NIST Special Publication 1190GB-4

Guide Brief 4 –

Determining Anticipated 

Performance 

This publication is available free of charge from: 

https://doi.org/10.6028/NIST.SP.1190GB-4
NIST Special Publication 1190GB-4 

Guide Brief 4 

Determining Anticipated 

Performance

This publication is available free of charge from: 

https://doi.org/10.6028/NIST.SP.1190GB-4

September 2016 

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director 
Certain commercial entities, equipment, or materials may be identified in this

document in order to describe an experimental procedure or concept adequately.

Such identification is not intended to imply recommendation or endorsement by the

National Institute of Standards and Technology, nor is it intended to imply that the

entities, materials, or equipment are necessarily the best available for the purpose.

National Institute of Standards and Technology Special Publication 1190GB-4 Natl. 

Inst. Stand. Technol. Spec. Publ. 1190GB-4, 12 pages (September 2016) 

CODEN: NSPUE2

This publication is available free of charge from: 

https://doi.org/10.6028/NIST.SP.1190GB-4
Guide Brief 4 – Determining Anticipated Performance

Purpose and Scope

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Guide Brief 4 – Determining Anticipated Performance

Applicable Section(s) of Guide: Volume 1, Section 4.1.4, Determine Anticipated Performance, p. 46

Guide Briefs supplement the Community Resilience Planning Guide 

for Buildings and Infrastructure Systems (NIST SP1190) 

Purpose and Scope

This Guide Brief supports Step 3, 

Determine Goals and Objectives. It

provides guidance for determining the 

anticipated performance of building 

clusters and infrastructure systems 

when subjected to hazard events. This 

information is needed to complete the 

performance goals tables, establish 

existing system performance, and 

determine resilience gaps.

It is intended for use by facility managers, operators, or consultants who have an engineering background 

but may not be familiar with how to estimate the anticipated performance of facilities after a disruptive 

event. The methodology described herein applies to building clusters and all supporting infrastructure 

systems. Guide Brief 4A, Example for Determining Anticipated Performance, presents an example of

how to assess a small water and wastewater system when subjected to a subduction earthquake.

1. Introduction

Determining the anticipated performance of the built environment, which includes post-event damage 

levels and the corresponding time to recover functionality, is a critical step for identifying gaps and needs 

for community resilience planning. It includes assessments of the performance of existing infrastructure 

systems and building clusters. The time and costs to recover functionality of the infrastructure system is 

based on its critical system components.

In the Riverbend example (Guide Chapter 9), the anticipated performance for the water systems response 

to an extreme flood event is shown as an “X” in the blue boxes of Table 9-28 (repeated here as Table 1). 

The desired performance goals represent the community’s plans for recovery that would minimize longterm impacts on the community, and are indicated as 30 %, 60 %, and 90 % of system functionality, 

recognizing the need for phased recovery at the community scale. The anticipated performance represents 

the time at which the major water infrastructure sub-systems are expected to recover to 90 % of the preevent capacity given their current condition, configuration, and recovery plans.
Guide Brief 4 – Determining Anticipated Performance

Introduction

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Table 1: Riverbend, USA water infrastructure performance goals for extreme flood (Guide Table 9-28)

Disturbance 1 Restoration Levels 2,3

Hazard Type Flood 30 % Function Restored

Hazard Level Extreme 60 % Function Restored

Affected Area Regional 90 % Function Restored

Disruption Level Severe X Anticipated Performance

Water Infrastructure Support 

Needed4

Design Hazard Performance

Phase 1

Short-Term

Phase 2

Intermediate

Phase 3

Long-Term

Days Weeks Months

0 1 1-3 1-4 4-8 8-12 4 4-24 24+

Source

Raw or source water and terminal reservoirs R, S, MS 30 % 60 % 90 % X

Raw water conveyance (pump stations and piping to 

WTP) R, S, MS 60 % 90 % X

Potable water at supply (WTP, wells, impoundment) R, S, MS 30 % 60 % 90 % X

Water for fire suppression at key supply points (to 

promote redundancy) R, S, MS 90 % X

Transmission (including Booster Stations)

Backbone transmission facilities (pipelines, pump 

stations, and tanks) R, S, MS 30 % 60 % 90 % X

Control Systems

SCADA or other control systems R, S, MS 30 % 60 % 90 % X

Distribution

Critical Facilities

Wholesale Users (other communities, rural water 

districts) R, S, MS 60 % 90 % X

Hospitals, EOC, Police Station, Fire Stations R, S, MS 60 % 90 % X

Emergency Housing

Emergency Shelters R, S, MS 60 % 90 % X

Housing/Neighborhoods

Drink water available at community distribution 

centers R, S, MS 30 % 60 % 90 % X

Water for fire suppression at fire hydrants R, S, MS 60 % 90 % X

Community Recovery Infrastructure

All other clusters R, S, MS 60 % 90 % X

Footnotes:

1 Specify hazard type being considered

Specify hazard level – Routine, Design, Extreme

Specify the anticipated size of the area affected – Local, Community, Regional

Specify anticipated severity of disruption – Minor, Moderate, Severe

2 30 % 60 % 90 % Desired restoration times for percentage of elements within the cluster

3 X Anticipated performance for 90 % restoration of cluster for existing buildings and infrastructure systems 

Cluster recovery times will be shown on the Summary Matrix

4 Indicate levels of support anticipated by plan

R = Regional; S= State; MS=Multi-State; C = Civil (Corporate/Local)
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The methodology to estimate the anticipated performance of an infrastructure system, such as a power, 

water, or transportation system, when subjected to a natural hazard event, includes:

• Section 2 – Determine Anticipated Performance of Infrastructure System Components. Assess 

system performance by considering the anticipated performance of the individual components. 

Anticipated performance of each critical component can be determined through fragility curves. 

Fragility curves are damage relationships expressed in terms of the hazard intensity or magnitude

and the corresponding probability of failure for the component. Sources of component fragility 

curves are provided. 

• Section 3 – Determine Anticipated Performance of Infrastructure System. Infrastructure system 

performance is estimated using the anticipated performance of the system components. System 

performance may also be affected by external site characteristics and factors (e.g., soil 

characteristics and expected ground motions in the case of earthquakes) that should be considered 

in combination with the performance of individual components. Other external factors, such as 

the performance of surrounding utilities or co-located facilities (e.g., pipes co-located on bridges),

may also impact system performance.

• Section 4 – Determine the Anticipated Performance of Building Clusters. A building cluster is a 

set of buildings that supports a common function, such as housing, health care, or retail sales. 

Fragility curves are also used to estimate the performance of building types within a building 

cluster. The anticipated performance of building clusters is determined in much the same way as 

infrastructure systems —the anticipated performance of individual building types is aggregated to 

determine the performance of the cluster.

• Section 5 – Use of Hazus. This Guide Brief concludes with a brief discussion of Hazus, a 

national damage and loss estimation tool distributed by FEMA. Hazus provides a wide variety of 

fragility curves for the anticipated performance of many building types and infrastructure system 

components for earthquake, flood, and hurricane hazards. 

An example of the approaches described is provided in Guide Brief 4A for the analysis of a water 

infrastructure system and an earthquake hazard.

2. Determine Anticipated Performance of Infrastructure System Components

This section provides guidance for determining the anticipated performance of infrastructure system

components for selected hazards. It is assumed that each community will identify multiple hazards that 

could potentially impact it, with three hazard levels for each hazard, as described in Guide Section 4.1.3, 

Define Community Hazards and Levels.

Divide the infrastructure system into its various components, as described in Guide Section 3.2, 

Characterize the Built Environment. Communities may assess several infrastructure systems, including 

transportation, energy, communication, and water and wastewater systems. A water system, for example,

might include a dam and a reservoir, transmission pipelines, a water treatment plant, pump stations, 

storage tanks, and distribution pipelines. Systems may contain many of the same component (e.g.,

multiple storage tanks or segments of distribution pipelines). Components with dependencies on other 

infrastructure systems should be identified, as the dependency can drive the optimal sequencing of 

response and recovery activities. Similarly, components that other infrastructure systems depend on 

should also be identified.

Identify the critical components required to keep the system functional. Components required to meet 

post-event demands need to be identified. Post-event demands differ from peak demands during normal 

operations, due to reduced demand or different demands during recovery (although there likely will be 
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overlap between these two conditions). For example, water systems facilities may not need to meet peak 

summer demand flows. Instead, meeting average, or winter, demands may suffice. With that in mind, a 

water utility may have a performance goal of delivering average winter demand within a defined 

timeframe, which is half of the peak daily demand. Components that play a secondary role may not be 

critical to recovery of system function.

A component’s performance is defined in terms of its anticipated level of functionality following the 

hazard event, its associated capacity, and the time and cost to recover the component functionality given 

the type and level of damage. Component recovery time is used as an input to determine the overall 

system recovery time. Any recovery costs that are avoided or reduced through pre-event planning or 

mitigation can be treated as benefits in a benefit-cost analysis. 

Estimate component performance with fragility curves. A fragility curve estimates the anticipated 

performance of a system component for a hazard level as the probability that some well-defined level of 

damage will occur, often expressed as a probability of failure. For example, a building may be evaluated 

for several types of damage based on the hazard type, such as loss of the roof or exterior cladding for 

wind events, foundation damage for flood events, or structural damage for earthquake events. Levels of 

damage can range from minor (i.e., a level of damage that can be quickly repaired with off-the-shelf tools 

and replacement parts) to complete (i.e., a level of damage that cannot be repaired, requiring the 

component to be completely replaced with a new component)1

. However, site specific evaluations should 

be carried out for components that may present a risk to the community when they are damaged.

Fragility curves provide a relationship 

between the hazard magnitude and the 

probability of damage for a component.

These are sometimes also referred to as 

fragility relationships or fragilities. Figure 1

shows an example fragility relationship for 

the probability of failure for two types of 

storage tanks and three earthquake ground 

shaking intensity levels. The failure mode 

could be overturning, buckling near the 

foundation, or other modes of failure that 

impact the functionality of the water tank

(refer to Guide Volume I, Table 4-5 for an 

example description of seismic hazard 

levels). Tank #1 is designated as having 

high vulnerability for this seismic 

environment as it was not designed to 

withstand the design event. Tank #1 has a 

small probability that the tank would fail 

during a routine event, about a 50 % chance

it would fail for a design event, and a 100 % 

chance it would fail for an extreme event. 

Tank #2 is designated as a low vulnerability 

tank as it was designed for the design level 

event. The Tank #2 probabilities of failures

are: near zero for the routine event, about 5 

% for the design event, and 20 % for the 

extreme event. Note the increasing probability of failure as the hazard intensity increases from routine to 

 1 Refer to Hazus Damage States, discussed in Section 6 of this Guide Brief (typical for all damage descriptors in this paragraph).

Figure 1. Example fragility relationships for two water 

storage tanks, with probability of failure versus 

earthquake ground shaking intensity (peak ground 

acceleration expressed as percent of gravity) 
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design to extreme levels. The shape of the fragility may differ depending on the nature of the component. 

If the component performance is clearly linked to a single element with a well-understood failure 

mechanism, the fragility curve will be steep, or close to vertical, as shown by Tank #1. If the component 

performance is based on several elements, so that there is less likelihood that the failure mode will occur, 

the fragility curve tends to be flatter, as shown by Tank #2. 

If there are several possible failure modes for a component, separate fragility curves may be developed 

for each failure mode to determine their relative influence on the component performance. For instance, 

bridge overpass structures may have failure of the supporting columns or failure of the bridge deck. Or,

electrical equipment located on the first floor of a building may be more vulnerable to a flood hazard than 

the integrity of building foundation; either type of damage would impact the intended function of the 

building. In some cases, the component failure probability can be adequately approximated as the 

probability of the most likely failure mode. In other cases, it may be necessary to combine multiple failure 

modes. If the failure modes are independent and their probabilities are small, a simple approximation is to 

add the failure probabilities together.

When a system has many of the same type of components, the probability of failure can be thought of as

the expected number of components failing out of 100 when subjected to a specified hazard intensity. For

the example in Figure 1, about 2 of 100 storage tanks would be expected to fail in a routine event; about 

45 of 100 would fail in a design event; and nearly all would fail in an extreme event. 

Map spatial hazard intensity for system components. A Geographic Information System (GIS) can

illustrate spatial variation in hazards, such as flood or seismic hazards, for critical components in 

distributed infrastructure systems A GIS map for each of the three hazard levels—routine, design, and 

extreme—can help visualize how system damage levels and extent varies for the three hazard levels. 

Sources of published fragilities. Fragility curves can be developed using various methods, or a 

combination of methods, as shown in Table 2. An overview of fragility curve development and use is 

provided by Porter (2016).2

Many fragility curves have already been developed, based on such methods, and are available in through 

the following sources. Hazus provides one of the broadest sets of publicly available fragilities (see 

Section 5). The U.S. Army Corps of Engineers developed fragility relationships to characterize system 

reliability for water resource structures (http://www.usace.army.mil/Media/News-Archive/Story-ArticleView/Article/477787/partnering-equals-teamwork/).

Researchers and academic institutions also develop fragility curves for buildings and infrastructure 

systems. Seismic fragilities have been developed by earthquake research centers in Buffalo, NY 

(NCEER/MCEER) (http://www.buffalo.edu/mceer.html); Champaign, IL (MAE Center)

(http://mae.cee.illinois.edu/); Memphis, TN (CERI) (http://www.memphis.edu/ceri/); and Berkley, CA 

(PEER) (http://peer.berkeley.edu/). The Center for Risk-Based Community Resilience Planning 

(http://resilience.colostate.edu/) is developing a community scale model of the built environment and 

associated social and economic functions, which includes a database of fragility curves. The Center 

fragility database will be available in 2017.

 2 Porter, K., 2016. A Beginner’s Guide to Fragility, Vulnerability, and Risk. University of Colorado Boulder, 92 pp., 

http://spot.colorado.edu/~porterka/Porter-beginners-guide.pdf
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Table 2: Examples of Fragility Curve Development Methods

Method Description/Example

Expert 

Opinion

Type of facility. An engineer familiar with performance of various categories of structures estimates 

the performance based on their experience and judgment. For example, steel moment frame structures 

perform well in earthquakes, whereas unreinforced masonry walls are much more prone to collapse.

Date of Construction. Building codes are regularly updated, reducing hazard vulnerability. An 

engineer familiar with code changes will understand the consequences of code changes on building

and infrastructure system performance.

Vulnerable subcomponents. The facility may have particularly vulnerable elements that are critical 

for maintaining functionality. For example, some types of breakers used in high voltage substations 

are vulnerable to earthquake ground motions, while other components may perform better for the 

same hazard intensity.

Empirical 

Damage 

Data

Damage data collected from multiple hazard events can be used in a regression analysis to develop a 

fragility curve. The damage data must include a detailed description of the component, the hazard 

intensity, and the performance of the particular component being evaluated. The pipe fragility curves 

developed by the American Lifelines Alliance used this technique.3

Analysis Structural analysis is used to determine the performance of potentially vulnerable elements for a 

range of hazard intensities. The analysis should consider the uncertainties in both the loads on the 

structure and the ability of the structure to resist those loads.

Testing Load tests demonstrate the performance (strain, displacement, etc.) as the hazard intensity is 

increased. Similarly, shake tables are used to qualify equipment using input ground motions from 

selected earthquakes. Repeated tests are generally required to capture the variability or uncertainty in 

the response. Additional consideration should be given to how well the test conditions represent the 

actual conditions to which the component will be exposed.

3. Determine Anticipated Performance of Infrastructure System 

The estimated probability of failure and associated damage state and level of functionality for system 

components for a hazard event informs the anticipated post-event performance of the overall 

infrastructure system. Overall system performance depends on the performance of the system 

components, the connectivity of the components within the system, and dependencies on other supporting 

systems. The anticipated system performance can be compared against the desired performance goals, as 

illustrated in Table 1, to identify performance gaps.

To determine the post-event functionality of a system component based on its fragility curve, consider the 

anticipated type of damage or failure mode and how that might affect its performance, and whether there 

are levels of performance, or simply a binary condition of ‘functional’ or ‘not functional.’ Many system 

components might be assumed non-functional for planning purposes if the probability of failure exceeds 

50 percent. For example, pump stations usually either work or do not work following an earthquake. A 

treatment plant may be considered partially functional if it can provide some level of primary treatment. 

Three general approaches are available for assessing post-event system performance: 1) similar event 

method, 2) workshop assessment method, and 3) system modeling method. The assessment of post-event 

system performance is then used to estimate the recovery cost and time for achieving the desired 

performance goals of the community and its buildings and infrastructure systems.

 3 American Lifelines Alliance, Seismic Fragility Formulations, Part 1 Guidelines, April 2001, 

http://www.americanlifelinesalliance.com/pdf/Part_1_Guideline.pdf; American Lifelines Alliance, Seismic Fragility 

Formulations, Part 2 Appendices, April 2001, http://www.americanlifelinesalliance.com/pdf/Part_2_Appendices.pdf
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3.1. Similar Event Method

The anticipated infrastructure system performance may be estimated based on the performance of a 

similar system for a similar hazard event. However, there are many variables to consider and such a 

process is not likely to result in quality results. For optimal results, consider an event where the 

topography or terrain and hazard event, the type and age of the infrastructure system, and the available 

resources to restore the system are similar. Adjustments should be made for any differences. 

3.2. Workshop Assessment Method

The workshop assessment method brings together a team with knowledge about various aspects of the 

infrastructure system, such as engineering and operations staff that make operating decisions every day.

As part of the evaluation, the team determines whether the post-event system can be operated without 

infrastructure system components that are identified as damaged or failed based on the performance 

assessment in Section 2.

The workshop process includes sequentially stepping through the decisions, materials, staffing, and 

functions required to recover the system and make it operable in phases that are consistent with the 

desired performance goals for the community, as indicated in Table 1. Some recovery steps will focus on 

a single component, but others may be able to consider system redundancies or alternate methods of 

obtaining labor and materials that may provide flexibility or alternatives. For example, for a power system 

that includes transmission lines, transmission substations, distribution substations, and distribution lines, 

redundant components can improve a plan for phased recovery (e.g., 30 % or 60 % operational) of electric 

power. 

System redundancy is highly dependent on the type of system and where the redundancy occurs within 

the system. Many power systems, for example, purchase bulk power from multiple providers, none of 

whom may be impacted by the hazard event. However, failure of a local substation may result in loss of 

service to the area if there is no redundancy in transmission or distribution lines. Many water utilities are 

self-contained, and may have only a single supply of water. If that supply is lost, the entire system may 

become non-functional. 

3.3. System Modeling Method

To assess infrastructure system performance, analytical models need to include system connectivity and

the post-event functionality level of system components and expected operational demands. Many 

infrastructure systems use models to evaluate their performance under normal operating conditions. Most 

of these models are deterministic and cannot account for uncertainties or component failures and their 

impact on the system. However, the model operator may be able to test the impact of one or two 

component outages on the system at a time. 

EPANET, developed by the Environmental Protection Agency (https://www.epa.gov/waterresearch/epanet), is one example of a commonly applied water network software application. EPANET is 

used in the water industry to assess the functionality of damaged water systems. Users can remove 

damaged system components (as determined separately by a fragility analysis) to determine the 

operational impact. If the system damage is extensive, the modeler can conduct sensitivity studies by

removing some but not all damaged system components to determine which components have the greatest 

impact. This approach is sometimes followed for assessing earthquake damage of water systems when 

failures of distribution pipelines are expected. Pipeline failures are grouped by pressure zone. The 

modeler then adds or removes the demand from the various pressure zones to evaluate overall impact on 

system functionality. Other system models allow probabilistic modeling when, for example, probabilities 
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of delivery are required, but implementation of such a model is time-consuming and may provide limited 

value.

The disadvantage of deterministic models is that they cannot incorporate fragilities and other uncertainties 

into the system analysis to better predict anticipated performance. Probabilistic risk assessment models 

are routinely used in the nuclear power industry where the consequences of failure can be extreme. These 

models typically use Monte Carlo simulation to calculate the probability of damage and the potential 

range of impacts on system functionality. Such models have been developed for several wholesale water 

delivery systems on the west coast to estimate probabilities of delivering water to local communities 

following various earthquake scenarios. These models are typically proprietary. These types of models 

are effective, but are expensive to develop and have limited application beyond the specific use for which

they were intended.

3.4. System Recovery Costs and Time

Recovery Costs. First-order recovery costs are sometimes estimated as a percentage of component 

replacement cost. With fragility estimates, a starting point for a first-order recovery cost estimate could be 

based on the percent probability of failure. For example, if a component has a replacement cost of $1 

million, and has a 30 % probability of failure (or when applied to estimate repair costs, an expected 

average cost) , it might be initially estimated to cost $300 000 for recovery. Note that the 30 % is an 

expected average cost, and that the uncertainty in the estimate can be large. Some facilities with a 30 % 

probability of failure may cost $500 000 (e.g., complete tank replacement – site/piping/control facility 

undamaged) to repair and others $100 000 (e.g., repair of elephant’s foot buckling). 

While such estimates are useful for early first-order recovery costs, a substantiated recovery cost can be 

obtained by detailed estimates of costs to purchase, ship, and install the component, as well as removing 

and disposing of the damaged components. Such estimates can then be updated over time.

Recovery Time. Recovery time estimates should consider post-hazard event environment for conducting 

operations, supporting staff, and receiving equipment and supplies. For instance, memorandums of 

understanding or contracts for post-event support can greatly accelerate the recovery process. Planning for 

reduced or altered support staff and the desired rate of recovery will highlight areas of concern. 

Identifying available transportation methods for incoming supplies and materials can provide options and 

flexibility during the recovery process, and inform the range of recovery times that might be expected. 

An estimate of infrastructure system recovery time, the time between the event and the time when it once 

again becomes 90 % functional (i.e., operating at normal capacity), is needed to complete the 

performance goals tables (see Table 1 as an example). The focus is on recovery of system function, which 

can include temporary measures until permanent repairs are completed. 

For components that are critical to recovery of system functionality, system engineers and operations 

personnel can estimate recovery time each component. If multiple critical components are out of service, 

the evaluation team would consider whether recovery of the components can be accomplished in parallel 

or sequentially. 

Considerations. Emergency equipment should be considered when evaluating recovery. For critical 

societal functions, infrastructure service can be temporarily met with emergency equipment, such as 

generators and portable toilets. As another example, tank trucks can deliver water to local distribution 

points. Communities may be able to achieve minimal system functionality (i.e., 30 % functionality) with 

emergency equipment.

Recovery of systems with numerous occurrences of the same type of damage or failure need to be 

considered. As an example, consider downed power lines. To estimate system or partial system 

restoration time, assume the time it takes to repair one downed line, multiply that time by the total number 
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of downed lines, and divide that total by the available number of crews. The time and costs to repair one 

downed power line following a hazard event may be longer than during normal operations. Some 

assumptions are required to determine the availability of crews. For instance, local crews may be delayed 

by family commitments. Or mutual aid crews may take days or more if transportation routes or fuel 

systems are damaged.

Equipment required for repairs may be the limiting factor in recovery rather than the availability of repair 

crews. For example, large excavators may be required to repair large water transmission mains. Following 

a hazard event, large excavators could be in high demand. Recovery priorities would be set by the state or 

county; the excavators may first be directed to clear debris from streets, next to restore water service, and 

finally wastewater service.

Availability of repair materials may also become an issue. For example, earthquakes often damage many 

distribution pipelines. Repairs require repair clamps, and utilities usually only stock a limited supply. 

Also consider the sources and time needed to acquire additional materials. Similarly, damaged equipment 

and materials with long lead times could hamper recovery. Large diameter pipe may have to be 

manufactured to repair water transmission main failures. For power utilities, damaged high voltage 

transformers may take months to replace.

In some cases, the hazard event may also damage other infrastructure systems that slow recovery work 

(e.g., transportation systems limiting access). Damaged underground power lines may be located above 

damaged sewers and sewers may need to be restored first because they are deeper.

A timeline for the expected sequence of such activities following the hazard event will help clarify and 

document the plans and support communication with other stakeholders and community members.

4. Determine the Anticipated Performance of Building Clusters

Determining the performance of building clusters has both similarities and differences relative to 

determining the performance of infrastructure systems. A building cluster is a set of buildings that 

supports a common function, such as housing, health care, or retail sales. The buildings in a cluster need 

not be located in a single geographic area.

The performance of one building may or may not depend on the performance of another building in the 

cluster. The dependency could be in terms of a specific function served by the building within the overall

building cluster. For example, one building in the financial services cluster may provide data storage – if 

it is not functional (with no redundancy or backup), other buildings dependent on the data storage may not 

be functional. In some cases, such as houses of worship, each building in a cluster may be functionally 

independent. In such cases, a community may have significant redundancy. 

4.1. Individual Building Fragilities

Building cluster components (e.g., each building) may be designed to different codes over time and may 

have different building materials and construction types. The anticipated performance of each building, or 

set of structurally similar buildings, may be evaluated using fragility curves. 

Building performance depends on its most probable failure modes for a given hazard type and magnitude. 

For example, the most probable failure mode might be structural collapse for extreme earthquake ground 

motions or equipment damage for design level floods. If a building has emergency equipment to mitigate 

the impacts of a known vulnerability, another failure mode may become significant for that building.

Approaches available to assess building component fragilities include expert opinion, empirical damage

information, and analysis (see Table 2). Like infrastructure system components, expert opinion can rely 
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Use of Hazus

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on the type of structure, date of construction, the code to which it was designed and vulnerable elements 

within the building.

The Applied Technology Council (ATC) has developed a methodology for assessing the performance of 

buildings subject to earthquakes for FEMA P-58-1, Seismic Performance Assessment of Buildings. The 

ATC methodology addresses both structural and non-structural system performance. The American 

Society of Civil Engineers (ASCE) developed guidance for evaluating existing buildings in ASCE 41-13,

Seismic Evaluation and Retrofit of Existing Buildings with Tier, 1, 2, or 3 analyses. While these 

documents go into great detail, a preliminary Tier 1 evaluation may be adequate for analysis of a building 

cluster component. 

4.2. Building Cluster Performance

Once the anticipated performance of building cluster components (individual buildings) is determined, all 

of the components are grouped together to determine the anticipated performance of the building cluster. 

For a building cluster with a large number of similar buildings, a representative sample could be used to 

establish the cluster fragility. The goals are to determine the expected degradation in functionality and the 

expected time required to return to 90 % functionality (see Table 1). To achieve 90 % functionality, it 

might be assumed that all components would have to be functional. However, in some instances, there 

may be temporary measures that allow 90 % operation while one or more components remain nonfunctional. For example, an operational facility that normally works one shift could be pushed to operate 

two shifts while the other facility is being restored.

5. Use of Hazus

Starting in the 1990s the National Institute of Building Sciences (NIBS) led a project named Hazus (a 

contraction of Hazards-US), funded by the Federal Emergency Management Agency (FEMA), to develop 

a comprehensive method to estimate damage and losses for earthquake, flood, and hurricane. The Hazus 

technical manuals for earthquake, flood, and hurricane hazards include fragility curves for buildings and 

many infrastructure systems and components.

The earthquake hazards modeled in Hazus are ground motion, liquefaction, landslide, and fault rupture. 

For flood, both riverine and coastal flooding are addressed. There is also an option to compute combined 

hurricane wind and storm surge damage using the coastal surge option in the hurricane and flood models. 

The building fragility curves in the hurricane model include the combined effects wind pressure, 

windborne debris, and rain infiltration, and there are separate curves for tree fall damage to residential 

structures. Infrastructure fragilities have not been addressed yet in the hurricane model. 

Hazus includes methods to quantify hazards, quantify the anticipated performance of buildings and 

infrastructure systems (referred to as lifelines in Hazus) to those hazards, and in some cases, to quantify 

the effects of a hazard event on the overall community. 

The Hazus technical manuals provide detailed information on the fragilities they use and are available 

from FEMA at http://www.fema.gov/media-library/assets/documents/24609. The Hazus software is 

available from FEMA at https://msc.fema.gov/portal/resources/hazus. To run the full loss assessment 

software package, Hazus must be installed as an extension to a commercially available geographic 

information system called ArcGIS. The Hazus earthquake fragility curves were developed by expert 

opinion for:
Guide Brief 4 – Determining Anticipated Performance

Use of Hazus

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This publication is available free

of charge from: http://doi.org/10.6028/NIST.SP.1190GB-4

• Buildings and facilities, including capacity and fragility curves for ground shaking, ground 

failure, and peak ground displacement. A special section addressed essential and high potential 

loss facilities. Fragilities are included for 15 categories of building types, including low, moderate 

and high building heights, and constructed with low, moderate or high-code design. Building 

types included wood, five steel categories, five concrete categories, and reinforced and 

unreinforced masonry.

• Transportation systems, including highway systems, railways, light rail, bus, ports and harbors, 

ferries, and airports.

• Lifeline utility systems, including potable water supply, wastewater, oil systems, natural gas, 

electric power, and communications.

• Induced damage for inundation, fire following earthquake, hazardous material release, debris, 

casualties, direct social and economic losses, indirect economic losses, and annualized losses.

Figure 2 shows an example set of a Hazus earthquake fragilities for concrete tanks.

Figure 2. Earthquake ground motion fragility curves for anchored on-ground concrete tank (after 

Hazus Earthquake Technical Manual Chapter 8, Direct Damage to Lifelines – Utility Systems)

The general damage descriptive terms are:

• Slight – Minor repairable damage. The component remains fully functional.

• Moderate – Repairable damage. The component can be repaired while remaining operable but 

potentially at reduced capacity or function.

• Extensive – Inoperable but repairable.

• Complete – Component cannot be reasonably repaired or the repair cost would exceed the cost to 

replace with a new facility.

System components with slight or moderate levels of damage are still considered functional. These 

damage descriptive terms can also be interpreted in terms of percent of replacement cost or some 

parameter relating it to recovery time.
Guide Brief 4 – Determining Anticipated Performance

Use of Hazus

12

______________________________________________________________________________________________________ 

This publication is available free

of charge from: http://doi.org/10.6028/NIST.SP.1190GB-4

Using the fragility curves in Figure 2 for on-ground concrete tanks, the functionality of such a tank 

following an earthquake can be estimated. For a 0.40 gn peak ground acceleration (where gn is 

acceleration due to gravity), it can be seen in Figure 2 that there is an 80 % chance the tank will have at 

least minor damage, a 35 % chance it will have at least moderate damage, a 10 % chance for at least 

extensive damage, and a 2 % chance for complete damage. Based on these fragilities, the tank will most 

likely experience minor to moderate damage. With moderate damage, the tank is repairable and can be 

repaired while remaining operable, but potentially at a reduced capacity. Therefore, there is a 90 % 

likelihood that the tank will remain operable at a peak ground acceleration intensity level of 0.40 gn.
This publication is available free of charge from:

http://doi.org/10.6028/NIST.SP.1190GB-4