Measuring the Giant - PICG Conference · 14/05/2015 · Measuring the Giant (or, How Big is the...

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Property Insurance Claims Group Conference

British Library Conference Center 14 May 2015

Measuring the Giant

(or, How Big is the Big One?) - Managing Natural Hazard Risk Before, During, & After Claims

Keith Porter, PE PhD Principal SPA Risk LLC

Research Professor University of Colorado, Boulder

© SPA Risk


May 14th 2015

The British Library Conference Centre

http://www.sparisk.com/

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You already live with the giant

0.0001

0.001

0.01

0.1

1

0.0001 0.01 1

Exce

edan

ce r

ate,

yr-1

Loss, fraction of portfolio value

1/250-year loss


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PROPERTY VULNERABILITY &

THE IP VALUE OF LOSS DATA

So let’s start quantifying him...


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Vulnerability

Relationship between

environmental input (like

shaking) and loss (like

repair cost)

Vulnerability data can be

valuable IP

0.00

0.25

0.50

0.75

1.00

0 0.5 1 1.5 2

S a (1.0 sec, 5%)

Dam

age

fact

or

Y

E[Y |S a =s ]

f Y |S =1g(y )


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Empirical vulnerability functions

x = ground motion (windspeed, ...) at property i

y = loss at property i

c = attributes of property i (age, material, height...)

Wesson et al. (2004)


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Empirical method: data to get

• Address or lat & lon (for shaking, windspeed, ...)

• Replacement cost new by coverage

• Repair costs by coverage (better: Xactimate est.)

• Structural material

• Structural system (frame, shearwall, ...)

• Number of stories

• Year built

• Occupancy (residential, etc.)

• Plans, photos of damage, ....


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Analytical vulnerability functions

• Design several realistic specimens

• Estimate structural response, damage, & repair

cost at many levels of shaking

• Combine them probabilistically

0.00

0.25

0.50

0.75

1.00

0.00 0.50 1.00 1.50

Mea

n d

am

ag

e fa

cto

r o

r C

OV

Sagm(1 sec, 5%), g

1.4 · sample COV

Typical

Poor

Superior

MDF

X

Y

Z

XY

Z

MODE- 1 : 0.43261 (sec)


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Analytical method: data to get

• Shaking (windspeed...)

instrumental records

• Structural and

architectural drawings

• Detailed costs (e.g.,

Xactimate)


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Aside: analytical (multi-site) BI modeling


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(Multi-site) red tagging

X direction capacity spectrum

0

1

2

3

4

5

6

7

0 0.5 1 1.5 2 2.5 3

Sd (in)

Sa (

g)

Red tagging often doesn’t contribute much to BI risk


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(Multi-site) equipment failure

0.00

0.25

0.50

0.75

1.00

0 1 2 3

Base acceleration, units of gravity

Fai

lure

pro

bab

ilit

y

Best conditions

No bracingNo spacers

No battery

restraints


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(Multi-site) operational failure

Legend

Or gate: the event connected

above occurs if any event

connected below occurs

And gate: the event

connected above occurs if all

events connected below occur

Transfer symbol: tree

continues elsewhere

Event: something

undesirable occurs

Basic event: an event whose

probability is quantified

without lower events

Primary Facility

Fails

Equipment

Systems Fail

Building FireData Processing

Equipment Fails

Building Support

Systems Fail

2 3 4

Red

tagged

Backup Facility

Fails

1

Operations Fail


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(Multi-site) system failure

0.00

0.25

0.50

0.75

1.00

0.00 0.50 1.00 1.50 2.00

Peak ground acceleration (g)

Fai

lure

pro

bab

ilit

y Current conditions,

correlated

Red tag

probability

Mitigate

generator

equipment Mitigate all

(a)

Current conditions,

uncorrelated

0.00

0.25

0.50

0.75

1.00

0.00 0.50 1.00 1.50 2.00

Peak ground acceleration (g)

Fai

lure

pro

bab

ilit

y

Current conditions,

uncorrelated

Red tag probability

Mitigate tape silos

Mitigate all

(c)

Current conditions,

perfect correlation

Primary Backup


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(Multi-site) BI risk

As-is

(5 yr)

Fix these weak links* Get

(5 yr)

Operations 0.8% Generator equipment 0.3%

Backup site 3.2% Generator equipment, fans,

EQSL, computers, raised

access floors, tape silos

0.1%

Ops & backup 0.1% With periodic evaluation ~10-6


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Expert opinion vulnerability

• Experts judge vulnerability

• We treat experts like data

• Useful when we lack data

• Inexpensive

• Long tradition of use

But:

• Underestimates uncertainty

• Least credible, verifiable 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0

Pro

babili

ty o

f C

olla

pse

0.2-Second Spectral Acceleration (g)

EXP2EXP3EXP7EXP9EXP11EXP12EXP13


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HOW BIG IS THE BIG ONE?

SHAKEOUT


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ShakeOut

• M7.8 hypothetical SoCal earthquake imagined in granular detail

• A science-based, community-oriented earthquake exercise

• 300 scientists, engineers, operators, and others created it

• 25 million people in 26 states, regions, countries participated in 2014

• Pamphlets, reports, journal articles, reports, games, YouTube vids, etc.

Google “USGS SAFRR”


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To answer two questions

1. What could happen in a large

earthquake near me?

2. How can I prepare for it?


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Stakeholders wanted a scenario

1 outcome

No probability

An earthquake

everyone should

be ready for


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150 year return period; 300 yr since last rupture

ShakeOut rupture unzips SE to NW, taking 90 seconds;

Shaking lasts >3 min.’s in LA & Ventura Northridge 1994

over in 30 sec’s


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Fault offset

Landers, California M 7.3 - 1992 earthquake; ~1 meter (3 feet) offset

Wairarapa fault, New Zealand - 1855 earthquake; ~18 meter (54

feet) right-lateral offset


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Palm

Springs

Los

Angeles

Bakersfield

Compare with

1994 Northridge

earthquake:

• 50x smaller

• Magnitude 6.7

• 57 deaths

• $40 billion

Shaking

Compare with

2008 Chino Hills

earthquake:

• 5000x smaller!

• Magnitude 5.4

• 0 deaths

• Minimal damage


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Damage to the building stock

• 300,000 significantly damaged

(1 in 16)

– Significant: repairs cost at least 10%

of replacement cost

• 45,000 complete losses (1%)

• Most dangerous:

– Brick

– Older concrete

• Most numerous:

– Older wood

• Less dangerous:

– Steel buildings built before 1994

1933 Long Beach, CA

1994 Northridge CA


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Older reinforced concrete

ShakeOut study by UCLA:

• 50 collapses

• 5,000 – 10,000 people in

collapsed buildings

• 100 red tagged buildings

1995 Kobe

1994 Northridge


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Woodframe buildings

• Most California

housing is wood

• Extensive damage

in past earthquakes

• Scenario: 175,000

wood buildings

significantly

damaged (1 in 25)

5/2/83

M6.5 Coalinga

1992 Mendocino

1989 M7.1

Loma Prieta


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Highrise steelframe buildings

• Connections fractured in

Northridge (1994)

• In Kobe (1995), 10s of

steel buildings collapsed

• No US buildings have

collapsed, but ShakeOut

is longer, stronger, and

richer in damaging motion

than Northridge.

• 600+ such buildings in

study area


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Highrise steelframe buildings

• Rupture-to-rafters analyses

• 5 collapses, 11-15 stories,

200,000-300,000 sq ft, 1,000

occupants each

• 10 red tags, 20 yellow

• Expert panel:

– “The possibility of some

collapses is quite credible.”

Kobe, Japan 1995

Mexico City, 1985


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Electric power

10 experts from 5 agencies find:

• Immediate loss of power throughout

region

• Collapse of some high-tension towers,

damage to transformers on poles

• Generators offline for inspection

LA, Riverside, & San Bernardino Counties:

• 30-50% of service restored in 24 hrs

• 75-90% restored in 3 days

• ~100% restored within 1-4 months

Transmission lines & power plants

1971 San Fernando

Earthquake


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Water & sewer

• Older pipe susceptible to

shaking, liquefaction, ...

– Some LA pipe 100+ yr old

• Aqueducts, tunnels cross

the fault several places

• Groundwater supply

needs power, pumps,

tanks, and other

damageable equipment

1994 Northridge Earthquake

1971 San Fernando Earthquake


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Water supply

19 reps from 8 agencies:

• Within 10 miles of fault & isolated areas:

damage impairs supply for up to 6 months

• Throughout much of study area, 1/2 of

customers lose service for up to 1 week

– Loss of power, damage to pumps, tanks, etc.

• In LA, Riverside, San Bernardino Counties,

5% of customers lose service 1-8 weeks


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Deaths & injuries

• Study by UCLA:

• 50,000 injured (to ERs)

• 1,800 killed

– 900 from fires, 900 from shake-

related building and

transportation damage

– Vs. 8,300 injured, 33 killed in

1994 Northridge Earthquake

• Up to 2/3 of hospital beds

unavailable in some counties Olive View Medical Center

1971 San Fernando earthquake

Evacuation of Sherra Cox, 1989 Loma

Prieta earthquake


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Fire following earthquake

2 largest peacetime

conflagrations: 1906 SF,

1923 Tokyo because:

• Numerous ignitions

• Degraded fire-resistive building

features

• Damaged water mains

• Saturated communications

• Traffic impacts

• More fires than firefighting

capabilities


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Fire following earthquake

• 1,600 ignitions requiring a fire engine

• 1,200 exceed capability of 1st engine

• Orange County & LA basin: dozens of

large fires merge into conflagrations

destroying 100s of blocks

• 200 million square feet burnt

≈ 133,000 single family dwellings

• Property loss: $65 billion

• No Santa Ana winds, not worst case

1989 Loma Prieta

1994 Northridge


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Property Damage: $113 billion

Fire-Damaged

Contents

($25b)

Highway

($0.4b)

Pipeline (water,

sewer, gas;

$1.1b)

Shake-

Damaged

Buildings

($32.7 billion)

Shake-

Damaged

Contents

($10.6b)

High-Rise

($2.2b)

High-Rise

Content ($0.7b)

Fire-Damaged

Buildings

($40b)

Business Interruption: $96 billion

Non-High-Rise

Buildings ($8

billion)

Fire ($22.4b)

Transportation

($0.5b)

Power ($7.3b)

Water ($53b)

Gas ($0.8b)

Ports ($1b) High-Rise

Buildings

($3.2b)

Monetary bottom line


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Important variables

• Demand surge could add 20%

• Fatalities are highly uncertain

• Few are insured; vast outmigration & long-term

economic malaise are possible

• Seismic retrofit can save up to $8 per $1 spent,

prevent loss of operations in critical facilities

• Above-code design & accelerated replacement of

obsolete infrastructure are the long-term fixes


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ARKSTORM


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British Library Conference Center 14 May 2015 Courtesy of James Done, NCAR

AR: Atmospheric River


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Great Floods in Santa Barbara Channel

40 40

0 500 1000 1500 2000

Year

212

440

603

1029

1418

1605


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K Street Sacramento, looking east

The 1861-1862 Floods

• 24 Dec 1861 – 21 Jan 1862:

nearly unbroken rains

• 300% to 500% of normal

precipitation in San Diego,

Sacramento, and SF

• Central Valley flooded 300

miles x 12 – 60 miles wide

• LA basin “generally

inundated”

• San Gabriel & San Diego

Rivers cut new paths to sea

K Street Sacramento, looking east


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ARkStorm Precipitation


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Rough estimates of

Flooding (drawing

from FEMA Flood

Insurance maps)

RUNOFF RETURN

PERIODS

PRECIPITATION

VIC

DFIRM

Estimating Flooding


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Flood Exercise Map


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Los Angeles & Orange Counties

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• Winds reach 125 mph

• 60 mph winds at Golden Gate, enough to close but not damage it

• Buildings & other structures do face damages.

Maximum wind speeds


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Highway damage: 100s of landslides

$100,000,000s in damage

Road closures of days to weeks


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24% of buildings flooded

Silicon Valley; SF Bayshore Sacramento

Orange & Los Angeles Counties Stockton


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Economics of the ARkStorm Property loss: ….........…………………….$350B

Property loss from flood: .........………….…97%

Demand surge: ………………….........…… 20%

Business interruption loss: ………...........$375B

Loss versus ShakeOut: ………………............3x

Probability versus ShakeOut: …………..........1x

CA sectors and counties affected: ............100%

Fraction insured: ……..................................12%

Long-term hit to the CA economy: ……...........?

Cost to enhance urban levees: …...........$25B?


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THE HAYWIRED SCENARIO


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Code’s surprising life-safety objective

The International Building Code does not aim

to provide earthquake-proof buildings. Its

goal for most buildings is life safety—limiting

collapse probability to ≤ 1% in 50 years—

with some damage reduction where

practical.


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After the Big One

International Building Code

aims to control the risk of this

But not this Both get this


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After the Big One

IBC does not aim to control this

Which gets this


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After the Big One

This, as scary as it might seem,

gets this


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To reduce BI, get fast inspections


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To reduce BI, get good inspectors


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To reduce BI, get efficient inspections

Google “FEMA ROVER” or “ROVER ATC-20”


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To reduce BI, preplan inspections


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New buildings in the Big One

Let’s call this “impairment”


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Code implication “Fraction impaired”


at ½ x code-mapped shaking


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60% impairment rate

over 3,300 km2

x 411 people/km2

≈ 1.4 million people

≈ 140,000 businesses


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3,300 km2 · 411 people/km2



8,000 km2 · 203 people/km2





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Other lessons of HayWired

• Old water pipe is California’s Achilles’ heel

• Some lifeline operators have not thought

about aftershocks

• Power failure could trap up to 25,000

people in 5,000 elevators

• Some telecoms are well prepared

– 8hr + batteries; generators on 90% of towers


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DEMAND SURGE MAKES THE

GIANT BIGGER


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Evidence of demand surge

• Great storm of 1703

– Reed thatching price

up 150-200%

• 1886 Charleston

earthquake

– Construction labor

prices doubled

• 1974 Darwin cyclone

– Building costs doubled


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Demand surge evidence: Xactimate

• Olsen & Porter collected prices of 6 baskets of goods: (residential or

commercial) x (material, labor, both)

• 53 US cities, quarterly for 2002-2010 Atlantic hurricane seasons

• Number of storms & peak windspeed in each city


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Labor costs drive demand surge


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SUMMARY / CLOSURE


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What gets measured gets managed

If we can better measure him,

we can better manage him


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QUESTIONS?

[email protected]

+1-626-233-9758


mailto:[email protected]

https://www.linkedin.com/pub/keith-porter/0/27/863

Measuring the Giant - PICG Conference · 14/05/2015 · Measuring the Giant (or, How Big is the...

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