Earthquake damage and casualties due to large earthquakes impacting Wellington Region

Earthquake damage and casualties due to large earthquakes impacting Wellington Region W. J. Cousins GNS Science Report 2013/41 July 2013

BIBLIOGRAPHIC REFERENCE Cousins, W. J. 2013. Earthquake damage and casualties due to large earthquakes impacting Wellington Region, GNS Science Report 2013/41. 12 p. W. J. Cousins, GNS Science, PO Box 30368, Lower Hutt 5040, New Zealand Institute of Geological and Nuclear Sciences Limited, 2013 ISSN 1177-2425 ISBN 978-1-972192-87-0

CONTENTS ABSTRACT... II KEYWORDS... II 1.0 INTRODUCTION... 1 2.0 COMPUTATION OVERVIEW... 2 3.0 RESULTS AND DISCUSSION... 3 4.0 PRECISION OF THE MODELLING... 5 5.0 ACNOWLEDGEMENTS... 6 6.0 REFERENCES... 6 TABLES Table 1.1 Earthquake scenarios used in the modelling.... 1 Table 3.1 Estimated costs for earthquake damage, and additional costs due to subsequent tsunami damage.... 3 Table 3.2 Numbers of collapsed buildings caused (a) by earthquake shaking, and (b) subsequent tsunami inundation.... 3 Table 3.3 Estimated deaths from earthquake and tsunami inundation.... 4 Table 3.4 Estimated injuries from earthquake and tsunami inundation.... 4 APPENDICES APPENDIX 1: STATE-BASED MODELLING OF DAMAGE AND INJURY FROM EARTHQUAKES... 9 APPENDIX FIGURES Figure A 1.1 Example of the relationship between shaking intensity and the probability of being in one or other of the defined damage states. The lines are the upper boundaries to the damage states, with the red line being the upper boundary of the DS5 state, and so on.... 11 APPENDIX TABLES Table A 1.1 Damage state definitions for buildings, and indicative consequences.... 10 Table A 1.2 Adopted correspondence between loss ratio and damage state.... 11 Table A 1.3 Casualty state definitions... 12 Table A 1.4 Examples of the relationship between building damage state and casualty state for two types of building, URM (unreinforced masonry) and Timber. As an example of the relationship, if a URM building is in DS3, then there is 95.76% probability of an occupant being in Casualty State 1 (CS1), 4% probability for CS2, 0.24% probability for CS3, and 0% probability for CS4 and CS5.... 12 GNS Science Report 2013/41 i

ABSTRACT Damage and casualties have been estimated for seven large earthquake scenarios located in the Wellington Region. Four of the earthquakes, involving rupture of the Wellington, Wairarapa and Ohariu Faults, and the Hikurangi Subduction Zone, are considered to be the most costly and deadly earthquakes likely impact the Wellington Region. In the modelling they generated shaking losses ranging from $9 billion to $16 billion, death numbers ranging from 400 to 1600 for a daytime event, 140 to 470 for night-time, and injury numbers from 4000 to 9000 daytime, 3000 to 7000 night-time. The remaining three scenarios involved the Wairau and BooBoo Faults, and the segment of the Wellington Fault that runs through the Tararua Range starting about 40 km north of Wellington. They resulted in much lower losses and casualties, ranging from $1 billion to $1.6 billion for losses, 10 to 60 daytime deaths, 1 to 14 night-time deaths, 300 to 400 daytime injuries, and 100 to 300 night-time injuries. Additional losses and casualties due to subsequent tsunami were also estimated for four of the scenarios. The necessary tsunami data were not available for the Ohariu and Wairau Fault cases, and the Tararua segment of the Wellington Fault was not considered for tsunami because it is entirely on-land. The tsunami made relatively modest contributions to the overall losses, but large contributions to the numbers of deaths. For the scenario involving rupture of Hikurangi Subduction Zone, the numbers of tsunami deaths greatly exceeded those caused by shaking damage (3200 vs. 370 for a daytime event, and 2500 vs. 140 for a night-time event). KEYWORDS Earthquake, fault rupture, tsunami, casualties, deaths, injuries, damage costs GNS Science Report 2013/41 ii

1.0 INTRODUCTION The purpose of this report is to present preliminary results from current work on estimating damage and casualties due to large earthquakes impacting the Wellington Region of New Zealand. It is intended to focus on the results, with only very brief comment on the earthquakes, the buildings and people models, and the methodology. Seven earthquake scenarios have been modelled (Table 1.1). Four were located within about 25km of Wellington City (Wellington Fault, Wairarapa 1855, Ohariu South, Subduction Zone), and three more than 25 km away (Tararua, Wairau, BooBoo). For further information on the earthquake sources see Stirling et al. (2012) [16]. With the exception of the Tararua scenario, all of the earthquake ruptures extended offshore, and so were expected to be accompanied by tsunami. Inundation data were available for four of the scenarios (Wellington Fault, Wairarapa 1855, Subduction Zone and BooBoo) so that combined shaking and tsunami impacts could be estimated for those four events. Table 1.1 Earthquake scenarios used in the modelling. Source Name Magnitude Earthquake Name Wellington Fault (Wellington-Hutt Valley segment) 7.5 Wellington Fault Wairarapa Fault (last ruptured in 1855) 8.2 Wairarapa 1855 Hikurangi Subduction Zone (rupture into Cook Strait) c. 8.9 Subduction Zone Ohariu Fault (southern section) 7.4 Ohariu South Wellington Fault (Tararua segment) 7.3 Tararua Wairau Fault 7.8 Wairau BooBoo Fault 7.6 BooBoo Detailed assets models had previously been developed for most of Wellington Region area as part of RiskScape, a risk modelling package being developed jointly by GNS and NIWA (Institute of Geological and Nuclear Sciences, www.gns.cri.nz, and National Institute of Water and Atmospheric Research, www.niwa.co.nz) (King & Bell [11] ). Attributes attached to each building in the models included the location, replacement value, structural type, age (i.e. era of building code) and quality (i.e. with or without structural deficiencies), and the numbers of occupants for night-time and work-daytime scenarios. The final model contained 194,000 buildings, occupied by 460,000 people. It was on a building-by-building basis for most of the region, the exception being residential buildings in the Wairarapa which were in the model but aggregated to Area Unit scale. Ground hazard ratings for shaking amplification, liquefaction and landsliding were also attached to the building locations. The hazard ratings were taken from GNS s in-house databases. They consisted of five-point ratings for each of the three phenomena, A to E for amplification (paralleling the A (strong rock) to E (very soft soil) ground classifications defined in the New Zealand Loadings Standard [15], and 1 (zero hazard) to 5 (very high hazard) for each of liquefaction and landsliding [2,13]. GNS Science Report 2013/41 1

2.0 COMPUTATION OVERVIEW For earthquakes, the computation procedure was as follows: Select an earthquake model (magnitude, location, mechanism of rupture). Estimate the MM (Modified Mercalli) intensity at each asset location using the Dowrick and Rhoades attenuation model for New Zealand [9]. Adjust the intensity to allow for ground hazards [3,11]. Estimate the repair cost using a damage ratio method [3]. Estimate the damage state for each building using a damage state vs. damage ratio relationship developed by Spence et al. [4,5,14]. See Appendix 1 for brief further information. Estimate the injury state for each person using damage state to injury state models developed by Spence et al. [4,5,14]. See Appendix 1 for brief further information. Post-earthquake tsunami impacts have previously been modelled [6] for four of the above earthquake scenarios, viz. Wellington Fault, Wairarapa 1855, Subduction Zone and BooBoo. Of the three remaining scenarios, the Wellington Fault (Tararua segment) is entirely on land and so will not generate a tsunami. Both the Ohariu Fault (southern section) and the Wairau Fault extend offshore into Cook Strait, by about 25 km for the Ohariu Fault and 40 km for the Wairau Fault. For comparison about 30 km of the Wellington Fault (Hutt-Valley segment) is offshore, about 10 km in Wellington Harbour and 20km in Cook Strait, and about 70 km of the Wairarapa Fault is offshore in Palliser Bay and Cook Strait. For tsunami, the computation procedure relied on sets of inundation depths that were generated in the earlier work [6]. Given those data, the steps were: Select the set of inundation depths, if any, for the earthquake model. Estimate the additional repair cost using a damage ratio method [1,6]. Estimate the additional numbers of casualties using casualty rates developed from historical data [1]. The entire process was repeated twenty times for each scenario, and median results were generated for presentation. GNS Science Report 2013/41 2

3.0 RESULTS AND DISCUSSION Total loss and casualty estimates are presented, by scenario, in Tables 3.1-3.4. In terms of damage cost, the Wellington Fault event is clearly the worst of the scenarios, at a little over $16 billion for the combined earthquake and tsunami losses. Three other scenarios also result in multi-billion dollar losses, Subduction Zone ($13 billion), Wairarapa 1855 ($11 billion) and Ohariu South ($9 billion). The remaining three scenarios cause much smaller losses in the range $1 to 1.5 billion. For most scenarios, the tsunami losses are much smaller than the preceding shaking losses. Only the Subduction Zone event generates a large tsunami loss (at nearly $2 billion). Table 3.1 Estimated costs for earthquake damage, and additional costs due to subsequent tsunami damage. Earthquake Scenario Building Damage Cost ($m) Earthquake Tsunami Total Wellington Fault 16,420 20 16,440 Wairarapa 1855 11,370 40 11,410 Subduction Zone 11,080 1,880 12,960 Ohariu South 9,070 > 0 > 9,070 Tararua 1,620 No tsunami 1,620 Wairau 1,510 > 0 > 1,510 BooBoo 1,094 1 1,095 Collapsed buildings are important because they are the cause of most earthquake deaths. From this point of view the Subduction Zone event is clearly the worst, resulting in more than twice as many collapses as the next worst event considered in the modelling, the Wellington Fault event, which in turn produces more than twice as many collapses as the Wairarapa 1855 event. The Tararua, Wairau and BooBoo cause relatively few collapses. The tsunami component of the Subduction Zone event is clearly dominant. Table 3.2 inundation. Numbers of collapsed buildings caused (a) by earthquake shaking, and (b) subsequent tsunami Earthquake Scenario Collapsed Buildings Earthquake Tsunami Total Wellington Fault 2,972 3 2,975 Wairarapa 1855 1,083 224 1,307 Subduction Zone 617 6,176 6,793 Ohariu South 713 > 0 > 713 Tararua 11 No tsunami 11 Wairau 9 > 0 > 9 BooBoo 7 0 7 GNS Science Report 2013/41 3

Death numbers (Table 3.3) broadly follow the trends observed with the collapse results. An exception is that the night-time death rates are consistently lower than the daytime rates because (a) at night people are mostly in timber houses, which are much less deadly than many of the types of commercial and industrial buildings that are occupied only during the day, and (b) much of the housing in Wellington Region is in hill suburbs and not exposed to tsunami inundation. Injury numbers (Table 3.4) tend to be dominated by the earthquake component. This occurs because many earthquake injuries occur in heavily damaged buildings, and earthquake shaking results in many more heavily damaged buildings than the total of heavily damaged and collapsed buildings caused by the tsunami inundation. Table 3.3 Estimated deaths from earthquake and tsunami inundation. Earthquake Scenario Deaths Daytime Deaths - Night-time Earthquake Tsunami Total Earthquake Tsunami Total Wellington Fault 1,635 253 1,888 474 81 555 Wairarapa 1855 364 150 514 128 197 325 Subduction Zone 351 3,199 3,550 136 2,455 2,591 Ohariu South 481 > 0 > 481 144 > 0 > 144 Tararua 9 No tsunami 9 1 No tsunami 1 Wairau 10 > 0 > 10 1 > 0 > 5 BooBoo 7 42 49 1 11 12 Table 3.4 Estimated injuries from earthquake and tsunami inundation. Earthquake Scenario Injuries - Daytime Injuries - Night-time Earthquake Tsunami Total Earthquake Tsunami Total Wellington Fault 9,001 256 9,257 6,987 76 7,063 Wairarapa 1855 4,486 132 4,618 4,048 131 4,179 Subduction Zone 4,007 2,881 6,888 4,001 1,978 5979 Ohariu South 4,145 > 0 > 4,145 3,058 > 0 > 3,058 Tararua 328 No tsunami 328 257 No tsunami 257 Wairau 368 > 0 > 368 193 > 0 > 193 BooBoo 278 41 319 117 10 127 One important point to note is that the above loss and casualty estimates are for the Wellington Region only, and that there will be additional losses outside of the Region. A second point is that the tsunami casualties are for the worst-case situation of no evacuation (and assumes that people do not go down to the beach to watch the tsunami arrive!). The above results are very brief regional-scale summaries of work that is at an early stage of development. On-going work will involve (a) breakdown to territorial authority level, (b) separation of injuries into critical, serious and moderate, (c) inclusion of other consequences, for example, numbers of short-term and long-term evacuees, and (d) discussion of consequences. GNS Science Report 2013/41 4

4.0 PRECISION OF THE MODELLING Many of the numbers in the tables are presented to 3-4 significant figure accuracy because of the need, in several places, to add a small number to a much larger number to get a total. For example, reasonable rounding of the numbers in the first row of Table 3.2 would give the sum 3000 + 3 = 3000, which is not very helpful. Parameters involved in loss modelling are often lognormal in character. Some well documented examples are building damage ratios from New Zealand earthquakes, which have been demonstrated to follow lognormal distributions with variance factors of approximately 3 about the median estimates [7,8,10]. In simple terms, if the median value of a parameter is 9, the 64 th percentile range is 3 to 27 (i.e. 9 / 3 to 9 x 3). All of the numbers in the tables are median estimates from multiple runs of the model. The natural variability in the estimates are likely to be at least as great as those observed for damage ratios. GNS Science Report 2013/41 5

5.0 ACNOWLEDGEMENTS Helpful reviews by Russ Van Dissen and Rob Buxton are appreciated. The project was supported by public research funding from the Government of New Zealand, and the Earthquake Commission and other local and government bodies through the It s Our Fault project. 6.0 REFERENCES 1. Beban, J.G., Cousins, W.J., Prasetya, G. & Becker, J. (2011). Modelling of the tsunami risk to Papamoa, Wairakei and Te Tumu and implications for the SmartGrowth strategy. GNS Science Consultancy Report CR2011/294. 133p. 2. Boon D., Perrin, N.D., Dellow, G.D., Van Dissen, R. & Lukovic, B. (2011). NZS 1170.5:2004 site subsoil classification of Lower Hutt. Proceedings, 9th Pacific Conference on Earthquake Engineering, Auckland, New Zealand, 14-16 April, 2011: Paper 013, 8 p. 3. Cousins, W.J. (2004). Towards a first-order earthquake loss model for New Zealand. Proceedings, 2004 Conference of the New Zealand Society for Earthquake Engineering, 19-21 March 2004, Rotorua. New Zealand Society for Earthquake Engineering. Paper No. 29. 4. Cousins, W.J., Spence, R. and So, E.K.M. (2006). Wellington area earthquake casualty estimation 2006 update. Client Report 2006/88. Institute of Geological & Nuclear Sciences, Lower Hutt. 5. Cousins, W.J., Spence, R. & So, E. (2008). Estimated Casualties in New Zealand Earthquakes. Proceedings, Australian Earthquake Engineering Conference AEES 2008. Ballarat, Victoria, 21-23 November 2008. AEES. 6. Cousins, W.J., Power, W.L., Destegul, U.Z., King, A.B., Trevethick, R., Blong, R., Weir, B. & Miliauskas, B. (2009). Earthquake and tsunami losses from major earthquakes affecting the Wellington Region. Proceedings, Conference of the New Zealand Society for Earthquake Engineering, April 3-5, 2009, Christchurch. Paper No. 24. 7. Dowrick, D.J. (1991). Damage costs to houses and farms as a function of intensity in the 1987 Edgecumbe earthquake. Earthquake Engineering and Structural Dynamics, 20:455-469. 8. Dowrick, D.J. and Rhoades, D.A. (1993). Damage costs for commercial and industrial property as a function of intensity in the 1987 Edgecumbe earthquake. Earthquake Engineering and Structural Dynamics, 22: 869-884. 9. Dowrick D.J. and Rhoades, D.A. (2005). Revised Models for Attenuation of Modified Mercalli intensity in New Zealand earthquakes. Bulletin of the New Zealand Society for Earthquake Engineering, 38(4), 185-214. 10. Dowrick, D.J., Rhoades, D.A. and Davenport, P.N. (2001). Damage ratios for domestic property in the 1968 Inangahua, New Zealand, earthquake. Bulletin of the New Zealand National Society for Earthquake Engineering, 34(3): 191-213. 11. King, A. and Bell, R. (2009). RiskScape Project: 2004-2008. GNS Science Consultancy Report 2009/247. 162p. 12. Rojahn, C. and Sharpe, R.L. (1985). Earthquake damage evaluation data for California. Report No. ATC-13, Applied Technology Council, California. GNS Science Report 2013/41 6

13. Semmens, S., Perrin, N.D., Dellow, G.D., Van Dissen, R. (2011). NZS 1170.5:2004 site subsoil classification of Wellington City. Proceedings, 9th Pacific Conference on Earthquake Engineering, Auckland, New Zealand, 14-16 April, 2011: Paper 007, 8 p. 14. Spence, R.J.S., Pomonis, A., Dowrick, D.J. & Cousins, W.J. (1998). Estimating human casualties in earthquakes: the case of Wellington. Proceedings, Sixth SECED (Society for Earthquake and Civil Engineering Dynamics) Conference, Seismic Design Practice into the Next Century. 26-27 March 1998, Oxford, UK: pages 277-286. 15. Standards New Zealand. (2004). Structural Design Actions Part 5 Earthquake Actions New Zealand. New Zealand Standard NZS1170.5:2004, 76p. 16. Stirling, M.W.; McVerry, G.H.; Gerstenberger, M.C.; Litchfield, N.J.; Van Dissen, R.J.; Berryman, K.R.; Barnes, P.; Beavan, R.J.; Bradley, B.; Clark, K.J.; Jacobs, K.; Lamarche, G.; Langridge, R.M.; Nicol, A.; Nodder, S.; Pettinga, J.; Reyners, M.E.; Rhoades, D.A.; Smith, W.D.; Villamor, P.; Wallace, L.M. (2012). National seismic hazard model for New Zealand: 2010 update. Bulletin of the Seismological Society of America, 102: 1514-1542. GNS Science Report 2013/41 7

APPENDICES

APPENDIX 1: STATE-BASED MODELLING OF DAMAGE AND INJURY FROM EARTHQUAKES The main steps in the procedure for estimating damage and casualties due to earthquake shaking were listed in Section 2 above. Appendix 1 provides further information on the final two steps in the sequence, viz.: Estimate the damage state for each building using a damage state vs. damage ratio relationship. Estimate the injury state for each person using a damage state to injury state model. Damage state is a rather nebulous concept, in that there seem to be almost as many shades of definition as there are risk modellers. Often the states are defined in terms of damage, with particular reliance on imprecise terms like non-structural damage, structural damage, severe damage, complete damage, partial collapse and collapse; or in terms of consequences, with usability, habitability and likelihood of casualties being the main ones considered. A key weakness is that the definitions of the states are fuzzy, which means that there are neither precise links between the various measures of shaking strength and the resultant damage state boundaries, nor precise links between the damage state boundaries and the consequences, be they financial loss, casualties or loss of functionality. Equally important is the lack of guarantee that a set of damage states derived for one hazard descriptor (e.g. Modified Mercalli Intensity, MMI), will match the set of damage states derived for another (e.g. spectral acceleration). Hence, to be reliable, a system has to be calibrated as a whole, from hazard descriptor to consequences. The damage state method, used in RiskScape [11] since 2009, has been developed from combination of (a) GNS models linking MMI to damage ratio, and (b) CARL (Cambridge Architectural Research Ltd.) models linking damage ratio to damage state and then to casualty state. The method has been calibrated as a whole by CARL using worldwide data on earthquake casualties. In 2006 GNS provided (CARL) with a set of damage ratio vs. MMI functions covering most of the building types found in New Zealand. CARL used the functions to generate, for each building type, the probabilities of being in each of damage states 1 to 5 as functions of MMI, [4,5,14]. Table A 1.1 defines the damage states, and provides qualitative expectations for levels of damage and numbers of injuries and deaths. GNS Science Report 2013/41 9

Table A 1.1 Damage state definitions for buildings, and indicative consequences. Damage State Nonstructural Damage Structural Damage Injuries Deaths Description DS0: None No No No No No damage DS1: Light DS2: Moderate DS3: Severe DS4: Partial Collapse DS5: Collapse Yes No Rare No Yes Yes Few No Yes Yes Some Rare Yes Yes Many Some Yes Yes Many Many Damage to claddings, small cracks in concrete and masonry infill walls, cracks in interior walls and ceilings Cracks in columns and beams of frames and in structural walls. Cracks in partition and infill walls, fall of brittle cladding and plaster, fall of mortar from joints of wall panels Cracks in columns and beam-column joints of frames. Spalling of concrete cover, buckling of reinforcing rods. Large cracks in partition and infill walls, failure of individual infill panels. Large cracks in structural elements with compression failure of concrete and fracture of reinforcing bars. Bond failure of beam reinforcing bars, tilting of columns or buildings. Collapse of a few columns or of a single upper floor. Volume Loss < 50%. Collapse of ground floor or parts (e.g. wings) of buildings. Volume loss of 50% or more The damage states were therefore derived indirectly. For each of the building classes, the GNS formula [3] MDR = A 10 B MMI - C was used to determine the mean damage ratio MDR at intensity level MMI, where constants A, B and C were defined for each of 43 building classes. Data underpinning the formula were derived from New Zealand and Californian earthquakes [7,8,10,12]. Using an analysis of its worldwide damage data, CARL developed the following formula for estimating the probability of the loss exceeding any given loss ratio, given the MDR, Φ -1 (R) = a Φ -1 (MDR) + b Φ -1 (LR) where Φ -1 refers to the inverse of the standard cumulative Gaussian distribution, R is the proportion of the sample with a loss ratio exceeding LR, and a and b are constants. In this formula, constants a and b depend on the building class. The loss ratio is closely related to damage state, and so the same formula can be used to estimate probability of exceedance of each damage state, if we define the corresponding loss ratios. CARL used the definitions listed in Table A 1.2. The assumed loss ratio ranges took account of observed collapse rates in New Zealand earthquakes, and worldwide damage data. GNS Science Report 2013/41 10

Table A 1.2 Adopted correspondence between loss ratio and damage state. Loss ratio Damage state Description 1 to 10% DS1 Light 10 to 35% DS2 Moderate 35 to 75% DS3 Severe 75 to 90% DS4 Partial collapse > 90% DS5 Collapse Figure A 1.1 is an example of the derived relationship between MMI and the probabilities of a building being in the defined damage states. At this point in the modelling a random number was generated, in the range 0 to 1, and used in combination with the estimated MMI to place the building into one or other of the damage states. As an illustration of the method, if the MMI is 10.0 and the random number is <= 0.03, then the building is assigned to DS5, if the number is >0.03 but <= 0.095 the building is assigned to DS4, if the number is >0.095 but <= 0.3 the building is assigned to DS3, and so on. Probability 1 0.9 DS1 (Light) 0.8 DS2 (Moderate) 0.7 DS3 (Severe) 0.6 DS4 (Partial Collapse) 0.5 DS5 (Collapse) 0.4 0.3 0.2 0.1 0 6.0 7.0 8.0 9.0 10.0 11.0 MM Intensity Figure A 1.1 Example of the relationship between shaking intensity and the probability of being in one or other of the defined damage states. The lines are the upper boundaries to the damage states, with the red line being the upper boundary of the DS5 state, and so on. Once the damage state of a building has been determined, a model based on worldwide data is used to assign each occupant of the building to a casualty state. The casualty states are as defined in Table A 1.3. The model linking damage and casualty states comprises a set of matrices of factors Mij, where the probability Pi of a person in a building in damage state j ending up in injury state i was given by Pi = Mij. There was a matrix of Mij factors for each of the main structural types [4,5,14]. Table A 1.4 provides two examples of the Mij matrices. The Mij numbers are probabilities, and so in the casualty computation a random number is used to assign a person to one or other of the casualty states, in much the same way as buildings were assigned to damage states. GNS Science Report 2013/41 11

Table A 1.3 Casualty state definitions. Casualty State Description of State CS0: None CS1: Light CS2: Moderate Injury CS3: Serious Injury CS4: Critical Injury CS5: Death Uninjured [First-Aid] Injuries that can be self-treated or treated by a first aider. Examples are bruising/contusion, minor cuts, sprains. [Doctor] Injuries that require expert treatment (paraprofessional or doctor), but which are not immediately life threatening if such treatment is not available. Examples are cuts requiring stitches, serious sprains, dislocations, significant burns (first degree, or second degree over small part of body), minor concussion (unconscious < 1 hr). [Hospital] Injuries requiring a greater degree of medical care and use of medical technology such as x-rays or surgery, but not expected to progress to a life threatening status, full recovery expected with suitable treatment. Examples are: open head or face wounds, concussion (unconscious > 1 hr), fractures (open, displaced or comminuted), dehydration or exposure, serious burns (third degree over small part of body, or second degree over large part of body). [Intensive Care] Injuries that pose an immediate life threatening condition if not treated adequately and expeditiously, or long-term disability. Examples are brain damage, spinal column injuries, nerve injuries, crush syndrome, internal organ failures due to crushing, organ puncture, other internal injuries, uncontrolled bleeding, traumatic amputations of arms or legs. [Undertaker] Well understood state (reincarnation excluded). Table A 1.4 Examples of the relationship between building damage state and casualty state for two types of building, URM (unreinforced masonry) and Timber. As an example of the relationship, if a URM building is in DS3, then there is 95.76% probability of an occupant being in Casualty State 1 (CS1), 4% probability for CS2, 0.24% probability for CS3, and 0% probability for CS4 and CS5. Casualty State Building Type: URM DS1 DS2 DS3 DS4 DS5 CS1: Uninjured, or Light Injury 1 1 0.9576 0.8883 0.736 CS2: Moderate Injury 0 0 0.04 0.07 0.12 CS3: Serous Injury 0 0 0.0024 0.035 0.08 CS4: Critical Injury 0 0 0 0.0007 0.004 CS5: Dead 0 0 0 0.006 0.06 Casualty State Building Type: Timber DS1 DS2 DS3 DS4 DS5 CS1: Uninjured, or Light Injury 1 1 0.9576 0.894174 0.85725 CS2: Moderate Injury 0 0 0.04 0.1038 0.12 CS3: Severe Injury 0 0 0.0024 0.0013 0.015 CS4: Critical Injury 0 0 0 0.000026 0.00075 CS5: Dead 0 0 0 0.0007 0.007 GNS Science Report 2013/41 12

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