Multi-Hazard Damage and Loss Estimates to Buildings and Lifelines. M. Capozzo, A. Rizzi, A. Barbosa, G.P. Cimellaro

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1 Multi-Hazard Damage and Loss Estimates to Buildings and Lifelines M. Capozzo, A. Rizzi, A. Barbosa, G.P. Cimellaro 1

2 Introduction In this research we examine and compare the vulnerabilities of a community by assessing building and highway bridges. EARTHQUAKE AT CSZ DAMAGE LOSS RESILIENCE CSZ Juan de Fuca Plate Pacific Plate Seaside, OR. Earthquake- induced ground shaking, ground failure, and tsunami inundation fragility functions are combined with regional hazard data to estimate the damage and direct economic loss of building and bridge. 2

3 Seaside Overview INVENTORY Unites State Census Population, Census, April 1, 2010 Age and sex Race and hispanic origin Visitors Bureau of LaborStatistics Occupancy Business Distribution Bridges Public buildings Urban distric 3

4 Earthquake Hazards 7-12% chance of magnitude 9.0 earthquake along Cascadia Subduction Zone in the next 50 years At Seaside PGA ~ 0.35g At Seaside PGD Liquefaction = 158 in 4

5 Damage state The damage states have been estimated by previous researches. 5

6 Direct Social Losses Shelter The direct social losses provided two estimates: The number of displaced household The number of people requiring only short-term shelter Where α ijkl is a costantdefined by the following relationship: The value of the α ijkl costantcan be calculated using a combination of shelter category «weights». 6

7 Direct Social Losses Casualties This module provides a methodology for estimating casualties caused only by building and bridge damage. Scenario time definition: 2:00 am (night time scenario) 2:00 pm (day time scenario) 5:00 pm (commute time scenario) Population distribution data: Residential population Commercial population Educational population Industrial population Commuting population Hotel population 7

8 Direct Social Losses Casualties Damage state probabilities The casualty model uses damage states computed by the direct physical damage module. The fragility curves describe the probability of reaching or exceeding each damage state given the level of ground motion. COMB The damage state necessary to the probability calculation is thus obtained: si. [ ] = [ ] = [ ] + [ ] [ ] [ ] P DS C P DS C GS DS C GF P DS C GS P DS C GF P DS C GS P DS C GF COMB 8

9 Direct Social Losses Casualties To come up with the calculation of the Pkilled I have followed this formula which is represented in this chart. P killed = P A * P E + P B * P F + P C * P G + P D * (P H * P J + P I * P K ) EN occupants killed = N occupants * P killed 9

10 Results The number of displaced households (due to loss of habitability) obtained is 910, while the number of people requiring only short-term shelter is

11 Results The map shows how the percentage of the number of killed for each building of Seaside changes. 11

12 Tsunami Hazards Major tsunami hazards provided from numerical analysis: Inundation area Max fluw depth Max. velocity Max. momentum flux Arrival time and duration of inundation Previous studies from DOGAMI in Oregon 1. DOGAMI report (Pilot study, 2006) at Seaside. 2. Open-file report series O-13-6, 2011 (Update for different sizes of future tsunami in CSZ based on paleoseismic data, Goldfinger, et al., It provides inundation map, max flow depth and max velocity for major coast cities.) 12

13 Moderate Damage from Tsuami Scenario 33 ( Mw. 9, m3, N3 ) applied as a tsunami scenario in this study. Multi-Hazard Damage and Loss Estimation: Application to Seaside, OR Dr. Hyoungsu Park 13

14 Extensive Damage from Tsuami Scenario 33 ( Mw. 9, m3, N3 ) applied as a tsunami scenario in this study. Multi-Hazard Damage and Loss Estimation: Application to Seaside, OR Dr. Hyoungsu Park 14

15 Complete Damage from Tsuami Scenario 33 ( Mw. 9, m3, N3 ) applied as a tsunami scenario in this study. Multi-Hazard Damage and Loss Estimation: Application to Seaside, OR Dr. Hyoungsu Park 15

16 Direct Social Loss: Casualty Estimation Input from Hazard Assessment Inventory and Exposure Warning time after earthquake Ambient condition of evacuation Casualty Estimation Output for Casualty Estimation The number of casualies consists of: the numberof fatalities the numberof injuries 16

17 Direct Social Loss: Casualty Estimation Distribution for people s response to the tsunami warning Final distribution including the effect of different walking speeds during evacuation Fig 1. Probability density function of the population ratio from the time of the tsunami warning 17

18 Direct Social Loss: Shelter Estimation Spatial distribution of: People Houseolds Income groups Age groups Hazard Assesment: Inundation areas Isolated areas Building Damage Assessment Shelter Estimation PEOPLE REQUIRING SHORT-TERM SHELTER The number of persons using publicly provided shelters: where α km is a costant defined as: 18

19 Results Good Ambient Conditions Fair Ambient Conditions Poor Ambient Conditions Number of Casualties Number of Fatalities Number of Injuries Tab. 1 Total number of casualty due to different ambient conditions for evacuation 19

20 Results Fig. 2 Fatalities due to good ambient conditions for evacuation 20

21 Results Fig. 3 Fatalities due to fair ambient conditions for evacuation 21

22 Results Total Number of Displaced Household due to damage buildings: DH da = 1861 households (30% of family) Total Number of Displaced Household living in non-damage residential units located in the area where the flood depth is greater than 1 foot: Dh in = 2432 households (38% of family) Total Number of Persons using short-term publicly provided shelters: STP = 975 individuals (20% of population) 22

23 Conclusion The damage and loss assessment was successfully implemented for earthquake and tsunami. Next steps: To estimate the indirect economic losses for building and bridges To evaluate the disaster resilience with the Cimellaro s framework. To combine the effect due to earthquake and tsunami. 23

24 Acknowledgements We would like to thank Dr. Hyoungsu Park, Mr. Shafiq Alam, Dr. Dan Cox., and Dr. Haizhong Wang for providing crucial information needed to developed our work and our university for the chance to carry out our final project at Oregon State University. Funding from the NIST-Resilience centeris acknowledged. 24