Determination of Individual Building Performance
Targets to Achieve Community-Level Social
and Economic Resilience Metrics
Wanting “Lisa” Wang, S.M.ASCE1; John W. van de Lindt, F.ASCE2; Brad Hartman3;
Harvey Cutler4; Jamie L. Kruse5; Therese P. McAllister, F.ASCE6; and Sara Hamideh7
Abstract: The retrofit of wood-frame residential buildings is a relatively effective strategy to mitigate damage caused by windstorms.
However, little is known about the effect of modifying building performance for intense events such as a tornado and the subsequent
social and economic impacts that result at the community level following an event. This paper presents a method that enables a com-
munity to select residential building performance levels representative of either retrofitting or adopting a new design code that computes
target community metrics for the effects on the economy and population. Although not a full risk analysis, a series of generic tornado
scenarios for different Enhanced Fujita (EF) ratings are simulated, and five resilience metrics are assigned to represent community goals
based on economic and population stability. To accomplish this, the functionality of the buildings following the simulated tornado is
used as input to a computable general equilibrium (CGE) economics model that predicts household income, employment, and domestic
supply at the community level. Population dislocation as a function of building damage and detailed sociodemographic US census-based
data is also predicted and serves as a core community resilience metric. Finally, this proposed methodology demonstrates how the
metrics can help meet community-level resilience objectives for decision support based on a level of design code improvement or
retrofit level. The method is demonstrated for Joplin, Missouri. All analyses and data have been developed and made available on
the open-source IN-CORE modeling environment. The proposed multidisciplinary methodology requires continued research to char-
acterize the uncertainty in the decision support results. DOI: 10.1061/(ASCE)ST.1943-541X.0003338. This work is made available
under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.
Author keywords: Community resilience; Community goals; IN-CORE; Multidisciplinary; Performance targets; Retrofit; Tornado.
Introduction In 2015, the US National Institute of Standards and Technology
(NIST) proposed a general framework to help communities develop
The performance of civil infrastructure systems supports community resilience plans for building clusters (a group of buildings that sup-
resilience but has been primarily controlled by probability-based port a community function such as education) and infrastructure
limit states design over the last several decades (e.g., ASCE 7-16). associated with social and economic systems (NIST 2015). Since
then, an increasing number of researchers have focused on physical
1Ph.D. Candidate, Dept. of Civil and Environmental Engineering, infrastructure systems and related distributed networks to quantita-
Colorado State Univ., Fort Collins, CO 80523. ORCID: https://orcid.org tively assess community-level resilience with multidisciplinary
/0000-0002-2399-6467. Email: lisa.wang@colostate.edu
2Harold Short Endowed Chair Professor, Dept. of Civil and Environ- measurements (e.g., Doorn et al. 2019; Wei et al. 2020; Wang et al.
mental Engineering, Colorado State Univ., Fort Collins, CO 80523 (corre- 2021; Roohi et al. 2020). According to McAllister (2016), engi-
sponding author) Email: jwv@engr.colostate.edu neering outcomes can be quantitatively coupled with socioeco-
3Ph.D. Candidate, Dept. of Economics, Colorado State Univ., Fort nomic performance, providing more flexible and informative
Collins, CO 80523. ORCID: https://orcid.org/0000-0002-0456-9339. support for risk-informed decision-making with the public interest
Email: bradhartman91@gmail.com in mind. Advancements in community resilience modeling can help
4Professor, Dept. of Economics, Colorado State Univ., Fort Collins, accelerate the development of building codes and standards to meet
CO 80523. Email: harvey.cutler@colostate.edu
5 the requirements of communitywide resilience goals of the broaderHCAS Distinguished Professor, Dept. of Economics, East Carolina
Univ., Greenville, NC 27858. ORCID: https://orcid.org/0000-0003-3864 built environment at a higher level, consistent with performance
-3231. Email: krusej@ecu.edu objectives of individual buildings throughout their service lives
6Community Resilience Group Leader and Program Manager, (e.g., Ellingwood et al. 2017; Masoomi and van de Lindt 2019).
Engineering Laboratory, National Institute of Standards and Technology, For example, in the United States, building codes and standards
Gaithersburg, MD 20899. ORCID: https://orcid.org/0000-0003-1733-4667. (e.g., ASCE 2016) have focused on life safety goals, but the role
Email: therese.mcallister@nist.gov of the individual building performance in fulfilling community
7Assistant Professor, School of Marine and Atmospheric Sciences, resilience goals is unknown (Ellingwood et al. 2017). In order to
Stony Brook Univ., Stony Brook, NY 11794. ORCID: https://orcid.org address this grand challenge over the next decade, there is a need to
/0000-0001-5298-9525. Email: sara.hamideh@stonybrook.edu link resilience design objectives with individual building perfor-
Note. This manuscript was submitted on October 4, 2021; approved on
January 12, 2022; published online on March 12, 2022. Discussion period mance levels (Wang et al. 2018). Physical performance of buildings
open until August 12, 2022; separate discussions must be submitted for has been quantitatively linked to communitywide social and eco-
individual papers. This paper is part of the Journal of Structural Engineer- nomic outcomes in only one study by Roohi et al. (2020), without
ing, © ASCE, ISSN 0733-9445. focusing on achieving community-level goals. Therefore, in this
© ASCE 04022045-1 J. Struct. Eng.
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paper, a systematic community-level analysis of linked physical, physical system) are based on the ability to achieve both social and
social, and economic systems is proposed to deaggregate perfor- economic goals at the community scale. This is accomplished by
mance targets of buildings to enable the community to achieve chaining the performance of the built environment to a computable
predefined socioeconomic communitywide resilience goals. The general equilibrium (CGE) model for economic metrics (i.e., house-
performance targets can be expressed in terms of individual building hold income, employment, domestic supply) and an existing popu-
fragilities to further guide the performance-based engineering de- lation dislocation algorithm for sociological metrics (i.e., household
sign of structural components given specific design features. or population dislocation) and ultimately determining the deaggre-
Community resilience goals mainly focus on robustness and gated performance targets for individual buildings to meet a speci-
rapidity (NIST 2015). The robustness goals emphasize improve- fied goal. The proposed methodology provides a structured but
ments in the performance of building components, and the rapidity flexible approach to support resilience decision-making by helping
goals are devoted to allocating limited resources and creating stakeholders develop integrative implementation strategies to im-
organizational guidelines to ensure community recovery is imple- prove their resilience. The proposed multidisciplinary methodology
mented effectively and efficiently (Wang et al. 2018; Wang and van builds on and integrates previous work (Wang et al. 2021), and con-
de Lindt 2021). The NIST Community Resilience Planning Guide, tinued research is needed to characterize uncertainty in the final
the San Francisco Planning and Urban Research Association, and decision support results.
the Oregon Resilience Plan provided examples of specifying the
desired time-to-recovery as performance goals for building clusters
at different functional levels (NIST 2015, 2020; OSSPAC 2013; Deaggregation of Community Resilience Goals
Poland 2009). Schultz and Smith (2016) developed rapidity resil- Fig. 1(a) summarizes the methodology used in this study to develop
ience objectives for housing, utility systems, and transportation individual residential building performance targets to achieve
individually when the community is exposed to flood events at dif- community-level resilience goals in terms of physical, social,
ferent return periods. However, only a few studies have focused on and economic metrics. The approach starts by articulating commu-
examining the achievement of robustness goals. Chang and Shino- nity resilience goals, such as less than an x% increase in unemploy-
zuka (2004) set a reliability goal of 95% likelihood of being able to ment immediately after an EF-3 tornado occurring anywhere in the
meet the objectives for water systems (e.g., major pump station community. The preliminary design for individual residential build-
loses function) in given seismic events. Sabarethinam et al. ings shown in Fig. 1(a) refers to structural combinations such as
(2019) estimated the likelihood of achieving robustness perfor- roof covering and is controlled by fragility functions. Please refer
mance goals (i.e., the performance of infrastructure systems from to the section “Wind Design to Achieve Community Resilience”
0% to 100%) for the coastal town of Seaside, Oregon, subjected to for more details about the design. Fig. 1(b) depicts the sequencing
combined seismic and tsunami hazards. Wang et al. (2018) used the of analyses for a given community and its physical, social, and eco-
direct loss ratio (DLR) and uninhabitable ratio (UIR) as the resil- nomic attributes; damage and functionality models; computable
ience goals for measuring the robustness of a residential building general equilibrium economic model; and population dislocation
cluster under tornado hazards, with the damage values linked to algorithm, which is introduced in later subsections of this paper,
direct loss and uninhabitability as defined from the HAZUS-MH to evaluate the hazard impacts and support community resilience
MR4 technical manual for consistency. planning. The percentage of residential buildings that were as-
In order to measure socioeconomic aspects of community resil- signed the specified retrofit were analyzed using values ranging
ience, researchers have proposed metrics that can be potentially from 0% to 100%, in intervals of 10%, for the community. The
considered indicators of community resilience. Potential indicators objective is to determine the percentage of buildings that should
of economic resilience include the unemployment rate, income be retrofitted such that the communitywide building performance
equality (e.g., based on gender and race or ethnicity), and business and socioeconomic metrics calculated in the resilience analysis
diversity (e.g., ratio of large to small businesses). Social resilience meet the community resilience goals. Community resilience goals
metrics reflect individual human and social needs, which can be would typically be community defined and could be adjusted based
represented in population changes and the distribution of sociode- on community-specific needs, but illustrative values are used in
mographic characteristics (e.g., age, race, education levels) over this study.
time (Burton 2015; Cutter et al. 2014), access to social services
and networks, and quality of life assessments. Some metrics can
reflect the multifaceted socioeconomic indicators of resilience. Damage and Functionality Model
For example, temporary and permanent population dislocation fol- Eq. (1) determines the building damage probability (Pdamage) using
lowing a disaster is a complex social and economic process jointly fragility functions for each building, which can be grouped by each
impacted by the functionality loss of physical systems and the so- building archetype and have been fitted to lognormal cumulative
ciodemographic characteristics (Wang et al. 2018). The effects of distribution functions (CDFs) controlled by two parameters (median
population dislocation can ripple through the local economy, social ? and standard deviation ?). The fragility functions (FrDS) represent
institutions, and building inventory. For example, local businesses the probability of exceeding damage state i (i.e., slight, moderate,
may lose both employees and customers and therefore decide to extensive, complete) for each building as a function of the intensity
close permanently and relocate. As residents and businesses leave measure (e.g., 3-s gust wind speed, spectral acceleration). For each
and relocate, tax revenue for local government shrinks, forcing lay- Monte Carlo realization of a tornado event, a uniformly distributed
offs that can induce more residents to leave (Mieler et al. 2015), as random variable Rj, between 0 and 1, is generated and compared to
well as shrinking resources for restoring and maintaining physical the building damage probabilities corresponding to the four dam-
infrastructure. age states. As shown in Eq. (2), if the realization experiences the
In the present study, building functionality, employment, do- moderate damage state or greater, then the building is assumed to
mestic supply, household income, and housing unit and population lose functionality in this study. The moderate damage state in tor-
dislocation are used as physical and socioeconomic resilience met- nado damage assessment means the building has moderate damage
rics in the context of a disaster. This is the first study in the literature to windows or doors and roof covering, but the building itself can
where structural performance goals selected for buildings (or any be occupied and repaired (Memari et al. 2018). For business, it
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2b) Damage 3a) Functionality
Models Models
Initial Interdependent
Community Description
at time = 0; PD = k 2c) Damage to
Physical 3b) Functionality of
1a) Built Environment Infrastructure Physical Infrastructure
1b) Social Systems
1c) Economic Systems
2a) Hazard 3c) CGE 3d) Social
Model Model Science Models
Start
3e) Direct and Indirect
Economic and Social Losses
(a) (b)
Fig. 1. (Color) (a) The framework of the deaggregation of community-level resilience goals; and (b) the sequence of analyses for community
resilience assessment and metrics.
would not be possible to have an operational business in the mod- CGE Model Description
erate damage state; thus, the building would be deemed nonfunc- CGE models assume that firms maximize profits and households
tional in the CGE analysis. The building functionality status (Ikfun;j) maximize welfare as a guide to making economic decisions.
of Eq. (2) is either functional (1) or nonfunctional (0) for each reali- CGE models are data-driven models that provide descriptions of
zation. The index j is representative of each realization of the how households, firms, and the local government interact to pro-
Monte Carlo simulation (j ¼ 1 to N) for each building k. Sub- duce goods and services for an economy. In recent years, CGE
sequently, the building functionality probability (Pfun) can be ap- models have become a particularly effective tool when applied to
proximated using Eq. (3) regional impact analysis of external shocks that are assimilated
Pkdamage;i ¼ FrkDSið ¼ Þ ð Þ
from other fields (e.g., Rose and Guha 2004; Rose and Liao 2005;
IM x 1
( Cutler et al. 2016; Attary et al. 2020). As such, financial shocks,health consequences of pollution, climate change, and, as this study
1 R > Fr
k ¼ j DS2 ð Þ conveys, natural hazards can all be linked with a CGE model toIfun;j
0 R ? 2Fr simulate economic outcomes. Prior to the extensive use of CGEj DS2
P models, Input–output (I-O) economic models were commonly used
k N ðIk ¼ Þ to model the impact of natural hazards (e.g., Rose and Liao 2005).N
Pk ? fun ¼ j¼1 fun;j 1 ð3Þ Although I-O models adequately simulate demand-side shocks,fun N N they have been limited in their ability to determine impacts to the
After the Monte Carlo simulation (MCS) building damage supply side, such as the loss of buildings and lifeline systems
analysis, the results are passed to the CGE economic analysis, (Koliou et al. 2020). Because the CGE model can address both
where the building is considered nonfunctional if the probability demand-side and supply-side factors, it is the tool of choice to ex-
of being in or exceeding DS2 (moderate damage) is greater than amine the impact of natural disasters.
0.5. The CGE is only run once after the structural analysis, and A social accounting matrix (SAM) organizes data for three en-
this full sequence shown in Fig. 1(a) is completed for each tornado tities, households, firms, and the local government, that represent
scenario to develop a suite of scenarios. the flow of resources in an economy at a point in time. A SAM is a
method to organize the data in a consistent way for modeling the
interactions between all three entities. The SAM, along with input
Computable General Equilibrium Model from other matrices, such as tax revenue, are input data to the CGE
The design or retrofit of infrastructure systems can be quantitatively model. See Schwarm and Cutler (2003) for an extensive description
related to community-level economic resilience metrics through a of a SAM. The SAM used in this study is based on data from the
dynamic economic impact model. In this study, the CGE model Bureau of Labor Statistics, Bureau of Economic Analysis, and US
served as the economic impact model to quantitatively evaluate the Census Bureau. In addition, county tax assessor data are used to
varying impacts of natural disasters on the local economy. The fol- obtain parcel-level physical characteristics of residential homes and
lowing section provides a brief summary of the CGE model and its business buildings. The buildings from this data set are merged
data. The implementation of the CGE model in this study is con- with building-specific archetypes to summarize the impact of a tor-
sistent with that of Wang et al. (2021); for further details on the nado on the functionality of these buildings.
CGE model or its data and assumptions, please refer to Cutler et al. CGE models are based on a range of fundamental microeco-
(2016) and Attary et al. (2020). nomic principles that include (1) utility-maximizing households
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that supply labor and capital and use the proceeds to pay for goods building functionality evaluations, to determine the dislocation
and services and taxes; (2) the production sector is based on per- probability of each building k in each census group m (Pkdis;m).
fectly competitive firms that choose profit-maximizing amounts of For each Monte Carlo realization, the population dislocation algo-
intermediate inputs, capital, land, and labor to produce goods and rithm can help predict whether the households leave their housing
services for both domestic consumption and export; (3) the unit immediately after a hazard event. For more details on the pop-
government sector collects taxes and uses tax revenues in order to ulation dislocation algorithm and the logistic regression model,
finance the provision of public services; and (4) the local economy please see Rosenheim et al. (2019) and Lin et al. (2008)
trades with the rest of the world. These principles help to formulate
the CGE model, which consists of a series of equations and is cali- k 1
brated when those equations exactly reproduce the data in the Pdis;i;m ¼ k k ð5Þ1þ e?ðc1þc2plossi;mþc3dsfmþc4pblackmþc5phispmÞ
SAM. The CGE model can then be used to simulate the outcomes
from a wide range of exogenous shocks, such as from a tornado. X4
Pk ¼ Pkdis;m dis;1;m × Pk kLinking the Building Functionality Model and the CGE damage;1 þ Pdis;i;mi¼2
Model
Capital stock within a community is the key variable of interest × ðPk kdamage;i ? Pdamage;i?1Þ ð6Þ
linking the functionality model to the CGE model. The market val-
ues of commercial and residential buildings were aggregated into a
goods, trade, and other commercial sector and three housing serv- Illustrative Example for Tornado Hazards
ices sectors (HS1, HS2, HS3). Goods, trade, and other are them-
selves aggregations of the North American Industry Classification In this study, simulated tornado wind fields defined as a peak three-
System (NAICS) sectors. Goods represent large manufacturing in- second gust were used. Joplin was selected as the testbed to per-
dustries, trade is mostly retail, and other is a combination of indus- form resilience assessments for tornado-induced events due to its
tries including services, health, and finance. This study focuses on history with a large double-vortex Enhanced Fujita 5 (EF5) tornado
residential buildings, where HS1 is lower-value homes, HS2 is in May of 2011. The purpose of the illustrative example was to
higher-value homes, and HS3 is rented residential buildings. determine the minimum percentage of wood-frame residential
Tornado damage to buildings, and their reduced functionality, is buildings that need to be retrofitted for the community to meet its
modeled as negative “shocks” in the CGE model. These shocks are resilience goals. These community-level resilience goals were de-
the connection point between engineering outputs and the CGE fined in terms of building functionality and social and economic
model. Eq. (4) calculates the sector shocks (? ) as a percentage metrics using the proposed methodology. All analyses and datas
of capital stock remaining were performed and are available in the open-source IN-COREP modeling environment. Please refer to Wang et al. (2020) for
nPCk k¼ s × P more details regarding the manual, data sets, and example note-?s ¼ k 1 fun;sn Ck ð4Þ books for the IN-CORE modeling environment. This example fo-k¼1 s cuses on the resilience assessment at the community level specific
where C = capital stock of each building k attributed to each to tornado events because tornadoes only strike a small footprint
sector s. area within a community. The resilience model and the retrofit
Incorporating the output from the engineering models into can be applied to a large urban area for other natural hazards such
external shocks enables the CGE model to estimate a range of post- as earthquake events (e.g., Roohi et al. 2020).
hazard economic losses such as employment effects and domestic
supply by sectors (Cutler et al. 2016). Furthermore, retrofit strat- Community Description
egies that mitigate damage to residential properties will attenuate
the shock to capital stock in the housing services sector and thus Joplin is a typical small to medium-sized community, located in
tend to reduce overall economic loss. southwest Missouri in the United States and spanning Jasper
and Newton counties. In this illustrative example, a total of 19 ar-
chetype buildings (e.g., residential, business, healthcare, education)
Population Dislocation Algorithm were used to represent the buildings within the community. Five
The population dislocation algorithm, which has input from the typical wood-frame residential buildings from Masoomi et al.
building damage analysis, and detailed sociodemographic data pre- (2018) with different footprint areas, roof structures, and number
dict the probability of dislocation immediately following the event of stories were used to describe all the residential buildings. The
(Girard and Peacock 1997; Peacock et al. 1997; Rosenheim et al. electric power network is generally regarded as the most impacted
2019). Eq. (5) uses a logistic regression model with five constants, infrastructure system by tornado (and most wind) events and was
c1 to c5, to estimate population dislocation probabilities (Pdis) for therefore also included herein to examine the dependency between
each damage state i based on property value loss (ploss) and build- the building infrastructure and electric power network. Transmis-
ing types (single-family or multifamily, dsf) for each building k sion or distribution substations and wood poles are the two types of
and neighborhood characteristics (percent of black, pblack, and vulnerable components included in the electric power network.
Hispanic populations, phisp) by each census groupm. The variable Other networks such as water, transportation, and telecommunica-
dsf is set to 1 if the number of estimated housing units was 1. The tion networks were not considered in this study but could be mod-
variable is 0 if the number of estimated housing units is greater eled in future work as needed. It is acknowledged that the
than 1. The logistic regression constants were not changed for this functionality of other network systems depends on the reliability
specific community, but the variables such as the percent of the of the electric power network (e.g., Unnikrishnan and van de Lindt
black and Hispanic population were updated based on the Census 2016; Zou and Chen 2019). For example, water towers are vulner-
Bureau’s data. Eq. (6) sums the dislocation probabilities for each able in that they need to be supplied with electric power (Masoomi
damage state (Pkdis;i;m). Damage state 1 (slight or no damage) is and van de Lindt 2018), so they may only last several days follow-
evaluated separately from damage states 2 to 4, consistent with the ing a tornado if backup generators for pumps are not available or
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Table 1. Built environment and human social system for Joplin testbed on the 2010 Decennial Census data and an existing housing unit
Joplin testbed Description Values allocation algorithm (see Rosenheim et al. 2019 for details). The
allocated housing units are also designated by race or ethnicity
Built environment and household income, in addition to tenure status, as shown in
Buildings Residential 24,903
Nonresidential 3,249 Table 1. The number of workers employed in Joplin in 2010
In total 28,152 was 39,831, and the total domestic supply was $3.04 billion. Please
Electric power network Substations 18 refer to Wang et al. (2021) for more details on the building inven-
Poles 23,857 tory, electric power network, housing unit characteristics, and
Human social system economy in Joplin.
Housing units Owner-occupied 11,344 Initial capital stock values come from the Newton and Jasper
Renter-occupied 9,435 County Assessor’s offices that encompass Joplin. The building
Vacant 2,455 level county assessor’s data and the building-level archetype data
Group quarters 22 used in the functionality model are from different sources. Fortu-
In total 23,261 nately, both data sets had detailed geographic coordinate location
Population Owner-occupied 26,873 information for every building. Therefore, in order to connect
Renter-occupied 20,949 individual building-level archetypes and functionality to economic
In total 49,810 sectors, the building-level sector information from the county as-
sessor’s office was merged with the archetype data sets using a GIS
spatial join algorithm. Building level data were then aggregated to
supplied. Additionally, damaged or fallen trees or poles can block the sector level.
the roads following tornadoes and cause adverse impacts on the
transportation networks (e.g., Hou and Chen 2020; Hou et al. Generic Tornado Models
2019).
Table 1 provides a summary of the built environment and social A series of generic tornadoes based on the gradient technique
systems for the testbed and example in this study. The number of (Standohar-Alfano and van de Lindt 2015) was used as the hazard
buildings and housing units in Joplin is 28,152 and 23,261 (multi- model impacting the community, resulting in physical damage to
family units will have multiple households in one building), respec- buildings and the electric power network and propagating eco-
tively, and the building data set was developed circa 2010 before nomic losses, household disruption, and population dislocation.
the 2011 Joplin tornado. Nonresidential buildings include 13 build- Tornadoes with different EF ratings (EF0–EF5) are associated
ing types, such as commercial buildings and social institutions, with different ranges of wind speeds. Fig. 2 shows the geometry
such as schools. The housing unit estimation was determined based of the gradient model for an EF2, EF3, and EF4 single tornado,
EF2: 50-60 m/s (111-135 mph)
EF1: 38-49 m/s (86-110 mph)
EF0: 29-37 m/s (65-85 mph)
0.37L
0.72L
L
(a)
EF3: 61-74 m/s (136-165 mph)
EF2: 50-60 m/s (111-135 mph)
EF1: 38-49 m/s (86-110 mph)
EF0: 29-37 m/s (65-85 mph)
0.32L
0.64L
0.88L
L
(b)
EF4: 75-89 m/s (166-200 mph)
EF3: 61-74 m/s (136-165 mph)
EF2: 50-60 m/s (111-135 mph)
EF1: 38-49 m/s (86-110 mph)
EF0: 29-37 m/s (65-85 mph)
0.21L
0.42L
0.70L
0.86L
L
(c)
Fig. 2. The geometry of generic tornado models for different EF ratings: (a) EF2; (b) EF3; and (c) EF4.
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W
W
0.83W W
0.65W 0.80W
0.79W
0.46W 0.50W
0.27W 0.34W 0.48W
Table 2. Community resilience goals based on core metrics
Physical service metrics Population stability metrics Economic stability metrics
% buildings
Tornado % buildings remaining
intensity remaining functional (due % households % change
(NCRPG functional to damage + dislocated(unit: % population % change in mean
Community hazard (due to electrical households) dislocated(unit: % change in in domestic household
goals level) damage) (%) power) (%) (%) people) (%) employment supply income
Goal A EF2 (routine) 98 95 1 1 0.2 0.5 0.2
Goal B EF3 (design) 96 89 3 3 0.5 1.0 0.5
Goal C EF4 (extreme) 94 83 5 5 0.8 1.5 0.8
respectively, where the width of the applied tornadoes is equal to The methodology presented herein is general and can be imple-
the average of the historical tornado data for the Enhanced Fujita mented for any hazard type. The socioeconomic goals defined for
rating (Attary et al. 2018). The start points, end points, and the di- the community, partially or wholly, do rely on a hazard-specific
rections of all tornado scenarios were assigned randomly within the analysis. For example, earthquake events commonly impact the
community boundaries. The NIST Community Resilience Planning entire community, whereas a tornado directly impacts a relatively
Guide (NCRPG) encourages communities to use routine levels small geographic footprint within a community, but the impact can
(i.e., hazard events that are more frequent with less consequential extend to the entire community in terms of social and economic
events that should not cause significant damage), design levels impacts. Additionally, building functionality is highly related to
(i.e., hazard events used to design structures), and extreme levels tornado intensity, tornado path and width, and housing density
(i.e., beyond design levels and likely to cause extensive damage) to (urban or rural).
address a range of potential damage and consequences (NIST 2020).
This study examined the community resilience impacted by 100
random tornadoes for each different intensity level (i.e., EF2, Multidisciplinary Community Resilience Goals
EF3, EF4) individually in line with the concept encouraged in the In this study, core resilience metrics inform three community sta-
NCRPG. Most tornadoes travel in paths from the southwest to- bility areas: physical services, economic activity, and population
wards the northeast (Suckling and Ashley 2006). Additionally, it stability. Physical service stability was estimated by determining
is important to mention that the building inventory was developed building functionality two different ways: with and without the
for Joplin exclusive of other nearby homes outside of the Joplin impact of the reliability of the electric power network. Percent
boundaries. Thus, some of the tornado scenarios might damage changes in employment, domestic supply (e.g., food, care, secu-
buildings outside of Joplin in the simulation, but they are not in- rity), and household income were used to jointly reflect the activity
cluded in the determination of physical damage and the associated of the local economy. Population stability was calculated as the
socioeconomic losses in this study. percent change in households being dislocated by housing unit
Table 3. Lognormal parameters for residential wood-frame building fragilities in this study
Original fragility Retrofit design in terms of
Building Damage functions (m/s) fragilities (m/s)
type Building description states ? ? ? ?
T1 Residential wood building, small rectangular DS1 3.68 0.13 3.68 0.14
plan, gable roof, one story DS2 3.56 0.14 3.85 0.12
DS3 3.63 0.13 3.98 0.11
DS4 3.68 0.14 4.16 0.13
T2 Residential wood building, small square DS1 3.60 0.13 3.60 0.13
plan, gable roof, two stories DS2 3.53 0.13 3.76 0.12
DS3 3.59 0.13 3.91 0.11
DS4 3.68 0.13 4.17 0.12
T3 Residential wood building, medium DS1 3.61 0.13 3.61 0.13
rectangular plan, gable roof, 1 story DS2 3.51 0.13 3.77 0.12
DS3 3.57 0.13 3.92 0.11
DS4 3.74 0.12 4.23 0.12
T4 Residential wood building, medium DS1 3.73 0.13 3.73 0.13
rectangular plan, hip roof, two stories DS2 3.65 0.13 3.87 0.12
DS3 3.71 0.13 4.00 0.11
DS4 3.76 0.13 4.28 0.12
T5 Residential wood building, large rectangular DS1 3.75 0.13 3.75 0.13
plan, gable roof, two stories DS2 3.65 0.13 3.88 0.12
DS3 3.70 0.13 3.98 0.11
DS4 3.64 0.15 4.06 0.14
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94.55°W 94.50°W 94.45°W 94.40°W
Retrofit for residential buildings
Retrofitted
Un-Retrofitted
Other building archetypes
94.55°W 94.50°W 94.45°W 94.40°W
(a)
94.55°W 94.50°W 94.45°W 94.40°W
Retrofit for residential buildings
Retrofitted
Un-retrofitted
Other building archetypes
94.55°W 94.50°W 94.45°W 94.40°W
(b)
94.55°W 94.50°W 94.45°W 94.40°W
Retrofit for residential buildings
Retrofitted
Un-retrofitted
Other building archetypes
94.55°W 94.50°W 94.45°W 94.40°W
(c)
Fig. 3. (Color) Residential buildings retrofitted randomly assigned through the community: (a) 0% retrofitted; (b) 40% retrofitted; and (c) 80% retrofitted.
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37.05°N 37.10°N 37.05°N 37.10°N 37.05°N 37.10°N
37.05°N 37.10°N 37.05°N 37.10°N 37.05°N 37.10°N
Table 4. Community resilience metrics for physical and social systems that benefit from residential building retrofits (mean values)
Physical service metrics Population stability metrics
The number of
Residential The number of buildings nonfunctional Housing unit Population
building buildings nonfunctional (due to damage + electrical dislocation (unit: dislocation (unit:
retrofits (%) (due to damage) power) housing units) people)
EF2
0 315 (1.1%) 981 (3.5%) 231 (1.0%) 478 (1.0%)
40 251 (0.9%) 971 (3.5%) 197 (0.9%) 409 (0.8%)
70 200 (0.7%) 963 (3.4%) 169 (0.7%) 350 (0.7%)
100 150 (0.5%) 955 (3.4%) 142 (0.6%) 295 (0.6%)
EF3
0 703 (2.5%) 1,387 (4.9%) 501 (2.2%) 1,021 (2.1%)
40 601 (2.1%) 1,377 (4.9%) 436 (1.9%) 894 (1.8%)
70 523 (1.9%) 1,368 (4.9%) 388 (1.7%) 796 (1.6%)
100 443 (1.6%) 1,360 (4.8%) 339 (1.5%) 692 (1.4%)
EF4
0 1,187 (4.2%) 2,583 (9.2%) 847 (3.6%) 1,711 (3.4%)
40 1,048 (3.7%) 2,570 (9.1%) 754 (3.2%) 1,532 (3.1%)
70 939 (3.3%) 2,558 (9.1%) 685 (2.9%) 1,392 (2.8%)
100 828 (2.9%) 2,547 (9.1%) 613 (2.7%) 1,231 (2.5%)
(or population) following a disruptive event. Three community roof-to-wall connection types (Wang et al. 2021). The typical de-
resilience goals (Goal A, Goal B, and Goal C) were targeted as sign would have regular asphalt shingles, 8d common nails spaced
routine level (EF2), design level (EF3), and extreme level (EF4) at 150=300 mm (6=12 in:) attaching roof sheathing panels to
tornado events, respectively, as indicated in Table 2. The commu- trusses, and two 16d toenails to connect the roof rafters over the
nity resilience goals may be viewed as modest but reasonable be- vertical studs. The retrofit design used regular asphalt shingles, roof
cause tornadoes typically strike a portion of the entire community, sheathing nails spaced at 150=150 mm (6=6 in:), and two H2.5
sometimes 5% to 10%. All residential and commercial buildings hurricane clips as roof-to-wall connections. A series of cases
outside the tornado path were not physically damaged but may still was examined, ranging from 10% of residential buildings in a com-
lose electric power. Therefore, two types of physical service metrics munity being retrofitted to 100%, to select how many residential
related to building functionality were proposed herein: considering buildings would need to be retrofitted to achieve the desired com-
the dependency between buildings and the electric power network munity resilience goals. Several of these scenarios are illustrated in
or neglecting the dependency of buildings on electric power. Fig. 3. The damage fragility curves for a suite of 19 building ar-
It is important to mention that each community is unique, with chetypes incorporating 13 nonresidential building types, each with
its own characteristics, and each will have its own specific resil- four damage states (i.e., slight, moderate, extensive, and complete),
ience goals and potential solutions. In this study, having clearly are available to cover the entire range of wind speeds (Masoomi
defined resilience goals in terms of core metrics is intended to dem- et al. 2018; Memari et al. 2018; Koliou et al. 2017; Masoomi
onstrate how a community can change a physical design of a com- and van de Lindt 2016).
ponent within its infrastructure (buildings in this case) to effect
change in their physical service, population, and economic stability Table 5. Economic stability metrics given different levels of residential
areas if a natural hazard were to strike. For example, keeping the building retrofits and tornado scenarios (mean values)
percentage of households dislocated below 5% is one of the social
resilience goals identified for tornadoes at the extreme hazard level. Economic stability metrics
Residential Employment Domestic supply Household income
building loss (unit: loss (unit: millions loss (unit: millions
Wind Design to Achieve Community Resilience retrofits (%) person) of $) of $)
Tornadoes are low-probability high-consequence events that often EF2
result in significant physical damage and socioeconomic impacts 0 78 (0.2%) 10.4 (0.3%) 2.0 (0.2%)
but have not been considered in the structural design codes and 40 62 (0.2%) 8.4 (0.3%) 1.6 (0.1%)
standards (e.g., ASCE 7-16) so far. That will change soon because 70 49 (0.1%) 6.9 (0.2%) 1.3 (0.1%)100 36 (0.1%) 5.3 (0.2%) 0.9 (0.1%)
tornadoes are planned to be included for Risk Category 3 and 4
buildings (e.g., hospitals, emergency operation centers, etc.) begin- EF3
ning in 2022. Some challenges such as pressure deficit, vertical 0 160 (0.4%) 22.0 (0.7%) 3.9 (0.3%)
components of the tornadic winds, and windborne debris in torna- 40 136 (0.4%) 19.2 (0.6%) 3.3 (0.3%)
does made it difficult to rationalize a design process for most build- 70 118 (0.3%) 17.0 (0.6%) 2.9 (0.3%)
100 99 (0.3%) 14.7 (0.5%) 2.5 (0.2%)
ings (e.g., Haan et al. 2010; van de Lindt et al. 2013; Masoomi and
van de Lindt 2017). In this study, basic construction improvements EF4
were modeled using modified fragilities for individual building per- 0 270 (0.7%) 36.8 (1.2%) 6.7 (0.6%)
formance. Table 3 presents building fragility functions for typical 40 236 (0.6%) 32.7 (1.1%) 5.9 (0.5%)
and retrofitted residential buildings with a different structural com- 70 211 (0.5%) 29.6 (1.0%) 5.3 (0.5%)
bination of roof coverings, roof sheathing nailing patterns, and 100 182 (0.5%) 26.2 (0.9%) 4.6 (0.4%)
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Community Resilience Metrics metrics in terms of physical services, economic activity, and pop-
After combining the fragility functions for retrofitted residential ulation stability were examined to explore the effect of wind mit-
buildings and the original fragility functions for other buildings igation retrofits on community resilience enhancement, that is, to
in the community model, the community assessment was per- link resilience goals at the community level with the selection of a
formed by chaining the algorithms, as described earlier. Resilience mitigation policy for building retrofit. Tables 4 and 5 indicate some
Fig. 4. (Color) Statistics of resilience metrics in terms of physical service and population stability: (a) building functionality without retrofit; (b) build-
ing functionality with 100% residential retrofit; (c) housing unit dislocation without retrofit; and (d) housing unit dislocation with 100% residential
retrofit.
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Table 6. Percentage of residential buildings requiring retrofit to achieve community resilience goals
Physical service metrics Population stability metrics
% buildings remaining
% buildings functional (due to % households % population
Community remaining functional damage + electrical dislocated (unit: dislocated (unit:
goals (due to damage) (%) power) (%) households) (%) people) (%)
Goal A 3.4 12.0 34.2 33.3
Goal B 8.0 6.0 17.5 14.0
Goal C 15.1 16.0 19.8 15.4
key findings for these core community resilience metrics in terms need to be retrofitted. However, the employment metrics control the
of the physical, economic, and social stability areas. The full suite retrofit level for the EF3 and EF4 tornado scenarios. The funda-
of results for buildings retrofitted at each of the different percent- mental contribution of this analysis methodology is the ability
ages for the building stock under different scenarios are not shown to essentially deaggregate the community-level resilience goals in
herein for brevity. As an example, when the community was im- terms of physical, social, and economic metrics into building retro-
pacted by the idealized EF4 tornadoes, the number of nonfunctional fit requirements. The goals themselves are flexible and can be ad-
buildings and housing units dislocated can be reduced by 11.7% justed by the analyst on a case-by-case basis. Additionally, it would
(1,187 to 1,048) and 11.0% (847 to 754), respectively when 40% also be possible to quantify the impact of a change in building code
of residential buildings are retrofitted. The percentages shown in for new construction following a tornado or with some modifica-
Table 4 are defined as the change in the metrics being measured tion to the methodology and examine the effect of implementing
(e.g., household dislocation) out of the total value that can be mea- new building code requirements over time as a community grows.
sured for that metric (e.g., households) for the community. Fig. 4
illustrates the histograms of typical metrics in terms of physical
service and population stability from 100 EF2 tornado scenarios as Conclusions
an example. The reason for a few extreme values at the left end in
the histograms is that the socioeconomic losses caused by the tor- Community resilience assessments help the community determine
nado event are also highly related to the attributes of the area hit by what is needed to improve its performance and long-term benefits
the tornado, such as population density. In more rural areas, both relative to the “do nothing” case. This study presented a method-
population and building density is lower, and tornadoes striking ology to determine building retrofit targets to achieve community-
these areas impact the local economy and cause household dislo- level physical, social, and economic resilience goals in support of
cation at a smaller scale compared to dense urban areas. community resilience decision-making. A series of tornado scenar-
Workers employed at damaged or nonfunctional commercial ios at different intensity levels was simulated and applied to an
buildings may face work interruption or job loss, leading to reduced illustrative community testbed. A set of core resilience metrics in-
household income and consumption expenditures. As part of the cludes the percent of buildings that are analytically predicted to
CGE simulation of this event, these values are calculated and rep- remain functional, the percent of households or population dislo-
resented in Table 5. Table 5 conveys that retrofitting played a sig- cated, and the percent change in the local economy (i.e., employ-
nificant role in mitigating economic impacts to domestic supply, ment, domestic supply, household income). The mitigation focused
especially employment and household income. From the lowest to on residential buildings, and the objective was to determine the
highest retrofit application (from 0% to 100%) for EF2 and EF3, a minimum percentage of residential buildings across a community
more than 36% reduction (from $3.9 million to $2.5 million) in that need to be retrofitted in order to achieve the multidisciplinary
household income loss and a 53.8% reduction (from 78 to 36) community resilience goals. Based on the work presented herein,
in employment loss is observed. and recognizing that uncertainty in the results is not addressed, the
The minimum percentage of residential buildings retrofitted to following preliminary conclusions can be drawn:
achieve the community-level resilience goals can be determined for • The percentage of loss of functionality to buildings and house-
each tornado scenario (e.g., average of EF rating tornado striking hold dislocation, as the key resilience metric in the study, may
anywhere in the community), as illustrated in Tables 6 and 7. The be reduced by approximately 11% when 40% of residential
column fields shown in Tables 6 and 7 are consistent with those buildings are randomly retrofitted throughout the community
representing each metric in Table 2. In order to meet all the multi- for the assigned EF4 tornado scenario. For the EF2 and EF3
disciplinary community resilience goals for EF2 tornadoes (see tornado scenarios, 40% of residential building retrofit may help
Goal A in Table 2), the metrics for household dislocation controlled mitigate the housing unit dislocation by approximately 14%.
the retrofit level and at least 34.2% of residential buildings would • Building retrofits can play a significant role in reducing capital
stock damage and further mitigating economic loss to domestic
supply, employment, and household income. From the lowest
Table 7. Percentage of residential buildings requiring retrofit to achieve (0%) to highest (100%) retrofit application for residential build-
community resilience goals ings for the EF2 and EF3 tornado scenarios, there would be
Economic stability metrics more than a 35% reduction in unemployment and more than
a 50% reduction in household income loss.
% change in % change in % change in • To meet all the multidisciplinary resilience goals for tornadoes
Community employment domestic supply mean household
goals (%) (%) income (%) in the routine level intensity (EF2) defined in this study, the
household dislocation metric controlled the retrofit level, and at
Goal A 28.7 13.1 19.4 least 34.2% of residential buildings would need to be retrofitted.
Goal B 21.5 18.7 11.6 For tornadoes at the design (EF3) and extreme (EF4) level haz-
Goal C 29.0 29.0 18.0 ard intensity, the employment metric controlled the retrofit level.
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The resilience goals are flexible and can be quantitively adjusted dsf = building types (single-family or multifamily);
for different levels based on community input and the unique FrDS = fragility functions;
needs of a community. Clearly, different multidisciplinary met- Ikfun;j = building functionality status;
rics may control the retrofit requirements for different hazard i = damage states;
intensities but are also specific to the resilience goals selected. j = each realization of Monte Carlo simulation;
This further underscores the need to consider goals across differ-
ent community stability areas. k = each building;
The study did not address budget constraints of the community m = each census group;
and costs to retrofit, which would further limit selections of differ- N = total number of Monte Carlo simulation realizations;
ent retrofit strategies for different households. Communities have Pdamage = building damage probability;
access to many funding sources outside of their own tax dollars Pdis = population dislocation probability;
for mitigation programs. The Federal Emergency Management Pkdis;i;m = dislocation probabilities for each damage state;
Agency (FEMA) Building Resilient Infrastructure and Commun- Pkdis;m = dislocation probability of each building in each census
ities (BRIC) and Department of Housing and Urban Development group;
(HUD) Community Development Block Grant–Disaster Recovery Pfun = building functionality probability;
(CDBG-DR) programs are two examples. pblack = percent of black population throughout census group;
Residential buildings were assumed to be retrofitted randomly phisp = percent of Hispanic population throughout census
without consideration of the community retrofit priorities for res- group;
idential buildings or individual capacity (e.g., high-income owners ploss = property value loss;
versus low-income renters). Rj = random variables between 0 and 1;Additionally, future studies will directly incorporate the CGE
model and population dislocation algorithm into the analysis sequence s = each sector;
to enable addressing uncertainty in the results. The results can then ?s = sector shocks;
reflect the uncertainty of the socioeconomic description specific for ? = medians of fragility functions; and
each hazard event. ? = standard deviation of fragility functions.
Addressing the previous limitations is beyond the scope of this
study, but future studies may include a risk-based cost-benefit
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