wind hazards & building loads in the context of community

Wind hazards &

building loads in the

context of community

resilience

NIST Center of Excellence for Risk-Based Community Resilience Planning

John W. van de LindtGeorge T. Abell Professor in Infrastructure

Co-Director, NIST Center for Risk-Based Community Resilience Planning

Hurricane Disaster Infrastructure Resilience and Planning Workshop II, May 25-26; Madison WI


Resilience Needs (Center Research Objectives)

• New measurement science to enable communities to study disaster resilience and make risk-informed decisions.

• Physics-based models of interdependent and cascading hazards and their effect on buildings, bridges and transportation infrastructure, and energy, water/wastewater, and communication networks.

• Modeling and integration of socio-economic support systems and their interfaces with physical infrastructure systems.

• Resilience-based community performance criteria and metrics.• Decision support system embedded in a state-of-the-research

computational environment (IN-CORE).


Thrust 1: The Development of IN-CORE

Individual Hazards

Multiple and

Competing Hazards

Nonstat. in Long-term Resilience

Assessment

Buildings Transpo.

Networks

Water & Wastewater Energy Comm.

Networks

Social Systems

Economic Systems

Optimization Strategies

Centerville

EQ

Tornado

Seaside

Oregon

EQ

Tsunami

Memphis

TN, MSA

EQ

Flood

Galveston, TX

Hurricane

-Surge

-Waves

-Wind

Two tiers


Tier 1 Research Tools: Executed completely within IN-CORE using libraries and plug-ins developed as part of the CoE.

Tier 2 Research Tools: Will run in IN-CORE but import data for the hazard portion of the analysis, e.g. a wind field shape file from an outside software (or other) source, overland surge flow from ADCIRC, etc.

Scenarios


• Earthquakes Tier 1• Rupture• Magnitude• Distance• Depth

• Earthquakes Tier 2• 3-D physics-based• Seismic wave

propagation• Example MMSA

• Windstorms – Tier 1• 3-sec wind gust over specified region

• Tornado – Tier 1 • Statistical representations• Historical estimated wind field from an

event

• Hurricane: Wind, Wave, and Surge – Tier 1• Data-driven wind field models from past

hurricanes• USACOE Coastal Hazard System

• Hurricane: Wind, Wave, and Surge – Tier 2• ADCIRC, SWAN• Surrogate Models

Scenarios


• WUI Fire – Tier 1• Propagation in wildlands using CA• Propagation inside a community

• Tsunami – Tier 1• ASCE 7 Procedures

• Tsunami – Tier 2• Time dependent numerical model• Specified bathymetry and bare-earth

topography

• Flood – Tier 1• NFIP Maps• Flood depth w/o velocity

• Flood – Tier 2• Fluvial (riverine)• Pluvial (excessive precipitation)• Coastal (sea level rise)• Example will be available for Wolf

River basin in Shelby Co., TN


Physical Infrastructure Modeling in IN-CORE

Buildings Transpo.

Networks

Water & Wastewater Energy Comm.

Networks

A few features:• Wind Damage and Repair fragilities – 19 building types• Damage for tsunami, floods• Combined wind-wave-surge building fragilities under development• Modeling of EPN and water networks with dependencies• Transportation systems – primarily bridges, road and railway• Aging and deterioration modeling

Physical Infrastructure: Transportation


• Simulate existing roadway and rail networks• Evaluation of critical routes for recovery• Assess impact of select retrofits on network performance• Hurricanes, floods, earthquakes, and tsunamis

• Damage fragilities include:• Roadway Bridges (as-built and retrofitted) for

earthquake and flood scour• Roadways for earthquake and hurricane

wave/surge• Railway bridges for earthquakes, hurricane, flood,

and tsunami)• Repair fragilities include:

• Bridges, roadways, railway bridges, rail tracks

Misra and Padgett, 2017

Physical Infrastructure: Water/WW


• Water/Waste water• Predict physical damage and functionality• Includes deterioration analysis• Critical path ID for recovery• Novel recovery functions based on priority,

available resources, etc.• Interdependencies with other physical and social

systems

Central

Water

Treatment

Plant

WellfieldAuxiliary

Pumping

Station

Guidotti et al, 2016

Physical Infrastructure: EPN, NGN, and TN


• Electrical Power Networks (EPN)• Modeling of generating plants, substations, transmission poles, power poles• CA algorithm to determine service areas based on capacity-demand balance• Repair duration algorithms with interdependencies and critical path ID

• Natural Gas Network (NGN)• Includes damage for processing plant, compressor

stations, pipelines, etc.• Network recovery modeling with critical path ID

• Telecommunication Networks (TN)• Wired and wireless/cellular• Recovery dependent on electrical poles

Attary et al, 2017

Physical Infrastructure: Buildings


• Seismic Analysis: Many existing fragilities• Modeling at both the individual and portfolio level• Default damage fragilities for new steel, RC, masonry, wood,

and mobile homes• Earthquakes and tsunamis; tornado and straight-line winds;

coastal storm surge, waves, and flooding• Four damage states• Five functionality states • New functionality taxonomy - interaction with the community• Interdependency with other physical infrastructure, services,

and the local economy

Lin and Wang, 2017

Attary et al, 2016


Social Science, Economics, and Decision

Social Systems

Economic Systems

Optimization Strategies

• Computable General Equilibrium Model• Social vulnerability modeling• Population Dislocation Modeling• Housing recovery models• Business Interruption models• Vacancy modeling for buildings• Influx of capital for financing recovery• Metrics to track/identify recovery

Decision Support for optimal:• Pre-event planning• Investments• Recovery planning

• Risk-informed!

Economic Modeling and Analysis


• Spatial dynamic computable general equilibrium model (SD-CGE)

• Incorporate infrastructure damage and population disruptions

• Provide range of economic and fiscal impact• Code changes, scenario type

• Includes:• Losses in production, wages, jobs in local econ sectors• Investigates with policy levers; optimal allocations of

resources

Emsi

Social Science Modeling and Analysis


• Population and employee dislocation

• Housing restoration and recovery

• Business interruption and restoration

• Identify socio-demographic and socio-economic vulnerability characteristics Daily Express

Dependency and Interdependency Modeling


• Buildings -- transportation, EPN, TN, water/WW, occupants• Transportation routes -- EPN, TN• Water/WW – (two way) -- population, businesses, services• Social systems and services -- buildings, EPN, TN, water/WW,

transportation, healthcare, education, governance• Economic systems and services -- buildings, EPN, TN, water/WW,

transportation

Community Goals and Metrics


• Community goals are aspirational levels for metrics

• Each analysis will contribute to community metrics• e.g. % buildings functional • 4 goal areas of community resilience

(stability areas)

CNN

DRR.com

Centerville, USA: A Virtual Community


Purpose: Provide researchers a simplified illustration of how individual infrastructure portfolios and infrastructure and their dependencies can be modeled; linkages to social and economic systems.

• 19 building archetypes for wind developed from Centerville

• Archival documentation in Sustainable and Resilient Infrastructure

• Illustrative example for EQ and tornado, but users can explore other hazards using Centerville

• The Centerville storyline was completed in early 2017; some extension ongoing


Hindcasting the Joplin Tornado


May 22, 2011 Tornado•EF5 multiple-vortex

•Fatalities:161, Injured: 1150

•Costliest single tornado in US history :

US$2.8 billion

•Population: 51,316 (2014 estimate)•Housing units: 23,322 •Median value of housing: US$103,300

City of Joplin, Missouri


Resolution Sensitivity Analysis

50x50 ft

100x100 ft

200x200 ft

500x500 ft

1000x1000 ft


Resolution Sensitivity Analysis

50x50 ft100x100 ft200x200 ft500x500 ft1000x1000 ft


Resolution Sensitivity Analysis Results (DS4>50%)Type 1000x1000ft 500x500ft 200x200ft 100x100ft 50x50ft Actual Event

T1-T5 9431 7092 5523 4968 4709 4551

T6 0 32 75 146 171 185

T7 81 115 136 138 140 139

T8 21 9 11 17 22 21

T9 0 0 0 8 9 9

T10 0 0 5 5 5 5

T11 0 0 0 2 1 1

T12 0 18 13 12 17 19

T13 0 1 6 13 20 21

T14 0 0 2 5 7 7

T15 0 1 5 6 5 6

T16 0 0 2 6 5 5

T17 110 49 24 13 14 11

T18 0 0 0 1 1 1

T19 0 69 109 172 204 218

Mode Scenario (DS4>50%)


Building’s Probability of Completing Repairs


Population Dislocation 20,820 People in the Tornado Path

87% Non-Hispanic White ResidentsRegion EF1 EF2 EF3 EF4 EF5 Total

Number of Residential Buildings 2592 1414 1771 823 613 7213

Population Estimation 7482 4081 5112 2376 1769 20820

Peacock, Walter Gillis, Betty Hearn Morrow, and Hugh Gladwin (1997). Hurricane Andrew: Ethnicity, Gender and the Sociology of Disaster. London: Routledge. Details also available in Clapp T. (2016)

From Texas A&M Univ. Andrew Data


Population Dislocation

25

Time EF1 EF2 EF3 EF4 EF5

1 Day 0.138 0.340 0.578 0.784 0.920

2 Months 0.085 0.274 0.539 0.780 0.931

5 Months 0.075 0.221 0.436 0.665 0.852

8 Months 0.055 0.164 0.338 0.549 0.757

10 Months 0.046 0.120 0.236 0.389 0.575

Probability of Population Dislocation

Testbed Community Modeling

73071

73069

73072

8.0 miles

9.0

mil

es

More than 90% of Norman’s population.

The area was divided into 0.16 square

kilometer (one-sixteenth square miles) grids.

Tornado-induced Wind Load

Parameters Description Approach A Approach B

Tornado Pressure Adjustment

Uplift Pressure

MWFRS Te 1.8 - 3.2

1.0

Ti 0.0

C&C Te 1.4 - 2.4

Ti 0.0

Lateral Pressure

MWFRS Te 1.0 - 1.5

Ti 1.0

C&C Te 1.2 - 2.0

Ti 0.0

External Pressure Coefficients, GCp ASCE 7-10 ASCE 7-16

1

• Two alternative approaches were proposed—namely approach A and B:

• Tornado-induced wind pressure can be calculated by (Masoomi and van de Lindt 2016):

h e p i pip q T GC T GC

Te = tornado external pressure adjustment

Ti = tornado internal pressure adjustment

• There is high level of uncertainty in Te and Ti.

Schools During Past Tornadoes

Xenia, Ohio 1974 (F5 tornado)

Michigan 1980 (F3 tornado)

• Xenia senior high school was demolished and reconstructed.

• St. Augustine elementary school was demolished and reconstructed.

Moore, Oklahoma 1999 (F4 tornado)• Kelly elementary school was demolished and reconstructed.

Enterprise, Alabama 2007 (EF4 tornado)• A school collapsed and 8 student fatalities were reported.

Joplin, Missouri 2011 (EF5 tornado)• Joplin High School, Franklin Technical Center, and Joplin East Middle

School were extensively damaged.

Moore, Oklahoma 2013 (EF5 tornado)• Briarwood Elementary School, Plaza Towers Elementary School, and

Highland East Junior High School were extensively damaged.

Damage States for School Building

Xenia senior high school

Xenia, Ohio

was hit by an F5 tornado

in 1974.

DS Roof Cover Failure Window/Door Failures Parapet FailureNon-load-bearing Wall

Failure

Roof Structural

Failure

Load-bearing Wall

Failure

0 ≤ 2% No No No No No

1 2%< and ≤15% 1 or 2 No No No No

2 15%< and ≤50% 1 or 2< and ≤25% No No No No

3 50% < 25% < Yes Yes No No

4 Typically 50% < Typically 25% < Typically Yes Typically Yes Yes Yes

Tornadic Fragility Flowchart

GCp : from ASCE 7-16

Te = Ti = 1.0

GCp : from ASCE 7-10

Te : from Hann et al. (2010)

Ti : 1.0 or 0.0 (Table 2)

Random Load

using equations (1) and (2)

Approach A

Approach B

Random ResistancesFailure Condition

of Components

Condition of

Damage States

MCS

Components

Failure Probability

Damage States

Occurrence Probability

After n MCS After n MCS

Plot

Fragilities After covering a wide range of wind speeds

Building Fragility Curves- Approach A

Type Construction Description

M1 Fully Grouted, M/S, PCL

M2 Partially Grouted, M/S, PCL

M3 Ungrouted, M/S, PCL

M4 Ungrouted, N, PCL

EF0 EF1 EF2 EF3 EF4

9022 45 67

DS1 DS2 DS3 DS4

URM1:

M/S, PCL

Fully Grouted

22 34 45 56 67

EF0 EF1 EF2 EF3

DS1 DS2 DS3 DS4

URM2:

M/S, PCL

Partially Grouted

22 34 45 56 67

EF0 EF1 EF2 EF3

DS1 DS2 DS3 DS4

URM3:

M/S, PCL

Ungrouted

DS1 DS3

DS4 DS2

URM4:

N, PCL

Ungrouted

EF0 EF1

4522 27 3631 40 50

0

0.2

0.4

0.6

0.8

1

50 75 100 125 150

P[ D

S >

ds |

v ]

3-Sec Gust Wind Speed (mph)

3-Sec Gust Wind Speed (km/h)

0

0.2

0.4

0.6

0.8

1

50 100 150 200

P[ D

S >

ds |

v ]



0

0.2

0.4

0.6

0.8

1

50 75 100 125 150

P[ D

S >

ds |

v ]



0

0.2

0.4

0.6

0.8

1

50 60 70 80 90 100 110

P[ D

S >

ds |

v ]



(m/s) (m/s)

(m/s) (m/s)

Community Components Properties

0

0.2

0.4

0.6

0.8

1

50 100 150 200

P[ L

S >

ls |

v ]



EF0 EF1 EF2 EF3 EF4

9022 45 67

(b) Approach B Fragilities

22 34 45 56 67

EF0 EF1 EF2 EF3

0

0.2

0.4

0.6

0.8

1

50 75 100 125 150

P[ L

S >

ls |

v ]



(a) Approach A Fragilities

Prob

abili

ty o

f Fai

lure

Prob

abili

ty o

f Fai

lure

(m/s) (m/s)

EF0 EF1 EF2 EF3 EF4

9022 45 67

DS1 DS2 DS3 DS4

0

0.2

0.4

0.6

0.8

1

50 100 150 200

P[ D

S >

ds |

v ]


3-Sec Gust Wind Speed (km/h)(m/s)

EF0 EF1 EF2 EF3 EF4

9022 45 67

DS1

DS2

DS3DS4

0

0.2

0.4

0.6

0.8

1

50 100 150 200

P[ D

S >

ds |

v ]


3-Sec Gust Wind Speed (km/h)(m/s)

(b) Approach B Fragilities(a) Approach A Fragilities

Roof cover fragility curves:

Building fragility curves:

Community Components Properties

A set of fragility curves for each

community component.

A set of repair times for each

community component.

Developed here Lopez et al., 2009

Assumed here

FEMA

Network Component DS1 DS2 DS3 DS4

µ σ µ σ µ σ µ σ

Residential Buildings Repair Time 5 1 20 2 90 10 180 20

Permitting Time 2 1 7 3 14 7 30 15

Workplace

Buildings

Repair Time 5 1 20 2 90 45 180 90


School

Buildings

Repair Time 5 1 20 2 180 90 730 180


Electric Power

Network

Transmission

Substation and

Distribution Substation

1 0.5 3 1.5 7 3.5 30 15

Transmission Tower - - - - - - 2 1

Sub-transmission Tower - - - - - - 1 0.5

Water

Network

Water Tower 1.2 0.4 3.1 2.7 93 85 155 120

Water Treatment Plant 0.9 0.3 1.9 1.2 32 31 95 65

1

Network Component DS1 DS2 DS3 DS4

m ξ m ξ m ξ m ξ

Residential Buildings

Small, 1-story 39.6 (88.7)

0.14 47.0

(105.1) 0.12

53.5 (119.7)

0.11 51.9

(116.2) 0.15

Small, 2-story 36.6 (81.9)

0.13 42.9 (96.1)

0.12 49.9

(111.6) 0.11

54.6 (122.1)

0.14

Medium, 1-story 37.0 (82.7)

0.13 43.4 (97.0)

0.12 50.4

(112.7) 0.11

61.6 (137.7)

0.12

Medium, 2-story 41.7 (93.2)

0.13 47.9

(107.2) 0.12

54.6 (122.1)

0.11 61.6

(137.7) 0.14

Large, 2-story 42.5 (95.1)

0.13 48.4

(108.3) 0.12

53.5 (119.7)

0.11 47.5

(106.2) 0.15

Mobile Home 35.5 (79.5)

0.09 42.2 (94.4)

0.09 49.2

(110.0) 0.11

52.7 (117.8)

0.12

Workplace Buildings

Light Industrial 34.6 (77.5)

0.10 41.9 (93.7)

0.09 45.4

(101.5) 0.09

49.2 (110.0)

0.09

Heavy Industrial 40.2 (90.0)

0.10 45.4

(101.5) 0.14

60.0 (134.3)

0.10 71.2

(159.2) 0.19

Small Big-box 29.1 (65.0)

0.08 36.6 (81.9)

0.09 69.4

(155.3) 0.11

76.7 (171.6)

0.10

Large Big-box 34.1 (76.3)

0.09 40.9 (91.4)

0.08 65.4

(146.2) 0.10

75.9 (169.9)

0.10

Small Unreinforced

Masonry 31.3 (70.1)

0.09 44.5 (99.5)

0.09 52.2

(116.8) 0.09

69.1 (154.5)

0.18

Medium Unreinforced

Masonry 36.4 (81.5)

0.13 40.7 (90.9)

0.11 52.2

(116.8) 0.10

66.4 (148.4)

0.21

Large Reinforced

Masonry 32.6 (73.0)

0.12 42.7 (95.6)

0.11 49.2

(109.9) 0.11

71.2 (159.2)

0.12

Extra Large Reinforced Masonry

33.6 (75.2)

0.12 42.3 (94.6)

0.11 49.2

(109.9) 0.10

71.9 (160.8)

0.12

2-story Reinforced

Concrete 32.3 (72.2)

0.08 40.7 (90.9)

0.09 48.2

(107.8) 0.08

57.7 (129.0)

0.11

4-story Reinforced

Concrete 41.1 (91.8)

0.13 45.8

(102.5) 0.09

65.0 (145.5)

0.08 77.1

(172.5) 0.08

School

Network

High School 41.5 (92.8)

0.09 46.3

(103.5) 0.11

52.7 (117.9)

0.10 79.4

(177.7) 0.13

Middle School 40.6 (90.9)

0.09 47.2

(105.6) 0.11

52.7 (117.9)

0.10 70.4

(157.6) 0.12

Elementary School 39.1 (87.4)

0.09 47.2

(105.6) 0.09

50.6 (113.3)

0.13 60.6

(135.6) 0.22

Electric Power

Network

Transmission

Substation and

Distribution Substation

33.4 (74.8)

0.20 39.5 (88.4)

0.20 48.6

(108.8) 0.20

60.8 (136.0)

0.20

Transmission Tower - - - - - - 60.8

(136.0) 0.12

Sub-transmission Tower - - - - - - 55.9

(125.0) 0.12

Water Supply

Network

Water Tower 34.4 (77.0)

0.15 40.7 (91.0)

0.15 50.1

(112.0) 0.15

62.6 (140.0)

0.15

Water Treatment Plant 36.9 (82.5)

0.15 43.6 (97.5)

0.15 53.6

(120.0) 0.15

67.1 (150.0)

0.15

Tornado Path Simulation

EF5EF4EF3EF2EF1EF0

Transmission Substation Transmission Line

20 20 24 160 18 18 7 10 4 3 2 1 4 7

26 21 160 4 18 18 6 3 2 4 2

2 15 200 18 3 14 7 2 6

2 1 1 1 200 350 4 9 6 2 10 5

3 3 60 100 50 150 25 2 300 6 7 9 4 2 1

3 4 60 100 100 5 2 20 20 3 2 2 6

18 20 20 100 100 30 20 180 13 50 3 100 2 5 7 4

3 10 10 20 20 100 50 180 60 200 10 120 20 2 8 1 100 100 3 2 5 8 5 4 4

4 2 20 12 75 50 200 200 600 1000 600 300 150 125 125 40 50 60 30 5 4 3 2

7 15 15 100 150 200 1000 600 50 100 75 75 100 100 100 30 4 4 4

150 150 900 900 50 50 50 150 150 125 100 100 150 80 10 8 1

40 40 150 50 1100 900 100 100 150 150 100 100 50 75 100 150 150 10 8 1 2 1

50 50 50 50 50 150 200 200 450 150 3 100 100 50 75 1 2

150 150 150 150 150 100 500 200 200 300 750 200 100 3 100 50 150 4

150 150 150 150 75 150 300 300 100 300 150 150 150 150 130 130 80 80 1

150 150 150 150 150 650 300 75 150 150 410 200 100 100 130 1 3 3

100 150 150 150 150 600 150 400 80 150 150 100 100 100 100 950 150 15 100 100 120 30

100 150 150 150 150 150 400 320 75 150 150 150 100 680 50 75 250 150 10 10 610

150 150 150 150 150 150 150 150 800 280 150 150 150 150 150 150 400 850 850 150 150 500 75 150 150 150 150 150 2

2 75 150 150 150 150 150 480 300 500 800 150 150 150 150 400 100 750 750 750 150 50 450 100 150 150 150 100 150 2

200 200 150 125 900 250 75 100 75 100 150 150 150 150 125 100 150 100 150 100 900 150 50 200 50 100 100 100

5 5 150 150 75 300 100 150 75 75 150 150 150 150 125 800 150 150 150 150 150 150 50 8 100 100 100

50 75 300 100 100 100 150 150 150 150 1200 1200 100 150 100 150 150 75 100 150 75 100 5

570 40 75 75 150 150 150 150 1200 1200 500 40 150 150 150 100 125 125 10 7 12 12

100 470 760 100 125 125 150 150 1200 1200 150 160 240 100 500 150 150 125 100 100 4 10

450 150 150 100 150 150 150 1200 1200 50 150 40 100 150 150 150 150 125 150 6

100 150 150 150 150 150 150 240 240 30 100 75 150 150 150 150

20 150 150 150 150 150 150 25 25 100 100 100 100 200 100 100

20 150 20 150 150 60 100 400 50 50 370 200 20 50 80

150 150 125 300 300 50 100 100 150 300

150 100 20 150 50 50 150 6

40 20 200 80 60 50 50 20

30 150 150 100 75 25 25

150 150 50 30 50

200 50 10 40

15 10 5 2

Indian Hills Rd

Franklin Rd

Tecumseh Rd

Rock Creek Rd

Robinson St

Alameda St

Lindsey St

Cedar Lane Rd

Post Oak Rd

Imhoff Rd

60th

Ave

NW

48th

Ave

NW

36th

Ave

NW

24th

Ave

NW

12th

Ave

NW

N P

orte

r Ave

12th

Ave

NE

24th

Ave

NE

36th

Ave

NE

35

35

9 9

9

77

77

77

Subtransmission LineDistribution Substation

Water Treatment Plant

Water Towers (Million Gallons): 0.5 1.5 2.0

High

School

Elementary

School

Middle

School 1 mile

EF4

The parameters needed to

simulate a tornado path:

• Center Point

• Direction

• Length

• Width

Gaussian copula model

was utilized for

generating the correlated

length and width:

Spatial Damage Simulation

EF Scale

3-Sec Gust Wind Speed, m/s (mph)

Wind Speed Range

(McDonald and Mehta, 2006) Mean Value

EF0 29-38 (65-85) 34 (75)

EF1 39-49 (86-110) 44 (98)

EF2 50-60 (111-135) 55 (123)

EF3 61-74 (136-165) 67 (150)

EF4 75-89 (166-200) 82 (183)

EF5 >89 (>200) 101 (225)

1

3-Sec Gust Wind Speed (m/s)

Prob

abili

ty o

f Fai

lure

No

Dam

age

DS1 DS2 DS3 DS4

A Set of Fragility Curves

EF0

EF1

EF2

EF3

100 Yard

V = N/A

V = 123 mph (55 m/s)

V = 98 mph (44 m/s)

V = 75 mph (34 m/s)

V = 123 mph (55 m/s)

V = 150 mph (67 m/s)

V = 75 mph (34 m/s)

In-Path Components

The range of

wind speed for

each EF scale:

Community Restoration Analysis

Generate an initiation time

Update time

Update the status of the repaired

components to “not-failed”

Allocate available resource units

to the damaged components in

the recovery sequence list

Create a recovery sequence list

Rep

eat u

ntil

all

com

pone

nts

in th

e li

st a

re r

ecov

ered

Calculate the performance indices

Release the corresponding

resource units

Restoration

Analysis

Record the percentage of affected

students, residential buildings, and

employees for each residential grid

Sort non-functional DSSs

based on their demand level

Find all paths from source

to each non-functional DSS

Select the DSS with

the highest priority

Select the path with the

shortest time for recovery

Add the damaged

components of the path to


Sort the remaining*

damaged components of the

network based on their

repair time and add them to


Rep

eat f

or a

ll no

n-fu

nctio

nal D

SS

*Because of redundancy in some

DSSs, there could be damaged

components that do not cause

non-functionality in the DSSs.

Recovery

Sequence

List

Plot mean restoration and

population outmigration curvesAfter n MCS

Assign a repair time to each

damaged component based

on its damage state

Population Outmigration Analysis

Plot restoration and population

outmigration curves

Community Components Properties:

1. Fragility Curves

2. Repair Time

3. Repair Cost

Community Modeling:

1. Topology of Components

2. Dependencies

3. Cross-Dependencies

Hazard Modeling:

1. Tornado path simulation

2. Find the in-path components

and associated EF intensities

Spatial Damage Simulation:

● Find the damage level for

each in-path component

(i.e., intrinsic failure status)

Find the extrinsic and

functionality failure statuses

for all community components

Restoration analysis for the EPN,

WSN, school network, residential

grids, and business grids

Mon

te C

arlo

Sim

ulat

ion

For example: for the EPN

Population Outmigration Analysis

0 0 0 0 0 2.34995 1.97134 2.39333 0 0 0 0 0 0 1.46287 1.53061 0.62792 0.86228 0.33601 0 0.1537 0 0 ####### ####### 0 0 0.22446 1.01076 0 0 0

0 0 0 0 0 0 3.35829 2.36823 0 0 0 0 ####### 0 1.69343 1.68446 0.45037 ####### ####### 0.19137 0 0 0 ####### 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 ####### 1.8969 0.36236 0 0 1.79788 0 0 0 ####### 1.21416 0 0.59616 ####### 0 0 0 0 0 0 0 0.66344 0 0 0

####### 0 0 ####### 0 0 0 ####### 0 ####### 0 0 0 0 0 ####### 0.75161 0.68676 ####### 0.92422 0 0 0 0 0 0 0 0 0 0 0.56092 0

0.21682 0 0 0.31752 0 7.64204 13.2528 6.47337 0 0 0 0 0 2.29715 0 ####### 0 0.46524 0.70986 0.87762 0.2577 ####### 0 0 0 0 0 ####### 0 0 0 0

####### 0 0 0.29584 0 8.00506 15.7704 13.6574 0 0 0.54813 ####### 0 0 0 0 0 0 0 0 0 0 2.04531 2.00739 ####### 0 ####### 0 ####### 0 0.66661 0

0 0 0 2.22289 2.95206 2.77573 15.8735 15.3104 0 0 0 0 1.09226 0 0 0 0 0 0 5.95417 ####### 0 11.3236 0 0 0 ####### 0.58341 1.33734 0 0 0.49176

0.35039 0 1.20202 1.15551 3.02505 2.78904 14.8148 8.16924 0 0 0 0 ####### 0 0 ####### 0.79405 0 ####### 0 0 0 12.2072 0 0.38562 ####### 0.49921 0.9582 0.82 0 0.48929 0.4848

0.28481 ####### 0 0 2.91547 1.54338 12.0789 8.19693 0 0 0 0 0 0 0 0 16.4513 0 13.2467 14.7895 5.3386 6.41244 0 0 8.9455 5.0384 0 0 0.71841 0.83692 0.33907 #######

0.78416 0 0 0 2.01499 2.01226 15.5594 42.2239 0 0 0 0 0 0 0 0 0 0 5.7028 12.6081 11.1183 9.88946 14.9416 14.9401 16.3763 4.5864 0 0.56896 0.69058 0.56789 0 0

0 0 0 0 0 0 25.5064 42.5118 0 0 0 0 0 0 0 0 5.59165 5.52958 6.01427 19.4834 22.3559 20.1326 14.7627 14.26 26.5628 12.5362 1.44521 1.00806 ####### 0 0 0

0 0 0 0 0 6.38756 27.398 15.6188 3.62639 0 0 0 0 0 0 0 0 19.5035 18.8026 13.6084 15.1872 7.61282 11.6012 15.5041 27.7821 25.8948 1.53657 1.19723 ####### 0 ####### #######

0 0 0 0 6.57188 7.45052 4.49899 4.64266 6.82052 21.0793 0 0 0 0 0 0 0 0 0 0 0 ####### 0 0 15.5694 14.7621 0 6.88628 12.9254 ####### 0 #######

0 0 0 0 20.6466 23.0942 13.525 15.238 22.1383 13.8232 0 0 0 0 0 0 0 0 0 22.8741 ####### 0 0 0 15.9027 0 7.32359 15.0423 0 0 0 0.37808

0 0 0 0 20.6394 27.428 29.3694 15.4795 10.9024 22.4511 0 0 0 0 0 0 0 68.3774 32.3742 33.7807 17.6547 17.339 0 0 20.629 19.4223 11.5037 10.3041 0 0 ####### 0

0 0 0 0 20.3695 28.4229 27.7252 15.5323 21.875 7.21468 0 0 0 0 0 0 0 17.0136 35.913 37.6805 0 0 0 0 15.877 14.9903 0 17.9677 ####### 0 0.3202 0.29749

0 0 0 19.9967 23.3156 25.8362 28.2725 28.6676 14.4562 14.5402 0 0 0 0 32.9901 38.5462 28.3288 28.3591 27.3774 28.3565 0 28.2335 1.66494 0 12.1058 0 0 0 0 0 0 0

0 0 0 21.2845 24.1136 26.5536 28.3831 30.0064 47.5732 14.6833 0 16.3801 33.9811 0 37.218 41.2276 30.0024 0 13.0333 20.3265 7.18721 28.5746 1.08531 0.94985 0 0 0 0 0 0 0 0

0 0 29.0171 34.0085 25.1647 26.1946 30.3541 29.2959 43.5751 45.1605 0 0 22.2015 22.8907 23.7105 42.2488 29.4336 28.3767 5.89992 9.47161 0 56.9161 13.5115 0 8.86884 17.7977 16.4297 15.4767 31.3467 27.6383 0 #######

0 ####### 14.774 31.3299 23.3167 24.4036 28.5568 29.5879 0 0 0 0 21.659 23.4433 23.2909 23.0494 0 18.5057 3.85927 0 0 55.2967 5.79122 3.47059 11.4501 17.121 16.8472 15.5413 20.0836 27.5656 ####### 0

0 0 0 0 53.7 51.72 29.4125 22.956 0 0 11.122 15.4737 22.7305 32.3976 28.9033 30.2664 25.8395 29.2001 23.2005 27.0789 22.6142 14.963 18.4234 12.2682 0 36.8396 17.1509 0 9.64811 17.1955 17.4399 16.0414

0 0 0 0 0.90055 1.19725 25.6455 26.9226 19.5792 0 15.9065 22.3289 23.2816 23.4299 30.2004 28.8243 26.5746 30.1511 23.8359 27.834 22.7138 22.7151 18.9173 18.1098 41.4739 40.8398 19.4443 1.11777 0 19.7791 18.5979 16.4194

0 0 0 0 0 0 0 12.366 18.7397 0 0 15.5058 36.5631 36.9784 17.0603 36.8735 26.6522 31.2004 0 0 43.9734 22.6451 30.7536 46.843 17.0422 30.2161 38.2184 23.1479 16.1842 19.5454 0 0.89168

0 0 0 0 0 0 0 0 0 0 0 6.15911 28.1129 27.6427 16.2942 37.728 32.1018 32.6116 0 0 0 17.6069 46.6359 45.0346 16.8937 16.0932 20.4802 18.634 2.17056 1.19609 2.03306 1.85164

0 0 0 0 0 0 0 0 0 0 25.7897 0 19.0436 39.7159 31.4273 30.5285 26.0817 26.992 0 0 58.7149 0 0 26.5435 11.7175 38.673 20.8767 16.8087 21.1036 18.915 0.44666 1.49702

0 0 0 0 0 0 0 0 0 0 0 0 57.4644 61.0356 23.009 37.0453 27.7785 27.6202 0 0 0 54.5688 15.0854 26.6057 36.8743 21.7639 21.3679 19.8188 25.0496 29.4461 0 0.62155

0 0 0 0 0 0 0 0 0 0 0 9.81096 15.9638 16.5947 32.0206 34.4093 26.052 26.3286 0 0 0 0 0 11.6629 12.7538 10.0946 20.2586 18.3037 29.1185 27.3902 0 0

0 0 0 0 0 0 0 0 0 0 0 1.57996 15.877 15.803 34.104 34.3546 25.109 25.9977 8.94716 7.86424 0 0 0 0 12.5742 12.0636 12.1606 12.5168 0 0 0 15.1702

0 0 0 0 0 0 0 0 0 0 0 0 0 1.95216 15.4617 2.17252 16.2394 15.1424 0 0 0 14.9737 0 15.7077 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14.9482 14.2969 12.3751 0 0 0 0 15.4365 30.5404 12.4726 0 0 0 19.9749 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13.261 9.40642 0 0 0 0 0 0 5.35463 0 6.63218 6.20749 19.469 0.55211 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19.1945 15.4926 6.2236 6.62621 0 2.27607 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7.17247 40.7426 41.025 23.6626 0 0 0 2.72291 2.97201 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 38.2422 41.1099 10.6797 0 0 0 5.7208 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 50.7486 11.257 1.72974 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.58763 1.9247 0 0 0.41773 ####### 0 0

Indian Hills Rd

Franklin Rd

Tecumseh Rd

Rock Creek Rd

Robinson St

Alameda St

Lindsey St

Cedar Lane Rd

Post Oak Rd

Imhoff Rd

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48th

Ave

NW

36th

Ave

NW

24th

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NW

12th

Ave

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0

10

20

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0 500 1000 1500 2000 2500

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Time (days)

0

5

10

15

20

25

30

0 500 1000 1500 2000 2500

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Time (days)

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10

20

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Time (days)

(a)

(b)

(c)

Tornado-Induced Casualties

The National Weather Service (NWS) Storm Prediction Center (SPC) has

a database of tornadoes in the United States since 1950. A tornado may strike a town in a populated area of a county or an

undeveloped part of a county.

The number of housing units and people in tornado path can be

approximated by using the tornado track information and the U.S. census

data. The U.S. census data for the years 1970, 1980, 1990, 2000, and 2010 and

the American Community Survey (ACS) 5-year estimate for the year 2015

were used in this study. The intercensal estimate was done for each tornado by simple linear

interpolation based on the year of occurrence.


Counties Census Tracts Block Groups Census Blocks

The EF4 tornado occurred on April 27, 2011 in the city of Tuscaloosa.

T

(a) (b) (c) (d)

(a) (b) (c) (d)


Touchdown:

Longitude: -87.9350

Latitude: 33.0297

Lift-off:

Longitude: -86.7436

Latitude: 33.6311

Buffer

Tornado Path

IntersectCensus

Boundary

Shapefile

Tuscaloosa

Greene

Jefferson

The error can be as

high as 80% when

the census data at

the county level is

used.(a) County-level

(b) Census Tract-level

(c) Block Group-level

(d) Census Block-level


Sources: Esri, HERE, DeLorme, USGS, Intermap, INCREMENT P, NRCan, Esri Japan, METI, Esri China (Hong Kong), Esri Korea, Esri (Thailand), MapmyIndia, NGCC,

© OpenStreetMap contributors, and the GIS User Community

Sources: Esri, HERE, DeLorme, USGS, Intermap, INCREMENT P, NRCan, Esri Japan, METI, Esri China (Hong Kong), Esri Korea, Esri (Thailand), MapmyIndia, NGCC,

© OpenStreetMap contributors, and the GIS User Community

(a)

(b)

The assumption of a straight line path may underestimate or overestimate the

affected community properties, e.g., Joplin tornado: A simplified angular

path which is close

to the observed path:

• P: 14,870

• H: 6,830

An assumed straight

path:

• P: 4,480

• H: 1,821


The intercensal estimate was done 20,001 tornadoes in the filtered

dataset.

Three models were developed:

Model 1: explanatory variables are LENGTH, INTENSITY, and

POPULATION.

Model 2: In addition to variables in Model 1 it has SEASON,

DAY, and EVENING.

Model 3: In addition to variables in Model 1 it has DAMAGE.

Designing for Community Resilience ?

7.3

96.0

1980

4.6

68.3

919

2.1

35.7

266

0.8

12.9

167

EF3

Closure


• Estimating building loads and damage is easy !

• Estimating the effect of these loads on community resilience is not !

Thank you.The Center for Risk-Based Community Resilience Planning is a NIST-funded Center of Excellence; the Center is funded through a cooperativeagreement between the U.S. National Institute of Standards andTechnology and Colorado State University (NIST Financial AssistanceAward Number: 70NANB15H044). The views expressed are those of thepresenter, and may not represent the official position of the NationalInstitute of Standards and Technology or the US Department ofCommerce.Numerous researchers contributed to the contents of this presentationand a sincere thank you is due to everyone affiliated with the Center forRisk-Based Community Resilience Planning.

Some work presented here was supported by the National ScienceFoundation (NSF) under Grant No. CMMI-1452725. This support isgratefully acknowledged. All views are those of the authors and do notnecessarily reflect the views of the NSF.

wind hazards & building loads in the context of community

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