Multi-Hazard Loss Estimations Using Socioeconomic Data

Tuesday Oct 29APRU – 2013

Multi-Hazard Loss Estimations Using Socioeconomic Data

J.R. Holliday* J.B. Rundle

University of California, Davis

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MotivationSeismic hazard “understood”

Seismic risk analysis possible– HAZUS-MH (FEMA)

– ST-RISK (Risk Engineering, Inc)

But not for the general population

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Motivation

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Earthquake Forecasts

• USA: USGS

– Uniform California Earthquake Rupture Forecast (UCERF)

– “24-Hour Aftershock Forecast Map”

– 2009 Earthquake Probability Mapping

– http://www.openhazards.com/

• World: CSEP

– Regional Earthquake Likelihood Models (RELM)

– Beyond RELM


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• USA: USGS





• World: CSEP


– Beyond RELM


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• USA: USGS





• World: CSEP


– Beyond RELM


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• USA: USGS





• World: CSEP


– Beyond RELM


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• USA: USGS





• World: CSEP


– Beyond RELM


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Ground Shaking

• Full propagation methods

– OpenSHA

– CyberShake

• Simple propagation approximations

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Ground Shaking


– OpenSHA

– CyberShake


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Ground Shaking


– OpenSHA

– CyberShake


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Ground Shaking


– OpenSHA

– CyberShake


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Simple Ground Shaking

• What’s necessary?

– Source location

– Target location

– Magnitude

• Possible enhancements

– Soil classification

– Basin effects

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– Source location

– Target location

– Magnitude



– Basin effects

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– Source location

– Target location

– Magnitude



– Basin effects

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– Source location

– Target location

– Magnitude



– Basin effects

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– Source location

– Target location

– Magnitude



– Basin effects

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Simple Ground Shaking (cont)

• Focus on PGA only

• Following Cua (2005)

– Parameterize PGA(r) with log polynomial

– Fit historic data (using shakemap.org)

– Estimate variance/uncertainty

– Test against global data

• Results are surprisingly good!

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– Parameterize PGA(r) with 4th order polynomial





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– Parameterize PGA(r) with 4th order polynomial





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Building Response

• Given PGA, how much damage will occur?

• How do we describe the building?

Building frame Wall type

Floor plan Square footage

Number of levels Chimney

Attached garage Wall anchors

Foundation type Year of construction

• Which of these are necessary?

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Building Response









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Building Response









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Building Response









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Building Response









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Building Response









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Building Response (cont)

• Simple Method

– Graf and Lee (Earthquake Spectra, 2009)

– Inputs

• PGA

• Construction Framing

– Output

• Fraction of building damaged

– Easy to “enhance”

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• Simple Method


– Inputs

• PGA


– Output



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• Simple Method


– Inputs

• PGA


– Output



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• Simple Method


– Inputs

• PGA


– Output



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• Simple Method


– Inputs

• PGA


– Output



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• Simple Method


– Inputs

• PGA


– Output



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• Simple Method


– Inputs

• PGA


– Output



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Putting It All Together

1. Create an earthquake forecast

2. Using the forecast as a density function, convolve a ground-shaking estimation with all possible earthquake sources

3. Create PGA Exceedance curves for all locations

4. Use the exceedance curves as inputs to damage calculations.

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Putting It All Together (cont)

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Quick Summary

• This procedure is fast

• Necessary data is available

• Science/theory exists for all steps

• Results are personalized and useful

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Quick Summary





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Quick Summary





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Quick Summary





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Quick Summary





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Enhancing the Damage Model

• Model works well in United States

• How can we go global?

• Expand to include International Building Codes

• Generalize for sub-IBC construction

• Need access to building and damage data

• Use social data for modeling parameters

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Enhancing the Damage Model (cont)

Damage Factor (DF)

• Function of Construction and Demand to Capacity Ratio (DCR)

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Damage Factor (DF)


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Damage Factor (DF)


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Damage Factor (DF)


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Damage Factor (DF)


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• Values determined from fit against American properties

• Assume these values for all IBC structures

• Fit structure types country by country

• Absorb uncertainty and shift into DCR ratio (into R)

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Framing System a b c

Wood-Frame -1.07093 2.47522 1.54108

Tilt-up -0.89917 2.14296 1.09176

Bearing Shear Wall -0.95119 2.24974 0.96355

URM Bearing Wall -0.47366 2.08034 0.58674

Moment-Resisting Space Frame -0.81928 2.32500 1.11061

Dual MRSF/Shear Wall -0.81325 2.22125 1.16141

Concentric Braced Frame -1.06169 2.78331 1.35868





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Wood-Frame -1.07093 2.47522 1.54108

Tilt-up -0.89917 2.14296 1.09176


URM Bearing Wall -0.47366 2.08034 0.58674








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Wood-Frame -1.07093 2.47522 1.54108

Tilt-up -0.89917 2.14296 1.09176


URM Bearing Wall -0.47366 2.08034 0.58674








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Wood-Frame -1.07093 2.47522 1.54108

Tilt-up -0.89917 2.14296 1.09176


URM Bearing Wall -0.47366 2.08034 0.58674








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Wood-Frame -1.07093 2.47522 1.54108

Tilt-up -0.89917 2.14296 1.09176


URM Bearing Wall -0.47366 2.08034 0.58674








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Wood-Frame -1.07093 2.47522 1.54108

Tilt-up -0.89917 2.14296 1.09176


URM Bearing Wall -0.47366 2.08034 0.58674








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𝑫𝑪𝑹=𝑺𝒂

𝑹×𝑽 /𝑾

Demand to Capacity Ratio

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𝑹×𝑽 /𝑾


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𝑹×𝑽 /𝑾


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Framing System R V/W T

Wood-Frame 2.5 – 6.5 0.05 – 0.4 0 – 1s

Tilt-up 2.5 – 5.5 0.05 – 0.5 0 – 1s

Bearing Shear Wall 2.5 – 5.5 0.03 – 0.5 0 – 2s

URM Bearing Wall 1.5 – 4.0 0.02 – 0.3 0 – 2s

Moment-Resisting Space Frame 2.0 – 8.5 0.03 – 0.5 0 – 2s

Dual MRSF/Shear Wall 3.0 – 8.5 0.03 – 0.5 0 – 2s

Concentric Braced Frame 3.0 – 8.0 0.03 – 0.5 0 – 2s


𝑹×𝑽 /𝑾


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• T can be estimated from the footprint and height of the structure

• R decreases with height (number of floors)

• R decreases DCR increases DF increases

• V/W can be constrained by year of construction

• IBC gives good indication of response to load

• Compliance with IBC folded into R


𝑹×𝑽 /𝑾

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𝑹×𝑽 /𝑾

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𝑹×𝑽 /𝑾

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𝑹×𝑽 /𝑾

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𝑹×𝑽 /𝑾

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𝑹×𝑽 /𝑾

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𝑹×𝑽 /𝑾

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𝑹×𝑽 /𝑾

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Examples

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Examples

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Examples

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Examples

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Examples

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Conclusions





• But… we need data!

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Conclusions






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Conclusions






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Conclusions






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Conclusions






Thank You

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Multi-Hazard Loss Estimations Using Socioeconomic Data

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