SEISMIC RISK ASSESSMENT OF THE TRANSPORTATION NETWORK IN CHARLESTON, SC

Transcription

1 SEISMIC RISK ASSESSMENT OF THE TRANSPORTATION NETWORK IN CHARLESTON, SC A Thesis Presented to The Academic Faculty by Emily Nilsson In Partial Fulfillment of the Requirements for the Degree Master of Science in Civil Engineering Georgia Institute of Technology May 2008

2 SEISMIC RISK ASSESSMENT OF THE TRANSPORTATION NETWORK OF CHARLESTON, SC Approved by: Dr. Reginald DesRoches, Advisor School of Civil and Environmental Engineering Georgia Institute of Technology Dr. Mulalo Doyoyo School of Civil and Environmental Engineering Georgia Institute of Technology Dr. Barry Goodno School of Civil and Environmental Engineering Georgia Institute of Technology Dr. Laurence Jacobs School of Civil and Environmental Engineering Georgia Institute of Technology Date Approved: March 31, 2008

3 ACKNOWLEDGEMENTS There are many people that I feel I must thank who have helped me through my journey here at Georgia Tech. First and foremost, I would like to thank my family for supporting me in my decision to come to this prestigious university. Their guidance and encouragement have only risen throughout my years here at Georgia Tech. The entire School of Civil and Environmental Engineering deserves thanks for providing me such a valuable education in engineering. More specifically, I would like to thank my committee members, Drs. Mulalo Doyoyo, Barry Goodno, and Laurence Jacobs, for their willingness to help me through my graduate studies. Additionally, I would like to acknowledge the support of this project by the Mid- America Earthquake Center, and for providing me many unique opportunities to enhance my studies. Without the help of Chris Navarro, Joshua Steelman, and many others at the University of Illinois, this research project would not have been nearly as successful. Finally, I would like to thank Dr. Jamie Padgett, and my advisor, Dr. Reginald DesRoches. Dr. Padgett has been my mentor and role model throughout my time as a research assistant. She has shown me everything from the ins and outs of a MAE Center meeting to how to be a successful, well-regarded woman in the engineering community. Similarly, the guidance that Dr. DesRoches has given me stretches far beyond the realm of this research project. His vast knowledge of earthquake engineering is only a small part of what he has shared with me in my years here at Georgia Tech. He has provided me with every opportunity for success through his mentorship and constant support. My studies at Georgia Tech surely would not have been as rewarding without the help of all those I have mentioned. I cannot thank them enough. iii

4 TABLE OF CONTENTS Page ACKNOWLEDGEMENTS LIST OF TABLES LIST OF FIGURES SUMMARY iii vi ix xiv CHAPTER 1 INTRODUCTION 1 CHAPTER 2 SEISMIC RISK ASSESSMENT OVERVIEW Previous Research on Seismic Risk Assessments 4 PART 1 CHAPTER 3 HAZARD ANALYSIS USGS Hazard vs. SC Hazard 10 CHAPTER 4 INVENTORY Bridge Inventory Processing Bridge Inventory Analysis Results GIS Implementation 23 CHAPTER 5 FRAGILITY CURVES Assumptions 28 CHAPTER 6 METHODOLOGY Summary Example Calculations 29 CHAPTER 7 DAMAGE AND FUNCTIONALITY RESULTS Scenarios Analysis of Damage 46 iv

5 7.3 Analysis of Functionality Analysis of Lifelines 52 CHAPTER 8 ECONOMIC LOSSES 60 PART 2 CHAPTER 9 MODAL INPUT VARIATION Seismic Hazard Bridge Fragilities Damage Ratios Sensitivity Study Results 70 CHAPTER 10 PRELIMINARY RECOMMENDATIONS 75 APPENDIX A: BRIDGE CLASSIFICATION PICTURES 78 APPENDIX B: SCENARIOS NOS. 1-3 AND 7-9 RESULTS 80 APPENDIX C: USGS SCENARIOS NOS. 4-6 RESULTS 108 REFERENCES 132 v

6 LIST OF TABLES Page Table 4.1: Bridge Distribution by County 13 Table 4.2: Bridge Classifications 13 Table 4.3: Bridge Classification Mapping to NBI Terminology 14 Table 4.4: Distribution of Bridge Classes within Study Area 15 Table 4.5: Bridge Inventory Statistics Number of Spans 16 Table 4.6: Bridge Inventory Statistics Maximum Span Length 17 Table 4.7: Bridge Inventory Statistics Deck Width 17 Table 4.8: Bridge Inventory Statistics Skew Angle 18 Table 4.9: Bridge Inventory Statistics Construction Year 19 Table 4.10: Bridge Inventory Statistics Mean Condition Ratings 20 Table 4.11: Bridge Inventory Statistics Liquefaction Potential 21 Table 4.12: Bridge Inventory Statistics Costing Data 22 Table 5.1: Fragility Curve Median PGA Values (g) 26 Table 5.2: Summary of Fragility Curve Assumptions 28 Table 6.1: Example Bridge Data Summary 30 Table 6.2: Fragility Curve Data Points from Corresponding Hazard Scenario 30 Table 6.3: LMF Values for Inventory 32 Table 6.4: Damage State Ranges per Bridge Classification and Hazard 32 Table 6.5: Bridge Repair Cost Ratios (D b ) 33 Table 6.6: Damage State Statistics per Bridge 34 Table 6.7: Statistical Costing Data per Bridge Classification 34 Table 6.8: Repair Cost Calculation Summary 36 vi

7 Table 7.1: MAEViz Scenarios 37 Table 7.2: Scenario No. 1 Damage State Distribution 2% in 50 (SC) 39 Table 7.3: Scenario No. 2 Damage State Distribution 5% in 50 (SC) 41 Table 7.4: Scenario No. 3 Damage State Distribution 10% in 50 (SC) 42 Table 7.5: Scenario No. 7 Damage State Distribution M w Table 7.6: Scenario No. 8 Damage State Distribution M w Table 7.7: Scenario No. 9 Damage State Distribution M w Table 7.8: Scenario No. 1 Damage States Along I-26 2% in 50 (SC) 54 Table 7.9: Scenario No. 1 Damage States Along US 17 2% in 50 (SC) 55 Table 7.10: Scenario No. 8 Damage States Along I-26 M w Table 7.11: Scenario No. 8 Damage States Along US 17 M w Table 8.1: Scenario No. 1 Direct Losses 2% in 50 (SC) 60 Table 8.2: Scenario No. 8 Direct Losses M w Table 9.1: Median Values (g) for DesRoches and Mander Fragility Curves 69 Table 9.2: Bridge Damage Ratios (D b ) - Basoz 70 Table 9.3: Bridge Damage Ratios (D b ) - REDARS 70 Table 9.4: Repair Cost Data Comparison 73 Table B.1: Scenario No. 2 Damage States Along I-26 5% in 50 (SC) 84 Table B.2: Scenario No. 2 Damage States Along US 17 5% in 50 (SC) 84 Table B.3: Scenario No. 3 Damage States Along I-26 10% in 50 (SC) 87 Table B.4: Scenario No. 3 Damage States Along US 17 10% in 50 (SC) 87 Table B.5: Scenario No. 7 Damage States Along I-26 M w Table B.6: Scenario No. 7 Damage States Along US 17 M w Table B.7: Scenario No. 9 Damage States Along I-26 M w Table B.8: Scenario No. 9 Damage States Along US 17 M w vii

8 Table B.9: Scenario No. 1 Functionality Results 2% in 50 (SC) 97 Table B.10: Scenario No. 2 Functionality Results 5% in 50 (SC) 98 Table B.11: Scenario No. 3 Functionality Results 10% in 50 (SC) 100 Table B.12: Scenario No. 7 Functionality Results M w Table B.13: Scenario No. 8 Functionality Results M w Table B.14: Scenario No. 9 Functionality Results M w Table B.15: Scenario No. 2 Direct Losses 5% in 50 (SC) 106 Table B.16: Scenario No. 3 Direct Losses 10% in 50 (SC) 106 Table B.17: Scenario No. 7 Direct Losses M w Table B.18: Scenario No. 9 Direct Losses M w Table C.1: Scenario No. 4 Damage State Distribution 2% in 50 (USGS) 108 Table C.2: Scenario No. 5 Damage State Distribution 5% in 50 (USGS) 109 Table C.3: Scenario No. 6 Damage State Distribution 10% in 50 (USGS) 110 Table C.4: Scenario No. 4 Damage States Along I-26 2% in 50 (USGS) 115 Table C.5: Scenario No. 4 Damage States Along US 17 2% in 50 (USGS) 115 Table C.6: Scenario No. 5 Damage States Along I-26 5% in 50 (USGS) 118 Table C.7: Scenario No. 5 Damage States Along US 17 5% in 50 (USGS) 118 Table C.8: Scenario No. 6 Damage States Along I-26 10% in 50 (USGS) 121 Table C.9: Scenario No. 6 Damage States Along US 17 10% in 50 (USGS) 121 Table C.10: Scenario No. 4 Functionality Results 2% in 50 (USGS) 124 Table C.11: Scenario No. 5 Functionality Results 5% in 50 (USGS) 125 Table C.12: Scenario No. 6 Functionality Results 10% in 50 (USGS) 127 Table C.13: Scenario No. 4 Direct Losses 2% in 50 (USGS) 129 Table C.14: Scenario No. 5 Direct Losses 5% in 50 (USGS) 129 Table C.15: Scenario No. 6 Direct Losses 10% in 50 (USGS) 130 viii

9 LIST OF FIGURES Page Figure 2.1: Seismic Risk Assessment Methodology 3 Figure 3.1: Hazard Map Comparison for Deterministic Scenarios 9 Figure 3.2: Hazard Map Comparison for 2% PE in 50 Year Scenarios 11 Figure 4.1: Skew Angle Schematic 18 Figure 4.2: NBI Condition Rating Descriptions 20 Figure 5.1: Fragility Curves for the Multi-Span Simply Supported Bridge Classes 26 Figure 5.2: Fragility Curves for the Single Span Simply Supported Bridge Classes 27 Figure 5.3: Fragility Curves for the Continuous Bridge Classes 27 Figure 6.1: Probability Exceedance Values for MSSS Concrete Bridge Classification 31 Figure 6.2: Probability Exceedance Values for MSSS Steel Bridge Classification 31 Figure 7.1: Scenario No.1 Probable Damage State Visual 2% in 50 (SC) 38 Figure 7.2: Scenario No.1 Collective Damage State Visual 2% in 50 (SC) 39 Figure 7.3: Scenario No.2 Probable Damage State Visual 5% in 50 (SC) 40 Figure 7.4: Scenario No.3 Probable Damage State Visual 10% in 50 (SC) 42 Figure 7.5: Scenario No.7 Probable Damage State Visual M w Figure 7.6: Scenario No.8 Probable Damage State Visual M w Figure 7.7: Scenario No.9 Probable Damage State Visual M w Figure 7.8: Scenario No.1 Damage State Distribution 2% in 50 (SC) 47 Figure 7.9: Scenario No.2 Damage State Distribution 5% in 50 (SC) 47 Figure 7.10: Scenario No.3 Damage State Distribution 10% in 50 (SC) 47 Figure 7.11: Damage State Comparison of Probabilistic Scenarios (SC) 48 Figure 7.12: Scenario No.7 Damage State Distribution M w ix

10 Figure 7.13: Scenario No.8 Damage State Distribution M w Figure 7.14: Scenario No.9 Damage State Distribution M w Figure 7.15: Damage State Comparison of Deterministic Scenarios 50 Figure 7.16: MAEViz Screen Capture Functionality at Day 0 2% in 50 (SC) 51 Figure 7.17: Functionality Comparison of Probabilistic Scenarios (SC) 51 Figure 7.18: Functionality Comparison of Deterministic Scenarios 52 Figure 7.19: Scenario No.1 Probable Damage States on I-26 2% in 50 (SC) 53 Figure 7.20: Scenario No.1 Probable Damage States on US 17 2% in 50 (SC) 53 Figure 7.21: Scenario No. 1 Lifeline Damage Comparison 2% in 50 (SC) 55 Figure 7.22: Scenario No. 8 Lifeline Damage Comparison M w Figure 7.23: Scenario No. 1 Lifeline Functionality Comparison 2% in 50 (SC) 58 Figure 7.24: Scenario No. 8 Lifeline Functionality Comparison M w Figure 8.1: Scenario No. 1 Direct Loss Distribution 2% in 50 (SC) 61 Figure 8.2: Scenario No. 2 Direct Loss Distribution 5% in 50 (SC) 62 Figure 8.3: Scenario No. 3 Direct Loss Distribution 10% in 50 (SC) 62 Figure 8.4: Scenario No. 7 Direct Loss Distribution M w Figure 8.5: Scenario No. 8 Direct Loss Distribution M w Figure 8.6: Scenario No. 9 Direct Loss Distribution M w Figure 8.7: Direct Loss Comparison 65 Figure 9.1: Sensitivity Study Framework 67 Figure 9.2: Summary of Cases Evaluated in the Sensitivity Study (Part 2) 71 Figure 9.3: Distributed Damage Comparison of Different Input Models - M w Figure 9.4: Distributed Damage Comparison of Different Input Models - M w Figure A.1: MSSS Concrete Bridge 78 Figure A.2: MSSS Steel Bridge 78 x

11 Figure A.3: MSC Concrete Bridge 78 Figure A.4: MSC Steel Bridge 79 Figure A.5: MSC Slab Bridge 79 Figure B.1: Scenario No. 1 Damage State by Bridge Type 2% in 50 (SC) 80 Figure B.2: Scenario No. 2 Damage State by Bridge Type 5% in 50 (SC) 80 Figure B.3: Scenario No. 3 Damage State by Bridge Type 10% in 50 (SC) 81 Figure B.4: Scenario No. 7 Damage State by Bridge Type M w Figure B.5: Scenario No. 8 Damage State by Bridge Type M w Figure B.6: Scenario No. 9 Damage State by Bridge Type M w Figure B.7: Scenario No. 1 Damage on I-26 by Bridge Type 2% in 50 (SC) 83 Figure B.8: Scenario No. 1 Damage on US 17 by Bridge Type 2% in 50 (SC) 83 Figure B.9: Scenario No. 2 Lifeline Damage Comparison 5% in 50 (SC) 85 Figure B.10: Scenario No. 2 Lifeline Functionality Comparison 5% in 50 (SC) 85 Figure B.11: Scenario No. 2 Damage on I-26 by Bridge Type 5% in 50 (SC) 86 Figure B.12: Scenario No. 2 Damage on US 17 by Bridge Type 5% in 50 (SC) 86 Figure B.13: Scenario No. 3 Lifeline Damage Comparison 10% in 50 (SC) 88 Figure B.14: Scenario No. 3 Lifeline Functionality Comparison 10% in 50 (SC) 88 Figure B.15: Scenario No. 3 Damage on I-26 by Bridge Type 10% in 50 (SC) 89 Figure B.16: Scenario No. 3 Damage on US 17 by Bridge Type 10% in 50 (SC) 89 Figure B.17: Scenario No. 7 Lifeline Damage Comparison M w Figure B.18: Scenario No. 7 Lifeline Functionality Comparison M w Figure B.19: Scenario No. 7 Damage on I-26 by Bridge Type M w Figure B.20: Scenario No. 7 Damage on US 17 by Bridge Type M w Figure B.21: Scenario No. 8 Damage on I-26 by Bridge Type M w Figure B.22: Scenario No. 8 Damage on US 17 by Bridge Type M w xi

12 Figure B.23: Scenario No. 9 Lifeline Damage Comparison M w Figure B.24: Scenario No. 9 Lifeline Functionality Comparison M w Figure B.25: Scenario No. 9 Damage on I-26 by Bridge Type M w Figure B.26: Scenario No. 9 Damage on US 17 by Bridge Type M w Figure B.27: Scenario No. 1 Functionality Distribution 2% in 50 (SC) 97 Figure B.28: Scenario No. 1 Weighted Bridge Functionality 2% in 50 (SC) 98 Figure B.29: Scenario No. 2 Functionality Distribution 5% in 50 (SC) 99 Figure B.30: Scenario No. 2 Weighted Bridge Functionality 5% in 50 (SC) 99 Figure B.31: Scenario No. 3 Functionality Distribution 10% in 50 (SC) 100 Figure B.32: Scenario No. 3 Weighted Bridge Functionality 10% in 50 (SC) 101 Figure B.33: Scenario No. 7 Functionality Distribution M w Figure B.34: Scenario No. 7 Weighted Bridge Functionality M w Figure B.35: Scenario No. 8 Functionality Distribution M w Figure B.36: Scenario No. 8 Weighted Bridge Functionality M w Figure B.37: Scenario No. 9 Functionality Distribution M w Figure B.38: Scenario No. 9 Weighted Bridge Functionality M w Figure C.1: Scenario No. 4 Probable Damage State Visual 2% in 50 (USGS) 109 Figure C.2: Scenario No. 5 Probable Damage State Visual 5% in 50 (USGS) 110 Figure C.3: Scenario No. 6 Probable Damage State Visual 10% in 50 (USGS) 111 Figure C.4: Scenario No. 4 Damage State Distribution 2% in 50 (USGS) 111 Figure C.5: Scenario No. 5 Damage State Distribution 5% in 50 (USGS) 112 Figure C.6: Scenario No. 6 Damage State Distribution 10% in 50 (USGS) 112 Figure C.7: Damage State Comparison of Probabilistic Scenarios (USGS) 113 Figure C.8: Scenario No. 4 Damage State by Bridge Type 2% in 50 (USGS) 113 Figure C.9: Scenario No. 5 Damage State by Bridge Type 5% in 50 (USGS) 114 xii

13 Figure C.10: Scenario No. 6 Damage State by Bridge Type 10% in 50 (USGS) 114 Figure C.11: Scenario No. 4 Lifeline Damage Comparison 2% in 50 (USGS) 116 Figure C.12: Scenario No. 4 Lifeline Functionality Comparison 2% in 50 (USGS) 116 Figure C.13: Scenario No. 4 Damage on I-26 by Bridge Type 2% in 50 (USGS) 117 Figure C.14: Scenario No. 4 Damage on US 17 by Bridge Type 2% in 50 (USGS) 117 Figure C.15: Scenario No. 5 Lifeline Damage Comparison 5% in 50 (USGS) 119 Figure C.16: Scenario No. 6 Lifeline Functionality Comparison 5% in 50 (USGS) 119 Figure C.17: Scenario No. 5 Damage on I-26 by Bridge Type 5% in 50 (USGS) 120 Figure C.18: Scenario No. 5 Damage on US 17 by Bridge Type 5% in 50 (USGS) 120 Figure C.19: Scenario No. 6 Lifeline Damage Comparison 10% in 50 (USGS) 122 Figure C.20: Scenario No. 6 Lifeline Functionality Comparison 10% in 50 (USGS) 122 Figure C.21: Scenario No. 6 Damage on I-26 by Bridge Type 10% in 50 (USGS) 123 Figure C.22: Scenario No. 6 Damage on US 17 by Bridge Type 10% in 50 (USGS) 123 Figure C.23: Scenario No. 4 Functionality Distribution 2% in 50 (USGS) 124 Figure C.24: Scenario No. 4 Weighted Bridge Functionality 2% in 50 (USGS) 125 Figure C.25: Scenario No. 5 Functionality Distribution 5% in 50 (USGS) 126 Figure C.26: Scenario No. 5 Weighted Bridge Functionality 5% in 50 (USGS) 126 Figure C.27: Scenario No. 6 Functionality Distribution 10% in 50 (USGS) 127 Figure C.28: Scenario No. 6 Weighted Bridge Functionality 10% in 50 (USGS) 128 Figure C.29: Functionality Comparison of Probabilistic Scenarios (USGS) 128 Figure C.30: Scenario No. 4 Direct Loss Distribution 2% in 50 (USGS) 130 Figure C.31: Scenario No. 5 Direct Loss Distribution 5% in 50 (USGS) 131 Figure C.32: Scenario No. 6 Direct Loss Distribution 10% in 50 (USGS) 131 xiii

14 SUMMARY The functionality of the transportation network following an earthquake event is critical for post-earthquake response and long-term recovery. The likely performance of a transportation network can be evaluated through a detailed seismic risk assessment. This paper presents an assessment of the seismic risk to the transportation network in the City of Charleston and the surrounding counties to support emergency response and the development of mitigation strategies and emergency planning efforts (such as lifeline selections). This study includes an inventory analysis of the 375 bridges in the Charleston area, and convolution of the seismic hazard with fragility curves analytically derived for classes of bridges common to this part of the country, damage-functionality relationships, and replacement cost estimates using relevant region-specific data. Using state-of-the-art tools, the distribution of potential bridge damage and functionality is evaluated for several scenario events, in order to aid in the identification of emergency routes and assess areas for investment in retrofit. Additionally, a sensitivity study is conducted to determine the criticality of a few of the different input models. Initial estimates of economic losses are assessed and preliminary recommendations for prioritizing retrofit are presented. xiv

15 CHAPTER 1 INTRODUCTION This study seeks to evaluate the performance of the transportation infrastructure in Charleston, SC under earthquake loading, with a focus on bridge and network performance. The specific area under investigation concerns the bridges along the I-26 corridor and along US 17, which are two main traffic arterials for access into and out of the city of Charleston. The study seeks to ascertain the essential transportation links (lifelines) via a seismic risk assessment (SRA). This assessment will provide city and state officials with the necessary data to prioritize bridge retrofit strategies that are both cost-effective and efficient in their application. Three hundred seventy five (375) bridges in and around the city are included in the assessment. The methodology used in the study evaluates likely bridge damage, bridge functionality, and economic losses due to repair and replacement of the structures. A portion of the study will consider the fact that the results from the SRA framework depend heavily on the tools and input models designated in the methodology. These include such items as ground motion models, fragility information on the bridges, repair cost information, and functionality. The adoption of different input models in the SRA framework could potentially have a significant effect on the overall results and conclusions of the study. The Charleston, SC region is used as an example to gain insight on the effect of using different seismic hazard maps, different fragility curves for evaluating the performance of bridges common to the region, as well as different 1

16 estimates of the damage ratio (or repair cost ratio) for assessing the direct losses based on the level of bridge damage suffered. The study will be divided into two parts. Part 1 of the study will include nine different hazard input models to give a broad range of potential earthquake scenarios for the region. All other factors incorporated in the earthquake runs of part 1 will be held constant. Bridge damage, functionality, and direct losses will be calculated and compared for lifeline analysis. The results of this part of the study will be most effective in future studies of retrofit analysis in the Charleston, SC area. Part 2 of the study focuses on the relative sensitivity of the highway bridge damage and loss estimates to different fragility and damage ratio input models. Two scenario earthquakes, two bridge fragility curves, and two damage ratio values are used in this part of the study. This results in eight different cases yielding comparable damage and loss estimates. Varying these input models can identify the critical components of the risk assessment framework that impact the overall results of a regional transportation network assessment. 2

17 CHAPTER 2 SEISMIC RISK ASSESSMENT OVERVIEW A seismic risk assessment involves performing an analysis of the consequences of a seismic event upon a particular area or network. In the case of this study, the seismic risk assessment pertains to the transportation network in Charleston, SC. A flowchart of the methodology can be found in Figure 2.1. Seismic Hazard Bridge / Network Inventory Bridge Fragilities and Damage Ratios Transportation Network Evaluation Bridge Damage Network Functionality Economic Losses Figure 2.1: Seismic Risk Assessment Methodology Input components of the assessment include the seismic hazard, bridge inventory, and input models such as fragility curves and damage ratios. These input components 3

18 were integrated into a visualization program called MAEViz, developed by the Mid- America Earthquake (MAE) Center. MAEViz incorporates a consequence-based risk management system that provides for sophisticated seismic risk assessments. The factors to be ascertained from MAEViz include bridge damage, bridge and network functionality, and economic losses. Earthquakes and other natural hazards are often very unpredictable and have a significant amount of uncertainty. One of the goals of the seismic risk assessment is to promote earthquake policy development given the tools that we have available, which can take into account the uncertainties on the demand and structural capacity. The implementation of a seismic risk assessment allows for proper planning to minimize the impacts of natural hazards. 2.1 Previous Research on Seismic Risk Assessments Whitman et. al. introduced a methodology known as Seismic Design Decision Analysis (SDDA) in The majority of the seismic risk assessments performed since then has been based off of this methodology, which considers effects of hazard, damage, and economic losses. In particular, the damage effects were studied as probabilities of different damage levels; this type of analysis is still done today. The generic methodology of a network SRA involves utilizing a seismic hazard for the study area to predict damage to the individual components of the network. More specifically, vulnerability functions, or most recently, fragility curves, along with the hazard, dictate the probability of the component being in various damage states. King et. al. (1997) discusses the benefits of creating generic classes in which to group components. This allows for the development of relationships for each class that quantify 4

19 the damage as a function of the hazard. Once the probable damage state is determined, repair costs and functionality / restoration times can be determined. After analyzing the components, the resulting data can be incorporated into the network system to assess the damage, repair costs, and functionality of the complete network. From this point, it is also possible to relate this network functionality to the entire social and economic losses experienced in the area. In 1991, the Applied Technology Council (ATC) began the focus on lifelines and whole network performance. An analysis of highway bridges was performed for many different earthquake loads, one of which was a magnitude 7.5 in the Charleston, SC region. Because such a broad range of earthquake scenarios and transportation networks were studied, there was little detail given to the Charleston case. Shinozuka et. al. (2000) used a Monte Carlo technique to suggest the states of network and bridge damage following a seismic event. Since the computational time for this simulation-based method was on the order of only a few seconds, it was determined to be an effective tool in a post-earthquake decision response system. Recent work on the SRA methodology has concentrated on guiding seismic risk reduction decision making. Giovinazzi (2005) provides some guidelines for SRAs and makes suggestions as to how decision makers can use the results for risk management, citing the importance of being able to create real time scenarios to aid in risk decisions in the first hours following a seismic event. The more complete the damage, functionality, and cost forecasts, the more prepared local officials will be for a seismic event. Werner and Taylor (2002) highlighted the importance of observing component functionality in addition to addressing the damage state. Knowing whether a bridge will 5

20 be fully closed (0% functional), partially open (50% functional), or fully open (100% functional) provides a means of analyzing networks as a whole. It is important to study a network and its comprising components under the same SRA. In this regard, one can legitimately link the damage of a component, such as a bridge, to the whole network. As this research will show, varying the input models has the potential to have a tremendous impact on the results of the study. Calculating costs (direct and indirect) on a national scale and accounting for the interaction among economic sectors was performed by Veneziano et. al. in Many studies show the costs of building damage or network highway damage, but Veneziano et. al. (2002) worked to evaluate how the different systems interact, resulting in increased losses. The scope of potential risk assessments gets larger as more capabilities become available with programs such as MAEViz, HAZUS, or REDARS. Recent studies done on transportation networks place a strong emphasis on indirect costs due to traffic flow and travel times. Werner (2000) estimated earthquake effects on immediate system wide traffic flow, economic impacts of highway damage, and post-earthquake traffic flows. King et. al. (1997) suggests the benefits of considering inventory outside the study area. The result introduces some level of error, but it can play an important role in determining post-earthquake emergency response. Chen and Eguchi (2003) consider unblocked reliability in addition to network vulnerability in their evaluation of the predicted effectiveness of a transportation system. They realized the fact that a potential building collapse in the vicinity of the network could cause a significant disruption in traffic flow. 6

21 There exists a great deal of research focused in the domain of analyzing specific regions, such as the San Francisco or New Madrid areas. A recent study by Kiremidjian et. al. in 2007, presents a risk assessment for the transportation network in the San Francisco Bay Area. 2,640 bridges were analyzed and bridge damage and repair costs were estimated. In addition, after linking the bridge data to the highway network by means of GIS implementation, time delays in traffic resulting from bridge closure were predicted. The bridge closures were presumed from the calculated functionality. Similar to the research presented in this document, the functionality and repair costs were functions of the estimated probable damage state. A primary goal of the Kiremidjian (2007) paper was to investigate the contributions of a liquefaction and landslide hazard in addition to the ground motion. The study found liquefaction to be the most detrimental hazard to the area, while landslides were the least damaging. It was noted, however, that the contributions of assorted hazards are dependent on the region analyzed. The following chapters will present a comparable SRA of the transportation network of Charleston, SC given various ground motion hazards. 7

22 PART 1 8

23 CHAPTER 3 HAZARD ANALYSIS The first step in evaluating the seismic risk for any region is to assume a seismic hazard. Two main hazard types are used in seismic risk analyses: deterministic hazard and probabilistic hazard. A deterministic hazard can be defined as a particular scenario event occurring at a certain location. Examples of deterministic hazards are shown in Figure 3.1. In contrast, a probabilistic hazard considers uncertainties, such as earthquake size and location, and concerns the frequency of earthquake occurrence. For instance, an earthquake with a 2% probability of exceedance in 50 years will have a return period of 2475 years. This means that this particular earthquake will occur, on average, approximately every 2475 years. (a) (b) Figure 3.1: Hazard Map Comparison for Deterministic Scenarios: (a) M w 5.3 and (b) M w 7.3 Although it is generally considered more appropriate to use deterministic hazards when performing a seismic risk assessment, both hazard types are used and compared in this study. For the deterministic hazard, three scenarios are used: earthquakes of 9

24 magnitude 4.0, 5.5, and 7.0 located at 32.9 o N, 80.0 o W, which is approximately 14.5 km outside of the Charleston city center near Summerville. For the probabilistic hazard, three scenarios are run using the following recurrence intervals: 2% probability of exceedance in 50 years (2% PE in 50), 5% probability of exceedance in 50 years (5% PE in 50), and 10% probability of exceedance in 50 years (10% PE in 50). These three probabilistic hazard scenarios are run using hazard map data obtained from both the United States Geological Survey (USGS) and the state of South Carolina (SC), equaling six (6) probabilistic hazard scenarios in addition to the three (3) deterministic hazard scenarios, giving a total of nine (9) hazard scenarios. 3.1 USGS Hazard vs. SC Hazard As stated, two seismic hazards maps were implemented in this study: USGS and SC. The former comes from the National Seismic Hazard Maps developed by the USGS, while the latter hazard comes directly from the state of South Carolina. Conventionally, the USGS hazard maps are used in any seismic analysis or risk assessment, but the state requested this study also consider the usage of their revised hazard maps in addition to the use of the USGS maps. A significant difference between the National Seismic Hazard Maps and the hazard maps developed by the state is that the state maps reflect actual geological conditions in the state of South Carolina. A generic site response model such as that used for the National Seismic Hazard Maps does not adequately represent the range of conditions in South Carolina. This is particularly the case for the coastal areas of the state such as Charleston and the surrounding areas where earthquake resistant design is most important. Figure 3.2 shows the difference between the two hazard maps. 10

25 (a) (b) Figure 3.2: Hazard Map Comparison for 2% PE in 50 Year Scenarios: (a) SC Hazard and (b) USGS Hazard 11

26 CHAPTER 4 INVENTORY 4.1 Bridge Inventory Processing Summary The first step to begin this investigation was data collection. First, we obtained the National Bridge Inventory (NBI) data for the state of South Carolina. From this data, the appropriate bridges were sorted by county and bridge identification number. Once the bridges in the study area were collected from the gross data, they were classified and analyzed. Data Extraction The initial NBI database given to us was quite exhaustive containing over 10,000 bridges with each bridge having over 126 fields of information. Bridge characteristics concerning geometry, materials, and construction year, as well location specifications (in latitude-longitude) were included within these categories. From this database, all the bridges in Charleston county, and a selected few from Berkeley, Dorchester, and Orangeburg counties were filtered out using Microsoft Access. These are the bridges that correspond to the I-26 corridor, along I-26 from Charleston to the Bowman exit. Additional bridges along US 17 from Beaufort, Colleton, Georgetown, Horry, and Jasper counties were filtered from the database resulting in a revised list containing only 375 bridges out of the overall 10,000. Refer to Table 4.1 for the bridge distribution for each corresponding county. 12

27 Table 4.1: Bridge Distribution by County SC County Qty. Berkeley County 15 Charleston County 273 Dorchester County 27 Orangeburg County 26 Other 34 Total 375 Bridge Classification From this revised list, an additional filtering process, again using Microsoft Access, was used to associate each bridge into one of the ten bridge classifications shown in Table 4.2. Table 4.2: Bridge Classifications Continuous Multi. Simply Supported Single Simply Supported MSC_Concrete MSSS_Concrete SS_Concrete MSC_Conc Box MSSS_Conc Box SS_Steel MSC_Steel MSSS_Steel MSC_Slab MSSS_Slab They were classified with the methodology used by Nielson (2005) in his fragility curve study. The classifications simply identify the bridges by both their span configuration simply supported (SS), multi-span simply supported (MSSS), multi-span continuous (MSC) as well as by their girder material type concrete or steel. Table 4.3 maps the previously mentioned bridge classifications with corresponding NBI terminology. 13

28 Table 4.3: Bridge Classification Mapping to NBI Terminology An overall distribution of the bridge classes can be seen in Table 4.4 and picture references for several bridge classes can be seen in Appendix A. It is noteworthy to mention several observations of the bridges in the study area: (1) only one bridge is included in the MSC_Concrete class, (2) 340 (90.67%) of the 375 bridges correspond to the bridge classifications used in the study, and (3) over 60% (64.80%) of the bridges are of the MSSS class (with approximately 30% being MSSS_Slab), while only 13.87% are continuous. Also, the Other bridge category contains all extra bridges not falling into one of the ten major classifications (i.e. Truss, Moveable, Tunnel/Culvert, Segmented Box Girder, and Box Single/Spread). 14

29 Table 4.4: Distribution of Bridge Classes within Study Area Bridge Type Qty. % MSC_Concrete % MSC_Steel % MSC_Slab % MSC_Conc Box % MSSS_Concrete % MSSS_Steel % MSSS_Slab % MSSS_Conc Box % SS_Steel % SS_Concrete % Other % Total % Statistical Analysis Once the bridges pertaining to the study area were extracted from the NBI database and identified according to the classes in Table 4.2, a statistical analysis was performed. This was done by analyzing each bridge class independently. Using a Microsoft Excel spreadsheet, statistics such as the mean, standard deviation, median, and mode were calculated for each bridge class for each of the following categories given in the NBI database: Number of spans Maximum span length Deck width Skew angle Year built Deck condition rating Superstructure condition rating Substructure condition rating These data categories are significant because they were similarly used, by Nielson (2005), in the fragility curve determination for each of the ten bridge classes. 15

30 4.2 Bridge Inventory Analysis Results Number of Spans The statistical analysis results for the number of spans for eight of the bridge classes are shown in Table 4.5. The simply supported classifications are not included since they all have just one span. An important observation is that there seems to be a high degree of variability for span number among the MSC_Slab, MSSS_Concrete, and MSSS_Steel classifications, as seen by their relatively high standard deviations. Also, the continuous slab bridges appear to contain more spans, on average, than all other bridge classifications. Table 4.5: Bridge Inventory Statistics Number of Spans Class % of Std. Mean Inventory Dev. Median Mode* MSC_Conc Box 0.27% MSC_Concrete 8.27% MSC_Slab 3.73% MSC_Steel 1.60% MSSS_Conc Box 16.27% MSSS_Concrete 16.53% MSSS_Slab 31.47% MSSS_Steel 0.53% Other 9.33% *The mode represents the most frequent value in the dataset. Maximum Span Length The results for the maximum span length, given in meters, are shown in Table 4.6. Overall, it appears that the continuous steel bridges have both the greatest mean maximum span length value as well as a greater variance relative to the multi-span simply supported bridges. 16

31 Table 4.6: Bridge Inventory Statistics Maximum Span Length Class Mean Std. Dev. Median Mode (m) (m) (m) (m) MSC_Conc Box MSC_Concrete MSC_Slab MSC_Steel MSSS_Conc Box MSSS_Concrete MSSS_Slab MSSS_Steel SS_Concrete SS_Steel Other Deck Width The results for the deck width, given in meters, are shown in Table 4.7. The results show that in the study area, the MSSS_Steel bridges have the greatest mean deck width compared to the other defined bridge classes. Also, the continuous bridge classes, excluding Concrete Box, have mean deck widths that are very similar. Table 4.7: Bridge Inventory Statistics Deck Width Class Mean Std. Dev. Median Mode (m) (m) (m) (m) MSC_Conc Box MSC_Concrete MSC_Slab MSC_Steel MSSS_Conc Box MSSS_Concrete MSSS_Slab MSSS_Steel SS_Concrete SS_Steel Other Skew Angle The skew angle is the angle measured between the centerline of the bridge supports and a line drawn perpendicular to the bridge centerline (Figure 4.1). 17

32 Figure 4.1: Skew Angle Schematic Several bridges in the study have horizontal curvature and hence, a variable skew angle. Bridges of this type were not considered in the skew angle data set. The results for the skew angle are shown in Table 4.8. Observation shows that nine of the ten classes have small mean skew angles (less than 15 degrees) with a large majority solely containing bridges with no skew angle whatsoever. This is consistent with the previous analysis by Nielson (2005), supporting the developing of fragility curves for bridges without consideration of skew angle. Only the MSSS_Steel and SS_Steel bridge classes appear to have large skew angles (greater than 15 degrees). Class Mean (Deg.) Table 4.8: Bridge Inventory Statistics Skew Angle Std. Dev. (Deg.) Median (Deg.) Mode (Deg.) 0 o (%) 1 o (%) o (%) > 30 o (%) MSC_Conc Box % 0.00% 0.00% 0.00% MSC_Concrete % 0.00% 0.00% 0.00% MSC_Slab % 0.00% 7.14% 0.00% MSC_Steel % 9.68% 0.00% 0.00% MSSS_Conc Box % 0.00% 0.00% 0.00% MSSS_Concrete % 3.28% 8.20% 4.92% MSSS_Slab % 0.85% 0.00% 0.00% MSSS_Steel % 14.52% 27.42% 24.19% SS_Concrete % 0.00% 0.00% 0.00% SS_Steel % 11.54% 15.38% 19.23% Other % 2.86% 0.00% 0.00% 18

33 Year Built The construction year is used to determine if seismic conditions were considered in design, since rigorous seismic design procedures were not implemented into non- California bridge design until 1990 (Nielson 2005). The results for construction year are given in Table 4.9. Note that all but one (98.39%) of the bridges in the MSSS_Steel class were built prior to 1990, and all classes, excluding the MSC_Concrete and MSSS_Conc Box classes, have at least one-third of their bridges built earlier than the 1990 transition year. Table 4.9: Bridge Inventory Statistics Construction Year Class Mean Std. Dev. Median Mode < 1990 (year) (year) (year) (year) (%) MSC_Conc Box MSC_Concrete MSC_Slab MSC_Steel MSSS_Conc Box MSSS_Concrete MSSS_Slab MSSS_Steel SS_Concrete SS_Steel Other Condition Ratings The results for the deck condition rating are shown in Table Refer to Figure 4.2 for the NBI code rating descriptions. Overall, it is seen that the mean condition ratings for the deck, the superstructure and the substructure are approximately 7, which is good condition with a few minor problems. Thus, most bridges in the study area appear to presently be without any major functional problems. 19

34 Table 4.10: Bridge Inventory Statistics Mean Condition Ratings Class Deck Superstructure Substructure MSC_Conc Box MSC_Concrete MSC_Slab MSC_Steel MSSS_Conc Box MSSS_Concrete MSSS_Slab MSSS_Steel SS_Concrete SS_Steel Other Figure 4.2: NBI Condition Rating Descriptions Liquefaction With the city of Charleston being a coastal city, the soil within the study area could perhaps liquefy under seismic excitation due to much of the soil profile being comprised of silt and sand. The liquefaction data for the bridges within the study area was obtained from the SC DOT and was incorporated into the NBI bridge data discussed and analyzed previously. The liquefaction potential for each bridge was denoted as the 20

35 following: (1) Y, meaning the site would liquefy; (2) N, meaning the site would not liquefy; or (3) U, meaning the liquefaction potential was unknown. Many of the bridges in the study area did not have liquefaction potential data. These bridges were assigned N (no) liquefaction potential. The liquefaction distribution by bridge classification is shown in Table The majority of the bridges in the inventory (61.07%) had no liquefaction information whatsoever while approximately one-third (32.8%) are expected to liquefy. The MSSS bridges contain approximately 65% (80 of 123) of the total number of bridges expected to undergo liquefaction. The present study does not incorporate liquefaction potential in the bridge vulnerability assessment. Therefore, it is anticipated that the damage and loss estimates presented herein are conservative estimates. Future phases of this research will address the impact of liquefaction on these figures. Table 4.11: Bridge Inventory Statistics Liquefaction Potential Class Y N U Blank LIQUEFY MSC_Conc Box % MSC_Concrete % MSC_Slab % MSC_Steel % MSSS_Conc Box % MSSS_Concrete % MSSS_Slab % MSSS_Steel % SS_Concrete % SS_Steel % Other % 21

36 Reconstruction and Repair Cost Repair and reconstruction costs are also required when performing a seismic risk analysis. These numbers are needed to determine the direct losses for a seismic event in any area. For this study, the repair costs for the bridges were determined and later incorporated into the earthquake scenarios to determine the total scenario economic losses. Costs for new bridge construction were obtained from the state of South Carolina, and from these numbers, cost data was calculated for each bridge class in terms of dollars per square foot of bridge ($/ft 2 ). The mean values of the data were used in the economic loss calculations. Table 4.12 provides a summary for the calculated costing data. The replacement cost associated with the remaining other bridge types (ie. truss) is approximated as the average of the detailed bridge types. Table 4.12: Bridge Inventory Statistics Costing Data Class CST/SQ.FT. μ σ MSC_Concrete Seg. Box Girder MSSS_Concrete SS_Concrete MSC_Steel MSSS_Steel SS_Steel MSC_Conc-Box MSSS_Conc-Box Box Single/Spread MSC_Slab MSSS_Slab Truss Moveable Tunnel/Culvert

37 4.3 GIS Implementation The bridge inventory definition of the study area was the first step in the seismic risk analysis for the city of Charleston. Once the bridge data was gathered, organized, classified and analyzed, the next step was the integration of the data into a modeling program to estimate the expected bridge damage for a given hazard and to perform the corresponding bridge and network (roadway) functionality analysis. This was achieved through a geographical information system (GIS). For this study, the bridge data was incorporated with roadway and terrain data to produce the GIS visualization for the risk and network analysis. Additional Inventory In addition to the bridge inventory previously discussed, roadway inventory was required to achieve a full network model for the risk assessment. This data was obtained from the state of South Carolina and implemented into ArcGIS along with the bridge inventory. As with all GIS systems, the program projected the information onto a geographical coordinate system which then created the geographical visualization. All visualized data then had corresponding information attributes (or fields) which could be modified. The NBI data served as the attribute data for the bridge inventory. It was reduced to approximately 40 attribute fields from the original 126. This served to give a summary of the NBI bridge information, while removing all irrelevant information from the raw data. The latitude-longitude data served to project the bridges onto the visualization map. They were subsequently superimposed over the additional inventory to complete the GIS model. 23

38 Difficulties with GIS Implementation Inventory mismatch was the greatest obstacle encountered with the GIS implementation. The majority of the bridges did not align with their corresponding roadways. The NBI data was highly inaccurate and the latitude-longitude data was often incorrect; yet, for the network model, it was imperative that all bridge points be within a certain location tolerance for their association to their corresponding roadway. Thus, all bridge points were individually checked for location accuracy and were modified (moved) manually where required. This proved to be quite cumbersome, but was necessary for a proper and accurate network model. 24

39 CHAPTER 5 FRAGILITY CURVES The final step in generating the seismic risk model was creating a mathematical and discrete manner to determine damage states of the bridges in the inventory. In this study, this is achieved by using fragility curves. These fragility curves depict the probability of meeting or exceeding different levels of damage conditioned upon the ground motion intensity. As previously mentioned, the analysis by Nielson (2005) provided the fragility curves for part 1 of this study, shown in Figures 5.1, 5.2, and 5.3. The specific fragility parameters of the Nielson (2005) curves are shown in Table 5.1. It is noteworthy to mention that his study did not provide a set of curves for the MSC_Conc Box bridge class. Thus, the curves for his MSC_Concrete bridge were used for the MSC_Conc Box bridges encountered in this study. To complete the analysis, we additionally assigned fragilities to the five other type bridges. The Segmented Box Girder bridge class was given the fragility of MSC_Concrete; the Box Single/Spread class was given the HAZUS HWB8 designation, a continuous concrete box girder; and the others were given the unclassified HAZUS label of HWB28. Details on these HAZUS designations can be found in FEMA (1999). 25

40 Table 5.1: Fragility Curve Median PGA Values (g) Bridge Class Slight Moderate Extensive Complete Dispersion, ζ MSC Concrete MSC Steel MSC Slab MSC Concrete Box MSSS Concrete MSSS Steel MSSS Slab MSSS Concrete Box SS Concrete SS Steel MSSS Concrete MSSS Concrete-Box (a) MSSS Slab (b) MSSS Steel (c) (d) Figure 5.1 Fragility Curves for the Multi-Span Simply Supported Bridge Classes: (a) MSSS Concrete, (b) MSSS Concrete-Box, (c) MSSS Slab and (d) MSSS Steel (Nielson 2005) 26

41 SS Concrete SS Steel (a) (b) Figure 5.2 Fragility Curves for the Single Span Simply Supported Bridge Classes: (a) SS Concrete and (b) SS Steel (Nielson 2005) MSC Concrete MSC Slab (a) MSC Steel (b) (c) Figure 5.3: Fragility Curves for the Continuous Bridge Classes: (a) MSC Concrete, (b) MSC Slab and (c) MSC Steel (Nielson 2005) 27

42 5.1 Assumptions Several assumptions were made in the formulation of these fragility curves. The majority of these assumptions dealt directly with generalizations of specific bridge components. For example, when considering the bridge bearings in the steel bridges, steel fixed and rocker bearings were assumed since they are conventionally used in steel bridges. Likewise, conventional fixed and expansion elastomeric pads were assumed for the concrete bridges. In both, variable properties were used to account for the uncertainty in bearing properties (i.e. stiffness, friction, etc.). Table 5.2 summarizes the remainder of the fragility assumptions. It should be noted that liquefaction was not considered in this analysis. Bearings: Steel fixed and rocker bearings (steel bridges) Fixed and expansion elastomeric pads (concrete bridges) Variable properties to account for uncertainty (i.e. stiffness, friction, etc.) Table 5.2: Summary of Fragility Curve Assumptions Columns: Non-seismically detailed circular columns Variable properties to account for uncertainty (i.e. strength, height, etc.) Abutments: Seat-type abutments with pile bents Variable properties to account for uncertainty (i.e. passive stiffness, active stiffness, etc.) Foundations: Pile foundations Adequately reinforced Variable properties to account for uncertainty (i.e. rotational stiffness, translational stiffness, etc.) Additional Assumptions: No seismic retrofit or seismic upgrade Non-seismic detailing MSSS and MSC bridges reflect response of typical highway overpass bridges (~ 3 spans typ.) Variable geometry and material properties to account for uncertainty Fragilities to reflect the potential vulnerability of a common/typical bridge class rather than unique bridge 28

43 CHAPTER 6 METHODOLOGY 6.1 Summary With the entire inventory defined and implemented into the GIS model and the incorporation of the fragility data associated with each bridge classification completed, the seismic scenario was now ready to be performed. There were two main goals to be achieved for each bridge in the inventory from each scenario modeled: (1) an expected damage state and (2) an expected repair cost. The following example shows the methodology used in the determination of these two goals. 6.2 Example Calculations Expected Damage State Determination Using the fragility curves and the hazard definition, each bridge site will have a specific peak ground acceleration (PGA) for each scenario. In this example, two bridges will be used: one from the MSSS_Concrete class and the other from the MSSS_Steel class. The information concerning these example bridges (from the NBI inventory data) is given in Table

44 Inventory Data Category Table 6.1: Example Bridge Data Summary NBI Data Item Number Ex. Bridge 1 Ex. Bridge 2 CLASSIFICATION --- MSSS_Concrete MSSS_Steel COUNTY FAC. INTERSECTED 6 I-26 Southern Railroad FAC. CARRIED 7 Aviation Ave. I-26 STR. NO YEAR BUILT SPANS STR. LENGTH m (250 ft) 46.6m (153 ft) DECK WIDTH m (68.2 ft) 15.5m (50.9 ft) LIQ --- N N Using the specific PGA values and the corresponding fragility curve sets (for each classification), the probabilities for each damage state are obtained for a given hazard scenario. For this example, the MSSS_Concrete and MSSS_Steel bridges experience PGA values of 0.70g and 0.54g respectively. Refer to Table 6.2 and Figures 6.1 & 6.2 for the process of obtaining the corresponding probabilities. Table 6.2: Fragility Curve Data Points from Corresponding Hazard Scenario Bridge Class PGA (g) Probabilities of Exceedance (PE[DS]) None Slight Moderate Extensive Complete MSSS_Concrete MSSS_Steel

45 MSSS Concrete PGA=0.70 Figure 6.1: Probability Exceedance Values for MSSS Concrete Bridge Classification MSSS Steel PGA=0.54 Figure 6.2: Probability Exceedance Values for MSSS Steel Bridge Classification Liquefaction effects will be considered at this step, though are not explicitly incorporated in this study. Refer to Table 6.3 for the Liquefaction Modification Factors (LMFs). These values will be implemented as multiplication factors to the median fragility values. For sites expecting liquefaction behavior, the median values will be reduced by some percentage of the nominal value. 31

46 Table 6.3: LMF Values for Inventory Liquefaction Modification LIQ? Factor Blank 1.00 N 1.00 U To be determined Y To be determined In this example, both bridges are not expecting liquefaction behavior, and thus the factor is 1.0. Therefore, the fragility data remains unchanged. With these exceedance values now modified, the probability for each expected damage state is calculated. This is done for each individual bridge, using the following formulas. Table 6.4 is the summary table for these calculations. P[ None] = 1 P[ Slight] P[ Slight] = P[ Slight] P[ Moderate] P[ Moderate] = P[ Moderate] P[ Extensive] P[ Extensive] = P[ Extensive] P[ Complete] P[ Complete] = P[ Complete] 0 = P[ Complete] Table 6.4: Damage State Ranges per Bridge Classification and Hazard Probabilities (P[DS]) Bridge Class PGA (g) None Slight Moderate Extensive Complete MSSS_Concrete 0.70 MSSS_Steel = = = The damage states from the fragility curves will be based off of the Repair Cost Ratios (D b ) shown in Table 6.5. A weighted average will be calculated using the best mean ratio values. The expected damage state for the individual bridges will then be determined from the weighted mean value. Due to overlap in the D b Ratio ranges, if the mean value falls into the overlap areas, the higher damage state will be used. 32

47 Damage State Table 6.5: Bridge Repair Cost Ratios (D b ) Range of D Best Mean D b Ratio b Ratios None Slight Moderate Extensive (if n < 3) Complete 2.0 x (bridge replacement cost)/n (if n 3) * (where n = number of spans) The mean damage state value, variance and standard deviation are calculated using the data from Tables 6.4 and 6.5. The formulas and numerical calculations for the MSSS_Concrete bridge follow, and the remainder of the calculations for this example are summarized in Table 6.6 For these calculations, the None damage state is neglected. Mean MSSS_Concrete μ D μ D = # DS Db j j= 1 P[ DS ] j 2.0 = 0.03 (0.40) (0.21) (0.15) n μ = 0.17 where: n = # of spans = 4 (see Table 6.1) Variance MSSS_Concrete σ 2 D = # DS ( Db j μd ) j= 1 D 2 Damage State: EXTENSIVE P[ DS ] j σ 2 D (0.03 μ D ) (0.08 μ D ) (0.25 μ D ) ( μ ) n σ 2 = where: n = # of spans = 4 (see Table 6.1) = D D Standard Deviation MSSS_Concrete σ = σ = D σ D D 33

48 Table 6.6: Damage State Statistics per Bridge MSSS_Concrete MSSS_Steel Mean, μ D Variance, σ D Standard Deviation, σ D Damage State EXTENSIVE EXTENSIVE Expected Repair Costs Once the damage states are determined, the expected repair costs can be calculated. In Table 6.7, the mean repair costs in dollars per square foot ($/ft 2 ) are given per bridge class (equivalent to Table 4.12). As stated before, these values were calculated from repair and construction data from the state of South Carolina. The other bridge type costing data can be found in Table Table 6.7: Statistical Costing Data per Bridge Classification Bridge Class CST/SQ.FT. ($/ft 2 ) μ replace σ replace μ + σ μ σ MSC_Concrete MSSS_Concrete SS_Concrete MSC_Steel MSSS_Steel SS_Steel MSC_Conc-Box MSSS_Conc-Box MSC_Slab MSSS_Slab The expected repair costs are calculated for each bridge classification group, using the following formula: L = # bridges i= 1 Cost D i b i done for each bridge class group separately where: Cost = μ A = μ ( w L ) i replace i i replace i deck i i 34

49 In the previous equation, μ replace is the mean repair cost per square foot (Table 6.7), and A is the total bridge deck area calculated from the bridge deck width, w structure length, L (Table 6.1). Db is the mean repair cost ratio taken from Table 6.5. deck, and the The mean and standard deviation costing values for each bridge class are calculated using the following formulas and methods. Table 6.8 gives the summary of these calculations. Mean MSSS_Concrete μ L μ L = # bridges i= 1 Cost μ i D i ( 68.2 ft 250 ) = ft $ ft μ L $196,000 Standard Deviation MSSS_Concrete σ L σ L = # bridges i= 1 ( Cost σ ) i Di ( ( ) ) $ ft = ft ft σ L 2 $208,000 Additionally, overall repair cost calculations are wanted for the entire inventory. The same methods are used to calculate these values. See the following calculations and Table 6.8 for their values. Loss Total Inventory L = # bridges Cost D = # bridges i bi i= 1 i= 1 Mean Total Inventory μ L μ L = # bridges i= 1 Cost μ i Di μ replacei ( w L ) deck i i D ( 68.2 ft 250 ft) $ ( 50.9 ft 153 ) = ft $ ft ft μ L $358,000 bi 35

50 Standard Deviation Total Inventory σ L σ L = # bridges i= 1 ( Cost σ ) i Di 2 2 ( ( 68.2 ft 250 ft) 0.18) $ 2 ( 50.9 ft 153 ) ( 0. ) 2 = $ 23 2 ft σ L ft $268,000 ft Table 6.8: Repair Cost Calculation Summary MSSS_Concrete $196,000 $208,000 MSSS_Steel $162,000 $169,000 μ L σ L Total Inventory $358,000 $268,000 The damage ratios referenced in this example were developed for California. In the absence of past earthquake damage and repair data for the South Carolina region, the assumed damage ratios provide a reasonable initial estimate. Part 2 of the study considers another set of damage ratios in addition to the ones used in this example. 36

51 CHAPTER 7 DAMAGE AND FUNCTIONALITY RESULTS 7.1 Scenarios The methodology presented was extrapolated into full scenarios for the entire bridge inventory in Charleston, SC. The models shown in Table 7.1 were performed using the seismic risk assessment (SRA) visualization program, MAEViz. This chapter focuses on the deterministic and South Carolina probabilistic scenarios. The results of the USGS probabilistic scenarios can be found in Appendix C. Table 7.1: MAEViz Scenarios Type No. Hazard Scenario 1 SC 2% PE in 50 Yrs. 2 SC 5% PE in 50 Yrs. Probabilistic 3 SC 10% PE in 50 Yrs. 4 USGS 2% PE in 50 Yrs. 5 USGS 5% PE in 50 Yrs. Deterministic 6 USGS 10% PE in 50 Yrs M w M w M w 7.0 2% PE in 50 Years (SC) Following the incorporation of the gathered inventory, the SC hazard, and the fragility curves into MAEViz, the expected damage states and functionality of all bridges in the inventory were calculated, as well as the bridge repair costs, which will be discussed in Chapter 8. Figure 7.1 gives a screen capture from MAEViz showing the 37

52 probable damage state of the bridges in and around the downtown area, and Table 7.2 summarizes the results from this scenario. It can be observed in the figure that the bridges closer to the epicenter of the earthquake are in higher damage states than bridges in other locations, such as the downtown area. This is due to the expected higher PGA values in the locations closer to the epicenter. Additionally, Figure 7.2 illustrates that, in fact, each damage state is associated with a probability of being in that damage state. Thus, there is a possibility of any damage state for each bridge. Damage States None Slight Moderate Extensive Complete Figure 7.1: Scenario No. 1 Probable Damage State Visual 2% in 50 (SC) 38

53 Type Table 7.2: Scenario No. 1 Damage State Distribution 2% in 50 (SC) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL Damage States None Slight Moderate Extensive Complete Figure 7.2: Scenario No. 1 Collective Damage State Visual 2% in 50 (SC) 39

54 5% PE in 50 Years (SC) Scenario No.2 was performed similarly to Scenario No.1 but with a return period of 5% PE in 50 years. Figure 7.3 gives the MAEViz screen capture of probable damage states and Table 7.3 summarizes the bridge inventory results. As expected, the bridge damage decreases as the PGA decreases. Note the moderately and extensively damaged bridges in the northwest corner of the map and the relatively less damaged bridges in the downtown area, where the majority are in probable damage states of slight or none. Damage States None Slight Moderate Extensive Complete Figure 7.3: Scenario No. 2 Probable Damage State Visual 5% in 50 (SC) 40

55 Type Table 7.3: Scenario No. 2 Damage State Distribution 5% in 50 (SC) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL % PE in 50 Years (SC) The MAEViz screen capture of probable damage states for Scenario No. 3 (10% PE in 50 years) is shown in Figure 7.4 and the tabulated results are shown in Table 7.4. Similar to the other return periods of 2% PE in 50 years and 5% PE in 50 years, this scenario shows the downtown area bridges appearing to be less damaged than the bridges located in areas with higher PGA values. 41

56 Damage States None Slight Moderate Extensive Complete Figure 7.4: Scenario No. 3 Probable Damage State Visual 10% in 50 (SC) Type Table 7.4: Scenario No. 3 Damage State Distribution 10% in 50 (SC) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

57 Deterministic Scenarios In addition to the probabilistic events that were evaluated, deterministic scenarios (No. 7-9) were performed. Screen captures corresponding to each scenario are shown in Figures 7.5, 7.6, and 7.7, and the results summary is given in Tables 7.5, 7.6 and 7.7. It is interesting to note that a M w 4.0 scenario results in only slight or moderate damage to 65 bridges, with the remaining bridges having no damage. Damage States None Slight Moderate Extensive Complete Figure 7.5: Scenario No. 7 Probable Damage State Visual M w

58 Type Table 7.5: Scenario No. 7 Damage State Distribution M w 4.0 Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL Damage States None Slight Moderate Extensive Complete Figure 7.6: Scenario No. 8 Probable Damage State Visual M w

59 Type Table 7.6: Scenario No. 8 Damage State Distribution M w 5.5 Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL Damage States None Slight Moderate Extensive Complete Figure 7.7: Scenario No. 9 Probable Damage State Visual M w

60 Type Table 7.7: Scenario No. 9 Damage State Distribution M w 7.0 Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL Analysis of Damage From these scenarios, several comparisons were made. First, a comparison between each of the probabilistic scenarios was performed followed by a comparison of the deterministic scenarios. Probabilistic Scenarios The results from Tables 7.2 through 7.4 are shown in the Figures 7.8, 7.9, and 7.10 with a compilation graphed in Figure As expected, there is a significant difference between the damage experienced during the three different hazard levels. In the 10% PE in 50 years earthquake, only 1 bridge had moderate or greater damage, compared with 139 bridges in the 5% PE in 50 years earthquake, and 291 (77.6%) in the 2% PE in 50 years earthquake. This is a direct result of the dramatic differences in ground acceleration between the different hazard levels. 46

61 2% PE in 50 Years Number of Bridges None Slight Moderate Extensive Complete Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure 7.8: Scenario No.1 Damage State Distribution 2% in 50 (SC) 5% PE in 50 Years Number of Bridges None Slight Moderate Extensive Complete Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure 7.9: Scenario No.2 Damage State Distribution 5% in 50 (SC) 10% PE in 50 Years Number of Bridges None Slight Moderate Extensive Complete Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure 7.10: Scenario No.3 Damage State Distribution 10% in 50 (SC) 47

62 Number of Bridges None Slight Moderate Extensive Complete Damage State 10% 5% 2% Figure 7.11: Damage State Comparison of Probabilistic Scenarios (SC) Deterministic Scenarios The results from Tables 7.5 through 7.7 are graphed in Figures 7.12, 7.13, and 7.14 with a comparison between scenarios shown in Figure A quick observation shows that the M w 7.0 seismic event does, as expected, produce the most damage among the bridge inventory having just over 280 bridges experiencing at least moderate damage. This is in comparison with 182 and 34 bridges corresponding to the M w 5.5 and M w 4.0 events, respectively. It can be concluded from the following figures, which show the SS Steel and SS Concrete classes in the relatively lower damage states, that the single span bridges perform much better than the multi-span bridges. Additionally, it should be noted that the MSC Steel and MSSS Steel bridges perform the worst. This is to be expected since their corresponding fragility curves, shown in Figures 5.1 and 5.3, seem to illustrate the highest bridge vulnerability within the inventory. 48

63 M w 4.0 Number of Bridges None Slight Moderate Extensive Complete Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure 7.12: Scenario No.7 Damage State Distribution M w 4.0 M w 5.5 Number of Bridges None Slight Moderate Extensive Complete Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure 7.13: Scenario No.8 Damage State Distribution M w 5.5 M w 7.0 Number of Bridges None Slight Moderate Extensive Complete Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure 7.14: Scenario No.9 Damage State Distribution M w

64 Number of Bridges None Slight Moderate Extensive Complete Damage State Figure 7.15: Damage State Comparison of Deterministic Scenarios 7.3 Analysis of Functionality While an assessment of the distribution of bridge damage is critical in a seismic risk assessment, this damage tells us very little about the impact on the transportation network. Figure 7.15 is a screen capture showing the various levels of bridge functionality at day 0 of a 2% PE in 50 year event. The expected combined functionality of the network for 2%, 5%, and 10% PE in 50 year events is illustrated in Figure The combined functionality denoted as WBF (weighted bridge functionality) is determined by taking a weighted average of the functionality of each bridge, n, in the network as shown in the following equation: WBF = 1 N N n Functionality n This provides a composite measure of the functionality of all of the bridges in the transportation network and reveals the anticipated restoration over time. 50

65 Functionality 0% 50% 100% Figure 7.16: MAEViz Screen Capture Functionality at Day 0 2% in 50 (SC) Weighted Bridge Functionality 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 2% 5% 10% 0% Number of Days Figure 7.17: Functionality Comparison of Probabilistic Scenarios (SC) 51

66 For a 10% PE in 50 year event, the system has a functionality of 94% for the first day, but is back up to 100% after that time. For the 5% PE in 50 year event, the system has a functionality of 52% for the first day, which improves to 81% between day 1 and day 7, and is up to 99% after day 7. The 2% PE in 50 year event results in a system functionality of 18% at the time of the event, 52% between day 1 and day 7, and 89% after 7 days. Figure 7.17 assesses the likely functionality of the bridges based on the three deterministic scenarios, and shows similar results to the probabilistic scenarios in terms of the trends in the functionality as the scenario earthquake increases in magnitude. 100% 90% Weighted Bridge Functionality 80% 70% 60% 50% 40% 30% 20% 10% % Number of Days Figure 7.18: Functionality Comparison of Deterministic Scenarios 7.4 Analysis of Lifelines I-26 and US 17 have been identified as two potential lifelines to get emergency services into and out of Charleston following an earthquake event. Figures 7.19 and 7.20 are screen captures showing the probable damage states of the bridge on I-26 and US 17, respectfully. 52

67 Damage States None Slight Moderate Extensive Complete Figure 7.19: Scenario No. 1 Probable Damage States on I-26 2% in 50 (SC) Damage States None Slight Moderate Extensive Complete Figure 7.20: Scenario No. 1 Probable Damage States on US 17 2% in 50 (SC) 53

68 Tables 7.8 and 7.9 and Figure 7.21 show the distribution of bridge damage for the two lifelines for the 2% PE in 50 year earthquake. Likewise, Tables 7.10 and 7.11 and Figure 7.21 show the lifeline results for a M w = 5.5 earthquake scenario. In the 2% PE in 50 year earthquake, I-26 has 14 bridges in the extensive to complete damage states, compared with 5 for US 17. There are several reasons for the increased level of damage on I-26 compared with US 17. First, I-26 has a large number of bridges in the MSSS Steel bridge class, which tends to be one of the most vulnerable types of bridges. Second, based on a hazard map, compared to US 17, I-26 is located in closer proximity to the areas of higher acceleration. Type Table 7.8: Scenario No. 1 Damage States Along I-26 2% in 50 (SC) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

69 Type Table 7.9: Scenario No. 1 Damage States Along US 17 2% in 50 (SC) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL % PE in 50 Years Number of Bridges I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 NONE SLIGHT MODERATE EXTENSIVE COMPLETE Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure 7.21: Scenario No. 1 Lifeline Damage Comparison 2% in 50 (SC) 55

70 Type Table 7.10: Scenario No. 8 Damage States Along I-26 M w 5.5 Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL Type Table 7.11: Scenario No. 8 Damage States Along US 17 M w 5.5 Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

71 M w 5.5 Number of Bridges I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 NONE SLIGHT MODERATE EXTENSIVE COMPLETE Damage State Figure 7.22: Scenario No. 8 Lifeline Damage Comparison M w 5.5 Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Evaluation of the system functionality for each of the routes shows that the system functionality of US 17 is higher than I-26 following the earthquake. The system functionality is 44% for US-17 versus 26% for I-26 for the first day, and increase to 71% and 54% for US-17 and I-26, respectively for days 3-7. After 7 days, US 17 has a functionality of 96%, while I-26 is somewhat lower with a weighted functionality of 87%. Figures 7.23 and 7.24 show the system functionality results of the 2% PE in 50 year scenario and the M w 5.5 earthquake, respectively. 57

72 2% PE in 50 Years Weighted Bridge Functionality % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Number of Days I-26 US17 Figure 7.23: Scenario No. 1 Lifeline Functionality Comparison 2% in 50 (SC) M w % 90.00% Weighted Bridge Functionality 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Number of Days I-26 US17 Figure 7.24: Scenario No. 8 Lifeline Functionality Comparison M w

73 Deterministic scenario events are likely more appropriate considerations for comparing emergency routes. The M w 5.5 earthquake, as well as others presented in Appendix B, produce less damage and yield higher functionality for US 17 relative to I- 26. The day zero and day one snapshots provide the greatest insight as to how accessible these routes are likely to be immediately after the event and following inspection. Comparisons at these days indicate that US 17 tends to have higher functionality and immediate access. For example, for the M w 5.5 event, US 17 has a weighted functionality of 68%, while I-26 has 45% at time zero. After 1 day, or following inspections, this may increase to nearly 86% and 73% respectively. Additional figures and tables for Scenarios Nos. 1-3 and 7-9 not referenced in Chapter 7 can be found in Appendix B. 59

74 CHAPTER 8 ECONOMIC LOSSES The calculation of expected economic losses is based on repair and construction data from the state of South Carolina and is computed as described in Chapter 6. Tables 8.1 and 8.2 show the expected direct losses separated by bridge type for Scenario Nos. 1 and 8, respectfully. Note, indirect costs are not accounted for in these tables. These extra costs, such as expenditures associated with traffic diversion, can be estimated as 7-20 times the calculated direct costs (ATC 1991). For purposes of analysis, a multiplication factor of 13 was used to calculate the estimated indirect costs. Indirect costs, and the estimated total costs (direct plus indirect), can be found in Figures , which illustrate the loss distribution by bridge type for Scenario Nos. 1-3 and 7-9. Table 8.1: Scenario No. 1 Direct Losses 2% in 50 (SC) Type Direct Loss MSC Concrete $77,000 MSC Steel $67,000,000 MSC Slab $960,000 MSC Conc Box $240,000 MSSS Concrete $5,700,000 MSSS Steel $11,000,000 MSSS Slab $2,600,000 MSSS Conc Box $99,000 SS Concrete $650,000 SS Steel $230,000 Other $5,200,000 TOTAL $93,756,000 60

75 Table 8.2: Scenario No. 8 Direct Losses M w 5.5 Type Direct Loss MSC Concrete $13,000 MSC Steel $26,000,000 MSC Slab $830,000 MSC Conc Box $200,000 MSSS Concrete $2,800,000 MSSS Steel $6,000,000 MSSS Slab $1,400,000 MSSS Conc Box $60,000 SS Concrete $510,000 SS Steel $94,000 Other $2,400,000 TOTAL $40,307,000 Loss 2% PE in 50 Years Direct Losses = $93,756,000 Estimated Indirect Losses ~ $1,200,000,000 Estimated TOTAL Losses ~ $1,293,756,000 $75,000, $60,000, $45,000, $30,000, $15,000, $0.00 MSC Conc MSC Steel MSC Slab MSC Conc Box MSSS Conc MSSS Steel MSSS Slab MSSS Conc Box SS Conc SS Steel Other Bridge Type Figure 8.1: Scenario No. 1 Direct Loss Distribution 2% in 50 (SC) 61

76 5% PE in 50 Years Direct Losses = $26,639,000 Estimated Indirect Losses ~ $350,000,000 Estimated TOTAL Losses ~ $376,639,000 $25,000, $20,000, Loss $15,000, $10,000, $5,000, $0.00 MSC Conc MSC Steel MSC Slab MSC Conc Box MSSS Conc MSSS Steel MSSS Slab MSSS Conc Box SS Conc SS Steel Other Bridge Type Figure 8.2: Scenario No. 2 Direct Loss Distribution 5% in 50 (SC) $2,000, % PE in 50 Years Direct Losses = $2,999,350 Estimated Indirect Losses ~ $39,000,000 Estimated TOTAL Losses ~ $41,999,350 $1,500, Loss $1,000, $500, $0.00 MSC Conc MSC Steel MSC Slab MSC Conc Box MSSS Conc MSSS Steel MSSS Slab MSSS Conc Box SS Conc SS Steel Other Bridge Type Figure 8.3: Scenario No. 3 Direct Loss Distribution 10% in 50 (SC) 62

77 Loss $5,000, $4,000, $3,000, $2,000, $1,000, $0.00 M w 4.0 Direct Losses = $6,344,900 Estimated Indirect Losses ~ $82,000,000 Estimated TOTAL Losses ~ $88,344,900 MSC Conc MSC Steel MSC Slab MSC Conc Box MSSS Conc MSSS Steel MSSS Slab MSSS Conc Box SS Conc SS Steel Bridge Type Figure 8.4: Scenario No. 7 Direct Loss Distribution M w 4.0 Other M w 5.5 Direct Losses = $40,307,000 Estimated Indirect Losses ~ $520,000,000 Estimated TOTAL Losses ~ $560,307,000 $40,000, $30,000, Loss $20,000, $10,000, $0.00 MSC Conc MSC Steel MSC Slab MSC Conc Box MSSS Conc MSSS Steel MSSS Slab MSSS Conc Box SS Conc SS Steel Other Bridge Type Figure 8.5: Scenario No. 8 Direct Loss Distribution M w

78 Loss M w 7.0 Direct Losses = $83,080,000 Estimated Indirect Losses ~ $1,100,000,000 Estimated TOTAL Losses ~ $1,183,080,000 $50,000, $40,000, $30,000, $20,000, $10,000, $0.00 MSC Conc MSC Steel MSC Slab MSC Conc Box MSSS Conc MSSS Steel MSSS Slab MSSS Conc Box SS Conc SS Steel Other Bridge Type Figure 8.6: Scenario No. 9 Direct Loss Distribution M w 7.0 For a M w 4.0 seismic event, economic losses are estimated to be close to $90 million dollars. This happens to be more than double what is expected of a 10% PE in 50 year scenario (~$42 million). In contrast, the more severe earthquake scenarios, M w 7.0 and 2% PE in 50 yrs, produce losses of more than $1 billion. As the earthquake scenarios increase in intensity, the economic losses increase exponentially. Figure 8.7 compares the direct losses of the nine scenarios. The difference between the probabilistic scenarios (SC versus USGS) is quite drastic. For the 2% PE in 50 yrs scenario, the USGS hazard yields direct losses that are more than double that expected with the SC hazard. Refer to Appendix C for more detailed results of the USGS Scenarios (Nos. 4-6). 64

79 $300,000, $250,000, $200,000, Loss $150,000, $100,000, $50,000, $0.00 SC 2% in 50 SC 5% in 50 SC 10% in 50 USGS 2% in 50 USGS 5% in 50 USGS 10% in Earthquake Scenario Figure 8.7: Direct Loss Comparison Additional figures and tables for scenarios 1-3 and 7-9 not referenced in Chapter 8 can be found in Appendix B. 65

80 PART 2 66

81 CHAPTER 9 MODAL INPUT VARIATION Part 2 of this study includes the sensitivity analysis of the various input parameters. The general framework used in this part of the study is presented in Figure 9.1. Figure 9.1: Sensitivity Study Framework Scenario earthquake events are used for the example presented herein, where the magnitude and location of the event must be specified. During the system analysis, fragility curves for classes of bridges common to the region are utilized in the evaluation of the expected level of damage to each bridge. The bridge damage coupled with information on the damage ratio (or fraction of replacement cost) and replacement cost 67

82 data for different bridge types permits an assessment of the losses. The following sections detail the different input models and scenarios which will be evaluated. 9.1 Seismic Hazard Two different scenario earthquake events are considered in part 2 of this study. This permits an evaluation of whether or not the conclusions of the study are dependent of the level of the hazard. The characteristic scenario events assessed for Charleston, SC are moment magnitude 5.3 and 7.3 located at the same location as the deterministic hazards used in part 1 of the study (32.9 N, 80.0 W). In order to estimate the level of ground shaking at the location of each bridge, a weighted average of different attenuation functions is used with an assumed underlying NEHRP soil class of D. This is to acknowledge the findings of past work which have indicated the importance of considering the epistemic uncertainty in ground motion models, particularly attenuation of ground motion for spatially distributed systems. Thus, the ground motion models themselves are not a focus of this part of the study and the epistemic uncertainty associated with them is captured and treated explicitly in each scenario, rather than evaluating the sensitivity of the results to different models. The scenarios evaluated result in considerably different levels of ground shaking in the Charleston, SC region. The question is raised, whether or not the ultimate damage and loss assessment will be comparably sensitive to input model variation at the higher and lower level earthquake events. 9.2 Bridge Fragilities Bridge fragility curves offer the probability of meeting or exceeding a level of damage given an intensity measure of the ground motion. As seen in part 1, the levels of 68

83 damage are qualitatively described as slight, moderate, extensive, and complete damage. Two different sets of fragility curves are compared in the study. One was developed by Mander (1999), while the other was created by Nielson (2005). The Nielson (2005) curves also known as the DesRoches curves - were used in part 1 of this study. The fragility curves and methodology proposed in Mander (1999) have subsequently been adopted by HAZUS and other SRA software. Table 9.1 shows a comparison between the values that make up the DesRoches and Mander curves. Table 9.1: Median Values (g) for DesRoches and Mander Fragility Curves DesRoches Mander Bridge Class Slight Moderate Extensive Complete Dispersion, ζ Slight Moderate Extensive Complete Dispersion, ζ MSC Concrete MSC Steel MSC Slab MSC Concrete Box MSSS Concrete MSSS Steel MSSS Slab MSSS Concrete Box SS Concrete SS Steel Damage Ratios The damage ratios used in part 1 of this study were developed by Basoz and Mander (1999). For comparison, scenarios were completed using REDARS damage ratios as well. The Basoz values are a function of the number of spans, while the REDARS values are not. Table 6.5, which shows the values of the Basoz repair cost ratios, is recreated in Table 9.2 for easy comparison to the REDARS values, shown in Table

84 Damage State Table 9.2: Bridge Damage Ratios (D b ) - Basoz Range of D Best Mean D b Ratio b Ratios None Slight Moderate Extensive (if n < 3) Complete 2.0 x (bridge replacement cost)/n (if n 3) * (where n = number of spans) Damage State Table 9.3: Bridge Damage Ratios (D b ) - REDARS Range of D Best Mean D b Ratio b Ratios None Slight Moderate Extensive Complete 1.0 (if n < 3) Sensitivity Study Results With two different earthquake scenarios, two different sets of fragility curves, and two sets of damage ratios, a total of eight cases were conduced in part 2 of the study, as shown in Figure 9.2. The ultimate damage and loss calculations were determined for each case. Comparisons were made between the various fragility curve and damage ratio combinations, resulting in an assessment of how sensitive each factor is in the damage and loss outcome. Figures 9.3 and 9.4 graphically show the differences in the level of damage for each of the 4 cases within both magnitudes. 70

85 Damage Ratio USGS Hazard Magnitude M w = 7.3 M w = 5.3 Fragility HAZUS DesRoches DesRoches HAZUS Basoz REDARS Basoz REDARS Basoz REDARS Basoz CASE 1 CASE 2 CASE 3 CASE 4 CASE 5 CASE 6 CASE 7 REDARS CASE 8 Figure 9.2: Summary of Cases Evaluated in the Sensitivity Study (Part 2) DesRoches/Basoz DesRoches/REDARS HAZUS/Basoz HAZUS/REDARS Number of Bridges None Slight Moderate Extensive Complete Damage State Figure 9.3: Distributed Damage Comparison of Different Input Models Mw =

86 Number of Bridges None Slight Moderate Extensive Complete Damage State DesRoches/Basoz DesRoches/REDARS HAZUS/Basoz HAZUS/REDARS Figure 9.4: Distributed Damage Comparison of Different Input Models Mw = 7.3 A comparison between the outputs of cases that have different fragility curves, but use the same damage ratios shows that in both cases - DesRoches/Basoz versus HAZUS/Basoz and DesRoches/REDARS versus HAZUS/REDARS HAZUS fragilities lead to higher damage states. In the latter assessment with M w 7.3, there are 6 bridges in the complete damage state when using the DesRoches fragilities, and 11 bridges in the same high damage state using HAZUS. 72

87 Table 9.4: Repair Cost Data Comparison Case Number Scenario Estimated Repair Cost Standard Deviation , HAZUS Fragilities, Basoz Damage Ratios 7.3, HAZUS Fragilities, REDARS Damage Ratios 7.3, DesRoches Fragilities, Basoz Damage Ratios 7.3, DesRoches Fragilities, REDARS Damage Ratios 5.3, DesRoches Fragilities, Basoz Damage Ratios 5.3, DesRoches Fragilities, REDARS Damage Ratios 5.3, HAZUS Fragilities, Basoz Damage Ratios 5.3, HAZUS Fragilities, REDARS Damage Ratios $71,400,000 $17,700,000 $197,000,000 $27,500,000 $105,000,000 $19,900,000 $267,000,000 $41,200,000 $50,900,000 $14,700,000 $125,000,000 $24,900,000 $27,900,000 $11,500,000 $74,200,000 $10,700,000 Table 9.4 summarizes the repair cost results of each of the eight cases. Unexpectedly, the DesRoches fragilities appear to have higher losses when compared to the HAZUS fragility results. Since HAZUS yielded higher damage states, one would anticipate higher losses as well. The damage ratios show a clear and distinct difference between their loss outputs. In every case, the REDARS damage ratios generate much higher losses than the Basoz counterparts. For example, the difference in losses between cases 3 (DesRoches/Basoz) and 4 (DesRoches/REDARS) is on the order of $100 million. It is apparent that the damage ratios have a much greater impact on the estimated losses than the fragility curves have. This notion is emphasized by the fact that the fragility that yielded higher damage states (HAZUS) did not, in fact, yield higher losses, as stated previously. The difference between cases 1 and 2 a comparison between two different damage ratios shows a loss increase of 176% when varying from Basoz to REDARS. Similarly, the difference between cases 2 and 4 a comparison between two different fragilities shows a loss increase of only 36% when varying from HAZUS to 73

88 DesRoches fragility curves. Making the same comparisons for the M w 5.3 cases (5-8) yields the same conclusions. Comparing cases 7 and 8 shows a loss increase of 166%, while the difference between cases 8 and 6 is a loss increase of only 68.5%. Thus, this confirms the fact that the damage ratios play a more integral role than the fragility curves in the determination of estimated losses, regardless of the earthquake magnitude. 74

89 CHAPTER 10 PRELIMINARY RECOMMENDATIONS Analysis of damage patterns for various earthquake scenarios shows that for the lower earthquake levels of M w 4.0 and 10% PE in 50 years, there are over 40 bridges in the slight or moderate damage states, indicating that these are earthquake levels that should be considered as scenarios which would require evaluation of damage by bridge engineers and inspectors. An event as large as a M w 7.0 may produce extensive or complete damage to over 50 bridges in the Charleston region and nearly 85% of the bridges may be expected to experience some level of slight through complete damage. Over 70% of the bridges had at least moderate damage in the M w 7.0 scenario and the 2% PE in 50 year hazard. In general, the bridge types which sustained the most damage during the various scenarios were the MSSS slab, MSSS steel girder, and MSC steel girder bridges. This is a function of the fact that the MSSS slab bridge is one of the most common bridges in the inventory and the MSC steel girder bridge is one of the most vulnerable. In general, nonseismically designed MSC steel and MSSS steel bridges, followed by MSC concrete and MSC slab bridges, tend to be the most vulnerable to higher levels of damage, as indicated by the bridge fragility studies. These bridge classes should be carefully considered for seismic retrofit through bridge specific evaluations. Evaluation of the seismic risk assessment suggests that US 17 would have a higher level of functionality compared to I-26 for the probabilistic and scenario events considered. This information can be used in several ways. First, the analysis shows that 75

90 there is a greater likelihood of being able to travel into and out of downtown Charleston using US 17 versus I-26, since the bridges, on average, would have higher functionality. In addition, in the M w 4.0 scenario for instance, US 17 would be expected to have full functionality based on the generalized bridges on the routes, while it would likely take 7 days to get all of the bridges on I-26 to 100% functionality. The lifeline comparison tends to indicate that US 17 is a preferred route. Immediate functionality following the event has a significant impact on the viability of an emergency route, which is estimated to be higher on US 17. If the seismic risk assessment information is being used for prioritizing retrofit, the information suggests that it would be more efficient to retrofit the bridges on US 17 compared with I-26, since there are fewer bridges requiring retrofit. There are additional issues that would need to be factored into the prioritization, including the bridge types and performance objectives. In addition, although more bridges on I-26 require retrofit, additional studies are required to determine if, in fact, it would be less expensive to retrofit I-26 versus US 17. To do this, detailed studies using retrofitted bridge fragility curves would be required. Additionally other alternate routes may be preferable, which should be examined in addition to US 17 and I-26. The results of the sensitivity study show quite convincingly that the values of the damage ratios have a significant impact on the estimated direct losses. Regardless of the hazard or fragility curves used, using REDARS damage ratios consistently increased losses by an estimated % from the Basoz damage ratio cases. As mentioned previously, the Basoz damage ratios, which were used in part 1 of the study, were initially intended for California bridges. Knowing the influence of variable damage 76

91 ratios, future studies should be done to evaluate refined damage ratios for South Carolina bridges. The sensitivity of the fragility curves was minor in comparison. The increase of losses due to a change in damage ratios was approximately 2-4 times higher than the change induced by a variation in fragility curves. Note, the results of this study do not include the effects of liquefaction, which would further increase the vulnerability of the transportation system. 77

92 APPENDIX A BRIDGE CLASSIFICATION PICTURES Figure A.1: MSSS Concrete Bridge Figure A.2: MSSS Steel Bridge Figure A.3: MSC Concrete Bridge 78

93 Figure A.4: MSC Steel Bridge Figure A.5: MSC Slab Bridge 79

94 APPENDIX B SCENARIOS NOS. 1-3 AND 7-9 RESULTS 2% PE in 50 Years 100 Number None Slight Moderate Extensive Complete 10 0 MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.1: Scenario No. 1 Damage State by Bridge Type 2% in 50 (SC) 5% PE in 50 Years Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.2: Scenario No. 2 Damage State by Bridge Type 5% in 50 (SC) 80

95 10% PE in 50 Years Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.3: Scenario No. 3 Damage State by Bridge Type 10% in 50 (SC) M w = 4.0 Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.4: Scenario No. 7 Damage State by Bridge Type M w

96 M w Number None Slight Moderate Extensive Complete 10 0 MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.5: Scenario No. 8 Damage State by Bridge Type M w 5.5 M w 7.0 Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.6: Scenario No. 9 Damage State by Bridge Type M w

97 2% PE in 50 Years (Bridges on I-26 Only) Number None Slight Moderate Extensive Complete 2 0 MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.7: Scenario No. 1 Damage on I-26 by Bridge Type 2% in 50 (SC) 2% PE in 50 Years (Bridges on US 17 Only) Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.8: Scenario No. 1 Damage on I-26 by Bridge Type 2% in 50 (SC) 83

98 Type Table B.1: Scenario No. 2 Damage States Along I-26 5% in 50 (SC) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL Type Table B.2: Scenario No. 2 Damage States Along US 17 5% in 50 (SC) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

99 5% PE in 50 Years Number of Bridges I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 NONE SLIGHT MODERATE EXTENSIVE COMPLETE Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure B.9: Scenario No. 2 Lifeline Damage Comparison 5% in 50 (SC) 5% PE in 50 Years % Weighted Bridge Functionality 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Number of Days I-26 US17 Figure B.10: Scenario No. 2 Lifeline Functionality Comparison 5% in 50 (SC) 85

100 5% PE in 50 Years (Bridges on I-26 Only) 18 Number None Slight Moderate Extensive Complete 2 0 MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.11: Scenario No. 2 Damage on I-26 by Bridge Type 5% in 50 (SC) 5% PE in 50 Years (Bridges on US 17 Only) None Number Slight Moderate Extensive 4 Complete 2 0 MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.12: Scenario No. 2 Damage on US 17 by Bridge Type 5% in 50 (SC) 86

101 Type Table B.3: Scenario No. 3 Damage States Along I-26 10% in 50 (SC) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL Type Table B.4: Scenario No. 3 Damage States Along US 17 10% in 50 (SC) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

102 10% PE in 50 Years Number of Bridges I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 NONE SLIGHT MODERATE EXTENSIVE COMPLETE Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure B.13: Scenario No. 3 Lifeline Damage Comparison 10% in 50 (SC) 10% PE in 50 Years % Weighted Bridge Functionality 99.00% 98.00% 97.00% 96.00% 95.00% I-26 US % Number of Days Figure B.14: Scenario No. 3 Lifeline Functionality Comparison 10% in 50 (SC) 88

103 10% PE in 50 Years (Bridges on I-26 Only) Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.15: Scenario No. 3 Damage on I-26 by Bridge Type 10% in 50 (SC) 10% PE in 50 Years (Bridges on US 17 Only) 18 Number None Slight Moderate Extensive Complete 2 0 MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.16: Scenario No. 3 Damage on US 17 by Bridge Type 10% in 50 (SC) 89

104 Type Table B.5: Scenario No. 7 Damage States Along I-26 M w 4.0 Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL Type Table B.6: Scenario No. 7 Damage States Along US 17 M w 4.0 Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

105 M w 4.0 Number of Bridges I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 NONE SLIGHT MODERATE EXTENSIVE COMPLETE Damage State Figure B.17: Scenario No. 7 Lifeline Damage Comparison M w 4.0 Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc M w 4.0 Weighted Bridge Functionality % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Number of Days I-26 US17 Figure B.18: Scenario No. 7 Lifeline Functionality Comparison M w

106 M w 4.0 (Bridges on I-26 Only) Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.19: Scenario No. 7 Damage on I-26 by Bridge Type M w 4.0 M w 4.0 (Bridges on US 17 Only) Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.20: Scenario No. 7 Damage on US 17 by Bridge Type M w

107 M w 5.5 (Bridges on I-26 Only) Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.21: Scenario No. 8 Damage on I-26 by Bridge Type M w 5.5 M w 5.5 (Bridges on US 17 Only) Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.22: Scenario No. 8 Damage on US 17 by Bridge Type M w

108 Type Table B.7: Scenario No. 9 Damage States Along I-26 M w 7.0 Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL Type Table B.8: Scenario No. 9 Damage States Along US 17 M w 7.0 Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

109 M w 7.0 Number of Bridges I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 I-26 US 17 NONE SLIGHT MODERATE EXTENSIVE COMPLETE Damage State Figure B.23: Scenario No. 9 Lifeline Damage Comparison M w 7.0 Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc M w 7.0 Weighted Bridge Functionality % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Number of Days I-26 US17 Figure B.24: Scenario No. 9 Lifeline Functionality Comparison M w

110 M w 7.0 (Bridges on I-26 Only) 14 Number None Slight Moderate Extensive Complete 0 MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.25: Scenario No. 9 Damage on I-26 by Bridge Type M w 7.0 M w 7.0 (Bridges on US 17 Only) Number None Slight Moderate Extensive Complete MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other Bridge Type Figure B.26: Scenario No. 9 Damage on US 17 by Bridge Type M w

111 Table B.9: Scenario No. 1 Functionality Results 2% in 50 (SC) Capacity State Days % % % % PE in 50 years Number of Bridges % 50% 100% Number of Days Figure B.27: Scenario No. 1 Functionality Distribution 2% in 50 (SC) 97

112 2% in 50 Years 100% 90% Weighted Bridge Functionality 80% 70% 60% 50% 40% 30% 20% 10% 0% Number of Days Figure B.28: Scenario No. 1 Weighted Bridge Functionality 2% in 50 (SC) Table B.10: Scenario No. 2 Functionality Results 5% in 50 (SC) Capacity State Days % % %

113 5% PE in 50 Years Number of Bridges % 50% 100% Number of Days Figure B.29: Scenario No. 2 Functionality Distribution 5% in 50 (SC) 5% PE in 50 Years 100% 90% Weighted Bridge Functionality 80% 70% 60% 50% 40% 30% 20% 10% 0% Number of Days Figure B.30: Scenario No. 2 Weighted Bridge Functionality 5% in 50 (SC) 99

114 Table B.11: Scenario No. 3 Functionality Results 10% in 50 (SC) Capacity State Days % % % % PE in 50 Years Number of Bridges % 50% 100% Number of Days Figure B.31: Scenario No. 3 Functionality Distribution 10% in 50 (SC) 100

115 10% PE in 50 Years 100% Weighted Bridge Functionality 99% 98% 97% 96% 95% 94% 93% Number of Days Figure B.32: Scenario No. 3 Weighted Bridge Functionality 10% in 50 (SC) Table B.12: Scenario No. 7 Functionality Results M w 4.0 Capacity State Days % % %

116 M w Number of Bridges % 50% 100% Number of Days Figure B.33: Scenario No. 7 Functionality Distribution M w 4.0 M w % Weighted Bridge Functionality 98% 96% 94% 92% 90% 88% 86% Number of Days Figure B.34: Scenario No. 7 Weighted Bridge Functionality M w

117 Table B.13: Scenario No. 8 Functionality Results M w 5.5 Capacity State Days % % % M w Number of Bridges % 50% 100% Number of Days Figure B.35: Scenario No. 8 Functionality Distribution M w

118 M w % 90% Weighted Bridge Functionality 80% 70% 60% 50% 40% 30% 20% 10% 0% Number of Days Figure B.36: Scenario No. 8 Weighted Bridge Functionality M w 5.5 Table B.14: Scenario No. 9 Functionality Results M w 7.0 Capacity State Days % % %

119 M w 7.0 Number of Bridges % 50% 100% Number of Days Figure B.37: Scenario No. 9 Functionality Distribution M w 7.0 M w 7.0 Weighted Bridge Functionality 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Number of Days Figure B.38: Scenario No. 9 Weighted Bridge Functionality M w

120 Table B.15: Scenario No. 2 Direct Losses 5% in 50 (SC) Type Direct Loss MSC Concrete $21,000 MSC Steel $20,000,000 MSC Slab $400,000 MSC Conc Box $97,000 MSSS Concrete $1,900,000 MSSS Steel $2,100,000 MSSS Slab $1,100,000 MSSS Conc Box $41,000 SS Concrete $200,000 SS Steel $21,000 Other $759,000 TOTAL $26,639,000 Table B.16: Scenario No. 3 Direct Losses 10% in 50 (SC) Type Direct Loss MSC Concrete $3,900 MSC Steel $1,900,000 MSC Slab $110,000 MSC Conc Box $25,000 MSSS Concrete $420,000 MSSS Steel $140,000 MSSS Slab $310,000 MSSS Conc Box $11,000 SS Concrete $45,000 SS Steel $450 Other $34,000 TOTAL $2,999,

121 Table B.17: Scenario No. 7 Direct Losses M w 4.0 Type Direct Loss MSC Concrete $1,100 MSC Steel $4,400,000 MSC Slab $230,000 MSC Conc Box $35,000 MSSS Concrete $560,000 MSSS Steel $550,000 MSSS Slab $380,000 MSSS Conc Box $8,700 SS Concrete $120,000 SS Steel $3,100 Other $57,000 TOTAL $6,344,900 Table B.18: Scenario No. 8 Direct Losses M w 7.0 Type Direct Loss MSC Concrete $40,000 MSC Steel $49,000,000 MSC Slab $1,400,000 MSC Conc Box $410,000 MSSS Concrete $6,200,000 MSSS Steel $14,000,000 MSSS Slab $2,600,000 MSSS Conc Box $120,000 SS Concrete $940,000 SS Steel $370,000 Other $8,000,000 TOTAL $83,080,

122 APPENDIX C USGS SCENARIO NOS. 4-6 RESULTS Type Table C.1: Scenario No. 4 Damage State Distribution 2% in 50 (USGS) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

123 Damage States None Slight Moderate Extensive Complete Figure C.1: Scenario No. 4 Probable Damage State Visual 2% in 50 (USGS) Type Table C.2: Scenario No. 5 Damage State Distribution 5% in 50 (USGS) Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

124 Damage States None Slight Moderate Extensive Complete Figure C.2: Scenario No. 5 Probable Damage State Visual 5% in 50 (USGS) Table C.3: Scenario No. 6 Damage State Distribution 10% in 50 (USGS) Type Damage State None Slight Moderate Extensive Complete TOTAL MSC Concrete MSC Steel MSC Slab MSC Conc Box MSSS Concrete MSSS Steel MSSS Slab MSSS Conc Box SS Concrete SS Steel Other TOTAL

125 Damage States None Slight Moderate Extensive Complete Figure C.3: Scenario No. 6 Probable Damage State Visual 10% in 50 (USGS) 2% PE in 50 Years Number of Bridges None Slight Moderate Extensive Complete Damage State Other SS Steel SS Conc MSSS Conc Box MSSS Slab MSSS Steel MSSS Conc MSC Conc Box MSC Slab MSC Steel MSC Conc Figure C.4: Scenario No. 4 Damage State Distribution 2% in 50 (USGS) 111