Supporting community resilience (public)

Supporting Community
Resilience
A-1

Community Needs Drive Functional Requirements for
the Built Environment
Resilience is “the ability to prepare for and adapt to changing conditions and to
withstand and recover rapidly from disruptions” (PPD 8 and PPD 21)
A-2

Community Resilience Planning Guide
Volume 1 – Methodology
• Introduction
• 6 Step Methodology
• Planning Example – Riverbend
• Glossary and Acronyms
Volume 2 – Reference
• Social Community
• Dependencies and Cascading Effects
• Buildings
• Transportation Systems
• Energy Systems
• Communication Systems
• Water & Wastewater Systems
• Community Resilience Metrics
A-3

6-Steps for Community Resilience
SIX-STEP GUIDE TO PLANNING FOR COMMUNITY RESILIENCE
FORM A COLLABORATIVE PLANNING TEAM
• Identify leader
• Identify team members
• Identify key stakeholders
UNDERSTAND THE SITUATION
Social Dimensions
• Characterize social functions & dependencies
• Identify support by built environment
• Identify key contacts
Built Environment
• Identify and characterize built environment
• Identify key contacts
• Identify existing community plans
Link Social Functions & Built Environment
• Define clusters
DETERMINE GOALS $ OBJECTIVES
• Establish long-term community goals
• Establish performance goals
• Define community hazards
• Determine anticipated performance
• Summarize results
PLAN DEVELOPMENT
• Evaluate gaps
• Identify team members
• Identify key stakeholders
PLAN PREPARATION, REVIEW, AND APPROVAL
• Document plan and strategy
• Obtain feedback and approval
• Finalize and approve plan
PLAN IMPLEMENTATION
AND MAINTENANCE
• Execute approved solution
• Evaluate and update
• Modify strategy as needed
1
2
3
4
5
6
A-4

Economic Decision Guide (EDG)
• Provides a standard methodology for evaluating
investment decisions for communities resilience
• Designed for use with NIST’s Planning Guide
‒ Provides a mechanism to evaluate and prioritize
resilience actions
• Frames the economic decision process
‒ Identifies and compares resilience-related
benefits & costs
• Across competing alternatives
• Versus the status quo (do-nothing)
A-5

Community Resilience Panel
• Mission
Reduce barriers to achieving community resilience by
promoting collaboration among stakeholders to improve
the resilience of buildings, infrastructure, and social
systems upon which communities rely.
• Goals
‒ Engage and connect community and cross-sector
stakeholders
‒ Identify policy and standards gaps and barriers
‒ Raise awareness of dependencies & cascading effects
‒ Contribute to community resilience documents
‒ Develop/maintain a Resilience Knowledge Base
A-6

Other Community Resilience Activities
Federal Agencies
DHS IP – Critical Infrastructure
• Regional Resilience Assessment Program
FEMA – Disaster Preparedness
• HAZUS tools
HUD – Housing and Infrastructure
• Community Development Block grants
EPA – Water Resiliency
• Tools for Enhancing Community Resilience
NOAA – Coastal Resilience
• Climate Resilience Toolkit
USACE – Ports, Waterways, Coasts
• Climate Preparedness & Resilience Program
A-7

Other Community Resilience Activities (2)
National Laboratories
Sandia National Laboratory
• Analysis tools for community and regional assessment of critical infrastructure,
economic impacts, and human behavior.
Argonne National Laboratory
• Assessment and analysis tools that support the resilient design of infrastructure
systems.
Brookhaven National Laboratory
• Resilient energy and smart grid systems
Idaho National Laboratory
• Resilient instrumentation, control, and cyber-physical systems
Pacific Northwest National Laboratory
• Delivery and reliability of electricity
National Renewable Energy Laboratory
• Renewable energy and energy efficiency technologies
A-8

NIST Research
• Community Resilience
‒ Assessment tools and metrics for community
resilience based on systems modeling methods
• Tornado hazard maps
‒ Multi-year effort to create science-based maps
will support tornado-resistant design standards
• Seismic performance of existing buildings
‒ Performance of slender concrete walls is being
evaluated that may lead to code changes
• Building failure and collapse mitigation
‒ New connections were developed for enhanced
performance using computational modeling
A-9

NIST-Funded Center of Excellence
• Awarded to 10 institution team led by Colorado State University.
• $4M/year program funded through a cooperative agreement.
• Objectives
‒ Develop an integrated, multi-scale, computational environment with systems-level models
‒ Develop data architectures and management tools to enable use of multi-disciplinary data
‒ Conduct studies to validate models and data tools for a variety of hazard events including:
• Tornado, hurricane, earthquake, flood, wildland-urban interface (WUI) fire
• Effects of climate change and aging infrastructure
• Envisioned products and end-users at 5 years
• Modeling environment for researchers
• Available incremental tools and metrics for community planners, designers, analysts, etc.
A-10

Overview of the NIST Center of Excellence
A-11

Civil Infrastructure
A-12
• Buildings
• Integrated transportation systems (roads and
bridges, air traffic, ports & harbors, locks & dams)
• Telecommunications facilities
• Power generation and distribution facilities
• Water/wastewater systems

Natural hazards
A-13
• Tornadoes
• Earthquakes and tsunamis
• Landslides
• Hurricanes
• Wildland Urban Interface Fires
• Floods and storm surge
• Sea level rise

A-14
Thrust 1: NIST-CORE
A Multidisciplinary computational environment with fully integrated
supporting databases: “The NIST-Community Resilience Modeling
Environment”.
Thrust 2: Data Management Tools for Community Resilience Systems
A standardized data ontology, robust data architecture, and effective
tools to support NIST-CORE.
Thrust 3: Resilience Data Architecture Validation Studies
Hindcasts and forecasts to test the data collection process and its
integration into NIST-CORE. Validate risk-informed intelligent search and
decision algorithms; field studies.

Current Standards for Risk Management
A-18
• Structural load requirements (ASCE Standard 7-10)
• Building design (AISC, AISI, ACI, AF&PA/ASCE)
• Bridges (AASHTO)
• Electrical transmission structures (EPRI)
• Water/wastewater distribution systems (AWWA)
• Offshore structures (API)
• Seismic PRA/Margins analysis (NRC, EPRI)
• DOE Facilities – Natural phenomena hazards (DOE)
• Dam safety (USACE, FEMA, BuRec)

Traditional engineering approaches to risk
management
A-19
‒Focus on individual hazards and facilities
‒Measures of performance are inconsistent
‒Margins of safety and functionality are not
commensurate with uncertainty
‒Risks cannot be benchmarked
‒Comparison of alternatives is difficult
‒Investments in risk mitigation and management may
be misdirected

What is community resilience?
A-20
“The ability of a community to prepare for
and adapt to changing conditions and to
withstand and recover from disruptions to its
physical and non-physical infrastructure.”

Managing resilience – an interdisciplinary endeavor
A-21
• Engineering
• Climatologists and geophysicists
• Computer science, information/communication
technology, software development
• Social, economic and political science
• Philosophy/ethics
• Stakeholders

Fundamental research issues
A-22
• Quantitative tools and metrics
• Interconnected and inter-dependent systems
• Numerous sources of uncertainties

Fundamental research issues
A-23
• Reality of climate change – modification to existing
decision support methods
• Existing buildings and infrastructure systems, new
construction
• Consistent performance goals

The Science
A-24
• Development of measurement
science and technology
• Ten-university-U.S. government
partnership
• Understanding and quantification
of factors that make a community
resilient to natural, technological,
and human-induced hazards
National Center for Supercomputing Applications

A-25
INPUT/MODELING
1.1.1 Ind. Hazards
1.1.2 Mult. Hazards
1.1.3 Climate Cha
1.2.1 Buildings
1.2.2 Transpo.
1.2.3 Water/Waste
1.2.4 Energy Net.
1.2.5 Telecom Net.
1.3.3 Econ. Net.
1.3.4 Social Syst.
1.3.1 Interdependency
1.3.2 Aging Infrastruct.
1.3.3 Uncertainty Prop.

A-26
INPUT/MODELING
1.1.1 Ind. Hazards
1.1.2 Mult. Hazards
1.1.3 Climate Cha
1.2.1 Buildings
1.2.2 Transpo.
1.2.3 Water/Waste
1.2.4 Energy Net.
1.2.5 Telecom Net.
1.3.3 Econ. Net.
1.3.4 Social Syst.
DATA/STANDARDIZATION
2.1 User requirements
2.2 Stan. Data Ontology
2.3 Data Man. Tools

A-27
INPUT/MODELING
1.1.1 Ind. Hazards
1.1.2 Mult. Hazards
1.1.3 Climate Cha
1.2.1 Buildings
1.2.2 Transpo.
1.2.3 Water/Waste
1.2.4 Energy Net.
1.2.5 Telecom Net.
1.3.3 Econ. Net.
1.3.4 Social Syst.
2.3 Data Man. Tools
DECISION
1.6.1 Ident./Define Baseline
1.6.2 Define Res. For Recovery
1.6.3 Define Perf. Models
1.6.4 Identify Infra/Other
1.8.1 Classical Optimization
1.8.2 Intelligent Search
1.8.3 Opt. of Investments
3.2.1 Field Study Dec. Algor.
3.2.2 Comm. Res. Testbed

A-28
INPUT/MODELING
1.1.1 Ind. Hazards
1.1.2 Mult. Hazards
1.1.3 Climate Cha
1.2.1 Buildings
1.2.2 Transpo.
1.2.3 Water/Waste
1.2.4 Energy Net.
1.2.5 Telecom Net.
1.3.3 Econ. Net.
1.3.4 Social Syst.
2.3 Data Man. Tools
DECISION
1.6.1 Ident./Define Baseline
1.6.2 Define Res. For Recovery
1.6.3 Define Perf. Models
1.6.4 Identify Infra/Other
1.8.1 Classical Optimization
1.8.2 Intelligent Search
1.8.3 Opt. of Investments
3.2.1 Field Study Dec. Algor.
3.2.2 Comm. Res. Testbed
SENSITIVITY/VALIDATION
1.5.1 Isolated Infra. Eval.
1.5.2 Coupled infra. Eval.
1.5.3 Full Event Hindcast
1.5.4 Quantif. Mod. Perf.
1.7.1 Effect of Mod. Res.
1.7.2 Effect of Scaling
1.7.3 Sensitivity Studies
3.1 Intermittent Beta Tst
3.3 Arch Validation Stud.
4.5 Filed Studies

Year 1 Goals
A-29
• Develop integrated models of physical, social, and economic
systems
• Expand hazard capabilities
• Establish the CGE modeling approach
• Develop network models for physical systems

Year 2 Goals
A-30
• Establish definitions for functionality, networked systems, and
their inter-relationships
• Develop models for community recovery including
dependencies
• Define intelligent decision algorithm architecture

Out Year Goals
A-31
• Develop risk-informed performance goals and tools
• Address the expected level of performance during the event
• Provide risk-informed guidelines suitable for standards and other
regulatory documents

Presentation Section 1:
Models for community resilience
assessment
A-32

Introduction of Testbeds
A-33
• Centerville – a virtual community
• Seaside, OR – a coastal tourist destination
• Shelby County, TN – previously modeled in MAE Center
research

Shelby County/Memphis, TN
A-35

The Centerville Testbed
A-36
• A simple community model with essential physical, social and economic
infrastructure components and systems;
• Algorithmic modules, linkages and interdependencies can be developed,
tested and verified independently through calculations, experience or
intuition;
• Software developers at NSCA require prototypical algorithms and datasets
so that they can begin coding NIST-CORE while waiting for more realistic
and complex algorithms and datasets to emerge;
• The common community model requires engineers, economists and
sociologists to begin working toward a common purpose immediately.

Centerville
A-38
(Infrastructure systems supporting community resilience)
• Physical systems – represent distinct topologies
‒ Buildings
‒ Transportation
‒ Water
‒ Electrical power
‒ Telecommunications
• Economic systems
• Social systems

Physical systems: Modeling of infrastructure
capacity, damage and fragility estimates, and
network models
A-39

Community Building Inventory
A-41
Objective: To model response of buildings to natural hazards at both individual and inventory
levels and at different resolutions
Building Classification System
Building
Fragilities
Functions
Inventory
Structural
Design
Loss of
Functionality
(downtime)
Direct/
Indirect
Losses
Recovery Time
and
Trajectory
106 building archetypes: 26 wood, 14 masonry,
40 steel & 26 reinforced concrete
Occupancies: Residential, commercial, industrial, educational,
healthcare, governmental, religious, etc.
Prioritized hazards: Earthquake, tsunami, tornado & earthquake
followed by tsunami
Econ. Systems;
Social Systems;
Uncertainties
Econ. Systems;
Social Systems;
Uncertainties;
Interdependency;
Decision algorithm

Community Building Inventory
A-42
Centerville
Total 15130 buildings in Centerville:
‐ 14890 residential buildings
‐ 151 commercial and retail buildings
‐ 70 industrial buildings
‐ 19 critical facilities
16 building types are used to assemble the 11 zones:
‐ 14890 residential buildings
‐ 151 commercial and retail buildings
‐ 70 industrial buildings
‐ 19 critical facilities

Transportation sector
A-43
Objective: To provide models and methods that enable risk and resilience assessment of
transportation systems at both component and network level
Component Fragility
Models
Risk and Resilience
Assessment
Decision-making Framework
for Mitigation Strategies
Transportation components: Highway/railway bridges, roadways, tunnels, railroads
Prioritized hazards: Earthquake, tsunami, flood, wind, surge-wave
Component level
Component Restoration
Models
Network Performance
Metrics
Network level
Network Restoration
Models

Transportation sector
A-44
Centerville
Major origins/destinations
Critical facilities
Interstate/state highways
Major community roads
Secondary community roads
Major intersection
Rock River
Bridges

Water and wastewater networks
A-45
Objective: To develop models of the physical water and wastewater systems and their
functional service to the community, including hazard-induced loads and effects
Damage Analysis
of Network Components
Water Flow Assessment
In the Post-event Scenario
Hazard Models and Intensity
Measures (IM) assessment

Water and wastewater networks
A-46
Centerville

Energy and power network
A-47
Objective: To develop models of the physical systems of energy and power networks and of
their functional service to the community
• Generation
• Transmission
• Distribution
Network
Analysis
Loss/Recovery
Analysis
Hazard Network
Interaction
Hazard
Analysis
EPN
Characterization

A-48
Centerville

A-49
EPN Tornado Analysis - Sample

A-50
Water and Electrical power networks
Modeling of systems’ dependencies
• Coupling of the WN and EPN to
model the dependency of the two
systems and capture impact of the
failures in one network on the
functionality of the other network
• The coupling induces a change in
the damage scenario and recovery
time of selected functionality
metrics

A-51
Physical components of the
Communications Infrastructure
for Clatsop, Oregon
Communication networks
Seaside
Example of a single-degree of freedom model for
mobile phone system restoration post-earthquake

A-52
Social Science Models: Social impacts and Social Vulnerability
• Provide an overview of our approach to social science modeling
• Examples of the data and mapping tools we are developing
• To enhance community resiliency planning
• For social science modeling
• Example of modeling population displacement

A-53
Social Science Models: Social impacts and Social
Vulnerability

A-54
Social Science Models: Social impacts
– Impacts on Institutional structure & community functions
‒ Education (schools), health (hospitals, clinics); Housing (SF, MF, rental,
owner-occupied); Child care; Emergency/Security, Food Security, etc.
– Demographic impacts
‒ Population displacement, dislocation, and loss, composition, etc.
– Economic impacts
‒ Business loss, interruption, failure, and movement, fiscal, etc.
– Psychological impacts
‒ PTSD, Anxiety, Depression, Substance Abuse, etc.
– Resiliency Outcomes:
‒ Recovery (speed and quality) & Adaptation

A-55
Vulnerability
Hazard
Exposure
Physical
Vulnerability
Social
Vulnerability
Community
Characteristics:
Hazard Agent

A-56
Vulnerability
• Critical For modeling social impacts and guiding
effective resiliency planning should be the
convergence of these three:
‒ Hazards exposure
‒ Physical vulnerability
‒ Social Vulnerability
These overlaps represent increased
hazard vulnerability and should be prime
targets for resiliency planning to reduce
risk through mitigation and recovery
planning activities.
Hazard
Exposure
Physical
Vulnerability
Social
Vulnerability

A-57
Social Science Models: Social Vulnerability
• Like physical vulnerability, but the focus is on
social units and their attributes
• Focus on attributes associated with social factors
and processes that generate vulnerabilities in
terms of a person or group’s capacity to
anticipate, cope, resist, and recovery from
disaster
• Race/Ethnicity, Gender, Education, Income, poverty,
Age, Wealth, Housing tenure (renter/owner).
• Populations with these characteristics are not
uniformly or randomly distributed in our
communities
• As a consequence we can develop tools and mapping
approaches to identify areas with varying degrees or
concentrations of socially vulnerable populations

A-58
Social Science Models: Social Vulnerability Attributes
• Census data – American Community
Survey (ACS) Data – 5-year estimates
• Census Block Group – high resolution
data that corresponds to Neighborhoods
• Ideal for planning purposes
• Developing first, second, and third
order social vulnerability indicators and
scales
• Higher resolution social vulnerability
data enhances:
• Mapping for resiliency planning
• Modeling social impacts and
recovery

A-59
Social Science Models: Social Vulnerability Indictors
Centerville
• Social vulnerability
characteristics are
included
• Income and race/ethnicity
• But quite simplified and
discrete housing areas.

A-60
Social Science Map: Single Parent Households with Children

A-61
Social Science Map: Single Parent Households with Children

A-62
Social Science Models: Modeling population Displacement
• The fine resolution Social
Vulnerability Data in
combination with
Engineering modeling
output and other data are
employed to develop social
science models
• Models are being developed
based on current social
science research and
empirical analysis of
existing data.

A-63
Social Science Models: Current Work
• Refinement of social vulnerability data and mapping tools
• Continuing work on Improving displacement and dislocation algorithms
• Literature reviews
• New analysis – Ike data
• New ways of linking engineering/social science modeling
• We also have working algorithms for estimating
• Causalities
• PTSD
• Extending SV approaches to building vulnerabilities
• Combining stress/strain analysis from engineering and social science
• Housing recovery modeling
• Reaching out to the practice community – American Planning Association

A-64
Economic Analysis
Description of a Computable General
Equilibrium (CGE) Model
Integration of the CGE model with the built
Environment
Deriving optimal mitigation policies

A-65
Income
(Spatial)
Labor Supply
Wage Payments
Expenditures for Goods and Services
Receipt of Goods and services
Tax Payments
Services
Tax Payments
Services
Expenditure Leakages
Household migration
Expenditures
Receipt of Goods and Services

A-66
Specifics of the CGE Model
Commercial Sectors – produce output using factors of
production and intermediate inputs
• Labor
• Physical Capital (buildings)
• Intermediate inputs
• Total factor productivity (TFP)
Households Income – wages and capital income
• Purchases goods and services
• Purchase or rent housing (buildings)

A-67
Integration with the Built Environment
Natural Hazard
• Buildings – impacts production, household income and
housing
• Transportation – impacts production (TFP) and
household purchases
• Electricity – ability to produce and quality of living
• Water – ability to produce and quality of living
• Telecommunication – affect TFP

A-68
Optimal Mitigation Policy
Want to understand how policies like retrofitting buildings and roads,
changes in building codes and spatial considerations for constructing new
buildings can impact the resilience of a community to a natural hazard
Consider many mitigation policies with the objective to minimize the
impacts on
• The level and distribution of household income
• Production of goods and services
• Population Dislocation
• Local government tax revenue

A-69
Centerville
Integration of physical, social and economic models
• Scenario earthquake hazard
• Building damage and loss
• Social and economic impact
• Decision analysis
• Accomplishments

A-71
Hazard-dependent damage
State probabilities

A-74
Expected loss and loss ratio by zone

A-75
Centerville Testbed
Social and economic metrics
• Population dislocation
• Employment/domestic output
• Level and distribution of household income

A-76
Population Dislocation (%)

A-77
Baseline Scenario: Mitigation Free
Consider alternative retrofitting policies to protect reinforce combinations of
commercial and residential buildings

A-78
Pre-event decision analysis
Mitigation Resource Allocation
Code improvements to mitigate impact to tax
Revenues and dislocation

A-79
Mitigation Resource Allocation
Allocate limited resources to retrofit building types in each
zone, to specific code levels with respect to the following
competing objectives:
‒ minimize economic loss (i.e., structure, non-structure, and
contents loss, provided by the engineering team)
‒ minimize total dislocation (OLS model provided by the Social
Science team)
Constraint:
‒ overall disparity in the dislocation rates by socio-economic status
does not increase

A-80
Mitigation Resource Allocation Baseline
Total direct economic loss: $856M
Total dislocation: 3,203 households
Evaluation optimization results under three budget levels:
‒ Low: 15% of ideal budget
‒ Medium: 30% of ideal budget
‒ High: 60% of the ideal budget
Ideal budget ($346M) is sufficient to retrofit all buildings to the
highest code level.

A-81
Mitigation Resource Allocation Baseline

A-82
Policy 1: Low dislocation Loss:$636,933,570 Dislocation: 1,467

A-83
Three Mitigation policies
Policy 1 5.5% and 9.9% reductions in the appraised value of residential and commercial,
respectively
respectively
respectively

A-84
Centerville
Accomplishments to date
• Topology of buildings, transportation, power and water infrastructure
complete;
• Fragilities of transportation, power and water components complete;
buildings in progress;
• Interfaces between physical, social and economic systems are
understood;
• Damage and loss estimation models have been tested and are being
implemented in NIST-CORE v.1;
• Engineers, economists and social scientists eager to work together!

B-1
Natural Hazards and Infrastructure Damage
Year 1 focus areas:
• Tornado
• Tsunami following Earthquake
• Wildland Urban Interface Fire
Year 2-3 focus areas:
• Hurricane Wind, Surge/Wave
• Landslide
• Precipitation-Rain, Snow
• Climate Change
• Multi hazards

B-3
Tornado Hazard Analysis
Historical tornado
tracks for
probabilistic
analysis.

B-4
Tornado Hazard Analysis
Probabilistic
tornado hazard
analysis from EF-0
To EF-5.

B-5
Tornado Characteristics
Tornado intensity, track width, length and direction are important characteristics
leading to community damage

B-6
Stochastic Variations in Tornado Characteristics
Stochastic variations in width, length and direction
Modeling of tornado
Characteristics to account for
varying intensity

B-7
Tornado Scenarios in NIST-CORE 1.0
Tornado Scenario:
Centerville testbed tornado
with user defined starting
point and prescribed length,
width and angle

B-8
Tornado Scenarios in NIST-CORE 1.0
Tornado Scenario:
Centerville testbed tornado
with user defined starting
point and random length,
width and angle

B-9
Tornado Damage and Loss Fragility Modeling
The EF3 Polk County tornado west of Osceola, Iowa, on June 20, 2011.

B-10
Tsunamis following Earthquakes
Tsunami inundation following an earthquake in Japan on March 11, 2011.

B-11
Nearfield Tsunami Threat for California, Oregon and Washington
Probability of full-rupture Cascadia Event is 18-22% in the next 50 years

B-12
Tsunami Generation, Propagation and Inundation Modeling
Use of NOAA’s ComMIT/MOST for tsunami generation and propagation and COULWAVE for inundation
Five Tsunami Intensity Measures:
• Arrive time
• Duration of flooding
• Maximum flow depth
• Maximum flow speed
• Maximum momentum flux
Life safety
Damage/loss
Seaside,
Oregon

B-13
Nearfield Tsunami Probability Hazard Analysis
Event tree to define tsunami probabilities Annual exceedance probability for depth at one location

B-14
Depth and momentum flux hazards for 1,000 year event
Flow depth and momentum flux hazards defined for the 1,000 year CSZ event

B-15
Characterization of the Built Environment
Tax Lot
Data
Google
Street
View
FEMA
Rapid
Visual
Screening
Construction
Material
Floor
Levels
Seismic
Code

B-16
Natural hazards and Infrastructure Damage
Tsunami fragility analysis for 1,000 year event based on flow depth
Probability of
Complete
Damage

B-17
Community-scale Damage Estimation

B-18
Multi-hazard Earthquake and Tsunami Damage Estimation
Probability of
Complete Damage
Tsunami + earthquake
Tsunami
Earthquake

B-19
Direct Loss Estimation for Earthquake and Tsunami Damage
Loss estimation
(2012 real market value)
Loss ($M)
Total direct loss
$1.23B

B-20
Wildland Urban interface Fire
In 2015, wildfires in California, Oregon and Washington burned 1,400,000 acres.

B-21
Framework for Evaluating WUI Hazard

B-22
Wildland Fire Propagation Model

B-23
Community Fire Propagation
Model
Step 1
Identify Ignitable Objects and boundary nodes to
develop corresponding path
Step 2
Calculate Weights (W) of each edge
W(1,J)
W(2,J)
W(3,J)
W(1,2)
W(1,3)
W(2,3)
W(2,2)
W(3,3)
Step 3
Identity most probable paths
Step 4
Calculate probability of most probable
paths for each boundary node

B-24
Year 2-3 Hazard Focus Areas
• Hurricane Wind, Surge/Wave
• Landslide
• Precipitation-Rain, Snow
• Climate Change
• Multi hazards

B-25
Hurricane Wind, Surge and Wave
Leverage existing efforts
• USACE Coastal Hazard System for Hurricane Storm Surge and Waves
• DHS Coastal Resilience Center for ADCIRC + SWAN output

B-26
Landslide Hazards
Leverage existing efforts for landslide hazard
• Example of landslide probability for Mw 9.0 CSZ event
• Use in multi-hazard and cascading hazard analyses

B-27
Sequence of damage from
earthquake aftershocks
(from Kam et al. 2011)
Earthquake Mainshock and
Aftershock

B-28
Fragility Analyses for Multi-Hazards
Fig B: Fragility surface for multiple Intensity Measures
Fig A: Methodology for combining earthquake and tsunami hazards

B-29
Interdependency
Defined as the relationship between two or more components, networks or systems. We focused
on civil infrastructure systems this first year. Our modeling approaches were (1) unified directed
graphs, (2) linked directed graph networks, and (3) linked system level input-output models.

B-30
Common characteristics of all models: fragilities and recovery functions.
Fragility is defined here as the conditional
probability of failure given an applied
Demand such as PGA (g).
Recovery curves represent physical system
Recovery or functionality over time.
Functionality is also called operability.

B-31
Role of Interdependency in Modeling Response and Recovery

B-32
Interdependency modeling using unified directed graph
Features
1. Inoperability of an individual node is
determined by using the Dynamic
Inoperability Input-output Model in
every time step.
2. Damage of a node is determined by
considering minimum required input
and output as well as the
relationship between the operability
and the physical integrity of the
facility represented by the node.

B-33
Illustration of the damage propagation
Fully connected network Initial damage of Node 2
‒ Node 2 is not fully
operational due to the
physical damage of the
node.
‒ If the minimum required
output is not met, the node
is designed as fully
inoperational
Damage propagation
Throughout the network
‒ Due to its dependency on
Node 2, Node 3 becomes
partially or fully
inoperational.
‒ Considering the
dependency of Node 4 on
Node 3 and 6, the
operability of Node 4will be
determined.

B-34
Features
3. Node-to-node level, across-system
dependency is modeled by
combining the importance of a node
to produce a service (including
power, telecommunication, and
water in the current model) and the
importance of the service in the
operation of another node.
4. Dependency relationships remain
unchanged throughout recovery.

B-35
Features
5. Inoperability of the individual systems
(power, telecommunication and water) can
be measured by network characteristic
parameters such as connectivity and
efficiency. For the calculations, the output
links of the fully damaged nodes are
removed.
6. Inoperability of the individual systems can
be used as inputs for the inoperability model
of socio-economic systems.

B-36
Dependency Model using Directed Graph Networks: Water functionality is dependent upon
electric power in this example from Centerville.
This approach used a
directed graph with
physical connectivity
between power
delivery components
and water system
components.
EPN/WN Analysis
o Component analysis Fragility functions
o Connectivity analysis Connectivity matrix
o Flow analysis

B-37
The third method
employed was the
system-level resiliency-
based interdependency
approach (RBIA). In this
approach the
similarities between
the risk and resiliency
formulations may be
seen.

B-38
Example of RBIA methodology
Begin with the power system fragility and recovery.
Derive interdependency matrix coefficients
for other systems.
Finally, simulate the complete set of recovery curves
for specific hazards using the Input-Output Model.Data sources include Duenas-Osorio
and Kwasinski (2012); Nojima et al.
(1995, 2005, 2013); and Park et al.
(2006)

B-39
The result of
RBIA modeling
are illustrated in
this figure. The
role of
interdependency
changes for pre-
event, during and
post-event
recovery.
RESILIENCY

B-40
• Synthesize modeling approaches
• Bridge the gap between the physical/
functional infrastructure models and social
acceptance.
Future Work

B-41
• NIST-CORE is the computation platform for NIST-COE
research.
• This demo shows how a common platform can integrate
different research done by different groups.
‒ Consistent User Interface and allowing to connect different
analyses
• These implementations are based on latest research of
NIST-COE.
NIST-CORE Demo: Overview

B-42
• Tornado
‒ Scenario tornado
‒ Building damage, Electric Power Network damage
• Tsunami
‒ Building damage
• Wildfire
NIST-CORE Demo: Overview

B-43
• Creating scenario tornado (Shelby county)
• Estimating building damage (Shelby county)
• Estimating electric power network damage
(Centerville)
NIST-CORE Demo: Tornado

B-45
• Ingesting tsunami hazard (Max. Momentum Flux
in 1,000 yr) for Seaside, OR
• Estimating building damage
NIST-CORE Demo: Tsunami

B-47
• Creating Wildfire Hazard
• This is a first dataset which is both spatial and
temporal.
NIST-CORE Demo: Wildfire

B-49
• Summary of major accomplishments
during Year 1
• Major challenges for Year 2 and beyond
The path forward

B-50
Hazard and infrastructure performance
• Expanded hazard capabilities from EQ to tornado, tsunami,
and WUI wildfire
• Extended fragility modeling approaches to new hazards,
including tornado and tsunami
• Developed network models for physical systems: water,
power, transportation
• Initiated research on recovery sequences and durations
Major accomplishments in Year 1

B-51
Integrated systems modeling and risk-informed decision
• Developed integrated models of physics, social, and economic systems
with initial dependencies in Centerville and Seaside Testbeds
• Modeled interdependencies between water and power networks in
Centerville
• Established the Computable Generalized Equilibrium (CGE) modeling
approach for economic impacts on communities
• Assessed impacts of physical damage on institutional structure,
community functions and demographics
• Identified user needs for database development and interfacing.
Major accomplishments in Year 1

B-52
• Natural hazard modeling and
system performance
• Functionality
• Recovery
• Climate change
• Decision support algorithms
Future Work in Year 2 and beyond

B-53
• Model hazards that involve coupled or cascading effects –
earthquake/tsunami/landslide, main shock-aftershock earthquake sequences,
fire following earthquake, hurricane wind/storm surge/waves/inundation
• Develop joint probabilities and statistical data on jointly occurring hazards
• Incorporate aging of civil infrastructure facilities
• Model debris and its impact on performance of infrastructure during and after
tornados, tsunamis, and flooding
• Identify scales of resolution for modeling hazards with spatially distributed
effects
• Develop resilience models for telecommunication facilities
Natural hazards and systems performance

B-54
• Establish connections between physical damage to buildings,
networked systems and lifelines and their functionalities
• Identify interdependencies between functionality recovery of
physical systems and community restoration patterns
• Assess impacts of physical damage on institutional structure
and community functions and demographics
• Advance social vulnerability modeling to reflect impacts which
may not be uniformly distributed across a community
• Extend social vulnerability models to businesses
Functionality

B-55
• Identify interactions between recovery of civil infrastructure
and community social and economic systems at different
phases in the recovery process
• Develop models for community recovery including
dependencies between physical, social, and economic
systems at appropriate spatial and temporal scales
• Collect and analyze full-scale data for validating recovery
models of interdependent systems
• Initiate processes and strategies for minimizing-recovery time
and creating a rapid recovery trajectory
Recovery

B-56
• Identify key modeling parameters and uncertainties in climate
change scenarios and their impact on infrastructure systems.
• Develop coupled hurricane/storm surge/wave/coastal
flooding models for coastal communities
• Develop computationally efficient methods for modeling
climate change-driven hazards and for propagating systemic
deep uncertainties in climate change models
• Identify feasible engineering solutions to mitigate community
risks under climate change
Climate change

B-57
• Define the intelligent decision support algorithm architecture to facilitate
consideration of competing objectives and different value systems
• Establish resolutions of community modeling required to support different
decisions and scalability of resilience assessment models to communities
of different sizes, contexts
• Develop risk-informed performance goals and tools to assess the integrity
of the built, economic, and social environments for resilient communities
• Provide decision support that consider special needs of vulnerable
populations, inter-generational equity, and sustainability
• Develop risk-informed guidelines for community resilience that are
suitable for standard and other regulatory documents
Decision support algorithms for risk-informed
decision-making

Supporting community resilience (public)

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Supporting community resilience (public)

Similar to Supporting community resilience (public) (20)

More from Md.Asif Rahman

More from Md.Asif Rahman (6)

Recently uploaded

Recently uploaded (20)

Supporting community resilience (public)