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Regional Seismic Damage Assessmentand

Interdependencies of CriticalInfrastructures during Earthquakes

Prof. Carlos E. VenturaUniversity of British Columbia

Workshop on Seismic Hazard and MicrozonationToronto, 13 January 2012

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Seismic Risk Studies in BC

Seismic risk in south-western BC Deals with damage, monetary losses and casualties

Critical Infrastructure Interdependencies Development of technology and tools to better understand the interdependency between critical infrastructure during natural and man-made disasters

Real-time monitoring of infrastructure in BCDevelopment of Internet-based technology for monitoring earthquakes and their effects in BC.

The methodology of each of these projects will be presented briefly.

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PGA / MMI

Select Probability Level

% Damage and $ Loss

Damage Table

Building TypeSeismicHazard

BuildingVulnerability

SeismicRisk

Elements of Seismic Risk

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Prevalent Material Type by Block

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Structural Damage in Vancouver Average MDF (%) by Block for MMI VIII

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Main geological units and the corresponding amplification factors are:

R2 1.0C1 1.5F 1.5C2 2.0O1 2.5

Geological Units in Victoria

These amplification factors are for strong shaking and long-period ground motion.

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Structural Damage in Victoriawith and without Site Amplification

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Monetary Losses

Economic losses estimated based on building use, replacement value and damage

FEMA Facility Dependent monetary loss estimation

0%10%20%30%40%50%60%70%80%90%

Office Hotel Hospital

Building type

Cost Distribution in Buildings

ContentsNonstructuralStructural

After Prof. E. Miranda

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Non-structural Damage

Average MDF for NSCs on UBC Campus

VI VII VIII IX X XI XII

Instrumental Intensity

Contents

Displacement

Acceleration

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Building Functionality 10

UBC Campus

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Collaborative work in BC to adapt HAZUS Methodology

to CANADA

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Recent collaborative work in BC

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Example of Earthquake Damage Scenarios

Developed by NRCan as part of this Collaborative Work

The following slides were kindly provided by Dr. M. Journeay of NRCan

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Disclaimer:

The results presented here are of verypreliminary nature and should only be usedto better understand the concepts describedin this presentation and to get a general ideaof the comparative impact of various types ofearthquakes that may affect the BC region

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UNDERSTANDING INTERDEPENDENCIES AMONG CRITICAL INFRASTRUCTURES

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System of Systems - interdependencies

Electric

Power Plant Substation

Transmission

FoodDistribution center

Production centerLocal store

PurificationPlantPump Station

Oil & Gas

Refinery

Oil Field Compressor Station

CommunicationsPhone

Internet

Mobile

Transportation

Local roadBridge Regional Highway

Emergency Responders

FirefighterParamedic

Hospital

911 E-Comm

Critical EventLocal road

• Many critical infrastructure Networks rely on one another in order to function• In the event of a disaster, Critical Infrastructures can sustain significant

damage which could render them inoperable• Important to identify infrastructure interdependencies in order to mitigate the

effects of a disaster

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How do we do it?

First, there are interdependencies within and in between infrastructures networks

Second, we need to recognize that interdependencies are time dependent and have very complex relationships

Third, we have to recognize that this is a difficult problem to solve because it is highly nonlinear and time dependent

The problem can be made more manageable by linearizing the interdependencies in segments of time, using Seismic Risk Assessment techniques for individual infrastructures and implementing a rational approach to combine the information available to determine the effect of these interdependencies

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I2Sim is a tool that we have developed to determine the consequences of the failure of one or more of the infrastructures

www.i2sim. ca

1Urgent

1 steam

distributor_to_Power_House

p_hospital

p_powerhouse

distributorcontroller

Waterstation

UBC_hospital

UBC Substation

currenttime

Steamstation

Powerhouse

_powertoph

External Water2

External Water

External Gas

Display

Channel 9

In1 Out1Channel 8

Channel 7

In1 Out1Channel 6

In1 Out1Channel 5

In1 Out1

Channel 4

In1 Out1Channel 3

In1 Out1Channel 2

In1 Out1Channel 11

In1 Out1Channel 10

1Channel 1

Node 1

Node 4

Node 3

Node 5

Node 2

1Urgent

1 steam

distributor_to_Power_House

p_hospital

p_powerhouse

distributorcontroller

Waterstation

UBC_hospital

UBC Substation

currenttime

Steamstation

Powerhouse

_powertoph

External Water2

External Water

External Gas

Display

Channel 9

In1 Out1Channel 8

Channel 7

In1 Out1Channel 6

In1 Out1Channel 5

In1 Out1

Channel 4

In1 Out1Channel 3

In1 Out1Channel 2

In1 Out1Channel 11

In1 Out1Channel 10

1Channel 1

Node 1

Node 4

Node 3

Node 5

Node 2

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Superimposed Layers

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Cell’s State

Physical Operability 100% (green)

Effective Operability 50% because of lack of water

Physical Operability 50% (yellow)

Effective Operability 0% because of lack of electricity

Sensory Information 0%

Sensory information 100%

electricity

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i2Sim Model

Cell 2

Channel i

Cell 1

Control points

Distributor

Aggregator

Structure + NSCs + Lifelines

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Interdependencies

Can be represented by the following equation:

[T]: Transportation matrix

[X]: Received Goods

[W]: Sent Goods

][]][[ wxT

xxxxxxxxx

p1p2p3

w1w2w3

r1r2r3

Sp1Sp2Sp3

Sw1Sw2Sw3

Sr1Sr2Sr3

p1 = power token value node 1p2 = power token value node 2...w1 = water token value node 1...

Sp1 = power source value node 1Sp2 = power source value node 2...Sw1 = water source value node 1...

x = internal transmission linky = interdependency link

p1 p2 p3 r1 r2 r3w1 w2 w3

w1w2w3

r1r2r3

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Real-Time Responsiveness

Closed solution much faster than open iterative solutions (e.g., agent-based modelling) by two or three orders of magnitude

As an example, a system of 3,000 cells with 15 inputs/outputs per cell (45,000 state variables) for a 10 hr scenario with ∆t = 5 minutes can be anylized in a few seconds of computer time

Interactive scenario playing is basically instantaneous

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2008011639

Cells Outputs

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

TIME (h)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

TIME (h)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

TIME (h)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

TIME (h)

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No Action (Sim)Alternative

actions

Action A1 (Sim)

Action A2 (Sim)

Real World

Action B1 (Sim)

Action B2 (Sim)

A, B = decision pointsDecision A- Take Action A2Decision B- Take Action B1Screens at A- Real World- No Action (Sim)- Action A1 (Sim)- Action A2 (Sim)

No Action (Sim)

Decision Making Scenario

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UBC Campus Case Study

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UBC Campus Case

Why modeling UBC campus? The UBC campus shares many

attributes of a small city 47000 daily transitory occupants 10000 full time residents own utilities providers

Information

After an earthquake, you will have losses in the services (electricity, water, etc.)What will be the overall functionality of UBC?Where to put the available resources?

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Campus Networks: GIS

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Campus Fiber Network

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45Earthquake Damage Assessment

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GIS: Decision Makers Risk Mapping

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Water system

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Global Interdependency of the Hospital

VI VII VIII IX X XI

Functio

nality Co

ndition

Global Interdependency between hospitalwater and electricity systems

H interdependencyElectricity to HWater Zone A12Hospital

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www.bcsims.ca

Internet-based tools for:

• Instant notification of levels of ground shaking (main shock and aftershocks)

• Real-time shake maps• Performance of

infrastructure• Emergency response

planning• Real-time maps of damage

distribution• Earthquake warning system

Collaborative effort between BCMOT-UBC-GSC (and BCMOE)

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Remarks

When interdependencies are taken into account, they can help develop more realistic risk reduction programs and emergency response plans.

Methodologies being developed are useful for the identification of regions of high seismic risk and the interdependencies among critical infrastructures

Real-time information tools, such as the BCSIMS project, and simulators, such as I2SIM, are powerful tools that allow the investigation of risk levels and interdependencies among Critical Infrastructure, so that consequences can be minimized.

Improving response to infrastructure failures is a necessary condition for disaster resilience

First priority during disaster situations is, and should be, human survival

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