Exposure Model Detailed Profiling and Quantification of the Exposure of Personnel to Geotechnical Hazards in Underground Mines

Transcription

1 This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia School of Civil and Resource Engineering, 2004 Exposure Model Detailed Profiling and Quantification of the Exposure of Personnel to Geotechnical Hazards in Underground Mines Michelle L. Owen Bachelor of Engineering (Civil Hons) Bachelor of Science (Mathematical Geophysics)

2 Exposure Model Detailed Profiling and Quantification of the Exposure of Personnel to Geotechnical Hazards in Underground Mines ABSTRACT This thesis presents an operationally applicable and reliable model for quantification of the exposure of underground mining personnel to geotechnical hazards. The model is shown to have the flexibility to apply to very different operational environments, within the context of mechanised metalliferous mines. It provides an essential component for carrying out quantitative geotechnical risk analyses of underground mines. Increasingly prevalent within the Australian mining industry are moves towards a riskbased philosophy instead of prescriptive design procedures. A barrier to this has been the lag in availability of resources (personnel and technical) required for the intensive effort of applying probabilistic methods to geotechnical engineering at mines. Tools and databases to facilitate the use of probabilistic methods of design and risk analysis are gradually making their way from the research domain into operations, just as they have done within other resource and energy sectors. However the complex and highly site-specific design and operating environment of underground mines make it a challenging task to develop useful and universal techniques. One of the missing components for quantitative risk analysis in mines has been an accurate model of personnel exposure to geotechnical hazards, from which meaningful estimates can be made of the probabilities of serious or fatal injury given a rockfall. Exposure profiling for geotechnical risk analysis at minesites has traditionally involved the simple classification of travelways and entry areas by their occupancy rate, not taking into account traffic and work characteristics which may significantly influence the risks. Therefore, it was the focus of this thesis to address that deficiency and progress the ability to perform semi-quantitative and quantitative risk analyses in mines. Risk is generally expressed as a combination of the consequence severity and likelihood of occurrence of the consequence. The Exposure modelled in this research is a measure of the likelihood of elements at risk being damaged and their probable degree of loss. In the mining environment, overly simplified or conservative estimates of personnel/asset vulnerability and exposure time can prevent realistic appraisals of i

3 Hazard Exposure Model design and operational alternatives. This may substantially hinder informed decisionmaking by camouflaging true high risk areas and distorting priorities for action. Although the basis for modelling the exposure of mobile underground equipment is also presented, the elements at risk focussed upon are the underground mine personnel. Hence the main consequences discussed in the case studies relate to personnel safety. This thesis addresses the issue of how to profile and track the exposure of mining personnel and assets to geotechnical hazards that create rockfall and rockburst damage potential in underground excavations. The underlying hypothesis is that personnel exposure to geotechnical hazards can be quantified in practice by accounting for the variables of exposure time, vulnerability to loss and hazard proximity. Through the review of risk and hazard analysis methodologies from related industry sectors, a model structure is established for quantifying the exposure of underground personnel to geotechnical hazards. This involves a structured profiling of a mine in terms of its workforce and equipment fleet, materials-handling arrangements, work locations, site-specific practices and activities related to production, development, service and technical support. These workforce profiles are input to the exposure model and the parameters of exposure time, vulnerability to loss, hazard proximity and hazard uncertainty are assigned. The model output is a rating that expresses the degree of exposure of underground personnel to geotechnical hazards. The exposure model parameters are related to temporal, spatial and vulnerability probabilities and are developed using both empirical data and mechanistic principles. The development and calibration of the exposure model requires a risk context into which the exposure must integrate. Several significant rockburst events from an Australian and a Canadian mine are used as case studies for demonstrating the application of the exposure model and exposure profiling process within a semiquantitative seismic risk analysis framework developed purely for model calibration purposes. Model validation is achieved through correlation of the rockfall incidence and injury statistics from Australian rockfall databases with the exposure model parameters and output. It is found that the exposure rating can be correlated with the likelihood of fatal injury in the event of a rockfall. ii

4 Hazard Exposure Model ACKNOWLEDGEMENTS I would like to gratefully acknowledge the assistance of my principal supervisor Professor Yves Potvin and co-supervisor Dr Barry Brady. Their guidance and support has been much appreciated. Professor Potvin s readiness to listen and offer helpful suggestions has been invaluable. Also acknowledged are the efforts of other staff at the Australian Centre for Geomechanics (ACG), particularly Mr Marty Hudyma, Ms Christine Neskudla and Ms Josephine Ruddle. The forum that the ACG and the MSRRM project provided for industry debate and the transfer of knowledge between researchers and mining industry practitioners has been of great value. The scholarship funding provided by the Robert & Maude Gledden Foundation has been much appreciated and, as well as keeping the mortgage paid, enabled me to undertake several months of study at Noranda s Brunswick mine in Atlantic Canada. I would like to thank the MSRRM project sponsors and, in particular, the geotechnical staff at Harmony Gold s Big Bell Mine, Noranda s Brunswick Mine, WMC s Perseverance Mine and BHP-Billiton s Cannington Mine for providing access to databases at their mines and valuable feedback as the research progressed. This study could not have been completed without the support of sponsoring mines in providing site accommodation and travel assistance. Finally, my profound thanks to my husband and family. They have followed the evolution of this PhD thesis with much interest, some bemusement and continual encouragement. iii

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6 Hazard Exposure Model SAFETY AND RISK TERMINOLOGY Occupational health and safety terminology (Minerals Council of Australia, Safety & Health Performance Report of the Australian Minerals Industry ): Fatal Injury (F): An injury that results in death. Lost Time Injury (LTI): An injury that results in a minimum of one full shift s absence (AS ). Severe Injury (SI): An injury that results in a minimum of two weeks off work. Incidence Rate (IR): The number of injuries per 1000 employees. IR = number of occupational injuries x 1000 number of employees Frequency Rate (FR): The number of occupational injuries expressed as a rate per million hours worked. FR = number of occupational injuries x 1,000,000 number of hours worked Fatal Injury Frequency Rate (FIFR): The number of fatal injuries per one million hours worked. Lost Time Injury Frequency Rate (LTIFR): The number of lost time injuries per one million hours worked. Risk terminology The risk definitions from the 1999 Australian/New Zealand Standard for Risk Management are (AS/NZS 4360:1999): Risk is a combination of Likelihood and Consequence where, Risk is the chance of something happening that will have an impact upon objectives. It is measured in terms of consequences and likelihood. v

7 Hazard Exposure Model Likelihood is used as a qualitative description of probability or frequency where frequency is a measure of the rate of occurrence of an event expressed as the number of occurrences of an event in a given time. It describes the likelihood of a hazardous event and resulting consequence. Consequence is the outcome of an event expressed qualitatively or quantitatively, being a loss, injury, disadvantage or gain. There may be a range of possible outcomes associated with an event. Hazard is a source of potential harm or a situation with a potential to cause loss. Other risk terminology from AS/NZS 4360:1999 or the Australian Geomechanics Society Landslide Risk Assessment Guidelines (AGS, 2000): Vulnerability is the degree of loss to a given element or set of elements within the area affected by the hazard. Expressed on a scale of 0 (no loss) to 1 (total loss). (AGS, 2000). Risk analysis : the systematic use of available information to determine how often specified events may occur and the magnitude of their consequences (AS/NZS 4360). the use of available information to estimate the risk to individuals or populations, property, or the environment, from hazards. Generally contains the following: scope definition, hazard identification, risk estimation. (AGS, 2000). Risk evaluation is the process used to determine risk management priorities by comparing the level of risk against predetermined standards, target risk levels or other criteria. Risk assessment is the overall process of risk analysis and risk evaluation. Risk treatment is the selection and implementation of appropriate options for dealing with risk. vi

8 Table of Contents EXPOSURE MODEL DETAILED PROFILING AND QUANTIFICATION OF THE EXPOSURE OF PERSONNEL TO GEOTECHNICAL HAZARDS IN UNDERGROUND MINES I ABSTRACT...I ACKNOWLEDGEMENTS... III SAFETY AND RISK TERMINOLOGY... V LIST OF TABLES... X LIST OF FIGURES... XIII 1. INTRODUCTION RISK ANALYSIS & SAFETY MANAGEMENT PRACTICES IN THE AUSTRALIAN MINING INDUSTRY RECENT SAFETY STATISTICS FOR THE AUSTRALIAN MINERALS INDUSTRY RESEARCH EFFORTS TOWARDS THE PREVENTION OF ROCK FALL FATALITIES RISK ANALYSIS CONCEPTS AND TECHNIQUES PROBABILISTIC METHODS IN GEOTECHNICAL ENGINEERING AND RISK ASSESSMENTS HYPOTHESIS/TOPIC OF RESEARCH RESEARCH OBJECTIVE, ORIGINALITY AND SIGNIFICANCE METHODOLOGY OF RESEARCH JUSTIFICATION FOR RESEARCH ORGANISATION OF THESIS LITERATURE REVIEW INTRODUCTION SYSTEMS AND MODELS System Definitions Model Construction Model Calibration and Validation RISK ANALYSIS TECHNIQUES USED WITHIN RESOURCE AND ENERGY SECTORS Nuclear Industry Offshore Oil and Gas Industry COMPARISON OF NUCLEAR AND OFFSHORE INDUSTRIES WITH MINING RISK ASSESSMENT OF GEOTECHNICAL HAZARDS IN THE CIVIL ENGINEERING INDUSTRY GEOTECHNICAL HAZARDS IN UNDERGROUND MINES General Factors Affecting Underground Excavation Stability Methods for Analysing Underground Geotechnical Hazards Physical Manifestation of Geotechnical Hazards on Underground Excavations Using Rockfall Databases for Model Calibration and Validation SEISMIC HAZARD IN UNDERGROUND MINES Introduction to Mining Induced Seismicity Seismic Hazard and Risk Techniques from the Literature The Role of the Mine Seismicity Risk Analysis Program (MS-RAP) Seismological Parameters used in Seismic Hazard Analysis DAMAGE POTENTIAL IN EXCAVATIONS SEISMIC RISK FRAMEWORKS CONCLUSION HAZARD EXPOSURE MODEL FOR UNDERGROUND MINES INTRODUCTION CONSTRUCTION OF A MODEL FOR QUANTIFYING EXPOSURE MAPPING THE EXPOSURE WITHIN A MINE General Methodology INITIAL CONCEPTUAL MODEL Data on Rockfalls and Injuries used to Establish Model Parameters Components of Exposure vii

9 Table of Contents 3.5. VULNERABILITY TO GEOTECHNICAL HAZARDS Personnel Exposure Equipment Exposure PROXIMITY TO GEOTECHNICAL HAZARDS EXPOSURE TIME GROUND HAZARD UNCERTAINTY AND HIGH RISK ACTIVITIES CALCULATION OF EXPOSURE RATING FOR ACTIVITIES CALCULATING THE EXPOSURE RATING FOR EXCAVATION CATEGORIES EXPOSURE PROFILES FOR DEVELOPMENT AND PRODUCTION CYCLES EXPOSURE PROFILES FOR ESTABLISHED EXCAVATIONS EXPOSURE PROFILES FOR FIXED ASSETS CHARACTERISING EXPOSURE FOR GENERAL MINE TRAFFIC RESULTS OF EXPOSURE RATING QUANTIFICATION SUMMARY AND CONCLUSIONS SEISMIC RISK FRAMEWORK FOR MODEL CALIBRATION INTRODUCTION USE OF MS-RAP FOR CLUSTERING SEISMIC DATA SEISMIC HAZARD RATING OF CLUSTERS CALCULATING SEISMIC HAZARD & DAMAGE POTENTIAL AT LOCATIONS ALONG DRIVES INTEGRATING EXPOSURE AND DAMAGE POTENTIAL RATINGS TO ESTIMATE SEISMIC RISK CONCLUSIONS DETAILED CASE STUDIES OF SEISMIC RISK & HAZARD EXPOSURE AT BIG BELL MINE BIG BELL MINE INTRODUCTION SLC LAYOUT AND OPERATIONS MINE GEOLOGY AND GEOTECHNICAL CHARACTERISTICS HISTORY OF ROCKBURSTING SEISMIC DATA SEISMIC HAZARD AND EXCAVATION DAMAGE POTENTIAL ANALYSIS SEISMIC HAZARD AND DAMAGE POTENTIAL FOR 17 TH JUNE 2000 ROCKBURST Description of Rockburst Seismic Hazard and Damage Potential Ratings SEISMIC HAZARD AND DAMAGE POTENTIAL FOR 9 TH JULY 2000 ROCKBURST Description of Rockburst Seismic Hazard and Damage Potential Ratings SEISMIC HAZARD AND DAMAGE POTENTIAL FOR 6 TH FEBRUARY Description of Rockburst Seismic Hazard and Damage Potential EXPOSURE OF PERSONNEL ON ROCKBURST DAMAGE LEVELS Personnel and Equipment Fleet Development and Production Cycles Mine Traffic Profiles and Workforce Distribution Summary Exposure Ratings for Excavation Categories Case-Specific Exposure Ratings SEISMIC RISK CHARTS FOR BIG BELL PERSONNEL CONCLUSIONS DETAILED CASE STUDIES OF SEISMIC RISK & HAZARD EXPOSURE AT BRUNSWICK MINE BRUNSWICK MINE INTRODUCTION BRUNSWICK MINE LAYOUT AND OPERATIONS MINE GEOLOGY AND GEOTECHNICAL CHARACTERISTICS HISTORY OF ROCKBURSTING SEISMIC DATA SEISMIC HAZARD AND EXCAVATION DAMAGE POTENTIAL ANALYSIS SEISMIC HAZARD AND DAMAGE POTENTIAL FOR 13 TH & 17 TH OCTOBER viii

10 Table of Contents Description of the 13 th October 2000 Rockburst Description of the 17 th October 2000 Rockburst Seismic Hazard and Damage Potential Charts SEISMIC HAZARD & DAMAGE POTENTIAL FOR 16 TH MAY 2002 ROCKBURST Rockburst Description Seismic Hazard and Damage Potential Charts EXPOSURE QUANTIFICATION FOR BRUNSWICK MINE ON ROCKBURST LEVELS Introduction Development and Production Cycles Mine traffic profiles and workforce distribution Summary Exposure Ratings for Excavation Categories Case-specific exposure ratings for October 2000 rockbursts Case-specific exposure ratings for May 2002 rockburst CHARTS OF PERSONNEL EXPOSURE TO ROCKBURSTS SEISMIC RISK TO PERSONNEL CONCLUSIONS EXPOSURE MODEL VALIDATION MODEL VALIDATION PROCESS EXPOSURE MODEL VALIDATION USING MINE ROCKFALL DATABASES CANNINGTON MINE ROCKFALL DATABASE PERSEVERANCE MINE ROCKFALL DATABASE ACG AUSTRALIAN ROCKFALL DATABASE CONVERTING THE EXPOSURE RATING TO PROBABILITIES OF INJURY CONSEQUENCE ASSESSMENT FOR FAILURES IN MINES DUE TO GEOTECHNICAL HAZARDS CONCLUSIONS FROM ANALYSES OF THE ROCKFALL DATABASES SUMMARY AND CONCLUSIONS NEED FOR A GEOTECHNICAL HAZARD EXPOSURE MODEL IN UNDERGROUND MINES EXPOSURE MODEL DEVELOPMENT EXPOSURE MODEL CALIBRATION AND VALIDATION FUTURE APPLICATIONS - INCORPORATING THE EXPOSURE MODEL INTO MS-RAP FUTURE APPLICATIONS - QUANTITATIVE SEISMIC RISK ANALYSIS FINAL CONCLUSIONS REFERENCES APPENDIX A Activity Time Distributions A1 A26 APPENDIX B Exposure Quantification Background Data B1-B12 APPENDIX C Mine Seismology Parameters and Seismic Clusters C1-C7 APPENDIX D Preliminary Calibration and Future Applications of the Hazard Exposure Model D1-D17 ix

11 Table of Contents LIST OF TABLES Table 1.1 Likelihood assessment matrix for geotechnical risk... 7 Table 1.2 Qualitative Geotechnical Risk Assessment Matrix... 7 Table 1.3 Likelihood Descriptions... 8 Table 1.4 Qualitative Measures of Likelihood... 9 Table 2.1 Overview of fire and explosion calculations Table 2.2 Overview of seismic hazard and rockburst calculations Table 2.3 Risk Assessment Approaches Table 2.4 Exposure table from GAP Table 2.5 Dynamic load versus excavation damage Table 2.6 Sample Seismic Risk Matrix Explicit Use of Exposure Rating Table 2.7 Seismic Risk Matrix Standard Consequence vs Likelihood Format Table 3.1 Problem Definition for Research Table 3.2 Suggested Classification Of Opening Types (DME 1997) Table 3.3 Underground Mine Excavation Categories Table 3.4 Description of rockfall fatalities in Australian underground metal mines Table 3.5 Personnel Vulnerability Ratings Table 3.6 Proximity Level Factor Table 3.7 Proximity Factor versus Underground Mine Fatality Data Table 3.8 Selected High Risk Activities Table 3.9 Exposure profiles applicable to various excavation categories Table 3.10 Summary statistics from mine efficiency study Table 3.11 Summary of Exposure Quantification for Big Bell Mine, Table 3.12 Summary of Exposure Quantification for Brunswick Mine, Table 3.13 Relative Levels of Exposure Rating Table 3.14 Exposure Model Terminology Table 3.15 Typical Hourly Exposure Parameters for Mine Activities Table 4.1 Relating Likelihood Descriptors with Probabilities Table 4.2 Seismic Hazard Definitions Table 4.3 Damage Potential Definitions Table 4.4 Criteria for determining f m Table 4.5 Magnitude-scaling factor f s Table 4.6 Variation in Values of Seismic Hazard Parameters Table 4.7 Criteria used to Rank the Seismic Hazard Parameters Table 4.7a Supplement to Table 4.7 using Moment Magnitudes Table 4.8 Semi-Quantitative Seismic Cluster Hazard Rating Table 4.9 Seismic source distances used to determine the level of dynamic loading and associated potential damage at an excavation site Table 4.10 Relative Levels of Exposure Rating Table 4.11 Semi-Quantitative Seismic Cluster Hazard and Damage Potential Ratings Table 4.12 Descriptions of Damage Potential Rating Table 4.13(a) Relative Risk Ratings based on the highest values of Exposure Categories x

12 Table of Contents Table 4.13(b) Relative Risk Ratings based on the mid-range of Exposure Categories Table 4.14 Criteria for Rating the Relative Risk Table 5.1 Risk Context for Calibration of Exposure Model using Big Bell case studies Table 5.2 Big Bell Mean Rock Mass Properties (after Turner & Player 2000) Table 5.3 Stress measurements within Big Bell mine (after Barrett & Player 2002) 152 Table 5.4 Rockburst Record to July 2000 (Player 2002) Table 5.5 MS-RAP clustering results for Big Bell data Table 5.6 Estimated Probabilities for Seismic Cluster Hazard Rating (ref: Chap 4.) Table 5.7 Adjusting drive hazard to obtain damage potential Table 5.8 Damage Potential Definitions Table 5.9 Summary Table for Seismic Hazard Rating of June Clusters Table 5.10 Summary Table for Seismic Hazard Rating of July Clusters Table 5.11 Summary Table for Seismic Hazard Rating of February 2002 Clusters Table 5.12 Average Big Bell Fleet and Operator Data for June July Table 5.13 Development cycle exposure ratings for Table 5.14 Development cycle exposure rating for Table 5.15 Production cycle exposure rating for 2000 and Table 5.16 Exposure Ratings for Travelways and Infrastructure Areas for early Table 5.17 Exposure Ratings for Travelways and Infrastructure Areas, early Table 5.18 Summary of Exposure Ratings for June-July 2000 Excavations Table 5.19 Summary of Exposure Quantification for Big Bell Mine, Table 5.20 Relative Levels of Exposure and Risk Ratings Table 6.1 Summary record of rockbursts and seismically induced falls of ground Table 6.2 MS-RAP clustering results for Brunswick data Table 6.3 Summary Table for Seismic Hazard Rating of October 2000 Clusters Table 6.4 Summary Table for Seismic Hazard Rating of May 2002 Clusters Table 6.5 Brunswick personnel numbers in Table 6.6 Brunswick personnel numbers in Table 6.7(a) Average stope characteristics Table 6.7(b) Production cycle timing based on an average stope size of 40,000 tonnes Table 6.8 Production cycle exposure ratings for 2000 and Table 6.9 Development cycle exposure ratings for 2000 and Table 6.10 Exposure Ratings for Travelways and Infrastructure Areas for late Table 6.11 Exposure Ratings for Travelways and Infrastructure Areas for mid Table 6.12 Summary Exposure Table for Brunswick Mine, Table 6.13 Summary of Exposure Quantification for Brunswick Mine, Table 6.14 Mine records of average resourcing per level for October Table 6.15 Exposure Rating of drives based on resource levels, early May Table 6.16 Relative Levels of Exposure and Risk Ratings Table 7.1 Relative Levels of Exposure Rating xi

13 Table of Contents Table 7.2 Cannington Mine Summary Rockfall and Injury Data Table 7.3 Comparison of model parameter E1 with Cannington injury rate data Table 7.4 Comparison of model parameters T1 and %HT with rockfall incidence. 258 Table 7.5 Ranges in exposure time parameters and rockfall incidence Table 7.6 Exposure Model Applied to Cannington Data Table 7.7 Exposure Model applied to Cannington data with revisions Table 7.8 Perseverance rockfall and injury records with associated activity times Table 7.9 Perseverance Injury Data versus Personnel Vulnerability Parameter Table 7.10 Perseverance rockfall data compared with exposure times Table 7.11 Applying the Exposure Model to Perseverance Data Table 7.12 Perseverance rockfall injury records Table 7.13 Australian rockfall database: injury information compared with factor E Table 7.14 Relative Exposure Ratings for Selected Activities from the Australian Rockfall Database Table 7.15 Exposure Ratings using mine-specific activity times for selected activities from the Australian Rockfall Database Table 7.16 Description of rockfall fatalities in Australian underground metal mines Table 7.17 Sample Risk Matrix Explicit Use of Exposure Table 7.19 Quantitative Risk Matrix (Annual P f ) for Small-Moderate Size Rockfalls Table 7.20 Annual fatality rates used to define risk levels xii

14 Table of Contents LIST OF FIGURES Figure 1.1 Probability density functions of load and capacity Figure 2.1 The system identification loop Figure 2.2 Framework for decisions on the tolerability of risk Figure 2.3 Iterative loop for risk assessment and risk control Figure 2.4 Main elements of the offshore QRA Figure 2.5 Average Apparent Stress Figure 2.6 Sample Seismic Decay Curve Figure 2.7 Recommended peak particle velocity distribution for support design Figure 2.8 Flow chart of the General Methodology for Assessment of Rockburst Potential Figure 3.1 Flowchart of activities contributing to exposure in a mine excavation.. 83 Figure 3.2 Pie chart showing the breakdown of the 494 rockfalls according to the location of failure within the drive Figure 3.3 Proportion of rockfalls resulting in injury Figure 3.4 (a) Time distribution for development charge-up pdf Figure 3.4 (b) Time distribution for development charge-up cdf Figure 3.5 Time distribution for Conventional Production Mucking Figure 3.6 Plan of the exposure of personnel to rockfalls for an active production level of Big Bell mine in early Figure 4.1 Example of a Big Bell mine level with seismic clusters rated using SCHR Figure 4.2 Recommended peak particle velocity distribution for support design. 137 Figure 4.3.a Figure 4.3.b Figure 5.1 Illustration of the radiating influence of a very high hazard seismic cluster on the seismic hazard rating of nearby drives Illustration of the radiating influence of a moderate hazard seismic cluster on the seismic hazard rating of nearby drives Long section of Big Bell mine showing the underground levels and open pit in early Figure 5.2 Plan of 510 level in Figure 5.3 Typical rockburst failure geometry Figure 5.4 Photograph of the failure plane in a 535 footwall drive rockburst Figure 5.5 Photograph at the entry to the damaged section of the 535 FWDN Figure 5.6 Hazard-rated Seismic Clusters to 17 th June 2000 for the 535 level Figure 5.7 Hazard rating of 535 level drives based on seismicity before 17 th June Figure 5.8 Relative Damage Potential Ratings for 535 level excavations Figure 5.9 Photograph of the 510 F83 rockburst damage Figure 5.10 Hazard-rated Seismic Clusters to 9 th July 2000 for the 510 level Figure 5.11 Seismic Hazard in 510 level drives Figure 5.12 Damage Potential in the 510 level drives Figure 5.13 Photograph of the sidewall ejection rockburst damage on the 560 level Figure 5.14 Photograph of the rockmass bulking in the 560 F64N Figure 5.15 Seismic clusters from 2000 that located near the 560 level xiii

15 Table of Contents Figure 5.16 Hazard-rated seismic clusters on the 560 level from 6 months of seismicity Figure 5.17 Seismic hazard in 560 level drives based on seismic data Figure 5.18 Seismic hazard in 560 level drives based on seismic data Figure 5.19 Damage potential ratings for the 560 level in early Figure 5.20 Map of Personnel Exposure to Rockbursts on the 535 Level, June Figure 5.21 Map of Personnel Exposure to Rockbursts on the 510 Level for July Figure 5.22 Map of Personnel Exposure to Rockbursts on the 560 Level for Feb Figure 5.23 Map of Personnel Exposure to Rockbursts on the 560 Level for Feb-2002 Figure 5.24 with excluded zones Plan of 535 level of relative seismic risk for the 17 th June 2000 period Figure 5.25 Plan of 510 level of relative seismic risk for the 9 th July 2000 period Figure 5.26 Plan of 560 level of relative seismic risk for the 6 th February 2002 period Figure 5.27 Plan of 560 level of relative seismic risk for the 6 th February 2002 period with excluded sections of the Footwall Drive North and Footwall Drive South Figure 6.1 Simplified longitudinal section of the mine looking west Figure 6.2 Rockburst damage in 326 XC, sublevel, associated with events of the 13 th October Figure XC rockburst and caving due to the 13 th October 2000 events Figure 6.4 Further damage in 326 XC after 17 th October 2000 rockburst Figure 6.5 Rockburst damage in the 327 heading due to 17 th October 2000 events Figure sublevel: Hazard-rated seismic clusters, April - October Figure 6.7 Seismic drive hazard for South sublevel, October Figure 6.8 Estimated Damage Potential for South sublevel, October Figure 6.9 View from the footwall access looking west to the intersection failure Figure 6.10 View of the south-west portion of the intersection failure Figure 6.11 Plan of the sublevel seismicity, showing clusters used in the risk analysis of the 16 th May 2002 rockburst Figure 6.12 Seismic Hazard in Drives on the sublevel, May Figure 6.13 Excavation Damage Potential on the sublevel, May Figure 7.1 Partial Event Tree for Annual Fatality Risk due to Rockfalls in a Decline xiv

16 Chapter 1 - Introduction 1. INTRODUCTION 1.1. Risk analysis & safety management practices in the Australian mining industry Increasingly prevalent within the Australian mining industry, particularly at the corporate level, is the impetus to usefully apply risk assessment methods beyond the traditional financial management area to areas of technical design and safety management. This move towards a risk-based philosophy instead of prescriptive design procedures follows trends in other industries such as the petro-chemical, civil/structural construction and nuclear sectors. It is also a development stipulated by mining industry regulators. Another recent trend in large Australian companies follows the lead of multinationals in Europe and the U.S.A. in expanding their corporate reporting to include information on their "triple bottom line" performance. The triple bottom line incorporates economic, social and environmental parameters and has become a focus since substantial stakeholder confidence in financial reporting has been lost in recent years with several well-publicised collapses of large and blue-chip corporations. According to PricewaterhouseCoopers' latest Management Barometer survey (PWC, May 2003), in Western Europe, 68 percent of large companies report social and environmental information in addition to required financial reports, while in the U.S.A. the proportion is lower but still significant at 41 percent. The relevance of these principles to this thesis is that the social and financial issues typically include workplace health and safety as a major component. Thus, initiatives to improve risk assessment and safety management practices are currently very topical Recent safety statistics for the Australian minerals industry The Minerals Council of Australia is the national body representing the exploration, mining and minerals processing sector and publishes annual safety and health data. The following commentary focuses on the underground mining sector safety performance 1

17 Chapter 1 - Introduction and quotes statistics selected from the Mineral Council s Safety & Health Performance Report of the Australian Minerals Industry (MCA ). In , the Australian minerals industry recorded 14 fatalities, with underground mining accounting for the majority (57%) of these deaths. In the decade , there have been 198 deaths recorded in the minerals industry, although the number of fatalities has varied widely from year to year, ranging from seven in to 33 in Since 1998, the clear majority (between 57% and 77%) of fatalities have been recorded in underground mines and in the past three years, the underground metalliferous sector has, on average, accounted for 66% of these fatalities in underground mines. In , half of the 14 fatalities recorded involved rockfalls, roof and rib collapses; four in the underground coal sector and the remaining three in the underground metalliferous sector (MCA ). Since 1995, the highest Fatality Injury Frequency Rate (FIFR) statistics have been in underground mining. More specifically from , the underground metalliferous sector FIFR was consistently the highest across the industry. However, the relatively high fatality rates do not reflect an overall disregard for safety as, by contrast, injury frequency statistics for the minerals industry have shown an exponential decrease over the past decade. The number of recorded lost time injuries (LTIs) for halved by and halved again to 2,200 LTIs in Similarly, the Lost Time Injury Frequency Rate (LTIFR) decreased from 42 in to 11 in but the data suggests that the industry LTIFR performance may have reached an asymptote such that future variations may reflect random fluctuations rather than genuine performance improvements or decrements (MCA ). For the underground metalliferous sector, the Lost Time Injury Frequency Rate (LTIFR) has remained fairly constant at since 1998 but is still one third of the LTIFR. The conclusions of the MCA ( ) report were that for Australian minerals industry fatality rates, 2

18 Chapter 1 - Introduction there is little evidence of a sustained improvement trend over the decade. This emphasises the need for minerals companies and governments to maintain an ongoing focus on fatality prevention Research Efforts Towards the Prevention of Rock Fall Fatalities In September 1997, the Mines Occupational Safety and Health Advisory Board (MOSHAB) was instructed by the Western Australian (WA) Minister for Mines to establish an inquiry into mining fatalities. This was initiated in response to a double fatality caused by rock fall in an underground mine in the Eastern Goldfields, which followed six other WA mining fatalities that year. MOSHAB established the tripartite Prevention of Mining Fatalities Taskforce to carry out the inquiry with particular attention directed to rock falls. Fatality data for the WA mining industry (surface and underground) from 1995 to 1st December 1997, showed that 56% of all fatalities occurred in the underground mining sector despite its accounting for only 10% of industry employees. Of these underground fatalities, 78% were due to rock fall. The Taskforce found that, while there had been a sustained improvement in occupational safety and health performance across the industry, the incidence of fatalities in the underground mining sector remains unacceptable and indicates a failure by this sector to adequately control the risk of exposure to rock falls. (MOSHAB 1997, p.iii). It also found that geotechnical risks, the most common cause of fatalities in 1996 and 1997, had not been adequately assessed or controlled and that Increased attention by industry and the inspectorate to geotechnical issues, particularly ground support in the workplace, is warranted. (MOSHAB 1997, p.iii). Various industry, government and academic initiatives since then have resulted in greater funding and resources for research efforts investigating rock falls and rockbursts in underground mines, as well as creating working groups of geotechnical engineering and rock mechanics practitioners with increased discussion and dissemination of information within the industry. The WA Government introduced legislative requirements for improved Geomechanics 3

19 Chapter 1 - Introduction practices through Regulations and MOSHAB also developed a Code of Practice for surface support. On the industry side, WMC Resources were particularly proactive in the late 1990 s in establishing an Elimination of Fatality Taskforce, resulting in a significant reduction in the number of rock fall injuries in their WA operations. Another industry initiative, the Ground Control Group (GCG) of Western Australia is a body of geotechnical and rock mechanics practitioners from within resource companies who regularly meet to discuss topical mining ground control issues, disseminate best practice information and formulate guidelines. Their Rock Fall Risk Assessment Guidelines for Underground Mine Access (GCG, 2000) aims to provide tools for assessing the geotechnical risks associated with rock falls. The rock fall definition adopted by these GCG guidelines is shown below (a) and other details are discussed in later sections of this thesis. Rockfall Definitions: (a) Volume of rock falling from the back, side-walls or face of an underground excavation where workers have access. Rock falls are classified as small rocks (typically in between rockbolts), wedges (larger than rockbolt patterns) or dynamic failures; rockbursts (unrelated to size, but ejected with high kinetic energy). (GCG 2000). (b) An uncontrolled fall of ground of significant size in an entry area, or an uncontrolled fall of ground of any size that causes (or potentially causes) injury or damage. (Potvin et al. 2001, p.1) For this thesis, which allows for an assessment of damage to mobile equipment and not just workers, the second more general definition has been adopted but is taken to include all classifications mentioned in definition (a). To maintain consistency of terminology with definition (b), hereinafter, all references other than direct quotes will be to rockfalls rather than rock falls. The second definition (b) is quoted from the report Towards the Elimination of Rockfall Fatalities in Australian Mines, which collected a database of rockfall records from underground mines throughout Australia. 4

20 Chapter 1 - Introduction The study was an initiative of the Australian Centre for Geomechanics 1 (ACG) and was supported by industry and the Minerals and Energy Research Institute of Western Australia (MERIWA). Results from the database analysis are discussed in more detail in later chapters but some pertinent findings included (Potvin et al. 2001, p.1): For the period covered by this study, the large majority of reported rockfalls occurred in drives within 10m of the active face. This is an area of high activity. The personnel involved in installing ground support, drilling the face and mucking in particular, have been exposed in varying degrees to these rockfalls. Almost one quarter of all reported rockfalls have resulted in injuries. There have been many large rockfalls reported (88 cases over 50 tonnes), but over 90% of the rockfalls causing injuries were smaller than 1 tonne Risk Analysis Concepts and Techniques Risk is defined as the chance of something happening that has an impact on objectives (such as damage, injury or loss) and is expressed as a combination of the consequence severity and its likelihood of occurrence. This thesis adopts risk definitions from the 1999 Australian/New Zealand Standard for Risk Management as summarised in the Terminology section of the preface. One could consider Consequence as a function of two components: 1. The Elements at Risk (AS/NZS 4360), which are the personnel or assets potentially subject to an injury or loss should the hazard occur; 2. The Exposure, which is a measure of the likelihood of elements at risk being damaged and their probable degree of loss. 1 The Australian Centre for Geomechanics (ACG) was established in Perth, Western Australia in 1992 as a joint venture between the University of Western Australian (UWA), the WA School of Mines and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) with the support of the State Government s Department of Mineral and Petroleum Resources. It is a non-profit organisation that conducts applied research into rock mechanics and other areas of geomechanics for the underground and surface mining sectors. 5

21 Chapter 1 - Introduction Exposure in this thesis is related to both the time spent within the range of influence of the hazard and the vulnerability to loss of the element at risk. In reviewing risk analysis methods for geotechnical hazards across other industries, one somewhat analogous situation was described in the Australian Geomechanics Society published guidelines on landslide risk management which defined a quantitative risk calculation as follows (AGS 2000a): R (DI) = P (H) x P (S:H) x P (T:S) x V (D:T) (1.1) Where, R (DI) = risk (annual probability of loss of life) P (H) = annual probability of the hazardous event (i.e. the landslide) P (S:H) = probability of spatial impact (e.g. of a building, vehicle, etc) by the hazard (the landslide) P (T:S) = temporal probability (e.g. of building occupation) given the spatial impact V (D:T) = vulnerability of the individual (probability of loss of life given the impact) The exposure quantification presented herein is consistent with the above breakdown of the risk components, although there are some differences in the detail of the calculations to allow for the complexities of geotechnical hazard and exposure characterisation in the underground mining environment. Referring back to the landslide risk calculation, the three listed probabilities P (S:H), P (T:S) and V (D:T) are accounted for as part of the proposed exposure rating system, as indicated by the labelled formula below. R (DI) = P (H) x P (S:H) x P (T:S) x V (D:T) GEOTECHNICAL HAZARD EXPOSURE Once the risk is calculated, it must be categorised into varying levels of severity and assessed against what is considered acceptable. For a coarse level of analysis, this 6

22 Chapter 1 - Introduction process is typically done through the use of a qualitative or semi-quantitative risk matrix. The following semi-quantitative matrices are from the Ground Control Group of Western Australia (GCG 2000) guidelines. The first matrix in Table 1.1 shows rockfall likelihood versus exposure to give an overall consequence likelihood. Table 1.2 is a geotechnical risk matrix, combining consequence severity and likelihood. Table 1.1 Likelihood assessment matrix for geotechnical risk (after GCG 2000) PROBABILITY OF ROCKFALL EXPOSURE HIGH LOW Continuous Almost certain Likely Moderate Unlikely Rare Likely Moderate Unlikely Rare Rare Moderate Unlikely Rare Rare Rare Unlikely Rare Rare Rare Rare No exposure Rare Rare Rare Rare Rare Table 1.2 Qualitative Geotechnical Risk Assessment Matrix (after GCG 2000) LIKELIHOOD CONSEQUENCES CATASTROPHIC MAJOR MODERATE MINOR INSIGNIFICANT (FATALITY) (SEVERE INJURY) (MTI) (FIRST AID) Almost certain E E H M L Likely E H M M L Moderate H M M L L Unlikely M L L L L Rare L L L L L Where: (E) Extreme Risk; (H) High Risk; (M) Moderate Risk; (L) Low Risk. Table 1.3 includes the GCG (2000) guideline definitions for their qualitative likelihood categories. Added to these descriptive categories is a final column quoting numerical probabilities associated with these or similar likelihood descriptions. The probabilities are from research on associating verbal phrases with numerical probabilities by 7

23 Chapter 1 - Introduction Lichtenstein & Newman (1967), as discussed by Baecher (1998) in his paper on Expert Elicitation in Geotechnical Risk Assessment. As a contrasting example, some indicative annual probability values from Appendix G in the AGS (2000a) landslide guidelines are listed in Table 1.4. The guidelines state that these probabilities may easily vary by an order of magnitude and it should be noted that they describe event occurrence under adverse conditions. In contrast, Table 1.3 uses very similar likelihood descriptors but with a different focus; the event occurrence under most circumstances. From their respective descriptions, the Rare category in Table 1.3 seems to correspond to the Possible to Likely categories in Table 1.4. Summers (2000) provides another likelihood table which contains qualitative descriptions ranging from very unlikely to highly likely with respective quantitative measures of less than 1/2000 to greater than 1/10. This variation in rationale between different authors can be a source of confusion and therefore quantitative likelihood measures are preferable. Table 1.3 Likelihood Descriptions Likelihood Description Almost certain Likely GCG Definition Is expected to occur in most circumstances Will probably occur in most circumstances Associated Probability Mean, Median, Std Deviation * 0.72, 0.75, 0.11 Moderate Might occur at some time 0.58, 0.60, 0.11 ( Rather ) 0.37, 0.49, 0.23 ( Possible ) Unlikely Could occur at some time 0.18, 0.16, 0.10 Rare May occur only in exceptional circumstances 0.07, 0.05, 0.07 * The closest matches to the description Almost certain, were: 1. Highly Probable with associated mean, median and standard deviation probabilities of 0.89, 0.90, 0.04, respectively as in Lichtenstein & Newman (1967); and 2. Virtually certain with probability equivalent of 0.99, and a range from 0.9 to 1.00, (Vick 1997). 8

24 Chapter 1 - Introduction In the underground mining environment it is not uncommon to see the manifestation of geotechnical hazards through such mechanisms as rockfalls, rockbursts, bulking, groundwater seepage and fault displacement. Mines will typically record at least a few rockfalls per year, so excavation damage can be expected under ordinary circumstances rather than only in adverse conditions. Therefore the quantitative likelihood measures in Table 1.3 were considered to be more relevant to this research than the wide range of probabilities related to landslide occurrence in Table 1.4. Table 1.4 Qualitative Measures of Likelihood (AGS 2000a) Descriptor Description Indicative Annual Probability Almost certain The event is expected to occur > 10-1 Likely The event will probably occur under adverse conditions Possible The event could occur under adverse conditions Unlikely The event might occur under very adverse circumstances Rare The event is conceivable but only under 10-5 exceptional circumstances Not Credible The event is inconceivable or fanciful < Probabilistic methods in geotechnical engineering and risk assessments The field of geotechnical engineering, whether practiced within mining, tunnelling, earthquake engineering or civil construction, involves significant uncertainties in the design process. These uncertainties can be broadly classified as either conceptual uncertainty or parameter uncertainty (Summers 2000). The first category involves a lack of knowledge about the process or mechanism, while the second is associated with uncertainties about the actual values taken by parameters or properties being considered. In geotechnical engineering, many of the design techniques are based on empirical data rather than a first principles understanding of the exact workings of the mechanisms 9

25 Chapter 1 - Introduction involved, particularly as there are often multiple potential failure paths. Examples of this include Bieniawski s (1993) Rock Mass Rating (RMR) and Barton s (1974) Qsystem charts and equations for classification of a rock mass and determination of ground support requirements. Another example is the use of seismic hazard maps based on historical earthquake records to determine the ground acceleration to use for building design in different locations. Therefore conceptual uncertainties are introduced into the design process for many geotechnical applications. The second category of parameter uncertainty is introduced through such conditions as: the variability of the soil or rock fabric and limited investigation allowed for the site characterisation; the ambiguity associated with the maximum and fatigue loading that the site will experience. The level of uncertainty about geotechnical models and parameters required for design tends to limit the usefulness of deterministic methods and instead naturally lend itself to probabilistic or stochastic techniques. Indeed, parametric studies of rock excavation stability have shown that the deterministic safety factor approach can sometimes produce misleading results when compared to actual probabilities of failure (Chen 1997). In discussing the assessment of slope stability against landslides, Aleotti & Chowdhury (1999, p.34) state that, In order to make decisions based not only on performance indicators but on the consequences of failure or of inadequate performance, it is again necessary to work within a probabilistic framework. As another example, modern structural design codes acknowledge the need to accommodate the variabilities of materials and loadings and use safety factors on material resistance and loading factors on forces to increase the separation between the two distributions (Figure 1.1). In mining, advanced probabilistic techniques are in common use within the specialised area of geostatistics for carrying out ore reserve analysis with only limited drilling data. 10

26 Chapter 1 - Introduction Loading Capacity f Factor of Safety < 1 Stress Figure 1.1 Probability density functions of load and capacity Risk analysis, by definition, involves probabilistic methodologies through the determination of likelihoods of hazard initiation and potential consequences. In the civil engineering sector, risk analysis has been adopted in the tendering process, for operational safety and also within the area of construction performance (Summers 2000). In underground mines, the calculation and management of risks associated with geotechnical issues such as rockfalls and rockbursts, is particularly challenging due to the additional complexities of: - the limited geological and geotechnical data available for feasibility studies and pre-construction design; - the large upfront capital costs for mine development and pressures to maximise production rate, which may conflict with adopting the mine design and schedule that would optimise compliance with geotechnical criteria; - the typically short service life required for mine excavations compared to civil tunnels so that expensive ground support options to minimise the probabilities of failure are not economically feasible; - the deterioration of the ground conditions with time and mining disturbance requiring ongoing maintenance activities such as scaling and rehabilitation of rock support and reinforcement; 11

27 Chapter 1 - Introduction - the difficulty of locating and assessing potentially critical discontinuities and ground properties such as faults, shear zones and variability in virgin stress conditions sufficiently in advance of production, let alone development; - even when problematic ground conditions are identified, there are limited opportunities to alter the mine design in a strategic way and instead reactive tactical measures are often adopted (e.g. upgrading the rock support and reinforcement); - the underground mining environment incorporates zones of ongoing construction (development headings, production areas) but also other locations with long-term services and infrastructure (declines, shafts, workshops, etc) so design criteria and design loadings vary significantly across the mine; - the usage of the mine tunnels evolves over time so that the activities occurring, number of personnel and types of equipment involved vary both spatially and temporally in the mine. This combination of operational complexities, design restrictions and material variability mean that stochastic (rather than deterministic) methods should be most applicable to account for the variation in design parameters and reflect the many uncertainties inherent in undertaking mine design and operational safety analyses. Whitman (2000) discusses the use of probabilistic techniques for evaluating geotechnical risks at various stages of a project. The focus is on civil engineering projects involving geomechanical or geoenvironmental issues such as dams, foundations, offshore facilities, site remediation and earthquake engineering. Mining, though not specifically mentioned, would reasonably fall within the category of large, capital-intensive, high geotechnical risk projects. Under the heading of When to Use Probabilistic Analysis, Whitman (2000) suggests that one can divide geotechnical engineering problems into two broad categories: 1. Those where the client relies upon codes, regulations, and accepted practice to ensure that he receives a satisfactory product. This category includes the vast majority of routine projects. 2. Those where the client, and/or a regulator, is active in a discussion of potential risks and ultimately assumes at least most of whatever risk is implied by the final 12

28 Chapter 1 - Introduction choice of design. Such projects are characterized by either the impossibility of eliminating risks completely or by a very high cost of reducing risks to an insignificant level. Thus it is in the interests of the client to become actively engaged in decision making. Projects of this type are less common, and typically are large in scale or involve unusual types of buildings or facilities, or both. However, there is no reason why probabilistic methods cannot be utilized in traditional problems if a client believes that doing so can be of potential benefit. The mining industry in Australia has moved in terms of philosophy from the first prescriptive category into the second risk-based one. However there is a lag in the availability of resources (personnel and technical) required for the intensive effort of applying probabilistic methods to geotechnical engineering at mines. Tools to facilitate the use of probabilistic methods of design and risk analysis are gradually making their way from the research domain into mine site operations, just as they have already done within other resource and energy industries such as oil and gas operations and nuclear plants. The techniques used by these industries are discussed in the next chapter. However the complex and highly individualised design and operating environment of underground mines, as described above, make it a challenging task to develop useful and universal techniques. This is acknowledged by the Ground Control Group (GCG 2000) who note in their rock fall risk assessment guidelines, that for estimation of the probability of a rock fall A purely quantitative approach relying on probability and statistics is beyond current technology and is currently not practical at the scale of a mine. It seems desirable, then, that new tools, methods or models that aim to enhance the ability of mine sites to carry out useful geotechnical risk assessment, should: initially provide a fairly simple, somewhat conservative but flexible methodology or model such as a rating system. It should be easily computerised in a universal format (such as Microsoft Excel), give results that can be compared across all locations within a mine, and be readily updateable and transferable to other sites; allow sufficient flexibility to add complexity and incorporate probabilistic elements to the model as understanding of geotechnical domains improves, 13

29 Chapter 1 - Introduction databases (of rock fall records, say) gain statistical validity or better analysis techniques become available. These criteria formed the basis for development of the exposure model presented in this thesis and its applicability to quantitative risk analysis of geotechnical hazards in an underground mine Hypothesis/Topic of Research Before risk can be calculated, there are numerous contributing and interacting subcomponents that must be considered to determine the consequences and their likelihoods. In discussing landslide hazard and risk assessment, Aleotti & Chowdhury (1999, p.24) note that there is the problem of quantitatively determining the vulnerability of the elements at risk. These complexities make it hard to achieve an accurate risk assessment. These comments are also applicable to the mining environment where overly simplified or conservative estimates of personnel/asset vulnerability and exposure time can prevent realistic appraisals of design and operational alternatives. This may substantially hinder informed decision-making by camouflaging true high risk areas and therefore distorting priorities for action. As mentioned in a previous section, in this thesis Consequence is calculated as a function of two components: 1. The Elements at Risk which are the personnel or assets potentially subject to an injury or loss should the hazard occur; 2. The Exposure, which is a measure of the likelihood of elements at risk being damaged and their probable degree of loss. The elements at risk focussed upon herein are the underground mine personnel, although some applications of the methodology to model the exposure of mobile underground equipment are also presented. Hence the main consequences discussed in the mine case studies relate to personnel safety. This thesis addresses the issue of how to profile and track the exposure of mining personnel and assets to geotechnical hazards that create rockfall and rockburst damage potential in underground excavations. The 14

30 Chapter 1 - Introduction underlying hypothesis in this thesis is that exposure can be quantified by accounting for the variables of exposure time, vulnerability to loss and hazard proximity Research Objective, Originality and Significance The objectives of this thesis are to devise a methodology for creating exposure profiles of elements at risk due to geotechnical hazards in the underground metalliferous mine environment; and to develop a model that usefully quantifies exposure and provides a numerical assessment that can be integrated into a geotechnical risk analysis. In the context of this thesis, the elements at risk being considered are : Primarily, the underground mine personnel, including underground crews, subcontractors, technical staff and regular visitors; and As a secondary consideration, the mobile equipment used or travelling underground, including drills, loader/mucking units, trucks, service vehicles, light vehicles, etc. The nature of the sponsoring mine sites and others who provided data has meant that the details within the exposure model presented herein relate directly to mechanised metalliferous underground mines. The principles behind the methodology, though, may be more generally applied within the mining industry. By concentrating on exposure, the application broadened beyond seismicity/rockburst issues to include all geotechnical hazards in underground mining that potentially lead to ground failures, such as rock falls and rock bursts. These hazards may include seismicity, critical discontinuities, groundwater, overstress and blasting. The concepts of system analysis and model building are presented in Chapter 2. Considerations of the available data and mine end-user requirements lead to the following being desired features of the exposure model: initial version to be a simple, somewhat conservative exposure model (a rating system); model validation using actual rockfall incident records; 15

31 Chapter 1 - Introduction the model to be computerised in a universal format (Microsoft Excel); the model must output repeatable results that can be compared across all locations within a mine; the model should be readily updateable and transferable to other sites; the model to be sufficiently flexible to add complexity and incorporate probabilistic elements to the model as data becomes available; the exposure model must progress the ability to perform semi-quantitative and quantitative risk analyses in mines. The originality of this research is evidenced by the lack, heretofore, of a methodology that focussed on the exposure of mine personnel and assets to geotechnical hazards. There is no other existing work that accounts for the operational complexities of an underground mining environment when creating exposure profiles and that uses empirical data and physical principles to quantify personnel vulnerability. This thesis establishes an exposure model that provides an integral part of the process to calculate geotechnical risk in underground mines. Applications include the ability to compare between different sites and improved operational and design decision-making as demonstrated through the case studies herein. Depending on the mine s requirements, the model s level of complexity may be varied to suit purposes from pre-mine planning through to daily operational management and detailed geotechnical risk analyses. Another significant benefit of this research, apart from the progress towards quantitative risk analysis, is that it is a technique that enables the comparison of the impact of various mine activities, work practices and equipment types on personnel safety Methodology of Research Underground Mine Familiarisation The first stage in researching the topic was to develop an understanding of the nature of geotechnical hazards affecting underground mines. Several months were spent on various sponsor mine sites in Western Australia, carrying out seismic data processing and accompanying the site geotechnical engineers on underground inspections. Both 16

32 Chapter 1 - Introduction gold and nickel underground mines were visited and mining techniques observed included sublevel caving and various methods of open stoping. At a later stage of the research, an additional three months were spent on site at Noranda s Brunswick mine in Atlantic Canada. This was very valuable in gaining an appreciation of a large underground mine outside the Australian context; a mine with a long history of mining, a good, pro-active safety record and a strong geotechnical department experienced in effectively dealing with rockfalls and seismicity. Literature review The next stage of research was to advance understanding of the topic of risk and, specifically, geotechnical risks in mining. This was accomplished through a thorough literature review encompassing the current state of risk analysis in mining, both in Australia and internationally; and risk assessment methodologies in use in other resource and energy industries including petroleum and nuclear operations. In addition, literature was reviewed on the techniques in use for geotechnical applications in civil engineering such as dam construction, tunnel excavation, road building, foundation design and earthquake engineering. It became clear that there was substantial scope for the development of mine-specific tools to assist in the risk assessment process, particularly for quantitative risk analysis in mining. Other sectors such as nuclear plants and offshore oil and gas operations were considerably more advanced in their use of probabilistic techniques and quantitative risk assessment. However there were limitations on the applicability of these techniques to the mining environment and its specific geotechnical and operational issues. One barrier to transference of these techniques to quantitative risk analysis in mines was the lack of data and methodologies dealing with the exposure of mine personnel and assets to geotechnical hazards. Information on exposure in mining focussed on occupational health and safety studies of noise, vibration and chemical hazards and were not relevant to the profiling of exposure to geotechnical hazards. Development of the conceptual exposure model and seismic risk framework An exposure model constructed in isolation from its intended application within a risk assessment context would be of limited use and very difficult to validate. Therefore a 17

33 Chapter 1 - Introduction conceptual framework for geotechnical risk analysis, concentrating initially on seismic hazards, was developed and discussed with practitioners of mine geotechnical engineering. A model must be built based on observations of the physical system, so an investigation was made of the type and quality of information on exposure that could be gathered at mine sites. This was done by collecting the most comprehensive exposure data available which was from computerised equipment/activity tracking databases (PITRAM) at BHP-Billiton s Cannington underground silver-lead-zinc mine and WMC s Perseverance underground nickel mine. The results of this data analysis and the earlier literature review refined the selection of realistic and available parameters to use in building an exposure model. It was found that the most analogous frameworks were provided by recent work published on landslide and roadway rockfall risk assessments. Initial application of the exposure model within a seismic risk framework Initial trials of a simplified seismic risk methodology were then carried out using microseismic data and exposure information which focussed on development/production cycle activities, gathered from sponsoring WA mine sites. These pilot studies confirmed that the exposure model could be integrated successfully into the developed risk framework. Also highlighted was the need for the exposure model to provide valid results despite potential shortfalls in the state of knowledge of the geotechnical hazards and excavation behaviour. Therefore, an additional parameter was added to the exposure model to account for mining areas where ground conditions were rapidly changing or not well known. This parameter reflected the degree to which ground hazard uncertainties could affect the personnel vulnerability and hazard proximity factors, as their modelled values were based on a thorough and current assessment of ground hazards having been carried out. Feedback on model trials 18

34 Chapter 1 - Introduction The results of applying the exposure model to development and production cycle activities were presented to project sponsors, ACG geomechanics course attendees and at the North American Rock Mechanics-Canadian Tunnelling Association (NARMS- TAC 2002) and Mine Planning & Equipment Selection (MPES 2003) international conferences. A paper on the seismic risk framework and initial trials was published in the proceedings of the NARMS-TAC conference, whereas the MPES paper concentrated on the details of constructing the exposure model and expanding its scope. The valuable feedback from geotechnical experts and experienced site personnel at these venues assisted with decisions on model refinements and confirmed that an industry need was being addressed by this work. Finalisation of the exposure model The successful testing and positive feedback allowed the structure of the exposure model to be finalised. The feedback also established the need for model flexibility to accommodate a wide range of user requirements and cope with the variability of mine records. Several levels of complexity were incorporated into the model structure, allowing outputs varying from generic values for excavation categories, through to detailed activity/location exposure ratings after customisation of the exposure parameters to match with site-specific identified mine hazards and work practices. This multi-tiered structure leant itself to integration into a semi-automated computer programme for the analysis of risk. This also satisfied ACG seismicity project requirements as explained further in the final chapter of this thesis. Data gathering to calibrate exposure model An extensive gathering of exposure data from a variety of underground mine environments was then embarked upon. These sites were Perseverance, Harmony s Big Bell underground gold mine, Cannington, and Brunswick. These mines were all mechanised metalliferous mines but reflected a range of underground mining methods, widely varied mine sizes, locations and operational characteristics. The information collated to characterise exposure in mines included: 19

35 Chapter 1 - Introduction Surveys of underground workforce and technical staff to verify typical work locations, equipment (vehicles) and travel patterns; Collection of site specific data on workforce profiles, equipment lists, mining layouts, daily tonnage produced, production and development cycles, materials handling, infrastructure and access characteristics; Collection of data on activity locations and durations from computerised databases at mine operations. In order to calibrate the exposure model, the exposure time for specific activities or work areas is readily measured or estimated. However estimations of the proximity of the work location to the hazard cannot be verified until the hazard manifests itself. Similarly, the degree of personnel and/or equipment vulnerability can only be assessed by looking at the historical record of the consequences of rockfalls. Therefore it was seen that the exposure model must be constructed within a risk framework and calibrated by information from risk databases. Case studies using the detailed exposure model were developed to calibrate the model and verify its adaptability to different mine sites. The data upon which the model was calibrated comprised: Review of safety incident reports from sites; Collection of rock fall records and seismic data where relevant; Review of the Australian rock fall database (Potvin et al. 2001); The detailed back-analysis of rockburst case studies at Big Bell mine. The exposure model was fully developed when the Brunswick mine in Canada was visited. This site visit provided further case studies for model calibration and confirmed that the model had sufficient flexibility to accommodate varied operational systems and to integrate with mine-specific hazard rating systems. As discussed later in Chapters 2 and 3, analysis of exposure must involve considerations of the nature and characteristics of the hazards. The case studies used in calibrating the exposure model trialled a seismic hazard characterisation methodology using processed seismic data from mine 20

36 Chapter 1 - Introduction microseismic networks both in sponsoring WA mines and at the Brunswick mine in Canada. Exposure model validation Model validation requires an independent data set so that statistical comparisons can be made between the system behaviour/parameters and model behaviour/parameters. Validation of the exposure model parameters was carried out using statistical data from the Australian rockfall database (Potvin et al. 2001) and other mine specific rockfall data from the comprehensive records at Cannington and Perseverance mines. Thesis Preparation The final stage of the research was preparation of this thesis which involved the collation and integration of the results of the literature review, data gathering, model construction and model calibration and validation. In addition, through the research process, other risk related applications, beyond the scope of this thesis but potentially of future benefit to the mining industry, became evident. It is considered that these probabilistic techniques may be integrated with the exposure model presented herein with only minor model modifications. This is discussed in the final chapter of this thesis in the section on recommendations for future work Justification for Research The study for this thesis originated within another research project run by the Australian Centre for Geomechanics. In 1999, the ACG established a research project into Mine Seismicity and Rockburst Risk Management (MSRRM), funded by industry and MERIWA. The MSRRM project was initiated as a response to the concerns of several underground mining operations in W.A. that rockbursts and mine seismicity had become a serious mine safety issue and a constraint to economic viability in several operations. In analysing their Australian rockfall database, Potvin et al. (2001) found that one quarter of all rockfall fatalities since the beginning of 1997 were associated with rockbursts. Brady (1990, preface) commented that the pervasiveness of rockbursts 21

37 Chapter 1 - Introduction suggests that they remain the major unresolved ground control problem in underground mining. The MSRRM project aims were to better define the nature and extent of the problem in Australian mines and develop risk analysis tools suited to local industry in both a technical and operational sense. In considering the framework for analysis of risks related to mining induced seismicity, it was obvious that generic risk definitions (AS/NZS 4360:1999) and readily available techniques were too broad for direct application to the calculation of risks associated with rockfalls, mining induced seismicity and rockbursting (Owen 2002). The complexities of analysing these highly technical topics in the changing conditions of an operational mining environment necessitated a more sophisticated characterization and combination of hazard, exposure and consequence to calculate risks. Literature reviews showed that an enormous amount of past and ongoing work has concentrated on characterising geotechnical hazards, with such systems as rockmass ratings, stability graphs and seismic hazard quantifications all falling under this generic category. This topic is briefly discussed in Chapter 2 and the reader is referred for further detail to such seminal works as Barton et al. (1974), Hoek & Brown (1980), Brady & Brown (1985) and Bieniawski (1993). In contrast, very little work has been done in the area of quantifying exposure to these hazards. The current state of exposure profiling for geotechnical risk assessment at mine sites involves, at best, the simple classification of travelways and entry areas by their occupancy rate (GCG 2000). This does not take into account traffic and work characteristics which may affect the likelihood of a rockfall impacting a vehicle or person, as well as the vulnerability of the person to serious or fatal injury given the rockfall impact. Therefore, a gap exists in the knowledge regarding exposure and specific techniques to be used for accurate quantification of exposure to geotechnical hazards in underground mines. It is the focus of this thesis to address that deficiency and progress the ability to perform semi-quantitative and quantitative risk analysis in mines. 22

38 Chapter 1 - Introduction As discussed in the previous sections, further justification for this research can be found in the context of industry and society s increasing focus on safety and the shift in design and operating philosophy from codified and regulatory to risk-based Organisation of Thesis The hypothesis, objectives and methodology of this research are presented in Chapter 1, following an introduction to risk and exposure concepts and definitions with their application within the underground mining environment. Chapter 2 introduces system and modelling concepts and presents the results of a literature review of exposure and risk analysis techniques with comparisons between mines and other industries (nuclear, oil & gas, civil) as systems for risk modelling and analysis. Then current work on geotechnical hazards in underground mines is presented, with a detailed examination of the assessment of mining-induced seismicity and potential excavation damage as a basis for the development of a seismic risk framework in which to calibrate the exposure model. The exposure model is developed in Chapter 3 which presents the model foundations, parameters, profiling spreadsheets and sample calculations. In order to calibrate the exposure model using available data, its application within a risk framework is required. There are three main stages to the model calibration and validation, involving three different types of information sources. The first stage of calibration is integral with the model construction and is detailed in Chapter 3. The main exposure model parameters are shown to be empirically founded by using the Australian rockfall database (Potvin 2001) statistics with the specific identified geotechnical hazard being the active excavation face. The second stage of model calibration is through application and the comparison of model results with actual results from case studies. The application chosen is a specialised subset of rockfalls, namely rockburst case histories. Seismicity, resulting in rockbursts, is selected as the hazard in order to minimise the number of variables within the risk model and thus avoid the introduction of extraneous errors to the calibration process. Another consideration in focussing on rockbursts for exposure model 23

39 Chapter 1 - Introduction application, is that there were generally good records of initiating events and subsequent consequences through the detailed investigations carried out when significant rockbursts occur. Therefore a semi-quantitative seismic hazard and damage potential methodology is developed in Chapter 4 and integrated with the exposure ratings to produce seismic risk ratings. Case studies using the exposure model in the back-analysis of seismic risk are presented in Chapters 5 and 6. Each chapter analyses three rockbursts, with Chapter 5 presenting Harmony s Big Bell mine cases and Chapter 6 looking at rockbursts at Noranda s Brunswick mine. Maps of seismic hazard, damage potential, exposure and seismic risk illustrate the results. By contrast, the large number of records and variety of potential contributing factors in the general rockfall category meant that rockfall databases are more useful for drawing statistical correlations. Chapter 7 details the third stage of the model calibration/validation using rockfall records as independent data sets for the exposure model validation. Firstly, rockfall and injury records from two large Australian mines with extensive rockfall databases covering several years are presented. The system behaviour, identified by trends in these rockfall records, is compared with that suggested by the modelled exposure parameters for the various mining activities. Subsequently, further model validation is attained by correlating exposure ratings with specific mine activities and work locations from the Australian rockfall database (Potvin et al. 2001). There is a final section in Chapter 7 on how to convert exposure times into probabilities using the geometric function. Examples are shown of the correlation of various exposure model results with the likelihood of fatal injury due to rockfalls in underground mines. Chapter 8 presents research conclusions and recommendations for further work. Potential future applications include the integration of the exposure model into specialised software that currently carries out analyses of seismic clusters, with the long-term goal being the real-time, quantitative analysis of seismic risk. 24

40 Chapter 2 Literature Review 2. LITERATURE REVIEW 2.1. Introduction The traditional treatments of exposure to workplace hazard are typified by their use within statistical safety indices such as the Frequency Rate (FR), which expresses the number of occupational injuries as a rate per million hours worked (Minerals Council of Australia): FR = number of occupational injuries x 1,000,000 (2.1) number of hours worked As is shown later in this chapter, the treatment of personnel exposure within other energy and resource sectors, such as the nuclear and offshore industries, may be only slightly more detailed. For example, the analysis of risks that are specific to a particular work area on an offshore platform necessitates a determination of the number of personnel in each different operational or off-duty area. Over a year, the cumulative time ( manhours ) associated with each area provides the exposure input for risk calculations. However the essentially static operating environment of a chemical plant or offshore platform (in terms of established work areas and associated personnel) does not compare well with the changing nature of workplaces in a mine. Underground mining involves a complex interaction of new development headings, construction cycles, established operations and the ongoing extraction of ore blocks. Of more relevance to this study of the geotechnical issues facing the underground mining workforce, are comparisons with civil engineering and construction sectors; specifically landslide and rockfall exposure and risk methodologies. As introduced in Chapter 1 and expanded upon in later sections herein, the AGS (2000a) landslide guidelines effectively quantify exposure by profiling the temporal and spatial nature of the elements at risk and then assessing the degree of their vulnerability to the hazard. For example, landslide exposure profiles would include the buildings and/or vehicles on or in the path of the slide (spatial profile), their typical occupancy rates (temporal 25

41 Chapter 2 Literature Review profile) and the effect on the occupants of the likely failure modes of the structure given the landslide characteristics (vulnerability of personnel). Generalisations about these exposure components (such as estimates of daily vehicle traffic) may be appropriate for the assessment of landslide hazard along a fixed length of roadway. However, the exposure varies so much across the different work areas in an underground mine, that exposure profiles and vulnerability assessments must be more detailed and work-site specific in order to have any value for operational decisionmaking. So again, a direct transference of the civil engineering exposure characterisation techniques to underground mines is not feasible. The time of exposure for various activities is an easily measured (or estimated) parameter, hence its widespread use as the only exposure parameter. The current state of exposure profiling for geotechnical risk assessment at mine sites involves, at best, the simple classification of travelways and entry areas by their occupancy rate, generally described as continuous, hourly, daily, weekly, etc (GCG 2000). This gives a qualitative temporal profile but is not a true stochastic model of exposure. It does not take into account the characteristics of the vehicles, traffic flow and work practices, all of which affect the likelihood of a rockfall impacting a vehicle or person, as well as the vulnerability of the person to serious or fatal injury given the rockfall impact. Therefore, a gap exists in the knowledge regarding exposure and specific techniques to be used for accurate quantification of exposure to geotechnical hazards in underground mines. This thesis addresses that deficiency. The reader will notice that there is a significant focus on risk and risk components within the literature review presented. This is not intended to divert attention from the primary objective of this thesis which is to present a model for exposure to geotechnical hazards in underground mines. However three reasons necessitate that the exposure model be developed and tested within a risk framework: 1. A model cannot be divorced from its end use. In this case the exposure model must be able to integrate into a quantitative or semi-quantitative risk framework. 2. Assessments of vulnerability (such as likelihood of fatal injury) are inextricably linked with the nature of the hazard. Personnel vulnerability to landslide hazards, 26

42 Chapter 2 Literature Review for example, can depend on factors including the volume of the slide, its velocity, type and depth, whether the slide buries the person, whether the person is in the open or inside a vehicle or building, and, if the latter, the likelihood and failure mode of the building or vehicle collapsing when struck (AGS 2000a). In the same way, the extent, velocity and size (e.g. mass per unit drive length) of rockfalls/rockbursts directly affects the likelihood of personnel sustaining a fatal injury if they or their vehicle(s) are struck. 3. As mentioned above, the exposure time for specific activities or work areas is readily measured or estimated. However comparing modelled versus actual values for the other two components of exposure (hazard proximity and vulnerability) is more problematic. Estimations of the proximity of the work location to the hazard cannot be verified until the hazard manifests itself. Similarly, in the absence of a crash-test dummy database for mine vehicles and pedestrians impacted by rockfalls, the degree of vulnerability can only be assessed by looking at the historical record of the consequences of rockfalls. Thus it can be seen that the exposure model must be constructed within a risk framework and calibrated by information from risk databases. In presenting the use of probabilistic techniques and reliability theory in geotechnical engineering, Whitman (1984, p.166) noted that the complexity of most engineering projects, with the interaction of numerous components and often multiple failure modes, meant that a project may be thought of as a system, and some of the techniques of systems analysis must be employed when evaluating the risk of failure for the project as a whole. The next section of this chapter examines the underground mining environment as a system for exposure and risk modelling purposes. Definitions of systems and models are presented and model development discussed in relation to the mine system characteristics. Then the results of an extensive review of the literature on the treatment of exposure and risk in other industries is discussed, in order to establish an appropriate framework for building the exposure model detailed in Chapter 3. 27

43 Chapter 2 Literature Review The geotechnical hazards and potential excavation damage to which underground mining personnel and assets may be exposed are then introduced, with a brief overview of current methods for analysing these hazards. Then the topic of seismic hazard and rockburst damage is reviewed in greater detail, providing a basis for development of the methodologies in Chapter 4 that are used in Chapters 5 and 6 to incorporate case studies of the personnel exposure model into a seismic risk framework for calibration purposes Systems and Models System Definitions Much of the scientific process involves taking observations from real-life situations (systems) and inferring models that explain or simulate some part of the system behaviour. Sophisticated use of dynamical system identification and detailed mathematical modelling is more typically associated with plant processes (nuclear, chemical, etc), advanced mechanical design (e.g. for aircraft) and electrical/electronic engineering applications. However, the general concepts and methodologies of system theory and model building can be applied to physical situations ranging from quite basic to highly complex. Further, the model itself has to be application dependent, so it may vary widely in form and intricacy. In his book on System Identification, Ljung (1999, pp.1-6) introduces the broad concept of a system as an object in which variables of different kinds interact and produce observable signals and a model as the assumed relationship among observed signals. More formally in system theory, a system is a part of the real world with well-defined physical boundaries...influenced by its surroundings or environment via its inputs and (it) generates influences on its surroundings by its outputs which occur through its boundary. (Hangos & Cameron 2001, p.20). In mathematical terms, again from Hangos & Cameron (2001, p.20), The system S maps the inputs to the outputs...internal to the system are the states x which allow a description of the behaviour at any point in time...the system to be modelled could be seen as the whole process plant, its environment, an operating unit or an item of equipment. Hence, to define our system we need to specify its 28

44 Chapter 2 Literature Review boundaries, its inputs and outputs and the physico-chemical processes taking place within the system. For this research, the large-scale system under consideration contains all accessible areas of the underground mine excavations. This definition encompasses all that is required for analysis of the personnel and mobile equipment exposure to underground geotechnical hazards within the scope of this thesis. However, to expand the exposure analysis to other elements at risk from these hazards, such as the ore resource or production tonnage, would require a larger system with its physical boundaries extended to include past, present and future mining areas. Similarly, when other influences, for example remote hazardous seismic energies, must be taken into account, the system boundaries become more difficult to define and may shift as new hazards (such as previously inactive faults) are identified Model Construction In building any model from observed data to represent part or all of the system being considered, the application, or purpose, of the modelling is a key issue which determines the type and structure of the model,...models of various sorts are constructed and used in engineering especially where undertaking experiments would not be possible, feasible or desirable for economic, safety or environmental reasons....the model should describe or reflect somehow the properties of the real system relevant to the modeling goal. (Hangos & Cameron 2001, p.22). In particular, the needs of the end user of the model must be taken in consideration. In most process engineering applications, the models are mathematical and use equations to represent certain characteristics of the system. Hangos and Cameron (2001, p.4) express the modelling process as linking together, a purpose P with a subject or physical system S and the system of equations M which represent the model. A series of experiments E can be applied to M in order to answer questions about the system S. Before discussing the model requirements for the specific mining application in this thesis, it is useful to describe some general types of models, distinguished by the system characteristics being considered and the state of knowledge of their underlying processes. A mechanistic or white-box model is a first principles engineering 29

45 Chapter 2 Literature Review approach which uses engineering knowledge of the physical or chemical processes taking place in the system, i.e. it is based on mechanisms or underlying phenomena. An empirical model is based on experimental results or observations and the parameters used in the equation fitting may have little or no physical meaning. When the model set is built, not upon the actual system characteristics, but by adjusting parameters to best fit the observations, this is termed a black-box model. In between cases, that is models combining mechanistic and empirical foundations with adjustable parameters based on physical interpretations, can therefore be referred to as grey boxes. Stochastic models are particularly significant to geotechnical engineering as they describe cases which involve natural random variations in space and/or time, typically represented by probability distributions rather than direct cause and effect phenomena. Applicable to stochastic models is the method of Bayesian updating, which can be used to update the probability of a given event as more information is obtained on the particular process or parameter. Where the cause and effect relations are clear, deterministic models can be defined. An example of the use of stochastic techniques in combination with deterministic models is in the development of limit-state structural engineering codes (AS/NZS 1170), which allows for variability in design loadings and resistances. The fast processing capabilities of modern computers has encouraged the use of Monte Carlo simulations for the probabilistic analysis of multiple interacting parameters. For an excellent summary of current practices with references and practical examples of probabilistic techniques used in geotechnical engineering, the reader is referred to Whitman (2000). When setting up the model, its time dependence must also be considered. Steady-state or static models describe an operating point of the system where it is assumed to be at steady state, such that...the simulation problem computes the output values o given specific inputs i, a model structure M and its parameters p. This is sometimes known as a rating problem. (Hangos & Cameron 2001, p.21). In the mining situation, adoption of a static model to study exposure would necessitate taking snapshot views of the mine, where the work and travel areas are fixed for specific times and exposure ratings determined for these particular situations. If the process model is developed to represent changes in the system over time, it is a dynamic simulation. 30

46 Chapter 2 Literature Review Lyung (1999) discusses model construction as involving three basic entities, namely a data set, a set of candidate models or a model structure, and finally a rule by which candidate models can be assessed using the data. Even before this is the problem definition phase in which the modeller defines the process, establishes the modelling goal and then reviews the data set and determines the validation criteria. These steps are part of the system identification loop as illustrated in Figure 2.1. Experiment Design Prior Knowledge Data Choose Model Set Choose Criterion of Fit Calculate Model Validate Model Not OK: Revise OK: Use it! Figure 2.1 The system identification loop (after Ljung 1999, p.15). In constructing a model, one common technique is to break down the system into subsystems whose properties are well understood. The underground mine comprises subsystems including new constructions (excavations) at various stages of the 31

47 Chapter 2 Literature Review development or production cycles, established travelways and infrastructure areas, ongoing maintenance and rehabilitation of long term excavations as well as inactive, perhaps unmaintained areas. In formulating and validating an exposure model for this complex environment, some comments from Breitung (1992) on structural reliability are relevant, namely that empirical Bayes methods and the statistical viewpoint of predictivism should be adopted. The empirical Bayes methods are suited to models where the data comes not from identical experiments (situations/structures) but from similar experiments. Even more than a civil/structural case, the mining situation incorporates significant temporal and spatial variations in materials (rockmass, ground support), loadings (virgin stress, mining-induced stress, blast vibrations) and exposure (distribution of personnel and equipment), such that a laboratory-style series of identical experiments is impossible to achieve. Thus, as for structural reliability, the empirical Bayes method is much more relevant to the source of data used for the formulation and validation of risk models in the underground mine system i.e. accident reports, geotechnical assessments, rockfall databases and mine records on activities and cycles. For this method, it is assumed that there is a sequence of experiments or observations, but the parameter of interest is different in each experiment. (Breitung 1992). So the objective of the methodology is the estimation of the a priori distribution rather than the parameter itself. For example, with each entry of mine personnel into an excavation, the parameter of most interest from an exposure viewpoint is the probability of fatal injury given a rockfall in the excavation. However, this probability will vary dependent on the nature of the rockfall (size, velocity, position in the excavation), the number and spatial positioning of the personnel, their level of protection, the time spent in the excavation, etc. All of these will be unique for each case and thus the parameter will never be exactly determined. Instead, one can use the empirical Bayesian approach and look at probable distributions of personnel undertaking the particular activities and prior records of rockfall nature and locations in order to estimate the likelihood of fatal (or other) injury given a fall of ground. When this estimated exposure distribution is then combined with the most 32

48 Chapter 2 Literature Review recent assessments of geotechnical hazard and rockmass integrity, an estimate can be made of the risk of fatal injury Model Calibration and Validation The concepts of model calibration and validation are linked because both use measured data but may be distinguished by their separate objectives and the different stages at which they occur in the modelling sequence. Hangos & Cameron (2001, p.299) note that statistical model calibration is usually done through model parameter and/or structure estimation. Within the chosen model structure, the best model is chosen during this parameter estimation procedure in model selection and calibration stages. In a grey-box model, there may be values of the model parameters and/or parts of the model structure that are not available, necessitating the use of experimental data from the real process system. However, because measured data contains measurement errors, the missing model parameters or structural elements can only be estimated. This estimation step is called statistical model calibration (Hangos & Cameron 2001, p.301). The end result of model calibration is a model that is well-defined, solvable and optimised to reduce unnecessary complexity yet still have sufficient parameters or variables to adequately describe the system processes. The model validation stage is where the selected (calibrated) model is checked against reality to see whether it is good enough for its required purpose. This assessment is based on analysing how the model relates to observed data, to prior knowledge, and to its intended use (Ljung 1999, p.14). In this step, the measured experimental data ( validation data ), is used to either compare the predicted outputs of the model to the measured data, or compare the estimated parameters of the model based on validation data to the true or previously estimated parameters. (Hangos & Cameron 2001, p.302). Model validation is the last stage of the modelling process, systematically described by Hangos & Cameron (2001, pp.26-29) as follows: 1. Define the problem describing the process system with the modelling goal and level of detail relevant to the modelling goal. 33

49 Chapter 2 Literature Review 2. Identify the controlling factors or mechanisms investigation of the physicochemical processes and underlying phenomena taking place in the system and relevant to the modelling goal. 3. Evaluate the data for the problem consideration of the measured process data and/or estimated model parameter values. 4. Develop a set of model equations obtaining either differential (conservation balance) equations or algebraic (constitutive) equations. 5. Find and implement a solution procedure identification of the mathematical form and ensuring degrees of freedom are satisfied. 6. Verify the model solution determining whether the model is behaving correctly; i.e. coded correctly and giving the intended answers. 7. Validate the model checking the quality of the model against independent observations or assumptions. There are various options for carrying out model validation including: verify experimentally the simplifying assumptions; compare the model behaviour with the process behaviour; develop analytical models for simplified cases and compare the behaviour; compare the model directly with process data. (Hangos & Cameron 2001, p.29). Usually only partial model validation is achievable with the data available. Should the model behaviour be found deficient in relation to the observed data, prior knowledge or intended use, then the model is rejected. Good performance in these respects will provide a certain level of confidence in the model. However, Ljung (1999, p.14) notes that, 34

50 Chapter 2 Literature Review A model can never be accepted as a final and true description of the system. Rather, it can at best be regarded as a good enough description of certain aspects that are of particular interest Risk Analysis Techniques used within Resource and Energy Sectors Before the development of the geotechnical hazard exposure model in Chapter 3, it is appropriate to review comparisons between mines and other industries (nuclear, oil & gas, civil) as systems for risk modelling and analysis Nuclear Industry The severe consequences of containment failure at a nuclear power station, as graphically demonstrated by the Chernobyl disaster, have meant that the nuclear industry is more conscious than most of the importance of structured risk assessments. For the same reasons, it is also an industry which is under enormous scrutiny by regulators. In the United Kingdom, the Nuclear Installations Act requires that the operator of a nuclear power station obtain a Nuclear Site Licence from the Nuclear Installations Inspectorate. Conditional to the granting and continuation of the Nuclear Site Licence are requirements on the operator to produce and periodically assess safety cases (Stochoe 1998). A comprehensive fault analysis and probabilistic safety assessment (PSA) comprise part of the documentation for safety cases (Western & Corkerton 1998). Ravindra (1997) presents the results of United States nuclear power plant examinations into externally initiated events (namely seismic events) which were carried out to comply with governmental severe accident policy. Seismic events were treated in these examinations by either a probabilistic risk assessment (PRA), also known as Probabilistic Safety Assessment (PSA) in other countries, or a seismic margin assessment. The second methodology is less relevant to this study but details can be found in publications by the U.S.A. Electric Power Research Institute (EPRI) and Nuclear Regulatory Commission (NUREG). 35

51 Chapter 2 Literature Review For the nuclear power industry, Ravindra (1997) summaries the seismic PRA as comprising: a seismic hazard analysis to estimate frequency distributions of peak ground motions during a specified time interval; seismic fragility evaluations which estimate conditional probabilities of structural or equipment failure, based on evaluations of the ground acceleration capacity (and its uncertainty) for each of the given components; systems/accident sequence analysis involving models of structural and equipment failure paths; assembling the results of the seismic hazard, fragility and system analyses to estimate plant/core damage frequencies and assess impacts on plant performance and integrity. The seismic hazard analysis for the PRA firstly involves seismic source identification and evaluation of the regional earthquake record to estimate the frequency-magnitude characteristics for the system. Then energy attenuation relations are developed to estimate the peak ground acceleration at the site and integrated with the seismic source characteristics to establish charts showing various design frequencies of exceedance of peak ground motion parameters. For seismic risk quantification purposes, a mean hazard curve is constructed, using a Bayesian estimate of the frequency of exceedance at any peak ground acceleration. The risk of plant failure is analysed by constructing fault trees that reflect sequences of failure of key system components. Boolean expressions represent these failure paths and are then integrated with the component fragility estimates to obtain probabilities for the various plant level failures as functions of the peak ground acceleration at the site. The probability density function (pdf) of the occurrence frequency of plant failure (severe core damage) is produced by integrating the family of plant fragility curves over the family of seismic hazard curves. 36

52 Chapter 2 Literature Review Further information with detailed methodologies for carrying out risk assessments of nuclear power plants and fossil fuel-fired power plants can be found in handbooks and guidelines produced by the American Society of Mechanical Engineers (ASME 1998), through its Research Committee for Risk-Based Technology. Applications of the risk methods beyond power plants to areas such as aviation and civil structures are also suggested. Some background to the development of these handbooks and discussion of their use is presented by Schmidt & Mauney (1998) Offshore Oil and Gas Industry PRA (or PSA) in the nuclear power generation industry predated their first application to the offshore petroleum industry by several years. In the offshore industry, the terminology generally used is Quantified or Quantitative Risk Assessment (QRA) and its first applications in the late 1970 s were mostly in the research domain and involved trial adaptations of the nuclear industry methods. In 1981, Norway became the first country to require that QRA be undertaken for all new offshore installations when the Norwegian Petroleum Directorate issued guidelines for safety evaluation. For many years afterward, Norway was the only country using QRA systematically (Vinnem 1999), until the severe accident on the Piper Alpha platform in 1988 mobilised UK authorities to introduce QRA requirements into legislation. Since 1992, when the Safety Case Regulations came into effect in the UK (UK HSE 1992), the offshore sector in the UK has been required to perform risk assessments as part of the safety cases on all new and existing installations. For more detail on current legislation and the development of safety cases and QRA in the offshore industry, the reader is referred to Vinnem (1999), Brandsaeter (2002) and Wang (2002). Methodologies used for oil and gas installations are similar to those for nuclear plant safety cases, since both involve modelling the plant or platform response to a set of accident initiating events. Event trees and fault trees (now mostly constructed using dedicated software) are used to map the various accident scenarios. Information on the frequencies of the initiating events, component failure modes and rates, repair times, etc is usually provided through a combination of generic and plant-specific operating experience. Other techniques have been adopted from the onshore petrochemical industry, including hazard and operability studies (HAZOPS), reliability of key safety 37

53 Chapter 2 Literature Review systems and techniques for modelling the consequences of hydrocarbon releases (Brandsaeter 2002). Vinnem (1999) in chapter 5 of his book, explains the construction of event trees and provides several examples related to offshore applications. The types of data sources that are used in QRA studies include accident statistics, failure databases, equipment failure databases, physical properties of various substances as well as generic data sources and company internal accident/ incident databases. Key points in relation to risk assessment is that one should always commence with a system description, a clear definition of the purpose (scope) of the risk assessment, and the establishment of acceptance criteria. The system description should comprise (Vinnem 1999): 1. Description of the technical system, relevant activities and operational phases; 2. Statement of the time period to which the analysis relates; 3. Statement of the personnel groups, the external environment and the assets to which the risk assessment relates; 4. Capabilities of the system in relation to its ability to tolerate failures and its vulnerability to accidental effects. The acceptance criteria may be both risk based and deterministic, but the application of numerical risk criteria may not always be appropriate considering the available data and level of uncertainty (Wang 2002). The UK Health and Safety Executive (HSE) framework for decisions on the tolerability of risk is reproduced in Figure 2.2 below. The figure shows three regions of acceptability: (1) intolerable, (2) as low as reasonably practicable (ALARP), and (3) broadly acceptable. Another way to show these same regions is in a risk matrix which may involve qualitative or quantitative categories. 38

54 Chapter 2 Literature Review Intolerable Region Risk cannot be justified on any grounds The ALARP Region Tolerable only if risk reduction is impracticable or if its cost is grossly disproportionate to the improvement gained Tolerable if cost of reductions would exceed the improvement gained Broadly Acceptable Region (No need to demonstrate ALARP) Necessary to maintain assurance that risk remains at this level Negligible risk Figure 2.2 Framework for decisions on the tolerability of risk (UK HSE 1992). The principal steps of a general risk analysis are illustrated in Figure 2.3. This format and terminology is similar to that shown in the AS/NZS 4390:1999 Risk Management standard. The second schematic (Figure 2.4) shows the major stages in an offshore QRA. The following discourse on the risk calculations used for offshore QRA is from Chapter 2 of Vinnem (1999) and the interested reader is referred to the text for a comprehensive analysis of the topic. Dimensions of risk are the different types of accident consequences and therefore include personnel risk, environmental risk, asset risk and impairment risk. The first four are self explanatory, while impairment risk involves study of the effect of accidents scenarios on escapeways, control functions and shelters. Personnel risk is mainly focussed on fatality risk, which can be expressed as a platform fatality risk, an individual risk or group risk. Platform fatality risk involves estimations of the Potential Loss of Life (PLL) or Fatalities Per Platform Year (FPPY) and can be considered as the fatality risk for the entire installation, covering all accident scenarios. It may be calculated from accident statistics or by QRA using event tree analysis. Each accident scenario is then an event tree terminal event with certain personnel consequences and an associated average number of fatalities. 39

55 Chapter 2 Literature Review The next risk dimension is individual risk, which can be expressed as FAR (Fatal Accident Rate) or AIR (Average Individual Risk). There are calculated using, FAR = PLL = PLL.10 8 (2.2) Exposed hours POB av AIR = PLL = PLL (2.3) Exposed individuals POB av. (8760/H) where, POB av = average annual number of staff on manning level H = annual number of offshore hours per individual (on-duty and off-duty hours). Note that 8760 is the number of hours in a year. The use of FAR values may be calculated for the entire crew but can also be determined as average values for select groups such as those associated with a specific work area or particular work activity. These latter applications are of more relevance in comparisons with the typical underground mining situation when considering geotechnical hazards. Extreme events which threaten the entire mine crew could include major water inrush, explosion and fires and, from the geotechnical perspective, exceptional tectonic events and instabilities such as major crown pillar collapse which have serious mine-wide consequences. Group risk relates to risk aversion, whereby a multiple fatality accident is considered less societally acceptable than several single fatality accidents. Therefore group risk relates to a transformation of the individual risk into an expression of the total effects of accidents on society or the exposed group. This relation is often represented by a logarithmic plot of accident frequency versus number of fatalities, known as an f-n curve. Referring to Figure 2.4, at the stage of hazard analysis, studies required include those for blowout hazards, riser/pipeline hazards, fire and smoke analysis, explosion analysis, collision hazards and structural failure analysis, among others. The results of these studies are then used in the next stage of analysis of critical risks where detailed examinations of probabilities and consequences are carried out, and event trees revised and studied. To demonstrate the calculation process for a particular hazard, an example from Chapter 4 of Vinnem (1999) is presented in Table

56 Chapter 2 Literature Review Risk analysis planning Risk acceptance criteria System definition Hazard identification Risk reducing measures Frequency analysis Consequence analysis RISK ESTIMATION RISK ANALYSIS Risk picture Risk evaluation unacceptable RISK ASSESSMENT acceptable Further risk reducing measures PART OF SAFETY MANAGEMENT AND RISK CONTROL Figure 2.3 Iterative loop for risk assessment and risk control (after Vinnem 1999, p.75). 41

57 Chapter 2 Literature Review Fatality risk analysis Environmental risk analysis Asset risk analysis Hazard Id Hazard analysis Analysis of critical risks Impairment analysis Figure 2.4 Table 2.1 Main elements of the offshore QRA (after Vinnem 1999, p.77). Overview of fire and explosion calculations (after Vinnem 1999, p.76) Fire and Explosion relating to blowout Release rate (often assumed to be constant) for flow inside/outside tubing, with/without unrestricted flow, etc. Calculation of flammable cloud size to serve as a basis for calculation of ignition probability and explosion overpressure. Calculation of ignition probabilities. Estimation of gas explosion overpressure. Calculation of conditional probabilities that a gas explosion causes overpressure exceeding specified values fatalities damage to equipment damage to structures damage to Shelter Area Calculation of the fire sizes for alternative scenarios and radiation impact at prescribed points which are critical with respect to trapping of personnel or safety functions. Calculation of the smoke impact at prescribed points which are critical with respect to trapping of personnel or safety functions. 42

58 Chapter 2 Literature Review Hazard and risk analyses involve considerable uncertainty, which may not be explicitly recognised or assessed in traditional deterministic or statistical approaches. Characteristic sources of uncertainty in QRA studies are statistical uncertainty, modelling uncertainty and uncertainty related to the adequacy of the risk assessment. The assessment, let alone quantification, of the latter two types of uncertainty is very problematic as they involve many unknowns. The assumptions made in an analysis have the cumulative effect of increasing the level of uncertainty associated with the latter stages of the QRA process compared with the initial steps. In his book, Vinnem (1999, p.36) adopts the Bayesian approach whereby, the risk estimates are considered to be expressions of the uncertainty related to whether accidents will occur or not. The implication of this consideration is that uncertainties shall not be quantified in QRA studies, because the risk assessment itself is an estimate of uncertainty...however...it will be important for the analysts to be aware of what is influencing the extent of uncertainty in the analysis Comparison of nuclear and offshore industries with mining If the same procedures as used in the nuclear sector PRA and offshore QRA were adapted to the underground mining system, an analogous calculation for say, rockbursting, could take the form shown in Table 2.2. Table 2.2 Overview of seismic hazard and rockburst calculations Rockbursting Calculations Analysis of data from seismic monitoring to identify the locations and characteristics of historical seismic sources. Numerical modelling of virgin + mining-induced stresses around excavations to identify potential areas of high stress and where overstressed areas are likely to shed load. Evaluation of applied maximum and confining stress against rockmass strengths to highlight future critically loaded areas and compare with historical seismic sources. 43

59 Chapter 2 Literature Review Calculation of maximum seismic magnitudes for the identified sources over the timeframe indicated by the recording period and frequency-magnitude curves. Calculation of the peak particle velocities (ppv) at excavation boundaries based on seismic magnitudes and attenuation with distance relationships. Integration of ppv-magnitude with frequency-magnitude curves to establish frequency of ppv exceedance. Determination of design ppv at each excavation boundary location by construction of a mean hazard curve, using a Bayesian estimate of the frequency of exceedance at any ppv. Calculation of the capacities of excavation boundaries with their planned (or installed) ground retention and reinforcement elements to withstand static + dynamic loading. Ground retention elements refer to surface support such as strapping and mesh which mobilise capacity after some ground displacement. Ground reinforcement involves strengthening of the rockmass, typically by rockbolts and cables. Construction of fault trees with Boolean expressions to show sequences of component failure (ground reinforcement elements, ground retention elements, rockmass elements) leading to excavation failure. Use of generic and site-specific data on component failure and uncertainties to assign probabilities to branches on the fault trees. Typical uncertainties would include ground support corrosion, fatigue and incorrect installation, variabilities in rockmass strength and unmapped discontinuities. Hence estimate excavation failure probabilities for the different fault tree sequences. Establishment of a family of fragility curves showing the probabilities of failure of excavations for various levels of ppv. Integration of excavation fragility curves over the design ppv curve to provide 44

60 Chapter 2 Literature Review frequencies of excavation failure under dynamic load (rockburst probabilities). Examination of the exposure of elements at risk at the potential rockbursting sites. Calculation of conditional probabilities that a seismic event causes dynamic loading exceeding specified values that result in fatalities, damage to equipment and/or damage to structures in the mine. Calculation of the rockburst size and impact for alternative scenarios at prescribed points which are critical with respect to the level of personnel exposure. It can be seen that this level of detailed, quantitative risk analysis involves a great deal of time and effort and an important point to note is that prior to the decision to start a QRA, careful consideration should be given to whether the data available is adequate to allow reliable conclusions to be reached. (Vinnem 1999). The QRA effort can be justified for the design or review of installations for which the operating conditions remain fairly constant and there is a good database of component capacities. Nuclear plants and oil and gas platforms both satisfy these criteria. Underground mines generally do not. However, satisfying the requirement for a good database on component capacities and uncertainties should be only a matter of time and coordinated effort across the mining industry, as happened within the nuclear and offshore sectors. The issue of reasonably static operating conditions, to justify the effort of the QRA/PRA before the analysed conditions become outdated, is more problematic. As discussed in Chapter 1, a mine is a combination of areas of ongoing construction and established areas for access, services and infrastructure. Also, most areas of a mine have a short design life and the use of high factors of safety in their design would make mining uneconomic. Thirdly, the actual operation of a mine actively disturbs its host rock mass environment, so there is a feedback loop from operations to geotechnical hazard. Some geotechnical consultants (AGS 2000b) express doubt about efforts to quantify hazard and consequence likelihoods, arguing that it may create a false sense of confidence in the accuracy of the state of knowledge and analysis results. However a counterargument is that geotechnical risk analyses will always involve uncertainties and 45

61 Chapter 2 Literature Review a probabilistic methodology (with parameter distributions) is the way to explicitly represent them. The level of confidence in the analysis may be expressed through stating the potential ranges of risk values for the various consequences. This may also assist in identifying those areas where efforts such as further site investigation, testing, etc would be best directed so as to increase confidence in the risk estimates and reduce the variations in risk values. Empirical methods, deterministic equations and qualitative assessments of hazard (such as design based on the maximum credible earthquake/flood/storm) all involve judgement on the best model structure and parameter values to adopt. So the uncertainty is implicit. Although qualitative and semi-quantitative assessments have their place (see Table 2.3 below), they do not allow for checks of the sensitivity of the risk to variations in the hazard and consequence parameters. This is an important and desirable outcome in the analysis of geotechnical risk in mines. Another argument for quantitative estimates is that risk measures can then be compared between different practitioners, time periods and projects. Only quantitative risk models can be validated statistically and compared with societal measures of acceptable risk. Table 2.3 Risk Assessment Approaches (from Lilly 2002) GOAL QUALITATIVE SEMI- QUANTITATIVE QUANTITATIVE To improve an actual problem Satisfactory Good Not necessary To plan for change Satisfactory Good Not necessary To select the best option Poor Satisfactory Good if data available To decide on Inadequate Satisfactory if the Good if data acceptability of risk acceptability rank is set available 46

62 Chapter 2 Literature Review In a recent study on how the absence of planning on the part of operators imperilled complex, engineered systems, Busby & Hughes (2003) analysed 59 incidents in the offshore industry and found that all the processes that were implicated in the failures were either changes in the state of the system, such as start-up and shut-down, or operations that could not be said to take place in a steady state: there were no cases involving routine, continual activity. Given the frequent exposure of operators in underground mines to non-steady state conditions, particularly in seismically active areas, there are implications for the identification and treatment of critical risks during mine work activities which should be addressed in the exposure model. This is developed further in Chapter 3. The issues discussed above, arising from the complex system that is an underground mine, make some modifications to traditional QRA necessary before it can be applied to a mine on anything other than a research or very limited basis. Even for offshore oil and gas applications, the data required for a QRA is often not available until the detailed design phase. To accommodate this and still allow safety related modifications to be made at a sufficiently early stage of the design process to be cost effective, some authors have suggested the initial use of the simpler design basis accident approach (Krueger & Smith 2003). This actually harks back to the era before QRA with the approach using credible worst case accidents and then focussing on estimating the consequences to the facility. Looking at underground mines as systems, the data set upon which hazard identification and capacity assessments can be made, becomes known incrementally as mining advances, rather than being fully defined at the planning stage. Similarly, the actual distribution of personnel and equipment, and their resultant exposure to geotechnical hazards, is a dynamic process. Therefore Bayesian updating or similar methodologies to systematically incorporate new information into initial assessments seems very relevant. Typical sources of updated data for mining include real-time seismic monitoring, ongoing geological mapping and geotechnical inspections of new excavations, and detailed mine schedules such as shift plans for work activities. 47

63 Chapter 2 Literature Review 2.5. Risk assessment of geotechnical hazards in the civil engineering industry As discussed by Whitman (2000) and introduced in Chapter 1 of this thesis, the adoption of risk-based techniques varies across the civil and geotechnical engineering industry. Newer applications such as the earthquake engineering of structures, offshore platform and foundation design and geo-environmental analyses have more readily adapted their design methodologies to explicitly account for uncertainties in design parameters by using probabilistic techniques. By contrast, many routine civil engineering projects, such as retaining walls and earth dams, rely on traditional deterministic methods of design and use accepted practice and minimum or codified factors of safety. The use of a safety case approach as described in previous sections, has made its way from nuclear and petroleum/offshore sectors across to advanced civil applications. An example of this is given by Llambias & Mercado (1998) in their paper on the design of a high speed railway system in earthquake zones in Taiwan. The methodologies used were very similar to those described by Ravindra (1997) for the probabilistic risk assessment of nuclear power plants subject to seismic loadings. Another instance of technology transfer is the use of event trees for the consequence-based risk analysis of dams, which is now standard practice for many large projects (Australian National Committee on Large Dams 1994). Of more direct relevance to comparisons with the stochastic nature of mining hazards and personnel exposure, are the civil engineering analyses of landslides and the assessment of roadways against rockfalls. Various hazard and risk assessment guidelines for landslides are presented by authors including Fell (1994), Aleotti & Chowdhury (1999) and AGS (2000a). Hoek (1998) presents a chapter on the analysis of rockfall hazards, explains the Rockfall Hazard Rating System (RHRS) developed by the Oregon State Highway Division and refers to a detailed rockfall risk case study by Bunce (1994). The case study by Bunce (1994) involved a fatality that occurred as a result of a rockfall onto a roadway in British Columbia. Various approaches to the estimation of the annual 48

64 Chapter 2 Literature Review probability of fatality due to rockfall were provided by Bunce (1994) and Bunce et al. (1997). One of these methods, using a geometric probability function to determine the likelihood of vehicle impacts for a given rockfall frequency, has been adopted by the Australian landslide guidelines (AGS 2000a). Another statistical approach using frequency-magnitude curves for rockfalls is given by Hungr et al. (1999). However Hoek (1998) and Budetta (2002) considered event tree analysis to be the best approach given the quality of input information on highway projects. Some comments by Hoek (1998, p.123) are quoted here as they are also applicable to the mining situation, Highway and railway construction in mountainous regions presents a special challenge to geologists and geotechnical engineers. This is because the extended length of these projects make it difficult to obtain sufficient information to permit reliability assessments to be carried out for each of the slopes along the route. This means that, except for sections which are identified as particularly critical, most highway slopes tend to be designed on the basis of rather rudimentary geotechnical analyses. The RHRS is a semi-quantitative method which combines ratings associated with rock slope and roadway characteristics. It also incorporates a parameter relating to personnel exposure, which it terms Average Vehicle Risk (AVR) and calculates using the formula below. AVR = ADT x Slope Length x 100% (2.4) Posted Speed Limit Where: ADT is the average daily traffic on the road in cars/hour, the Slope Length is measured in miles and the Posted Speed Limit in miles/hour. Another RHRS parameter which is of interest for exposure modelling, is the Percent of Decision Sight Distance (DSD) category, which rates the line-of-sight distance that drivers on the roadway have to react to unexpected hazards. The RHRS is increased for road sections where there is insufficient distance for the driver to react in time to avoid rockfall hazards. More sophisticated rockfall/landslide risk analyses should evaluate triggering mechanisms, the rockfall or slide run-out parameters (maximum distance reached, flow velocity, thickness and distribution of deposits or rockfall block size), the vulnerability of the roads situated at the toe of the slope and the type of vehicles. Vulnerability depends on several factors including the vehicle speed and length, the available decision 49

65 Chapter 2 Literature Review sight distance, the traffic volume, the length of the landslide/rockfall risk section of the route, the number of occupants in a vehicle and the type of vehicle (Budetta 2002). Chowdhury & Flentje (2003) in discussing slope reliability analysis for landslide risk management, conclude that conventional deterministic methods of geotechnical analysis need to be supplemented by probabilistic techniques to account for uncertainties and variabilities in design parameters. Reliability is generally defined as the probability that a system survives for some specified period of time (Lewis 1997). Some reliability concepts that may be applicable to aspects of geotechnical engineering were covered in the classic paper by Whitman (1984) and more recently by Pine (1992). Specific applications to landslides are presented in Chowdhury & Flentje (2003). One reliability parameter found to be useful in geotechnical analyses is the reliability index, defined as follows: For a safety factor probability density function (pdf) with a mean FS and standard deviation FS, one can define a reliability index, such that, = FS FS (2.5) Both Whitman (1984) and Chowdhury & Flentje (2003) list some values of slope failure probabilities calculated from reliability indices. When the probability distributions of capacity and demand are not described by standard distributions or not fully known, other methods can be used to estimate failure probabilities. Aleotti & Chowdhury (1999) provide a comprehensive review of current techniques for landslide hazard assessment and list three commonly used methods of probability calculation: First Order Second Moment (FOSM), Point Estimate and Monte Carlo Simulation methods. A recent detailed application of a FOSM method to the reliability-based design of rock slopes is presented by Duzgun et al. (2003). More advanced techniques again relate back to QRA type analyses, with conditional probabilities assigned to different failure paths and event trees used to establish the consequences of failure. Further development of techniques for the reliability analyses of structures subject to dynamic 50

66 Chapter 2 Literature Review (e.g. seismic) loading is presented by Huh & Haldar (2000), who integrate response surface methods (RSM) with FOSM and a stochastic finite element method (SFEM). Importantly, landslide and roadway rockfall analyses both incorporate spatial and temporal variations in personnel exposure, as is also necessary for modelling the exposure of mine personnel. Substantial variations in the vulnerability of exposed people to injury is another feature common to both landslide and mine environments. This vulnerability is dependent on the characteristics of the physical manifestation of the hazard (e.g. velocity of landslide, size of rockfall) as well as the nature of any protection between the hazard and the person (e.g. a building, vehicle). The conditional probabilities involved were introduced previously in Chapter 1 and are discussed further in Chapter 3. From the literature review discussed above, it was concluded that the most analogous situation to underground mining exposure to geotechnical hazards was that for the assessment of the exposure of structures and roadways to landslide hazards. This conclusion was based on the similarity in model parameters, as both cases involve measures of spatial probability, temporal probability and vulnerability. The next sections discuss the specific nature of geotechnical hazards in underground mines and typical methods for their assessment Geotechnical Hazards in Underground Mines General The term geotechnical rather than geomechanical hazards has been used herein to maintain consistency with leading authors such as Whitman (1984) and local guidelines including DME (1997). Geotechnical engineering includes the fields of engineering geology, hydrogeology, soil and rock mechanics and mining seismology. A geotechnical domain is a volume of rock with generally similar geotechnical rock mass properties. A typical first pass determination of the extent of these domains in mines uses rock mass classification methods such as: 1. the Rock Mass Rating system or RMR system (Bieniaswski 1993); 51

67 Chapter 2 Literature Review 2. the Rock Quality system or Q-system (Barton et al. 1974), (Barton & Grimstad 1994); 3. the Mining Rock Mass Rating system or MRMR system (Laubscher 1990). These classification systems use semi-quantitative ratings to describe the rock mass quality in the particular geotechnical domain and provide indications of whether ground support is required. More detailed geotechnical domaining techniques involve sophisticated characterisations of the number and orientations of structural sets (discontinuities including joints, shear zones, bedding planes, etc) and other rockmass properties. The DME (1997) guidelines discuss soft rock, hard rock and seismic rock conditions as three general categories of ground conditions that may be encountered in underground mines. The guidelines define these categories as: Soft Rock Conditions Low strength, jointed or sheared, plastic rock in a low stress environment ; Hard Rock Conditions High strength, well jointed, elastic rock in a low stress environment Seismic Rock Conditions High strength, brittle, sparsely jointed, elastic rock in a moderate to high rock stress environment that is prone to mining induced seismicity Factors Affecting Underground Excavation Stability The main factors influencing the ground conditions as well as their associated uncertainties are listed in DME (1997, pp ) as including: 1. Geological structure The rock mass is not a continuum but is comprised of a large number of discontinuity bound potential blocks the size, shape, orientation, location and number of which are largely unknown. Geological structure refers to all the natural planes of weakness in the rock mass including joints, faults, shears, bedding planes, foliation and schistosity; 52

68 Chapter 2 Literature Review 2. Rock stress The forces or stresses acting in large volumes of the rock mass are generally unknown and are subject to variation (possibly as a result of block interactions or rock anisotropy), however "point" measurements of the rock stress field are possible. The overstressing of a rockmass may result in the development and propagation of stress fractures, shearing and dilation along discontinuities and seismicity. Other problems may arise from destressing and/or reductions in confining stresses leading to a loss of rockmass integrity; 3. Rock strength The strength of the rock mass is not well known and is difficult to measure in large volumes of rock; and large scale rock testing is difficult and expensive to conduct (however, it may be estimated by back analysis). Rock strength can be highly sensitive to confining stress and the time dependent behaviour of the rock mass is also not well known; 4. Groundwater Water under pressure in the joints defining rock blocks reduces the frictional resistance to shearing. Another potential effect of groundwater in mines is its deleterious effect on the strength (and increase in the deformability) of some rock types such as those with sensitive clay seams; 5. Blast damage Blast damage to the rock mass, particularly from large scale blasting operations, is an additional factor that has generally not been well quantified. 6. Mine excavations Size, number, shape, type and orientation of openings and their interaction with the other five factors. Hoek & Brown (1980) discuss four principal sources of instability of underground excavations: (a) Instability due to adverse structural geology... (b) Instability due to excessively high rock stress... (c) Instability due to weathering and/or swelling... (d) Instability due to excessive groundwater pressure or flow 53

69 Chapter 2 Literature Review The hazards due to adverse structural geology and excessively high rock stress are more typical of hard rocks. Rockfalls due to structure occur in rocks which are faulted and jointed. Hoek & Brown (1980) note that stability may be improved by relocating or reorientating the excavations, but usually extensive support, such as rockbolting and cabling, is required. High rock stress problems may be alleviated by changing the shape and layout of excavations but ground support is also important. The geotechnical hazards associated with excavating through heavily weathered, poor quality rock, fill or overburden soil at shallow depth more typically involve squeezing or flowing ground. The excavation methods, such as soft tunnel boring, and types of ground support used to manage these hazards are similar to those adopted for civil tunnelling applications, and are distinct from those in better quality rock. The timely installation of support is very important in these zones given the short stand-up times of unsupported excavations in soil or poor quality rock. Another situation where geotechnical hazards associated with weathering and/or swelling may be encountered is in isolated seams within more competent rock. Reducing the exposure of rock surface to moisture changes is a typical remedial measure (Hoek & Brown 1980). The issue of groundwater is of most concern when it occurs together with one of the other sources of geotechnical hazard. Remedial measures include drainage to reduce water pressure and the redirection of flow by grouting Methods for Analysing Underground Geotechnical Hazards Techniques for analysing the geotechnical hazards encountered in underground mining can be found in the discussions of rock mechanics and the design of underground excavations in Hoek & Brown (1980), Brady & Brown (1994) and Hoek, Kaiser & Bawden (1995). The MCA (2003) publication of a reference manual for the management of rockfall risks in underground metalliferous mines provides a summary list of tools and techniques commonly used for the design of excavation spans and support regimes. Software tools include DIPS, UNWEDGE (Rocscience 2001) and SAFEX (Windsor 1993). Complex numerical models of stresses and strains in the rock mass can be built using programs such as FLAC3D (Brummer et al. 2003) and Map3D (Wiles 1993). 54

70 Chapter 2 Literature Review The variation in geotechnical parameters across the rockmass and the typical deterioration of the excavation condition over time make the use of stochastic methods for stability assessment very relevant. Examples showing the adaptation of deterministic geotechnical engineering methods to probabilistic analyses were initially presented by authors such as Whitman (1984), Pine (1992) and Chen, Jia & Ke (1997) Physical Manifestation of Geotechnical Hazards on Underground Excavations Brady & Brown (1994, p.9) note that the ultimate design objective for a mine is to control the rock displacements into and around the excavations, and go on to specify four rock mechanics objectives for the performance of a mine structure: (a) (b) (c) (d) to ensure the overall stability of the complete mine structure, defined by the main ore sources and mined voids, ore remnants and adjacent country rock; to protect the major service openings throughout their designed duty life; to provide secure access to safe working places in and around the centres of ore production; to preserve the mineable condition of unmined ore reserves. From the previous subsection, geotechnical hazards associated with underground mining were seen to include: critical geological structures such as faults and shear zones groundwater dynamic loads from blasting and seismicity mining induced overstressing or destressing of the rockmass. However the common physical manifestation of these hazards on the mine structure, and the focus of mine design, is rockmass displacement, potentially resulting in rockfalls. 55

71 Chapter 2 Literature Review Using Rockfall Databases for Model Calibration and Validation Referring back to the earlier sections on systems and modelling, the end result of model calibration is a model that is well-defined, solvable, optimised for simplicity and describes the desired system processes. To achieve this, an established calibration technique involves the application and comparison of model results with actual results from case studies. Using rockmass displacement or rockfall cases within a risk framework for exposure model calibration is problematic, as the number of geotechnical variables to be accounted for would tend to make the risk modelling results too imprecise to be useful. Gravitational potential energy is an inescapable hazard affecting mine excavations but combines in a complex interaction with other contributory factors to rockfalls, as discussed earlier in this section. Civil engineering studies of landslides and rockfalls risks affecting highways have also encountered these issues (Chowdhury & Flentje 2003), and used combinations of deterministic and probabilistic analysis methods. A limitation to using statistical methods in underground rockfall assessments has been the lack of adequate mine rockfall databases. The Australian Centre for Geomechanics (ACG) undertook a project, in partnership with industry and MERIWA, to collate records from Australian underground metalliferous mines. The result is a comprehensive database collected from a group of 26 Australian underground mining operations. The initial findings from analysis of this database are presented in Potvin et al. (2001). It was recognised that there are numerous parameters that can conspire to produce a rockfall so the project report concentrated on identifying priority areas where improvements could be made. Potvin et al. (2001) observe that, Rockfalls are a major driver of safety statistics in underground mines. Despite good progress in reducing rockfall injuries in recent years, fatalities and severe injuries are still at an unacceptable level...by developing a better global understanding of the main causes of rockfalls, the high risk areas can be identified and solutions developed to address these issues as part of a system-based approach rather than allowing single issue parameters to drive the agenda. 56

72 Chapter 2 Literature Review The value of the Australian underground rockfall database to this research is that it provides the best chance to date of identifying statistically significant trends in the rockfall and related injury records. These trends can then be used to validate the hazard exposure model. However, the numerous and complex contributory factors to the rockfalls recorded in the database effectively preclude its use in comparing actual with modelled risk results as part of the detailed modelling of exposure to specific geotechnical hazards. To allow this type of model calibration, it is necessary to limit the risk analysis to a manageable set of variables and minimise modelling uncertainties. Therefore, case studies were chosen from a specialised subset of geotechnical hazards, namely seismic hazards. In this way, the topic of seismic hazard and rockburst damage potential was explored in some detail in order to find characteristic and readily measured seismic parameters to back-analyse rockburst case histories. It is emphasised that the objective was to suggest broadly representative relative ratings rather than a definitive seismic hazard methodology. The following section presents the results of this literature search Seismic Hazard in Underground Mines Introduction to Mining Induced Seismicity A seismic event is a transient vibration or stress wave caused by an inelastic deformation in a rockmass whereas a rockburst is a seismic event that causes significant visible rockmass damage. The rockfall definition adopted for this research from Potvin et al. (2001) includes rockbursts as a subset of the rockfall category. Seismicity associated with underground mining operations is primarily caused by the progressive build up of high stress levels in the rock mass remaining around an excavation as it is enlarged by mining (DME 1997). Seismic events may also be caused by the reduction of some principal stresses, inducing fault slip. The underground mining processes of blasting and extraction of rock significantly disturb the rockmass. Excess stresses are redistributed and potential energies stored or released as the progressive removal of rock from a stope causes the stress originally carried by that rock to be transferred to nearby abutments and/or pillars. If high enough, the induced 57

73 Chapter 2 Literature Review rock stresses can lead to sudden movement on pre-existing planes of weakness in the rock mass and/or failure through the intact rock mass. Displacements of the rock mass allow the partial dissipation of high rock stress levels. The brittle fracture and displacement of rock is associated with seismic events and may manifest itself as rock noise, small rock falls, rock ejected into excavations at high velocity, the large scale collapse of excavations, and bursting of pillars or faces in development headings or stopes. Mining induced seismicity can be recorded by vibration monitoring systems involving networks of geophones and/or accelerometers installed through the mine. Information on the location, size and other characteristics of the seismic event can then be obtained for analysis of the potential hazard Seismic Hazard and Risk Techniques from the Literature The standard definition of seismic hazard is the probability of occurrence of a seismic event equal to or exceeding a specified level within a given period of time, P(M>M 1 t 1 <t<t 2 ). Stewart (1995) tracked several seismic hazard parameters for rockburst-prone sites in South African gold mines. Some more recent results on seismic hazard characterisation have come from South Africa and are published in the SIMRAC report GAP 303 by van Aswegen et al. (1999). Detailed hazard assessments in South Africa considered various seismological source parameters (as listed in Appendix C) and mapped peak ground motions. A review of the state of the art in seismic risk assessment in South Africa was published by Brink et al. (2000) in the SIMRAC report GAP 608. The report concentrated on the current techniques used or proposed for characterisations of the seismic hazard and potential excavation damage, and is one of the few papers on seismicity that distinguished between hazard and risk. It adopted the same approach as herein, that the seismic hazard is the seismicity which has the potential to cause harm or loss. However, the authors then associated the event magnitude frequency definition (P[M>M 1 t 1 <t<t 2 ]) with seismic risk instead of seismic hazard. The contention of this thesis is that risk must involve consideration of both the hazardous event and its potential consequence to elements exposed to damage. 58

74 Chapter 2 Literature Review Therefore the seismic event frequency relates to hazard, not risk, characterisation. The exposure of the workforce is only mentioned briefly by Brink et al. (2000), with a very general table shown based on percentage of total workforce exposed to the seismic hazard (reproduced in Table 2.4). The geotechnical risk assessment systems for various mines and regulators are summarised but the procedures are predominantly for hazard identification rather than true risk analysis. No definitive system was in place and proven for a wide variety of environments. More recently, Hagan & Brink (2001) presented ideas for seismic risk assessment in South African gold and platinum mines with a seismic risk definition that is more consistent with the risk conventions adopted for this thesis. They defined seismic risk as: Risk = Seismic Hazard * Induced Physical Hazard * Systemic Vulnerability. The suggested profiling of worker exposure was very simple with time-of-day distributions of personnel across large areas of the mine. This is similar to another publication by van Aswegen (2001:GAP610) that assesses the seismic exposure of mine personnel over a day, by combining time-of-day seismicity statistics with the average number of people in the area of concern for each hour of the day. The very large scale mining volumes assessed (involving the exposure of hundreds of workers each hour) and labour-intensive mining methods mean these techniques are not particularly applicable to the typical Australian and Canadian mechanised mine environments. Table 2.4 Exposure table from GAP 608 (Brink et al. 2000) Risk rating for P 3 p 1 Description of the time distribution of people 1 0 to 20% of max. number of people exposed 2 20 to 40% of max. number of people exposed 3 40 to 60% of max. number of people exposed 4 60 to 80% of max. number of people exposed 5 80 to 100% of max. number of people exposed 59

75 Chapter 2 Literature Review The Role of the Mine Seismicity Risk Analysis Program (MS-RAP) A major problem in assessing seismic hazard is dealing with the large quantity of data and the spatial and temporal variations of that data. In an operating mine environment, it is often unrealistic to analyse seismic parameters for seismic hazard and risk assessment on a daily basis. A purpose built seismic data management program is being designed by the ACG s Mine Seismicity and Rockburst Risk Management project to aid in assessing seismic hazard for large amounts of data. The MS-RAP (Mine Seismicity Risk Analysis Program) will aid a mine-site user in analysing large quantities of seismic data, and ultimately map seismic hazard and risk on an interactive, on-line basis. MS-RAP is being incrementally developed, with four stages to the software. The main function of MS-RAP Version 1 was to manage seismic data and aid the user in historical analysis of seismic event mechanism. MS-RAP Version 2 focuses on the quantification of seismic hazard. MS-RAP Versions 3 and 4 will be risk analysis tools, incorporating seismic hazard, excavation damage potential and exposure. Version 4 will be a means to deliver a seismic risk methodology similar in philosophy to that presented in the case studies herein (Hudyma 2003). The first version of MS-RAP was used in this research to check the event quality of large databases of mine seismicity and then to determine seismic clusters based on spatial event density. This allowed the next stage of the hazard analysis to concentrate on a manageable number (typically 30 to 50) of seismic clusters that incorporated over 95% of the total seismic energy while reducing the clutter of extraneous small seismic events. The program screens for potential errors in the seismic event database and classifies the seismic events based on four quality tests (Hudyma 2003): 1) Check for a high location error. 2) Check if there is an invalid field in one of the input data fields (typically this is a zero value in a source parameter such as seismic energy or seismic moment). 60

76 Chapter 2 Literature Review 3) Check if the seismic event is significantly outside the population of other seismic events. 4) Check if the seismic event deviates significantly from the Log (Seismic Energy) versus Log (Seismic Moment) relation as established using the other seismic data. If an event passes the four tests, its data quality is specified as "Good event tested". If the event fails one or more of the tests, it is specified as "Potential outlier tested". A clustering algorithm is then initiated within MS-RAP to sort the seismic data into clusters of spatially related events. The MS-RAP algorithm searches for high concentrations of seismic events and groups the events in that volume according to user defined sensitivity parameters. These sensitivity parameters are the cluster size, isolation distance, minimum number of events in cluster and maximum number of clusters. The last two parameters are self-explanatory. The cluster size is the radius of the cluster in metres and the larger it is specified, the more events will be found in each cluster. The smaller the cluster size, the more clusters that MS-RAP will find. Hudyma (2003) notes that ideally, the cluster size is set so that individual seismic sources can be identified as individual clusters. To achieve this, the cluster size should be as small as possible. As a starting point, consider a cluster size that is smaller than the sublevel spacing in the mine. The isolation distance parameter is intended to speed up the clustering analysis by eliminating events that locate greater than the specified distance from any another event. For an isolation distance of 20, if there is no other recorded seismicity within 20 metres of the particular seismic event, then the event is assumed to be isolated and is eliminated from the clustering process. The isolation distance is internally adjusted to scale up (by a factor of 1.5) for events with a local magnitude greater than 0. For a nominated 20 metre isolation distance, this meant an increase in the isolation distance to 30 metres for the larger events Seismological Parameters used in Seismic Hazard Analysis There are numerous seismological parameters that may be used to evaluate the seismicity in a region, as discussed by McGarr (1984), van Aswegen & Butler (1993), 61

77 Chapter 2 Literature Review Mendecki (1993), Kijko & Funk (1994), Brummer & Kaiser (1995) and van Aswegen (2001). In their paper, van Aswegen & Butler (1993), state that the Application of basic seismological principles in the quantitative analyses of rock mass behaviour can indicate the variation in space and time of rockburst potential in mines. The classical description of seismic activity, according to Kijko & Funk (1994), is based on three parameters: the seismic activity rate = N/T, where N is the number of events with magnitude greater than or equal to a defined magnitude level m min during a specified time period T. (2.6) the parameter b in the Gutenberg-Richter relation, which takes the form log N(m) = a bm (2.7) where N(m) is the number of events not less than magnitude m, and a and b are parameters. 3. the maximum regional magnitude, m max, which may be estimated from empirical relations involving the tectonic and fault parameters (if relevant and known), from the extrapolation of frequency-magnitude trends and strain rates, or from statistical procedures. Another parameter discussed extensively in seismic hazard literature is apparent stress. This scalar quantity is a measure of the ratio between seismic energy E and moment M and differentiates seismic source regions of different stress state and/or rock mass properties (van Aswegen & Butler 1993). Of most interest are concentrations of events of small magnitude but high apparent stress, as they may indicate the build up of elastic strain in the area of an asperity in the rockmass, potentially preceding an eventual violent failure. Apparent stress has been recognised as a reliable, model independent measure of dynamic stress release at the source (Mendecki 1993). It has been seen as important in assessing the influence of seismicity on nearby excavations since the ground motion (e.g. peak particle velocity) is proportional to the stress drop (McGarr 1984). Work by Cichowicz (1997) showed that factors other than just the 62

78 Chapter 2 Literature Review seismic source magnitude and distance to excavation affected the damage observed at a site, and found that very large stress drop events cause more damage. Apparent stress will tend to increase with increasing event magnitude so an averaging method is more useful to eliminate this effect and instead focus on apparent stress variations associated with local rock mass conditions. A methodology used at Brunswick mine (Simser 2000) was considered relevant for this research. This method, illustrated in the Figure 2.5, uses the straight line fit to data on a log E vs log M plot to calculate an average apparent stress which is a single number that can be directly compared to other areas on the mine. As Simser (2000) explained, Problems can arise with the straight line fit if the volume selected has too few events or if the data comes from vastly different populations within the volume. Time is also a big factor; average apparent stress will decrease or increase depending on changes in the local rock mass condition. These potential complications were assumed to be largely eliminated by the analysis herein of individual spatial clusters of seismicity and selection of an appropriate timeframe related to mining activity. The quality-tested seismic cluster data used in the analysis yielded a slightly reduced median moment value of log M = 8.3, so this was used instead of the log M = 9 from the historical data ( ) used by Simser (2000) and shown in Figure 2.5. A well-established technique in evaluating the temporal hazard posed by seismicity is to analyse curves of decay in seismic activity following a blast or, over the long term, the time-of-day distributions (van Aswegen 2001: GAP 610). These decay curves plot the number of seismic events over time (in hours) and typically show an exponential decay in seismicity post-blast, as illustrated in Figure 2.6. They may be used in daily operations to decide exclusion times and zones for personnel. Re-entry of equipment and/or personnel would only be allowed when the seismic activity dropped to a level judged low enough to be safe. Variations in the decay curve, such as spikes of activity that cannot be correlated with blast times, indicate that the system is not reaching equilibrium in a predictable way. 63

79 Chapter 2 Literature Review log E log E = c + d(log M) seismic event 9 log M assume an average log moment of 9 for all polygons, calculate an energy value based on straight line fit of data, then calculate apparent stress = G*E/M Figure 2.5 Average Apparent Stress (after Simser 2000) Seismic Event Frequency Number of Events N Time (hours after blast) Figure 2.6 Sample Seismic Decay Curve Similarly, if significant seismicity is occurring concurrently but well distant from known triggers such as blasting or large seismic events, it may show that the rockmass in the mine environment is in a critical state of stress. That is, the result seen (significant seismicity) is disproportionately large compared to the apparent trigger (small stress change induced by distant blast/seismic events). 64

80 Chapter 2 Literature Review Jesenak, Kaiser & Brummer (1993) noted that the damaging effects of blasting were less than those for seismic events which induced equivalent peak ground velocities. This has been attributed to the multiple stress cycles and longer vibration durations of seismic events compared to blasting. Therefore using peak particle velocity (ppv) criteria established for seismic events, should result in conservative criteria for potential blast influence. Ma & Brady (1999) noted that several studies have reported a relation between ppv (or acceleration) in the ground wave and the extent of excavation damage. It has been suggested (Jesenak, Kaiser & Brummer 1993), that rockfalls could be expected at ppv of 300 mm/s. In their paper on scaling laws for the design of rock support, Kaiser & Maloney (1997) provide a support design chart integrating distance from the source with ppv and seismic magnitude levels (reproduced in Figure 2.7). Using this chart, for a Richter magnitude 2.5 event, the design ppv drops below 300mm/s for locations beyond an approximate distance of 75m from the seismic source. A Richter magnitude of 2.5 was selected as representative of the largest seismic events in the mine databases studied for this research and was approximately equivalent to the magnitude of 2.7 associated with the 350,000 tonne mass blast of the West Ore Zone at Brunswick mine in 2001 (Simser 2001). The determination of the relative hazard scales used later in this thesis was based on similar principles to those stated by Kijko & Funk (1993). However adaptations were made to take advantage of the dense seismic networks typical of the Australian and Canadian mine cases and therefore focus in on individual seismic sources by analysing seismic clusters instead of regional seismic records. The requirement was for a seismic hazard analysis method that could be largely automated and did not rely on site personnel carrying out extensive investigation of individual seismic clusters or sources. Techniques were needed that could be universally applied across different mine sites with comparable results. Some inbuilt redundancy was considered necessary to prevent the hazard analysis from potentially being overwhelmed by inaccuracies introduced in the clustering process, particularly in best-fit estimates of the Gutenberg-Richter relation to seismic clusters where complete seismic histories may not be available. A similar rationale was adopted to that used by 65

81 Chapter 2 Literature Review Alcott, Kaiser & Simser (1998) in selecting criteria for their Rockburst Hazard Assessment (RHA) methodology; i.e. the use of scalar parameters as more suited to routine analyses. The resultant seismic hazard parameters and their integration with exposure in a seismic risk framework are presented in Chapter Damage Potential in Excavations Much work has been done in Canada on excavation damage potential, with publications by Kaiser & Maloney (1997) for the design of rock support and by Kaiser et al. (1992) proposing various descriptive damage rating scales based on falls of ground and support damage. Links between damage severity, site characteristics and peak particle velocity (ppv) were investigated in these papers. Discussed in Jesenak, Kaiser & Brummer (1993) were the concepts of scaled distance to seismic source and target magnitude (apparent seismic magnitude experienced at the specific excavation compared to the known source magnitude). Local conditions at site can include rockmass properties including RMR or Q as well as information on system stiffness and local fracture states as indicated by observation, modelling and/or seismic parameter trends. Kaiser and Maloney (1997) suggested using the following procedure to determine ground motion parameters for the far-field, that is at distances R>1.5[M 0 / ] 1/3 : 1. Plot logrv against log(m 0 ) or log E s or M L 2. Determine a reasonable upper bound limit (e.g. 95% confidence line) with the slope fixed at the theoretical value of a*=0.5 for, log Rv = a*logm 0 +logc* (2.8) Hedley (1992) proposed the relations shown in Table 2.5, associating excavation damage to peak particle velocity (ppv) based on observations from Ontario mines. Kaiser et al. (1992) agreed with the lower damage threshold of 50mm/s and suggested that significant damage can be expected over the ppv range of 400 to 800mm/s. 66

82 Chapter 2 Literature Review Table 2.5 Dynamic load versus excavation damage Peak Ground Motion ppv < 50mm/s Damage Scale No damage 50 < ppv < 300mm/s Falls of loose rock 300 < ppv <600mm/s Falls of ground ppv > 600mm/s Severe damage These relations were considered more applicable to: (a) (b) intact/ slightly jointed rock; or highly fractured zones around an excavation where some proportion of the seismic energy released is required to break the ground or form new fractures. Jesenak, Kaiser & Brummer (1993) distinguished between these two cases and a third state where the ground is highly fractured and also contains well defined structures, creating potentially unstable wedges. This latter case with a static factor of safety close to unity was susceptible to seismically induced falls of ground because they were easily triggered by low levels of acceleration and could thus occur at greater distance from the seismic source. Using seismic data from several mines including El Teniente, Creighton and Brunswick to calibrate the constants in Equation (2.8), Kaiser & Maloney (1997) presented a chart of conservative ppv ranges for locations at distance (R) from large seismic events. This chart (reproduced in Figure 2.7), was intended to assist with ground support design under dynamic loading from events with stress drops of typically less than 2.5 MPa, and was applicable to rockmass condition (b) as described above. With the use of the upper bound 95% limit to produce the chart below, Kaiser & Maloney (1997) stated that the design ppvs at any given distance from the source are greater than the average by a factor of 2.5. Another point to note from the chart is that the near-field shaded zone represents, not the near-field in a seismological sense, but the zone within about two times the source radius (2 x r 0 ) from the centre of the seismic source. Beyond this 67

83 Chapter 2 Literature Review distance, Kaiser, McCreath & Tannant (1995) showed that the far-field relation dominates the peak ground motion Nuttli Magnitude mn m/s 300mm/s 100mm/s 30mm/s Richter Magnitude ML mm/s Distance from Source R [m] Figure 2.7 Recommended peak particle velocity distribution for support design (Relationship II: a* = 0.5, C* = 0.25 m 2 /s). Higher values are expected above the shaded near-field zone (after Kaiser & Maloney 1997). This chart and the ppv damage ranges proposed by Hedley (1992) were used in mapping seismic hazard and damage potential in drives for the case studies presented in Chapters 5 and 6. However, the design loading/excavation damage potential philosophy was applied using seismic clusters as the source hazard rather than individual seismic events. The rationale was that one cannot gain much from an individual seismic event as to what the future ground hazard may be; whereas a seismic cluster better represents the behaviour of a volume of rock and enables trends to be assessed, leading to more confidence in forecasts of future hazard. An additional consideration to the use of spatial clusters of seismicity was that similar or more damage to an excavation may be realized through a number of seismic events with relatively smaller magnitudes, as opposed to the damage due to a single seismic event with very strong motion (Ma & Brady 1999, p.5). The following flow diagram (Figure 2.8) is from Board & Tinucci (1993) and illustrates a general methodology for the assessment of rockburst potential in underground mines. 68

84 Chapter 2 Literature Review Mining Plan Stope Layout Stope Sequencing Location and Orientation of development Geologic Structure Location and Orientation of Structures Estimate of Fault Props In Situ Stress Estimate Probabilistic or Deterministic Analysis Possible Large Scale (Class II) Rockbursts Re- Examine Mine Plan Yes Estimate of Rockburst Potential Predict Probability of Major Events Define regions of Likely Violent Failure Estimate Max. Seismic Moment Produced Is There a Need to Re-examine Stope Layout or Sequencing to Minimise Failure Potential? Or, Should Remedial Measures be Used? No Final Estimate of Seismic Potential Location and Magnitude of Possible Events For Small Scale (Class I) Rockbursts, Examine Use of Destressing, Increased Support or Geometry Changes Dynamic Effects of Seismic Event Estimates of PPV and Stress Change in Region Around Event Damage Assessment Estimate of Damage Locations Using PPV Estimate of Type and Extent of Damage (explosive or keyblock) Re-examine Location or Orientation of Development? Final Support Recommendations Final Mine Plan Check Predictions Against Seismic Data During Mining Adjust Design As Needed Figure 2.8 Flow chart of the General Methodology for Assessment of Rockburst Potential (after Board & Tinucci 1993). 69

85 Chapter 2 Literature Review The stages in the above flow chart that are of most relevance later on in this thesis for the calibration of the exposure model within a seismic risk context are the Estimate of Rockburst Potential, Final Estimate of Seismic Potential, Dynamic Effects of Seismic Event and Damage Assessment Seismic Risk Frameworks The seismic risk framework summarised below and used in the case studies in Chapters 5 and 6 is based on the author s work discussed in Owen & Hudyma (2001) and Owen, Hudyma & Potvin (2002) with some updates based on maintaining consistency with reliability and seismic PRA definitions. Seismic risk is presented here in terms of combining seismic hazard, damage potential and exposure for each consequence of interest. The uncertainties involved in the numerous geotechnical and mining factors, which may influence both the occurrence and the effect of seismicity, introduce various probability terms into the risk calculation including: Probability of initiation of a seismic source to produce a certain magnitude event in a given time period. This is part of the seismic hazard characterisation step and from a family of frequency-magnitude curves, a design maximum event magnitude (M max ) and its likelihood or frequency (F) can be estimated. Probability of various levels of dynamic loading (peak ground motion) at each excavation site of interest based on seismic source magnitudes and the proximity (scaled distance SD) of the seismic sources to the excavation sites. Probability of damage occurring at each excavation site of interest. This involves an integration of the local site characteristics, SC, (rockmass fragility) and the peak ground motions to give the maximum probable excavation damage or damage potential (D). Probability of exposure of elements at risk (personnel, equipment/services, production) to potential damage. This leads to levels of Exposure (E). 70

86 Chapter 2 Literature Review The combination of these factors leads to a range of possible Consequences with varying levels of probability. The Consequence magnitude or severity could be descriptive (e.g. negligible severe loss) or may incorporate costs. Therefore, for each specific CONSEQUENCE of interest, SEISMIC RISK = SEISMIC HAZARD LIKELIHOOD x DAMAGE POTENTIAL x EXPOSURE = F x D(Mmax, SD, SC) x E (2.9) As mentioned in Chapter 1, combining these components for a risk assessment is typically done, for coarse analyses, through the use of a risk matrix which incorporates the Consequences and their Likelihoods. The choice of the final form of the seismic risk matrix may depend on the application to which it is being put. Two different forms are shown below. The first matrix below considers the specific probability of rockbursting as the physical manifestation of the geotechnical hazard, instead of the more general rockfall likelihood. The numbers within the matrix ranging from 1 to 50 are the product of the row and column values which were arbitrarily chosen as measures of relative likelihood. The shading of the matrix from top left to bottom right reflects changes in risk levels from very low low moderate high extreme risk. This matrix was used (Owen & Hudyma 2001) for calculating a semi-quantitative seismic risk to personnel at a mine. The columns of the matrix relate to various consequences of personnel exposure to rockfall/rockburst hazards. These range from no exposure, implying negligible consequence to personnel; through to high exposure of personnel, with potentially severe consequences of serious injuries or fatalities. The exposure rating incorporates both the likelihood of personnel being in the area of interest and their vulnerability (or likelihood of injury) to rockbursts. Thus in this first matrix, the exposure is linked directly with the likelihood of serious consequences to personnel. The rows of the matrix correspond to the probability of occurrence of significant excavation damage. These probabilities are based on the damage potential of the excavation i.e. a combination of the seismic hazard (probability and 71

87 Chapter 2 Literature Review size/severity of events from a seismic source) and the damaging effects of the resultant dynamic load on the local excavation site given its particular site characteristics. Table 2.6 Sample Seismic Risk Matrix Explicit Use of Exposure Rating LIKELIHOOD OF SERIOUS CONSEQUENCE TO PERSONNEL BASED ON EXPOSURE LEVEL Very Low (Negligible exposure) Low Moderate High LIKELIHOOD OF ROCKBURST Very Low Low Moderate High Very High Severe (High Prob. of Fatality) The second matrix in Table 2.7 is more reminiscent of a generic risk matrix with the Consequences listed across the top and the Likelihood of these Consequences ranked down the side of the matrix. This overall likelihood then incorporates all the probabilities associated within the seismic risk definition including: Probability of dynamic loading of the excavation site due to a nearby seismic event/source (seismic hazard at excavation); Probability of the excavation being damaged by dynamic load (excavation fragility); Probability of personnel being in the vicinity at the time of the burst (temporal and spatial exposure); Probability of personnel injury/fatality given a rockburst impact (exposure vulnerability). It is considered that the combination of all of these different probabilities into one value may tend to obscure the meaning of values used in the geotechnical risk assessment. 72

88 Chapter 2 Literature Review For this reason, the first matrix form is preferred for this research and will be discussed further in Chapter 7 and Appendix D. Table 2.7 Seismic Risk Matrix Standard Consequence vs Likelihood Format CONSEQUENCE TO PERSONNEL None Low Moderate High Severe LIKELIHOOD OF CONSEQUENCE Very Low Low Moderate High Very High Conclusion Through the review of risk and hazard analysis methodologies from comparable industry sectors and the comparison of these other operations with the underground mining environment, a need was identified for the development of a location and activity-specific method for quantifying the exposure of underground personnel to geotechnical hazards. It was seen that the development and application of quantitative risk (QRA) and probabilistic safety assessments (PSA) is considerably more advanced in other energy and resource sectors such as the nuclear and offshore petroleum industries. Within the field of civil engineering, design methods and geotechnical risk assessment techniques are increasingly adopting probabilistic methodologies. The closest comparable situation from another industry sector to the geotechnical hazards encountered underground was the assessment of the exposure of structures and roadways to landslide hazards as this also involved measures of spatial probability, temporal probability and vulnerability. However the complexities and stochastic nature of the workforce distribution across an underground mine were found to necessitate a unique methodology for quantifying exposure for this environment. The development of such an exposure methodology or model requires a risk context into which the exposure must integrate, to be of any value to an end-user. It was seen that the geotechnical hazards in underground excavations manifested as rockmass displacement or rockfalls. A new database created by the ACG contains rockfall 73

89 Chapter 2 Literature Review records from 26 Australian underground metalliferous mines and enables statistical trends to be identified for use in model calibration. A specific risk context, with limited number of geotechnical parameters, was also found to be necessary for model calibration through the investigation of individual case studies. The risk context addressed by the case studies in this thesis is that caused by mining induced seismic hazards. Therefore some exploration was made of the current work being carried out in the fields of seismic hazard and damage potential analysis. The various probabilities associated with the quantification of exposure and seismic risk were also discussed, prior to a more specific analysis in Chapter 4. The development of the geotechnical hazard exposure model is presented in the next chapter. 74

90 Chapter 3 Hazard Exposure Model for Underground Mines 3. HAZARD EXPOSURE MODEL FOR UNDERGROUND MINES 3.1. Introduction This chapter presents the development of a model to quantify the exposure of underground mining personnel (and associated mobile equipment) to geotechnical hazards such as rockfalls and rockbursts. The Australian Geomechanics Society published guidelines on landslide risk management (AGS 2000a) were introduced in Chapter 1. From the literature reviewed in Chapter 2, it was concluded that the most analogous situation to underground mining exposure to geotechnical hazards was in the civil engineering sector for the assessment of the exposure of structures and roadways to landslide hazards. These assessments involve measures of spatial probability, temporal probability and vulnerability and similar measures are also directly relevant in underground mines. The landslide guidelines (AGS 2000a) defined a quantitative risk calculation, based on work by Bunce et al. (1997), as follows: R (DI) = P (H) x P (S:H) x P (T:S) x V (D:T) (3.1) Where, R (DI) = risk (annual probability of loss of life) P (H) = annual probability of the hazardous event (i.e. the landslide, rockfall, etc) P (S:H) = probability of spatial impact (e.g. of a building, vehicle, etc) by the hazard (the landslide) P (T:S) = temporal probability (e.g. of building occupation) given the spatial impact V (D:T) = vulnerability of the individual (probability of loss of life given the impact) 75

91 Chapter 3 Hazard Exposure Model for Underground Mines The exposure quantification presented herein, as well as the seismic risk framework described in later chapters, are both consistent with the above breakdown of the risk components. However there are some differences in the detail of the calculations to account for the complexities of geotechnical hazard, excavation fragility and exposure characterisation in the underground mining environment. Referring back to the landslide risk calculation, the three listed probabilities P (S:H), P (T:S) and V (D:T) are accounted for as part of the proposed exposure rating system detailed herein, as indicated by the labelled equation 3.1 below. R (DI) = P (H) x P (S:H) x P (T:S) x V (D:T) GEOTECHNICAL HAZARD EXPOSURE = PROXIMITY x DURATION x VULNERABILITY As discussed in Chapter 1, an enormous amount of past and ongoing work has concentrated on characterising geotechnical hazards. Systems such as rockmass ratings, stability graphs and seismic hazard quantifications all fall under this generic category. In contrast, very little work has been done in the area of quantifying the exposure of personnel and equipment to these hazards. The time of exposure for various activities is an easily measured (or estimated) parameter, hence its widespread use as the only exposure parameter in many cases. This was seen for the characterisation of hazard exposure in the literature reviewed on the offshore petroleum industry in the previous chapter. The current state of exposure profiling for geotechnical risk assessment at mine sites involves, at best, the simple classification of travelways and entry areas by their occupancy rate. This rate is generally described as continuous, hourly, daily, weekly, etc as in GCG (2000) and Potvin et al. (2001). This gives a qualitative temporal profile but is not a true stochastic model of exposure. It does not take into account the characteristics of the vehicles, traffic flow and work practices, all of which affect the likelihood of a rockfall impacting a vehicle or person. Another factor not accounted for by current risk methods in mines is the vulnerability of the person to serious or fatal injury should a rockfall impact occur. 76

92 Chapter 3 Hazard Exposure Model for Underground Mines Therefore, a gap exists in the knowledge regarding exposure and specific techniques to be used for accurate quantification of exposure to geotechnical hazards in underground mines. It is thus the focus of this thesis, with a detailed quantification methodology presented in the following sections. The way that it progresses the ability to carry out semi-quantitative or quantitative risk analysis in mines is demonstrated in later chapters Construction of a Model for Quantifying Exposure Referring back to Section 2.2 and Figure 2.1 for the stages involved in a formal system identification and model construction, the first phase is the problem definition. For this thesis, the problem definition is summarised in the following table, with the observations comprising the required data set shown in italics. Table 3.1 Problem Definition for Research PROBLEM DEFINITION Process the operation of an underground mine Modelling Goal to describe quantitatively the exposure of mine personnel and equipment to geotechnical hazards in an underground mine. Validation criteria check that model rates appropriately activities which are known from injury and fatality records to involve higher risk*; use mines rockfall statistics, incident databases and record of risk treatment measures to assess modelled exposure profiles against the recorded trends; back analyse specific rockfall/rockburst case studies using the actual hazard data + the mine-specific exposure model to compare modelled against actual consequences. 77

93 Chapter 3 Hazard Exposure Model for Underground Mines * One of the findings from the Australian underground mine rockfall database study (Potvin et al. 2001) was that activities that present the highest risk are: barring down, rockbolting and charging. They account for over two thirds of the rockfall fatalities since Therefore ensuring that the exposure model rates these activities appropriately should be one of the validation criteria, as is discussed in Chapter 7. In choosing a model set and determining a criterion of fit for selecting the most appropriate model, the guiding principle adopted was that: given the presence of geotechnical hazard and a resulting rockfall in a mining excavation, then the hazard exposure model should output a result representing the relative likelihood of severe consequence to personnel (i.e. serious injury or fatality) and/or equipment (costly damage or destruction). A linear model structure, with parameters representing independent probabilities that contribute to the likelihood of severe consequence resulting from a rockfall, was therefore judged appropriate. This was also consistent with the risk calculation (AGS 2000a) shown earlier in Equation 3.1. Just as in system analysis it is common to divide a system into subsystems, so it is recommended practice in geotechnical engineering to divide an excavation into geotechnical domains. A geotechnical domain is a volume of rock with generally similar rock mass properties such as characteristics of the planes of weakness (orientation, spacing, persistence, etc), degree of weathering and/or alteration, intact rock uniaxial compressive strength (UCS), permeability, deformation modulus and rock stress field (DME 1997). Similarly, the mine excavations should be divided into exposure domains based on their typical usage. A particular excavation location may undergo several changes in exposure domain over its design life. The underground mine comprises subsystems including new constructions (excavations) at various stages of the development or production cycles, established travelways and infrastructure 78

94 Chapter 3 Hazard Exposure Model for Underground Mines areas, ongoing maintenance and rehabilitation of long term excavations as well as inactive, perhaps unmaintained areas. Model construction initially involves considering observations of the system that the model is to use as input. As was discussed in Chapter 2, the types of data sources used in offshore QRA studies include accident statistics, failure databases, equipment failure databases, physical properties of various substances as well as generic data sources and company internal accident/ incident databases. For the underground mine system, similar information sources are relevant i.e. accident reports, geotechnical assessments, rockfall databases, mine fleet (vehicles/equipment) characteristics and mine records on activities and cycles. Given these data sources, the most relevant model category to be adopted for exposure analysis is a grey box type of model, which combines mechanistic and empirical foundations by having adjustable parameters based on physical interpretations. It was noted in Chapter 2 that empirical Bayes methods are suited to models where the data comes not from identical experiments (situations/structures) but from similar experiments. Therefore the objective of the methodology is in estimation of the a priori distribution rather than the parameter itself (Breitung 1992). Even more than a civil/structural case, the mining situation incorporates significant temporal and spatial variations in materials (rockmass, ground support), loadings (virgin stress, mininginduced stress, blast vibrations) and exposure (distribution of personnel and equipment), such that a laboratory-style series of identical experiments is impossible to achieve. With each entry of mine personnel into an excavation, the parameter of most interest from an exposure viewpoint is the probability of injury (in particular fatal injury) given a rockfall in the excavation. However, this probability will vary dependent on the nature of the rockfall (size, velocity, position in the excavation), the number and spatial positioning of the personnel, their level of protection, the time spent in the excavation, etc. All of these will be unique for each case and thus the parameter will never be exactly determined. Instead, one can use the empirical Bayesian approach and look at probable distributions of personnel undertaking the particular activities and prior records of rockfall nature and 79

95 Chapter 3 Hazard Exposure Model for Underground Mines locations in order to estimate the likelihood of fatal (or other) injury given a fall of ground. When this estimated exposure distribution is then combined with the most recent assessments of geotechnical hazard and rockmass integrity, an estimate can be made of the risk of fatal injury. This is the approach adopted for development of the exposure model in this chapter. The first stage of exposure analysis for a mine will therefore involve profiling the mine in terms of materials handling arrangements, personnel and equipment travel patterns, development and production cycle activities and equipment types. For this research, the large-scale system under consideration contains all accessible areas of the underground mine excavations, encompassing all locations required for analysis of the personnel and mobile equipment exposure to underground geotechnical hazards. Stochastic models have been found to be particularly significant to geotechnical engineering as they describe cases which involve natural random variations in space and/or time and this is certainly applicable to exposure analysis for underground mines. The exposure model must also incorporate the flexibility to represent static operating conditions and be able to integrate probabilistic elements to represent stochastic processes and uncertainties in exposure parameters. In the mining situation, adoption of a static model to study exposure would necessitate taking snapshot views of the mine, where the work and travel areas are fixed for specific times and exposure ratings determined for these particular situations. For the model described in the following sections, the shortest time period considered is the individual activity time for activity exposure quantification and then the activity exposure ratings can be averaged over one shift for spatial analysis of exposure across different excavation categories and locations. If a model is developed to represent changes in the system over time, it is a dynamic simulation. The predictability of mining cycles enables a static exposure model (a rating case) linked to repetitive mine activities and universal location categories to remain applicable over extended time periods, equivalent to a dynamic simulation. 80

96 Chapter 3 Hazard Exposure Model for Underground Mines 3.3. Mapping the Exposure within a Mine General Methodology The first stage in looking at the exposure of workforce and assets across an underground mine is to describe the overall system characteristics and its subsystems: 1) Mining method(s) used e.g. sublevel caving, open stoping, etc. 2) Materials handling systems and infrastructure decline, shaft, underground crushing, conveying, haulage, etc. 3) The development cycle activities and approximate cycle time. 4) The production cycle activities and approximate cycle time. 5) Subsidiary service and support activities which typically involve more mobile crews such as equipment maintenance and repairs, workplace inspections, ventilation work, reconditioning of ground support, etc. Categorisation of locations within the mine by excavation type is the next step. The DME (1997) Geotechnical Guidelines list the following excavation types, separated into permanent (design life longer than about 2 years) and temporary. Table 3.2 Suggested Classification Of Opening Types (DME 1997) PERMANENT OPENINGS Shafts Declines and inclines Main level development Escapeways Intake airways Return airways Offices and lunch rooms Refuge bays Workshops Electrical substations TEMPORARY OPENINGS Stopes Stope accesses Drill drives Sill drives Cut-off rise Mill holes (drawpoints) Extraction drives Working party magazines 81

97 Chapter 3 Hazard Exposure Model for Underground Mines PERMANENT OPENINGS Crusher excavations Conveyor excavations Main pump stations Main magazines Fuel storage bays Vehicle service stations TEMPORARY OPENINGS Stope ventilation rises For the purposes of the exposure modelling in this thesis, the following more general classification has been used, with the further breakdown of categories as required to suit specific mine case studies: Table 3.3 Underground Mine Excavation Categories Excavation Area Excavation Category 1 Decline/Incline (established levels) 2 Shaft 3 Infrastructure area (workshop, stores, crusher, etc) 4 Ventilation/service area (airways, elec. subs, pump stations, etc) 5 Inactive levels accesses and drives 6 Development area near working face 7 Development area accesses/drives 8 Production level drilling horizon near working face 9 Production level drilling horizon accesses/drives 10 Production level mucking horizon near working face 11 Production level mucking horizon accesses/drives 12 Designated no entry area 13 Designated no unauthorised entry/limited access area As mentioned, for specific cases, further breakdown of these categories may be necessary to distinguish between, for example, conventional and remote mucking areas 82

98 Chapter 3 Hazard Exposure Model for Underground Mines and varying tramming distances to stockpiles/orepasses. However these twelve general categories have been found to be broadly applicable across most minesites. Each of these excavation categories will have typical activities associated with it, varying from general traffic to haulage to ground support installation. The mixture and timing of these activities will combine to give an overall exposure for the particular excavation over the time period of interest. Figure 3.1 is a conceptual flowchart showing this methodology. Excavation Category Time Distributions (e.g. per shift, daily, weekly) General traffic Activity 1 Activity 2 Activity 3 Tramming in or between work areas Visits/ inspections Mobile maintenance crews Service crews Shift change/ crib Jumbo Drilling Ground Support Charge-up, etc Haulage Loader tramming Jumbo, etc tramming Figure 3.1 Flowchart of activities contributing to exposure in a mine excavation Initial conceptual model The prototypes of the exposure model were developed using data from surveys of mine personnel as well as some preliminary results from the Australian Rockfall Database research (Potvin et al. 2001) including the rockfall definition adopted for this thesis, 83

99 Chapter 3 Hazard Exposure Model for Underground Mines ROCKFALL: An uncontrolled fall of ground of significant size in an entry area, or an uncontrolled fall of ground of any size that causes (or potentially causes) injury or damage Data on Rockfalls and Injuries used to Establish Model Parameters Key findings from Potvin et al. (2001) which were of relevance to this exposure research were: The large majority of reported rockfalls occurred in drives within 10m of the active face. 83% of the personnel injury cases (where details were recorded) occurred within 10m of the active face and the trend for equipment damage was similar with 71% of the known cases recorded within 10m of the active face. Despite significant numbers of large rockfalls reported, over 90% of the rockfalls causing injuries were smaller than one tonne. Since 1999, the risk of rockfall injuries overwhelmingly came from relatively small rocks, detaching from the space between bolts in areas where no surface support was installed. Almost one quarter of all reported rockfalls resulted in injuries. Since 1998, the industry has made significant improvement in controlling rockfalls and the number of rockfall injuries has declined notably. This success was attributed to a number of changes in practices near the active face, including single pass bolting and the increasing use of surface support as well as the implementation of more stringent reporting standards, ground control management plans and risk assessment tools. Between 1999 and 2000, an average of 75 rockfalls were reported causing eight injuries annually, with seven of these occurring where there was no surface support. The risk of being injured by small rockfalls remained higher near the active face, but it is not confined to this area. Further away from the active face, there 84

100 Chapter 3 Hazard Exposure Model for Underground Mines seemed to be a recurrent risk of rockfalls resulting in at least two injuries per annum. A database of 23 rockfall fatalities recorded between 1993 and 2001 (Table 3.4) showed the average annual number of rockfall fatalities fluctuated between zero and three. There was no discernable improvement in recent years, and one quarter of all rockfall fatalities since the beginning of 1997 were associated with rockbursts. After 1997, the occurrence of unsupported rockfall fatalities near the active face of drives was no longer the dominant issue. In recent years, fatal rockfalls were generally greater than two tonnes (often greater than 20 tonnes) and occurred in supported ground further away (more than three metres) from the active face. 85

101 Chapter 3 Hazard Exposure Model for Underground Mines Table 3.4 Description of rockfall fatalities in Australian underground metal mines (after Potvin et al. 2001) Year Distance Weight Rockfall Ground Bolt type Surface from of failure support support active face (tonnes) origin installed? installed? Activity* Possible contributing factors metres 0.4 Backs No None No WD Lack of ground support, scaling in high headings " 3 metres 1 Face No None No CB Rock possibly ejected off the face " < 6 metres 0.3 Backs No None No BD U 1995 < 3 metres 10 Hanging wall No None No BD (entry stope) Scaling procedure 1996 < 3 metres 20 Backs No Grouted bolts No B Method of installation, procedure " < 3 metres 1.5 Backs Yes FRS No BD Procedure " U U U U U U U U " < 3 metres 250 Backs Yes FRS No BD/I Ground condition assessment " < 6 metres 1.5 Hanging wall No None No B (entry stope) Seismicity, Inadequate support design " < 3 metres 1.5 Face & Backs No None No B Inadequate support design, procedure " < 3 metres Backs No FRS No B Procedure 1997 > 50 metres 0.05 Backs No FRS No S Scaling inspection procedure " < 3 metres Face Yes (backs) HGB Yes (backs) C High rock stress levels, scaling/charging procedure " 15 metres 3 Wall Yes Cables, RB's Yes (backs) RL Seismicity " Open Stope 1000 Backs No None No CL Breach of procedure " < 3 metres > 50 Backs yes cable dowels, FRS No C Seismicity " < 3 metres > 50 Backs yes cable dowels, FRS No C Seismicity 1999 U U Wall U U U D Support standards " < 3 metres 8 Brow Yes None None C Scaling/inspection/charging 2000 < 3 metres 5 Backs U U U CB Procedure " > 50 metres >50 Backs Y FRS No B (rehab.) Seismicity, procedures 2001 > 50 metres 200 Backs Yes Cables, RB's No BD Scaling procedures, supervision " > 50 metres 200 Backs Yes Cables, RB's No BD Scaling procedures, supervision * B = Bolting (5) D = Drilling (1) BD = Barring down (5-6) I = Inspecting (1) C = Charging (4) RL = Remote loading (1) CB = Changing bit (1) S = Surveying (1) CL = Conventional loading (1) WD = Watering down (1) 86

102 Chapter 3 Hazard Exposure Model for Underground Mines The main conclusions drawn from the above findings for application to this research into exposure were: The area close to the active face of drives is recognised as a particularly hazardous location, common to many mines. This includes the unsupported ground (face and/or backs) within a few metres of the face as well as supported ground slightly further away. Therefore, in building the exposure model, this information can be used as a basis for estimating the effect of the proximity of the workplace to the known hazard such that: model input = distance between personnel and known hazard; model output = component of exposure. With the development cycle creating almost continuous exposure of personnel at active faces, the combination of high exposure and hazard create a very high risk workplace, as reflected in the injury statistics. Therefore the time spent by personnel in particular locations is of significance for the exposure model. The large proportion of injuries caused by small to moderate sized rockfalls (<1 tonne) indicates that the exposure model should account for personnel vulnerability to these conditions as well as to the large falls of ground. These small rockfalls would be of minor significance to most heavy vehicles (and personnel inside) but have severe consequences for personnel outside vehicles Components of Exposure Through the literature review and the above analysis of underground mining rockfall databases and accident records, the exposure model parameters were identified. In developing a procedure for quantifying the exposure of underground personnel and equipment to geotechnical hazards, three components were considered critical and are explained and quantified in the following sections. These were: 1) The vulnerability of the personnel/asset should a fall of ground occur (their relative level of protection). 87

103 Chapter 3 Hazard Exposure Model for Underground Mines 2) The proximity of the worker/equipment to the geotechnical hazard. 3) The amount of time exposed, incorporating the number of people, typical activity time and the proportion of this time spent in any particularly hazardous duties Vulnerability to Geotechnical Hazards Personnel Exposure In quantifying the exposure of personnel, a key factor is the level of protection the worker has should a rockfall/rockburst occur. Obviously someone working outside their vehicle with the standard Personnel Protective Equipment (i.e. hard hat) is more vulnerable than when they are inside a cab with a Rollover Protective Structure (ROPS) or Falling Object Protective Structure (FOPS). A rock of just a few kilograms mass falling a couple of metres could severely injure a person, whereas a much larger slab (greater than about one tonne) would be required to crush the cab of a machine with a protective structure installed. Conversely though, the cab of a machine working in a heading provides a much larger target for a vertical impact than does an isolated person. The result of combining (multiplying) these two opposing factors to give an overall personnel vulnerability rating (E1) is shown in the following table. Table 3.5 Personnel Vulnerability Ratings Protection Type E1 Enclosed ROPS/FOPS 1 cab 0.8 ROPS/FOPS, open cab 1 FOPS canopy 2 Unreinforced cab 20 Personal Protective Equipment 50 Note: 1. Herein, the designation of ROPS/FOPS means that the machine has both a rollover structure and a protective canopy or roof. 88

104 Chapter 3 Hazard Exposure Model for Underground Mines These values determined for E1 were based upon the impact energy ratings for FOPS (AS ), ROPS (AS ) and hard hats/helmets (AS 1801) in the Australian Standard testing procedures. For example, the FOPS canopy is rated to an impact energy of 11,600 J, the ROPS to approximately 21,500 J (impact absorption for a typical wheeled loader or dump truck), whereas the hard hat can only withstand 50 J of impact energy. So the Helmet:FOPS:ROPS energy absorption ratio was 1:232:430. Allowing then for the relative target areas (say a 1.5m x 1.5m canopy compared to a 0.5m x 0.5m person s footprint ) gave a reduction factor of 9; reducing the Helmet:FOPS:ROPS ratio to approximately 1:25:50. A small, arbitrary allowance was also made for the slightly increased protection provided by an enclosed ROPS cab compared to an open structure. For the case of an unreinforced cab (e.g. a standard light vehicle), the target areas remain similar to the ROPS/FOPS case but the impact energy absorption capacity is much less and the fall height may be slightly greater. Section presents calculations that suggest the roof of a standard light vehicle, such as a Toyota Landcruiser, can withstand an equivalent impact energy of about 10% of the FOPS rating. Hence E1 = 2 x 10 = 20 was considered appropriate Calculations for the ROPS/FOPS impact energy The ROPS energy rating of 21,000 J used above was adjusted from the AS calculation to reflect the difference between a rockfall impact and a rollover impact. The impact energy rating for a ROPS structure is based on a 360 rollover that has the machine maintaining contact with the ground surface. In AS testing, the test load may be spread over a distance up to 80% of the frame side dimension. Therefore the rollover impact load is more gradually applied and distributed more evenly than for the equivalent mass of rock falling on to a ROPS/FOPS. In contrast, the FOPS structure is tested in AS using a 0.4m diameter sphere, a much more concentrated impact than the ROPS testing, as it is intended to model rockfalls. Taking some typical heavy mine equipment such as a CAT R1700 LHD (loader) and a 73B rigid frame truck, the equivalent ROPS rockfall rating of about 21,500 J was calculated as follows: 89

105 Chapter 3 Hazard Exposure Model for Underground Mines Using a canopy side dimension of 1.5m, the allowable load distribution for rollover testing is 0.8 x frame dimension, so approximately 1 to 1.2 m. Therefore, compared to FOPS testing with a 0.4m load dimension, there is a scale factor of about 0.4/1.1 = The R1700 loader has a ROPS rollover impact energy capacity of U = (M/10000) 1.25 = 60,000 J where U is the lateral energy in Joules and M is the representative mass of the vehicle in kilograms. Reducing this by the scale factor of 0.36 gives an energy capacity of 21,500 J for the purposes of rockfall impact. This is equivalent to a 1,000 kg rock falling just over 2 m. For a 40 tonne rigid dump truck (e.g. CAT 73B), U = 0.73 M = 29,000 J. So applying a similar ROPS/FOPS load distribution, the truck would have an equivalent rockfall impact energy absorption capacity of about 29,000 J x 0.36 = 10,500 J. Allowing for the reduction in the rockfall impact energy by a factor of 0.5 compared to loader impacts because of the reduced clearance between truck roof and excavation backs, this means the truck can withstand the same rockfall mass as the loader, i.e. 1,000 kg rockfall. Considering the structural mechanics, a ROPS/FOPS structure may have the same canopy as a vehicle with FOPS only, but with additional framing elements to withstand the rollover impacts. These additional supports will reduce the effective span of the canopy structure and therefore it seems reasonable that there be a 2:1 impact energy absorption ratio of ROPS/FOPS compared to FOPS only Calculations for the light vehicle impact energy Although light vehicles used in mining have a rollcage installed which improves the rollover performance, this may provide little benefit against a penetrating impact such as a rockfall, and it does not strengthen the windscreen. 90

106 Chapter 3 Hazard Exposure Model for Underground Mines If a 3 tonne light vehicle, such as a Toyota Landcruiser, can be expected to withstand a static force of 1.5g applied over 0.7m x 1.8m while deforming no more than 0.13 m (NHTSA 1999), this equates to a deformation energy of about 4,000 J/m 2. However the dynamic load and sharper impact of a rockfall must also be considered, as in the ROPS case above. Comparing the NHTSA test platen dimensions with the FOPS test dimensions results in a scale factor of (0.4 2 /1.4), reducing the energy from 4,000 to 500 J/m 2, about 10% of the FOPS capacity rating. Another calculation which confirms an impact energy capacity close to 10% of the FOPS rating was based on a report by Henderson and Paine (1998) which quoted research by Murray (1994) suggesting that a more suitable test for roof crush strength would be to drop a vehicle on its roof from a height of 0.5m. It was claimed that most cars currently on the Australian market would crush to the level of a typical occupant s head with a drop of only 38 mm. For a 3 tonne vehicle, this equates to a roof impact energy of 1120 J, an order of magnitude less than the FOPS rating of 11,600 J. As an estimate of the effect of a rollcage, the ROPS standard (AS ) lateral impact energy (U) rating for a 3 tonne vehicle was compared to that for a 35 tonne loader. For this latter case, the value of E1 = 1 has been calculated previously. The respective values for the 3 tonne and 35 tonne vehicles were U = 2,775 J and U = 59,840 J; a ratio of 1:21, consistent with Table 3.5. Therefore the value of E1 = 20 for light vehicles is justified Comparison with Lateral Impacts The personnel vulnerability factors of Table 3.5 have also been checked for the cases of side wall buckling or bursting. Many complexities exist and are difficult to quantify precisely. With that qualification, the following calculations attempt to approximate relative impact energies and target areas to determine applicable E1 values for the various cases involving lateral impacts. 91

107 Chapter 3 Hazard Exposure Model for Underground Mines (a) Highest Vulnerability Level For a person standing outside a vehicle, their exposure to a small (2kg 2 ) rockfall from the backs was assessed to be the same as if the rock was ejected (at say 2m/s 3 ) from the sidewall. This was based on producing the same impact energy of 50J (hard hat rating) given: the relative target areas (2m x 0.5m vs 0.5m x 0.5m = 1m 2 :0.25m 2, i.e. 400%); applicable excavation areas to produce lateral versus vertical impacts (3m wall span versus 5m back span, i.e. 60%); and frequency of recorded falls from walls/faces versus backs (approx 42%) as shown in Figure 3.2 from Potvin et al. (2001). Rockfall origin - all mines Backs Wall Unknown Face Corner Figure 3.2 Pie chart showing the breakdown of the 494 rockfalls according to the location of failure within the drive (after Potvin et al. 2001). So the relative probability of a person standing in an excavation being injured by a lateral compared to vertical rock impact is p = 400% x 60% x 42% = 1.0. This relative probability calculation was supported by the injury statistics from the Australian rockfall database which showed the percentage of rockfalls which resulted in 2 Based on a block size with side dimension similar to a mesh strand spacing (approx mm). 3 Based on typical velocities reported in the SIMRAC report GAP 709 (Milev et al. 2000). 92

108 Chapter 3 Hazard Exposure Model for Underground Mines injuries was the same for falls from backs and falls from walls or faces (22-23%). This is illustrated in Figure 3.3. Personnel Injury Consequences versus Rockfall Origin 250 Number of rockfalls Number of rockfalls resulting in injury Rockfalls with no injury consequences 50 0 Backs Walls and Face Figure 3.3 Proportion of rockfalls resulting in injury So E1 = 50 for the most vulnerable case (lowest level of protection to personnel) was found to still be applicable for lateral impacts. (b) Lowest Vulnerability Level Looking at the best case (highest level of protection) of a person inside the cab of a heavy vehicle with ROPS/FOPS, their protection against injury from lateral impacts depends on a combination of: 1. the likelihood of an adjacent rockfall occurring in such a way as to actually impact the cab; and 2. the lateral impact energy resistance of the cab. These factors were considered as follows: The side impact force will be reduced by a factor of 1/g (~10%) compared to a vertical impact for the same mass of rock. A ROPS structure is rated to absorb a certain lateral impact energy (U) resulting from a rollover, as demonstrated earlier. Therefore the vertical posts of the ROPS 93

109 Chapter 3 Hazard Exposure Model for Underground Mines frame can also help to provide protection against a side rockfall impact if they are hit. A FOPS only frame is rated for vertical impact not lateral energy absorption. Compared to a vertical impact on a ROPS/FOPS canopy, there is less of a barrier (more openings) on the side of a cab to prevent rock penetrating the operator s space inside the cab. Conservatively assuming only two posts, the proportion of protective side steel area is about 10% of the canopy steel area. Therefore, the amount of protection provided to an operator within a ROPS/FOPS cab against a side impact compared to a vertical impact can be approximated by: reduced force factor x 1/reduced barrier factor = 10% x 1/10% = 1. So a personnel vulnerability factor of E1 = 1 is still appropriate for a side impact on a ROPS/FOPS vehicle. (c) Intermediate Vulnerability Levels The previous calculation does not apply to the FOPS only case because this structure is not rated for lateral impact. For the FOPS situation, the simplest way to determine an appropriate E1 value is to compare and scale it with the case of an unprotected person standing in the excavation. For example, unlike someone standing on the floor of the excavation, a person in a vehicle is not exposed to rockfalls/ejections from the lower portion of walls (1.5 to 2m height). In addition, the roof/canopy provides some protection to the person in the cab against rockfalls from high up on the wall (becomes equivalent to a vertical impact). So only about 20-40% of the wall area may be relevant to generating rock ejections/falls that can impact a person in a heavy vehicle from a sideways direction; half that for a person standing in the excavation. This is the same for rockfalls due to sidewall buckling, spalling, etc for a person working close to the wall. Similarly, for large falls of ground from sidewall failure where the person is working further from the wall, say at the centre of drive, a greater proportion of the wall would have to fail (or ejection velocity be greater) before a person within a heavy vehicle is harmed, compared to a person outside the vehicle. 94

110 Chapter 3 Hazard Exposure Model for Underground Mines Thus, again there is an associated likelihood ratio of about 1:2. So the relative probability of a rock fallen/ejected from a sidewall seriously impacting a person in a heavy vehicle with FOPS compared to someone outside is approximately: p = reduced wall factor x reduced force factor = 50% x 1/10 = 0.05 Thus the personnel vulnerability factor E1 = 50 x 0.05 = 2.5. This was considered sufficiently similar to the vertical impact case (E1=2), given the assumptions and uncertainties involved in the quantifications. For light vehicles with rollcages, it has already been shown that there is an approximate lateral impact energy ratio of 1:20 between a 3 tonne loader and one with E1 = 1. The values for E1 in Table 3.5 can therefore be applied for both lateral and vertical impacts Equipment Exposure For the case of equipment exposure, the vulnerability parameter E1 can generally be left out of the calculation (i.e. E1=1). The installation of additional protective structures against rockfalls/rockbursts could be used as a justification to include a reduced E1 factor in the exposure calculation. Similarly, a detailed profiling of equipment exposure could use E1 as a measure of loss and cost so that E1=1 implies 100% loss of the piece of equipment and a reduced E1 means that the equipment may be damaged but not written off totally. These applications are supported by the exposure model but are beyond the scope of this research, which is focused on safety issues. It was also found that there were insufficient database records of equipment damage caused by rockfalls to be able to calibrate and validate equipment exposure models Proximity to Geotechnical Hazards Another complexity which was taken into account for the exposure profiling was the proximity of the operator and equipment to known geotechnical hazards. These more hazardous areas would typically include active development faces, brows of stopes, backs that are unsupported or have no surface support, and areas of seismic activity. Rockfall statistics (Potvin et al. 2001) suggest that other potentially hazardous locations 95

111 Chapter 3 Hazard Exposure Model for Underground Mines are intersections, which accounted for approximately 16% of all recorded rockfalls in the database ( ), despite their relatively small plan area compared to drive areas. Large spans and inadequate cablebolt design contribute to making intersections susceptible to large rockfalls. For the detailed application of the exposure model to analyse a specific area of concern in a mine, the proximity of work activities to all geotechnical hazards identified by inspection and monitoring would be used. These identified hazards could include priority areas for scaling and rehabilitation, displacing faults and shears, areas of water ingress, etc. Table 3.6. shows the proximity rating (E2) given to the various ranges of distance from the hazardous ground. These values were developed by extrapolation from injury and fatality records in the ACG rockfall database research (Potvin et al. 2001), and calibrated using Table 3.7. The relative numbers of injuries recorded within varying distances of the face of active headings provided the basis for the E2 values. Extrapolation was required because of the different distance categories in the rockfall database compared to those deemed appropriate for this exposure rating. For example, 70% of the recorded injuries occurred within 10m of the face and less than 10% occurred more than 50m from the face but these categories were considered too broad for the detailed exposure profiling of mine activities. Table 3.6 Proximity Level Factor Proximity to geotechnical hazard E2 <5m m m 2 >30m 1 The best case (lowest) proximity factor, against which all the other distance categories were scaled, was chosen to be equal to one. This was to maintain consistency with the best case for the vulnerability parameter, for which lowest values are also close to unity. 96

112 Chapter 3 Hazard Exposure Model for Underground Mines The highest proximity category (E2 = 10) was selected to be within five metres of the identified hazard (e.g. active face) based on: The majority of the fatalities due to rockfalls (see Table 3.4) were recorded for distances less than six metres from the active face. Five metres is a typical drive dimension (height, width) and therefore blasted muckpile extent, meaning that loaders mucking in a heading will typically be working at about this distance from the face or brow. Given typical jumbo boom lengths, five metres is an approximate distance for the jumbo cab to be back from the face. Given typical drive heights, a full-height slab peeling off the face will extend five metres into the drive. The next proximity category (E2 = 5) extends another 10 metres from the hazard, typical of the length of drive occupied by large mining equipment such as LHDs and cable-bolters. The distance range is also representative of the placement of operator stations (line of sight) for remote mucking. It was considered that a working area extending up to 30 metres from the hazard was representative of most mining activities as it allowed work space for the heavy mining equipment (such as jumbos, bolters, LHDs, charge-up wagons, etc), as well as for the associated service, maintenance and inspection crews parking up their light vehicles and/or service trucks in the area. The rockfall fatality statistics for Australian underground metalliferous mines (Table 3.4) were recorded with slightly different distance ranges. The information is summarised in Table 3.7 below and compared with the proximity factor ranges to check consistency between the fatality trends and the exposure parameter values. The fatality data has been separated into two tables. The first (Table 3.7a) comprises the entire database from 1993 to It was noted (Potvin et al. 2001) that substantial improvements in the ground support and reinforcement practices near the face were widely implemented in underground mines from about This was reflected in a 97

113 Chapter 3 Hazard Exposure Model for Underground Mines reduction in serious incidents near the face recorded in more recent years. To eliminate this bias in the statistics, a second table was created (Table 3.7b), comprising the fatalities from 1993 to 1998 only. The small number of fatalities in the record preclude more than a cursory statistical analysis. However, there was a reasonable match (shown by the same colour shading) between the relative exposure proportions suggested by the proximity factor ranges and the percentage of fatalities recorded for the corresponding distance categories. This provided an initial calibration for the E2 exposure parameter. The exposure proportions in Table 3.7c were calculated as shown in Table 3.7d. Table 3.7 Proximity Factor versus Underground Mine Fatality Data (a) Total number of fatalities (excl. 2 with unknown location): 21 Distance from active face/ brow < 3m < 6m < 18m > 50m Fatalities : Proportion of total: 57% 67% 81% 19% (b) Total number of fatalities (excl. 1 with unknown location): i.e. prior to main improvements of practices near face 16 Distance from active face/ brow < 3m < 6m < 18m > 50m Fatalities : (c) Proportion of total: 63% 75% 94% 6% Exposure Model Proximity Factor Ranges: Distance from hazard: < 5m 5-15m 15-30m >30m Proximity Factor E2: Proportions: 56% 28% 11% 6% Cumulative %: 56% 84% 95% 100% (d) Proximity Factor Proportion Calculations: Distance from hazard: < 5m 5-15m 15-30m >30m SUM Proximity Factor E2: Proportion calcs: =10/18 =5/18 =2/18 =1/18 Resultant Proportion: 56% 28% 11% 6% 100% 98

114 Chapter 3 Hazard Exposure Model for Underground Mines 3.7. Exposure Time In the mine sites studied to date, there have been records available on the typical timing of major activities, usually sourced from the mine planning departments. These activity times had some variation, particularly for production tasks when the stope sizes differ significantly. However, when calculated on a per shift basis, most of these activity times compared well across different time periods. They were also reasonably consistent across different mines as long as similar equipment and work practices had been used. Therefore the typical activity times per shift that were recorded can be adopted with a fair degree of confidence. Both to provide a basis for comparison across mine sites and for cases where records are not available such as for a new mine, time distributions of the major activities were calculated. To quantify the exposure time for both underground workers and equipment, data was collected from several weeks of PITRAM records at two different Australian underground mines. These were BHP-Billiton s Cannington silver/lead/zinc mine and WMC s Leinster Nickel Operation Perseverance mine. From this data, time distributions were developed for major activities (jumbo drilling, mucking, charge-up, etc) for each active excavation type such as development headings or stopes. These time distributions may be used for analysis of a generic or planned mine site or where, for whatever reasons, the actual activity times are not well known. For these purposes it is suggested that, rather than adopting the average or median (50%) value, the activity time used for the most critical mine activities should conservatively be the time from the cumulative distribution (cdf) chart corresponding to the 80% range; this more conservative estimate of activity time is referred to herein as T80. In other words, in 80% of cases, the activity takes no more than time T80 for completion. The validity of adopting the median or T80 times will depend on the site specific operating conditions and analysis objectives, as well as the shape of the time distribution. 99

115 Chapter 3 Hazard Exposure Model for Underground Mines Development Charge-Up LogLogistic(-1.71, 3.21, 5.85) X <= % X <= % Relative Frequency Mean = Mean = Operating Time (hrs/shift) Figure 3.4 (a) Time distribution for development charge-up pdf Development Charge-Up 1 X <= % X <= % Cumulative Probability Mean = 1.65 Mean = Operating Time (hrs/shift) Figure 3.4 (b) Time distribution for development charge-up cdf An example of a probability density function (pdf) and cumulative distribution function (cdf) for the development charging activity time can be seen in Figure 3.4. The charts for other major activities are presented in Appendix A. 100

116 Chapter 3 Hazard Exposure Model for Underground Mines For the analysis of the exposure of mobile equipment to geotechnical hazards, the activity time for the machine must represent the total time per shift spent by the machine in the work area. It would therefore be a summation of the operating time and any standby, repair and park-up periods that the machine spends in the location under review. Some examples are provided in Appendix A. Workforce surveys were undertaken in order to establish exposure profiles for various mine personnel. These surveys were circulated to mine production and development crews (including shiftbosses and supervisors), service and maintenance personnel and technical/management staff including geologists, engineers and surveyors. The surveys included questions as to the time spent in the main work areas as well as that for travelling in the decline and accesses, time spent working on inactive levels and vehicles or equipment used. Examples of completed survey forms are provided in Appendix B Ground Hazard Uncertainty and High Risk Activities The parameter Exposure cannot be analysed thoroughly without reference back to the hazard assessment because the vulnerability of the element at risk is inextricably linked with the nature of the hazard to which it is exposed. In the landslide analogy where (repeating eqn 3.1), R (DI) = P (H) x P (S:H) x P (T:S) x V (D:T), the vulnerability or degree of loss component, V (D:T), describes the likelihood of fatalities given a landslide occurring and impacting buildings, vehicles, etc. The landslide characteristics such as its velocity, areal extent, etc can all influence the way that the slide impacts. For example, it has been noted in the AGS 2000a guidelines that research into landslides in Hong Kong (Hong Kong Vulnerability Ranges from Finlay et al. 1999) that a person is more likely to survive a partial burial which is a more typical consequence of a slow moving landslide, than the crushing injuries associated with high velocity debris flows. In their quantitative risk analysis of earthquake-induced landslides in Hong Kong, Wong and Ho (2000) use probabilities of fatal consequence (in terms of fatality per year) of 1/6 and 1/8 for major cut-slope failure impacting 101

117 Chapter 3 Hazard Exposure Model for Underground Mines buildings and vehicles respectively, but reduce these probabilities by a factor of 10 for minor (local) slope failures. When considering the consequences to underground mine personnel of a particular hazard such as an area of instability in the back of a drive (potential rockfall), the exposure depends on the time spent by personnel underneath that unstable area and the likely injury should the potential fall of ground occur. If the vulnerability of the personnel is high, there is a correspondingly high likelihood of fatal injury should the rockfall occur when personnel are in the hazardous area. Comparing this with the exposure of a piece of equipment, say a truck, to the same rockfall hazard, the vulnerability may imply a certain proportion of loss so that a high vulnerability corresponds to total loss (write-off) of the vehicle while a low vulnerability means only minor damage is expected should the truck be in the vicinity when the fall of ground takes place. It can be seen that the vulnerability is dependent on the connection between the elements at risk and the nature of the hazard. A rockfall in a drive which hits a person will likely cause a substantially different degree of loss compared with the same rockfall impacting a heavy vehicle. More complicated is comparing differing natures of hazard (such as size, velocity, etc) and their effect on both proximity and vulnerability estimates. For example, a person standing in a drive may have the same vulnerability if hit on the head by a 20kg rock falling from the back as by a 20 tonne rock fall; i.e. high likelihood of fatal injury. However for a heavy vehicle, an isolated 20kg impact will likely cause minimal damage (low vulnerability to loss), whereas the 20 tonne rock fall could destroy most of the vehicle. Inaccurate assessments of the extent of the hazard would also result in the proximity factor being incorrectly estimated. For example, the unsupported area above an active development face may be recognised as a hazard and appropriate procedures implemented to reduce personnel exposure in this immediate area. However if a rockfall initiates at an active face but propagates up the drive then the previous estimates of worker proximity are invalid. 102

118 Chapter 3 Hazard Exposure Model for Underground Mines Therefore, when assessing potential consequences, uncertainty about the nature of the hazard that is present could result in incorrect determinations of proximity and vulnerability, and hence a wildly inaccurate estimate of consequence severity. In an effort to account for this sensitivity within the exposure model, there is an added component that allows for an increase in the exposure rating should the nature and/or severity of the ground hazard be unclear or unknown. Given the many significant uncertainties involved in geotechnical hazard identification and assessment in underground mining, it was deemed appropriate to allow for a conservative increase in exposure for situations where the ground conditions are rapidly changing or the area is otherwise due for a geotechnical inspection. Ideally, the level of confidence in the ground hazard assessment would be quantified as part of the hazard analysis. However, for the purposes of presenting an exposure model that is useful in a wide variety of underground mine operations and can be validated by rockfall databases, the model must accurately represent the degree of personnel exposure regardless of the site-specific hazard analysis methodologies. Therefore, the modelled uncertainty was instead associated with the activities being undertaken at times and/or in mining areas which were generally recognised as particularly hazardous. Of particular interest were the potentially higher risk activities (refer Table 3.8) such as manual scaling, inspecting unstable ground, working close to an unsupported face and entering old, unmaintained mining areas. In these cases, the geotechnical hazards may not be well identified or understood because of rapidly changing or variable ground conditions. As explained earlier, it is reasoned that the vulnerability (hence exposure) increases as knowledge of the hazards decreases. Supporting research from the offshore industry was presented in Chapter 2 (Busby & Hughes, 2003) where all the accidents studied involved the system being in a state of change, not in a predictable steady-state. Hence the relative proportion of time spent by the worker in performing the higher risk work has been estimated and incorporated in the exposure profile as an additional component. This uncertainty component was termed the ground hazard uncertainty parameter and designated %HT ( hazardous time proportion or percentage of time spent in hazardous areas or tasks). 103

119 Chapter 3 Hazard Exposure Model for Underground Mines A corollary to this methodology is that when measures such as detailed geotechnical investigations are carried out, the uncertainty may be reduced and associated tasks therefore fall into the known geotechnical hazards group, with a consequent reduction in the exposure rating and hence risk. This can be directly applied to the cases of reentry following a blast and entering old, unmaintained mining areas, where the first personnel through have the highest exposure, but after inspection the uncertainty and thus exposure decreases. Table 3.8 Selected High Risk Activities High Risk Activities Re-entry following stope firing Inspecting unstable ground/hang-ups, etc Installation of ground support/rehabilitation Manual scaling Working at unsupported face/brow Working near backs/pillars without surface support Entering old, unmaintained mining areas This explicit inclusion of a hazard component within an exposure assessment is not unprecedented. The Q-system is a rock support and reinforcement design method that is based on the rock mass quality number or Q-value (Barton, Lien & Lunde 1974). This method has been widely adopted in geotechnical engineering, as have the Rock Mass Rating (RMR) and Mining Rock Mass Rating (MRMR) systems. An update to the Q-system is given in a paper by Barton and Grimstad (1994) where the main changes relate to the stress reduction factor (SRF) and the excavation support ratio (ESR). These values, together with other numerical values associated with Rock Quality Designation (RQD), factors describing joint characteristics, water inflow and drive back span, are used in calculating a Q-value and estimating the level of rock support and reinforcement required. The ESR value is dependent on the type (or usage) of excavation that is being assessed and ranges from 2-5 for temporary mine openings to for permanent, high exposure excavations such as underground nuclear power stations, railway stations and public facilities. The excavation back span is divided by the ESR and the resultant value used in design charts to establish the recommended 104

120 Chapter 3 Hazard Exposure Model for Underground Mines level of ground support and reinforcement. Therefore the ESR is used as a partial safety factor which is dependent on the exposure profile of the excavation, but is used within a hazard assessment/design methodology. Another example, discussed in Chapter 2, is the use of decision sight distances in RHRS (Pierson et al. 1990), which adjusts the rockfall hazard rating for sections of road where the exposure of vehicles and personnel is increased by their lack of timely knowledge of approaching hazards Calculation of Exposure Rating for Activities This section details the steps involved in calculation of the exposure rating for an activity and gives some typical examples. The individual activity ratings must then be combined to determine the maximum exposure rating of an excavation area over a shift. This next stage is discussed in later sections. Step 1. Calculate the Hourly Exposure Rating (E/hr)* for different activities based on: 1. The vulnerability of the person or asset (= factor E1) 2. The proximity of the activity to known geotechnical hazards (= factor E2) 3. For general activities, multiply these two factors to give E/hr = E1 x E2. 4. For areas/tasks where greater uncertainty is involved in the geotechnical hazard assessment, add a high risk task component using the proportion of total activity time taken for this task (%HT) and the worst case proximity factor (E2=10). 5. So overall E/hr = E1 x E2 + (%HT x E1 x 10) (3.2) * Note that this term is called the Hourly Exposure Rating to indicate that the activity time component (in hours) is not included at this stage. 105

121 Chapter 3 Hazard Exposure Model for Underground Mines Examples follow of the calculation and comparison of hourly personnel exposure ratings for three major mining activities: production mucking (bogging), development jumbo boring, and charging a face. Example 1 Low Hourly Personnel Exposure Loader mucking in production heading: Enclosed ROPS/FOPS cab E1=0.8 Machine working within 5m of brow E2 = 10 Good, supported ground, operator stays in machine, no high risk task %HT=0 E/hr = E1 x E2 + %HT x E1 x 10 = 0.8 x x 0.8 x 10 = 8 ~ 10 Example 2 Moderate Hourly Personnel Exposure Development jumbo operator boring a face: Operator under a FOPS canopy E1=2 When in cab, operator is 5-10m from face E2 = 5 From worker surveys, approximately 20% of time is spent outside the jumbo and closer to the unsupported face, e.g. changing drill bits. Therefore %HT=20%, E1 = 50 E/hr = E1 x E2 + %HT x E1 x 10 = 2 x % x 50 x 10 = 110 ~ 100 Example 3 High Hourly Personnel Exposure Development charge-up crew loading an active development face: Personnel protected only with PPE E1=50 Working within 5m of face E2 = 10 Personnel working in direct contact with unsupported face and beyond last row of bolts in the back %HT=100% E/hr = E1 x E2 + %HT x E1 x 10 = 50 x % x 50 x 10 =

122 Chapter 3 Hazard Exposure Model for Underground Mines Step 2. Incorporate the number of personnel and time of exposure (from minespecific records or generic values from this research), to calculate an overall exposure rating per shift for each activity. EXPOSURE RATING (E.activity /shift) = Hourly Exposure Rating per person (E/hr) x x No. Personnel (N) Activity Time per shift ( t) = E/hr x N t (3.3) This calculation is demonstrated in the following examples, which use the previously determined hourly exposure ratings. Example 4 (continued from Example 1) Low Hourly Personnel Exposure E/hr = E1 x E2 + %HT x E1 x 10 = 0.8 x x 0.8 x 10 = 8 From the generic time distribution chart (Figure 3.5) for a production loader: Mean time = 4.2 hrs/shift, Median (T50) = 3.2 hrs/shift, T80 = 6.7 hrs/shift. Using activity time t = T80, and one operator: N t = 1 x 6.7 = 6.7 manhrs/shift. Exposure Rating E.activity /shift = E/hr x N t = 8 x (6.7) = 54 Example 5 (continued from Example 3) High Hourly Personnel Exposure E/hr = E1 x E2 + %HT x E1 x 10 = 50 x % x 50 x 10 = 1000 From the generic time distribution chart (Figure 3.4b) for development charge-up crew: Mean time = 1.65 hrs/shift, T50 = 1.5 hrs/shift, T80 = 2.36 hrs/shift. Using activity time t = T80, and assuming 2 people charging face: N t = 2 x 2.36 = 4.7 manhrs/shift. Exposure Rating E.activity /shift = E/hr x N t = 1000x 4.7 = 4,

123 Chapter 3 Hazard Exposure Model for Underground Mines 1 Conventional Production Loading (Muck only) X <= % X <= % Cumulative Probability Mean = 4.20 Mean = Operating Time (hrs/shift) Figure 3.5 Time distribution for Conventional Production Mucking It can be seen that there was a ten-fold increase in the hourly exposure ratings for a development jumbo operator, spending an estimated 20 percent of the time outside the jumbo, compared to a conventional production loader operator who remains inside the machine. Similarly, the hourly exposure rating for a member of the charge-up crew, who is largely unprotected at the face, was a factor of 10 greater than the exposure of a jumbo operator. Based on the generic time distributions, the exposure rating which includes the time spent in the work area, was approximately E.activity /shift = 50 for production loading near a stope, compared to a value of 4,700 for development charge-up; a difference of two orders of magnitude Calculating the Exposure Rating for Excavation Categories Table 3.9 lists the exposure profiles required for various excavation areas. These exposure profiles are created by combining the individual worker profiles within common travel and work areas. Before the site-specific geotechnical hazards are located and assessed, worker exposure profiles comprise: the vulnerability factor (E1) associated with their vehicles/machines; 108

124 Chapter 3 Hazard Exposure Model for Underground Mines default or assumed hazard proximity factors (e.g. lowest level E2 = 1); and the time spent by the workers within or travelling past the area. Table 3.9 Excavation Area Exposure profiles applicable to various excavation categories. Excavation Category Calculation Method for Maximum Exposure per Shift 1 Decline (established levels) Mine traffic and tramming 2 Shaft Total U/G workforce at start of shift 3 Infrastructure area Local workforce profile 4 Inactive levels accesses and drives 5 Development area* near working face 6 Development area accesses/drives 7 Production level drilling horizon near working face 8 Production level drilling horizon accesses/drives 9 Production level mucking horizon near working face 10 Production level mucking horizon accesses/drives Mobile crew profile Development cycle profile Development cycle tramming + traffic Production drilling cycle profile Production drilling cycle tramming + traffic Production mucking cycle Production mucking cycle tramming + traffic 11 Designated no entry area Negligible exposure 12 Designated no unauthorised entry/limited access area Limited inspections only * Focussed on ongoing level and decline development but may also include vertical development such as ventilation rises between levels. The exposure profiles for vertical excavations in an operational mine are generally covered within a production subcategory where cut-off rises/slots (blasted or bored) are used to initiate stoping. Shaft sinking is considered a specialised subcategory (with low personnel exposure using modern methods) and was not relevant to the mines studied; typically being a pre-mining activity outside the normal operational mining environment. 109

125 Chapter 3 Hazard Exposure Model for Underground Mines The following sections provide details of how these various exposure profiles can be determined Exposure Profiles for Development and Production Cycles The systematic methodology that has been developed calculates the typical maximum exposure rating per shift for an active development heading or production area. The steps involved are: 1. List the activities in the development or production cycle. E.g. For a development heading: Fire face, muck heading, install ground support, bore face, etc. 2. Include regular associated activities such as survey, geotechnical inspections, geologist sampling, repairs, shiftboss inspections. 3. For each of these activities list the number of people involved (N) and their typical time spent in the excavation area performing the task ( t). The product of these two (N t) gives the manhours associated with that activity. 4. For each activity calculate the Hourly Exposure Rating E/hr as described in the previous section. 5. The Exposure Rating for each activity is then simply, E.activity /shift = N t. E/hr (3.4) 6. Select all the activities that are possible to accomplish within one shift and sum their Exposure Ratings. This value is the maximum exposure rating per shift for the development heading or production area and is referred to as E max for the particular excavation category. i.e. E max = E max.location/shift = shift (N t. E/hr) (3.5) Examples of this quantification are given in the case study chapters and Appendix B. 110

126 Chapter 3 Hazard Exposure Model for Underground Mines Exposure profiles for established excavations The times of exposure of personnel and mobile plant in established excavations such as shafts and infrastructure areas (workshops, crushers, etc) were calculated as an average over the whole workforce and typical shift exposure profiles analysed. For example, in an underground workshop, the exposure largely comprised mechanics working there for the majority of the shift. For a shaft access operation, the maximum exposure around the shaft during a shift was at the start of day-shift with most of the underground workforce using the morning cage-run. Other areas such as the magazine, store or fuelbay had low exposure comprising mostly of short visits by relevant personnel to collect supplies. Similarly, exposure of personnel on inactive levels generally comprised occasional ground inspections, ventilation checks and perhaps some ground support reconditioning. The tables for estimation of these exposure profiles are in Appendix B. As a guide to times spent in locations other than the work areas, an efficiency study carried out by site personnel at a large Australian mine operation was analysed, and the results were summarised in Table The PITRAM data from Cannington mine also provided estimates of non-productive times (repairs, standby, crib, etc) as summarised in Appendix A. Table 3.10 Summary statistics from mine efficiency study INFRASTRUCTURE TRAFFIC Cage/ U/G Crew Crib Stores Handover in Toyota Tramming (hrs/shift) (hrs/shift) (hrs/shift) (hrs/shift) (hrs/shift) MAX MIN MEAN STD DEV

127 Chapter 3 Hazard Exposure Model for Underground Mines For most established excavations and infrastructure areas, if a specific work activity such as shaft maintenance or decline rehabilitation is being carried out, then the consideration of higher risk tasks and specific hazards becomes more relevant. Otherwise, the parameter values that can generally be adopted (in the absence of identified nearby geotechnical hazards), are: ground hazard uncertainty factor %HT = 0; vulnerability factor E1 = 50 (pedestrians) or E1 = 20 (general traffic); and proximity factor E2 = Exposure profiles for fixed assets Other assets which may be exposed to loss due to geotechnical hazards include the ore resource, infrastructure development such as conveyors and crushing stations, shafts and so on. The exposure of these types of assets will generally be constant within their specific location in the mine and damage potential map rather than being directly associated with a workforce activity. For these assets, time distributions of exposure are not applicable. Instead, the proportion of the asset exposed (its vulnerability to loss) is relevant. For example, severe damage to a cross-cut may result in not being able to recover part of the ore in the stope. The vulnerability to loss of this proportion of the ore resource therefore describes it exposure. Assessing the exposure and risk to these fixed assets is beyond the scope of the present work which instead concentrates on workforce and mobile equipment exposure Characterising exposure for general mine traffic The amount of traffic through an underground mine will depend on the size of the workforce, its materials handling arrangements (e.g. haulage vs conveying, decline vs shaft, orepass vs stockpile) and crew mobility. Tables in Appendix B detail the typical travel profiles of the various underground crews and staff. Before travel profiles were determined, the typical activity times (e.g. time spent inspecting a face, collecting a sample, servicing equipment in a heading, collecting supplies from the store, etc) and 112

128 Chapter 3 Hazard Exposure Model for Underground Mines number of work areas visited within a shift were estimated for each underground worker, based on shift records and worker surveys. Taking the time for these work activities out of the shift duration leaves the total time available for travel and tramming between work places. This available time can then be distributed over the relevant travelways, including the decline and level accesses. The times listed in Table 3.10 were also used as a guide. The typical daily tonnage hauled, truck capacity and number of trucks in the fleet determine the number of haulage cycles per shift. The distribution of haulage time over the decline and accessways depends on the speed of travel and haulage distances. The manhours spent travelling through the accesses, drives and decline were thus calculated for the haulage, general mine traffic and equipment tramming in each area. Each of these traffic categories was then treated as a separate activity for calculating the maximum exposure for the travelway using the procedure detailed in Section 3.9 above. Within each category, the average level of protection (E1), proximity to geotechnical hazards (E2 = 1 for the general case) and ground hazard uncertainty parameter (%HT=0 for traffic) were combined with the exposure time in manhours to give the exposure ratings per shift for each traffic category. The final step was a summation of these individual ratings to produce maximum exposure ratings per shift for the various travelways. Should a section of an excavation be changed from a purely traffic category to an actual work area (e.g. scaling the decline), then these specific activities would be modelled by increased exposure model parameters (i.e. %HT>0 and E2>1) so the resultant E activity/shift would be added to increase the exposure rating for that part of the travelway. It should be noted that these calculated maximum exposure ratings for travelways such as declines and level accesses reflect the combined traffic intensity over the shift for the excavation category and do not imply this level of activity at all points at all times. This means that the associated likelihood of injury (given a rockfall) relates to the category rather than to a specific point in the travelway. This method is consistent with the treatment of the development and production heading categories, where the worst case 113

129 Chapter 3 Hazard Exposure Model for Underground Mines combination of activities during a shift was used to calculate the maximum exposure rating. For cases where particular locations in a travelway, such as an intersection, are being closely examined, then the average amount of traffic past a point can be read or determined from the traffic profile table and used to estimate the probability that a vehicle or pedestrian will be in the given area when a rockfall occurs. To calibrate the exposure rating in the travelways with some probability of a rock falling onto a moving vehicle, methods using geometric probability density functions from landslide risk management guidelines (AGS 2000a) and Bunce et al. (1997) were adopted. The resultant temporal probability represents the number of rockfalls that would be expected to occur before one impacts a vehicle. This calculation requires an estimate of the number of rockfalls per day for the travelway section. As a starting point for this, the average number of rockfalls/year per Australian mine was taken from the ACG rockfall database (Potvin et al. 2001). These calculations are detailed in Chapter Results of exposure rating quantification The following two tables summarise the results of the exposure profiling and model application detailed in Appendix B for one Australian and one Canadian mine site. Shift and personnel records covering early 2002 from Big Bell mine were studied for the first analysis. Due to the significant amounts of ground rehabilitation and installation of specialised secondary support (conebolts) being carried out during this period, an additional Rehabilitation category was added as seen in Table Over the time period in question, Big Bell generally had three production drills, three to four loaders, four jumbos, two integrated toolcarriers (ITs) and a cable-bolter available. This equated to about 16 to 18 work areas for production, development headings and rehabilitation and/or secondary ground support zones. For the purpose of calculating some representative (maximum) ratings for active level travelways, it was assumed that three stopes were being worked during each shift on a production level, and that four development headings were active during a shift on a 114

130 Chapter 3 Hazard Exposure Model for Underground Mines development level. The maximum exposure ratings per shift for the traffic on generic active levels were then determined based on one third of the service and support resources being apportioned to a busy production drilling level, and two thirds to a busy development level. It was assumed that the traffic associated with production mucking horizons during normal operations comprised loader tramming and truck haulage only. These relative proportions can be easily adjusted when particular mining levels are studied in detail, as is demonstrated later. Data from mine records covering June July 2002 at Noranda s Brunswick mine were used to create the second exposure profile. The major differences between the two mines were as follows. Firstly Brunswick is a shaft operation with no decline to surface, hence the large exposure in the infrastructure areas relating to the underground workshops and refuges/lunchrooms/offices. Secondly, Brunswick mine used mobile equipment which, with the exception of some trucks, typically had the protective canopies removed to accommodate their ramp traffic control system (manual pull-cords hanging from the backs). Therefore, the personnel vulnerability factor E1 was substantially higher in most locations within the Canadian mine than for an equivalent Australian site, leading to higher exposure ratings. This was particularly notable for the mucking areas with the vulnerability factor increased from Big Bell s E1=0.8 to 1, up to E1=50 for Brunswick mine. It should be noted that the very high exposure ratings for the infrastructure areas in Table 3.12 simply denote a large concentration of personnel in the area (protected only by PPE) and do not necessarily imply a high risk to personnel. These infrastructure areas are typically in stable geotechnical domains and are well supported, with thick layers of shotcrete for example. Therefore the geotechnical hazards or probabilities of rockfall should generally be low for these areas. 115

131 Chapter 3 Hazard Exposure Model for Underground Mines Table 3.11 Summary of Exposure Quantification for Big Bell Mine, 2002 Excavation E max per Excavation Category Area shift 1a Decline (near active levels) 993 1b Decline (upper/inactive levels) Shaft n/a 3 Infrastructure area (workshop, stores, fuelbay, magazine, cribroom, crusher), time-of-day dependent 1,775 4 Inactive levels accesses and drives vehicles/pedestrians 382 / Development area near working face 6,784 6 Development area accesses/drives 1,565 7a Production level drilling horizon near working face, including ring prep & charge 9,368 7b Production level drilling horizon near working face, prod drill only 1,928 8 Production level drilling horizon accesses/drives 384 9a Production level mucking horizon near working face, including re-entry inspections 3,915 9b Production level mucking horizon near working face, muck only Production level mucking horizon accesses/drives, loader tramming + haulage Designated, barricaded no entry area 0 12 Designated no unauthorised entry/limited access area Rehabilitation scale + services + secondary ground support 6,

132 Chapter 3 Hazard Exposure Model for Underground Mines Table 3.12 Summary of Exposure Quantification for Brunswick Mine, 2002 Excavation E max per Excavation Category Area shift 1 Ramp 4,450 2 Shaft Infrastructure area (workshop, stores, fuelbay, magazine, cribroom, crusher) Note: This exposure rating mostly relates to workshop and refuges/offices Inactive levels accesses and drives (will reduce for resource-spread over several levels) 14,744 2,381 5 Development area near working face 9, Development area accesses/drives (for concentration of development resources on one level) Production level drilling horizon near working face (with scaling, ring prep, uphole clean-out, charging etc) 2,644-5,288 13,365 8 Production level drilling horizon accesses/drives 1,160 9 Production level mucking horizon near working face 8, Production level mucking horizon accesses/drives 1, Designated no entry area 0 12 Designated no unauthorised entry/limited access area 88 The exposure ratings from Table 3.11 were used as a basis for creating maps showing the relative exposure for different levels of the Big Bell mine for the time period studied. After studying shift records to determine the status of different work areas, exposure ratings were assigned to accesses, drives, service areas, development and production headings. These ratings were then colour-coded for plotting on mine plans 117

133 Chapter 3 Hazard Exposure Model for Underground Mines by division of the numerical range of exposure ratings into the descriptive categories shown in Table Table 3.13 Relative Levels of Exposure Rating E/hr E max Relative Exposure Equivalent exposure Rating time for E1=50, E2 =10 < 20 <500 Very Low < 0.5 manhrs/shift Low manhrs/shift Moderate manhrs/shift High 5-15 manhrs/shift > 500 >7500 Very High > 15 manhrs/shift The exposure ratings for accesses and drives in Table 3.11 were derived from Big Bell mine traffic profiles assuming a certain proportion of the active stopes and development headings were being worked on the level being analysed. To assign exposure ratings to specific locations on a level, the shift records over a two week period in January 2002 were studied. The example shown here concerned the 535 level. Drives and accesses were described by their function such as: Active level decline Main production level access South/North access or drive Direct development access Direct production drill/muck access Haulage drive, etc From the relative number of shifts during which work was carried out over this period, each of the production areas, development headings and secondary support locations on the damage levels could be categorised as low, medium or high priority. Comparing this mining activity to the other levels in the mine for the time period, it was found that the 535 level accounted for 54% of the production mucking effort, 20% of the 118

134 Chapter 3 Hazard Exposure Model for Underground Mines production drilling in the mine, 40% of the secondary ground support locations and just 12% of the active development headings. These proportions were then used to scale the exposure ratings for the lower priority headings and for the traffic in drives and accesses so that the intensity of traffic, support and service activities reflected the amount of nearby mining activity. 535 Level Personnel Exposure Map (late January 2002) stope fronts PROD. MUCK HEADINGS PROD. DRILL HEADINGS DRIVES EXCLUDED FROM MINING TRAFFIC v high high mod low v low NEW DEVELOPMENT 700 E DECLINE N Figure 3.6 Plan of the exposure of personnel to rockfalls for an active production level of Big Bell mine in early Summary and Conclusions This chapter has detailed a methodology for profiling mine hazard exposure and developed a model for quantifying the exposure of underground personnel to geotechnical hazards. The exposure model parameters were developed using both empirical data and mechanistic principles. Examples of the application of this hazard exposure model to two underground mines were shown, with exposure ratings calculated for the various drives, development and production headings based on the mining activities and traffic profiles determined from shift records and survey data. The classification of personnel exposure into categories, ranging from very low to very high 119

135 Chapter 3 Hazard Exposure Model for Underground Mines likelihood of injury in the event of a rockfall, was then illustrated with a colour-coded plan of an active mining level. A summary of the exposure model terminology and abbreviations introduced in this chapter is given in Table Typical hourly exposure ratings for various mine activities, based on information from Big Bell and Perseverance mines, are listed (alphabetically) in Table Table 3.14 Exposure Model Terminology Exposure Term Abbreviation Description Vulnerability Factor E1 0.8 to 50 Proximity Factor E2 1 to 10 Number of personnel N Number of people involved in a work activity Activity Time t Typical time spent on an activity in a shift Ground Hazard Uncertainty Parameter %HT % of time spent in hazardous task Hourly Exposure Rating E/hr E1 x E2 + %HT x E1 x 10 Activity Exposure Rating E.activity /shift N t. E/hr Maximum Exposure Rating per shift for an Excavation Category E max activities/shift (N t. E/hr) 120

136 Chapter 3 Hazard Exposure Model for Underground Mines Table 3.15 Typical Hourly Exposure Parameters for Mine Activities Activity E1 E2 %HT E/hr Bog/Muck development % Bore development face % 110 Charge development face % 1000 Charge rings % Diamond drilling % 50 Drilling for primary ground support % 260 Drilling for secondary support 2 5 5% 35 Geotech inspection/ sampling % 350 Inspections/ mapping % Installing primary ground reinforcement/support (no FOPS) % 750 Installing primary ground reinforcement/support (with FOPS) Installation of secondary ground reinforcement/ support (no FOPS) Installation of secondary ground reinforcement/ support (with FOPS) % % % 110 Manual scaling % 1000 Mechanical scaling (with FOPS) % 120 Production drilling 2 5 5% 35 Production mucking conventional % 5 30 Production mucking remote % 100 Re-install services % 275 Repairs in heading % Repairs in workshop % 50 Ring preparation and clean out % 350 Services % 50 Shotcreting/ fibrecreting % 300 Surveying % 125 Travelling % 20 Water down/check area %

137 Chapter 3 Hazard Exposure Model for Underground Mines The integration of the hazard exposure model with other risk components is shown in Chapter 4, using seismicity as the initiating geotechnical hazard. Then Chapters 5 and 6 demonstrate applications of the exposure model within this seismic risk framework. The back-analysis of rockburst case studies and comparison of actual versus modelled consequences provides calibration of the exposure methodology and seismic risk analysis. The validation of the hazard exposure model through correlations with rockfall and injury records for various Australian underground metalliferous mines is shown in Chapter 7. Then examples are shown of probabilistic analyses to calculate risks to personnel from rockfalls using model data. 122

138 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model 4. SEISMIC RISK FRAMEWORK FOR MODEL CALIBRATION 4.1. Introduction It was shown in earlier chapters that the hazard exposure model must be constructed within a risk framework and calibrated by information from risk databases. A seismic risk context was the basis for selection of the specific case studies used for calibration purposes. The aim was to minimise the number of geotechnical hazard variables necessary for inclusion in a relevant risk framework. The following sections present the details of the seismic risk methodology that was used to calibrate the hazard exposure model through the rockburst case studies presented in Chapters 5 and 6. It must be emphasised that the seismic risk methodology is presented as a technique to broadly approximate seismic hazard and excavation damage severities, and thus enable calibration of the hazard exposure model. The method was designed to suit the particular mine sites studied and is not intended to be a definitive seismic hazard analysis tool. As discussed in Chapter 2, seismic risk is presented here in terms of combining seismic hazard, damage potential and exposure for each consequence of interest, which involves consideration of the following probabilities: 1. Probability of initiation of a seismic source to produce a certain magnitude event in a given time period, typically expressed as a design maximum event magnitude (M max ) and its likelihood or frequency (F). For the case studies, the behavioural trends of seismic clusters were analysed in order to estimate the level of hazard. 2. Probability of various levels of dynamic loading (peak ground motion) at each excavation site of interest based on seismic source magnitudes and the proximity (scaled distance SD) of the seismic sources to the excavation sites. 3. Probability of damage occurring at each excavation site of interest. This involves an integration of the rockmass fragility, as defined by local site characteristics 123

139 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model (SC), and the peak ground motions to give the maximum probable excavation damage or damage potential (D). There is no universal or widely accepted technique for determining (and updating) rockmass fragility curves, particularly those that would account for the effect of different forms of ground support and reinforcement. Probabilistic techniques are more advanced for the study of rockmass behaviour under static (e.g. gravity) loading. For example, specific programmes such as DIPS and UNWEDGE (Rocscience 2001) may be used to analyse the probability of creating unstable wedges through the intersection of mapped joint sets. The principles of excess shear stress (ESS) on discontinuities (Ryder 1987) combined with probabilistic descriptions of rockmass fractures have been suggested for the simulation of seismic behaviour in mines (Board & Tinucci 1993). Board (1994) demonstrates the seismic simulation based on the probabilistic fracture generation for local mine areas with complex stress modelling. However, the transfer of these methods from the research domain and to the mine-scale has not been proven for a wide range of environments. Instead, for dynamic loading, mine site engineers tend to combine standard rockmass property measures (e.g. UCS, Young s Modulus) and classifications (e.g. Q, MRMR) with local site experience of failure mechanisms in order to prioritise areas for further action. They do not have quantitative techniques for calculating the probabilities of local excavation failure such as rockbursting and seismically induced falls of ground. 4. Probability of hazard exposure of elements at risk (e.g. personnel) and their vulnerability to loss (e.g. fatal injury) from the potential damage. This leads to levels of Exposure (E) as discussed in the previous chapter. The combination of these factors leads to a range of possible consequences with varying levels of probability such that for each specific CONSEQUENCE of interest, SEISMIC RISK =HAZARD LIKELIHOOD x DAMAGE POTENTIAL x EXPOSURE = F x D(M max, SD, SC) x E (4.1) The consequences of concern in the case studies presented were severe or fatal injuries to personnel caused by rockbursting or seismically induced falls of ground (SIFOG). 124

140 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model According to Vinnem (1999, p.151), when addressing the fatality risk in the context of major hazards, it is probably impossible to distinguish rigidly between fatalities and severe injuries...and... probably no point in doing so. A similar approach is adopted herein, where the term fatality risk is used to cover both fatalities and serious injuries, and it is assumed that there is probably some fixed (though unknown) ratio between fatality and injury numbers. The first step of the hazard characterisation for the mine case studies was sorting of the mine s microseismic data into seismic clusters, using the MS-RAP programme as explained in the next section. Then each cluster was assigned a hazard rating based on the trends in the seismic parameters used to describe the cluster. This hazard rating could be equated with a probable maximum magnitude for each cluster, with the assumption that the future behaviour of the cluster (at least in the short-term) could be forecast from representative historical seismicity. As an example, substantial changes in mining strategies, such as the suspension of mining on a level, would negate this assumption. The next stage of analysis involved an estimation of the level of dynamic loading at each excavation site based on distance from seismic clusters and the use of the design peak particle velocity chart from Kaiser & Maloney (1997). This enabled level maps to be produced showing the relative degrees of seismic hazard in all drives. Then these dynamic loads were used to estimate potential excavation damage through assessment of the local characteristics of each excavation site. As mentioned above, there is no universal technique for determining the rockmass fragility curves for each excavation site. Therefore, for the purposes of this research (i.e. calibrating the exposure model), it was considered appropriate to examine the rockburst histories for trends in failure mechanisms and, where relevant, adopt the site-specific qualitative ranking methods used by the mines geotechnical engineering personnel. From this, level plans showing relative degrees of damage potential could be produced. Detailed exposure profiles of mine activities were developed, specific to the time period for each rockburst case study. Exposure ratings were calculated based on these exposure profiles, using the model parameters as described in Chapter 3. The damage 125

141 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model potential rating was then integrated with the personnel exposure rating at each excavation site to calculate levels of risk. Tables linking qualitative descriptions of likelihood with descriptions and some estimated probabilities were presented in Chapter 1 and used as a basis for relating the seismic hazard and damage potential assessments to likelihood measures as shown in Tables 4.2 and 4.3 below. The probabilities reflect the chance of occurrence of the event within the relevant timeframe associated with the case studies; largely dependent on the seismic record length which varied between three and six months, and on the planned continuity of mining i.e. how far into the future would past mining conditions still be representative. Therefore a three month time window was considered most relevant and was arbitrarily selected. The estimated probability ranges for the likelihood descriptions in GCG (2000), plus two additional likelihood categories, are shown in Table 4.1. These probability ranges were based on figures from Table 1.1 and may be used in combination with the hazard and damage ratings in quantitative analyses. Table 4.1 Relating Likelihood Descriptors with Probabilities Qualitative Likelihood Category Approximate Probability Range Almost Certain > 0.95 Very Likely Likely Moderate Unlikely Rare Negligible < 0.01 As previously discussed in Chapter 2, and seen below in Table 4.2, a Richter magnitude of M L = 2.5 (+/- 0.5) was selected as representative of the largest and most damaging seismic events in the mine databases studied for this research. From the mines studied, the lower limit to seismically induced damage was approximately M L = 0 to

142 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model Table 4.2 Seismic Hazard Definitions Likelihood of Qualitative Hazard Description M L = 0.5 Likelihood of M L = 1.5 Likelihood of M L = 2.5 Very Low unlikely rare negligible Low moderate unlikely rare Moderate likely moderate unlikely High very likely likely moderate Very High almost certain very likely likely Table 4.3 Damage Potential Definitions Qualitative Damage Potential Description Likelihood of shakedown Likelihood of rockfall or strainburst Likelihood of rockburst Very Low unlikely rare negligible Low moderate unlikely rare Moderate likely moderate unlikely High very likely likely moderate Very High almost certain very likely likely The fine points of the seismic risk analysis process are developed through the following sections of this chapter. Mine-specific details and analysis results are presented in Chapters 5 and Use of MS-RAP for Clustering Seismic Data This program was introduced in Chapter 2. It was used in the case studies to screen for potential errors in the database and classify the seismic events based on the four quality tests. The clustering algorithm was then initiated within MS-RAP to sort the seismic data into clusters of spatially related events. Cluster sorting runs were carried out on the seismic data recorded prior to each of the rockbursts chosen for the risk analyses. For the Big Bell analyses, the minimum number of events in a cluster was set at 15 and 101 clusters chosen as the maximum number of clusters. The results of the clustering runs 127

143 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model were output to Microsoft Excel spreadsheets, in which the rest of the cluster analysis was carried out Seismic Hazard Rating of Clusters The trends exhibited by the seismic events within each cluster were analysed to determine the level of seismic hazard associated with the cluster. Six parameters were chosen as components of an overall hazard rating. These comprised: 1. descriptors of the seismic event frequency-magnitude relationship, 2. estimators of the probable maximum event magnitude within the cluster, 3. a measure of the stress change at the seismic source, and 4. a parameter reflecting the spatio-temporal correlation between production blasts and the occurrence of large seismic events within the cluster. For the purposes of this research, the requirements for the semi-quantitative seismic hazard methodology were that it: could be largely automated and did not rely on the extensive investigation of individual seismic sources and clusters, as this would be an unrealistic expectation for the application of the methodology by geotechnical personnel at mine sites; could be universally applied across different mine sites (for seismicity in the approximate range of Richter -3 to 3) with comparable results; had some inbuilt redundancies to prevent potential errors introduced in the clustering process by incomplete seismic histories of areas from overwhelming or preventing the hazard analysis; used estimates of the actual frequency-magnitude distribution for each cluster and not only a best-fit Gutenberg-Richter relation which may be heavily influenced by the small to moderate event magnitude range; 128

144 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model The parameters used to calculate a seismic cluster hazard scale were therefore: 1. m est: This is an estimate of m max for the cluster using a simple statistical procedure (Kijko and Funk 1994) to estimate a future maximum seismic magnitude using the previous two largest events in the record. The seismic moment is considered a useful measure of the size of an event as it relates to the deformation at the seismic source (McGarr 1984). Ordering the seismic record in terms of seismic moment magnitude for each cluster, where M 1 is the largest moment magnitude on record for the cluster and M n is the n-th largest, then m est = M 1 + (M 1 M 2 ) (4.2) 2. b: The b-value from the Gutenberg-Richter frequency-magnitude relation for each cluster was found by a least-squares best-fit linear regression to the straight line portion of the logarithmic frequency-magnitude curve. A low b-value implies a large proportion of the seismic events in the cluster have high magnitudes, which is considered to be an indication of increased hazard compared to high b-values. 3. a/b: The other parameter from the Gutenberg-Richter relation is the a-value which reflects the number of events and hence relative activity level in the seismic record being studied. Within the hazard scale, the value of a/b (which equals the magnitude axis intercept of the trendline) was calculated. This value was used as another estimate of the maximum magnitude in the cluster, based this time on the population trend rather than the largest two events. [Note: an alternative but similar parameter, which should be proportional to Hazard Magnitude (van Aswegen 2001a), may be found useful. This is a 2 /2b, which reflects the triangular area under the frequency-magnitude trend for events 0<m<a/b]. 4. Weighted sum of magnitudes, w i N i : This was another way of characterising the seismicity from the event magnitudes within the cluster, without assuming that the population fits to the Gutenberg-Richter relation. It is equivalent to the prorating of events described by van Aswegen (2001b). The non-parametric methods for estimating m max developed by Kijko & Funk (1994) and recommended for use by van Aswegen et al. (1999) are based on seismic events 129

145 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model greater than a moment magnitude of about 3. For the mines studied for this thesis, a magnitude 3 is the upper size limit of the event range and the use of data ranging in size from about -1 through to 3 is necessary. Therefore, the numbers of events within each magnitude category N i = N(x i < M L < 1+ x i ) were multiplied by weighting factors w i which increased with the magnitude category increase (i.e. by a factor of 10) and these products were then summed. w i N i = log(x i ).N i ] (4.3) = 0.1N(-1<M L <0) + 1.N(0<M L <1) + 10N(1<M L <2) +100N(2<M L <3). 5. A._av or AL (M typ ): The apparent stress level is equivalent to the average apparent stress when calculated using a fixed seismic moment selected as typical for the area of interest (van Aswegen 2001a). This average apparent stress parameter, as used by Simser (2000) at the Brunswick mine, has been explained in Chapter 2. The apparent stress for a seismic event is recognised as a model-independent measure of the stress change at the seismic source and is calculated from the radiated energy (E), seismic moment (M) and rockmass modulus of rigidity (G); A = GE/M. (4.4) For the hazard scale developed herein, a trend line, known as the E:M relation and spanning the data range for each cluster, was fitted to the logarithmic plot of Energy:Moment data such that: log E = c + d logm (4.5) where c and d are constants. Using the seismic data in all clusters at a mine, M typ was calculated as the median seismic moment. Then, for each cluster, the E:M relation was used to estimate the energy E(M typ ) for this median moment and thus the average apparent stress, A_av = G.E(M typ ) /M typ (4.6) 6. Blast miscorrelation parameter f bm : The seismic decay and energy attenuation techniques described in Chapter 2 were adapted to provide a simplified method for analysing the correlation of seismic cluster activity with blasting. 130

146 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model In considering the everyday practicalities of implementing a seismic hazard scheme at mine sites, it was decided that the hazard rating should be increased, using a miscorrelation parameter f bm, if large seismic events within a cluster were occurring well outside stope blast windows (temporal and spatial windows). This less predictable behaviour could reflect that the seismic source area was in a critical state with only small perturbations required to trigger significant events. In practice, it also reduces confidence in the effectiveness of standard risk mitigation measures such as exclusion zones and times. Seismic events with local magnitude (equivalent to Richter Magnitude for the mines studied) of at least zero, comprised the database for blast correlation. In order to characterise the behaviour of the seismic clusters in response to blasting, the individual analysis of potentially thousands of seismic decay curves was not a practical method for use within an operational mine environment. Also, the limited number of events within some seismic clusters meant that time-of-day distributions would not be statistically significant. Instead, different levels of spatial and temporal event correlation with the most recent prior blast were considered, as shown in the table below. These criteria were necessarily dependent on the available detail of the blast records (in terms of time and spatial co-ordinates). Those events which fell within 75m and 12 hrs (typically one shift) of a stope blast were considered best correlated. The choice of a 75m range was based on energy attenuation considerations as described in Chapter 2, and also typical sublevel dimensions versus blast-related damage. In the mines studied, 25 to 35 metres was a typical level/sublevel spacing, therefore a 75m range of blast influence would potentially include two to three levels above and below the active stope. A slightly higher hazard level was associated with events that occurred 12 to 24 hrs post blast and still within the 75m range. Those events with poor spatial correlation to blast locations (i.e. beyond 75m distance) were given the two highest hazard levels, with events falling outside the 12 hr time window topping the miscorrelation scale. 131

147 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model Table 4.4 Criteria for determining f m Spatial window Temporal window Miscorrelation factor f m < 75m from blast < 12 hrs post-blast 0 < 75m from blast hrs post-blast 0.5 > 75m from blast < 12 hrs post-blast 1 > 75m from blast > 12 hrs post-blast 2 The miscorrelation factor was then scaled by the event magnitude using the midpoint of the magnitude range as the scaling factor. Table 4.5 Magnitude-scaling factor f s Magnitude range Scaling Factor 0 < M L < f s 1 < M L < < M L < This meant that each event with magnitude M L > 0 had an assigned magnitudescaled blast miscorrelation factor, f bm = f s x f m (4.7) Then, for each seismic cluster the individual event factors were summed, 132

148 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model f bm.cluster = f bm.event (4.8) The six parameters were thus calculated for the seismic clusters associated with the Big Bell mine from April to July For each parameter, its distribution of values across the seismic clusters was analysed, with the results summarised in Table 4.6. Then each parameter was assigned a ranking (R i ) from zero to four, representing the level of seismic hazard (from very low through to very high) associated with the value of the parameter. These parameter ranges are shown in Table 4.7. Table 4.6 Variation in Values of Seismic Hazard Parameters App Stress (kpa) Blasting f bm m est w i N i b a/b A. av Max Min Median Mean Std Deviation CoV Table 4.7 Criteria used to Rank the Seismic Hazard Parameters Hazard Rank R i m est w i N i b * a/b * A.av (kpa) f bm very low 0 m est < b > 1.2 a/b <-0.5 0< < 0.5 f bm < 2 low 1-1<m est < <b< <a/b< 0 0.5< < 1.5 2<f bm <5 moderate 2 0<m est < <b< <a/b<1 1.5< < 2.5 5<f bm <10 high 3 1<m est < <b<0.8 1<a/b< < < 5 10<f bm <15 very high 4 m est > 2 > 81 b< 0.6 a/b > 1.5 > 5 f bm >15 * For each cluster, b-values and a-values were also calculated using moment magnitudes and the distributions of these parameters compared to the local magnitude cases. The following criteria were found to provide equivalent rankings to those in Table 4.7, and may therefore be used when, for whatever reason, moment magnitudes are preferred over local magnitudes. Table 4.7a Supplement to Table 4.7 using Moment Magnitudes Hazard Rank R i b a/b very low 0 b >1.35 a/b < low <b< <a/b< 0.32 moderate <b< <a/b< 1 high <b< <a/b< 1.5 very high 4 b < 0.75 a/b >

149 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model The six individual parameter rankings were then summed as shown in equation 4.7, to give the overall seismic cluster hazard rating. The SCHR ranges from the lowest rating of zero, up to a maximum of 24. Seismic Cluster Hazard Rating SCHR = R i (4.7) It should be noted that this hazard scale can be simply adapted to include more than six hazard parameters if that is deemed appropriate. The SCHR was used as a semiquantitative hazard scale as shown in Table 4.8. The upper limits of the numeric ranges were calculated as demonstrated by these examples: Example 1. Cluster Y has five seismic hazard parameters ranked as low (R 1 =R 2 =R 3 =R 4 =R 5 =1) and one parameter ranked as moderate (R 6 = 2). Therefore, Y:SCHR = R i = 5 x x 2 = 7 = upper limit of the Low Hazard range Example 2. Cluster Z has three seismic hazard parameters ranked as moderate (R 1 =R 2 =R 3 =2), two parameters ranked as high (R 4 =R 5 =3) and one as low (R 6 =1). Therefore, Z:SCHR = R i = 3 x x 3 + 1x1= 13 = upper limit of the Moderate Hazard range. The SCHR value, based as it was on several seismic magnitude trends, was related to a qualitative likelihood description of the probable maximum magnitude using the seismic hazard definitions in Table 4.2. This table listed seismic magnitudes of 0.5, 1.5 and 2.5 as the bases for probability comparisons. It must be emphasised that the likelihood of occurrence of a certain size event is affected by the length of the seismic record, as one has less confidence in recurrence times greater than the record duration. There is also the assumption that the future mining environment and seismicity will be reflected in the current seismic trends. Back-analyses of time records of mining and seismic activity may be used to check the validity of this assumption, provided the seismic monitoring system characteristics are also stable. It is of less consequence to the use of the methodology in an operating mine as the MS-RAP seismic database will be continuously updated. In this way, the actual ongoing rockmass response will be 134

150 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model captured, rather than the hazard analysis being the static snapshot shown herein for model calibration purposes. The methodology discussed in Brummer (2000) was used to provide an approximate statistical comparison between SCHR values and event probability ranges and is detailed in Appendix D. Based on these correlations (Appendix D, Table D.6), acknowledging that they are preliminary and represent a potentially wide range of seismic magnitudes, Table 4.8 relates the SCHR ranges with qualitative and associated quantitative probabilities of occurrence of a magnitude 2.5 (+0.5) event in the seismic cluster vicinity within a selected timeframe. The timeframe must be appropriate to the seismic record period and mine plans and therefore, based on the Big Bell and Brunswick case studies, a three to six month period was considered most relevant. Figure 4.1 illustrates the clustering and hazard rating of the seismicity on a production level of the Big Bell mine. A seismic cluster may be rated as a very high hazard, but if it locates far from any entry excavations, the risk to personnel is minimal. Therefore the next stage of the seismic risk analysis was to compare the seismic cluster locations with drive locations in order to map the relative degrees of seismic hazard in all drives. This procedure is detailed in Section 4.4. Table 4.8 Semi-Quantitative Seismic Cluster Hazard Rating Estimated Likelihood of Event Overall Cluster Hazard Rating Occurring in Time Period (typ. 3-6 months dept on record period) Qualitative Description Quantitative Value = Sum of Hazard Ranks Likelihood of M L = 2.5 Tentative Probability Range Very Low 0 to 1 negligible < 0.01 Low 2 to 7 rare Moderate 8 to 13 unlikely High 14 to 19 moderate Very High > 20 likely

151 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model 485 Level: Hazard-rated Seismic Clusters based on seismicity to 9 July E v high high mod low N Figure 4.1 Example of a Big Bell mine level with seismic clusters rated using SCHR Calculating Seismic Hazard & Damage Potential at Locations along Drives This stage of the seismic risk analysis involved an estimation of the level of dynamic loading at each excavation site based on distance from seismic clusters and the use of the design peak particle velocity (ppv) chart from Kaiser & Maloney (1997), reproduced here in Figure 4.2. This chart and the ppv damage ranges proposed by Hedley (1992) were used in developing Table 4.9, in which ppv ranges and their related levels of excavation damage were associated with distances between the excavation site and seismic sources of specified magnitude. 136

152 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model Nuttli Magnitude mn m/s 300mm/s 100mm/s 30mm/s Richter Magnitude ML mm/s Distance from Source R [m] Figure 4.2 Table 4.9 Recommended peak particle velocity distribution for support design (Relationship II: a* =0.5, C* = 0.25 m2/s). Higher values are expected above the shaded near-field zone (after Kaiser & Maloney 1997). Seismic source distances used to determine the level of dynamic loading and associated potential damage at an excavation site. Peak particle velocity range Excavation Damage Classification Approximate design distance R from seismic source for ppv range (from Figure 4.2) M L = 2.5 M L = 1.5 M L = 0.5 ppv < 50mm/s No damage > 700 m > 150 m R > 75m 50 < ppv < 300mm/s Falls of loose rock m m 3 75 m 300 < ppv < 600mm/s Falls of ground m m within nearfield ppv > 600mm/s Severe damage R < 45 m R < 15 m within source zone 137

153 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model A Microsoft Excel macro was then written for this study in order to automate the following tasks: The survey coordinates of locations along drives were compared with the coordinates of every clustered seismic event. For each SCHR category, the minimum distance between the drive location and the seismic events in the SCHR category was reported. For example, the locations of all the seismic events which fell in clusters rated as high hazard were compared with the drive coordinates and the distance from the closest event therefore became the distance from the drive location to the high seismic hazard zone. In the resulting spreadsheet, each drive location had five minimum distances listed against it, each relating to a seismic hazard category from very low to very high. Then the distance relations from Table 4.9 were applied to determine the most critical combination of seismic source hazard and distance (i.e. dynamic loading) for each drive location as follows: The excavation site received a Very High drive hazard rating if it had a minimum distance of less than 30m to a very high SCHR event; It received a High drive hazard rating if it was less than 45m from a very high SCHR event, or less than 30m from a high SCHR event; The site received a Moderate drive hazard rating if it located within 75m of a very high SCHR event, 45m of a high SCHR event, or 30m of a moderate SCHR event; It received a Low drive hazard rating if the excavation site located within 105m of a very high SCHR event, 75m of a high SCHR event, 45m of a moderate SCHR event, or 20m of a low SCHR event; 138

154 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model The site received a Very Low drive hazard rating if it was less than 135m from a very high SCHR event, 105m from a high SCHR event, 75m from a moderate SCHR event, 45m from a low SCHR event, or 20m from a very low SCHR event; If the minimum distances fell outside these ranges, the excavation site was assigned a Negligible drive hazard rating. Figures 4.3a and 4.3b illustrate how this process worked, using examples of a seismic cluster with a very high hazard rating and a moderate SCHR respectively. It can be seen how the drive hazard rating increased for excavation locations as they approached the seismic cluster. It should be stressed that a very high SCHR event meant that the event fell within a seismic cluster which had been rated as very high hazard. It does not make any assumptions about the magnitude of this closest seismic event. The largest seismic event within a cluster may well lie on the opposite side of the cluster from the excavation site being considered. Cluster rated as Very High Hazard Legend 30m 30m 30m 30m 135m Excavations with drive hazard rating depending on cluster proximity Drive Hazard V high High Mod Low V low Figure 4.3.a Illustration of the radiating influence of a very high hazard seismic cluster on the seismic hazard rating of nearby drives. 139

155 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model Cluster rated as Moderate Hazard Legend Drive Hazard 30m 30m 75m Excavations with drive hazard rating depending on cluster proximity Figure 4.3.b Illustration of the radiating influence of a moderate hazard seismic cluster on the seismic hazard rating of nearby drives. The procedure enabled level maps to be produced showing the relative degrees of seismic hazard in all drives. Therefore, each location along a drive had an estimate of applied dynamic loads that could be used as an initial indicator of the potential excavation damage using Tables 4.3 and 4.9. The actual response of the excavation site to the dynamic loads also depends on the local characteristics such as the excavation span, drive orientation and presence of discontinuities. As already discussed, for the purposes of this research, it was considered appropriate to use the mine-specific rockburst trends and geotechnical assessments to adjust, where necessary, the seismic hazard in drives to better represent the actual excavation damage potential. From this, level plans showing relative degrees of damage potential could be produced and compared with the recorded instances of rockbursts and seismically induced falls of ground Integrating Exposure and Damage Potential Ratings to Estimate Seismic Risk Exposure ratings for all relevant locations in the mine under review were determined by following the exposure profiling methodology and applying the exposure model described in Chapter 3. The exposure ratings were divided into the following categories 140

156 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model from very low to very high that represent increasing likelihoods of injury given a rockfall: Table 4.10 Relative Levels of Exposure Rating E max /shift Relative Exposure Rating Typical Excavation Categories <500 Very Low restricted entry areas, production mucking horizon accesses Low inactive levels, declines, dev level drives, infrastructure areas Moderate near brows prod charge, re-entry mucking High development headings, ground support rehab areas >7500 Very High busy u/g workshops, cribrooms and offices, headings with no surface support or damaged support In order to determine the seismic risk to personnel, three risk components must be combined: Seismic Hazard Likelihood, Damage Potential and Exposure. The Damage Potential is a function of the seismic event magnitude, the distance between seismic source and excavation site, and the local site characteristics so the risk equation of Equation 4.1 can be written as before: SEIS. RISK = SEIS. HAZ. LIKELIHOOD x DAMAGE POTENTIAL x EXPOSURE R = F x D(M max, SD, SC) x E For the case studies in this thesis, the excavation damage potential was quantified based on the seismic hazard rating assigned to the drives as described previously. The damage potential ratings were chosen to reflect the top of the SCHR ranges for each descriptive category, as shown in Table Highly detailed geotechnical assessments of the excavations would perhaps allow for more definitive analyses of the variation in local rockmass fragility. Then it may be more appropriate for the damage potential rating categories to cover a range of values, similar to those for seismic hazard, rather than 141

157 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model conservatively adopting the highest value in the SCHR range. However this level of detail was not justified for the case studies in this thesis, where the main objective is to calibrate the hazard exposure model. Table 4.11 Semi-Quantitative Seismic Cluster Hazard and Damage Potential Ratings Qualitative Description SCHR and Drive Hazard Damage Potential Rating Very Low 0 to 1 1 Low 2 to 7 7 Moderate 8 to High 14 to Very High > Integrating this with the damage potential definitions from Table 4.3, one obtains Table 4.12 which links the damage potential rating with descriptions of seismically induced excavation damage. The damage modes which were considered ranged from the shakedown of loose or highly fractured material up to severe rockbursting. Table 4.12 Descriptions of Damage Potential Rating Damage Potential Rating Qualitative Damage Potential Description Likelihood of shakedown Likelihood of rockfall or strainburst Likelihood of rockburst 1 Very Low unlikely rare negligible 7 Low moderate unlikely rare 13 Moderate likely moderate unlikely 19 High very likely likely moderate 25 Very High almost certain very likely likely 142

158 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model To determine the relative degree of seismic risk to personnel, the maximum exposure ratings per shift (E max ) were scaled by a factor of 1/500 to bring them within a similar range of values to the damage potential ratings. Then, the damage potential ratings (D) of the entry excavations were multiplied by the scaled exposure ratings E max /500, to obtain relative risk values (RR). Two relative risk matrices were used to determine the appropriate risk rating criteria to use for assessment. The first matrix in Table 4.13a used the highest value of E max /500 within each exposure category. The second matrix, Table 4.13b used a mid-range value for E max /500. The colour-coded shadings of these matrices differentiate between the categories of relative risk, varying from, very low (blue) low (green) moderate (yellow) high (orange) very high (red) extreme (purple). By comparing the values and trying various shading combinations in the two matrices, the criteria for relative risk ratings were decided upon. They are listed in Table Some sample determinations of relative risk rating based on these criteria are as follows: 1. D = 19 (high) combined with E max = (high) RR = 19 x (10 to 15) = , i.e. very high. 2. D = 13 (moderate) with E max = (moderate) RR = 13 x (6 to 10) = , i.e. high. 3. D = 7 (low) with E max = (low) RR = 7 x (2 to 5) = 14 35, i.e. moderate. 143

159 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model Table 4.13(a) Relative Risk Ratings based on the highest values of Exposure Categories RR = D x E max /500 Exposure per shift Very Low Low Moderate High Very High E max / Damage Potential Rating (D) Very Low >20 Low >140 Moderate >260 High >380 Very High >500 Table 4.13(b) Relative Risk Ratings based on the mid-range of Exposure Categories RR = D x E max /500 Exposure per shift Very Low Low Moderate High Very High E max / Damage Potential Rating (D) Very Low Low Moderate High Very High

160 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model Table 4.14 Criteria for Rating the Relative Risk Qualitative Description Risk Rating RR = (D. E max )/ 500 Very Low R < 0.5 Low 0.5 < R < 12 Moderate 12 < R < 70 High 70 < R < 190 Very High 190 < R < 300 Extreme R > 300 For the level of risk to represent a true likelihood of injury, the final calculation must involve a measure of the seismic hazard frequency such as the recurrence time for the maximum probable event. For each of the case studies in the next two chapters, a period covering between three and six months of the seismic record was found relevant to the seismic hazard analysis. At Big Bell during full production, the Integrated Seismic Systems (ISS) seismic record to July 2000 showed an average event frequency of up to 0.05 per shift for events greater than a local magnitude (M L ) of 1.5 (damaging rockfall/strainburst level). When the Australian Geological Survey Organisation (AGSO) regional records were added to cover 11 months from August 1999 July 2000, the average event frequency was 7/11 per month or about 0.01/shift for magnitudes greater than 1.5. By comparison, seismic records for Brunswick mine site revealed: For the six month period to May 2002, 55 events larger than M L = 0.5; 6 with magnitude greater than 1.5; and one greater than 2.5 (M L =2.6). For the six month period from April to September 2000, 58 events larger than M L = 0.5; and 17 events with magnitude greater than 1.5 (max = 2.4). This equates for M L > 1.5 to an average event frequency of 0.02/shift for 2002 and 0.05/shift for

161 Chapter 4 Seismic Risk Framework for Calibrating the Exposure Model Therefore the seismic hazard likelihood for the two different mine sites was very similar for the time periods stated. This means that the relative risk ratings should be comparable between the two sites, though it will depend on the number and extent of hazardous seismic zones Conclusions In this chapter, a risk framework has been proposed for analysing seismic hazards and excavation damage potential and then integrating them with the exposure model. The methodology provides semi-quantitative risk ratings and some estimated correlations with probability values were presented to enable a conversion to quantitative risk analysis. Applications of the exposure model within this seismic risk framework are presented in the following two chapters. The seismic hazard and damage potential procedures, proposed here to estimate seismic risk, are not presented as definitive and defensible methodologies. Instead, the requirement to demonstrate the application of the exposure model within specific risk contexts required approximate measures of geotechnical hazard and excavation response. The particular hazard selected to demonstrate, and hence calibrate, the exposure model was that of mining-induced seismic energy. Therefore the excavation response of interest was the rockburst potential; a small subset of the much larger rockfall category. The selection of seismic hazard, potentially resulting in rockbursting of excavations, was in order to provide a better solution procedure for the model by minimising the number of parameters that required inclusion for representative determinations of damage potential, exposure and hence risk. Existing statistical methods to calculate seismic risk were found to generally be too broad in scale to suit the required application to specific locations within a mine. The limitations of the seismic database available for the case studies and, more generally, for many mining operations, precluded intensive statistical treatments. Instead, use was made of an existing statistical method to draw approximate correlations between probability values and seismic hazard trends. 146

162 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine 5. DETAILED CASE STUDIES OF SEISMIC RISK & HAZARD EXPOSURE AT BIG BELL MINE 5.1. Big Bell Mine Introduction Harmony Gold s Big Bell Mine is located near Cue in the Murchison goldfields of Western Australia. Prior to its closure in 2003, Big Bell mine was a low grade, high tonnage operation using longitudinal sub-level caving (SLC) as the mining technique. Following several damaging seismic events during 1999 and early 2000, and a fatality in September 2000, the mine entered a redevelopment phase with production sourced from upper levels of the mine. From late 2001, after rehabilitation of damaged areas and redesign of the footwall access drives, production mining on the midlevels of the mine gradually recommenced (Barrett & Player 2002). Gold was first discovered at Big Bell 100 years ago. Early prospecting and mining was succeeded by underground mining by the American Smelting and Refining Co. (ASARCO) until The next mining phase was open-pit mining from 1988 to 1993, firstly by a joint venture between Australian Consolidated Minerals (ACM) and Placer Pacific Pty Ltd, and later by Normandy Poseidon Ltd. The final phase of operations commenced in 1994 with underground mine development accessing remnant mineralisation (Player 1998). Then in 1997, mining of the first level of the longitudinal sublevel cave commenced, producing approximately 1.7 Mt of gold ore annually. New Hampton Goldfields Ltd purchased the Big Bell mine from Normandy in January 2000, and were in turn taken over by Harmony Gold of South Africa in Although indications of a high stress regime were apparent during the mass blast open stoping on upper levels in 1996, Big Bell mine started experiencing significant mining related seismic events with accompanying rockbursts from February Large, damaging events were recorded by the Australian Geological Survey Organisation (AGSO) in August and November 1999 with magnitudes ranging from 1.7 to 2.4 on the Richter scale. These events prompted the short-term installation of a portable 8-channel CSIR-Miningtek system, to cover the three levels of the northern producing stopes, before delivery of a full scale mine-wide Integrated Seismic System (ISS), which was 147

163 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine commissioned in February Details of this system are given in Turner & Player (2000) and updated by Barrett & Player (2002), who note the increase in system sensitivity in December 2001 when an additional eight triaxial sensors were installed, increasing the number of sensors to seventeen. From early April 2000, reliable seismic data from the ISS system became available. This research covers the 22 month period from April 2000 until early February 2002 and back-analyses the three rockbursts that occurred during this period. Using data collected from mine operations and planning records, workforce exposure profiles were determined for the periods leading up to each rockburst event. Then, for each rockburst, the exposure model was used to quantify the personnel exposure associated with individual locations across the specific level which experienced major damage. As was explained in the introductory chapters, the exposure model, which incorporates not only time but also hazard proximity and degree of vulnerability (lack of protection), can only be realistically calibrated within a specific risk context. The risk context for the Big Bell case studies is summarised in Table 5.1. Table 5.1 Risk Context for Calibration of Exposure Model using Big Bell case studies. RISK COMPONENT CASE STUDY CONTEXT DESCRIPTION IN THESIS Hazardous Energy: Mining induced seismicity. Seismic Hazard Potential impact on system: Rockbursting Excavation Damage Potential RELEVANT PARAMETERS Seismic magnitude and frequency (likelihood) of occurrence Damage severity (size, extent) and likelihood. Exposure of Elements at Risk Underground mine personnel exposed to rockbursts Exposure Model Measures of exposure time, proximity and vulnerability. Consequences of concern: Injury to underground mine personnel within range of the rockfall/rockburst. Relative Risk Hazard frequency x Damage Potential x Exposure Potential barriers or mitigators to consequence occurring: 1) ground retention system to limit rock displacement; 2) protection of personnel by being within or shielded by a vehicle. Part of Excavation Damage Potential Part of Exposure Model Measure of energy absorption of ground support system. Vulnerability measure. 148

164 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Therefore, in order to calibrate the exposure model, the semi-quantitative methodology developed for assessing seismic hazard and excavation response, presented in Chapter 4, was used in conjunction with the detailed exposure profiles for the mine to determine relative risk ratings across the damage levels. In this way the actual rockburst consequences could be compared with the modelling results SLC Layout and Operations Adoption of the mining method of longitudinal sublevel caving under a caving hangingwall was discussed extensively in Player (1998). The method was a top-down approach and during the early part of this study period, production at Big Bell was focussed on the 485RL, 510RL and 535RL levels (metres below surface). Figure 5.1 shows a long section view of the mine. Figure 5.1 Long section of Big Bell mine showing the underground levels and open pit in early A plan of the 510 level is presented in Figure 5.2 below, showing the features typical of the mid levels (485 to 560 levels). Each production level was accessed from a footwall 149

165 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine decline from the surface. Typical level development comprised a footwall strikeparallel haulage drift (FWD), off which several cross-cuts were driven into the orebody. Mining on each level was initiated from three slot raises located at the north, middle and south of the orebody. Rib pillars were formed by mining outward from the central slot while retreating inwards from the north and south extremities. Mass blasting of the retreat pillars completed mining on the level. F83N X83 F83S Level 510 H77S central slot X64 hangingwall ore drive FWDN F58S footwall ore drive NEA FWDS CENTRAL ACC SEA exhaust airway 650 Easting footwall haulage drive DECLINE Northing Figure 5.2 Plan of 510 level in The sublevel vertical interval was 25m. In plan, the orebody is lenticular with a width varying between 10m and 40m. Twin oredrives were developed through the wider, central sections of the orebody; the parallel footwall and hangingwall ore drives. In 1999, the ore drive dimensions were reduced to 4.5m high to 5m wide and all development was designed with an arched profile. During the first half of 2000, mine production equipment consisted of two longhole drill rigs (Simba 4356), four Elphinstone loaders and seven Elphinstone 73D trucks used for hauling the ore to surface. A production crew of 18 personnel worked 12 hour shifts. Mine development was carried out by a contractor, typically using two development jumbos, and Orica had the explosives contract. When a hang-up occurred in the cave, a 150

166 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine cannon was used to fire a projectile charge and knock it down. Dealing with frozen ground involved the construction of bund walls before bulk emulsion was pumped and the target bombed Mine Geology and Geotechnical Characteristics The Big Bell deposit is hosted by a greenstone sequence within the Murchison province of the Yilgarn block, and the ore body strikes at 30 from magnetic north and dips at approximately 72 to the east. The deposit is pervasively foliated, significantly influencing the response of the rock mass to stress redistribution (Player 2001). A strike-parallel graphitic shear structure, which varies in thickness from 2 cm to 45 cm, is located between 5 m and 20 m into the footwall of the orebody, intersecting the mine workings. Another major graphitic shear lies approximately 100 m into the footwall. Pegmatites are variably developed and intrude all rock types. Details of the mine stratigraphy are provided in Barrett & Player (2002) but the main point of note for this study is the differentiation between the geotechnical properties of the orezone schists (ALSH, BISH & CRSH) and the more massive and brittle footwall amphibolite (AMPH). Barrett & Player (2002) report average intact rock strengths for the main rock types as: Footwall amphibolite Ore zone (KPSH & ALSH) - Hangingwall biotite schist (BISH) MPa 160 MPa 95 MPa Geotechnical mapping identified seven joint sets but generally two to three sets, plus the foliation, were present at any one location (Player 2001). The following tables list the rockmass properties and stresses measured at depth using the HI-cell overcoring method. 151

167 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Table 5.2 Big Bell Mean Rock Mass Properties (after Turner & Player 2000) Rock Type UCS 50 Young s Modulus (GPa) Poisson s Ratio Density (kg/m 3 ) AMPH ALSH BISH CRSH Table 5.3 Stress measurements within Big Bell mine (after Barrett & Player 2002) Year Level Principal Stress Magnitude (MPa) Dip ( ) Bearing ( ) Major (footwall) Intermediate Minor Major (footwall) Intermediate Minor Major (orezone) Intermediate Minor Major (footwall) Intermediate Minor

168 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine 5.4. History of Rockbursting The high and deviatoric stress regime of the mine is evident from Table 5.3 above, and another point of note is that the orientation of the major principal stress is perpendicular to the strike of the orebody at depth. Big Bell mine has had a history of rock noise and small-scale strainbursting related to pegmatite intrusions, flat-dipping joints and/or local stress concentrations located in deeper sections of the decline, cross-cuts, ore drives and footwall drives (Turner & Player 2000). Prior to the onset of the rockbursts and seismically induced falls of ground, the primary failure mechanism was intense stress-induced fracturing above the backs of the ore drives which was accompanied by dilation, furthering the shear of footwall foliation planes (Player 2001). This could result in the loss of brows and blasthole closure issues and necessitated the use of strong brow support. Turner & Player (2000) proposed that the main mechanism for the seismic activity of concern in Big Bell mine was the shear failure of intact rock and tight foliation surfaces, as illustrated in Figure 5.3. A photograph showing an example of this mode of failure at Big Bell is presented in Figure 5.4. They attributed the large magnitudes to a combination of very high stress levels, a competent rock mass capable of storing a significant amount of energy prior to failure, and the orientation of footwall drives which were parallel to foliation and maximum shear stress below stope face positions. Numerical modelling of stresses using MAP3D showed, as expected, that the high virgin stresses combined with the extent of the stoping and caved zones resulted in very high indicated stress levels ahead of and below stoping abutments (Turner & Player 2000). More specifically, the authors report on back-analyses of August 1999 to March 2000 rockbursts to estimate the contributory stress environment, that found the maximum principal stress peaking in the footwall drive below the stope faces and either side of the intersections with cross-cuts. 153

169 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine W E Tight foliation planes Shear through intact rock (shattered, powdery) Stress failure through intact rock Shear along foliation planes Figure 5.3 Typical rockburst failure geometry (after Turner & Player 2000). Figure 5.4 Photograph of the failure plane in a 535 footwall drive rockburst (photo courtesy of J. Player, Harmony Gold). 154

170 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Table 5.4 summarises the history of damaging rockbursts at Big Bell mine. Before the installation of the ISS system in early 2000, the seismic events which were too small to be detected by the AGSO regional network (i.e. less than about M L = 1.7) had no magnitude estimate and was listed as no record (nr). Other events that could not be associated with a specific seismic event were generally those occurring during stope blasting which interfered with the sensor trace. The three rockbursts selected as case studies for seismic risk analysis in this thesis are shown in bold. Table 5.4 Rockburst Record to July 2000 (Player 2002) Date 12-Feb Jun-99 7-Jul-99 9-Aug Aug 25-Nov Nov-99 6-Apr Apr-00 8-May May Jun-00 4-Jul-00 9-Jul-00 2-Sep-00 6-Feb-02 M L AGSO or (ISS) Level Northing Location/ Drive Reference Volume (m3) displaced nr F64N 4 nr FWDS 5 nr FWDN FWDN FWDN FWDN FWDN 40 nr FWDN 3 nr FWDN 1 (0.7) FWDS/CENTRAL 15 nr FWDN (2.2) FWDN FWDS (2.3) F83N/F83S 300 nr 10 (0.7) F83N (fatality) nr (1.6) F70S 3 155

171 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine The three rockbursts were selected based mostly on maximising the length of the preceding ISS seismic event record. Since the ISS records commenced in April 2000, this restricted the choice of rockburst cases considerably. The other requirement was that there be significant excavation damage (several tonnes) directly related to rockbursting, rather than simply shakedown or seismically induced falls of ground. It was decided not to analyse the 4 th July 2000 event as a separate case study due to its small damage extent and timing just prior to the large rockburst on the 9 th July Normal production mining operation on the mid-lower mine levels were suspended, and there was a gap in the seismic record due to sensor damage over the months immediately following the large rockburst event on 9 th July 2000, precluding detailed SCHR analysis of the small event of 2 nd September Based on the rockfall record, the excavations most prone to rockbursts were the footwall drive (FWD) and the footwall ore drive, in particular the sections north of the central access and either side of cross-cut intersections. Rockfalls but not rockbursts had been recorded in the hangingwall ore drives and decline/level accesses. This rockburst trend accorded well with assessment of the hangingwall biotite schist and orezone as more ductile and less strong than the footwall amphibolite. When this information is combined with the results of the numerical models of stress reported by Turner & Player (2000) as discussed above, the basis for the footwall drives being more predisposed to the violent release of strain energy stored in the rock mass become more clear. That is, the production level drives close to the footwall of the orebody were lying within a strong, brittle, highly stressed rockmass unit (striking parallel to foliations and normal to the maximum principal stress), adjacent to more ductile and deformable schistose orezone and hangingwall zones. Highly stressed rib pillars on production levels also contributed to the mining induced stress environment, before the mining strategy was changed in mid 2000 to prevent the formation of retreat pillars. These rib pillars formed relatively stiff remnants within a deforming orezone and, as load was progressively shed from the hangingwall to the footwall, both the pillars and adjacent footwall ore drives were therefore prone to seismicity and sudden instability. 156

172 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Before August 1999, the standard ground support and reinforcement comprised grouted friction stabiliser bolts (splitsets) together with point anchor bolts and RF81 mesh. Unplated but grouted twin strand cable bolts were installed in backs at truckbays and the intersections of crosscuts and strike drives. An upgrade to this support regime after August 1999 involved greater use of cable bolts and the partial introduction of cone bolts. (Barrett & Player 2002). Details are provided in later sections of the specific ground support regime and excavation damage related to the three rockburst case studies Seismic Data The seismic event data used in the analysis was from a database recorded by the ISS system, filtered for events recorded on at least five sensors and then manually processed (Grinceri 2002). The processing had been carried out by geotechnical personnel on site, including Big Bell staff, researchers and consultants. The database consisted of 11,500 events recorded over the period from 4 April 2000 to 10 February At various times during this period, there were some interruptions to the seismic record due to sensor damage and system upgrades. The interruptions and changes in system sensitivity prevented meaningful comparisons of seismic activity rate over time. The seismic data associated with each of the three time periods of interest was imported from the event database into the MS-RAP program which was described briefly in Chapter 2. The MS-RAP sensitivity parameters are the cluster size, isolation distance, minimum number of events in cluster and maximum number of clusters. For the final two parameters, respective values of 15 events and 101 clusters were adopted. With Big Bell sublevel intervals of 25m, final clustering runs were carried out using a cluster size of 20 metres (after initial unsatisfactory trials of 15 metres), and an isolation distance of 20 metres. Sorting runs were carried out on the seismic data recorded prior to each of the three rockbursts chosen for the risk analyses. The large seismic events (on 17-Jun-00, 9-Jul- 00 and 6-Feb-02) associated with the rockburst damage were part of the seismic database used in the MS-RAP clustering but were then excluded from the subsequent 157

173 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine cluster hazard analysis. This was to ensure the cluster hazard rating depended only on the seismicity in the months preceding the damaging events. Table 5.5 summarises the final MS-RAP sorting results. The unsorted events were irrelevant to further analysis within this study as they represented only a very small proportion of the total radiated seismic energy and were mostly located around the mine development in the upper levels of the mine, deep in the footwall or in the hangingwall. Despite the short record period, the first two clustering runs seemed to be successful in clustering the seismicity associated with development, stoping and remnant pillars, as well as other clusters that located in the footwall and were associated with the second graphitic shear structure. The majority (greater then 97%) of the radiated seismic energy was incorporated in the seismic clusters. Table 5.5 MS-RAP clustering results for Big Bell data Cluster Time range of Total no. No. of No. of seismic Proportion of Run sorted data of seismic clusters events sorted seismic energy events into clusters in clusters 1 4/Apr 17/Jun/00 4, , % 2 4/Apr - 9/Jul/00 5, , % 3 4/Apr/00-6/Feb/02 10, ,206 98% 4 1/Aug/01-6/Feb/02 1, % There were some difficulties associated with using the results of the third clustering run which was intended to analyse the 6 th February 2002 rockburst. In addition to the disruption in mining production and development on the damaged 510 and 535 levels during June and July 2000, after the fatality in September 2000 all mining on the mid to lower levels of the mine was suspended. Production from the mid-levels recommenced in January These disruptions, combined with the interruptions in the seismic record, reduced confidence in the value of the third clustering run which involved the 158

174 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine entire seismic database (4-Apr-00 to 6-Feb-02). Therefore a final clustering run was carried out using only the seismic data recorded in the six months prior to the February 2002 rockburst. This data was not cluttered with seismicity from stressed areas that had been released during the year 2000 rockbursts and mining activities. The proportion of seismic energy in clusters was slightly lower, close to 90%, due to the scatter of seismic data related to remnant mining of the upper levels of the mine which was excluded from further analysis. It is shown later that this analysis provided a much better definition of the seismic hazard associated with the altered stress environment and more recent mining activity Seismic Hazard and Excavation Damage Potential Analysis The focus of this thesis and the case studies is to develop and demonstrate applications of an exposure model for geotechnical hazards in underground metalliferous mines. However the calibration of the model results requires comparison with actual rockfall consequences and must therefore be carried out within a risk framework. The case studies selected were rockburst incidents, so an appropriate seismic risk framework was necessary. For the purposes of this research, an empirically-based, semi-quantitative seismic risk framework was proposed in Chapter 4. A major component was the seismic cluster hazard rating (SCHR) methodology. For each of the Big Bell rockburst case studies, the seismic clusters were analysed and SCHR values calculated. These represented different likelihoods of probable maximum seismic event magnitudes. From Chapters 2 and 4, it was seen that a complete seismic hazard description involves both magnitude and frequency measures. This may be expressed as the probability of occurrence of a seismic event of, or greater than, a certain magnitude within a specified timeframe. Table 5.6 restates the empirically based relative seismic hazard rating association with large event likelihoods. The time covered by data within individual seismic clusters may not be sufficient to establish reasonable event recurrence times on a cluster by cluster basis. This is relevant to the short seismic record between April 2000 and the rockbursting events in June and July Therefore some general statistics for the overall mine were examined. 159

175 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine From examination of the Big Bell rockburst history (Barrett & Player 2002) as reproduced in Table 5.4, it was seen that the major rockbursts were associated with seismic magnitudes of M L = 1.6 and greater. Small, localised areas of damage, more typical of strainbursts, were related to seismic magnitudes of approximately 0.5. The largest event on record had M L = 2.4. Within eleven months (August 1999 July 2000), there were seven recorded damaging events with seismic magnitudes greater than 1.5, as listed in Table 5.4. This equates to a damaging event frequency of 0.64 per month or approximately 1 every 90 shifts. This probability increases to 1 in 70 shifts if the smaller strainburst type events between February 1999 and July 2000 are included. The ISS record only began in April 2000 so cannot be matched exactly with the damage statistics. However the first four months of data recorded a monthly average of 0.4 events of magnitude greater than 0.5, and 0.05 events greater than 1.5. Therefore, every 2.5 shifts, one could expect at least a magnitude 0.5 event (strainburst potential), and every 20 shifts an event greater than magnitude 1.5 (rockburst potential). It must be noted that these general statistics do not apply equally to all mining areas but are relevant to the seismically active middle and lower levels below approximate RL 435. Table 5.6 Estimated Probabilities for Seismic Cluster Hazard Rating (ref: Chap 4.) Estimated Likelihood of Event Occurring in Time Cluster Seismic Hazard Period (say 3 months) Descriptions Qualitative Description SCHR Likelihood of M L = 1.5 Likelihood of M L = 2.5 Tentative Probability Range Very Low 0 to 1 rare negligible < 0.01 Low 2 to 7 unlikely rare Moderate 8 to 13 moderate unlikely High 14 to 19 likely moderate Very High > 20 very likely likely

176 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Since there is not currently an accepted method for determining the local excavation vulnerability (or fragility) to dynamic loading, a high degree of fragility was initially assumed for the Big Bell excavations. Therefore the preliminary damage potentials for excavations on the rockburst damage levels were assumed equivalent to the seismic drive hazard ratings. The drive hazard reflected the expected dynamic loading (ppv) at the excavation site and was determined using the SCHR values and proximity of the excavations to the seismic sources as described in Chapter 4. Since the case study context was the analysis of exposure and risk due to rockbursting, the ratings were then adjusted based on the rockfall/rockburst record and what it suggested about local excavation vulnerability to seismically induced damage. As the only excavations with recorded rockburst damage were the footwall drive (FWD) and the footwall ore drive, the assumption of high vulnerability to dynamic loading was justified and therefore the damage potential for these drives was maintained at the initial drive hazard rating. This included the exhaust airways (NEA and SEA) at each end of the FWD. So, for example, a section of the northern FWD (FWDN) which was found to have a high seismic drive hazard was assigned a high damage potential. Rockfalls but not rockbursts had been recorded in the hangingwall ore drives and decline/level accesses, so the excavation fragility under dynamic load for these areas seemed to be substantially lower than in the footwall strike-parallel drives. This was consistent with their differences in site characteristics such as drive orientation, foliation alignment relative to the maximum principal stress direction 1, rockmass strength, stiffness and/or stress. Given that there were seven recorded instances of dynamic loads associated with events of M L >1.6 that damaged only strike drives and not the nearby cross-cuts, one can estimate the likelihood of rockbursting in the latter excavations at less than 1 in 7 or P<0.14. The occurrence of a rockburst in these excavations in the near future under similar mining conditions could thus be described as unlikely or rare (Table 4.1). This equates to a moderate or low damage potential respectively (using the definitions within Tables 4.12 & 5.8), despite high to very high seismic hazard. Therefore the final damage potential considered most representative for these areas was chosen to be two categories lower than the drive hazard rating, as shown in Table 5.7. For example, an area of decline with a high seismic drive hazard was 161

177 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine assigned a low damage potential, and a cross-cut with a very high seismic drive hazard rating became a moderate damage potential. The associated likelihood of various damage severities such as strainbursting or severe rockbursting was estimated in Chapter 4 and are repeated in Table 5.8. Table 5.7 Adjusting drive hazard to obtain damage potential Seismic Drive Hazard Excavation damage potential Likelihood of ppv high enough for strainbursting Likelihood of ppv high enough for rockbursting Qualitative Hazard Description FWDN & FW oredrives Other drives such as XCs, decline, etc Rare Negligible Very Low Very Low Negligible Unlikely Rare Low Low Negligible Moderate Unlikely Moderate Moderate Very Low Likely Moderate High High Low Very likely Likely Very High Very High Moderate Table 5.8 Damage Potential Definitions Qualitative Damage Potential Description Likelihood of rockfall or strainburst Likelihood of rockburst Very Low rare negligible Low unlikely rare Moderate moderate unlikely High likely moderate Very High very likely likely The following sections provide details of the seismic hazard and damage potential ratings for the time periods prior to each of the three rockburst case studies. Colour-coded plans of the damage levels illustrate the relative seismic hazard and excavation damage potential. Then, sections 5.10 and 5.11 present the results of the personnel exposure profiling and ratings produced by the exposure model for the times and mine levels associated with the rockburst events. 162

178 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Finally, the relative seismic risk ratings were calculated by combining the excavation damage potential and the personnel exposure ratings according to the procedure in Chapter 4. Maps of the relative risk across the damage levels were produced and the actual consequences of the rockbursts compared to the model results Seismic Hazard and Damage Potential for 17 th June 2000 Rockburst Description of Rockburst Following the advent of damaging seismic events in 1999, site engineers implemented a 24 hr exclusion period for the 50m of development below the production blast. The installation of seismic-resistance support conebolts was commenced in footwall haulage drives (FWDN, FWDS), starting with the 460 level. The exclusion regime was reduced for these areas to 25m and re-entry after 12 hrs. The seismic resistant support and reinforcement initially consisted of cable bolts, cone bolts, debonded gewi bars, and split sets (Barrett & Player 2002). On the 17 th June 2000, a seismic event recorded as local magnitude 2.2 occurred well outside the exclusion time. Damage was caused to about 130m of the northern footwall drive (FWDN) on the 535 level. Mobilised material consisted of 60 m 3 or about 170 tonnes of ejected rock, as well as seismically induced falls of ground. The rockfall size was initially estimated at greater than 500 tonnes. Damage was partially controlled on the footwall side of the drive and the adjacent truckbay by conebolts installed in the backs. However, some of the damage extended higher into the backs than the conebolt length and other falls occurred at joins in the mesh (Barrett & Player 2002). Significantly, there was no damage seen in adjacent drives and cross-cuts. At the time of the rockburst, production drilling, development and ground support activities were being carried out in the northern oredrives. This resulted in five employees being trapped for over two hours before climbing out over the rubble. A long hole rig and two drill jumbos were trapped for 28 shifts. The footwall drive suffered significant damage and required extensive rehabilitation works. Photographs of the damaged drive can be seen in Figures 5.4 and

179 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Seismic Hazard and Damage Potential Ratings Table 5.9 lists selected seismic clusters from the MS-RAP analysis with their six hazard parameters (R 1 to R 6 ) being summed to produce the SCHR or seismic cluster hazard rating. The clusters that located within a sublevel of the 535 level were included in the table below and the full list of seismic clusters used in the analysis can be found in Appendix C. The cluster names describe the cluster level and drive location. Figure 5.5 Photograph at the entry to the damaged section of the 535 FWDN (photo courtesy of J.Player, Harmony Gold). 164

180 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Table 5.9 Cluster Name- June Sort Summary Table for Seismic Hazard Rating of June Clusters m est: R 1 b R 2 a/b R 3 w i N i R 4 A._av R 5 f bm R 6 SCHR Relative Hazard Rating 510 FWDS/CENT Mod 510 X77/F Low F77N Mod F83S High FWDN/X High FWDN/X High H64N Low NEA Mod 535 F70N-FWDN Mod 535 F74N-FWDN Low F65N- FWDN Low FWDN/CENT Mod FWDS/CENT Low X Low X High The hazard-rated clusters were plotted on plans of the main production and development levels. The chart in Figure 5.6 shows the seismic clusters for the 535 mining level, as well as the large seismic event associated with the rockburst damage on 17 th June There were four clusters rated as high hazard on the 535 level, two on the FWDN and two that located between the footwall drive and the footwall ore drive. According to Table 5.7, a high seismic hazard indicates the occurrence of an event of M L = in the vicinity of the cluster can be estimated as moderate to likely. 165

181 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine 535 Level: Seismic Clusters to 17 June E 535 Level 700 high SCHR mod SCHR low SCHR June RL N Figure 5.6 Hazard-rated Seismic Clusters to 17 th June 2000 for the 535 level The effect of the high SCHR clusters on nearby drives was seen when the seismic events in the Table 5.9 clusters were compared with the 535 drive co-ordinates in order to determine the seismic hazard in drives. This procedure was detailed in Chapter 4, and Figure 5.7 presents the resulting drive hazard map. Damage potentials were then assigned to the drives by keeping the same rating as the hazard level for the FWDN and footwall ore drives. Other excavations were derated by two hazard levels, as was shown in Table 5.7, with the associated likelihood of the drive experiencing a damaging peak particle velocity (ppv) from the critical seismic source as listed in Table 5.8. The damage potentials across the 535 level are illustrated in Figure 5.8. The level plan in Figure 5.7 shows the high seismic hazard for the oredrives close to development and stoping areas. Also of note is the high to very high seismic hazard for the FWDN and cross-cuts (XCs) running off it. The very high hazard zone to the north was due to seismic activity related to the northern closure pillar located on the 485 level. The very high hazard seismic cluster around the 485 pillar can be seen in the plan of the 510 level in Figure The high hazard areas of the decline were associated with scattered seismicity on lower levels deep into the footwall, associated with the second graphitic shear structure intersecting the decline. 166

182 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Level 535 Seismic Hazard Map for 17 June CROS S-CUT ORE DRIVES CENTRAL SLOT V High High Mod Low V Low TRUCK BAY LATER DEVELOPMENT NEA FWDN DECLIN FWDS SEA LATER DEVELOPMENT E N Figure 5.7 Hazard rating of 535 level drives based on seismicity before 17 th June The final damage potentials in the map in Figure 5.8 more accurately portray the susceptibility of the footwall ore drive and FWDN to seismic damage due to their unfavourable foliation intersections and strong, brittle rockmass characteristics. Also plotted on the plan is the actual rockburst damage. It can be seen that the damage zone was coincident with an extensive area of high damage potential. It was also noted that the damage did not extend into the truckbay which was reinforced with longer conebolts than the adjacent FWDN. There was a small section of FWDN rated as very high damage potential because of its close proximity to intense seismic activity related to the northern closure pillar above. This area was not significantly damaged by the M L = 2.2 seismic event, as the event located near the more southerly high hazard cluster as shown in Figure

183 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Level 535 Damage Potential Map for 17 June 2000 V High High Mod Low V Low neg Rockburst Damage Zone Prior seismic damage, 8-May-00, FWDS near 3665N E 700 Prior seismic damage, 23-May-00, FWDN TruckBay near 3775N N Figure 5.8 Relative Damage Potential Ratings for 535 level excavations Seismic Hazard and Damage Potential for 9 th July 2000 Rockburst Description of Rockburst On the 9 th July 2000 at 9am, a seismic event recorded as local magnitude 2.3 caused severe damage to a northern ore drive on the 510 level. The event occurred well outside the blast exclusion period and was the most damaging event sustained by the mine. Over 60m of the F83 ore drive either side of the X83 cross-cut sustained severe rockbursting damage with close to 1,000 tonnes of fallen material. The area had been reinforced with cablebolts and supported by mesh and straps as shown in Figure 5.9. Service and scaling activities were scheduled for the shift in the X83 heading. Four personnel were working in the vicinity of the rockburst but were evacuated safely after the event with no physical injuries. A light vehicle was extensively damaged and mine operations from the 485 level down were suspended. Access to the 510 northern ore drive area was restricted until removal of the 485 closure pillar above. 168

184 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Figure 5.9 Photograph of the 510 F83 rockburst damage (photo courtesy of J. Player, Harmony Gold) Seismic Hazard and Damage Potential Ratings The same methodology as in Section was used for the seismic hazard and damage potential analysis of the 510 level seismicity recorded prior to the 9 th July event. The full list of 41 seismic clusters analysed for the period up to 9 th July can be found in Appendix C. Table 5.10 lists those clusters which located within one sublevel of the 510 damage level. The seismic hazard components and resulting SCHR values are presented below. These hazard-rated clusters for the 510 level are plotted in Figure 5.10, showing the very high hazard seismic cluster in the northern ore drive area, associated with the retreat pillar above. 169

185 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Table 5.10 Cluster Name - July Sort Summary Table for Seismic Hazard Rating of July Clusters m est R 1 b R 2 a/b R 3 w i N i R 4 A._av R 5 f bm R 6 SCHR Relative Hazard Rating 485 FWDS Sth Mod 485 FWDS/X Mod 485 X75/F75S Mod 485 X V High 485 X86/FWD Mod F80N V High FWDN/NEA Mod NEA High X75/F75N Low XC Mod 510 FWDS/X Mod F77S Low FWDN High NEA High X High X High 535 CENT ACC/FWDS Mod 535 CENT. ACC/FWDS Mod 535 F65S High 535 F78N Mod 535 FWDS nth of Tbay Low 535 X70/FWDN Mod 535 X74/FWDN fw High id 535 XC65-F65N Low The seismic drive hazard map in Figure 5.11 clearly highlights the hazardous zone in the northern footwall drives and FWDN. This suggested that the occurrence of a magnitude seismic event was likely to very likely in this general area, generating significant peak particle velocities in the very high hazard rated drives. There were also high hazard zones across much of the 510 level including sections of the decline, ore 170

186 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine drives, the FWDN and adjacent cross-cuts. This implied that a large seismic event in the vicinity was moderate to likely. Figure 5.12 shows the damage potential determination based on the same principles as in the previous rockburst case; i.e. derating excavations other than the FWD and footwall ore drives. A very high damage potential signified that rockburst damage in these drives was likely should the nearby seismic source generate an event of a M L = The actual rockburst damage can be seen to locate in the very high damage potential area of the F83 footwall ore drive. 510 Level: Seismic Clusters to 9 July E low SCHR mod SCHR high SCHR very high SCHR 17June RL July RL N Figure 5.10 Hazard-rated Seismic Clusters to 9 th July 2000 for the 510 level It can be seen in Figure 5.12 that the small (2m 3 ) rockburst that occurred on the 4 th July 2000 located in an area of drive rated as having a moderate likelihood of a strainburst or small rockburst occurring. It may be that the additional excavations for truck bays off the footwall drive in this area created local stress concentrations and increased the area s seismic fragility. The small rockburst of 6 th April 2000 in a similar location in the FWDN supports this tentative hypothesis. 171

187 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Level 510: Seismic Hazard Map for 9 July FW ORE DRIVE XC83 XC77 CENTRAL SLOT ORE DRIVES FWDN FWDS 700 E v high high mod 600 low v low neg DECLINE N Figure 5.11 Seismic Hazard in 510 level drives Level 510: Damage Potential Map for 9 July FW ORE DRIVE XC XC CENTRAL SLOT ORE DRIVES Prior seismic damage, 9-Apr-00, FWDN near 3775N FWDN FWDS 700 E v high high mod low v low neg Rockburst Damage Zone DECLINE Prior seismic damage, 4-July-00, FWDS near 3600N N Figure 5.12 Damage Potential in the 510 level drives 172

188 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine 5.9. Seismic Hazard and Damage Potential for 6 th February Description of Rockburst On the 6 th February 2002, a seismic event with M L = 1.7 resulted in significant damage to the 560 level footwall strike drive F64N. Ground reinforcement and support comprising 4m conebolts and meshing limited the back damage to bulking, with 700mm of displacement over a 30m length of drive (Figure 5.14). However, the sidewalls of the oredrive failed violently along and below the lowest row of conebolts and mesh. The rockburst involved close to 10 tonnes of material ejected from the sidewalls, failing several conebolts and lifting the mesh (Figure 5.13). The adjacent hangingwall drive and cross-cuts were undamaged except for some minor shakedown of loose scats. Figure 5.13 Photograph of the sidewall ejection rockburst damage on the 560 level (photo courtesy of J. Player, Harmony Gold). At the time of the rockburst, nearby work locations included the hangingwall oredrive which was an active development area and had recently been fired. There was a grouting crew in the F64N/X64 area, installing conebolts as secondary ground support 173

189 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine close to the rockburst location. There were no injuries to personnel nor was any equipment damage recorded. Figure 5.14 Photograph of the rockmass bulking in the 560 F64N (photo courtesy of J. Player, Harmony Gold) Seismic Hazard and Damage Potential For the analysis of the seismicity prior to the rockbursting event in February 2002, the complete seismic record from April 2000 was first examined. However there had been significant changes in the mine over the two year period, including the large rockbursts in mid 2000, the suspension of mining of the lower mine levels for several months, and the removal of retreat pillars. This meant that the seismicity from 2000 (Figure 5.15), which dominated the seismic record was not representative of the 2002 situation. Instead, a six month time period was chosen and the seismicity recorded from August 2001 to early February 2002 was used to carry out a new MS-RAP clustering run. The new seismic clusters that located within one sublevel of the 560 level are listed in Table 5.11 and shown in Figure

190 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Both seismic drive hazard maps are presented below. Figure 5.17 shows the drive hazard associated with 22 months of seismic data while Figure 5.18 shows the results of the six month analysis. It was seen that Figure 5.17 had extensive areas of high hazard across the level but these were largely a result of the intense seismic activity of A much clearer representation of the seismic hazard for early 2002 is observed in Figure The X64 cross-cut and intersecting ore drives were highlighted as a high seismic hazard area in this latter plan, consistent with the rockburst damage zone. Table 5.11 Summary Table for Seismic Hazard Rating of February 2002 Clusters Cluster Name: Aug01 Feb02 Sort m est: R 1 b R 2 a/b R 3 w i N i R 4 A._av R 5 f bm R 6 SCHR Relative Hazard Rating 535 F65S 535 FWDN/X F60S 535 FWDN/Tbay 535 X83-NEA 560 F70N 560 NEA 560 FW SHEAR 3580N 560 X FW SHEAR X70/F70S 560 F70N 560 F63N F78S 560 CIA 585 Level FW SHEAR 3650N 585 Level FW SHEAR 3630N High Mod Mod Mod Mod Mod Mod Mod Mod Mod Mod Mod Low Mod Low Mod Mod 175

191 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine 560 Level: Seismic Clusters to 9 July low SCHR 750 mod SCHR high SCHR 700 E 6 Feb 02 RL N Figure 5.15 Seismic clusters from 2000 that located near the 560 level 560 Level: Seismic Clusters August 2001-February low SCHR 800 mod SCHR high SCHR Feb 02 RL-557 E N Figure 5.16 Hazard-rated seismic clusters on the 560 level from 6 months of seismicity. 176

192 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Level 560 Seismic Hazard Map for 6 Feb 2002 (using database Apr Feb 2002) 560 high mod low Rockburst Damage Zone 850 ORE DRIVE DEVELOPMENT 800 FWDN FWDS 750 E NEA SEA 700 CIA 650 DECLINE N Figure 5.17 Seismic hazard in 560 level drives based on seismic data. Level 560 Seismic Hazard Map for 6 Feb 2002 (using database Aug Feb 2002) 560 high mod low v low neg 850 ORE DRIVES X FWDN 750 FWDS E DECLINE N Figure 5.18 Seismic hazard in 560 level drives based on seismic data. 177

193 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine All further analyses were carried out using the six month seismic record. Some slight variations were made to the assessment of damage potentials for the 560 level drive. The 560-FWDN alignment was not parallel to the orebody strike, differing from the 510 and 535 footwall drives. Therefore foliations were intersected at a different angle, disrupting the identified critical failure mechanism. The associated rockbursting potential was thus considered to be reduced to a range below that for the strike-parallel footwall drives but above that for the strike-normal (cross-cut) excavations. Since the FWDN drive hazard was rated as moderate, the damage potential was therefore reduced by one category to low. The other modification to the standard methodology was for the hangingwall ore drives. Following the large rockbursts in June and July 2000, geotechnical engineering consultants for the mine designated a 75m zone around the ore drives as at higher risk of sustaining a M L > 0 event. Drives in this zone were to have the seismic resistant ground reinforcement and support installed (Barrett & Player 2002). To maintain consistency with this assessment, the hangingwall ore drives on the 560 level were assigned a damage potential rating just one category less than the seismic drive hazard. This meant that the high hazard H64 drive received a moderate damage potential rating, while the moderate hazard northern hangingwall oredrives were assigned a low damage potential. Given the history of rockbursting at the mine, these damage potential assumptions for the hangingwall ore drives were conservative. The resulting damage potentials across the 560 level were plotted as shown in Figure The location of the rockburst in the F64N oredrive can be seen to coincide with the area assessed to have a moderate likelihood of rockbursting as indicated by the high damage potential. 178

194 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Level 560 Damage Potential Map for 6 Feb 2002 (using database Aug Feb 2002) 560 high mod low v low neg Rockburst Damage Zone E N Figure 5.19 Damage potential ratings for the 560 level in early Exposure of Personnel on Rockburst Damage Levels Personnel and Equipment Fleet Exposure profiling was carried out for two time periods in order to determine the distribution of personnel across the mine using human resource, equipment, mine planning and worker survey records. The first time period covered the June July 2000 case studies and the second used data from January February The personnel and equipment fleet records for these periods were analysed, with some data from the year 2000 shown below in Table The details of the mining fleet were sourced from the mine workshop/maintenance department and the typical number of machines operating each shift was determined from shift records (production and development books). 179

195 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Table 5.12 Average Big Bell Fleet and Operator Data for June July 2000 Data from Shift Records & Mine Maintenance Supervisor BB=prod + rehab, M = dev decline & levels Typical numbers of machines & operators per shift No. in Operating Machine Type fleet BB/M Machines Operators Crew Assignment Production Boggers 3 BB 3 3 UG Miner - prod bog Cannon on small remote bogger 1 BB UG Miner Production Trucks 7 BB 5 5 UG Miner -prod haul Production Drills 3 BB 3 3 UG Miner - prod drill Grd Supp Jumbos (rehab/some dev) 1 BB 1 2 Grd Supp Crew Normet charge-up 1 BB UG Miner/Blast Crew IT 2 BB 1 2 Grd Supp+Service Crews Service truck 1 BB 1 1 Service Crew Scaling machine 1 M 1 1 Grd Supp Crew IT (development) 2 M 2 3 Dev Contr/ Service Crew Dev. Boggers 2 M 2 2 Dev Contr - bog Dev. Trucks 3 M 3 3 Dev Contr - haul Dev. Jumbos 2 M 2 4 Dev Contr - drill Normet charge-up 1 M 1 2 Dev Contr - blast Raisebore/Clegg Hammer 1 other 1 2 Dev Contr - vertical Totals Development and Production Cycles The exposure model parameters were determined for each of the development and production cycles activities in order to calculate the maximum exposure ratings over a shift for active headings. The significant difference in the development cycles between 2000 and 2002 was the inclusion of conebolt installation as part of development within seismically hazardous zones on the mid lower mine levels by The different exposure values for the two time periods are presented in Tables 5.13 and For production headings, the main change was that tele-remote mucking for stope re-entry became standard for problem areas by 2002 but this did not change the maximum E/shift values. Table 5.15 documents these production heading exposure ratings. 180

196 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Table 5.13 Development cycle exposure ratings for 2000 Development activities: Crews No. of people Typ. activity time Manhrs per shift E1 E2 %HT E/hr crew Total Exposure/ Shift (N) ( t) N( t) E/hr E/hr x N t Fire face shotfirer % Fume clearance n/a shiftboss/ shotfirer/ geotech % Water down/ check area Muck development (incl. misfire checks) bogger operator % Installation of ground reinforcement/ support jumbo & IT % Clean up fall dirt bogger % 10 5 Bore face dev drill % Charge face charge up crew % survey % Other support/ service services % activities repairs % Overall Sum Max Manhrs/Shift = 21 Emax/shift = 6418 Average E/hr for heading =

197 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Table 5.14 Development cycle exposure rating for 2002 Development activities: Crews No. of people Typ. activity time Manhrs per shift E/hr crew E. activity /Shift (N) ( t) N( t) E/hr x N t Fire face shotfirer Fume clearance n/a Water down/check area shiftboss/ shotfirer/ geotech Bog development bogger operator Installation of ground reinforcement/ support jumbo Drill cone bolt holes jumbo Clean up fall dirt bogger Install cone bolts - grout only grout crew Bore face dev drill Charge face charge up crew Tension cone bolts grout/service crew survey Other support/ service activities services repairs Overall Sum Max Manhrs/Shift = 23 Emax/shift = 6784 Average E/hr for heading =

198 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Table 5.15 Production cycle exposure rating for 2000 and 2002 Production activities: Job Titles No. of people Typ. activity time Comment Manhrs per shift Cumul. No. Shifts E/hr E. activity /shift Hrs N( t) E/hr.N t Fire stope shotfirer 1 - Exclusion period Re-install services/ scale service crew Bogging - normal operation boggers 1 84 variable depending on shot size variable drill 120m/shift Production drilling prod drillers mm holes Ring preparation charge-up crew Ring clean out charge crew or driller 2 8 variable (250m/shift) - size of shot/number of holes to clean out Charge up charge-up crew 2 3 variable, 1t of emulsion per hr to charge, 1 hr to tie in Secure services service crew Other service/ support survey activities geotech/ sampler shiftboss inspect repairs Overall Sum E/hr averaged over total cycle = 115 Max E/shift for Re-entry+Mucking= 3915 Max Manhrs/Shift= (mainly ring prep & Max E/shift for Mucking only= 137 Emax/shift = 9368 clean-out) Max E/shift for Prod Drill + service/support

199 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Mine Traffic Profiles and Workforce Distribution For mine traffic and working area profiles, all production, development, service and support crews as well as supervisory and technical staff comprising the underground mine workforce were included in a spreadsheet. The spreadsheet had listings for decline travel, infrastructure areas (crib, stores, workshop, magazine etc), inactive/standby levels, active level travel/ tramming and number of active headings visited. Other columns listed the total number of hours/shift worked underground and the typical number of underground visits per week (relevant to technical support staff). Hence the tabulation of personnel working and travelling times was carried out, allowing the determination of typical total manhours per shift spent in various travelways, infrastructure areas and oredrives over the whole workforce (Tables 5.16 and 5.17). The resultant exposure ratings were calculated using average personnel vulnerability and proximity factors. For general traffic, an average E1 value of 20 (light vehicle) and E2 =1 (>30m from hazard), was assumed. For the sections of drive with high or very high damage potential, the proximity factor was increased to E2 = 2, (15 30m from hazard). Table 5.16 Travelways Exposure Ratings for Travelways and Infrastructure Areas for early N t manhrs/ shift Av # trips/ shift Av. E1 Av. E2 E/shift = E1.E2.N t General Decline Traffic Haulage: Haul truck drivers Other traffic: Mobile crews + LV drivers +transport U/G crews Total Decline Traffic near Active Levels Haulage Haul truck drivers Other traffic: U/G operators + mobile crews + LV drivers Total Active Level Drives and Accesses Production Level (assume 3 stope areas worked/ shift) Development Level (assume 4 headings U/G operators and mobile crews Haul trucks and loaders U/G operators and mobile crews

200 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Travelways N t manhrs/ shift Av # trips/ shift Av. E1 Av. E2 E/shift = E1.E2.N t worked/shift) Haul trucks and loaders Vertical Development near airways Clegg Hammer Inactive Levels Infrastructure Area (total over crib, store, magazine, w/shop) Table 5.17 Exposure Ratings for Travelways and Infrastructure Areas, early Travelways N t manhrs/ shift Av # trips/ shift Av. E1 Av. E2 E/shift = E1.E2.N t General Decline Traffic Haulage Haul truck drivers Other traffic Mobile crews + LV drivers + transport U/G crews Total Decline Traffic near Active Levels Haulage Haul truck drivers Other traffic U/G operators + mobile crews + LV drivers Total Active Level - Drives and Accesses Production Level (max of 3 stope areas worked/ shift) Development Level (assume max of 4 headings worked/ shift) U/G operators and mobile crews Haul trucks and loaders SUM: 415 U/G operators and mobile crews Haul trucks and loaders SUM: 1565 TOTAL SUM: 1980 Inactive Levels Infrastructure Area (total over crib, store, magazine, w/shop) Summary Exposure Ratings for Excavation Categories Summary tabulations were then made of the exposure rating values determined through the exposure profiling discussed in previous sections. The general exposure ratings of the excavation areas for mid 2000 and early 2002 are listed in Table 5.18 and

201 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine below. The range of values for travelways in were due to increases in the proximity factor from E2 = 1 to E2 = 2 for high-very high damage potential areas. Table 5.18 Excavation Area Summary of Exposure Ratings for June-July 2000 Excavations Excavation Category E max per shift E/hr 1a Decline (near inactive levels) b Decline (near active levels) Infrastructure area (workshop, stores, fuelbay, magazine, cribroom, crusher) Note: Time-of-day dependent (esp. crib) Inactive levels accesses and drives Development area near working face Development area accesses/drives a 6b Production level drilling horizon near working face (ring clean out reqd) Production level drilling horizon - near working face, no ring clean/out, prod drilling c 7 8a 8b 9 10 Production level drilling horizon near working face, ring prep & charge-up Production level drilling horizon accesses/drives Production level mucking horizon near working face (re-entry+mucking) Production level mucking horizon near working face, muck only Production level mucking horizon accesses/drives Combined Prod & Dev Level - accesses/drives Designated no entry area 0 n/a 12 Designated no unauthorised entry/limited access area Scale + Services + Sec Grd Support

202 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Table 5.19 Summary of Exposure Quantification for Big Bell Mine, 2002 Excavation E max per Excavation Category Area shift 1a Decline (near active levels) 993 1b Decline (upper/inactive levels) Shaft n/a 3 Infrastructure area (workshop, stores, fuelbay, magazine, cribroom, crusher), time-of-day dependent Inactive levels accesses and drives vehicles/pedestrians 382 / Development area near working face Development area accesses/drives a Production level drilling horizon near working face, incl. ring prep & charge b Production level drilling horizon near working face, prod drill only Production level drilling horizon accesses/drives 384 9a Production level mucking horizon near working face, incl. re-entry inspections b Production level mucking horizon near working face, muck only Production level mucking horizon accesses/drives, loader tramming + haulage Designated, barricaded no entry area 0 12 Designated no unauthorised entry/limited access area Rehabilitation scale + services + secondary ground support 6400 As explained in Chapter 3, for the purpose of calculating representative ratings for active level travelways, it was assumed that three stopes were being worked during each shift on a production level, and that four development headings were active during a shift on a development level. The maximum exposure ratings per shift for the traffic on 187

203 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine generic active levels were then determined based on one third of the service and support resources being apportioned to a busy production drilling level, and two thirds to a busy development level. It was assumed that the traffic associated with production mucking horizons during normal operations comprised loader tramming and truck haulage only. These relative proportions can be easily adjusted when particular mining levels are studied in detail, as is demonstrated in the next section Case-Specific Exposure Ratings The summary exposure ratings for accesses and drives were derived from Big Bell mine traffic profiles assuming a certain proportion of the active stopes and development headings were being worked on the level being analysed. For the case study analyses, the summary values from Tables 5.18 and 5.19 were adjusted using the specific shift records for at least one week prior to each rockburst. Drives and accesses were described by their function such as active level decline, main production level access, direct development access, etc. Then, from the relative number of shifts during which work was carried out over relevant period, each of the production areas, development headings and secondary support locations on the damage levels could be categorised as low, medium or high priority. Comparing this mining activity to the other levels in the mine over the same time period, the following proportions were found: 17 th June 2000, 535 Level: 12% of production muck/haul resources, 38% of production drilling resources and 12% of development resources, 2-3 active stoping areas per shift. 9 th July 2000, 510 Level: 45% of production resources, 36% of the development effort, two production drill and two production mucking areas per shift. 6 th February 2002, 560 Level: effectively nil production, four high priority development headings (same as assumed for Table 5.18) so no adjustment required. These relative proportions were then used to scale the exposure ratings for the traffic in drives and accesses so that the intensity of traffic, support and service activities 188

204 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine reflected the amount of nearby mining activity. The maximum exposure per shift rating was also reduced for low priority headings. Therefore, based on the relative status of different work areas in the week or so prior to the rockburst, exposure ratings were assigned to accesses, drives, service areas, development and production headings. Colour-coded personnel exposure maps were produced by division of the numerical range of exposure ratings into the descriptive categories shown in Table The exposure maps are presented below in Figures 5.20 to 5.23 and the busiest mine areas, typically high priority development, rehabilitation or production drill headings, are clearly depicted. Table 5.20 Relative Levels of Exposure and Risk Ratings E max /shift Relative Exposure Rating Risk Rating R = (D. E max )/ 500 <500 Very Low R < Low 0.5 < R < Moderate 12 < R < High 70 < R < ,999 Very High 190 < R < 300 > 15,000 Extreme R > 300 The next stage of analysis for each rockburst case study was to combine the exposure ratings and damage potentials for each rockburst level so that relative risk ratings can be determined and plotted as explained in Section 5.5. The relative risk criteria in Table 5.20 were used to produce the risk charts shown in Section

205 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Level 535: Personnel Exposure Map for week prior to 17 June stope fronts v high high 700 E mod low v low 650 neg N Figure 5.20 Map of Personnel Exposure to Rockbursts on the 535 Level, June 2000 Level 510 Personnel Exposure Ratings (during week up to 9 July 2000 Rockburst) v low low E high v high N Figure 5.21 Map of Personnel Exposure to Rockbursts on the 510 Level for July

206 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Level 560 Personnel Exposure Map (for early February 2002) 560 high mod low Rockburst Damage Zone E N Figure 5.22 Map of Personnel Exposure to Rockbursts on the 560 Level for Feb-2002 Level 560 Personnel Exposure Map (for early February 2002) 560 high mod low v low Rockburst Damage Zone 850 F70N XC70 H64N XC64 F64S Footwall haulage drives excluded from mine activity access 800 FWDN FWDS 750 E NEA DECLINE N Figure 5.23 Map of Personnel Exposure to Rockbursts on the 560 Level for Feb-2002 with excluded zones 191

207 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Seismic Risk Charts for Big Bell Personnel The following charts illustrate the modelled relative seismic risk to personnel. Comparing actual consequences with the modelled higher risk areas: When the 535 level rockburst occurred, there were 5 drill operators/offsiders working in the northern footwall oredrives who became trapped for a short time and had to evacuate through the damage area. These work areas were modelled as zones of very high to extreme seismic risk to personnel. The rockburst damage occurred in the FWDN with no resulting injuries, matching well with the moderate risk rating assigned to this drive. The extreme risk rating assigned to the 510 level northern FWDN and footwall oredrive corresponded with the rockburst damage area and the very close proximity of four personnel. No injuries resulted but the extensive damage caused to a light vehicle highlights the degree of risk and validates the model results. For the 560 level rockburst in the F64N oredrive, there were no injury consequences but there was a ground support crew working in the vicinity. The very high seismic risk modelled for the drive is therefore validated. It was therefore seen that when the actual consequences to personnel were examined, and particularly the near-miss situations taken into account, all three rockburst case studies were well represented by the modelled exposure and seismic risk. 192

208 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Level 535 Map of Seismic Risk to Personnel (for week prior to 17 June 2000) operator and offsider outside drill rig 2 operators in drill rig 1 operator in drill rig stope fronts extreme E v high high 700 mod low v low 650 Rockburst Damage Zone CIA N Figure 5.24 Plan of 535 level of relative seismic risk for the 17 th June 2000 period. Level 510: Risk to Personnel from Rockburst (9-Jul-00 rockburst) personnel in vicinity of rockburst escaped, but LV extensively damaged E extreme v high high mod low v low Rockburst Damage Zone N Figure 5.25 Plan of 510 level of relative seismic risk for the 9 th July 2000 period. 193

209 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Level 560 Seismic Risk to Personnel Map for 6 Feb 2002 (using database Apr Feb 2002) 560 v high high mod low Rockburst Damage Zone 850 When the rockburst occurred, a grouting crew was working nearby E N Figure 5.26 Plan of 560 level of relative seismic risk for the 6 th February 2002 period. Level 560: Seismic Risk to Personnel for 6 Feb 2002 (excluding footwall haulage drives from mine traffic) 560 v high high mod low v low Rockburst Damage Zone 850 When the rockburst occurred, a grouting crew was working nearby E N Figure 5.27 Plan of 560 level of relative seismic risk for the 6 th February 2002 period with excluded sections of the Footwall Drive North and Footwall Drive South. 194

210 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Conclusions This chapter has introduced the geotechnical and operational environment of Harmony s Big Bell mine and presented the details of three rockbursts. These rockbursts were used as case studies for demonstrating the application of the exposure model and exposure profiling process within a seismic risk framework. It was seen, firstly, that the semiquantitative seismic hazard and damage potential methodology appropriately rated the excavations in which damage occurred. This was not essential to the application of the exposure model but was necessary to achieve a meaningful calculation of seismic risk and thereby compare actual rockburst consequences with those indicated by combining the exposure and damage potential. The process of developing appropriate exposure profiles for the levels being reviewed was presented in detail through Section Then the application of the exposure model to the work areas and travelways, based on the profiled distributions of personnel for the relevant time periods, was illustrated by colour-coded plans of the damage levels. Finally the personnel exposure was combined with the estimated drive damage potentials to produce relative risk ratings across the levels. The relative risk was then compared with the actual injury, damage and near-miss consequences of the rockbursts. For the first rockburst case, there were areas of the damaged sublevel where several operators and machines were trapped for some time after the rockburst. When the exposure model was applied within the proposed seismic risk framework, the areas in question were appropriately found to be rated at very high to extreme risk levels. The second rockburst case study involved four personnel being in the rockburst vicinity. They escaped without injury but extensive damage was caused to a light vehicle. This rockburst area was rated as extreme risk by the model. Similarly, the very high seismic risk modelled for the damage location in the final rockburst case was consistent with the close proximity of a work crew when the sidewall of the ore drive failed violently. 195

211 Chapter 5 - Detailed Case Studies of Seismic Risk and Hazard Exposure at Big Bell Mine Conversely, the modelling of many excavation areas as having low levels of seismic risk was consistent with the lack of seismic damage and negligible consequence to personnel in these drives and accesses. Therefore, it was found in all cases that there was no contradiction between the modelled seismic risk and the actual risk suggested by the consequence analysis. This does not imply a proof (or validation) of the proposed seismic risk methodology because of the limited amount of rockburst data available for analysis. However, since the damage potential evaluation matched well with the actual damage, even given the database limitations, the correspondence between modelled and actual risk progressed the calibration of the exposure model. 196

212 Chapter 6 Detailed Case Studies of Seismic Risk and Hazard Exposure at Brunswick Mine 6. DETAILED CASE STUDIES OF SEISMIC RISK & HAZARD EXPOSURE AT BRUNSWICK MINE 6.1. Brunswick Mine Introduction Noranda s Brunswick Mine (BMS) is located near Bathurst, New Brunswick in Atlantic Canada. It is a 9,000 tonnes per day underground lead-zinc-copper-silver mine which has been operating continuously since Mining depths of up to 1200 m below surface, high extraction ratios and a deviatoric stress regime have contributed to produce areas of highly stressed rockmass and a significant history of seismicity (Simser & Andrieux 1999). Brunswick mine provided a good environment for testing the flexibility of the hazard exposure model as it was substantially larger in extent with more complex operations than the Big Bell mine. During the third quarter of 2002, at the time that this study was being carried out, Brunswick mine geotechnical personnel were carrying out a risk assessment of a planned pillar extraction the North Regional Pillar. They expressed an interest in the exposure model being applied to the Brunswick underground mine, with the aim of potentially integrating it with their other geotechnical risk methodologies. This research covers a 2 year period from April 2000 until May 2002 and back-analyses three rockbursts that occurred during this period. Using data collected from mine operations and planning records, workforce exposure profiles were determined for the periods leading up to each rockburst event. Then, for each rockburst, the exposure model was used to quantify the personnel exposure associated with individual locations across the specific sublevel which experienced major damage. The risk context for the Brunswick case studies was the same as for Big Bell, and was summarised in the previous chapter in Table 6.1. In order to calibrate the exposure model, the semi-quantitative methodology developed for assessing seismic hazard and excavation response, presented in Chapter 5, was used in conjunction with the detailed exposure profiles for the mine. In this way relative risk ratings across the damage levels 197

213 Chapter 6 Detailed Case Studies of Seismic Risk and Hazard Exposure at Brunswick Mine were determined and the actual rockburst consequences could be compared with the modelling results Brunswick Mine Layout and Operations Previous mining methods at Brunswick included mechanised cut-and-fill but the principal extraction techniques more recently in use have been primary-secondary open stoping with delayed backfilling, end-slicing (modified avoca) and pillarless pyramidal open stoping. Both tactical and strategic changes have been made over time at the mine to address major seismic and stress related issues. As a strategic measure, the current mining uses long hole open stopes in a pillarless sequence to prevent stress concentrations and hazardous seismicity in secondary pillars. Other measures introduced during the late 1990s included paste backfill, smaller stope sizes, fan drilling from smaller overcuts instead of wide overcuts and vertical drilling, the widespread use of shotcrete, increased cablebolting and the adoption of modified conebolts as a seismic resistant reinforcement technique (Simser, Joughin & Ortlepp 2002). Stopes were reduced from an average size of 75,000 tonnes to 39,000 tonnes. Figure 6.1 shows a generalized mine longitudinal section from Simser, Joughin & Ortlepp (2002) with the 21,346 seismic events recorded from January 2000 to December 2000 shown by magnitude-scaled circles. The hatched areas represent stoping and the circled area is the 1000 South Bulk Zone where the October 2000 rockbursts occurred. The strike length shown is 1.5 km, the top of the mined out area outcrops on surface, with the deepest mining being 1.2 km below. At Brunswick mine, the access from surface is via shafts with substantial infrastructure located underground including large workshops, offices and crushing facilities. Ramps (declines/inclines) connect levels and sublevels. Levels in the mine comprise several sublevels, typically with vertical spacing of 25 to 30m. In plan, the layout on each sublevel is very complex, extending over 1.2km north-south along strike and 0.5km east-west with numerous ore lenses. 198

214 Chapter 6 Detailed Case Studies of Seismic Risk and Hazard Exposure at Brunswick Mine Figure 6.1 Simplified longitudinal section of the mine looking west (Simser, Joughin & Ortlepp 2002) Mine Geology and Geotechnical Characteristics Brunswick Mine is a large massive sulphide deposit that extends to a depth of 1.2 km, strikes north to south and dips steeply to the west at about 75º. It has a multi-lens orebody. The principal stress direction at the mine is sub-horizontal east/west with a magnitude approximately twice the vertical stress. The intermediate principal stress is sub-horizontal north/south (on strike) at about 1.6 times the vertical, and the minor principal stress is vertical and approximately equal to the overburden (Simser, Joughin & Ortlepp 2002). A distinct contrast in rockmass quality exists between the dyke intrusions, the metasediment country rock sequences and the massive sulphide deposit. Highly laminated, 199

215 Chapter 6 Detailed Case Studies of Seismic Risk and Hazard Exposure at Brunswick Mine chloritic rocks make up the hangingwall and footwall. The hangingwall sequence has unconfined compressive strengths in the 30 to 40 MPa range while the footwall is slightly stronger with UCS up to 70 MPa. The massive sulphide material (lead/zinc rich ore, or pyrite/pyrhotite rich waste) generally has unconfined compressive strengths exceeding 200 MPa. Typical joint spacing in the sulphides is in terms of metres, with the rock being very competent and brittle but prone to stress fracturing which can seriously degrade the local rockmass quality (Simser, Joughin & Ortlepp 2002). Seismicity generally occurs in the strong, brittle massive sulphides or dyke material, whereas ground in the metasediment sequences tends to unravel along the laminations (Simser & Andrieux 1999) and generally be aseismic. Rock strength testing (Gaudreau 1998) yielded the following average unconfined compressive strengths: Massive sulphides - Porphyry Dyke Waste metasediments MPa 182 MPa MPa Approximate Q values for these three rock types are 33, 17 and 7 respectively (Milne et al. 1998). The in situ Young s Modulus (E) for the massive sulphides is approximately 70 GPa whereas the weaker metasediments can have an in situ modulus as low as 8 GPa (Simser & Andrieux 1999). The stiffness contrast caused by the intrusion of waste metasediment stringers into the massive sulphides can lead to significant seismicity. Violent failure may potentially result from the storing of strain energy in the brittle surrounding sulphides after the weak metasediments fail at lower load History of Rockbursting The main seismic source mechanisms identified at Brunswick are, according to (Simser & Andrieux (1999): seismicity associated with brittle rocks and structure, stiffness contrasts between adjacent rock types causing squeezing of the softer medium, stress- 200

216 Chapter 6 Detailed Case Studies of Seismic Risk and Hazard Exposure at Brunswick Mine induced pillar failures, seismicity associated with discrete geological structures, and strain-bursting. Table 6.1 summarises the history of damaging rockbursts at Brunswick mine. The table is a summary drawn from Brunswick mine fall of ground reports. It lists those fall of ground events which were associated with seismicity and could be described as rockbursts or seismically induced falls of ground (SIFOG). The cases chosen for this thesis to demonstrate the exposure model within a seismic risk framework are highlighted in bold type. Criteria for selection were: that the falls of ground were clear rockburst events (i.e. not simply shake down of loose material or relaxed rockmass); that the timing of the falls of ground were known and could be associated with specific, recorded seismic activity; that the amount of displaced material was significant (at least several tonnes); that there was a good record of mining activity prior to the rockbursts, particularly dates of production blasts; that records existed for personnel exposure quantification and comparison with actual consequences. The requirement for good and readily available personnel and blasting records restricted the timeframe for selecting case studies from the period from late 1999 onwards. The three cases chosen were: 1. the 6,000 tonne rockburst on 13 th October 2000 on the sublevel 2. the 2,150 tonne rockburst on 17 th October 2000 on the sublevel 3. the 150 tonne strainburst on 16 th May 2002 on the sublevel. All three rockbursts located in the southern zones of the mine. Details are provided in later sections of the specific ground support regime and excavation damage related to the three rockburst case studies. 201

217 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine Table 6.1 Summary record of rockbursts and seismically induced falls of ground Date Sublevel Fall/Burst Type Description Rocktype(s) 22-Mar SIFOG 22-24/3/1996: West Ore Zone, 5th ore stringer, failure in north-east corner of stope massive sulphide 30t 11-Jun SIFOG stope, main ore zone. Large back failure from Nth abutment (against stope) to Sth abutment and from HW to FW. chloritic iron formation and massive sulphide 01-Jul Rockburst/SIFOG 51-8 Stope, back failure 3500t 18-Jul stringer area. Simultaneous fogs and Stope (north side). 18-Jul SIFOG stope, large back failure waste and ore Volume or weight displaced 6500t, 30m longx15m wide x 8m peak 2325t ore, 1400t waste, 8m peak 300t metaseds, 500t ore, 12m long x 9mwidex3.5m peak 18-Jul SIFOG stope, north side, back failure mainly toward HW side of stope (West). metaseds and ore 19-Jul to 22-Jul , 2 Rockbursts/SIFOG 51-8, 53-8 Stope areas ore, dyke, metaseds 53,000t 22-Jul SIFOG FW Drift, damage in back waste sulphide 5t 07-Mar Rockburst/SIFOG stope, failure of back near entrance. massive sulphide 500t, 10x6x2m 07-May Jun SIFOG Rockburst, ejection of slab & fragments 46-7 drawpoint pillar, damage from upper third of pillar's east wall, no damage in backs massive sulphide 5t 16N X/C near corner of F-1 Drift North, damage in north upper corner waste sulphide 0.73t, 0.17m3 100t, 9mE/W x 1mN/S x 3mvert 20-Aug Rockburst access, wall failure massive sulphide F1 Drift South by 37-8 Stope, damage in east wall of F-1 08-Oct SIFOG drift waste sulphide 5t, 1.2m3 18-Nov SIFOG Access, damage in upper wall massive sulphide 4.4t, 5.1m2 x 0.2mth 24-Jan Rockburst/SIFOG Stope Overcut, back collapsed in 2 places massive sulphide 60t, 2x2x x3x1 01-Mar SIFOG HW drive, failure of back massive sulphide 108t, 5mx5mx1th. 01-Jul SIFOG between the sub 21orepass and a pastefill pipe cutout, thin portion of the pillar wall spalled/shook down massive sulphide 8.6t, 0.2m th. 202

218 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine Date Sublevel Fall/Burst Type Description Rocktype(s) Volume or weight displaced 13-Jul Pillarburst in stope pillar - wall failure. massive sulphide 89t 08-Aug SIFOG Intersection of F-1 Drift & 16N X-Cut, damage in north wall waste sulphide 12.5t, 2.9m3 13-Sep Rockburst 126 X/C, wallburst massive sulphide 11t 21-Sep Strainburst 45-7 drawpoint pillar/haulage intersection, damage in upper west corner massive sulphide 2t, 0.5m3 13-Oct Rockburst Intersection between 326 X/C & stope access. Some material quite fine - had been ejected violently. massive sulphide 6000t 17-Oct Rockburst 327 X/C, back collapsed. Also further damage in 326 X/C. massive sulphide 2150t, 20x5x5m 23-Jun Rockburst/SIFOG 145S-9 (5-way intersection). 2-3 m of back had fallen out previously (historically). 2-3 m further break in this fog to create 5m overbreak of back. massive sulphide? 18-Nov SIFOG stope XC/Drill Drift Intersection, failure in back through to brow of freshly blasted stope massive sulphide 1200t 16-May Strainburst access area, backs of the intersection between access drift (E-W) & ore sill (N-S). 150t, 6x5x1.5m Falls from backs and walls in area, damage at 06-Sep SIFOG/Strainburst Contained damage 23-Sep corners, stress abutments. small Deformation of walls at intersection of 4100/499 access with FWD. Bulking and bent plates. massive sulphide 203

219 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine 6.5. Seismic Data The largest recorded seismic event at Brunswick mine occurred in 1984 and registered at Richter magnitude 3.5. Three events larger than Richter 3 were recorded over the following ten years (Hudyma 1995). In the more recent years applicable to this research, events of Richter has been representative of the largest magnitudes. Local seismic monitoring began at Brunswick mine in 1986 with the installation of an analogue 32 channel Electrolab MP250 system. This was progressively expanded to a 64 channel full waveform system capable of locating seismic events to within 10m using uniaxial accelerometers. In 1994, a fully triaxial Integrated Seismic System (ISS) was installed, allowing seismic source parameters to be analysed (Hudyma 1995). At Brunswick mine, an increase in seismic activity after a large stope blast (production firing generally at 18:00) is normal and is routinely managed by systematic checks of the seismic data by the ground control group at site. When abnormal activity such as a large event or intense flurry of seismicity occurs, ground control staff are automatically paged and it is standard practice to shutdown affected areas until the seismic activity returns to acceptable levels (Simser 2000). The seismic event data used in the analysis was from a database recorded by the ISS system and manually processed by the BMS seismic system analyst/technician Terry MacDonald. The database comprised approximately 32,000 events between 1 st April 2000 and 31 st May 2002 with Local Richter Magnitudes (ML) in the order of 3 to 3 and location errors typically in the 5 to 10m range. The seismic data associated with each of the three time periods of interest was imported from the event database into the MS- RAP program which was described briefly in Chapters 2 and 4. The MS-RAP sensitivity parameters used for the Brunswick cluster analyses were a cluster size and isolation distance of 30m, a minimum number of events per cluster of 15 and 300 as the maximum number of clusters. Sorting runs were carried out on the seismic data recorded in the six months prior to each of the three rockbursts chosen for the risk analyses. Only two sorting runs were required, one for the October 2000 rockbursts and the other for the May 2002 event. 204

220 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine Because of the close timing and location of the two October 2000 rockbursts, there was no need to carry out separate clustering analyses. Table 6.2 summarises the final MS-RAP sorting results. The proportions of energy in clusters reported in Table 6.2 were calculated after the removal of a few large magnitude but isolated seismic and remote events. Because such large magnitude events can dominate the cumulative seismic energy record in the database, they distort the apparent effectiveness of the seismic clustering routines. These large unsorted events were mostly isolated events located on the middle-upper levels of the mine, in the south abutments or deep below working levels and, being remote from entry excavations and the mining areas of interest, were not relevant to further analysis of the seismic clusters within this study. It should be noted that these large events remain within the MS-RAP database and so are available for other studies, such as of the minewide seismicity, or should future mining plans extend the entry areas within range of these previously remote events. Table 6.2 Cluster Run MS-RAP clustering results for Brunswick data Time range of sorted data Total no. of seismic events No. of clusters No. of seismic events sorted into clusters Proportion of seismic energy in clusters 1 1/Apr/00 10/Oct/00 6, , % 2 15/Nov/01-14/May/02 6, , % The seismic data initially selected for the rockburst analysis were all events from April 2000 to 10 th October 2000 in clusters that located on the 1000 sublevels, the 850 sublevels (above ) and the 1125 sublevels (below ). These clusters are plotted in the chart in Figure 6.6. The chart also shows the delineation that was then drawn between the seismicity in the southern sections of the mine (south of 23900N) and that in the northern zones. Just as the seismic data selected for further analysis was constrained to an appropriate vertical range (from RL ), the horizontal extent was, for the purposes of the specific rockburst case studies, also limited. Being over 300m away from the rockburst locations, the seismic events north of 23900N were considered to have insignificant effect on the zones being studied. 205

221 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine The seismic data recorded between 1996 and 2000 within 2 sub-levels of the south sublevel was also analysed, for completeness of the drive hazard rating. The focus was on those areas around Sth which were seismically active up to the year 2000 but not in the six month period prior to the October 2000 rockbursts. This early seismicity was assessed to have only, at most, a moderate hazard rating. Therefore the seismic activity from years prior to 2000 was considered to be of little significance to the seismic hazard in the drives of interest during October For the May 2002 rockburst case study, again the seismicity recorded in the six months prior was considered the most appropriate for assessing drive hazard in early May This was validated by re-evaluating the drive hazard using seismic data recorded over the two year period between April 2000 and May This expanded analysis provided less differentiation of drive hazard across the sublevel, with larger areas rated as high drive hazard. It also missed a very high hazard cluster which was identified by the six month cluster hazard analysis. Therefore the expanded analysis was less useful and the six-month timeframe was adopted as the most relevant. Using broad timeframes of data which span several mining phases presents the risk that there will be areas of the mine where the hazard is rated incorrectly because, for example, they have become damaged and destressed. The WOZ mass blast of mid 2001 substantially redistributed stresses within the lower levels of the mine. Thus the spatial clustering of seismicity recorded from represents too broad a timeframe for hazard analysis. Instead, it was considered more appropriate to incorporate a temporal component by separating the data recorded during mid from that of late A final consideration in choosing the six-month timeframe for seismic analysis was to adopt a methodology consistent with the needs and resources of the eventual end-users, that is, the site-based geotechnical personnel. Apart from research or special project environments, practical limitations on time, personnel and computing resources would tend to preclude extensive back-analysis of several years of seismic data in order to assess seismic hazard in drives on a regular basis. 206

222 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine 6.6. Seismic Hazard and Excavation Damage Potential Analysis For the Brunswick case studies, there was a small change made to the criteria for assessing a seismic cluster s average apparent stress. Instead of the top hazard rank applying for an average apparent stress greater then 5,000 Pa, the range was extended to 10,000 Pa. Intermediate rankings were similarly adjusted by a doubling of the Big Bell apparent stress limits. The range in average apparent stress values for Brunswick was substantially greater than was found for a similar seismic moment at Big Bell. This may be attributed to a stronger rockmass in the seismically active areas, with the average UCS for Brunswick massive sulphides (210 MPa) up to twice the mean for Big Bell rock types ( MPa). Therefore, increasing the hazard ranking criterion for apparent stress by a factor of two for Brunswick was appropriate. This approach, with the selection of a top range starting at 10,000 Pa, was also consistent with values for average apparent stress used for analyses at Brunswick as reported by Simser (2000). Then, as for the Big Bell cases in Chapter 5, for each of the Brunswick rockburst case studies, the seismic clusters were analysed and hazard rating (SCHR) values calculated. By the same methodology as described in previous chapters, a seismic hazard rating was ascribed to the drives and accesses in the southern area of the sublevel and the sublevel. With the levels extending over one kilometre from north to south, it was unnecessary to consider all the seismicity recorded across the level when analysing seismic hazard related to the rockbursts occurring in the south bulk zone. Instead, the drive hazard process concentrated on analysing all the seismic clusters within 300m of the burst location. In the previous chapter, Big Bell mine s simple geometry and geology, as well as the consistency in mechanism and locations of historical rockburst damage, justified the use of a simple technique for determining the damage potential of drives. The types of drives (i.e. decline, cross-cuts and hangingwall ore drives) that had no previous history of rockburst damage, despite high levels of dynamic loading in nearby excavations, were assessed as more resistant. Therefore the damage potential was adjusted as described in Section 5.6. The differentiation in seismic fragility between different drive categories was based solely on the rockbursting history and assessments provided to the mine by geotechnical consultants. No attempts were made to distinguish between the 207

223 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine increased seismic fragility due to excavation characteristics (such as orientation, structure, etc) and that possibly due to rockmass degradation in excavations subjected to repeated dynamic loading (fatiguing). For the Brunswick mine, its large extent, complex geology and variation in rockburst mechanism precluded a similar conversion between seismic hazard and damage potential in drives. Known contributing factors to other rockbursts at BMS included: the presence of waste metasediment intrusions into massive or waste sulphides; proximity to the main dyke and certain shear zones; highly stressed remnants and abutments combined with a relaxation in local confining stresses; the rotation of principal stresses around a drive following a significant mining step, creating locking points in the stressed rockmass where strain may accumulate and later release violently; and joints in the rockmass, often not visible until after the rockburst exposes them. For the sublevel drives under review, there were no particular zones where all these factors could be conclusively excluded. At Brunswick, a mine-specific rockmass seismic fragility analysis had been carried out by a former mine geologist, whose ranking system is known as the Godin rating (Diedrichs et al. 2002). Godin assessed stopes for the presence of rockmass characteristics known to be contributory to seismic damage. Were such a system to be extended to cover areas other than stopes, it could conceivably be used in conjunction with the seismic drive hazard to determine more precise damage potentials. It is more problematic to account for the presence of deepseated joints and other discontinuities that do not daylight into drives, but which contribute to increasing the propensity to local violent excavation failure and may only be identified after the event. The installation of rockburst resistant support, such as conebolts with heavy mesh and strapping, should not affect the likelihood of rockburst damage occurring in these supported areas but may reduce the damage severity and limit its extent. The first 208

224 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine significant installation of dynamic support commenced in August The support system comprised modified conebolts (MCB) in combination with heavy gauge mesh straps and chainlink mesh. By October 2000, only the South Hangingwall Access drive (SHW-3 ACC) and a small portion of the 326 crosscut (XC) had rockburst support installed. These two areas are indicated on the damage potential map in Figure 6.8. For the sublevel south of 23900N, by May 2002, there was rockburst support in the XC that was installed in preparation for the West Ore Zone (WOZ) mass blast in It may be appropriate to reduce the damage potential for areas with seismic resistant support, however this would depend on a comparison of rock failure characteristics with the condition, coverage and length of support elements. That sort of assessment is beyond the scope of this work and since only small sections of a few drives were involved, the effect of dynamic support in reducing the damage potential was not included in the risk analyses for the Brunswick case studies. It was conservatively assumed that the same moderate to high degree of fragility applied across the levels of interest due to: the absence of a widely accepted method for determining the local excavation seismic fragility; and being unable to definitively exclude potential contributory factors to rockbursting on the BMS sublevels studied. Therefore the preliminary damage potentials for excavations on the rockburst damage levels were assigned equivalent values to the seismic drive hazard ratings. The drive hazard reflected the expected dynamic loading (measured in ppv) at the excavation site. It was determined using the SCHR values and proximity of the excavations to the seismic sources, as described in Chapter 4. The following sections provide details of the seismic hazard and damage potential ratings for the time periods prior to each of the rockburst case studies. Colourcoded plans of the damage levels illustrate the relative seismic hazard and excavation damage potential. Then, sections 6.9 and 6.10 present the results of the personnel 209

225 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine exposure profiling and ratings produced by the exposure model for the times and mine levels associated with the rockburst events. Finally, the relative seismic risk ratings were calculated by combining the excavation damage potential and the personnel exposure ratings according to the procedure in Chapter 5. Maps of the relative risk across the damage levels were produced and the actual consequences of the rockbursts compared to the model results Seismic Hazard & Damage Potential - 13 th & 17 th October Description of the 13 th October 2000 Rockburst Two large rockbursts occurred in October 2000 on the sublevel, approximately 900 m below surface, in the south-western area of the mine known as the South Bulk Zone. The sublevel is located 30m below previous cut and fill mining (sill pillar) and above a pillarless pyramid sequence. In addition, the local stress conditions in the South Bulk Zone were greatly influenced by mining in the hangingwall lens (Simser, Joughin & Ortlepp 2002). In discussing the first rockburst on the 13 th October 2000, Simser, Joughin & Ortlepp (2002) report that the burst area had been seismically active throughout the preceding week. Geotechnical site personnel noted small magnitude seismicity and minor cracking in the shotcreted tunnels in the area and implemented several proactive workplace closures. On the evening of the 13 th October, a ML 1.6 seismic event occurred and seismic activity increased, prompting the withdrawal of labour from the work zone. The major damage was assumed to have occurred at midnight when a magnitude 2.5 seismic event was recorded. This severe failure involved a violent shake down of intensely stress fractured material and extensive caving beyond the 325 XC intersection. It initiated a formal shutdown of the zone until a full investigation could be completed. Photographs of the damage are presented in Figures 6.2 to 6.4, courtesy of B. Simser and the Brunswick ground control group. The 326 cross-cut (326 XC) intersection which failed on the 13 th October sustained further damage on the 17 th October, with material ejected from a portion of the wall in front of the intersection. A very thin band of waste meta-sediments was in the centre of the wall burst. 210

226 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine Installation of yielding support had commenced in the 326 XC but only a small section of the drive had been completed at the time of the burst. Simser, Joughin & Ortlepp (2002) describe the typical stiff ground reinforcement and support regime in this area of the mine as comprising, 7m twin cablebolts in the back; 2.3m resined rebar on a 1.5m by 1.5m pattern; 10cm aperture weld mesh screen with 3.7mm diameter wire in the back; regular shotcrete over the mesh; and steel fibre reinforced shotcrete in the walls (no weldmesh screen in walls). The yielding support package included heavy chain link mesh, weld mesh straps and a close pattern of 2.3m long modified (resin-grouted) conebolts. This small area of the drive experienced some bolt displacement and cracking of the shotcrete but the damage was contained by the support. Simser, Joughin & Ortlepp (2002) note, however, that 2.3m long cone-bolts could not be expected to support the 6m high intersection failure and therefore proactive closures of seismically active areas regardless of rockburst support is an essential risk treatment measure. Figure 6.2 Rockburst damage in 326 XC, sublevel, associated with events of the 13 th October

227 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine Figure XC rockburst and caving due to the 13 th October 2000 events. Figure 6.4 Further damage in 326 XC after 17 th October 2000 rockburst (note fan now on floor). 212

228 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine Description of the 17 th October 2000 Rockburst The second rockburst occurred on the 17 th October 2000, presumably concurrent with the ML =2..7 seismic event in the vicinity. The major rockburst damage occurred in the 327 cross-cut (327 XC), immediately to the north of the 326 XC. The 327 XC had already sustained damage from the seismicity of the 13 th October 2000, with floor heave, upper corner wall blowouts, and tensile cracks in the back. After 17 th October 2000, the back of the drift collapsed over a length of 20m and up to a height of approximately 5m (Simser, Joughin & Ortlepp 2002). Figure 6.5 Rockburst damage in the 327 heading due to 17 th October 2000 events (photo courtesy of B. Simser and the Brunswick ground control group) Seismic Hazard and Damage Potential Charts Tables 6.3 presents a summary of the SCHR values for the 38 seismic clusters relevant to the damage level. Then these seismic clusters are plotted in Figure

229 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine Table 6.3 Cluster Name: Apr00 Oct00 Sort Summary Table for Seismic Hazard Rating of October 2000 Clusters m est: R 1 b R 2 a/b R 3 w i N i R 4 A._av R 5 f bm R 6 SCHR Relative Hazard Rating Nth (19) Mod Nth (39) Mod Sth High Nth 142N-8 STOPE Mod Nth XC Mod Nth 144N-9 ACC Mod Nth STOPE High Nth 145S-9 STOPE Mod Sth STOPE Mod Sth STOPE High Nth ( 3) Mod Nth (20) Mod Nth (35) High Nth (38) Low Sth 226-2HW Mod Sth 228-9B High Sth Low Nth Mod Sth V High Sth 328-9B Mod Sth V High Nth Mod Sth STOPE High Nth ( 9) Mod Nth (27) Mod Nth (36) Low Nth Low Nth SOUTH VOID ACC Low Sth 330-9B Mod Sth Mod Sth Mod Sth 76-5 ( 2) High Sth 76-5 (23) Mod Nth Low Sth Low Nth ( 4) Mod Nth (33) Low Nth Mod 214

230 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine The plan of the sublevel in Figure 6.6 shows the main concentration of seismic activity in the South Bulk Zone. This intense activity was consistent with an increase in the abutment stresses in this South Bulk Zone caused by stoping below and the commencement of stoping directly to the west. Potential release mechanisms were provided by the intrusion of weaker metasediment waste stringers through the strong, brittle massive sulphides of the orezone. Level Seismic Clusters for 6 mths prior to 10-Oct clusters sth of 23,900N used for rockburst analysis E N Low SCHR Mod SCHR High SCHR V High SCHR ML=2.2, 13/10/00 23:57 ML=2.4, 13/10/00 23:58 ML=2.7, 17/10/00 Figure sublevel: Hazard-rated seismic clusters, April - October The very large seismic events that occurred on the 13 th and 17 th October 2000 are also indicated on the plan above in Figure 6.6. These events located on the periphery or at some distance from the high to very high hazard clusters, but it is unclear whether this was due to stress migration to abutments or to the difficulties in precisely locating such large, complex events when processing. The resultant drive hazard is shown in Figure 6.7, with the 325, 326, 327 and 328 crosscuts highlighted as very high seismic hazard areas. Then Figure 6.8 illustrates the estimated damage potential, based solely on the drive hazard ratings. The rockbursts 215

231 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine that occurred on both the 13 th and 17 th October 2000 can be seen to have located in the very high seismic drive hazard/damage potential areas of the South Bulk Zone Level South Seismic Hazard in Drives Map (using 6 months of seismic data to 10-Oct-2000) E v high high mod low v low neg N Figure 6.7 Seismic drive hazard for South sublevel, October Level South Damage Potential Map (based on seismicity for 6 months prior to 10-Oct-2000) previous burst, area, 24/1/00 could reduce damage potential slightly for 2 areas with conebolt support v high high mod low E v low neg 13-Oct-00 Rockburst Damage Oct-00 Extended Rockburst/ Cave Damage 17-Oct-00 Rockburst Damage N Figure 6.8 Estimated Damage Potential for South sublevel, October

232 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine 6.8. Seismic Hazard & Damage Potential for 16 th May 2002 Rockburst Rockburst Description On the 16 th May 2002, a rockburst occurred in the intersection of the stope access on the sublevel. Underground observations (Gaudreau 2002) suggested that the south-west portion of the intersection ejected violently, followed by a gravitydriven structurally-controlled failure of the remainder of the intersection. The cablebolts in the burst portion were snapped, while unravelling had generally occurred 3 elsewhere. The total volume displaced was estimated at 50m or about 215 tonnes. The rockburst was assumed to have been triggered by a large (M L = 2.5) seismic event that located approximately 100m to the east of the intersection. This event did not appear to have damaged closer excavations. Upon investigation of the rockburst, the mine s ground control group (Gaudreau 2002) identified a sub-vertical joint located in the southwest corner of the intersection as an important part of the violent failure mechanism. Gaudreau (2002) and Simser & Andrieux (2002) considered the likely mode of failure to be a clamped wedge mechanism in which a pre-existing discontinuity (in this case the sub-vertical joint) hindered the propagation of stress fractures around the drive. This could effectively produce a local asperity in the shoulder of the drive in which strain energy stored while stress accumulated until some trigger, such as a local rotation in stresses or a dynamic load, induced a sudden, violent failure. One point of interest, applicable to both this burst and the 13 th October 2000 failure, which was noted by Simser & Andrieux (2002) was that for the 326 X/C and the intersection rockbursts... in each case, the pre-existing discontinuity plane that is inferred to have played an important role in the failure did not appear to daylight into the drift, which made it next to impossible to detect before-hand - in other words, it is highly unlikely that standard geological mapping would have identified these features. The rockburst occurred in an active travelway but no personnel were in the immediate vicinity and no injuries or equipment damage resulted. Photographs of the intersection failure are presented in Figures 6.9 and

233 Chapter 6 Detailed Case Studies of Risk and Exposure at Brunswick Mine Figure 6.9 View from the footwall access looking west to the intersection failure. (Photo courtesy of D. Gaudreau and the Brunswick Ground Control Group). 218