lee tues 4pm ligurian.pdf

8/12/2019 LEE TUES 4PM LIGURIAN.pdf

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UsingRobustSimulationtoCharacterize Uncertainties in

CatastropheLoss

Assessments

Yajie J.Lee,CraigE.Taylor,Zhenghui Hu,WilliamP.Graf,

CharlesK.

Huyck

RAACatModeling2014

February1113,2014,Orlando,FL

ImageCat, Inc.

Supportingthe

global

risk

and

disaster

management

needs

of

today,

using

the

technologiesoftomorrow


2/27

EQECatfounded

HAZUS BuildingModels

CloudComputing

Dynamic Financial Analysis

AIRfounded

RMS

founded

-

o ust mu at on

(recognized uncertai

nty)

Determinis tic Cat Scenarios

IBMPCU

sage

Risk Curves ('EP') forsynthetic catalogs

Governmentstudies [Wiggins,

ESSA, Friedman]

Tracking ExposureAccumulations in Zones

Realistic DisasterScenarios

1970 1980 1990 2000 2010 2020San FernandoValley

LomaPrieta

Northridge Tohoku +Christchurch

Algermissen & Perkins Frankel et al Petersen et alUSGS Probabili stic

1996 2002 2008 201419901976


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AFramework

for

More

Robust

Uncertaint

Assessment

RobustSimulation

CurrentApproach

$M)

Amount(

Los

AverageReturnInterval(Years)


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Scientificchallenges

WhatisRobustSimulation?

USGSnationalseismicmaps

g y

Portfoliolosses

Conc usions


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Scientificchallenge:Whatassumptionsaremadeaboutmaximum

events?

BUT!Japanesegeologistshadrecognizedlargereventsinthedecadebeforetheearthquakefromancienttsunamirecords

~ . M9.6couldoccurinanysubductionzones

Thoku wasM9.0,ruptured5segments

(Imagesource: Steinetal.,TectonicPhysics,2012)


6/27

Scientificchallenge:Howaccuratelyareseismicsourcesmapped?

NewMadrid

(USGSNSHM,2014)

M6.8 to M8.0

Reoccurrencemodel

500to50,000years

Cluster &Noncluster

o e au mo e or

nuclearpowerplantPseudofaults


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Scientificchallenge:Howstrongwillthegroundshake?

GMPEsforSubduction Earthquakes(Abrahamson2012)

GMPEsforCEUS(USGS NSHM,2014)


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Scientificchallen e:Howwelldoesabuildin erformduringgroundshaking?

(Forcebasedparadigm)

(Displacementbased

aradi m

(A1970ConcreteFrame)

Increasingbuildingperiods

(CODA:CodeOrientedDamageAssessmentmethod,

adaptedfromATC13,Graf&Lee,Spectra,2009)


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WhatAssum tionsaremadeaboutmissin data?

Lo

ss

Time

Loss

ss

Lo

Time


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,answer?

elihood

Asinglenumber?

Lik

Severity

OrAdistribution

Likelihood basedwhatwenowknow?

Severity


11/27

Scientificchallenge:Howdoweaccountforuncertainty?

Twotypeso mo e e uncertainties

Innermodelrandomness(aleatory)

Givenenoughtime,thevalueswillberealized

afterasufficient

number

of

event

cycles

Outermodelvariation(epistemic)

Lackofknowled e scientific modeluncertaint

Onlytime

or

future

research

will

tell

the

correct

model


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RobustSimulation:Representationoffutureriskthroughsimulationofasuiteofpossibleanswersthatintegratesvalidscientificdisagreementandstochasticmodelingofunknownvariables.

ity

Sever

ReturnInterval


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A licationofrobust

estimation:e sm c azar assessment


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Logic will be more complex in 2014 NSHM models!

2008USGSNationalSeismicHazardMapping(NSHM).


15/27

A site in San Francisco A site in Los Angeles

475year(USGS) 475year(USGS)

1412

0

2

4

6

8

10

12

Count

2 6 .1 4 8 2 6 .3 4 8 2 6 .5 4 8 2 6 .7 4 8 2 6 .9 4 8

0

2

4

6

8

10

Coun

t

0.0

2

0.0

60

.1

0.1

4

0.1

8

0.2

2

0.2

60

.3

0.3

4

0.3

8

0.4

2

0.4

60

.5

0.5

4

0.5

8

0.6

2

0.6

60

.7

0.7

4

0.7

8

0.8

2

0.8

60

.9

0.9

4

0.9

8

PGA (g)

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

0.

PGA (g)

Groundmotiondistributionat475yearreturninterval


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Applicationof

robust

estimation:

singleproperty


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A

sample

mid

rise

office

buildingLosAngeles,1965

Concreteshearwall

Concreteshearwall: 35%

Concreteframe: 17.5%

Steelframe: 17.5%

Woodframe: 10%

Steelconcentricbracedframe: 20%


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Comparisonwith

Federico

Waismans 2010RAAearthquake


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(Waisman,2010)


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ImageCat (ConventionalApproach)

(Waisman,2010)


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ARobustEstimateofUncertainty

RobustSimulation

ConventionalEPcurvent($M

)

LossAmo



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ARobustEstimateofUncertainty

nt($M

)

LossAmo


UsingmultipleanswersfromvariousCatmodelersdoesnotnecessarily

disclosethefullrangeofuncertaintyinpotentialCatrisks.


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o ust mu at on:

Acce tsmodelim erfection

Acknowledgesmodellimitations

Revealsuncertaintyassociated

with

imperfect

knowledgeEncouragesmodeldivergence

Fewersurprises(Black

Creditordiscreditextreme


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Providesacontrolled

Modeltraceability Preserves coherenc and

integrity

Usesnonparametr cstat st csMinimizesneedforcomplex

classicalstatistics

Propagatesuncertaintythroughlayers,simplifyingcomplex


25/27

Simplifiestimesequenceoflosses

Clusteredevents,e.g.,NMSZ,

Simplifiesmultiperilmodeling

Flood

Winterstorm

EarthquakeTsunami

TerrorThedrunkardswalk


26/27

Asingleviewofrisksuppressesmodeluncertaint andim lies

Thailand

illusoryprecisiontomodelingfutureriskwithmanyunknowns.

Re ulators and modelers havebeguntostressunderstandingmodellimitationsand

uncertainty

Thoku

Uncertaintycanchangewhenmoreinformationisacquired

toassessuncertaintyadequatelyandallocateresourcesaccordin l

Christchurch


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