Multi-hazard Loss Estimation Methodology 

 

Earthquake Model 

 

 

HAZUS-MH MR3 

 

Technical Manual 

 

 

Developed by: 

Department of Homeland Security 

Emergency Preparedness and Response Directorate 

FEMA 

Mitigation Division 

Washington, D.C. 

 

 

Under a contract with: 

National Institute of Building Sciences 

Washington, D.C. 

 

 2003, Federal Emergency Management Agency 

(Secured by Assignment) 

 

HAZUS is a trademark of the Federal Emergency Management Agency 




FOREWORD 

 

The research and development and studies that provided the basis for this 
publication were conducted pursuant to a contract with the Federal 
Emergency Management Agency (FEMA) by the: 

 

 


 

The National Institute of Building Sciences (NIBS), located in Washington, DC, 
is a non-governmental, non-profit organization, authorized by Congress to 
encourage a more rational building regulatory environment, to accelerate the 
introduction of existing and new technology into the building process and to 
disseminate technical information. 

 

Copies of this report are available through the Federal Emergency Management 
Agency. For information contact FEMA @ www.fema.gov/hazus or: 

 

FEMA Distribution Center 

P.O. Box 2012 

Jessup, Maryland 20794-2012 

Tel.: 1 800-480-2520 

Fax: 301-362-5335 

 

HAZUS is a trademark of the Federal Emergency Management Agency. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 2003, Federal Emergency Management Agency 

(Secured by Assignment) 

 




ACKNOWLEDGMENTS 

 

HAZUS-MH and HAZUS-MH MR1 

 

Earthquake Committee 

 

Chairman, William Holmes, Rutherford & Chekene, San Francisco, California 

Roger Borcherdt, U.S. Geological Survey, Menlo Park, California 

David Brookshire, University of New Mexico, Albuquerque, New Mexico 

Richard Eisner, California Office of Emergency Services, Oakland California 

Robert Olson, Robert Olson & Associates, Inc., Sacramento, California 

Michael O'Rourke, Rensselaer Polytechnic Institute, Troy, New York 

Henry Lagorio, University of California at Berkeley, Berkeley, California 

Robert Reitherman, Consortium of Universities for Research in 
Earthquake Engineering, Richmond, California 

Robert Whitman, Massachusetts Institute of Technology, Cambridge, 
Massachusetts, Chairman Emeritus 

 

Building Damage Subcommittee 

 

William Holmes, Rutherford & Chekene, San Francisco, California 

Robert Whitman, Massachusetts Institute of Technology, Cambridge, Massachusetts 

 

Shake Beta Subcommittee 

 

Chairman, William Holmes, Rutherford & Chekene, San Francisco, California 

Robert Whitman, Massachusetts Institute of Technology, Cambridge, Massachusetts 

Roger Borcherdt, U.S. Geological Survey, Menlo Park, California 

Richard Eisner, FAIA, California Office of Emergency Services, Oakland 
California 

Michael O=Rourke, Rennselaer Polytechnic Institute, Troy, New York 

 

Casualties Subcommittee 

 

Chairman, Robert Reitherman, Consortium of Universities for Research in 
Earthquake 
Engineering, Richmond, California 

Richard Eisner, California Office of Emergency Services, Oakland California 

William Holmes, Rutherford & Chekene, San Francisco, California 

Robert Olson, Robert Olson & Associates, Inc., Sacramento, California 

Henry Lagorio, University of California at Berkeley, Berkeley, California 

 

 

PBS&J, Atlanta, Georgia 

Jawhar Bouabid, Program Manager; Scott Lawson 

 

 

Kircher & Associates, Palo Alto, California 
Charles Kircher 




San Jose State University Foundation, San Jose, California 
Thalia Anagnos 

Special thanks to the Environmental Protection Agency for its assistance in 
integrating 
ALOHA into the Earthquake Model. 

 

Special thanks to the National Oceanic and Atmospheric Administration for making 
FloodWAV and FloodVIEW available and its assistance in integrating them into the 
Earthquake Model. 

 

 Earthquake Model Validation 

 

Comartin-Reis, Stockton, California 
Craig Comartin, Evan Reis 


Earthquake Model Software Development 

 

Software Committee 

 

Chairman, Dick Bilden, Consultant, Reston, Virginia 

Co-Chairman, Mike Haecker, Consultant, Austin, Texas 

Dan Cotter, Terrapoint, The Woodlands, Texas 

Gerry Key, Computer Sciences Corporation, San Diego, California 

Tracy Lenocker, Lenocker and Associates, Inc., Orange, California 

Ken Lewis, KVL and Associates, Inc., Scottsdale, Arizona 

Frank Opporto, DHS, EP&R Directorate (FEMA), Information Services Technology 
Division, 
Washington, D.C. 

Dirk Vandervoort, POWER Engineers, Inc., Boise Idaho 

Leslie Weiner-Leandro, DHS, EP&R Directorate (FEMA), Information Services 
Technology 
Division, Washington, D.C. 

 

PBS&J, Atlanta, Georgia 
Michelle Palmer, Program Manager; Mourad Bouhafs, Sandeep Mehndiratta, Eduardo 
Escalona, Nabil Bouhafs 

 

Beta Test Subcommittee HAZUS-MH 

 

Darryl Davis, Corps of Engineers Hydrologic Engineering Center, Davis, 
California 

 Neil Grigg, Colorado State University, Fort Collins, Colorado 

Charles Kircher, Kircher & Associates, Palo Alto, California 

Tracy Lenocker, Lenocker and Associates, Inc., Orange, California 

 Kenneth Lewis, KVL and Associates, Inc., Scottsdale, Arizona 

 Masoud Zadeh, Consultant, San Jose, California 

 

Beta Test Communities HAZUS-MH 

 

 Division of Emergency Management, Tallahassee, Florida; Washington State 
Emergency 
Management, Camp Murray, Washington; Whatcom County Public Works, Bellingham, 
Washington; Johnson County, Olathe, Kansas; Mecklenburg County Stormwater 
Services, 




Charolotte, North Carolina; Louisiana State University, Baton Rouge, Louisiana; 
Charleson 
County Building Services, North Charleston, South Carolina 

 

Beta Test Subcommittee  HAZUS-MH MR1 

 

Douglas Bausch, Department of Homeland Security, Emergency Preparedness and 
Response Directorate (FEMA), Washington, D.C. 

Richard Eisner, Governor's Office of Emergency Services, Oakland, California 

John Knight, South Carolina Emergency Management Division, Columbia, South 
Carolina 

Kevin Mickey, The Polis Center, Indianapolis, Indiana 

Mark Neimeister, Delaware Geological Survey, Newark, Delaware 

Lynn Seirup, New York City Office of Emergency Management, New York, New York 

 

Beta Test Subcommittee  HAZUS-MH MR2 

 

 Douglas Bausch, Department of Homeland Security, Federal Emergency Management 
Agency, 
Washington, D.C. 

 John Knight, South Carolina Emergency Management Division, Columbia, South 
Carolina 

 Kevin Mickey, The Polis Center, Indianapolis, Indiana 

 Joe Rachel, Department of Homeland Security, Federal Emergency Management 
Agency, 
Washington, D.C. 

 Ken Wallace, Department of Homeland Security, Federal emergency Management 
Agency, 
Washington, D.C. 

 Bryan Siltanen, Advanced Systems Development, Inc., Arlington, VA 

 

Beta Test Subcommittee  HAZUS-MH MR3 

 

Douglas Bausch, Department of Homeland Security, Federal Emergency Management 
Agency, Washington, D.C. 

John Buechler, The Polis Center, Indianapolis, IN 

Susan Cutter, Hazards and Vulnerability Research Institute, University of South 
Carolina, Columbia, SC 

Aiju Ding, CivilTech Engineering, Inc., Cypress, TXs 

Craig Eissler, Geo-Tech Visual Power Tec Texas Geographic Society, Austin, TX 

Melanie Gall, Hazards and Vulnerability Research Institute, University of South 
Carolina, Columbia, SC 

Shane Hubbard, The Polis Center, Indianapolis, IN 

David Maltby, Geo-Tech Visual Power Tech/First American Flood Data Services, 
Austin, TX 

Kevin Mickey, The Polis Center, Indianapolis, IN 

David Niel, APPTIS Inc., McLean, VA 

Jack Schmitz, The Polis Center, Indianapolis, IN 

Bryan Siltanen, APPTIS Inc., McLean, VA 

Hilary Stephens, Greenhorne & O'Mara, Inc., Laurel, MD 

Eric Tate, Hazards and Vulnerability Research Institute, University of South 
Carolina, 
Columbia, SC 

 




 

 

 

HAZUS-MH and HAZUS-MH MR1 Shell Development 

 

PBS&J, Atlanta, Georgia 

 Mourad Bouhafs, Program Manager (HAZUS-MH); Pushpendra Johari, Program Manager 
(HAZUS-MH MR1); Sandeep Mehndiratta 

 

 Special thanks to ESRI for its assistance in coordinating ArcGIS with HAZUS-MH. 

 

Project Sponsorship and Oversight 

 

Department of Homeland Security, Federal Emergency Management Agency, Mitigation 
Division, 
Washington, D.C. 

 
Frederick sharrocks, Section Chief, Assessment & Plan for Risk; Cliff Oliver, 
Chief, Risk 
Assessment Branch; Edward Laatsch, Chief, building Science and Technology; Eric 
Berman, 
Project Officer; Claire Drury, Project Officer; Paul tertell, Michael Mahoney, 
Stuart Nishenko, 
ScottMcAfee, Paul Bryant 

 

FEMA Technical Monitors 

 Douglas Bausch, FEMA Region 8; John Ingargiola, Douglas Bellemo, Allyson 
Lichtenfels, 
Divisional Coordination 

 

Program Management 

 

National Institute of Building Sciences, Washington, D.C. 

 

Philip Schneider, Director, Multihazard Loss Estimation Methodology Program; 
Barbara 
Schauer, Senior Project Manager 

 




MESSAGE TO USERS 

 

The HAZUS-MH Earthquake Model is designed to produce loss estimates for use by 
federal, 
state, regional and local governments in planning for earthquake risk 
mitigation, emergency 
preparedness, response and recovery. The methodology deals with nearly all 
aspects of the built 
environment, and a wide range of different types of losses. Extensive national 
databases are 
embedded within HAZUS-MH, containing information such as demographic aspects of 
the 
population in a study region, square footage for different occupancies of 
buildings, and numbers 
and locations of bridges. Embedded parameters have been included as needed. 
Using this 
information, users can carry out general loss estimates for a region. The HAZUS-
MH 
methodology and software are flexible enough so that locally developed 
inventories and other 
data that more accurately reflect the local environment can be substituted, 
resulting in increased 
accuracy. 

 

Uncertainties are inherent in any loss estimation methodology. They arise in 
part from incomplete 
scientific knowledge concerning earthquakes and their effects upon buildings and 
facilities. They 
also result from the approximations and simplifications that are necessary for 
comprehensive 
analyses. Incomplete or inaccurate inventories of the built environment, 
demographics and 
economic parameters add to the uncertainty. These factors can result in a range 
of uncertainty in 
loss estimates produced by the HAZUS-MH Earthquake Model, possibly at best a 
factor of two 
or more. 

 

The methodology has been tested against the judgment of experts and, to the 
extent possible, 
against records from several past earthquakes. However, limited and incomplete 
data about actual 
earthquake damage precludes complete calibration of the methodology. 
Nevertheless, when used 
with embedded inventories and parameters, the HAZUS-MH Earthquake Model has 
provided a 
credible estimate of such aggregated losses as the total cost of damage and 
numbers of casualties. 
The Earthquake Model has done less well in estimating more detailed results - 
such as the number 
of buildings or bridges experiencing different degrees of damage. Such results 
depend heavily 
upon accurate inventories. The Earthquake Model assumes the same soil condition 
for all 
locations, and this has proved satisfactory for estimating regional losses. Of 
course, the 
geographic distribution of damage may be influenced markedly by local soil 
conditions. In the 
few instances where the Earthquake Model has been partially tested using actual 
inventories of 
structures plus correct soils maps, it has performed reasonably well. 

 

Users should be aware of the following specific limitations: 

 

 
While the HAZUS-MH Earthquake Model can be used to estimate losses for an 
individual building, the results must be considered as average for a group of 
similar 
buildings. It is frequently noted that nominally similar buildings have 
experienced vastly 
different damage and losses during an earthquake. 




 

 
When using embedded inventories, accuracy of losses associated with lifelines 
may be 
less than for losses from the general building stock. The embedded databases and 
assumptions used to characterize the lifeline systems in a study region are 
necessarily 
incomplete and oversimplified. 




 

 
Based on several initial studies, the losses from small magnitude earthquakes 
(less than 
M6.0) centered within an extensive urban region appear to be overestimated. 




 




 
Because of approximations in modeling of faults in California, there may be 
discrepancies in motions predicted within small areas immediately adjacent to 
faults. 




 

 
There is considerable uncertainty related to the characteristics of ground 
motion in the 
Eastern U.S. The embedded attenuation relations in the Earthquake Model, which 
are 
those commonly recommended for design, tend to be conservative. Hence use of 
these 
relations may lead to overestimation of losses in this region, both for scenario 
events and 
when using probabilistic ground motion. 




 

 
SQL Server MSDE has a size limit of 2 GB per database, which limits the size of 
the 
region you can analyze to 2000 census tracts. Since the data for the three 
hazards share 
the 2 GB limit, the maximum size of multihazard regions will be less. To work-
around 
the 2 GB database limit, the full version of Microsoft SQL Server 2000 must be 
used. 
Refer to Appendix N for details. 




 

 
To maximize the size of a study region within the 2000 census tract limit, set 
your virtual 
memory size from a minimum of 1024 MB to a maximum of 2048 MB. 




 

 
Multihazard loss analysis is limited to the 22 states that experience hurricane, 
flood and 
earthquake hazards. 




 

 
Rapid loss estimation for large study regions of 1000 - 2000 census tracts might 
require 1 
- 2 hours to run an analysis. 




 

 

HAZUS-MH should still be regarded as a work in progress. Additional damage and 
loss data 
from actual earthquakes and further experience in using the software will 
contribute to 
improvements in future releases. To assist us in further improving HAZUS-MH, 
users are invited 
to submit comments on methodological and software issues by letter, fax or e-
mail to: 

 

Philip Schneider Eric Berman 

National Institute of Building Sciences Department of Homeland Security 

1090 Vermont Avenue, N.W., Federal Emergency Management Agency 

Washington, D.C. 20005 Mitigation Division 

Tel: 202-289-7800, Ext. 127 500 C Street, S.W. 

Fax: 202-289-1092 Washington, DC 20472 

E-Mail:HAZUSGenHelp@nibs.org Tel: 202-646-2884 
Fax: 202-646-2577 
E-Mail: Eric.Berman@dhs.gov 




Limitations of the HAZUS-MH MR3 Software 

 

 

Installation 

 

.. 
HAZUS-MH MR3 is certified to run on ArcGIS 9.2 SP2. Tests have shown that 
HAZUS-MH MR3 is unable to fully function on the ArcGIS 9.2 platform only. 
SP1 is required, buts SP2 is preferable. ArcGIS 9.2 SP2 is available form the 
ESRI website. 




 

.. 
HAZUS-MH MR3 is certified to run on MS Windows 2000 SP2, SP3 and SP4 
and Windows XP SP1 and SP2. A user is allowed to install HAZUS-MH MR2 on 
MS Windows 2000 and XP for Service Packs higher than SP4 and SP2 
respectively, but HAZUS-MH MR2 is not certified to work flawlessly with those 
service packs. 




 

.. 
HAZUS-MH MR3 must be uninstalled only with the Windows Add/Remove 
Programs utility. For details on uninstalling, please consult the User Manuals. 




 

.. 
Users who plan to operate HAZUS-MH MR3 in a network environment will be 
able to perform HAZUS operations, such as importing, but not study region 
creation. 




 

Study Region Size 

 

.. 
The database management system of HAZUS-MH MR3 is SQL 2005 Express. 
The system has a size limit of 4 GB per database, which limits the size of the 
regions to 3,000 census tracts, equivalent to an area with a population of about 
9 
million. For a multi-hazard study region that includes data for all three 
hazards, 
the 4 GB limit will permit an even smaller study region. To work around this, 
the 
full version of Microsoft SQL Server 2000 Personal and Developer Editions must 
be used (see Appendix N in the user manuals). 




 

.. 
To maximize the size of a study region that may be analyzed, set the virtual 
memory size from a minimum of 2048 MB to a maximum of 4096 MB. For the 
earthquake model, the virtual memory size may be increased from a minimum of 
1024 MB for optimal operation. Here are the steps for setting the virtual memory 
size: 




 

1- 
Click on Start | Settings | Control Panel | System | 




 

2- 
Click on the Advanced Tab 




 

3- 
Click on the Performance Options button 




 

4- 
Click the Change button under Virtual Memory 







 

5- 
Click Apply 




 

.. 
To speed up the study region aggregation process, state databases can be copied 
to the local hard-disk. If necessary, the Registry can be updated so that HAZUS-
MH looks to the location where you copied the data on the local hard disk rather 
than to the default DVD location. 




 

The HAZUS-MH MR3 installation allows the user to specify the folder where the 
state data will be copied through the Choose Data Path dialog in the 
installation 
wizard. If, at the time of installation, the user specifies the folder where the 
data 
wil be copied after installation, they only need to perform Step 1 as described 
below. If at the time of installation the User does not specify the folder where 
the 
state data will be copied by the user after installation, or if they want to 
change 
the folder specified during installation, then Steps 2 through 4 for updating 
the 
Registry should be completed. 

 

NOTE: The Choose Data Path dialog in the installation process only specifies 
the folder where the state data will be copied by the user from the DVD after 
installation has completed. This dialog doesnt copy the data from the DVD to 
the 
specified folder; that has to be done manually by the user after installation. 

 

1- 
Copy one or more of the state data folders (e.g., NCI), both the DVD 
identification files (e.g., D1.txt ^ 4.txt) and syBoundary.mdb from the Data 
DVD to a folder on your hard drive (e.g., D:\HAZUSData\). As an example, 
the following graphic illustrates how the data for the state of South Carolina 
would be organized under the HAZUS Data folder. 




 

2- 
Next, point the program to the data folder on your local hard drive. To do this, 
click the Start button and select Run to open the Run window, type 
regedit in Run window edit box and click the OK button to open the 
Registry Editor. Navigate through the folders listed in the Registry Editor to 
the following location: 




 

HKEY_LOCAL MACHINE | SOFTWARE | FEMA | HAZUS-MH | General 

 

3- 
Now look at the right side of the window and find the entry called 
DataPath1. Double click on DataPath1 to open the Edit String window 
and enter the full name of the folder on the hard drive that contains the data 
copied from the DVDs in the edit box. Click the OK button to update the 
DataPath1 value. 




 

IMPORTANT: Make sure the path ends with a \ and do not change any of the 
other registry settings. 

 




4- 
Close the Registry Editor by choosing Exit from the File menu of the Registry 
Editor. 




 

 

Capabilities 

 

.. 
Transferring data, including importing study regions, from HAZUS99, 
HAZUS99-SR1, HAZUS99-SR2 and HAZUS-MH to HAZUS-MH MR2 will 
require the assistance of technical support. 




 

.. 
Inventory data and subsequently the Level 1 analysis functionality are 
unavailable 
for the US held territories, but are available for Puerto Rico. 




 

.. 
Components for independently developed data sets in the default inventory data 
might not line up on maps, for example, the placement of bridges and roads, and 
facilities. This situation can be addressed by updating the default inventory 
data 
with user supplied data. 




 

.. 
Rapid loss estimates for large study regions of 1000-2000 census tracts might 
require 0.5 to 1.5 hours analysis time. 




 

 

InCAST 

 

.. 
Since InCAST development predated the development of the Earthquake Model 
in HAZUS-MH, data types used for different types of hazard specific data in 
InCAST are not compatible with those used in HAZUS-MH MR2. Additionally, 
InCAST does not capture all hazard specific attributes used in HAZUS-MH MR2. 




 

InCAST can be used to capture earthquake hazard data which can be imported 
into HAZUS-MH MR2 from hzIncast table. However, the following fields should 
not be imported: BldgType, Kitchen, Dinning and Sleeping. The full capability of 
InCAST can be used to capture data for import into the inventory table of the 
AEBM module. 

 

Technical Support 

 

.. 
Technical support is available via telephone, e-mail, or FAX. The numbers and 
address are listed on the HAZUS software package and under the Help menu in 
the software. Information on HAZUS updates, software patches, and FAQs are 
available at www.fema.gov/hazus/. 




 

 

 

WHATS NEW IN HAZUS-MH - EARTHQUAKE MODEL 




Data 

 
New 2002 aggregated data for square footage, building count, building exposure, 
content exposure and demographics. 


 
New site specific inventory data (2000 or later) 


 
New 2002 USGS probabilistic ground shaking maps and new USGS attenuation 
functions 


 
Multi-familily dwellings (RES3) are split into five categories Duplex (RES3A), 
Triplex / Quads (RES3B), Multi-dwellings - 5 to 9 units (RES3C), Multi-
dwellings - 10 to 19 units (RES3D), Multi-dwellings - 20 to 49 units (RES3E) 
and Multi-dwellings - 50+ units (RES3F). 


 
Expansion of attributes in the Demographics data. The following categories were 
added: Gender information, more income brackets, number of students and 
building count by age. 


 
Precompiled default replacement values for all site specific data 




Methodology 

 
Direct usage of square footage, building exposure and content exposure in 
building damage and economic loss computations 


 
Streamlined the restoration functions for pipelines. Now all pipelines are 
restored 
in the same ways as potable water pipelines. 


 
Streamlined damage for bridges 


 
Lifeline buildings will now use pushover analysis for damage computations 


 
Facility added in the Sceanario Wizard to set the whole study region one soil 
type, liquefaction susceptibility category, and landslide susceptibility 
category 
and water depth. 


 
Show current scenario dialog modified to view current hazard settings for soil, 
liquefaction landslide and water depth. 


 
Occupancy mapping scheme can be viewed by Specific Occupancy to General 
Building type relation and withing every such relation as a General Building 
Type 
to Specific Building Type distribution. 


 
Streamlined economic parameters into inventory replacement values and loss 
ratios by damage states. 




Other Features: 

 
New three tier application architecture that can be esily ported for internet 
based 
implementation. 







 
New Hazard Query Wizard to handle complex relational queries with the 
underlying MSDE database. 


 
Feature added to enable third party modules. Currently ALOHA/MARPLOT and 
FLDWV/FLDVIEW have been enabled. Refer to Appendix L and Appendix M of 
Users Manual for details. 




 




WHATS NEW IN HAZUS-MH MR1 

 EARTHQUAKE MODEL 

 

Data 

 
Updated USGS probabilistic hazard data. 


 
Updated valuations for the general building stock. 


 
Data for Puerto Rico. 




 

Methodology 

 
Capability to port USGS shake maps into the HAZUS-MH MR1 geodatabase 
format. 


 
Capability to add new custom building types. 




 

Other Features 

 
Operation on the new ArcGIS 9.0 SP1 platform. 


 
Capability to run HAZUS-MH MR1 without administrative rights. 


 
Optimized software for rapid loss assessment. 




 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 




WHATS NEW IN HAZUS-MH MR1 

 EARTHQUAKE MODEL 

 

Data 

 

.. 
2005 valuation data for all occupancy classes. 




 

.. 
Means location factors for residential and non-residential occupancies on a 
county 
basis. 




 

.. 
Updated and validated valuation data for single-family residential housing and 
manufactured housing based on comparisons with other national databases. 




 

.. 
Zeros substituted for any negative values calculated for the daytime, nighttime, 
working commercial, working industrial and commuting populations. 




 

.. 
Construction age and values by decade for every census block with floor area 
(square footage). 




 

 

Methodology 

 

.. 
Optimized Building Damage Module that uses seven combinations of design 
levels and building quality levels (reduced from nine) as follows. 




 

1) High  Code 

2) Moderate  Code 

3) Low  Code 

4) Pre  Code 

5) Special High  Code 

6) Special Moderate  Code 

7) Special Low  Code 

 

.. 
Technical Manual Chapter 4 guidance for selecting attenuation functions. 




 

.. 
Corrected Wyoming and Montana default mapping schemes. 




 

 

Other Features 

 

.. 
Updated BIT that is coordinated with the seven combinations of design levels 
and building quality levels in the Building Damage Module. 




 

.. 
Keyboard operation of all user interface operations with some exceptions. 




 




.. 
Operation on the ArcView 9.1 SP1 platform. 




 

.. 
Certified on Windows XP SP2 




 

.. 
Operation on the MDAC 2.8 data access engine from Microsoft. 




 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 




TABLE OF CONTENTS 

 

 

 Page No. 

 1. Introduction to FEMA Loss Estimation Methodology 

 1.1 
Background......................................................................
.....................1-1 

 1.2 Technical Manual 
Scope.......................................................................1-1 

 1.3 Technical Manual 
Organization............................................................1-2 

 

 2. Overall Approach and Framework of Methodology 

 2.1 Vision 
Statement.......................................................................
............2-1 

 2.2 Project 
Objectives......................................................................
...........2-4 

 2.3 Description of Loss Estimation 
Methodology......................................2-6 

 

 3. Inventory Data: Collection and Classification 

 3.1 
Introduction....................................................................
.......................3-1 

 3.2 Direct Damage Data - Buildings and 
Facilities....................................3-1 

 3.2.1 General Building 
Stock.............................................................3-1 

 3.2.1.1 
Classification................................................................3-
1 

 3.2.1.2 Specific Occupancy to Model Building Type 

Mapping.......................................................................3-
4 

 3.2.2 Essential 
Facilities....................................................................3-
5 

 3.2.2.1 
Classification................................................................3-
5 

 3.2.2.2 Occupancy to Model Building Type Mapping.............3-6 

 3.2.3 High Potential Loss 
Facilities...................................................3-6 

 3.2.3.1 
Classification................................................................3-
7 

 3.3 Direct Damage Data - Transportation 
Systems....................................3-7 

 3.3.1 Highway 
Systems......................................................................3-8 

 3.3.1.1 
Classification................................................................3-
8 

 3.3.2 
Railways........................................................................
............3-8 

 3.3.2.1 
Classification................................................................3-
9 

 3.3.3 Light 
Rail............................................................................
....3-10 

 3.3.3.1 
Classification..............................................................3-10 

 3.3.4 
Bus.............................................................................
.............3-12 

 3.3.4.1 
Classification..............................................................3-12 

 3.3.5 Ports and 
Harbors....................................................................3-13 

 3.3.5.1 
Classification..............................................................3-13 

 3.3.6 Ferry 
................................................................................
........3-14 

 3.3.6.1 
Classification..............................................................3-15 

 3.3.7 
Airports........................................................................
...........3-16 

 3.3.7.1 
Classification..............................................................3-16 

 3.4 Direct Damage Data - Lifeline Utility 
Systems..................................3-19 

 3.4.1 Potable Water 
Supply.............................................................3-19 

 3.4.1.1 
Classification..............................................................3-19 




 3.4.2 Waste 
Water...........................................................................
.3-20 

 3.4.2.1 
Classification..............................................................3-20 

 3.4.3 Oil 
Systems.........................................................................
....3-21 

 3.4.3.1 
Classification..............................................................3-21 

 3.4.4 Natural Gas 
Systems...............................................................3-22 

 3.4.4.1 
Classification..............................................................3-22 

 3.4.5 Electric 
Power.........................................................................3-
23 

 3.4.5.1 
Classification..............................................................3-23 

 3.4.6 
Communication...................................................................
....3-23 

 3.4.6.1 
Classification..............................................................3-23 

 3.5 Hazardous Materials 
Facility..............................................................3-24 

 3.6 Direct Economic and Social 
Loss.......................................................3-24 

 3.6.1 Demographics 
Data.................................................................3-24 

 3.6.2 Default Occupancy Class Square Foot Inventory...................3-27 

 3.7 Indirect Economic 
Data......................................................................3-27 

 3.8 
References......................................................................
.....................3-29 

 Appendix 3A 
................................................................................
........3-30 

 Appendix 3B 
................................................................................
........3-48 

 Appendix 3C 
................................................................................
........3-56 

 

 4. Potential Earth Science Hazards (PESH) 

 4.1 Ground 
Motion..........................................................................
...........4-1 

 4.1.1 
Introduction....................................................................
...........4-1 

 4.1.1.1 Form of Ground Motion / Site Effects..........................4-3 

 4.1.1.2 Input Requirements and Output Information................4-3 

 4.1.2 Description of 
Methods............................................................4-4 

4.1.2.1 
Basis for Ground Shaking.............................................4-5 


4.1.2.2 
Standard Shape of Response Spectra............................4-7 


4.1.2.3 
Attenuation of Ground Shaking..................................4-11 


4.1.2.4 
Amplification of Ground Shaking - Local Site 

Conditions...................................................................4-
22 




 4.1.3 Guidance for Expert-Generated Ground Motion Estimation..4-25 

 4.2 Ground 
Failure.........................................................................
...........4-26 

 4.2.1 
Introduction....................................................................
.........4-26 

 4.2.1.1 
Scope...........................................................................
4-26 

 4.2.1.2 Input Requirements and Output Information..............4-26 

 4.2.2 Description of 
Methods..........................................................4-27 

 4.2.2.1 
Liquefaction................................................................4-27 

 4.2.2.1.1 Background................................................4-27 

4.2.2.1.2 Liquefaction Susceptibility........................4-27 

4.2.2.1.3 Probability of Liquefaction........................4-29 

4.2.2.1.4 Permanent Ground Displacements.............4-33 

4.2.2.2 
Landslide.....................................................................4-
36 

4.2.2.2.1 Background................................................4-36 

4.2.2.2.2 Landslide Susceptibility.............................4-36 




4.2.2.2.3 Probability of Having a Landslide 

Susceptible Deposit...................................4-40 

4.2.2.2.4 Permanent Ground Displacements.............4-40 

 4.2.2.3 Surface Fault 
Rupture.................................................4-42 

4.2.2.3.1 Permanent Ground Displacements.............4-42 

 4.2.3 Guidance for Expert-Generated Ground Failure Estimation..4-43 

 4.2.3.1 Input Requirements and Output Information..............4-43 

4.2.3.1.1 Liquefaction................................................4-43 

4.2.3.1.2 Landsliding.................................................4-44 

4.2.3.1.3 Surface Fault Rupture.................................4-44 

 4.2.3.2 
Liquefaction................................................................4-45 

4.2.3.2.1 Background................................................4-45 

4.2.3.2.2 Susceptibility..............................................4-47 

4.2.3.2.3 Liquefaction Probability.............................4-48 

4.2.3.2.4 Permanent Ground Displacement...............4-49 

 4.2.3.3 
Landsliding.................................................................4-50 

4.2.3.3.1 Background................................................4-50 

4.2.3.3.2 Landslide Susceptibility.............................4-50 

4.2.3.3.3 Probability of Landsliding..........................4-51 

4.2.3.3.4 Permanent Ground Displacements.............4-51 

 4.2.3.4 Surface Fault 
Rupture.................................................4-52 

4.2.3.4.1 Permanent Ground Displacements.............4-52 

 4.3 
References......................................................................
.....................4-53 

 Appendix 4A 
................................................................................
........4-58 

 

 5. Direct Physical Damage - General Building Stock 

 5.1 
Introduction....................................................................
.......................5-1 

 5.1.1 
Scope...........................................................................
..............5-1 

 5.1.2 Input Requirements and Output Information............................5-3 

 5.1.3 Form of Damage 
Functions......................................................5-3 

 5.2 Description of Model Building 
Types..................................................5-6 

 5.2.1 Structural 
Systems....................................................................5-8 

 5.2.2 Nonstructural 
Components.....................................................5-11 

 5.3 Description of Building Damage 
States..............................................5-12 

 5.3.1 Structural 
Damage..................................................................5-13 

 5.3.2 Nonstructural 
Damage............................................................5-21 

 5.4 Building Damage Due to Ground 
Shaking.........................................5-23 

 5.4.1 
Overview........................................................................
.........5-23 

 5.4.2 Capacity 
Curves......................................................................5-24 

 5.4.3 Fragility 
Curves......................................................................5-35 

5.4.3.1 
Background.................................................................5-35 


5.4.3.2 
Development of Damage State Medians.....................5-36 


5.4.3.3 
Development of Damage State Variability.................5-37 


5.4.3.4 
Structural Damage......................................................5-39 


5.4.3.5 
Nonstructural Damage - Drift - Sensitive 

Components................................................................5-43 







5.4.3.6 
Nonstructural Damage - Acceleration - Sensitive 
Components................................................................5-49 




 5.4.4 Structural Fragility Curves - Equivalent Peak Ground 
Acceleration....................................................................
........5-54 

 5.5 Building Damage Due to Ground 
Failure...........................................5-61 

 5.5.1 
Overview........................................................................
.........5-61 

 5.5.2 Fragility Curves - Peak Ground Displacement.......................5-61 

 5.6 Evaluation of Building 
Damage..........................................................5-62 

 5.6.1 
Overview........................................................................
.........5-62 

 5.6.2 Damage Due to Ground 
Shaking............................................5-62 

5.6.2.1 D
emand Spectrum Reduction for Effective 

Damping.........................................................................
...5-62 


5.6.2.2 
Damage State Probability...........................................5-66 




 5.6.3 Ground Damage due to Combined Ground Failure and 

Ground 
Shaking......................................................................5-
67 

 5.6.4 Combined Damage to Occupancy Classes.............................5-69 

 5.7 Guidance for Expert 
Users..................................................................5-69 

 5.7.1 Selection of Representative Seismic Design Level................5-70 

 5.7.2 Development of Damage Functions for Other Buildings.......5-71 

 5.8 Building Damage 
References.............................................................5-72 

 

 6. Direct Physical Damage - Essential and High Potential Loss Facilities 

 6.1 
Introduction....................................................................
.......................6-1 

6.1.1 
Scope...........................................................................
..............6-1 

6.1.2 Essential Facilities 
Classification.............................................6-1 

6.1.3 Input Requirements and Output Information............................6-3 

6.1.4 Form of Damage 
Functions......................................................6-4 

6.2 D
escription of Model Building 
Types..................................................6-5 


6.3 D
escription of Building Damage 
States................................................6-6 


6.4 B
uilding Damage Due to Ground Shaking - Special Buildings...........6-6 

6.4.1 
Overview........................................................................
...........6-6 

6.4.2 Capacity Curves - Special 
Buildings........................................6-7 


6.4.3 F
ragility Curves - Special Buildings.......................................6-12 


6.4.3.1 B
ackground.................................................................6-12 


6.4.3.2 S
tructural Damage - Special Buildings.......................6-12 


6.4.3.3 N
onstructural Damage - Drift Sensitive.....................6-15 


6.4.3.4 N
onstructural Damage - Acceleration - 

Sensitive Components.......................................................6-20 






6.4.4 Structural Fragility Curves - Equivalent PGA........................6-24 
6.5 D
amage Due to Ground Failure - Special Buildings..........................6-28 


6.6 E
valuation of Building Damage - Essential Facilities........................6-28 


6.6.1 O
verview.........................................................................
........6-28 


6.6.2 D
amage Due to Ground Shaking............................................6-28 


6.6.2.1 D
emand Spectrum Reduction for Effective 

Damping - Special Buildings............................................6-29 


6.6.2.2 D
amage State Probability...........................................6-30 











6.6.3 C
ombined Damage Due to Ground Failure and Ground 




 
Shaking.........................................................................
..........6-31 
6.6.4 C
ombined Damage to Essential Facilities..............................6-32 




6.7 G
uidance for Expert 
Users..................................................................6-32 


6.7.1 S
election of Representative Seismic Design Level................6-32 


6.7.2 H
igh Potential Loss Facilities.................................................6-
33 


6.7.2.1 I
ntroduction.................................................................6-33 


6.7.2.2 I
nput Requirement and Output Information...............6-33 


6.7.2.3 F
orm of Damage Function and Damage Evaluation...6-34 






6.8 E
ssential Facility and HPL Damage References................................6-34 


6.9 R
estoration 
Curves..........................................................................
....6-35 




 

 

 7. Direct Physical Damage to Lifelines-Transportation Systems 

 7.1 Highway Transportation 
System..........................................................7-1 

 7.1.1 
Introduction....................................................................
...........7-1 

 7.1.2 
Scope...........................................................................
..............7-1 

 7.1.3 Input Requirements and Output Information............................7-3 

 7.1.4 Form of Damage 
Functions......................................................7-4 

 7.1.5 Description of Highway 
Components.......................................7-4 

 7.1.6 Definition of Damage 
States.....................................................7-8 

 7.1.7 Component Restoration 
Curves................................................7-9 

 7.1.8 Development of Damage Functions........................................7-
11 

 7.1.9 Guidance for Loss Estimation Using Advanced Data and 

 Models 
................................................................................
...7-39 

 7.1.10 
References......................................................................
.........7-17 

 Figures for Section 
7.1........................................................................7-18 

 7.2 Railway Transportation 
System..........................................................7-24 

 7.2.1 
Introduction....................................................................
.........7-24 

 7.2.2 
Scope...........................................................................
............7-24 

 7.2.3 Input Requirements and Output Information..........................7-25 

 7.2.4 Form of Damage 
Functions....................................................7-25 

 7.2.5 Description of Railway System Components.........................7-26 

 7.2.6 Definition of Damage 
States...................................................7-27 

 7.2.7 Component Restoration 
Curves..............................................7-30 

 7.2.8 Development of Damage Functions........................................7-
32 

7.2.9 G
uidance for Loss Estimation Using Advanced Data and 

Models 
................................................................................
...7-39 




 7.2.10 
References......................................................................
.........7-39 

 Figures for Section 
7.2........................................................................7-40 

 7.3 Light Rail Transportation 
System.......................................................7-50 

 7.3.1 
Introduction....................................................................
.........7-50 

 7.3.2 
Scope...........................................................................
............7-50 

 7.3.3 Input Requirements and Output Information..........................7-50 

 7.3.4 Form of Damage 
Functions....................................................7-51 

 7.3.5 Description of Light Rail System Components......................7-51 




 7.3.6 Definition of Damage 
States...................................................7-52 

 7.3.7 Component Restoration 
Curves..............................................7-54 

 7.3.8 Development of Damage Functions........................................7-
54 

7.3.9 Guidance for Loss Estimation Using Advanced Data and 

Models..........................................................................
..........7-55 

 7.3.10 
References......................................................................
.........7-55 

 Figures for Section 
7.3........................................................................7-56 

 7.4 Bus Transportation 
System.................................................................7-57 

 7.4.1 
Introduction....................................................................
.........7-57 

 7.4.2 
Scope...........................................................................
............7-57 

 7.4.3 Input Requirements and Output Information..........................7-57 

 7.4.4 Form of Damage 
Functions....................................................7-58 

 7.4.5 Description of Bus System Components................................7-59 

 7.4.6 Definition of Damage 
States...................................................7-59 

 7.4.7 Component Restoration 
Curves..............................................7-60 

 7.4.8 Development of Damage Functions........................................7-
61 

7.4.9 Guidance for Loss Estimation Using Advanced Data and 

Models..........................................................................
..........7-61 

 7.4.10 
References......................................................................
.........7-62 

 7.5 Port Transportation 
System................................................................7-63 

 7.5.1 
Introduction....................................................................
.........7-63 

 7.5.2 
Scope...........................................................................
............7-63 

 7.5.3 Input Requirements and Output Information..........................7-63 

 7.5.4 Form of Damage 
Functions....................................................7-64 

 7.5.5 Description of Port 
Components.............................................7-65 

 7.5.6 Definition of Damage 
States...................................................7-65 

 7.5.7 Component Restoration 
Curves..............................................7-67 

 7.5.8 Development of Damage Functions........................................7-
68 

7.5.9 Guidance for Loss Estimation Using Advanced Data and 

Models..........................................................................
..........7-69 

 7.5.10 
References......................................................................
.........7-70 

 Figures for Section 
7.5........................................................................7-71 

 7.6 Ferry Transportation 
System..............................................................7-75 

 7.6.1 
Introduction....................................................................
.........7-75 

 7.6.2 
Scope...........................................................................
............7-75 

 7.6.3 Input Requirements and Output Information..........................7-76 

 7.6.4 Form of Damage 
Functions....................................................7-76 

 7.6.5 Description of Ferry System Components..............................7-77 

 7.6.6 Definition of Damage 
States...................................................7-77 

 7.6.7 Component Restoration 
Curves..............................................7-79 

 7.6.8 Development of Damage Functions........................................7-
79 

7.6.9 Guidance for Loss Estimation Using Advanced Data and 

Models..........................................................................
..........7-79 

 7.6.10 
References......................................................................
.........7-79 

 7.7 AIRPORT TRANSPORTATION SYSTEM..................................7-80 




 7.7.1 
Introduction....................................................................
.........7-80 

 7.7.2 
Scope...........................................................................
............7-80 

 7.7.3 Input Requirements and Output Information..........................7-80 

 7.7.4 Form of Damage 
Functions....................................................7-81 

 7.7.5 Description of Airport 
Components.......................................7-82 

 7.7.6 Definition of Damage 
States...................................................7-82 

 7.7.7 Component Restoration 
Curves..............................................7-84 

 7.7.8 Development of Damage Functions........................................7-
85 

 7.7.9 Guidance for Level 3 Loss Estimation...................................7-
86 

 7.7.10 
References......................................................................
.........7-86 

 Figures for Section 
7.7........................................................................7-87 

 Appendix 7A 
................................................................................
........7-89 

 Appendix 7B 
................................................................................
........7-90 

 Appendix 7C 
................................................................................
........7-94 

 Appendix 7D 
................................................................................
........7-95 

 

 8. Direct Damage to Lifelines-Utility Systems 

 8.1 POTABLE WATER 
SYSTEMS........................................................8-1 

 8.1.1 
Introduction....................................................................
...........8-1 

 8.1.2 
Scope...........................................................................
..............8-1 

 8.1.3 Input Requirements and Output Information............................8-3 

 8.1.4 Form of Damage 
Functions......................................................8-4 

 8.1.5 Description of Potable Water System Components..................8-5 

 8.1.6 Definition of Damage 
States.....................................................8-7 

8.1.6.1 D
amage State Definitions for Components Other 

than 
Pipelines......................................................................8
-7 


8.1.6.2 
Definition of Damage States for Pipelines...................8-9 




 8.1.7 Component Restoration 
Curves................................................8-9 

 8.1.8 Development of Damage Functions........................................8-
12 

 8.1.9 System 
Performance...............................................................8-18 

8.1.10 
Guidance for Loss Estimation Using Advanced Data 

and 
Models..........................................................................
....8-19 




 8.1.11 
References......................................................................
.........8-20 

 Figures for Section 
8.1........................................................................8-21 

 8.2 Waste Water 
System..........................................................................
.8-34 

 8.2.1 
Introduction....................................................................
.........8-34 

 8.2.2 
Scope...........................................................................
............8-34 

 8.2.3 Input Requirements and Output Information..........................8-34 

 8.2.4 Form of Damage 
Functions....................................................8-34 

 8.2.5 Description of Waste Water System Components..................8-35 

 8.2.6 Definition of Damage 
States...................................................8-36 

8.2.6.1 D
amages States Definitions for Components other 

than Sewers/Interceptors...................................................8-36 


8.2.6.2 
Damage States Definitions for Sewers/Interceptors...8-37 







 8.2.7 Component Restoration 
Curves..............................................8-37 

 8.2.8 Development of Damage Functions........................................8-
38 

8.2.9 Guidance for Loss Estimation Using Advanced Data and 

Models..........................................................................
..........8-40 

 8.2.10 
References......................................................................
.........8-41 

 Figures for Section 
8.2........................................................................8-42 

 8.3 OIL 
SYSTEMS.........................................................................
.........8-46 

 8.3.1 
Introduction....................................................................
.........8-46 

 8.3.2 
Scope...........................................................................
............8-46 

 8.3.3 Input Requirements and Output Information..........................8-46 

 8.3.4 Form of Damage 
Functions....................................................8-47 

 8.3.5 Description of Oil System Components.................................8-47 

 8.3.6 Definition of Damage 
States...................................................8-48 

8.3.6.1 D
amage states definitions for Components other 

than 
Pipelines....................................................................8-
48 


8.3.6.2 
Damage State Definitions for Pipelines......................8-49 




 8.3.7 Component Restoration 
Curves..............................................8-50 

 8.3.8 Development of Damage Functions........................................8-
50 

8.3.9 Guidance for Loss Estimation Using Advanced Data and 

Models..........................................................................
..........8-53 

 8.3.10 
References......................................................................
.........8-53 

 Figures for Section 
8.3........................................................................8-54 

 8.4 NATURAL GAS 
SYSTEMS............................................................8-60 

 8.4.1 
Introduction....................................................................
.........8-60 

 8.4.2 
Scope...........................................................................
............8-60 

 8.4.3 Input Requirements and Output Information..........................8-60 

 8.4.4 Form of Damage 
Functions....................................................8-61 

 8.4.5 Description of Natural Gas System Components...................8-61 

 8.4.6 Definition of Damage 
States...................................................8-61 

8.4.6.1 D
amage States Definitions for Compressor 

Stations......................................................................8-
62 


8.4.6.2 
Damage States Definitions for Pipelines....................8-62 




 8.4.7 Component Restoration 
Curves..............................................8-62 

 8.4.8 Development of Damage Functions........................................8-
62 

8.4.9 Guidance for Loss Estimation Using Advanced Data and 

Models..........................................................................
..........8-63 

 8.4.10 
References......................................................................
.........8-63 

 8.5 Electric Power 
System..........................................................................
..........8-64 

 8.5.1 
Introduction....................................................................
.........8-64 

 8.5.2 
Scope...........................................................................
............8-64 

 8.5.3 Input Requirements and Output Information..........................8-64 

 8.5.4 Form of Damage 
Functions....................................................8-65 

 8.5.5 Description of Electric Power System Components...............8-65 




 8.5.6 Definition of Damage 
States...................................................8-66 

 8.5.7 Component Restoration 
Curves..............................................8-68 

 8.5.8 Development of Damage Functions........................................8-
69 

8.5.9 
Power Outage & Performance Evaluation for Electric 

Power 
Systems........................................................................8
-72 




8.5.10 Guidance for Loss Estimation Using Advanced Data and 

Models..........................................................................
..........8-74 

 8.5.11 
References......................................................................
.........8-75 

 Figures for Section 
8.5........................................................................8-76 

 8.6 COMMUNICATION SYSTEMS....................................................8-
84 

 8.6.1 
Introduction....................................................................
.........8-84 

 8.6.2 
Scope...........................................................................
............8-84 

 8.6.3 Input Requirements and Output Information..........................8-84 

 8.6.4 Form of Damage 
Functions....................................................8-85 

 8.6.5 Description of Communication System Components.............8-85 

 8.6.6 Definition of Damage 
States...................................................8-85 

 8.6.7 Component Restoration 
Curves..............................................8-86 

 8.6.8 Development of Damage Functions........................................8-
87 

8.6.9 Guidance for Loss Estimation Using Advanced Data and 

Models..........................................................................
..........8-87 

 8.6.10 
References......................................................................
.........8-88 

 Figures for Section 
8.6........................................................................8-89 

 Appendix 8A 
................................................................................
........8-92 

 Appendix 8B 
................................................................................
........8-97 

 Appendix 8C 
................................................................................
........8-99 

 Appendix 8D 
................................................................................
......8-102 

 Appendix 8E 
................................................................................
......8-107 

 

9. Induced Damage Models - Inundation 

 9.1 
Introduction....................................................................
.......................9-1 

 9.1.1 
Scope...........................................................................
..............9-1 

 9.1.2 Form of Inundation 
Estimate....................................................9-1 

 9.1.3 Input Requirements and Output Information............................9-3 

 9.1.3.1 Input 
Requirements.......................................................9-3 

9.1.3.1.1 Dam Failure..................................................9-3 

9.1.3.1.2 Levee Failure................................................9-4 

9.1.3.1.3 Input Requirements - Tsunami.....................9-6 

9.1.3.1.4 Input Requirements - Seiche........................9-6 

 9.1.3.2 Output 
Information.......................................................9-7 

9.2 Description of 
Methodology.................................................................9-7 

 9.2.1 Dam 
Failure.........................................................................
.....9-7 

 9.2.2 Levee 
Failure.........................................................................
...9-8 

 9.2.3 
Tsunami.........................................................................
............9-8 

 9.2.4 
Seiche..........................................................................
..............9-8 




9.3 Guidance for Expert-Generated 
Estimates...........................................9-8 

 9.3.1 Dam 
Failure.........................................................................
.....9-9 

 9.3.2 Levee 
Failure.........................................................................
.9-12 

 9.3.3 
Tsunami.........................................................................
..........9-12 

 9.3.4 
Seiche..........................................................................
............9-13 

9.4 Inundation 
References......................................................................
..9-14 

 

 

 10. Induced Damage Models - Fire Following Earthquake 

 10.1 
Introduction....................................................................
.....................10-1 

 10.1.1 
Scope...........................................................................
............10-1 

 10.1.2 Form of Damage 
Estimates.....................................................10-3 

 10.1.3 Input 
Requirements.................................................................10-
3 

 10.2 Description of 
Methodology...............................................................10-4 

 10.2.1 
Ignition........................................................................
............10-4 

 10.2.2 
Spread..........................................................................
...........10-7 

 10.2.3 
Suppression.....................................................................
......10-11 

 10.2.3.1 Discovery 
Time.......................................................10-11 

 10.2.3.2 Report 
Time............................................................10-11 

 10.2.3.3 Arrival 
Time............................................................10-12 

 10.2.3.4 Control 
Time...........................................................10-13 

10.2.3.4.1 Room and Content Fires.........................10-13 

10.2.3.4.2 Structure Fire- Engines Needed.............10-13 

10.2.3.4.3 Structure Fire- Water Needed.................10-14 

10.2.3.4.4 Engines Available...................................10-16 

10.2.3.4.5 Water Available......................................10-17 

10.2.3.4.6 Fire Spread with Partially Effective 

Suppression...........................................10-17 

10.2.3.4.7 Fire Spread at Natural Fire Breaks.........10-18 

 10.3 Guidance for Expert-Generated 
Estimates.......................................10-19 

 10.4 
References......................................................................
...................10-20 

 

 11. Induced Damage Models - Hazardous Materials Release 

 11.1 
Introduction....................................................................
.....................11-1 

 11.1.1 
Scope...........................................................................
.............11-1 

 11.1.2 Classification of Hazardous 
Materials.....................................11-3 

 11.1.3 Input Requirements and Output Information...........................11-3 

 11.2 Description of 
Methodology...............................................................11-6 

 11.3 Guidance for Expert-Generated 
Estimates.........................................11-6 

 11.4 
References......................................................................
.....................11-9 

 Appendix 11A 
................................................................................
......11-12 

 Appendix 11B 
................................................................................
......11-19 

 

 12. Induced Damage Methods - Debris 

 12.1 
Introduction....................................................................
.....................12-1 

 12.1.1 
Scope...........................................................................
............12-1 




 12.1.2 Form of Damage 
Estimate......................................................12-1 

 12.1.3 Input Requirements and Output Information..........................12-1 

 12.2 Description of 
Methodology...............................................................12-3 

 12.2.1 Debris Generated From Damaged Buildings...........................12-3 

 12.3 Guidance for Expert-Generated 
Estimates.........................................12-8 

 12.4 
References......................................................................
.....................12-8 

 

 13. Direct Social Losses - Casualties 

 13.1 
Introduction....................................................................
.....................13-1 

13.1.1 
Scope...........................................................................
............13-1 


13.1.2 
Form of Casualty 
Estimate......................................................13-3 


13.1.3 
Input 
Requirements.................................................................13-
4 




 13.2 Description of 
Methodology.............................................................13-19 

13.2.1 
Earthquake Casualty Model..................................................13-19 


13.2.2 
Alternative Estimation of Casualty Rates.............................13-21 


13.2.3 
Casualties due to Outdoor Falling Hazards..........................13-22 


13.2.4 
Casualty Rates Resulting from Bridge Collapse...................13-23 




 13.3 
References......................................................................
...................13-24 

 

 14. Direct Social Losses - Displaced Households Due to Loss of Housing 

 Habitability and Short-Term Shelter Needs 

 14.1 
Introduction....................................................................
.....................14-1 

 14.1.1 
Scope...........................................................................
............14-1 

 14.2 Displaced Households - Form of Loss 
Estimate.................................14-3 

 14.2.1 Input Requirements - Displaced Households..........................14-3 

 14.2.2 Description of 
Methodology...................................................14-3 

 14.3 Short Term Shelter Needs - Form of Loss 
Estimate...........................14-5 

 14.3.1 Input Requirements - Short Term Shelter Needs....................14-5 

 14.3.2 Description of 
Methodology...................................................14-6 

 14.3.3 User-defined Changes to Weight and Modification Factors..14-8 

 14.3.4 Guidance for Estimates Using Advanced Data and Models...14-8 

 14.4 Guidance for Estimating Long-Term Housing Recovery...................14-8 

 14.5 
References......................................................................
.....................14-9 

 

 15. Direct Economic Losses 

 15.1 
Introduction....................................................................
.....................15-1 

 15.1.1 
Scope...........................................................................
............15-4 

 15.1.2 Form of Direct Economic Loss Estimates..............................15-6 

 15.1.3 Input 
Requirements.................................................................15-
6 

 15.2 Description of Methodology: 
Buildings.............................................15-7 

 15.2.1 Building Repair and Replacement Costs................................15-
8 

 15.2.1.1 Default Values for Building Repair Costs..............15-10 

 15.2.1.2 Default Values for Regional Cost Variation...........15-17 

 15.2.1.3 Procedure for Updating Building Cost Estimates...15-18 

 15.2.2 Building Contents 
Losses.....................................................15-18 




 15.2.3 Business Inventory 
Losses....................................................15-22 

 15.2.4 Building Repair Time/Loss of Function...............................15-
24 

 15.2.5 Relocation 
Expenses.............................................................15-29 

15.2.6 L
oss of 
Income.......................................................................15-
33 




 15.2.6.1 Recapture 
Factors...................................................15-34 

 15.2.7 Rental Income 
Losses...........................................................15-35 

 15.2.8 Guidance for Estimate Using Advanced Data and Models 
Analysis........................................................................
........15-37 

 15.3 Description of Methodology: 
Lifelines.............................................15-39 

 15.3.1 Transportation 
Systems.........................................................15-45 

 15.3.1.1 Highway Systems....................................................15-
45 

 15.3.1.2 Railway 
Systems.....................................................15-46 

 15.3.1.3 Light Rail 
Systems..................................................15-47 

 15.3.1.4 Bus 
Systems............................................................15-48 

 15.3.1.5 Port 
Systems............................................................15-48 

 15.3.1.6 Ferry 
Systems..........................................................15-49 

 15.3.1.7 Airport 
Systems......................................................15-50 

15.3.2 Utility 
Systems......................................................................15-
51 

15.3.2.1 Potable Water Systems..........................................15-51 

15.3.2.2 Waste Water Systems............................................15-52 

15.3.2.3 Oil 
Systems............................................................15-53 

15.3.2.4 Natural Gas Systems.............................................15-54 

15.3.2.5 Electric Power Systems.........................................15-54 

15.3.2.6 Communication Systems.......................................15-54 

 15.4 
References......................................................................
...................15-55 

 Appendix 15A 
................................................................................
......15-56 

 Appendix 15B 
................................................................................
......15-60 

 Appendix 15C 
................................................................................
......15-63 

 Appendix 15D 
................................................................................
......15-66 

 

 16. Indirect Economic Losses 

16.1 
Introduction....................................................................
.....................16-1 

16.2 What are Indirect 
Losses?...................................................................16-1 

16.2.1 Supply Shortages and Forward Linked Losses.......................16-3 

16.2.2 Demand Effects and Backward Linked Losses......................16-4 

16.2.3 Regional vs. National 
Losses..................................................16-4 

16.3 Interindustry 
Models..........................................................................
.16-5 

16.3.1 A Primer on Input-Output Techniques...................................16-
6 

16.3.2 An Illustration of Backward Linked Losses...........................16-9 

16.3.3 The Impact of Outside Reconstruction Aid on the 

 Region and the 
Nation..........................................................16-10 

16.4 The Indirect Loss 
Module.................................................................16-11 

16.4.1 Damage-Linkage to the Direct Loss Module........................16-12 

16.4.2 Supply-Side Adjustments and Rebalancing the Economy...16-14 

16.4.3 The Time 
Dimension............................................................16-18 

16.4.4 The Effect of Rebuilding and Borrowing.............................16-18 




16.4.5 T
he Issue of Aggregation......................................................16-
19 




16.5 R
unning the 
Model...........................................................................
16-19 


16.5.1 D
efault Data Analysis Inputs, Operation and Output............16-20 


16.5.1.1 U
ser Input and Default Data...................................16-20 


16.5.1.2 C
alculation of Indirect Loss....................................16-23 


16.5.1.3 T
he Format of the Output.......................................16-25 






16.5.2 User-Supplied Data 
Analysis................................................16-26 
16.5.2.1 I
MPLAN Input-Output Data...................................16-26 


16.5.2.2 S
pecifying Indirect Loss Factors............................16-28 




16.5.3 Advanced Data and Models Analysis.................................16-32 
16.5.3.1 E
xtending the Indirect Loss Module.......................16-32 


16.5.3.2 A
lternative Modeling Techniques..........................16-34 




16.6 E
xample 
Solutions.......................................................................
.....16-35 


16.6.1 S
imple One-Sector Supply Shock - No Excess Capacity......16-35 


16.6.2 T
he Northridge Earthquake...................................................16-35 


16.6.3 T
he Sensitivity of Indirect Loss to Capacity, Damage 




 and 
Reconstruction...............................................................16-
42 
16.6.4 Observation About Indirect 
Loss...........................................16-54 




16.7 
References......................................................................
...................16-55 

Appendix 16A 
................................................................................
......16-56 

 17. Annualized Losses 

16.1 
Introduction....................................................................
.....................17-1 

16.2 Average Annualized Earthquake Loss Computation..........................17-
2 

 

 




Chapter 1 

Introduction to the FEMA Loss Estimation Methodology 

 

 

1.1 Background 

 

The Technical Manual describes the methods for performing earthquake loss 
estimation. 
It is based on a multi-year project to develop a nationally applicable 
methodology for 
estimating potential earthquake losses on a regional basis. The project has 
being 
conducted for the National Institute of Building Science (NIBS) under a 
cooperative 
agreement with the Federal Emergency Management Agency (FEMA). 

 

The primary purpose of the project is to develop guidelines and procedures for 
making 
earthquake loss estimates at a regional scale. These loss estimates would be 
used 
primarily by local, state and regional officials to plan and stimulate efforts 
to reduce risks 
from earthquakes and to prepare for emergency response and recovery. A secondary 
purpose of the project is to provide a basis for assessing nationwide risk of 
earthquake 
losses. 

 

The methodology development and software implementation has been performed by a 
team of earthquake loss experts composed of earth scientists, engineers, 
architects, 
economists, emergency planners, social scientists and software developers. The 
Earthquake Committee has provided technical direction and review of work with 
guidance from the Project Oversight Committee (POC), a group representing user 
interests in the earthquake engineering community. 

 

1.2 Technical Manual Scope 

 

The scope of the Technical Manual includes documentation of all methods and data 
that 
are used by the methodology. Loss estimation methods and data are obtained from 
referenced sources tailored to fit the framework of the methodology, or from new 
methods and data developed when existing methods and data were lacking or were 
not 
current with the state of the art. 

 

The Technical Manual is a comprehensive, highly technical collection of methods 
and 
data covering a broad range of topics and disciplines, including earth science, 
seismic/structural engineering, social science and economics. The Technical 
Manual is 
written for readers who are expected to have some degree of expertise in the 
technical 
topic of interest, and may be inappropriate for readers who do not have this 
background. 

 

As described in Chapter 2, a separate User Manual describes the earthquake loss 
estimation methodology in non-technical terms and provides guidance to users in 
the 
application of the methodology. The methodology software is implemented using 
Geographical Information System (GIS) software as described in the Technical 
Manual. 

 




1.3 Technical Manual Organization 

 

The Technical Manual contains sixteen chapters. Chapter 2 describes the overall 
framework of the methodology and provides background on the approach developed 
used 
to meet the projects objectives. Chapter 3 discusses inventory data, including 
classification schemes of different systems, attributes required to perform 
damage and 
loss estimation, and the data supplied with the methodology. Sources and methods 
of 
collection of inventory data are not covered in Chapter 3, but may be found in 
the User 
Manual. 

 

Chapters 4 through 16 cover, respectively, each of thirteen major components or 
subcomponents (modules) of the methodology. Each of the major components and 
subcomponents are described in Chapter 2. A flowchart is provided in Chapter 2 
as a 
"road map" of the relationships between modules of the methodology. This 
flowchart is 
repeated at the beginning of each chapter with the module of interest high-
lighted to 
show input from and output to other modules of the methodology. 




Chapter 2 

Overall Approach and Framework of Methodology 

 

This chapter describes the overall approach used by the developers to meet the 
objectives 
of the project, the components and subcomponents of earthquake loss estimation 
and 
their relationship within the framework of methodology. 

 

2.1 Vision Statement 

 

The overall approach for the project is based on the following "vision" of the 
earthquake 
loss estimation methodology. 

 

The earthquake loss estimation methodology will provide local, state and 
regional officials with the tools necessary to plan and stimulate efforts to 
reduce 
risk from earthquakes and to prepare for emergency response and recovery from 
an earthquake. The methodology will also provide the basis for assessment of 
nationwide risks of earthquake loss. 

 

The methodology can be used by a variety of users with needs ranging from 
simplified estimates that require minimal input to refined calculations of 
earthquake loss. The methodology may be implemented using either 
geographical information system (GIS) technology provided in a software 
package or by application of the theory documented in a Technical Manual. An 
easily understood User Manual will guide implementation of the methodology by 
either technical or non-technical users. 

 

The vision of earthquake loss estimation requires a methodology that is both 
flexible, 
accommodating the needs of a variety of different users and applications, and 
able to 
provide the uniformity of a standardized approach. The framework of the 
methodology 
includes each of the components shown in Figure 2-1: Potential Earth Science 
Hazard 
(PESH), Inventory, Direct Physical Damage, Induced Physical Damage, Direct 
Economic/Social Loss and Indirect Economic Loss. As indicated by arrows in the 
figure, 
modules are interdependent with output of some modules acting as input to 
others. In 
general, each of the components will be required for loss estimation. However, 
the 
degree of sophistication and associated cost will vary greatly by user and 
application. It 
is therefore necessary and appropriate that components have multiple levels 
(multiple 
modules) of detail or precision when required to accommodate user needs. 

 

Framing the earthquake loss estimation methodology as a collection of modules 
permits 
adding new modules (or improving models/data of existing modules) without 
reworking 
the entire methodology. Improvements may be made to adapt modules to local or 
regional needs or to incorporate new models and data. The modular nature of the 
methodology permits a logical evolution of the methodology as research 
progresses and 
the state-of-the-art advances. 

 

 




 

8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Figure 2.1 Flowchart of the Earthquake Loss Estimation Methodology. 

 




Most users will implement the methodology using the GIS-based software 
application 
provided by NIBS. After initial inventory entry, the program will run 
efficiently on 
desktop computer. The GIS technology provides a powerful tool for displaying 
outputs 
and permits users to "see" the effects of different earthquake scenarios and 
assumptions. 
A User Manual will guide users in program manipulation, input of new data, and 
changes 
to existing data. 

 

Certain users may not wish to use the software application, or may want to 
augment the 
results with supplementary calculations. In such cases, users can refer to the 
Technical 
Manual for a complete description of models and data of each module. The 
Technical 
Manual is useful to technical experts, such as those engineers and scientists 
that have 
conducted previous earthquake loss studies, but might be inappropriate for non-
technical 
users. 

 

Both technical and non-technical users are guided in the application of the 
methodology 
by the User Manual, which addresses important implementation issues, such as: 

 

(1) Selection of scenario earthquakes and PESH inputs 

(2) Selection of appropriate methods (modules) to meet different user needs 

(3) Collection of required inventory data, i.e., how to obtain necessary 
information 

(4) Costs associated with inventory collection and methodology implementation 

(5) Presentation of results including appropriate terminology, etc. 

(6) Interpretation of results including consideration of model/data uncertainty. 

 

The three project deliverables are shown in Figure 2.2. 

 

 

Figure 2.2 Project Deliverables. 

Technical ManualHAZUSSoftware ApplicationVolume IVolume IIVolume 
IIIHAZUSHAZUSHAZUSTechnical ManualHAZUSSoftware ApplicationHAZUSSoftware 
ApplicationVolume IVolume IIVolume IIIHAZUSHAZUSHAZUSUsers ManualHAZUSVolume 
IUsers ManualHAZUSVolume ITechnical ManualHAZUSSoftware 
ApplicationHAZUSSoftware ApplicationVolume IVolume IIVolume 
IIIHAZUSHAZUSHAZUSTechnical ManualHAZUSSoftware ApplicationHAZUSSoftware 
ApplicationVolume IVolume IIVolume IIIHAZUSHAZUSHAZUSUsers ManualHAZUSVolume 
IUsers ManualHAZUSVolume IUsers ManualHAZUSVolume IUsers ManualHAZUSVolume 
IHAZUSVolume I



2.2 Project Objectives 

 

The development of an earthquake loss estimation methodology has been defined by 
the 
eight General Objectives outlined in the NIBS/FEMA "Task Plan for Tasks 2 and 
5," 
October 18, 1993. The following sections summarize the approach taken to meet 
each 
objective. 

 

Accommodation of User Needs 

 

The methodology utilizes a modular approach with different modules addressing 
different user needs. This approach avoids the need to decide on who is the 
designated 
user. The needs of most, if not all, users are accommodated by the flexibility 
of a 
modular approach. 

 

The GIS technology permits easy implementation by users on desktop computers. 
The 
visual display and interactive nature of a GIS application provides an immediate 
basis for 
exchange of information and dialog with end-users of the results. The User 
Manual 
provides appropriate terminology and definitions, and user-oriented descriptions 
of the 
loss estimation process. 

 

State-of-the-Art 

 

The methodology incorporates available state-of-the-art models in the earthquake 
loss 
estimation methodology. For example, ground shaking hazard and related damage 
functions are described in terms of spectral response rather than MMI. Modules 
include 
damage loss estimators not previously found in most studies, such as induced 
damage 
due to fire following earthquake and indirect economic loss. A nationally 
applicable 
scheme is developed for classifying buildings, structures and facilities. 

 

Balance 

 

The methodology permits users to select methods (modules) that produce varying 
degrees 
of precision. The User Manual provides guidance to users regarding the selection 
of 
modules that are appropriate for their needs and which have a proper balance 
between 
different components of earthquake loss estimation. 

 

Flexibility in Earthquake Demand 

 

The methodology incorporates both deterministic (scenario earthquake) and 
probabilistic 
descriptions of spectral response. Alternatively, the proposed methodology 
accepts user-
supplied maps of earthquake demand. The software application is structured to 
also 
accept externally supplied maps of earthquake ground shaking. 

 

"Uncertainty" in earthquake demand due to spatial variability of ground motion 
is 
addressed implicitly by the variability of damage probability matrices (DPM's) 
or 
fragility curves. Uncertainty in earthquake demand due to temporal variability 
(i.e., 




earthquake recurrence rate) or uncertainty in the magnitude of earthquake 
selected for 
scenario event may be readily evaluated by the users. 

 

Once the data is input into the software application, any number of scenario 
events can be 
evaluated. The User Manual provides guidance for the consideration of 
uncertainty, 
including that associated with earthquake demand. 

 

Uses of Methodology Data 

 

The User Manual provides recommendations for collecting inventory data that will 
permit use of the data for non-earthquake purposes. Inventory information will 
come 
from databases supplied with the methodology and/or collected in databases 
compatible 
with the software. Such data will be available to users for other applications. 

 

Accommodation of Different Levels of Funding 

 

The methodology includes modules that permit different levels of inventory 
collection 
and associated levels of funding. For example, the methodology permits 
simplified 
(Default Data Analysis) estimates of damage and loss, using primarily default 
data 
supplied with the software application. These estimates of damage/loss do not 
require 
extensive inventory collection and can be performed on a modest budget. More 
precise 
damage/loss (User-Supplied Data Analysis) estimates require more extensive 
inventory 
information at additional cost to the user. The User Manual provides guidance to 
users 
regarding trade-offs in cost and accuracy of results. 

 

Standardization 

 

The methodology includes standard methods for: 

 

(1) Inventory data collection based on census tract areas 

(2) Using database maps of soil type, ground motion, ground failure, etc. 

(3) Classifying occupancy of buildings and facilities 

(4) Classifying building structure type 

(5) Describing damage states 

(6) Developing building damage functions 

(7) Grouping, ranking and analyzing lifelines 

(7) Using technical terminology 

(8) Providing output. 

 

Non-Proprietary 

 

The methodology includes only non-proprietary loss estimation methods. The 
software 
application is non-proprietary to the extent permitted by the GIS-software 
suppliers. 

 

2.3 Description of Loss Estimation Methodology 

 




The earthquake loss estimation methodology is an improvement over existing 
regional 
loss estimation methodologies, since it more completely addresses regional 
impacts of 
earthquakes that have been omitted or at best discussed in a qualitative manner 
in 
previous studies. Examples of these impacts are service outages for lifelines, 
estimates of 
fire ignitions and fire spread, potential for a serious hazardous materials 
release incident, 
and indirect economic effects. In addition, strength of this methodology is the 
ability to 
readily display inputs and outputs on GIS-based maps that can be overlaid. By 
overlaying maps the user is able to experiment with different scenarios and ask 
"what if" 
questions. 

 

As discussed in Section 2.1, the methodology is modular, with different modules 
interacting in the calculation of different losses. Figure 2.1 shows each of the 
modules 
and the flow of information among them. It can be seen that, because of the 
complexity 
of earthquake damage and loss estimation, the model is complex. One advantage of 
the 
modularity of the methodology is that it enables users to limit their studies to 
selected 
losses. For example, a user may wish to ignore induced physical damage when 
computing direct economic and social losses. This would eliminate the lower left 
portion 
of the flow diagram along with corresponding input requirements. A limited study 
may 
be desirable for a variety of reasons, including budget and inventory 
constraints, or the 
need to obtain answers to very specific questions. 

 

The methodology has been developed with as much capability as possible. However, 
there are certain areas where methods are limited. For example, the methodology 
calculates potential exposure to flood (e.g., dam break) or fire (following 
earthquake) in 
terms of the fraction of a geographical area that may be flooded or burned, but 
does not 
have methods for rigorous calculation of damage or loss due to flooding or fire. 
Consequently, these two potential contributors to the total loss would not be 
included in 
estimates of economic loss, casualties or loss of shelter. 

 

A limiting factor in performing a study and quality of the inventory is the 
associated cost. 
Collection of inventory is without question the most costly part of performing 
the study. 
Furthermore, many municipalities have limited budgets for performing an 
earthquake 
loss estimation study. Thus, the methodology is structured to accommodate 
different 
users with different levels of resources. 

 

While most users will develop a local inventory that best reflects the 
characteristics of 
their region, such as building types and demographics, the methodology is 
capable of 
producing crude estimates of losses based on a minimum of local input. Of 
course, the 
quality and uncertainty of the results is related to the detail of the inventory 
and the 
economic and demographic data provided. Crude estimates would most likely be 
used 
only as initial estimates to determine where more detailed analyses would be 
warranted. 

 

At the other end of the spectrum, a user may wish to make detailed assessments 
of 
damage to and service outages for lifelines. Detailed analyses of lifelines 
require 
cooperation and input from utilities and transportation agencies. Lifeline 
systems require 
an understanding of the interactions between components and the potential for 
alternative 




pathways when certain components fail. Thus, without cooperation of utilities, 
the user 
is limited in the quality of analysis that can be performed. 

 

The proposed loss estimation methods are capable of providing estimates of 
damage to 
and service outages for lifelines with a minimum of cooperation from lifeline 
operators. 
These estimates, of course, will have a great deal of uncertainty associated 
with them. 
However, they will be useful for planning purposes and for an initial estimate 
to 
determine where more detailed analyses would be warranted. Many lifeline 
operators 
perform their own detailed earthquake loss studies that incorporate detailed 
models of 
their systems. 

 

Three types of analysis are defined to describe implementation of the 
methodology by 
users with different needs and resources. These types and their definitions are 
somewhat 
arbitrary, and the boundaries between the three types are not well defined. The 
three 
types are defined as follows: 

 

Default Data Analysis: This is the simplest type of analysis requiring minimum 
effort 
by the user as it is based mostly on input provided with the methodology (e.g. 
census information, broad regional patterns of seismic code adoption and 
earthquake resistance of classes of construction, etc.). The user is not 
expected 
to have extensive technical knowledge. While the methods require some user-
supplied input to run, the type of input required could be gathered by 
contacting 
government agencies or by referring to published information. At this level, 
estimates will be crude, and will likely be appropriate only as initial loss 
estimates to determine where more detailed analyses are warranted. 

 

Some components of the methodology cannot be performed in a Default Data 
Analysis since they require more detailed inventory than that provided with the 
methodology. The following are not included in the Default Data Analysis: 
damage/loss due to liquefaction, landslide or surface fault rupture; damage/loss 
due to tsunamis, seiche or dam failure. At this level, the user has the option 
(not 
required) to enter information about hazardous substances and emergency 
facilities. One week to a month would be required to collect relevant 
information depending on the size of the region and the level of detail the user 
desires. 

 

User-Supplied Data Analysis: This type of analysis will be the most commonly 
used. 
It requires more extensive inventory data and effort by the user than Default 
Data Analysis. The purpose of this type is to provide the user with the best 
estimates of earthquake damage/loss that can be obtained using the standardized 
methods of analysis included in the methodology. It is likely that the user will 
need to employ consultants to assist in the implementation of certain methods. 
For example, a local geotechnical engineer would likely be required to define 
soil and ground conditions. 

 




 All components of the methodology can be performed at this level and loss 
estimates are based on locally (user) developed inventories. At this level, 
there 
are standardized methods of analysis included in the software, but there is no 
standardized User-Supplied Data Analysis study. As the user provides more 
complete data, the quality of the analysis and results improve. Depending on 
the size of the region and the level of detail desired by the user, one to six 
months would be required to obtain the required input for this type of analysis. 

 

Advanced Data and Models Analysis: This type incorporates results from 
engineering 
and economic studies carried out using methods and software not included 
within the methodology. At this level, one or more technical experts would be 
needed to acquire data, perform detailed analyses, assess damage/loss, and 
assist the user in gathering more extensive inventory. It is anticipated that at 
this level there will be extensive participation by local utilities and owners 
of 
special facilities. There is no standardized Advanced Data and Models Analysis 
study. The quality and detail of the results depend upon the level of effort. 
Six 
months to two years would be required to complete an Advanced Data and 
Models Analysis. 

 

To summarize, User-Supplied Data Analysis and Advanced Data and Models Analysis 
represent a broad range of analyses, and the line between one type of analysis 
and 
another is fuzzy. The above definitions are provided to understand the scope and 
flexibility of the methodology, not to limit its application. The primary limit 
on the type 
of analysis will be the user's ability to provide required data. 

 

Even with perfect data, which can never be obtained, the methodology would not 
be able 
to precisely estimate earthquake loss. Simply put, predictive methods are 
approximate 
and will often have large amounts of uncertainty associated with damage and loss 
estimates. A discussion of uncertainty and guidance for users performing 
earthquake loss 
estimation is provided in the User Manual. 

 




Chapter 3 

Inventory Data: Collection and Classification 

 

 

3.1.

 Introduction 

 

This chapter describes the classification of different buildings and lifeline 
systems, 
data and attributes required for performing damage and loss estimation, and the 
data 
supplied with the methodology. The different systems covered in this chapter 
include 
buildings and facilities, transportation systems, utility systems, and hazardous 
material facilities. In addition, census data, county business patterns, and 
indirect 
economic data are discussed. Sources and methods of collecting inventory data 
can 
be found in the Users Manual. 

 

Required input data include both default data (data supplied with the 
methodology) 
and data that must be supplied by the user. Data supplied with the methodology 
include default values of classification systems (i.e., mapping relationships) 
and 
default databases (e.g., facility location, census information, and economic 
factors). 
Default data are supplied to assist the user that may not have the resources to 
develop 
inventory data and may be superseded by better information when the user can 
obtain 
such for the study region of interest. 

 

3.2.

 Direct Damage Data - Buildings and Facilities 

 

This section deals with the general building stock, essential facilities, and 
high 
potential loss facilities. 

3.2.1.

 General Building Stock 

 

The general building stock (GBS) includes residential, commercial, industrial, 
agricultural, religious, government, and educational buildings. The damage state 
probability of the general building stock is computed at the centroid of the 
census 
tract. The entire composition of the general building stock within a given 
census tract 
is lumped at the centroid of the census tract. The inventory information 
required for 
the analysis to evaluate the probability of damage to occupancy classes is the 
relationship between the specific occupancy class and the model building types. 
This 
can be computed directly from the specific occupancy class square footage 
inventory. 

 

All three models (Earthquake, Wind and Flood) use key common data to ensure that 
the users do not have inventory discrepancies when switching from hazard to 
hazard. 
Generally the Earthquake Model and Hurricane display GBS data at the census 
tract 




level while the Flood Model displays GBS data at the census block.

1

 The key GBS 
databases include the following: 

 

 Square footage by occupancy. These data are the estimated floor area by 
specific occupancy (e.g., COM1). For viewing by the user, these data are also 
rolled up to the general occupancies (e.g., Residential). 




 Full Replacement Value by occupancy. These data provide the user with 
estimated replacement values by specific occupancy (e.g., RES1). For viewing by 
the user, these data are also rolled up to the general occupancies (e.g., 
Commercial). 

 



 Building Count by occupancy. These data provide the user with an estimated 
building count by specific occupancy (e.g., IND1). For viewing by the user, 
these 
data are also rolled up to the general occupancies (e.g., Government). 

 



 General Occupancy Mapping. These data provide a general mapping for the 
GBS inventory data from the specific occupancy to general building type (e.g., 
Wood). 

2 

 



 Demographics. This table provides housing and population statistics for the 
study region. 

 

3.2.1.1.

 Classification 

 

The purpose of a building inventory classification system is to group buildings 
with 
similar damage/loss characteristics into a set of pre-defined building classes. 
Damage 
and loss prediction models can then be developed for model building types which 
represent the average characteristics of the total population of buildings 
within each 
class. 

 

The building inventory classification system used in this methodology has been 
developed to provide an ability to differentiate between buildings with 
substantially 
different damage and loss characteristics. The following primary parameters 
affecting building damage and loss characteristics were given consideration in 
developing the building inventory classification system. 

1 In order to allow for future alignment between the Hurricane and Flood Models, 
the Hurricane 
Model will display and perform analysis at the census block level if the user 
has included the Flood 
Model in the study region. 

2 Generally, all three models will agree, however, a user can modify the general 
occupancy mapping at 
the census block level in the Flood Model thereby requiring them to select an 
average value at the 
tract level in the other two models, which will result in variances. This should 
not be an issue for 
users making this type of change. 




 

 Structural parameters affecting structural capacity and response 

 Basic structural system (steel moment frame) 

 Building height (low-rise, mid-rise, high-rise) 

 Seismic design criteria (seismic zone) (Refer to Chapter 5) 

 Nonstructural elements affecting nonstructural damage 

 Occupancy (affecting casualties, business interruption and contents damage) 

 Regional building practices (Refer to Chapter 5) 

 Variability of building characteristics within the classification 

 

To account for these parameters, the building inventory classification system 
consists 
of a two-dimensional matrix relating building structure (model building) types 
grouped in terms of basic structural systems and occupancy classes. 

 

The basic model building types are based on FEMA-178 (FEMA, 1992) building 
classes. Building height subclasses were added to reflect the variation of 
typical 
building periods and other design parameters with building height. Mobile homes, 
which are not included in the FEMA-178 classification, were also added. A 
listing of 
structural building types, with corresponding labels, descriptions, and heights, 
is 
provided in Table 3.1. 

 

The general building stock is also classified based on occupancy. The occupancy 
classification is broken into general occupancy and specific occupancy classes. 
For 
the methodology, the general occupancy classification system consists of seven 
groups (residential, commercial, industrial, religion/nonprofit, government, 
education 
and lifelines). There are 33 specific occupancy classes. The building occupancy 
classes are given in Table 3.2, where the general occupancy classes are 
identified in 
boldface. The distribution of specific occupancies classes within each general 
occupancy class can be computed for each census tract based on the occupancy 
square footage inventory (Section 3.6). These relationships are in a form shown 
in 
Table 3A.1 of Appendix 3A. 

 




3-4 
Table 3.1: Building Structure (Model Building) Types 
Height 
No. Label Description Range Typical 
Name Stories Stories Feet 
1 
2 
W1 
W2 
Wood, Light Frame (= 5,000 sq. ft.) 
Wood, Commercial and Industrial 
(> 5,000 sq. ft.) 
1 - 2 
All 
1 
2 
14 
24 
3 
4 
5 
S1L 
S1M 
S1H 
Steel Moment Frame 
Low-Rise 
Mid-Rise 
High-Rise 
1 - 3 
4 - 7 
8+ 
2 
5 
13 
24 
60 
156 
6 
7 
8 
S2L 
S2M 
S2H 
Steel Braced Frame 
Low-Rise 
Mid-Rise 
High-Rise 
1 - 3 
4 - 7 
8+ 
2 
5 
13 
24 
60 
156 
9 S3 Steel Light Frame All 1 15 
10 
11 
12 
S4L 
S4M 
S4H 
Steel Frame with Cast-in-Place 
Concrete Shear Walls 
Low-Rise 
Mid-Rise 
High-Rise 
1 - 3 
4 - 7 
8+ 
2 
5 
13 
24 
60 
156 
13 
14 
15 
S5L 
S5M 
S5H 
Steel Frame with Unreinforced 
Masonry Infill Walls 
Low-Rise 
Mid-Rise 
High-Rise 
1 - 3 
4 - 7 
8+ 
2 
5 
13 
24 
60 
156 
16 
17 
18 
C1L 
C1M 
C1H 
Concrete Moment Frame 
Low-Rise 
Mid-Rise 
High-Rise 
1 - 3 
4 - 7 
8+ 
2 
5 
12 
20 
50 
120 
19 
20 
21 
C2L 
C2M 
C2H 
Concrete Shear Walls 
Low-Rise 
Mid-Rise 
High-Rise 
1 - 3 
4 - 7 
8+ 
2 
5 
12 
20 
50 
120 
22 
23 
24 
C3L 
C3M 
C3H 
Concrete Frame with Unreinforced 
Masonry Infill Walls 
Low-Rise 
Mid-Rise 
High-Rise 
1 - 3 
4 - 7 
8+ 
2 
5 
12 
20 
50 
120 
25 PC1 Precast Concrete Tilt-Up Walls All 1 15 
26 
27 
28 
PC2L 
PC2M 
PC2H 
Precast Concrete Frames with 
Concrete Shear Walls 
Low-Rise 
Mid-Rise 
High-Rise 
1 - 3 
4 - 7 
8+ 
2 
5 
12 
20 
50 
120 
29 
30 
RM1L 
RM2M 
Reinforced Masonry Bearing Walls 
with Wood or Metal Deck 
Diaphragms 
Low-Rise 
Mid-Rise 
1-3 
4+ 
2 
5 
20 
50 
31 
32 
33 
RM2L 
RM2M 
RM2H 
Reinforced Masonry Bearing Walls 
with Precast Concrete Diaphragms 
Low-Rise 
Mid-Rise 
High-Rise 
1 - 3 
4 - 7 
8+ 
2 
5 
12 
20 
50 
120 
34 
35 
URML 
URM 
M 
Unreinforced Masonry Bearing 
Walls 
Low-Rise 
Mid-Rise 
1 - 2 
3+ 
1 
3 
15 
35 
36 MH Mobile Homes All 1 10 
Chapter 3  Inventory Data: Collection and Classification 



3-5 
Table 3.2: Building Occupancy Classes 
Label Occupancy Class Example Descriptions 
Residential 
RES1 Single Family Dwelling House 
RES2 Mobile Home Mobile Home 
RES3 Multi Family Dwelling 
RES3A Duplex 
RES3B 3-4 Units 
RES3C 5-9 Units 
RES3D 10-19 Units 
RES3E 20-49 Units 
RES3F 50+ Units 
Apartment/Condominium 
RES4 Temporary Lodging Hotel/Motel 
RES5 Institutional Dormitory Group Housing (military, college), Jails 
RES6 Nursing Home 
Commercial 
COM1 Retail Trade Store 
COM2 Wholesale Trade Warehouse 
COM3 Personal and Repair Services Service Station/Shop 
COM4 Professional/Technical Services Offices 
COM5 Banks 
COM6 Hospital 
COM7 Medical Office/Clinic 
COM8 Entertainment & Recreation Restaurants/Bars 
COM9 Theaters Theaters 
COM10 Parking Garages 
Industrial 
IND1 Heavy Factory 
IND2 Light Factory 
IND3 Food/Drugs/Chemicals Factory 
IND4 Metals/Minerals Processing Factory 
IND5 High Technology Factory 
IND6 Construction Office 
Agriculture 
AGR1 Agriculture 
Religion/Non/Profit 
REL1 Church/Non-Profit 
Government 
GOV1 General Services Office 
GOV2 Emergency Response Police/Fire Station/EOC 
Education 
EDU1 Grade Schools 
EDU2 Colleges/Universities Does not include group housing 
3.2.1.2. Specific Occupancy-to-Model Building Type Mapping 
Default mapping schemes for specific occupancy classes (except for RES1) to 
model 
building types by floor area percentage are provided in Tables 3A.2 through 
3A.16 of 
Appendix 3A. Table 3A.2 through 3A.10 provide the suggested mappings for the 
HAZUS-MH MR3 Technical Manual 



Western U.S. buildings and are based on information provided in ATC-13 (1985). 
Tables 3A.11 through 3A.16 provide the mapping for buildings in the rest of the 
United States and are based on proprietary insurance data, opinions of a limited 
number of experts, and inferences drawn from tax assessors records. Table 3C.1 
in 
Appendix 3C provides regional classification of the states. Table 3A.17 through 
3A.21 provide model building distribution for the specific occupancy class 
RES1 
on a state-by-state basis. Tables 3A.2 through 3A.10 provide the mapping based 
on 
the height of buildings and the age of construction. The user must provide, for 
census 
tracts on the west coast, the proportion of buildings in low, mid, and high rise 
categories, and the proportion of buildings in the three categories according to 
age 
(pre- 1950, 1950-1970, and post 1970). These proportions are used to compute a 
weighted sum of matrices in Table 3A.2 through Table 3A.10 to arrive at the 
default 
specific occupancy class to model building type mapping. For the rest of the 
United 
States, Tables 3A.11 through 3A.16 provides the mapping based on the height of 
buildings only and the user must provide the proportion of buildings in low-, 
mid-, 
and high-rise categories to compute the default specific occupancy class to 
model 
building type mapping. The default mapping provided in Tables 3A.2 through 3A.16 
should be considered as a guide: Accurate mapping may be developed based on the 
particular building type distribution within in the study region. 





3.2.1.3.

 The Default General Building Stock Database 





The general building stock inventory was developed from the following 
information: 

 





 Census of Population and Housing, 2000: Summary Tape File 1B Extract on CD-
ROM / prepared by the Bureau of Census. 

 





 Census of Population and Housing, 2000: Summary Tape File 3 on CD-ROM / 
prepared by the Bureau of Census. 

 





 Dun & Bradstreet, Business Population Report aggregated by Standard Industrial 
Classification (SIC) and Census Block, May 2002. 

 





 Department of Energy, Housing Characteristics 1993. Office of Energy Markets 
and End Use, DOE/EIA-0314 (93), June 1995. 

 



 Department of Energy, A Look at Residential Energy Consumption in 1997, 
DOE/EIA-0632(97), November 1999. 

 



 Department of Energy, A Look at Commercial Buildings in 1995: 
Characteristics, Energy Consumption, and Energy Expenditures, DOE/EIA-0625(95), 
October 1998. 

 

The US Census and the Dun & Bradstreet data were used to develop the general 
building stock inventory by Census Block and then rolled up to Census Tract. The 




3-7 
three reports from the Department of Energy (DOE) helped in defining regional 
variations in characteristics such as number and size of garages, type of 
foundation, 
and number of stories. The inventorys baseline floor area is based on a 
distribution 
contained in the DOEs Energy Consumption Report. An approach was developed 
using the same report for determining the valuation of single-family residential 
homes 
by accounting for income as a factor on the cost of housing. 
Initially the methodology created the opportunity for the user to develop 
conflicting 
or discrepant square footage totals for single-family residential structures 
within a 
census block between the inventory database and the valuation database. The 
solution was to integrate the regional DOE distributions with the income factors 
developed for determining valuation. To do this, default values for typical 
square 
footage per single-family home were developed from Energy Information 
Administration (EIA) data on heated floor space. These default data, shown in 
Table 
3.3, are provided by region and income group. The breakdown reflects not only 
how 
typical housing size varies across the U.S., but also how in general, higher 
income 
areas tend to contain larger single-family homes. 
Consequentially, the default typical square footage data was derived from a 
detailed, 
unpublished database provided by the EIA. Only information on families in 
singlefamily 
residences, aggregated across all foundation/basement types, was used. The 
raw database included information on the number of households by region, income 
category, and housing floor space. Regional data were available by 9 multi-state 
census divisions (e.g., New England). 
The very nature of the default data, both in occupancy classifications and 
extent of 
coverage (national) requires the use of a baseline database collected in a 
consistent 
manner for the nation. The data source changes depending on the general use of 
the 
inventory being explored. For example, to determine the total floor area (square 
feet) 
of single-family residences by census block, one uses a data source like the 
Census 
data. While sufficient for residential occupancy, the Census data does not 
address 
non-residential occupancy classifications. 
The development of the default inventory required two major datasets for the two 
main elements of the built environment. To create the default inventory for 
residential structures, the US Department of Commerces Census of Housing was 
used. For commercial and industrial structures, a commercial supplier, Dun & 
Bradstreet (D&B) was contacted. The project team performed the aggregation to 
the 
census data, while D&B performed the aggregation to their own data (due to its 
proprietary nature). 
The STF1B census extract at the census block level allows for the quick 
quantification of the single-family residential environment. When combined with 
the 
STF3A census extract at the census block group level, the STF1B can provide a 
better proxy of the multi-family environment than using one extract alone. In 
both 
the single-family and multi-family proxies, the proposed methodology represents 
an 
HAZUS-MH MR3 Technical Manual 



improvement over using single average values similar to the existing HAZUS99 
data. 

 

The STF3A extract also provides information that is useful in developing 
distributions for the age of buildings within each census block group as well as 
valuable demographic data. 

 

The D&B provides a realistic representation of the non-residential environment. 
Based on the site specific data contained within their database, D&Bs data is 
used to 
provide a reasonable assessment of the non-residential environment. The 
processing 
of the D&B data is discussed in more detail in Section 3.2.1.4. 

3.2.1.4. 
Specific Occupancy Square Footage 




 

Single-Family Residences (RES1) 





 

The following discussion highlights the data development effort for the RES1 
square 
foot values by block. The Census Extract STF1B provides estimates of the single 
family attached and detached housing units on a block-by-block basis. Several 
other 
sources of information were used to develop distributions of square footage 
relative 
to the income of the census block group. The DOE distributions of income factors 
was used to develop a ratio of the census block group income (STF3A field 
P08A001) and the average income for the region (the nine multi-state census 
divisions). 





The EIA data provided information regarding the heated floor area in 
relationship to 
income. Income was reported in 25 categories (e.g., $20,000-$22,499) that were 
converted into five relative income groups for consistency with the inventory 
valuation methodology. Housing floor space data were provided in 7 categories 
(e.g., 
2,000-2,399 sq. ft.), which, for purposes of computing typical floor space, were 
represented by the midpoint of the range (e.g., 2,200 sq. ft.). This enabled 
average 
floor space to be calculated for the 9 census divisions and 5 relative income 
categories. 




3-9 
Table 3.3 Typical Square Footage Per Unit (Main Living Area) by 
Census Division (R)1 
R = New England 
Income Ratio: Basement 
No (j=1) Yes2 (j=2) 
Ik < 0.5 1300 975 
0.5 = Ik < 0.85 1500 1125 
0.85 = Ik < 1.25 1800 1350 
1.25 = Ik < 2.0 1900 1425 
Ik = 2.0 2200 1650 
R = Middle Atlantic 
Income Ratio: Basement 
No (j=1) Yes2 (j=2) 
Ik < 0.5 1300 975 
0.5 = Ik < 0.85 1500 1125 
0.85 = Ik < 1.25 1700 1275 
1.25 = Ik < 2.0 1900 1425 
Ik = 2.0 2200 1650 
R = East North Central 
Income Ratio: Basement 
No (j=1) Yes2 (j=2) 
Ik < 0.5 1300 975 
0.5 = Ik < 0.85 1600 1200 
0.85 = Ik < 1.25 1700 1275 
1.25 = Ik < 2.0 1800 1350 
Ik = 2.0 2500 1875 
HAZUS-MH MR3 Technical Manual 



3-10 
Table 3.3 Typical Square Footage Per Unit (Main Living Area) by 
Census Division (R)1 (Continued) 
R = West North Central 
Income Ratio: Basement 
No (j=1) Yes2 (j=2) 
Ik < 0.5 1300 975 
0.5 = Ik < 0.85 1500 1125 
0.85 = Ik < 1.25 1800 1350 
1.25 = Ik < 2.0 1800 1350 
Ik = 2.0 2300 1725 
R = South Atlantic 
Income Ratio: Basement 
No (j=1) Yes2 (j=2) 
Ik < 0.5 1400 1050 
0.5 = Ik < 0.85 1600 1200 
0.85 = Ik < 1.25 1700 1275 
1.25 = Ik < 2.0 2000 1500 
Ik = 2.0 2300 1725 
R = East South Central 
Income Ratio: Basement 
No (j=1) Yes2 (j=2) 
Ik < 0.5 1300 975 
0.5 = Ik < 0.85 1400 1050 
0.85 = Ik < 1.25 1700 1275 
1.25 = Ik < 2.0 1900 1425 
Ik = 2.0 2500 1875 
R = West South Central 
Income Ratio: Basement 
No (j=1) Yes2 (j=2) 
Ik < 0.5 1300 975 
0.5 = Ik < 0.85 1700 1275 
0.85 = Ik < 1.25 1800 1350 
1.25 = Ik < 2.0 1900 1425 
Ik = 2.0 2500 1875 
Chapter 3  Inventory Data: Collection and Classification 



3-11 
Table 3.3 Typical Square Footage Per Unit (Main Living Area) by 
Census Division (R)1 (Continued) 
R = Mountain 
Income Ratio: Basement 
No (j=1) Yes2 (j=2) 
Ik < 0.5 1200 900 
0.5 = Ik < 0.85 1500 1125 
0.85 = Ik < 1.25 1700 1275 
1.25 = Ik < 2.0 1800 1350 
Ik = 2.0 2600 1950 
R = Pacific 
Income Ratio: Basement 
No (j=1) Yes2 (j=2) 
Ik < 0.5 1300 975 
0.5 = Ik < 0.85 1500 1125 
0.85 = Ik < 1.25 1700 1275 
1.25 = Ik < 2.0 1900 1425 
Ik = 2.0 2100 1575 
Notes: 
1 Based on data from the Energy Information Administration, Housing 
Characteristics 1993; 
2 (Area of main living area if basement present) = 0.75 x (Area of main living 
area if no 
basement). This adjustment allows consistent application of the Means cost 
models, in 
which basement areas are added-on, and are assumed to be 1/3 of main living 
area. 
While the US Census data does have data defining the median income for each 
census block, there is data for the median income for each census block group. 
This 
value will be applied to each block within the block group. With the a median 
income for each census block, and the median income for the census region, it is 
possible to define an Income Ratio that can be used to determine the square 
footage 
for buildings with and without basements. Table 3.4 below shows the 9 census 
regions, the states within those regions and the values used to compute the 
Income 
Ratio. The value from the Census STF3A field P08A001 is the median income for 
the census block group that will be applied to every census block within the 
group. 
The distribution of basements is a summation or roll-up of the foundation type 
distribution discussed later in this section. 
HAZUS-MH MR3 Technical Manual 



3-12 
Table 3.4 Income Ratio and Basement Distribution by Census Region 
Region (States) Income Ratio Percent with 
Basement 
Percent without 
Basement 
AL P053001 / 36,268 25 75 
AK P053001 / 52,492 13 87 
AZ P053001 / 39,653 32 68 
AR P053001 / 30,082 5 95 
CA P053001 / 45,070 13 87 
CO P053001 / 49,216 32 68 
CT P053001 / 50,647 81 19 
DE P053001 / 47,438 23 77 
DC P053001 / 38,005 23 77 
FL P053001 / 37,305 23 77 
GA P053001 / 41,481 23 77 
HI P053001 / 45,657 13 87 
ID P053001 / 37,760 32 68 
IL P053001 / 46,649 68 32 
IN P053001 / 41,315 68 32 
IA P053001 / 41,560 75 25 
KS P053001 / 38,393 75 25 
KY P053001 / 36,826 25 75 
LA P053001 / 32,500 5 95 
ME P053001 / 39,815 81 19 
MD P053001 / 52,846 23 77 
MA P053001 / 45,769 81 19 
MI P053001 / 46,034 68 32 
MN P053001 / 50,088 75 25 
MS P053001 / 31,963 25 75 
MO P053001 / 44,247 75 25 
MT P053001 / 32,553 32 68 
NE P053001 / 39,029 75 25 
NV P053001 / 43,262 32 68 
NH P053001 / 48,029 81 19 
NJ P053001 / 51,739 76 24 
NM P053001 / 34,035 32 68 
NY P053001 / 40,822 76 24 
NC P053001 / 38,413 23 77 
ND P053001 / 33,769 75 25 
Chapter 3  Inventory Data: Collection and Classification 



3-13 
Table 3.4 Income Ratio and Basement Distribution by Census Region 
(Continued) 
Region (States) Income Ratio Percent with 
Basement 
Percent without 
Basement 
OH P053001 / 41,972 68 32 
OK P053001 / 34,020 5 95 
OR P053001 / 41,915 13 87 
PA P053001 / 41,394 76 24 
RI P053001 / 43,428 81 19 
SC P053001 / 36,671 23 77 
SD P053001 / 35,986 75 25 
TN P053001 / 35,874 25 75 
TX P053001 / 39,296 5 95 
UT P053001 / 46,539 32 68 
VT P053001 / 40,908 81 19 
VA P053001 / 47,701 23 77 
WA P053001 / 46,412 13 87 
WV P053001 / 29,217 23 77 
WI P053001 / 45,441 68 32 
WY P053001 / 38,291 32 68 
Once the parameters above had been defined, it is possible to develop an 
algorithm 
that allows for the estimation of the RES1 or single-family residential square 
footage 
for the entire nation. This algorithm is: 
RES1 (sq. ft.) = Total Single Family Units (STF1B 
H1BX0002) *[(Percent of units with basement) * (floor area 
w/basement based on income ratio and region) + (Percent of 
units without basement)*(floor area w/o basement based on 
income ratio and region] where Income Ratio = STF3A 
P08A001/regional income 
For a sample New England census block, 81% Basement 19% no basement and an Ik 
of 0.67: 
RES1 (sq. ft.) = [STF1BX0002] * [(0.81)*(1,125) + (0.19)*(1,500)] 
Multi-Family and Manufactured Housing (RES3 and RES2) 
Developing the multi-family (RES3A through RES3F) and manufactured housing 
(RES2) inventory requires additional information and effort compared to the 
singlefamily 
occupancy classification. In the 1999 census extract, the STF1B (census block 
data) extract identifies only those housing units within the 10 or more unit 
HAZUS-MH MR3 Technical Manual 



classification, unfortunately, the 2000 census extract no longer provided that 
information. Therefore in order to define of the multi-family units, it is 
necessary to 
utilize the STF3A extract. The multi-family definition in the STF3A extract 
identifies Duplex, 3-4 Unit, 5-9 unit, 10-19 unit, 20-49 unit, and 50+ 
dwellings. 
Additionally the STF3A census data provides a definition of the Manufactured 
Housing (MH) units within a block group and therefore the RES2 was processed at 
the same time. The census data has an other classification for that will be 
ignored 
since this classification represent a very small portion of the universe of 
housing units 
and there is no other damage functions that can be assigned to these 
facilities. 
Examples of the Other Census classification include vans and houseboats. 

 

Unlike the single family residential that used the Housing Characteristics 1993 
to 
define heated floor area, assessor data from around the United States, including 
that 
from the six Proof-of-Concept (POC) communities, was reviewed to develop 
preliminary estimates of average floor area for multi-family housing. This data 
was 
then peer reviewed by engineering experts to develop an average floor area per 
number of units for the unit ranges provided by the census data. Table 3.5 shows 
the 
distribution of the floor area by unit. The associated equations provide an 
example of 
the calculations that have taken place. 

 

Table 3.5 Floor Areas for Multi-Family Dwellings (RES2 & RES3A-RES3F) 

 





Units 

Duplex 
3-4 




5-9 

10-19 
20-49 




50+ 

Manufactured 
Housing 




Other 

Floor Area 
1,500 




750 

800 

750 

700 

650 
Single Wide  950 
Double Wide  1,350 




Not 
Required 

 

Previously, the flood model team had a complex process that allowed for a more 
accurate block level distribution. However, when the US Census Bureau modified 
the SF1 extract to eliminate information regarding the single-family and large 
multi-
family fields, it became necessary to modify the data manipulation process. The 
multi-family data was still available in the SF3 extract at the census block 
group 
level. The only available process was to distribute the census block group data 
homogeneously throughout the census blocks. The distribution process is 
facilitated 
by finding the ratio of total housing units per census block (H1BX0001) with 
respect 
to the total housing units per census block group (H0010001). This ratio was 
then 
used to as a multiplier to distribute the census block group level multi-family 
data 
into each census block. 

 

Step 1:

 Develop the ratio of total housing units for each census block 




Unit Ratio = (H1BX0001)/(H0010001) 





Step 2:

 Distribute the multi-family housing units throughout each census block 





For example: 

 
Duplex units per block = H0200003*Unit Ratio 




 

Step 3:

 Derive Floor area per occupancy classification 

 

Manufactured Housing (sq. ft.) = Census Block RES2 (from Step 2)* 
(0.75 * 950 + 0.25 * 1,400) 

Duplex (sq. ft.) = (Census Block Duplex from Step 2) * 1,500 

 

3-4 Units (sq. ft.) = (Census Block 3-4 units from Step 2) * 750 

 

5-9 Units (sq. ft.) = (Census Block 5-9 units from Step 2) * 800 

 

10-19 Units (sq. ft.) = (Census Block 10-19 units from Step 2) * 750 

 

20-49 Units (sq. ft.) = (Census Block 20-49 units from Step 2) * 700 

 

50+ Units (sq. ft.) = (Census Block 50+ units from Step 2) * 700 

 

By using the above distribution, the valuation can be more specifically tailored 
to 
each floor plan. This has the potential future benefit of allowing the user to 
modify 
the floor area for multi-family units. For example in future releases, it may be 
possible to provide the user the capability to modify the average floor area for 
duplexes to 2,000 Sq Ft per unit if this more closely reflected the users 
community. 
This should then lead to a net decrease in the total number of units for the 
RES3A 
occupancy classification. 

 

The floor areas presented for manufactured housing are based on review of 
various 
internet websites for manufactured housing sales (new and used), housing 
manufacturers, and finally additional US Census Bureau data. There was a great 
deal 
of information regarding sales and shipment of manufactured housing since the 
1970s, but there was very little information regarding the attrition rate 
experienced 
over the same 30-year span. Charting information from the Manufactured Housing 
Institute, Figure 3.1 shows that there has been a general growth trend in the 
size of 
the units since the 1980s for both the single wide and doublewide (also known 
as 
single-section and multi-section) manufactured housing. 




 



02004006008001000120014001600180019801981198219831984198519861987198819891990199
1199219931994199519961997Year of SaleAverage Floor Area

Single Wide

Double Wide

 

Figure 3.1 Manufactured Housing Growth Over Time 

 

The recently released American Housing Survey for the US, 1997

3

 (September, 
1999) contained estimated floor areas for manufactured housing (labeled Mobile 
Home in the Census tables) based on a surveyed population of over 8 million 
manufactured homes across the United States. The survey does not differentiate 
between single-section and multi-section units, but when the values are charted 
the 
distribution presents natural points to estimate these dimensions. Figure 3.2 
shows 
the distribution of floor area by number of structures from the survey. Using 
this 
distribution, it is possible to estimate representative values for single-
section and 
multi-section units of 950 Square Feet and 1,400 Square Feet respectively. 

3 US Department of Housing and Urban Development and US Census Bureau, American 
Housing 
Survey for the United States H150/97, Office of Policy Development an Research 
and the US Census 
Bureau, September 1999. 






05001000150020002500020040060080010001200140016001800200022002400260028003000320
0340036003800400042004400Floor Area (Square Feet)
Number of Mobile Home Units (Thousands)
US Census Total Units Surveyed = 8,301,000

 

Figure 3.2 Number of Mobile Home Units by Floor Area  1990 US Census Data 
Housing Characteristics 

 

Non-Residential Occupancy Classifications

 

 

The HAZUS99 Earthquake Model inventory used the D&B business inventory at the 
census tract level for all non-residential structures and those facilities that 
are 
commercial in nature but provide housing for people such as hotels (RES4) and 
nursing homes (RES6). The D&B data represents approximately 76 percent 
(approximately 14 million) of the total estimated businesses in the United 
States 
(approximately 19 million). While initially this might seem like a low 
representation, 
the D&B database accounts for 98 percent of the gross national product. D&B 
states 
that the remaining businesses are likely to be smaller and home-based. If true, 
the 
proxy inventory established for the residential dwellings will account for these 
businesses in the total damage estimates. 

 

D&B provided the data aggregated on the SIC definitions used previously in the 
development of the HAZUS99 Earthquake Model (HAZUS99 Users Manual, 1997 
Table Appendix A.19, page A-23). The D&B data obtained for the Flood Model 
provided floor area for businesses at the census block level. It should be noted 
that 
D&B performs regular random sampling of businesses in their database to obtain 
the 
actual floor area. D&B then utilizes proprietary algorithms to estimate the 
floor area 




for the remaining businesses. According to D&B, floor area is sampled for 
approximately 25 percent of their business database and the remainder is 
modeled. 





With their data, D&B provided a count of businesses, the total floor area 
(modeled 
and sampled), and the total number of employees. During a review of the data, it 
was 
discovered that D&B had some data aggregated at the census block groups and 
tracts 
level. Review of the data determined that these errors were consistent with 
automated georeferencing processes and are likely to represent those businesses 
where the addresses did not match directly with D&Bs reference street base. D&B 
performed an additional review and ascertained that this was in fact the cause 
of this 
aggregation. It was felt, however, that the tract and block group data could be 
safely 
distributed to the census blocks based on weighted averages of commercial 
development within the blocks. Review of the results of this effort showed 
little net 
impact and continued agreement with ground truth data. 

 

The D&B data contained information on all non-residential uses including some 
agricultural facilities, general government offices, schools, and churches. 
Again, 
comparison with POC data and other available data showed relatively good 
agreement. 

 

3.2.1.5.

 Building Replacement Costs 

 

Building replacement cost models within HAZUS are based on industry-standard 
cost-estimation models published in Means Square Foot Costs (R.S. Means, 2002). 
Replacement cost data are stored within HAZUS at the census tract and census 
block 
level for each occupancy class. For each HAZUS occupancy class, a basic default 
structure full replacement cost model (cost per square foot) has been 
determined, and 
are provided in Table 3.6. Commercial and industrial occupancies have a typical 
building replacement cost model associated with each occupancy class (e.g., 
COM4, 
Professional/Technical/Business Services, is represented by a typical, 80,000 
square 
foot, 5 to 10 story office building). In most cases, the typical building chosen 
to 
represent the occupancy class is the same as was used in the original HAZUS 
earthquake model (based on Means, 1994), except for single family residential, 
multi-
family residential, and industrial uses. Both primary default (in bold) and 
alternate 
models (in italics) are provided in the table. As shown, in some cases the 
alternate 
costs are very similar to the primary default cost (e.g., medium and large 
dormitories), although a number of alternate costs vary quite a bit (medium 
hotel vs. 
medium motel). Square foot costs presented in the table have been averaged over 
the 
various alternatives for exterior wall construction (e.g., wood siding over wood 
frame, brick veneer over wood frame, stucco on wood frame or precast concrete, 
concrete block over wood joists or precast concrete, etc.). For non-residential 
structures, the default configuration assumes structures without basements. 








The RES1 (single family residential) replacement cost model is the most complex, 
utilizing socio-economic data from the census to determine an appropriate mix of 
construction classes (Economy, Average, Custom and Luxury) and associated 
replacement cost models. The algorithm is described in Section 3.2.1.5.1.4. 
Within 
Means, basements are not considered in the base cost of the structure and are 
handled 
as an additive adjustment (additional cost per square foot of main structure). 
Table 
3.7 provides Means (2002) replacement costs for the various single family 
dwelling 
configurations available in the default building inventory (1, 2, and 3 story 
and split-
level), assuming a typical size of 1,600 square feet. Costs have been averaged 
for the 
various alternatives for exterior wall construction. 





Because the default single family residential (SFR) damage model is based on the 
FIA credibility-weighted depth damage functions, whose coverage extends to 
garages, the replacement cost of garages will also be included in the basic 
replacement cost. Relevant Means models for SFR garages include costs by 
construction class (economy, average, custom, and luxury), for detached and 
attached 
1-car, 2-car and 3-car garages, constructed of wood or masonry. For 
incorporation 
into HAZUS, costs by size and construction class have been averaged for 
attached/detached and various materials. Average costs associated with garage 
types 
included in the default inventory for single family residential structures (1-
car, 2-car 
and 3-car) were provided in Table 3.8. 
3.2.1.5.1.




 Single-Family Residential Valuation Algorithm 



The algorithm defined below will be used to develop the valuation for single-
family 
residential buildings at the census block level. This algorithm utilizes socio-
economic data from the census to derive an appropriate Means-based cost for each 
census block. The earthquake and wind models shall use a roll-up of the 
results 
from the flood model calculations. Some round-off error will occur, but this 
cannot 
be avoided. 



The valuation algorithm can be summarized mathematically in equation (1) below: 


 
Where: 

 
VRES1, k is the total estimated valuation for single-family 
residences (RES1) for a given census block (k). VRES1, 

 
4 4 4 4 
VRES1, k = (ARES1, k)*[ S S wi,k*wj,k*Ci,j] + (ARES1, k)*wl,k*[ S S 
?wi,k*wj,k*Ci,j,l] 
i=1 j=1 i=1 j=1 
4 4 
+(RES1Cnt k)*[ S ? S wi,k*wm,k*Ci, m] (3-1) 
i=1 j=1 









k is editable when viewing the dollar exposure by 
specific occupancy table. 

 

ARES1,k is the total single-family residential (RES1) floor area 
(square feet) for a given census block (k) found in the 
square foot by specific occupancy table. ARES1,k is 
editable when viewing the square foot by specific 
occupancy table. 

 

i the Means construction class (1 = Economy, 2 = 
Average, 
3 = Custom, 4 = Luxury). 

 

wi,k is the weighting factor for the Means construction class 
(i) for the given census block (k) and is determined 
from the income ratio range as shown in Table 14.4 
below. Values are displayed in percent to the user and 
are editable when viewing the dollar exposure 
parameters tables. 

 

j the number of stories class for single-family (RES1) 
structures 
(1 = 1-story, 2 = 2-story, 3 = 3-story, and 4 = split 
level) 

 

wj,k is the weighting factor for the Number of Stories class 
(j) for the given census block (k) depending on the 
census region of that block (by state FIPS). Weighting 
factors were developed from regional construction type 
distributions as discussed in Section 3. Values are 
displayed in percent to the user and are editable when 
viewing the dollar exposure parameters tables. 

 

Ci,j is the single-family (RES1) cost per square foot for the 
given Means construction class (i) and number of 
stories class (j). RES1 replacement costs are seen in 
the third column of Table 14.2. Values are editable 
when viewing the dollar exposure parameters tables. 

 

l the basement status available for single-family 
residences 
(1 = yes, 2 = no). 

 




wl,k is the weighting factor for basements for the given 
census block (k) depending on the census region of that 
block (by state FIPS). Weighting factors were 
developed from regional foundation type distributions 
as discussed in Section 3. Values are displayed in 
percent to the user and are editable when viewing the 
dollar exposure parameters tables. Default will be 
established based on whether the block is a coastal or 
non-coastal block. 

 

Ci,j,l the additional cost, per square foot of the main 
structure, for a finished basement for the given Means 
construction class (i) and number of stories class (j), as 
shown in Table 14.2, Column 4. Note: Ci,j,l = 0 when l 
= 2. Values are editable when viewing the dollar 
exposure parameters tables. 

m the garage combinations available for single-family 
residences (1 = 1-car, 2 = 2-car, 3 = 3-car, 4 = carport, 
and 5 = none). 

 

wm,k is the weighting factor for the garage type (m) for the 
given census block (k) depending on the census region 
of that block (by state FIPS). Weighting factors were 
developed from regional construction type distributions 
as discussed in Section 3. Values are displayed in 
percent to the user and are editable when viewing the 
dollar exposure parameters tables. 

 

Ci,m the additional replacement cost for a given garage type 
(m), for the given Means construction class (i) as 
shown in Table 14.3. Note: Ci,m = 0 when m = 4 
(covered carport) or m = 5 (none). Values are editable 
when viewing the dollar exposure parameters table. 

 

RES1Cnt the count of RES1 structures within the given census 
block (k) taken directly from the Building Count by 
occupancy table. 

 

As the algorithm shows, the basic replacement cost per square foot is a function 
of 
the Means construction class, the number of stories and an additional cost per 
square 
foot of the main structure for the existence of a finished or unfinished 
basement. 
Finally, there is an additional cost per housing unit based on the garage 
associated 
with the structure. The valuation parameters are presented in a series of tables 
in 
Section 3.2.1.5.1.4 of this document. 




 

3.2.1.5.2.

 Manufactured Housing Valuation Algorithm 

 

It is necessary to clarify that RES2 within HAZUS99 and HAZUSMH, while 
designated Manufactured Housing, represents Mobile Homes and not single-family 
pre-manufactured housing. The US Census provides a detailed count of the mobile 
homes within each census block and this quantity is used to develop the total 
floor 
area (square foot) of the RES2 occupancy classification. The total floor area 
was 
developed assuming a typical floor area and average distribution of singlewide 
to 
doublewide mobile homes. Unlike other occupancy classifications, there are no 
allowances for variation of floor heights (number of stories) or other valuation 
parameters. The valuation of manufactured housing is the straight multiplication 
of 
the total floor area by the baseline replacement cost per square foot. The cost 
per 
square foot (CRES2) is defined in Table 3.6 in the valuation parameters section 
(Section 3.2.1.5.1.4) of this document 

 

The algorithm for manufactured housing is defined in equation (2) below: 

 

VRES2,k = ARES2,k *CRES2 (3-2) 

 

Where: 

 

VRES2,k is the total estimated valuation for Manufactured 
Housing (RES2) for a given census block (k). VRES2, k 
is editable when viewing the dollar exposure by 
specific occupancy table. 

 
ARES2,k is the total Manufactured Housing (RES2) floor area 
(square feet) for a given census block (k) found in the 
square foot by specific occupancy table. ARES2,k is 
editable when viewing the square foot by specific 
occupancy table. 

 


CRES2 is the Manufactured Housing (RES2) cost per square 
foot. RES2 replacement costs are Table 14.1 
($30.90/SqFt). The value is editable when viewing the 
dollar exposure parameters tables. 





The flood model has accounted for differential areas between singlewide and 
doublewide manufactured housing in the total floor area, it is assumed that the 
cost 
per square foot does not vary greatly between the two structure types. 

 




3.2.1.5.3.

 Other Residential and Non-Residential Occupancies 

 

The algorithm for the remaining residential occupancies (RES3-RES6) and all non-
residential (COM, IND, EDU, REL, GOV, and AGR) occupancies is not as complex 
as the single family model but allows for the potential incorporation of a 
distribution 
for number of stories. It should be noted that the replacement costs seen in 
Table 3.6 
are an average replacement cost by occupancy. In other words, the replacement 
cost 
is averaged across structure types, stories and construction classes to produce 
the 
values in Table 14.1. 


The algorithm for the remaining residential occupancies and non-residential 
occupancies can be seen in equation (3) below: 




 

Vx,k = .Ax,k*Cx (3-3) 

 

Where: 

 

x defines the remaining occupancy classifications (x 
ranges from 3 to 28 for the remaining occupancies, i.e., 
RES5, COM1, REL1, etc.) for which the cost is being 
calculated. 

 

Vx,k is the total estimated valuation for the specific 
occupancy (x) (such as RES4, COM3, or IND6) for a 
given census block (k). Vx, k is editable when viewing 
the dollar exposure by specific occupancy table. 

 

Ax,k is the total floor area (square feet) for a specific 
occupancy (x) (such as RES3, COM8, IND4, GOV1, 
etc.) for a given census block (k) found in the square 
foot by specific occupancy table. Ax,k is editable when 
viewing the square foot by specific occupancy table. 

 

Cx is the cost per square foot for the specific occupancy 
(x). The replacement costs are seen in Table 14.1 
below by specific occupancy. Values are editable when 
viewing the dollar exposure parameters tables. 

 

At this time, the flood model depreciation models for non-single-family 
residential 
structures will not depend on features such as the number of stories. A 
distribution of 
number of stories will still be developed in the dollar exposure parameters 
table since 




3-24 
the creation of such depreciation models are seen as a potential enhancement in 
future 
versions of the HAZUS Flood Model. 
3.2.1.5.4. Valuation Tables 
The following tables present the baseline valuation parameters for the variables 
discussed in Section 14.4 of this document. Each of these parameters is editable 
by 
the user. 
Table 3.6 Default Full Replacement Cost Models (Means, 2002) 
HAZUS Occupancy Class 
Description Sub-category Means Model Description (Means 
Model Number) 
Means 
Cost/SF 
(2002) 
RES1 Single Family Dwelling See Table 14.2 
RES2 Manufactured Housing Manufactured 
Housing Manufactured Housing (N/A)1 $30.90 
Multi Family Dwelling  Duplex SFR Avg 2 St., MF adj, 3000 SF $67.24 
small Triplex/Quads SFR Avg 2 St., MF adj, 3000 SF $73.08 
Multi Family Dwelling  5-9 units Apt, 1-3 st, 8,000 SF (M.010) $125.63 
medium 10-19 units Apt., 1-3 st., 12,000 SF (M.010) $112.73 
20-49 units Apt., 4-7 st., 40,000 SF (M.020) $108.86 
50+ units Apt., 4-7 st., 60,000 SF (M.020) $106.13 
RES3 
Multi Family Dwelling  
large 
Apt., 8-24 st., 145,000 SF (M.030) $111.69 
Hotel, medium Hotel, 4-7 st., 135,000 SF(M.350) $104.63 
Hotel, large Hotel, 8-24 st., 450,000 SF (M.360) $93.47 
Motel, small Motel, 1 st., 8,000 SF (M.420) $94.13 
RES4 Temp. Lodging 
Motel, medium Motel, 2-3 st., 49,000 SF (M.430) $110.03 
Dorm, medium College Dorm, 2-3 st, 25,000 SF 
(M.130) $118.82 
Dorm, large College Dorm, 4-8 st, 85,000 SF 
RES5 Institutional Dormitory (M.140) $113.31 
Dorm, small Frat House, 2 st., 10,000 SF (M.240) $99.50 
RES6 Nursing Home Nursing home Nursing Home, 2 st., 25,000 SF 
(M.450) $104.62 
Dept Store, 1 st Store, Dept., 1 st., 110,000 SF 
(M.610) $71.54 
Dept Store, 3 st Store, Dept., 3 st., 95,000 SF (M.620) $88.73 
Store, small Store, retail, 8,000 SF (M.630) $79.23 
Store, medium Supermarket, 44,000 SF (M.640) $69.09 
Store, convenience Store, Convenience, 4,000 SF (M.600) $83.59 
COM1 Retail Trade 
Auto Sales Garage, Auto Sales, 21,000 SF 
(M.260) $70.84 
COM2 Wholesale Trade Warehouse, medium Warehouse, 30,000 SF (M.690) $61.91 
Warehouse, large Warehouse, 60,000 SF (M.690) $56.58 
Chapter 3  Inventory Data: Collection and Classification 



Warehouse, small 

Warehouse, 15,000 SF (M.690) 

$70.43 

Garage, Repair 

Garage, Repair, 10,000 SF (M.290) 

$86.81 

Garage, Service sta. 

Garage, Service sta., 1,400 SF 
(M.300) 

$113.91 

Funeral Home 
Funeral home, 10,000 SF (M.250) 


$97.66 
Laundromat 


Laundromat 3,000 SF (M.380) 
$135.64 




COM3 

Personal and Repair 
Services 
Car Wash 


Car Wash, 1 st., 800 SF (M.080) 
$198.28 


Office, Medium 
Office, 5-10 st., 80,000 SF (M.470) 


$98.96 
Office, Small 




Office, 2-4 st., 20,000 SF (M.460) 

$102.69 

COM4 

Prof./ Tech./Business 
Services 

Office, Large 

Office, 11-20 st., 260,000 SF (M.480) 

$88.21 

COM5 

Banks 

Bank 

Bank, 1 st., 4100 SF (M.050) 

$153.97 




3-26 
Table 3.6 Default Full Replacement Cost Models (Means, 2002) (Continued) 
HAZUS Occupancy Class 
Description Sub-category Means Model Description (Means 
Model Number) 
Means 
Cost/SF 
(2002) 
Hospital, Medium Hospital, 2-3 st., 55,000 SF (M.330) $144.60 
COM6 Hospital 
Hospital, Large Hospital, 4-8 st., 200,000 SF (M.340) $125.60 
Med. Office, medium Medical office, 2 st., 7,000 SF 
(M.410) $129.82 
COM7 Medical Office/Clinic 
Med. Office, small Medical office, 1 st., 7,000 SF 
(M.400) $118.01 
Restaurant Restaurant, 1 st., 5,000 SF (M.530) $137.02 
Restaurant, fast food, 4,000 SF 
Restaurant, Fast food (M.540) $121.49 
Bowling Alley Bowling Alley, 20,000 SF (M.060) $72.31 
Country Club Club, Country, 1 st., 6,000 SF (M.100) $135.23 
Social Club Club, Social, 1 st., 22,000 SF (M.110) $95.39 
Racquetball Court Racquetball Court, 30,000 SF (M.510) $111.23 
COM8 Entertainment & 
Recreation 
Hockey Rink Hockey Rink 30,000 SF (M.550) $115.13 
Movie Theatre Movie Theatre, 12,000 SF (M.440) $102.35 
COM9 Theaters 
Auditorium Auditorium, 1 st., 24,000 SF (M.040) $109.60 
Parking Garage Garage, Pkg, 5 st., 145,000 SF 
(M.270) $34.78 
COM10 Parking 
Parking Garage, 
Underground Garage, UG Pkg, 100,000 SF (M.280) $49.20 
Factory, small Factory, 1 st., 30,000 SF (M.200) $73.82 
IND1 Heavy 
Factory, large Factory, 3 st., 90,000 SF (M.210) $78.61 
Warehouse, medium Warehouse, 30,000 SF (M.690) $61.91 
IND2 Light Factory, small Factory, 1 st., 30,000 SF (M.200) $73.82 
Factory, large Factory, 3 st., 90,000 SF (M.210) $78.61 
College Laboratory College Lab, 1 st., 45,000 SF (M.150) $119.51 
IND3 Food/Drugs/Chemicals Factory, small Factory, 1 st., 30,000 SF (M.200) 
$73.82 
Factory, large Factory, 3 st., 90,000 SF (M.210) $78.61 
College Laboratory College Lab, 1 st., 45,000 SF (M.150) $119.51 
IND4 Metals/Minerals Factory, small Factory, 1 st., 30,000 SF (M.200) $73.82 
Processing 
Factory, large Factory, 3 st., 90,000 SF (M.210) $78.61 
College Laboratory College Lab, 1 st., 45,000 SF (M.150) $119.51 
IND5 High Technology Factory, small Factory, 1 st., 30,000 SF (M.200) $73.82 
Factory, large Factory, 3 st., 90,000 SF (M.210) $78.61 
Warehouse, medium Warehouse, 30,000 SF (M.690) $61.91 
IND6 Construction Warehouse, large Warehouse, 60,000 SF (M.690) $56.58 
Warehouse, small Warehouse, 15,000 SF (M.690) $70.43 
Warehouse, medium Warehouse, 30,000 SF (M.690) $61.91 
AGR1 Agriculture Warehouse, large Warehouse, 60,000 SF (M.690) $56.58 
Warehouse, small Warehouse, 15,000 SF (M.690) $70.43 
REL1 Church Church Church, 1 st., 17,000 SF (M.090) $114.08 
Chapter 3  Inventory Data: Collection and Classification 



3-27 
Table 3.6 Default Full Replacement Cost Models (Means, 2002) (Continued) 
HAZUS Occupancy Class 
Description Sub-category Means Model Description (Means 
Model Number) 
Means 
Cost/SF 
(2002) 
Town Hall, small Town Hall, 1 st., 11,000 SF (M.670) $90.30 
Town Hall, medium Town Hall, 2-3 st., 18,000 SF 
(M.680) 
$112.94 
Courthouse, small Courthouse, 1 st., 30,000 SF (M.180) $130.71 
Courthouse, medium Courthouse, 2-3 st., 60,000 SF 
(M.190) 
$136.81 
GOV1 General Services 
Post Office Post Office, 13,000 SF (M.500) $86.83 
Police Station Police Station, 2 st., 11,000 SF 
(M.490) 
$136.10 
GOV2 Emergency Response Fire Station, small Fire Station, 1 st., 6,000 SF 
(M.220) $105.53 
Fire Station, medium Fire Station, 2 st., 10,000 SF (M.230) $110.34 
High School School, High, 130,000 SF (M.570) $92.80 
Elementary School School, Elementary, 45,000 SF 
(M.560) 
$90.22 
Jr. High School School, Jr. High, 110,000 SF (M.580) $95.21 
Library Library, 2 st., 22,000 SF (M.390) $103.94 
EDU1 Schools/Libraries 
Religious School Religious Educ, 1 st., 10,000 SF 
(M.520) 
$112.19 
College Classroom College Class. 2-3 st, 50,000 SF 
(M.120) 
$114.68 
College Laboratory College Lab, 1 st., 45,000 SF 
(M.150) 
EDU2 Colleges/Universities $119.51 
Vocational school School, Vocational, 40,000 SF 
(M.590) 
$93.96 
Notes: 
1 Manufactured Housing Institute, 2000 cost for new manufactured home 
HAZUS-MH MR3 Technical Manual 



3-28 
Table 3.7 Replacement Costs (and Basement Adjustment) for RES1 Structures 
by Means Constructions Class (Means, 2002) 
Means 
Construction 
Class 
Height 
Class 
Average Base 
cost per 
square foot 
Adjustment for 
Finished Basement 
(cost per SF of 
main str.) 
Adjustment for 
Unfinished 
Basement 
(cost per SF of 
main str.) 
1 story 55.23 16.95 6.35 
2 story 59.58 9.85 4.20 
3-story N/A  use 2 st N/A  use 2 st N/A  use 2 st 
Economy 
Split level* 55.30 12.32 5.02 
1 story 79.88 21.15 7.35 
2 story 79.29 13.80 4.85 
3-story 84.81 10.97 3.78 
Average 
Split level 74.94 16.42 5.77 
1 story 99.59 31.90 11.65 
2 story 99.63 18.75 7.15 
3-story 105.83 13.78 5.35 
Custom 
Split level 93.81 23.35 8.78 
1 story 122.25 37.75 13.20 
2 story 117.55 22.35 8.10 
3-story 124.00 16.48 6.05 
Luxury 
Split level 111.13 27.82 9.95 
Table 3.8 Single Family Residential Garage Adjustment (Means, 2002) 
Means Construction Class Garage Type Average Additional Garage Cost 
per Residence 
1 car $10,700 
Economy 2 car $16,700 
3 car $22,600 
1 car $11,000 
Average 2 car $17,100 
3 car $23,000 
1 car $12,500 
Custom 2 car $19,700 
3 car $26,600 
1 car $14,700 
Luxury 2 car $23,300 
3 car $31,700 
Chapter 3  Inventory Data: Collection and Classification 



3-29 
Table 3.8 Weights (percent) for Means Construction/Condition Models 
Weights (w) for: 
Income 
cLg cCg cAa cEp 
Ik < 0.5 - - 70 30 
0.5 = Ik < 0.85 - - 100 - 
0.85 = Ik < 1.25 - - 100 - 
1.25 = Ik < 2.0 - 60 40 - 
Ik = 2.0 40 60 - - 
3.2.1.6. Contents Replacement Cost 
Contents replacement value is estimated as a percent of structure replacement 
value. 
The NIBS Flood Module will utilize the same contents to structure value ratios 
as are 
employed in the NIBS Earthquake Module (Table 15.5 in the HAZUS 1999 
Technical Manual), provided in Table 3.10. 
Table 3.10 Default HAZUS Contents Value Percent of Structure Value 
No. Label Occupancy Class Contents Value (%) 
Residential 
1 RES1 Single Family Dwelling 50 
2 RES2 Mobile Home 50 
3 RES3 Multi Family Dwelling 50 
4 RES4 Temporary Lodging 50 
5 RES5 Institutional Dormitory 50 
6 RES6 Nursing Home 50 
Commercial 
7 COM1 Retail Trade 100 
8 COM2 Wholesale Trade 100 
9 COM3 Personal and Repair Services 100 
10 COM4 Professional/Technical/ 
Business Services 
100 
11 COM5 Banks 100 
12 COM6 Hospital 150 
13 COM7 Medical Office/Clinic 150 
14 COM8 Entertainment & Recreation 100 
15 COM9 Theaters 100 
16 COM10 Parking 50 
Industrial 
17 IND1 Heavy 150 
18 IND2 Light 150 
19 IND3 Food/Drugs/Chemicals 150 
HAZUS-MH MR3 Technical Manual 



3-30 
Chapter 3  Inventory Data: Collection and Classification 
No. Label Occupancy Class Contents Value (%) 
20 IND4 Metals/Minerals Processing 150 
21 IND5 High Technology 150 
22 IND6 Construction 100 
Agriculture 
23 AGR1 Agriculture 100 
Religion/Non/Profit 
24 REL1 Church/Membership Organization 100 
Government 
25 GOV1 General Services 100 
26 GOV2 Emergency Response 150 
Education 
27 EDU1 Schools/Libraries 100 
28 EDU2 Colleges/Universities 150 
3.2.2. Essential Facilities 
Essential facilities are those facilities that provide services to the community 
and 
should be functional after an earthquake. Essential facilities include 
hospitals, police 
stations, fire stations and schools. The damage state probabilities for 
essential 
facilities are determined on a site-specific basis (i.e., the ground motion 
parameters 
are computed at the location of the facility). The purpose of the essential 
facility 
module is to determine the expected loss of functionality for these critical 
facilities. 
Economic losses associated with these facilities are computed as part of the 
analysis 
of the general building stock (general building stock occupancy classes 12, 26, 
27 and 
28). The data required for the analysis include mapping of essential facilitys 
occupancy classes to model building types or a combination of essential 
facilities 
building type, design level and construction quality factor. In addition, the 
number of 
beds for each hospital and the number of fire trucks at each fire station are 
required. 
The fire truck information is used as input for the fire following earthquake 
analysis 
(Chapter 10). 
3.2.2.1. Classification 
The essential facilities are also classified based on the building structure 
type and 
occupancy class. The building structure types of essential facilities are the 
same as 
those for the general building stock presented in Table 3.1. The occupancy 
classification is broken into general occupancy and specific occupancy classes. 
For 
the methodology, the general occupancy classification system consists of three 
groups (medical care, emergency response, and schools). Specific occupancy 
consists 
of nine classes. The occupancy classes are given in Table 3.11, where the 
general 
occupancy classes are identified in boldface. Relationships between specific and 
general occupancy classes are in a form shown in Table 3B.1 of Appendix 3B. 



3-31 
Table 3.11: Essential Facilities Classification 
Label Occupancy Class Description 
Medical Care Facilities 
EFHS Small Hospital Hospital with less than 50 Beds 
EFHM Medium Hospital Hospital with beds between 50 & 150 
EFHL Large Hospital Hospital with greater than 150 Beds 
EFMC Medical Clinics Clinics, Labs, Blood Banks 
Emergency Response 
EFFS Fire Station 
EFPS Police Station 
EFEO Emergency Operation Centers 
Schools 
EFS1 Grade Schools Primary/ High Schools 
EFS2 Colleges/Universities 
3.2.2.2. Occupancy to Model Building Type Relationship 
Default mapping of essential facility occupancy classes to model building types 
is 
provided in Tables 3B.2 through 3B.16 of Appendix 3B. For the regional 
designation 
of a particular state, refer to Table 3C.1 in Appendix C. The default mapping of 
specific occupancy to model building type mapping is based on general building 
stock occupancy classes 12, 26, 27 and 28. 
3.2.3. High Potential Loss Facilities 
High potential loss facilities are facilities that are likely to cause heavy 
earthquake 
losses if damaged. For this methodology, high potential loss (HPL) facilities 
include 
nuclear power plants, dams, and some military installations. The inventory data 
required for HPL facilities include the geographical location (latitude and 
longitude) 
of the facility. Damage and loss estimation calculation for high potential loss 
facilities are not performed as part of the methodology. 
HAZUS-MH MR3 Technical Manual 



3.2.3.1.

 Classification 

 

Three types of HPL facilities are identified in the methodology (dams, nuclear 
power 
facilities and military installations) are shown in Table 3.12. The dam 
classification 
is based on the National Inventory of Dams (NATDAM) database (FEMA, 1993). 

 

Table 3.12: High Potential Loss Facilities Classification 

Label 

Description 

 

Dams 

HPDE 

Earth 

HPDR 

Rock fill 

HPDG 

Gravity 

HPDB 

Buttress 
HPDA Arch 
HPDU Multi-Arch 
HPDC Concrete 
HPDM Masonry 
HPDS Stone 
HPDT Timber Crib 
HPDZ Miscellaneous 
Nuclear Power Facilities 
HPNP Nuclear Power Facilities 
Military Installations 
HPMI Military Installations 
3.3. Direct Damage Data - Transportation Systems 
The inventory classification scheme for lifeline systems separates components 
that 
make up the system into a set of pre-defined classes. The classification system 
used 
in this methodology was developed to provide an ability to differentiate between 
varying lifeline system components with substantially different damage and loss 
characteristics. Transportation systems addressed in the methodology include 
highways, railways, light rail, bus, ports, ferries and airports. The 
classification of 
each of these transportation systems is discussed in detail in the following 
sections. 
The inventory data required for the analysis of each system is also identified 
in the 
following sections. 
For some transportation facilities, classification of the facility is based on 
whether the 
equipment is anchored or not. Anchored equipment in general refers to equipment 
designed with special seismic tie-downs or tiebacks, while unanchored equipment 
refers to equipment designed with no special considerations other than the 
manufacturer's normal requirements. While some vibrating components, such as 
pumps, are bolted down regardless of concern for earthquakes, as used here 
anchored means all components have been engineered to meet seismic criteria 



3-33 
which may include bracing (e.g., pipe or stack bracing) or flexibility 
requirements 
(e.g., flexible connections across separation joints) as well as anchorage. 
3.3.1. Highway Systems 
A highway transportation system consists of roadways, bridges and tunnels. The 
inventory data required for analysis include the geographical location, 
classification, 
and replacement cost of the system components. The analysis also requires the 
length 
of each highway segment. 
3.3.1.1. Classification 
The classes of highway system components are presented in Table 3.13. For more 
details on how to classify these components, refer to section 7.1.5 of Chapter 
7. 
Table 3.13: Highway System Classification 
Label Description 
Hignhway Roads 
HRD1 Major Roads 
HRD2 Urban Roads 
Highnway Bridges 
HWB1 Major Bridge - Length > 150m (Conventional Design) 
HWB2 Major Bridge - Length > 150m (Seismic Design) 
HWB3 Single Span  (Not HWB1 or HWB2) (Conventional Design) 
HWB4 Single Span  (Not HWB1 or HWB2) (Seismic Design) 
HWB5 Concrete, Multi-Column Bent, Simple Support (Conventional Design), Non-
California (Non- 
CA) 
HWB6 Concrete, Multi-Column Bent, Simple Support (Conventional Design), 
California (CA) 
HWB7 Concrete, Multi-Column Bent, Simple Support (Seismic Design) 
HWB8 Continuous Concrete, Single Column, Box Girder (Conventional Design) 
HWB9 Continuous Concrete, Single Column, Box Girder (Seismic Design) 
HWB10 Continuous Concrete, (Not HWB8 or HWB9) (Conventional Design) 
HWB11 Continuous Concrete, (Not HWB8 or HWB9) (Seismic Design) 
HWB12 Steel, Multi-Column Bent, Simple Support (Conventional Design), Non-
California (Non-CA) 
HWB13 Steel, Multi-Column Bent, Simple Support (Conventional Design), California 
(CA) 
HWB14 Steel, Multi-Column Bent, Simple Support (Seismic Design) 
HWB15 Continuous Steel (Conventional Design) 
HWB16 Continuous Steel (Seismic Design) 
HWB17 PS Concrete Multi-Column Bent, Simple Support - (Conventional Design), 
Non-California 
HWB18 PS Concrete, Multi-Column Bent, Simple Support (Conventional Design), 
California (CA) 
HWB19 PS Concrete, Multi-Column Bent, Simple Support (Seismic Design) 
HWB20 PS Concrete, Single Column, Box Girder (Conventional Design) 
HWB21 PS Concrete, Single Column, Box Girder (Seismic Design) 
HWB22 Continuous Concrete, (Not HWB20/HWB21) (Conventional Design) 
HWB23 Continuous Concrete, (Not HWB20/HWB21) (Seismic Design) 
HWB24 Same definition as HWB12 except that the bridge length is less than 20 
meters 
HWB25 Same definition as HWB13 except that the bridge length is less than 20 
meters 
HWB26 Same definition as HWB15 except that the bridge length is less than 20 
meters and Non-CA 
HWB27 Same definition as HWB15 except that the bridge length is less than 20 
meters and in CA 
HWB28 All other bridges that are not classified (including wooden bridges) 
HAZUS-MH MR3 Technical Manual 



3-34 
Highwnay Tunnels 
HTU1 Highway Bored/Drilled Tunnel 
HTU2 Highway Cut and Cover Tunnel 
3.3.2. Railways 
A railway transportation system consists of tracks, bridges, tunnels, stations, 
and fuel, 
dispatch and maintenance facilities. The inventory data required for analysis 
include 
the geographical location, classification and replacement cost of the 
facilities, 
bridges, tunnels, and track segments. The analysis also requires the length of 
the 
railway segments. 
3.3.2.1.Classification 
The various classes of railway system components are presented in Table 3.14. 
For 
more details on how to classify these components refer to section 7.2 of Chapter 
7. 
Table 3.14: Railway System Classification 
Label Description 
Railway Tracks 
RTR1 Railway Tracks 
Railway Bridges 
RLB1 
Steel, Multi-Column Bent, Simple Support (Conventional Design), Non- 
California (Non-CA) 
RLB2 
Steel, Multi-Column Bent, Simple Support (Conventional Design), California 
(CA) 
RLB3 Steel, Multi-Column Bent, Simple Support (Seismic Design) 
RLB4 Continuous Steel (Conventional Design) 
RLB5 Continuous Steel (Seismic Design) 
RLB6 Same definition as HWB1 except that the bridge length is less than 20 
meters 
RLB7 Same definition as HWB2 except that the bridge length is less than 20 
meters 
RLB8 
Same definition as HWB4 except that the bridge length is less than 20 meters 
and Non-CA 
RLB9 
Same definition as HWB5 except that the bridge length is less than 20 meters 
and in CA 
RLB10 All other bridges that are not classified 
Railway Urban Station 
RST Rail Urban Station (with all building type options enabled) 
Railway Tunnels 
RTU1 Rail Bored/Drilled Tunnel 
RTU2 Rail Cut and Cover Tunnel 
Railway Fuel Facility 
RFF Rail Fuel Facility (different combinations for with or without anchored 
components and/or with or without backup power) 
Railway Dispatch Facility 
RDF Rail Dispatch Facility (different combinations for with or without anchored 
components and/or with or without backup power) 
Railway Maintenance Facility 
RMF Rail Maintenance Facility (with all building type options enabled) 
Chapter 3  Inventory Data: Collection and Classification 



3-35 
3.3.3. Light Rail 
Like railways, light rail systems are composed of tracks, bridges, tunnels, and 
facilities. The major difference between the two is with regards to power 
supply, 
where light rail systems operate with DC power substations. The inventory data 
required for analysis include the classification, geographical location, and 
replacement cost of facilities, bridges, tunnels, and tracks. In addition, the 
analysis 
requires the track length. 
3.3.3.1. Classification 
Table 3.15 describes the various classes of light rail system components. For 
more 
details on how to classify these components refer to section 7.3 of Chapter 7. 
Table 3.15: Light Rail System Classification 
Label Description 
Light Rail Tracks 
LTR1 Light Rail Track 
Light Rail Bridges 
LRB1 
Steel, Multi-Column Bent, Simple Support (Conventional Design), Non-California 
(Non-CA) 
LRB2 
Steel, Multi-Column Bent, Simple Support (Conventional Design), California 
(CA) 
LRB3 Steel, Multi-Column Bent, Simple Support (Seismic Design) 
LRB4 Continuous Steel (Conventional Design) 
LRB5 Continuous Steel (Seismic Design) 
LRB6 Same definition as HWB1 except that the bridge length is less than 20 
meters 
LRB7 Same definition as HWB2 except that the bridge length is less than 20 
meters 
LRB8 
Same definition as HWB4 except that the bridge length is less than 20 meters and 
Non-CA 
LRB9 
Same definition as HWB5 except that the bridge length is less than 20 meters and 
in CA 
LRB10 All other bridges that are not classified 
Light Rail Tunnels 
LTU1 Light Rail Bored/Drilled Tunnel 
LTU2 Light Rail Cut and Cover Tunnel 
DC Substation 
LDC1 Light Rail DC Substation w/ Anchored Sub-Components 
LDC2 Light Rail DC Substation w/ Unanchored Sub-Components 
Dispatch Facility 
LDF Light Rail Dispatch Facility (different combinations for with or without 
anchored components and/or with or without backup power) 
Maintenance Facility 
LMF Maintenance Facility (with all building type options enabled) 
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3-36 
3.3.4. Bus System 
A bus transportation system consists of urban stations, fuel facilities, 
dispatch 
facilities and maintenance facilities. The inventory data required for bus 
systems 
analysis include the geographical location, classification, and replacement cost 
of bus 
system facilities. 
3.3.4.1. Classification 
Table 3.16 describes the various classes of bus system components. For more 
details 
on how to classify these components refer to section 7.4 of Chapter 7. 
Table 3.16: Bus System Classification 
Label Description 
Bus Urban Station 
BPT Bus Urban Station (with all building type options enabled) 
Bus Fuel Facility 
BFF Bus Fuel Facility (different combinations for with or without anchored 
components and/or with or without backup power) 
Bus Dispatch Facility 
BDF Bus Dispatch Facility (different combinations for with or without anchored 
components and/or with or without backup power) 
Bus Maintenance Facility 
BMF Bus Maintenance Facilities (with all building type options enabled) 
3.3.4.2.Ports and Harbors 
Port and harbor transportation systems consist of waterfront structures, 
cranes/cargo 
handling equipment, warehouses and fuel facilities. The inventory data required 
for 
ports and harbors analysis include the geographical location, classification and 
replacement cost of the port and harbor system facilities. 
3.3.4.3. Classification 
Table 3.17 describes the various classes of port and harbor transportation 
system 
components. For more details on how to classify these components refer to 
section 
7.5 of Chapter 7. 
Chapter 3  Inventory Data: Collection and Classification 



3-37 
Table 3.17: Port and Harbor System Classification 
Label Description 
Waterfront Structures 
PWS Waterfront Structures 
Cranes/Cargo Handling Equipment 
PEQ1 Stationary Port Handling Equipment 
PEQ2 Rail Mounted Port Handling Equipment 
Warehouses 
PWH Port Warehouses (with all building type options enabled) 
Fuel Facility 
PFF Port Fuel Facility Facility (different combinations for with or without 
anchored components and/or with or without backup power) 
3.3.4.4.Ferry 
A ferry transportation system consists of waterfront structures, passenger 
terminals, 
fuel facilities, dispatch facilities and maintenance facilities. The inventory 
data 
required for ferry systems analysis include the geographical location, 
classification 
and replacement cost of ferry system facilities. 
3.3.4.5. Classification 
Table 3.18 describes the various classes of ferry transportation system 
components. 
For more details on how to classify these components refer to section 7.6 of 
Chapter 
7. 
Table 3.18: Ferry System Classification 
Label Description 
Water Front Structures 
FWS Ferry Waterfront Structures 
Ferry Passenger Terminals 
FPT Passenger Terminals (with all building type options enabled) 
Ferry Fuel Facility 
FFF Ferry Fuel Facility (different combinations for with or without anchored 
components and/or with or without backup power) 
Ferry Dispatch Facility 
FDF Ferry Dispatch Facility (different combinations for with or without 
anchored components and/or with or without backup power) 
Ferry Maintenance Facility 
FMF Piers and Dock Facilities (with all building type options enabled) 
3.3.5. Airports 
An airport transportation system consists of control towers, runways, terminal 
buildings, parking structures, fuel facilities, and maintenance and hangar 
facilities. 
HAZUS-MH MR3 Technical Manual 



3-38 
The inventory data required for airports analysis include the geographical 
location, 
classification and replacement cost of airport facilities. 
3.3.5.1. Classification 
Table 3.19 describes the various classes of airport system components. For more 
details on how to classify these components refer to section 7.7 of Chapter 7. 
Table 3.19: Airport System Classification 
Label Description 
Airport Control Towers 
ACT Airport Control Tower (with all building type options enabled) 
Airport Terminal Buildings 
ATB Airport Terminal Building (with all building type options enabled) 
Airport Parking Structures 
APS Airport Parking Structure (with all building type options enabled) 
Fuel Facilities 
AFF Airport Fuel Facility (different combinations for with or without anchored 
components and/or with or without backup power) 
Airport Maintenance & Hangar Facility 
AMF Airport Maintenance & Hangar Facility (with all building type options 
enabled) 
ARW Airport Runway 
Airport Facilities - Others 
AFO Gliderport, Seaport, Stolport, Ultralight or Baloonport Facilities 
AFH Heliport Facilities 
3.4. Direct Damage Data - Lifeline Utility Systems 
Lifeline utility systems include potable water, waste water, oil, natural gas, 
electric 
power and communication systems. This section describes the classification of 
lifeline utility system and their components, and data required to provide 
damage and 
loss estimates. 
3.4.1. Potable Water System 
A potable water system consists of pipelines, water treatment plants, wells, 
storage 
tanks and pumping stations. The inventory data required for potable water 
systems 
analysis include the geographical location and classification of system 
components. 
The analysis also requires the replacement cost for facilities and the repair 
cost for 
pipelines. 
3.4.1.1. Classification 
Chapter 3  Inventory Data: Collection and Classification 



3-39 
Table 3.20 describes the various classes of potable water system components. For 
more details on how to classify these components refer to section 8.1 of Chapter 
8. 
Table 3.20: Potable Water System Classification 
Label Description 
Pipelines 
PWP1 Brittle Pipe 
PWP2 Ductile Pipe 
Pumping Plants 
PPPL Large Pumping Plant ( > 50 MGD ) [different combinations for with or 
without anchored components] 
PPPM Medium Pumping Plant ( 10 to 50 MGD ) [different combinations for with 
or without anchored components] 
PPPS Small Pumping Plant ( < 10 MGD ) [different combinations for with or 
without anchored components] 
Wells 
PWE Wells 
Water Storage Tanks (Typically, 0.5 MGD to 2 MGD) 
PSTAS Above Ground Steel Tank 
PSTBC Buried Concrete Tank 
PSTGC On Ground Concrete Tank 
PSTGS On Ground Steel Tank 
PSTGW On Ground Wood Tank 
Water Treatment Plants 
PWTL Large WTP ( > 200 MGD ) [different combinations for with or without 
anchored components] 
PWTM Medium WTP ( 50-200 MGD ) [different combinations for with or without 
anchored components] 
PWTS Small WTP ( < 50 MGD ) [different combinations for with or without 
anchored components] 
3.4.2. Waste Water 
A waste water system consists of pipelines, waste water treatment plants and 
lift 
stations. The inventory data required for waste water systems analysis include 
the 
geographical location and classification of system components. The analysis also 
requires the replacement cost for facilities and the repair cost for pipelines. 
3.4.2.1. Classification 
Table 3.21 describes the various classes of waste water system components. For 
more details on how to classify these components refer to section 8.2 of Chapter 
8. 
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3-40 
Table 3.21: Waste Water System Classification 
Label Description 
Buried Pipelines 
WWP1 Brittle Pipe 
WWP2 Ductile Pipe 
Waste Water Treatment Plants 
WWTL Large WWTP ( > 200 MGD ) [different combinations for with or without 
anchored components] 
WWTM Medium WWTP ( 50-200 MGD ) [different combinations for with or 
without anchored components] 
WWTS Small WWTP ( < 50 MGD ) [different combinations for with or without 
anchored components] 
Lift Stations 
WLSL Large Lift Stations ( > 50 MGD ) [different combinations for with or 
without anchored components] 
WLSM Medium Lift Stations ( 10 MGD - 50 MGD) [different combinations for 
with or without anchored components] 
WLSS Small Lift Stations ( < 10 MGD ) [different combinations for with or 
without anchored components] 
3.4.3. Oil Systems 
An oil system consists of pipelines, refineries, pumping plants and tank farms. 
The 
inventory data required for oil systems analysis include the geographical 
location and 
classification of system components. The analysis also requires the replacement 
cost 
for facilities and the repair cost for pipelines. 
3.4.3.1. Classification 
Table 3.22 describes the various classes of oil system components. For more 
details 
on how to classify these components refer to section 8.3 of Chapter 8. 
Chapter 3  Inventory Data: Collection and Classification 



3-41 
Table 3.22: Oil System Classification 
Label Description 
Pipelines 
OIP1 Welded Steel Pipe with Gas Welded Joints 
OIP2 Welded Steel Pipe with Arc Welded Joints 
Refineries 
ORFL Large Refinery ( > 500,000 lb./day ) [different combinations for with or 
without 
anchored components] 
ORFM Medium Refinery ( 100,000 - 500,000 lb./ day) [different combinations for 
with 
or without anchored components] 
ORFS Small Refinery ( < 100,000 lb./day ) [different combinations for with or 
without 
anchored components] 
Pumping Plants 
OPP Pumping Plant [different combinations for with or without anchored 
components] 
Tank Farms 
OTF Tank Farms with Anchored Tanks [different combinations for with or without 
anchored components] 
3.4.4. Natural Gas Systems 
A natural gas system consists of pipelines and compressor stations. The 
inventory 
data required for natural gas systems analysis include the geographical location 
and 
classification of system components. The analysis also requires the replacement 
cost 
for facilities and the repair cost for pipelines. 
3.4.4.1. Classification 
Table 3.23 describes the various classes of natural gas system components. For 
more 
details on how to classify these components refer to section 8.4 of Chapter 8. 
Table 3.23: Natural Gas System Classification 
Label Description 
Buried Pipelines 
NGP1 Welded Steel Pipe with Gas Welded Joints 
NGP2 Welded Steel Pipe with Arc Welded Joints 
Compressor Stations 
NGC Compressor Stations [different combinations for with or without 
anchored components] 
HAZUS-MH MR3 Technical Manual 



3.4.5.

 Electric Power 

 

An electric power system consists of substations, distribution circuits, 
generation 
plants and transmission towers. The inventory data required for electric power 
systems analysis include the geographical location, classification and 
replacement 
cost of the facilities. 

3.4.5.1.

 Classification 

 

Table 3.24 describes the various classes of electric power system components. 
For 
more details on how to classify these components refer to section 8.5 of Chapter 
8. 

 
Table 3.24: Electric Power System Classification 

 

Label 

Description 


Transmission Substations 

ESSL 

Low Voltage (115 KV) Substation [different combinations for with or 
without anchored components] 

ESSM 

Medium Voltage (230 KV) Substation [different combinations for with or 
without anchored components] 
ESSH 

High Voltage (500 KV) Substation [different combinations for with or 
without anchored components] 

 

Distribution Circuits 

EDC 

Distribution Circuits (either Seismically Designed Components or Standard 
Components) 

 

Generation Plants 

EPPL 

Large Power Plants ( > 500 MW ) [different combinations for with or 
without anchored components] 

EPPM 

Medium Power Plants ( 100 - 500 MW ) [different combinations for with or 
without anchored components] 

EPPS 

Small Power Plants ( < 100 MW ) [different combinations for with or 
without anchored components] 

 

 

3.4.6.

 Communication 

 

In the loss estimation methodology, a communication system consists of telephone 
central offices. The inventory data required for communication systems analysis 
include the geographical location and the classification. The analysis also 
requires 
the replacement cost of the facilities. 

3.4.6.1.

 Classification 

 

Table 3.25 describes the various classes of central offices. For more details on 
how 
to classify these components refer to section 8.6 of Chapter 8. 






3-43 
Table 3.25: Communication Classification 
Label Description 
Central Offices 
CCO Central Offices (different combinations for with or without anchored 
components and/or with or without backup power) 
Stations or Transmitters 
CBR AM or FM radio stations or transmitters 
CBT TV stations or transmitters 
CBW Weather stations or transmitters 
CBO Other stations or transmitters 
3.5. Hazardous Materials Facilities 
Hazardous material facilities contain substances that can pose significant 
hazards 
because of their toxicity, radioactivity, flammability, explosiveness or 
reactivity. 
Significant casualties or property damage could occur form a small number or 
even a 
single hazardous materials release induced by an earthquake, and the consequence 
of 
an earthquake-caused release can vary greatly according to the type and quantity 
of 
substance released, meteorological conditions and timeliness and effectiveness 
of 
emergency response. Similarly to the case of critical faculties with a potential 
for 
high loss, such as large dams, the methodology does not attempt to estimate 
losses 
caused by earthquake which caused hazardous materials releases. Thus, the 
hazardous materials module of HAZUS is limited to inventory data concerning the 
location and nature of hazardous materials located at various sites. Section 
11.1.2 
describes the scheme used to define the degree of danger of hazardous materials. 
3.6. Direct Economic and Social Loss 
In this section, information related to inventory data required to determine 
direct 
economic and social loss is presented. The two main databases used to determine 
direct economic and social loss are demographic and building square footage 
databases. 
3.6.1. Demographics Data 
The census data are used to estimate direct social loss due to displaced 
households, 
casualties due to earthquakes, and the estimation quality of building space 
(square 
footage) for certain occupancy classes. The Census Bureau collects and publishes 
statistics about the people of the United States based on the constitutionally 
required 
census every 10 years, which is taken in the years ending in "0" (e.g., 1990). 
The 
HAZUS-MH MR3 Technical Manual 



Bureau's population census data describes the characteristics of the population 
including age, income, housing and ethnic origin. 

 

The census data were processed for all of the census tracts in the United 
States, and 
29 fields of direct importance to the methodology were extracted and stored. 
These 
fields are shown in Table 3.26 and are supplied as default information with the 
methodology. The population information is aggregated to a census tract level. 
Census tracts are divisions of land that are designed to contain 2500-8000 
inhabitants 
with relatively homogeneous population characteristics, economic status and 
living 
conditions. Census tract divisions and boundaries change only once every ten 
years. 
Census tract boundaries never cross county boundaries, and all the area within a 
county is contained within one or more census tracts. This characteristic allows 
for a 
unique division of land from country to state to county to census tract. Each 
Census 
tract is identified by a unique 11 digit number. The first two digits represent 
the 
tract's state, the next three digits represent the tract's county, while the 
last 6 digits 
identify the tract within the county. For example, a census tract numbered 
10050505800 would be located in Delaware (10) in Sussex County (050). 

 

 

 




3-45 
Table 3.26: Demographics Data Fields and Usage 
Module Usage 
Description of Field Shelter Casualty Occupancy 
Class 
Lifelines 
Total Population in Census Tract * * 
Total Household in Census Tract * * 
Total Number of People in General Quarter * 
Total Number of People < 16 years old * 
Total Number of People 16-65 years old * 
Total Number of People > 65 years old * 
Total Number of People - White * 
Total Number of People - Black * 
Total Number of People - Native American * 
Total Number of People - Asian * 
Total Number of People - Hispanic * 
Total # of Households with Income < $10,000 * 
Total # of Households with Income $10 - $20K * 
Total # of Households with Income $20 - $30K * 
Total # of Households with Income $30 - $40K * 
Total # of Households with Income $40 - $50K * 
Total # of Households with Income $50 - $60K * 
Total # of Households with Income $60 - $75K * 
Total # of Households with Income $75 - $100K * 
Total # of Households with Income > $100k * 
Total in Residential Property during Day * 
Total in Residential Property at Night * 
Hotel Occupants * 
Vistor Population * 
Total Working Population in Commercial Industry * 
Total Working Population in Industrial Industry * 
Total Commuting at 5 PM * 
Total Number of Students in Grade School * 
Total Number of Students in College/University * 
Total Owner Occupied - Single Household Units * * 
Total Owner Occupied - Multi-Household Units * * 
Total Owner Occupied - Multi-Household Structure * * 
Total Owner Occupied - Mobile Homes * * 
Total Renter Occupied - Single Household Units * * 
Total Renter Occupied - Multi-Household Units * * 
Total Renter Occupied - Multi-Household Structure * * 
Total Renter Occupied - Mobile Homes * * 
Total Vacant - Single Household Units * 
Total Vacant - Multi-Household Units * 
Total Vacant - Multi-Household Structure * 
Total Vacant - Mobile Homes * 
Structure Age <40 years * 
Structure Age >40 years * 
HAZUS-MH MR3 Technical Manual 



3.6.2.

 Default Occupancy Class Square Foot Inventory 

 
The default square footage estimates for occupancy classes RES1, 2,3,5, are 
based on 
census data on the number of dwelling units or the number of people for that 
occupancy class. Table 3.27 provides the conversion factors for these occupancy 
classes. These conversion factors are obtained from expert opinion and 
modifications 
to ATC-13 values. The conversion factors were also calibrated against tax 
assessors 
data for region-specific counties. The square foot estimates are calculated 
using the 
following expression: 

 

 SFI = UD * CF (3-4) 

where, 
SFI = building square footage for an occupancy class 

 
UD = unit of data for that occupancy class 
CF = conversion factor for that occupancy class (Table 3.27) 

 
The building square footage estimates for the remaining occupancy classes were 
obtained using a building square footage inventory database purchased from the 
Dun 
and Bradstreet Company (D&B). The square footage information was classified 
based on Standard Industrial Code (SIC) and provided at a census tract 
resolution. 
The SIC codes were mapped to NIBS occupancy classes using the mapping scheme 
provided in Table 3.27. There is no default information for occupancy class 
COM10. 




 

3.7. Indirect Economic Data 

 

The indirect economic data refers to the post-earthquake change in the demand 
and 
supply of products, change in employment and change in tax revenues. The user 
can 
specify the levels of potential increase in imports and exports, supply and 
product 
inventories and unemployment rates. 

 





3-47 
Table 3.27: Mapping of Standard Industrial Codes, Conversion Factors to 
Estimate Occupancy Square Footage and Square Footage Per Occupancy Class 
Source of Data 
Label Occupancy Class Census Dun and Bradstreet 
Unit of 
Data 
Conversion 
Factor 
SIC Code 
Residential 
RES1 Single Family Dwelling # of Units variable 
RES2 Mobile Home # of Units 1000 sq. t./unit 
RES3 Multi Family Dwelling # of Units 1000 sq. t./unit 
RES4 Temporary Lodging 70 
RES5 Institutional Dormitory # in Group 
Quarters 
700 sq. 
ft./person 
RES6 Nursing Home 8051, 8052, 8059 
Commercial 
COM1 Retail Trade 52, 53, 54, 55, 56, 57, 59 
COM2 Wholesale Trade 42, 50, 51 
COM3 Personal/Repair Services 72,75,76,83,88 
COM4 Prof./Technical Services 40, 41, 44, 45, 46, 47, 49, 61, 62, 63, 64, 
65, 67, 73, 78 (except 7832), 81, 87, 89 
COM5 Banks 60 
COM6 Hospital 8062, 8063, 8069 
COM7 Medical Office/Clinic 80 (except 8051, 8052, 8059, 8062, 8063, 
8069) 
COM8 Entertainment & Rec. 48, 58, 79, (except 7911), 84 
COM9 Theaters 7832, 7911 
COM10 Parking 
Industrial 
IND1 Heavy 22, 24, 26, 32, 34, 35 (except 3571, 3572), 
37 
IND2 Light 23, 25, 27, 30, 31, 36 (except 3671, 3672, 
3674), 38, 39 
IND3 Food/Drugs/Chemicals 20, 21, 28, 29 
IND4 Metals/Minerals 
Processing. 
10, 12, 13, 14, 33 
IND5 High Technology 3571, 3572, 3671, 3672, 3674 
IND6 Construction 15, 16, 17 
Agriculture 
AGR1 Agriculture 01, 02, 07, 08, 09 
Religion/Non/Profit 
REL1 Church/ N.P. Offices 86 
Government 
GOV1 General Services 43, 91, 92 (except 9221, 9224), 93, 94, 95, 
96, 97 
GOV2 Emergency Response 9221, 9224 
Education 
EDU1 Schools 82 (except 8221, 8222) 
EDU2 Colleges/Universities 8221, 8222 
HAZUS-MH MR3 Technical Manual 



3.8.

 References 

 

ATC - 13 (1985). Earthquake Damage Evaluation Data for California, Applied 
Technology Council, Redwood City, CA. 

 

FEMA, 1993. "Water Control Infrastructure, National Inventory of Dams 1992," 
FEMA 246, Federal Emergency Management Agency and U.S. Army Corps of 
Engineers, Washington, D.C., October 1993. 

 

R.S. Means (2002), Means Square Foot Costs 

 

U.S. Bureau of the Census, May 1991. Standard Tape File 1 (STF-1A). 

 

U.S. Bureau of the Census, May 1992. Standard Tape File 3 (STF-3). 

 






3-49 
APPENDIX 3A 
General Building Stock 
Table 3A.1: Distribution Percentage of Floor Area for Specific Occupancy 
Classes within each General Occupancy Class. 
General Occupancy Class 
Specific Occupancy Class 
RES COM IND AGR REL GOV EDU 
No. Label Occupancy Class 1 2 3 4 5 6 7 
1 RES1 Single Family Dwelling . 
2 RES2 Mobile Home . 
3 RES3 Multi Family Dwelling . 
4 RES4 Temporary Lodging . 
5 RES5 Institutional Dormitory . 
6 RES6 Nursing Home . 
7 COM1 Retail Trade . 
8 COM2 Wholesale Trade . 
9 COM3 Personal and Repair Services . 
10 COM4 Professional/Technical . 
11 COM5 Banks . 
12 COM6 Hospital . 
13 COM7 Medical Office/Clinic . 
14 COM8 Entertainment & Recreation . 
15 COM9 Theaters . 
16 COM10 Parking . 
17 IND1 Heavy . 
18 IND2 Light . 
19 IND3 Food/Drugs/Chemicals . 
20 IND4 Metals/Minerals Processing . 
21 IND5 High Technology . 
22 IND6 Construction . 
23 AGR1 Agriculture 100 
24 REL1 Church 100 
25 GOV1 General Services . 
26 GOV2 Emergency Response . 
27 EDU1 Schools . 
28 EDU2 Colleges/Universities . 
. The relative distribution varies by census tract and is computed directly from 
the specific 
occupancy class square footage inventory. For Agriculture (AGR) and Religion 
(REL) there 
is only one specific occupancy class, therefore the distribution is always 100%. 
HAZUS-MH MR3 Technical Manual 



3-50 
Table 3A.2: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, Pre-1950, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 36 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1L RM2L URML MH 
1 RES1 For State-Specific Res1 Distribution, Refer to Table 3A.17 
2 RES2 100 
3 RES3 73 1 1 1 6 3 3 1 9 2 
4 RES4 34 2 1 2 1 19 16 3 4 18 
5 RES5 20 5 1 1 28 18 6 21 
6 RES6 45 10 5 10 20 10 
7 COM1 22 2 6 3 20 17 1 6 23 
8 COM2 8 3 4 2 41 18 1 3 5 2 13 
9 COM3 28 1 1 3 18 7 1 8 33 
10 COM4 27 2 1 3 19 15 7 26 
11 COM5 27 2 1 3 19 15 7 26 
12 COM6 8 5 2 11 11 27 2 1 27 6 
13 COM7 25 5 2 10 10 15 2 1 20 10 
14 COM8 8 12 1 2 3 16 27 4 5 1 21 
15 COM9 5 20 7 15 20 3 10 20 
16 COM10 8 8 18 43 7 1 6 3 6 
17 IND1 3 29 13 2 2 15 14 7 1 4 2 8 
18 IND2 4 14 8 22 1 18 16 1 1 2 13 
19 IND3 1 18 8 3 3 20 22 2 3 20 
20 IND4 2 24 12 7 2 13 16 2 2 6 14 
21 IND5 21 5 5 3 35 2 10 2 15 2 
22 IND6 32 3 2 10 18 8 7 13 7 
23 AGR1 56 3 2 14 2 9 1 13 
24 REL1 22 8 2 21 15 5 8 19 
25 GOV1 9 8 1 3 4 12 42 4 6 11 
26 GOV2 45 2 37 3 13 
27 EDU1 11 6 3 3 21 21 4 9 22 
28 EDU2 2 5 10 5 15 20 20 5 18 
* Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



3-51 
Table 3A.3: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, 1950-1970 , West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 36 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1L RM2L URML MH 
1 RES1 For State-Specific Res1 Distribution, Refer to Table 3A.18 
2 RES2 100 
3 RES3 72 1 2 2 1 6 2 8 3 3 
4 RES4 55 1 2 2 2 3 11 2 18 1 3 
5 RES5 39 3 3 1 8 16 6 18 1 5 
6 RES6 70 3 1 1 5 20 
7 COM1 34 3 1 3 2 4 13 5 10 1 18 2 4 
8 COM2 12 4 5 5 3 3 18 22 1 19 4 4 
9 COM3 12 3 5 5 2 3 23 4 12 1 22 4 4 
10 COM4 34 3 3 1 2 3 17 5 3 23 4 2 
11 COM5 34 3 3 1 2 3 17 5 3 23 4 2 
12 COM6 32 5 2 4 3 16 6 28 4 
13 COM7 46 13 1 3 3 9 20 5 
14 COM8 13 17 12 3 3 13 6 30 3 
15 COM9 10 10 30 5 10 5 30 
16 COM10 5 8 20 34 5 20 6 2 
17 IND1 10 25 30 3 7 14 9 2 
18 IND2 8 5 14 17 4 10 5 22 3 12 
19 IND3 14 16 6 1 5 17 28 1 10 2 
20 IND4 18 25 9 11 10 7 15 3 2 
21 IND5 4 9 3 2 4 20 35 3 15 4 1 
22 IND6 30 1 15 7 4 20 3 20 
23 AGR1 51 4 8 12 2 10 11 2 
24 REL1 20 4 1 3 3 24 4 37 4 
25 GOV1 21 6 3 2 2 26 5 4 2 27 2 
26 GOV2 50 13 7 20 10 
27 EDU1 25 3 4 5 4 20 4 2 29 4 
28 EDU2 5 2 12 5 20 50 6 
* Refer to Table 3C.1 for states classifications. 
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Table 3A.4: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, Post-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 36 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1L RM2L URML MH 
1 RES1 For State-Specific Res1 Distribution, Refer to Table 3A.19 
2 RES2 100 
3 RES3 73 2 3 6 1 1 9 5 
4 RES4 53 3 2 3 4 13 20 2 
5 RES5 33 3 3 6 5 24 23 3 
6 RES6 70 5 5 20 
7 COM1 26 9 1 2 1 6 10 1 15 5 21 3 
8 COM2 8 4 1 3 4 2 12 41 3 19 3 
9 COM3 13 3 2 2 3 3 13 20 5 34 2 
10 COM4 35 3 2 1 3 4 15 8 3 24 2 
11 COM5 35 3 2 1 3 4 15 8 3 24 2 
12 COM6 31 6 1 1 7 4 13 7 28 2 
13 COM7 47 16 5 4 6 2 20 
14 COM8 4 23 8 1 3 2 15 4 1 32 7 
15 COM9 5 27 20 12 4 27 5 
16 COM10 8 8 6 3 49 3 13 7 3 
17 IND1 11 19 28 3 2 1 9 11 3 11 1 1 
18 IND2 3 13 9 6 3 10 41 3 12 
19 IND3 2 15 10 5 3 12 28 7 18 
20 IND4 1 26 18 5 4 1 11 1 12 5 15 1 
21 IND5 1 12 8 2 3 10 38 7 17 1 1 
22 IND6 30 4 6 11 8 16 6 14 5 
23 AGR1 40 8 11 8 3 11 1 15 1 2 
24 REL1 23 12 3 1 6 26 1 3 22 3 
25 GOV1 8 15 4 3 7 2 32 4 16 9 
26 GOV2 40 3 7 23 10 7 3 7 
27 EDU1 24 9 6 1 5 3 16 3 4 3 21 5 
28 EDU2 5 10 10 5 20 5 40 5 
* Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



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Table 3A.5: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, Pre-1950, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
3 RES3 15 4 5 1 19 25 8 23 
4 RES4 18 4 12 1 20 20 8 17 
5 RES5 16 1 5 40 20 18 
6 RES6 20 5 35 20 10 10 
7 COM1 8 6 3 21 34 11 1 16 
8 COM2 8 27 53 5 7 
9 COM3 18 22 42 5 13 
10 COM4 25 7 10 2 22 16 9 9 
11 COM5 25 7 10 2 22 16 9 9 
12 COM6 18 4 6 1 35 19 8 9 
13 COM7 20 5 5 30 20 10 10 
14 COM8 25 20 40 5 10 
15 COM9 30 10 40 10 10 
16 COM10 10 5 2 55 18 3 2 5 
17 IND1 
18 IND2 10 5 75 10 
19 IND3 32 3 1 1 14 41 3 5 
20 IND4 25 3 1 9 52 10 
21 IND5 35 10 30 5 20 
22 IND6 20 80 
23 AGR1 25 75 
24 REL1 10 90 
25 GOV1 30 15 5 3 23 10 4 10 
26 GOV2 
28 EDU2 10 20 60 3 5 2 
* Refer to Table 3C.1 for states classifications. 
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Table 3A.6: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, 1950-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occup. 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
3 RES3 10 15 6 4 37 1 21 6 
4 RES4 9 24 9 5 34 1 14 4 
5 RES5 6 1 11 9 45 18 10 
6 RES6 15 10 15 5 25 25 5 
7 COM1 7 25 5 3 31 22 7 
8 COM2 21 3 2 34 1 34 5 
9 COM3 10 3 28 54 5 
10 COM4 17 18 9 9 18 2 23 4 
11 COM5 17 18 9 9 18 2 23 4 
12 COM6 14 10 14 5 23 3 23 8 
13 COM7 15 10 15 5 25 25 5 
14 COM8 5 28 52 10 5 
15 COM9 5 30 50 10 5 
16 COM10 5 8 8 7 39 8 18 7 
17 IND1 10 20 40 20 10 
18 IND2 15 10 50 20 5 
19 IND3 11 4 10 30 20 1 15 9 
20 IND4 100 
21 IND5 10 5 13 32 30 10 
22 IND6 
23 AGR1 
24 REL1 80 10 10 
25 GOV1 15 6 15 11 28 2 18 5 
26 GOV2 5 10 10 5 60 10 
28 EDU2 20 15 5 35 15 10 
* Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



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Table 3A.7: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, Post-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
3 RES3 9 23 8 10 28 7 12 3 
4 RES4 16 28 8 11 18 3 13 3 
5 RES5 9 10 11 16 34 4 11 5 
6 RES6 25 10 15 10 35 5 
7 COM1 34 9 3 12 17 5 15 5 
8 COM2 20 17 15 10 8 15 15 
9 COM3 11 17 3 10 17 12 17 13 
10 COM4 37 10 12 9 15 3 9 5 
11 COM5 37 10 12 9 15 3 9 5 
12 COM6 25 9 15 10 33 1 6 1 
13 COM7 25 10 15 10 35 5 
14 COM8 10 90 
15 COM9 10 90 
16 COM10 4 8 3 4 66 8 6 1 
17 IND1 
18 IND2 
19 IND3 62 5 1 23 4 1 3 1 
20 IND4 100 
21 IND5 18 14 3 34 13 5 10 3 
22 IND6 
23 AGR1 
24 REL1 5 90 5 
25 GOV1 25 11 15 22 12 4 9 2 
26 GOV2 25 20 35 20 
28 EDU2 20 5 10 25 25 10 5 
* Refer to Table 3C.1 for states classifications. 
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Table 3A.8: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, High Rise, Pre-1950, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
3 RES3 39 1 2 8 24 23 3 
4 RES4 45 3 3 8 20 18 3 
5 RES5 15 5 10 30 40 
10 COM4 47 10 4 1 21 16 1 
11 COM5 47 10 4 1 21 16 1 
12 COM6 56 9 1 1 24 8 1 
13 COM7 
16 COM10 
23 AGR1 
25 GOV1 53 5 5 3 30 3 1 
28 EDU2 5 5 35 40 15 
* Refer to Table 3C.1 for states classifications. 
Table 3A.9: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, High Rise, 1950-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
3 RES3 30 21 6 13 24 3 3 
4 RES4 48 10 9 12 19 1 1 
5 RES5 20 15 25 30 5 5 
10 COM4 40 26 18 6 7 1 2 
11 COM5 40 26 18 6 7 1 2 
12 COM6 35 27 17 4 15 1 1 
13 COM7 
16 COM10 
23 AGR1 
25 GOV1 46 13 22 10 8 1 
28 EDU2 35 20 20 25 
* Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



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Table 3A.10: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, High Rise, Post-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
3 RES3 44 6 5 18 20 5 2 
4 RES4 56 10 6 16 9 2 1 
5 RES5 25 18 20 37 
10 COM4 56 10 14 14 5 1 
11 COM5 54 10 15 15 5 1 
12 COM6 45 6 19 13 17 
13 COM7 
16 COM10 
23 AGR1 
25 GOV1 52 14 14 14 6 
28 EDU2 30 10 10 50 
* Refer to Table 3C.1 for states classifications. 
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Table 3A.11: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, Mid-West* 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 36 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1 
L 
RM2 
L 
URML MH 
1 RES1 For State-Specific Res1 Distribution, Refer to Table 3A.20 
2 RES2 100 
3 RES3 75 2 23 
4 RES4 50 3 2 45 
5 RES5 20 4 13 2 22 4 2 33 
6 RES6 90 10 
7 COM1 30 2 4 11 6 7 5 5 2 28 
8 COM2 10 2 4 11 6 7 2 10 2 14 2 2 28 
9 COM3 30 2 4 11 6 7 5 5 2 28 
10 COM4 30 2 4 11 6 7 5 5 2 28 
11 COM5 30 2 4 11 6 7 5 5 2 28 
12 COM6 2 4 2 2 6 21 4 33 6 2 18 
13 COM7 30 2 4 11 6 7 5 5 2 28 
14 COM8 30 2 4 11 6 7 5 5 2 28 
15 COM9 2 6 14 8 10 4 13 2 22 4 15 
16 COM10 2 4 11 6 7 6 21 4 33 6 
17 IND1 5 10 25 13 17 2 7 2 12 2 5 
18 IND2 10 2 4 11 6 7 2 10 2 14 2 3 27 
19 IND3 10 2 4 11 6 7 2 10 2 14 2 3 27 
20 IND4 5 10 25 13 17 2 7 2 12 2 5 
21 IND5 10 2 4 11 6 7 2 10 2 14 2 2 28 
22 IND6 30 2 4 11 6 7 5 5 2 28 
23 AGR1 10 2 4 11 6 7 2 10 2 14 2 2 28 
24 REL1 30 3 5 3 4 5 5 2 2 41 
25 GOV1 15 14 21 7 6 4 3 30 
26 GOV2 14 7 17 4 12 3 43 
27 EDU1 10 5 12 5 7 11 50 
28 EDU2 14 6 12 2 8 11 10 37 
* Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



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Table 3A.12: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, Mid-West* 
Specific Model Building Type 
No. Occupancy 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
3 RES3 10 7 3 14 39 7 2 18 
4 RES4 10 7 3 14 37 2 7 2 18 
5 RES5 25 62 2 11 
6 RES6 
7 COM1 3 20 16 6 11 27 2 5 2 8 
8 COM2 7 3 14 37 2 7 3 27 
9 COM3 3 20 16 6 11 27 2 5 2 8 
10 COM4 3 20 16 6 11 27 2 5 2 8 
11 COM5 3 20 16 6 11 27 2 5 2 8 
12 COM6 3 20 16 6 12 30 2 6 5 
13 COM7 3 20 16 6 11 27 2 5 2 8 
14 COM8 3 20 16 6 11 27 2 5 2 8 
15 COM9 
16 COM10 2 14 10 4 17 43 2 8 
17 IND1 
18 IND2 7 3 14 37 2 7 3 27 
19 IND3 7 3 14 37 2 7 3 27 
20 IND4 
21 IND5 7 3 14 37 2 7 3 27 
22 IND6 
23 AGR1 7 3 14 37 2 7 3 27 
24 REL1 3 20 16 6 11 27 2 5 2 8 
25 GOV1 20 24 11 9 5 31 
26 GOV2 
28 EDU2 7 14 9 13 13 44 
* Refer to Table 3C.1 for states classifications. 
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Table 3A.13: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, High Rise, Mid-West* 
Specific Model Building Type 
No. Occup. 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
3 RES3 3 13 4 16 44 7 7 6 
4 RES4 3 13 4 16 44 7 7 6 
5 RES5 26 74 
10 COM4 7 29 9 12 32 4 4 3 
11 COM5 7 29 9 12 32 4 4 3 
12 COM6 7 29 9 13 36 2 2 2 
13 COM7 7 29 9 12 32 4 4 3 
16 COM10 5 19 6 18 52 
23 AGR1 2 6 2 16 44 11 11 8 
25 GOV1 
28 EDU2 
* Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



3-61 
Table 3A.14: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, East Coast* 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 36 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1L RM2L URML MH 
1 RES1 For State-Specific Res1 Distribution, Refer to Table 3A.21 
2 RES2 100 
3 RES3 62 3 2 2 5 4 22 
4 RES4 48 5 4 4 8 4 3 3 3 3 15 
5 RES5 7 7 6 6 17 6 3 8 6 5 5 24 
6 RES6 22 11 8 8 8 3 2 4 3 5 4 22 
7 COM1 14 20 15 5 16 3 2 2 4 2 17 
8 COM2 10 21 15 7 16 3 2 2 3 4 17 
9 COM3 25 7 5 11 5 3 2 2 6 4 30 
10 COM4 26 11 8 4 9 4 2 3 5 4 24 
11 COM5 13 13 9 13 10 5 3 2 2 5 3 22 
12 COM6 2 22 15 18 10 4 2 5 4 3 2 13 
13 COM7 24 10 7 15 8 3 2 3 4 4 20 
14 COM8 19 19 13 6 15 3 2 2 3 3 15 
15 COM9 5 20 13 12 2 16 7 2 3 3 3 2 12 
16 COM10 10 7 8 30 11 6 14 12 2 
17 IND1 5 22 15 4 2 17 7 3 3 3 3 3 13 
18 IND2 10 15 9 15 11 5 3 2 2 4 5 19 
19 IND3 7 25 18 3 19 4 2 2 2 3 2 13 
20 IND4 7 26 19 3 20 3 2 2 2 3 13 
21 IND5 5 25 17 3 2 20 7 3 3 3 2 10 
22 IND6 10 21 14 7 2 16 5 2 2 2 2 3 14 
23 AGR1 48 8 6 12 7 2 3 2 12 
24 REL1 36 4 4 3 2 2 2 7 6 34 
25 GOV1 7 24 16 3 19 5 3 2 1 3 3 13 
26 GOV2 8 16 11 4 13 8 3 2 4 3 4 5 19 
27 EDU1 13 17 13 13 5 3 2 2 5 5 22 
28 EDU2 4 18 13 14 8 3 2 4 3 5 4 22 
* Refer to Table 3C.1 for states classifications. 
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3-62 
Table 3A.15: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, East Coast* 
Specific Model Building Type 
No. Occupancy 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
3 RES3 3 4 6 3 14 13 57 
4 RES4 9 12 3 18 9 2 11 7 29 
5 RES5 7 10 3 23 11 3 12 5 26 
6 RES6 
7 COM1 23 29 2 8 5 3 5 5 20 
8 COM2 23 30 3 8 4 3 5 5 19 
9 COM3 10 13 3 5 4 11 10 44 
10 COM4 14 19 2 5 7 4 9 7 33 
11 COM5 15 21 2 6 8 5 8 6 29 
12 COM6 21 27 2 8 12 6 2 7 2 13 
13 COM7 15 20 2 5 7 4 9 6 32 
14 COM8 22 30 3 8 5 3 5 5 19 
15 COM9 
16 COM10 10 13 3 38 17 6 11 2 
17 IND1 
18 IND2 22 28 2 8 10 5 2 6 3 14 
19 IND3 25 32 3 9 6 4 4 3 14 
20 IND4 
21 IND5 24 32 3 9 9 6 5 2 10 
22 IND6 
23 AGR1 19 25 2 7 4 2 7 6 28 
24 REL1 5 9 2 4 3 12 12 53 
25 GOV1 24 30 3 9 7 5 5 3 14 
26 GOV2 
28 EDU2 17 23 2 6 10 5 2 8 4 23 
* Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



3-63 
Table 3A.16: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, High Rise, East Coast* 
Specific Model Building Type 
No. Occup. 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
3 RES3 8 21 8 34 17 2 5 5 
4 RES4 8 21 8 34 17 2 5 5 
5 RES5 6 16 6 40 20 3 5 4 
10 COM4 15 36 15 15 8 2 9 
11 COM5 15 36 15 15 8 2 9 
12 COM6 14 35 14 17 8 2 2 8 
13 COM7 15 38 15 14 8 2 8 
16 COM10 5 12 5 43 21 4 6 4 
23 AGR1 7 4 18 20 42 9 
25 GOV1 
28 EDU2 
* Refer to Table 3C.1 for states classifications. 
Table 3A.17: Distribution Percentage of Floor Area for Model Building Types 
within RES1 Building Occupancy Class, Pre-1950, West Coast 
Model Building Type 
State State State 1 9 13 19 29 34 
FIPS* Abbreviation W1 S3 S5L C2L RM1L URML 
02 AK Alaska 99 1 
04 AZ Arizona 60 25 16 
06 CA California 99 1 0 
08 CO Colorado 76 15 9 
15 HI Hawaii 92 1 4 3 
16 ID Idaho 95 3 2 
30 MT Montana 98 1 1 
35 NM New Mexico 74 16 10 
32 NV Nevada 97 2 1 
41 OR Oregon 99 1 
49 UT Utah 82 11 7 
53 WA Washington 98 1 1 
56 WY Wyoming 92 5 3 
* State FIPS are two digit unique number representative of each state and US 
territory. 
Refer to Table 3C.1 of Appendix C for a complete list of State FIPS. 
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3-64 
Table 3A.18: Distribution Percentage of Floor Area for Model Building Types 
within RES1 Building Occupancy Class, 1950-1970, West Coast 
Model Building Type 
State State State 1 9 13 19 29 34 
FIPS Abbreviation W1 S3 S5L C2L RM1L URML 
02 AK Alaska 99 1 
04 AZ Arizona 60 36 4 
06 CA California 99 1 0 
08 CO Colorado 76 21 3 
15 HI Hawaii 92 1 6 1 
16 ID Idaho 95 4 1 
30 MT Montana 98 2 
35 NM New Mexico 74 23 3 
32 NV Nevada 97 3 
41 OR Oregon 99 1 
49 UT Utah 82 16 2 
53 WA Washington 98 2 
56 WY Wyoming 92 7 1 
Table 3A.19: Distribution Percentage of Floor Area for Model Building Types 
within RES1 Building Occupancy Class, Post-1970, West Coast 
Model Building Type 
State State State 1 9 13 19 29 34 
FIPS Abbreviation W1 S3 S5L C2L RM1L URML 
02 AK Alaska 99 1 
04 AZ Arizona 60 40 
06 CA California 99 1 0 
08 CO Colorado 76 24 
15 HI Hawaii 92 1 7 
16 ID Idaho 95 5 
30 MT Montana 98 2 
35 NM New Mexico 74 26 
32 NV Nevada 97 3 
41 OR Oregon 99 1 
49 UT Utah 82 18 
53 WA Washington 98 2 
56 WY Wyoming 92 8 
Chapter 3  Inventory Data: Collection and Classification 



3-65 
Table 3A.20: Distribution Percentage of Floor Area for Model Building Types 
within RES1 Building Occupancy Class, Mid-West 
Model Building Type 
State State State 1 19 34 
FIPS Abbreviation W1 C2L URML 
05 AR Arkansas 87 13 
19 IA Iowa 92 8 
17 IL Illinois 77 1 22 
18 IN Indiana 80 20 
20 KS Kansas 91 9 
21 KY Kentucky 88 12 
22 LA Louisiana 89 11 
26 MI Michigan 86 14 
27 MN Minnesota 95 1 4 
29 MO Missouri 76 24 
28 MS Mississippi 94 6 
38 ND North Dakota 98 2 
31 NE Nebraska 89 1 10 
39 OH Ohio 76 24 
40 OK Oklahoma 71 29 
46 SD South Dakota 97 3 
47 TN Tennessee 90 10 
48 TX Texas 100 
55 WI Wisconsin 90 10 
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3-66 
Table 3A.21: Distribution Percentage of Floor Area for Model Building Types 
within RES1 Building Occupancy Class, East Coast 
Model Building Type 
State State State 1 19 34 
FIPS Abbreviation W1 C2L URML 
01 AL Alabama 95 5 
09 CT Connecticut 96 4 
11 DC District of Columbia 21 3 76 
10 DE Delaware 71 1 28 
12 FL Florida 25 5 70 
13 GA Georgia 93 7 
25 MA Massachusetts 96 4 
24 MD Maryland 71 1 28 
23 ME Maine 99 1 
37 NC North Carolina 90 10 
33 NH New Hampshire 97 1 2 
34 NJ New Jersey 91 9 
36 NY New York 85 1 14 
42 PA Pennsylvania 66 34 
44 RI Rhode Island 98 2 
45 SC South Carolina 92 8 
51 VA Virginia 75 25 
50 VT Vermont 96 2 2 
54 WV West Virginia 72 28 
Chapter 3  Inventory Data: Collection and Classification 



3-67 
APPENDIX 3B 
Essential Facilities 
Table 3B.1: Distribution Percentage of Floor Area for Specific Occupancy 
Classes within each General Occupancy Class 
General Occupancy Class 
Specific Occupancy Class Medical Care Emergency Response Schools 
No. Label Occupancy Class 1 2 3 
1 EFHS Small Hospital X 
2 EFHM Medium Hospital X 
3 EFHL Large Hospital X 
4 EFMC Medical Clinics X 
5 EFFS Fire Station X 
6 EFPS Police Station X 
7 EFEO Emergency 
Operation Centers 
X 
8 EFS1 Grade Schools X 
9 EFS2 Colleges/ 
Universities 
X 
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Table 3B.2: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, Pre-1950, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1L RM2L URML 
1 EFHS 8 5 2 11 11 27 2 1 27 6 
2 EFHM 8 5 2 11 11 27 2 1 27 6 
3 EFHL 8 5 2 11 11 27 2 1 27 6 
4 EFMC 8 5 2 11 11 27 2 1 27 6 
5 EFFS 45 2 37 3 13 
6 EFPS 45 2 37 3 13 
7 EFEO 45 2 37 3 13 
8 EFS1 11 6 3 3 21 21 4 9 22 
9 EFS2 2 5 10 5 15 20 20 5 18 
Table 3B.3: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, 1950-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1L RM2L URML 
1 EFHS 32 5 2 4 3 16 6 28 4 
2 EFHM 32 5 2 4 3 16 6 28 4 
3 EFHL 32 5 2 4 3 16 6 28 4 
4 EFMC 32 5 2 4 3 16 6 28 4 
5 EFFS 50 13 7 20 10 
6 EFPS 50 13 7 20 10 
7 EFEO 50 13 7 20 10 
8 EFS1 25 3 4 5 4 20 4 2 29 4 
9 EFS2 5 2 12 5 20 50 6 
* Refer to Table 3C.1 for states classifications. 
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Table 3B.4: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, Post-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1L RM2L URML 
1 EFHS 31 6 1 1 7 4 13 7 28 2 
2 EFHM 31 6 1 1 7 4 13 7 28 2 
3 EFHL 31 6 1 1 7 4 13 7 28 2 
4 EFMC 31 6 1 1 7 4 13 7 28 2 
5 EFFS 40 3 7 23 10 7 3 7 
6 EFPS 40 3 7 23 10 7 3 7 
7 EFEO 40 3 7 23 10 7 3 7 
8 EFS1 24 9 6 1 5 3 16 3 4 3 21 5 
9 EFS2 5 10 10 5 20 5 40 5 
Table 3B.5: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, Pre-1950, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
1 EFHS 18 4 6 1 35 19 8 9 
2 EFHM 18 4 6 1 35 19 8 9 
3 EFHL 18 4 6 1 35 19 8 9 
4 EFMC 18 4 6 1 35 19 8 9 
5 EFFS 
6 EFPS 
7 EFEO 
9 EFS2 10 20 60 3 5 2 
* Refer to Table 3C.1 for states classifications. 
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Table 3B.6: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, 1950-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
1 EFHS 14 10 14 5 23 3 23 8 
2 EFHM 14 10 14 5 23 3 23 8 
3 EFHL 14 10 14 5 23 3 23 8 
4 EFMC 14 10 14 5 23 3 23 8 
5 EFFS 5 10 10 5 60 10 
6 EFPS 5 10 10 5 60 10 
7 EFEO 5 10 10 5 60 10 
9 EFS2 20 15 5 35 15 10 
Table 3B.7: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, Post-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
1 EFHS 25 9 15 10 33 1 6 1 
2 EFHM 25 9 15 10 33 1 6 1 
3 EFHL 25 9 15 10 33 1 6 1 
4 EFMC 25 9 15 10 33 1 6 1 
5 EFFS 25 20 35 20 
6 EFPS 25 20 35 20 
7 EFEO 25 20 35 20 
9 EFS2 20 5 10 25 25 10 5 
* Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



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Table 3B.8: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, High Rise, Pre-1950, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
1 EFHS 56 9 1 1 24 8 1 
2 EFHM 56 9 1 1 24 8 1 
3 EFHL 56 9 1 1 24 8 1 
4 EFMC 56 9 1 1 24 8 1 
9 EFS2 5 5 35 40 15 
Table 3B.9: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, High Rise, 1950-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
1 EFHS 35 27 17 4 15 1 1 
2 EFHM 35 27 17 4 15 1 1 
3 EFHL 35 27 17 4 15 1 1 
4 EFMC 35 27 17 4 15 1 1 
9 EFS2 35 20 20 25 
Table 3B.10: Distribution Percentage of Floor Area, for Model Building Types 
within Each Building Occupancy Class, High Rise, Post-1970, West Coast* 
(after ATC-13, 1985) 
Specific Model Building Type 
No. Occupancy 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
1 EFHS 45 6 19 13 17 
2 EFHM 45 6 19 13 17 
3 EFHL 45 6 19 13 17 
4 EFMC 45 6 19 13 17 
9 EFS2 30 10 10 50 
* Refer to Table 3C.1 for states classifications. 
HAZUS-MH MR3 Technical Manual 



3-72 
Table 3B.11: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, Mid-West* 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1L RM2L URML 
1 EFHS 30 2 4 11 6 7 5 5 2 28 
2 EFHM 2 4 2 2 6 21 4 33 6 2 18 
3 EFHL 2 4 2 2 6 21 4 33 6 2 18 
4 EFMC 30 2 4 11 6 7 5 5 2 28 
5 EFFS 14 7 17 4 12 3 43 
6 EFPS 14 7 17 4 12 3 43 
7 EFEO 14 7 17 4 12 3 43 
8 EFS1 10 5 12 5 7 11 50 
9 EFS2 14 6 12 2 8 11 10 37 
Table 3B.12: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, Mid-West* 
Specific Model Building Type 
No. Occupancy 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
1 EFHS 3 20 16 6 11 27 2 5 2 8 
2 EFHM 3 20 16 6 12 30 2 6 5 
3 EFHL 3 20 16 6 12 30 2 6 5 
4 EFMC 3 20 16 6 11 27 2 5 2 8 
5 EFFS 
6 EFPS 
7 EFEO 
9 EFS2 7 14 9 13 13 44 
* Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



3-73 
Table 3B.13: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, High Rise, Mid-West* 
Specific Model Building Type 
No. Occupancy 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
1 EFHS 7 29 9 12 32 4 4 3 
2 EFHM 7 29 9 13 36 2 2 2 
3 EFHL 7 29 9 13 36 2 2 2 
4 EFMC 7 29 9 12 32 4 4 3 
7 EFEO 
9 EFS2 
Table 3B.14: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Low Rise, East Coast* 
Specific Model Building Type 
No. Occup. 1 2 3 6 9 10 13 16 19 22 25 26 29 31 34 
Class W1 W2 S1L S2L S3 S4L S5L C1L C2L C3L PC1 PC2L RM1L RM2L URML 
1 EFHS 24 10 7 15 8 3 2 3 4 4 20 
2 EFHM 2 22 15 18 10 4 2 5 4 3 2 13 
3 EFHL 2 22 15 18 10 4 2 5 4 3 2 13 
4 EFMC 24 10 7 15 8 3 2 3 4 4 20 
5 EFFS 8 16 11 4 13 8 3 2 4 3 4 5 19 
6 EFPS 8 16 11 4 13 8 3 2 4 3 4 5 19 
7 EFEO 8 16 11 4 13 8 3 2 4 3 4 5 19 
8 EFS1 13 17 13 13 5 3 2 2 5 5 22 
9 EFS2 4 18 13 14 8 3 2 4 3 5 4 22 
* Refer to Table 3C.1 for states classifications. 
HAZUS-MH MR3 Technical Manual 



3-74 
Table 3B.15: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, Mid Rise, East Coast* 
Specific Model Building Type 
No. Occupancy 4 7 11 14 17 20 23 27 30 32 35 
Class S1M S2M S4M S5M C1M C2M C3M PC2M RM1M RM2M URMM 
1 EFHS 15 20 2 5 7 4 9 6 32 
2 EFHM 21 27 2 8 12 6 2 7 2 13 
3 EFHL 21 27 2 8 12 6 2 7 2 13 
4 EFMC 15 20 2 5 7 4 9 6 32 
5 EFFS 
6 EFPS 
7 EFEO 
9 EFS2 17 23 2 6 10 5 2 8 4 23 
Table 3B.16: Distribution Percentage of Floor Area for Model Building Types 
within Each Building Occupancy Class, High Rise, East Coast* 
Specific Model Building Type 
No. Occupancy 5 8 12 15 18 21 24 28 33 
Class S1H S2H S4H S5H C1H C2H C3H PC2H RM2H 
1 EFHS 15 38 15 14 8 2 8 
2 EFHM 14 35 14 17 8 2 2 8 
3 EFHL 14 35 14 17 8 2 2 8 
4 EFMC 15 38 15 14 8 2 8 
7 EFEO 
9 EFS2 
 Refer to Table 3C.1 for states classifications. 
Chapter 3  Inventory Data: Collection and Classification 



3-75 
APPENDIX 3C 
States Classifications 
Table 3C.1: Regional Distribution of States 
State Fips State 
Abbreviation 
State Name Group 
02 AK Alaska West 
01 AL Alabama East 
05 AR Arkansas Mid-West 
04 AZ Arizona West 
06 CA California West 
08 CO Colorado West 
09 CT Connecticut East 
11 DC District of Columbia East 
10 DE Delaware East 
12 FL Florida East 
13 GA Georgia East 
15 HI Hawaii West 
19 IA Iowa Mid-West 
16 ID Idaho West 
17 IL Illinois Mid-West 
18 IN Indiana Mid-West 
20 KS Kansas Mid-West 
21 KY Kentucky Mid-West 
22 LA Louisiana Mid-West 
25 MA Massachusetts East 
24 MD Maryland East 
23 ME Maine East 
26 MI Michigan Mid-West 
27 MN Minnesota Mid-West 
29 MO Missouri Mid-West 
28 MS Mississippi Mid-West 
30 MT Montana West 
37 NC North Carolina East 
38 ND North Dakota Mid-West 
31 NE Nebraska Mid-West 
33 NH New Hampshire East 
34 NJ New Jersey East 
35 NM New Mexico West 
32 NV Nevada West 
36 NY New York East 
39 OH Ohio Mid-West 
40 OK Oklahoma Mid-West 
41 OR Oregon West 
42 PA Pennsylvania East 
44 RI Rhode Island East 
HAZUS-MH MR3 Technical Manual 



Table 3C.1(cont.): Regional Distribution of States 

State Fips State 
Abbreviation 
State Name Group 
45 SC South Carolina East 
46 SD South Dakota Mid-West 
47 TN Tennessee Mid-West 
48 TX Texas Mid-West 
49 UT Utah West 
51 VA Virginia East 
50 VT Vermont East 
53 WA Washington West 
55 WI Wisconsin Mid-West 
54 WV West Virginia East 
56 WY Wyoming West 
60 AS American Samoa West 
66 GU Guam West 
69 MR Northern Mariana Islands West 
72 PR Puerto Rico East 
78 VI Virgin Islands East 

Chapter 3  Inventory Data: Collection and Classification 




Chapter 4 

Potential Earth Science Hazards (PESH) 

 

 

Potential earth science hazards (PESH) include ground motion, ground failure 
(i.e., 
liquefaction, landslide and surface fault rupture) and tsunami/seiche. Methods 
for 
developing estimates of ground motion and ground failure are discussed in the 
following 
sections. Tsunami/seiche can be included in the Methodology in the form of user-
supplied inundation maps as discussed in Chapter 9. The Methodology, 
highlighting the 
PESH component, is shown in Flowchart 4.1. 

 

4.1 Ground Motion 

 

4.1.1 Introduction 

 

Ground motion estimates are generated in the form of GIS-based contour maps and 
location-specific seismic demands stored in relational databases. Ground motion 
is 
characterized by: (1) spectral response, based on a standard spectrum shape, (2) 
peak 
ground acceleration and (3) peak ground velocity. The spatial distribution of 
ground 
motion can be determined using one of the following methods or sources: 

 

 Deterministic ground motion analysis (Methodology calculation) 

 USGS probabilistic ground motion maps (maps supplied with HAZUS-MH) 

 Other probabilistic or deterministic ground motion maps (user-supplied maps) 

 

Deterministic seismic ground motion demands are calculated for user-specified 
scenario 
earthquakes (Section 4.1.2.1). For a given event magnitude, attenuation 
relationships 
(Section 4.1.2.3) are used to calculate ground shaking demand for rock sites 
(Site Class 
B), which is then amplified by factors (Section 4.1.2.5) based on local soil 
conditions 
when a soil map is supplied by the user. The attenuation relationships provided 
with the 
Methodology include eight (8) attenuations relationships for the Western United 
States 
(WUS) and four (4) for the Central and Eastern United States (CEUS). 

 

In the Methodologys probabilistic analysis procedure, the ground shaking demand 
is 
characterized by spectral contour maps developed by the United States Geological 
Survey (USGS) as part of 2002 update of the National Seismic Hazard Maps 
(Frankel et. 
al, 2002). The Methodology includes maps for eight probabilistic hazard levels: 
ranging 
from ground shaking with a 39% probability of being exceeded in 50 years (100 
year 
return period) to the ground shaking with a 2% probability of being exceeded in 
50 years 
(2500 year return period). The USGS maps describe ground shaking demand for rock 
(Site Class B) sites, which the Methodology amplifies based on local soil 
conditions. 

 




8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Flowchart 4.1: Ground Motion and Ground Failure Relationship to other Modules 
of the Earthquake Loss Estimation Methodology 

 

 

User-supplied peak ground acceleration (PGA) and spectral acceleration contour 
maps 
may also be used with HAZUS-MH (Section 4.1.2.1). In this case, the user must 
provide 
all contour maps in a pre-defined digital format (as specified in the Users 
Manual). As 
stated in Section 4.1.2.1, the Methodology assumes that user-supplied maps 
include soil 
amplification. 

 

4.1.1.1 Form of Ground Motion Estimates / Site Effects 




 

Ground motion estimates are represented by: (1) contour maps and (2) location-
specific 
values of ground shaking demand. For computational efficiency and improved 
accuracy, 
earthquake losses are generally computed using location-specific estimates of 
ground 
shaking demand. For general building stock the analysis has been simplified so 
that 
ground motion demand is computed at the centroid of a census tract. However, 
contour 
maps are also developed to provide pictorial representations of the variation in 
ground 
motion demand within the study region. When ground motion is based on either 
USGS 
or user-supplied maps, location-specific values of ground shaking demand are 
interpolated between PGA, PGV or spectral acceleration contours, respectively. 

 

Elastic response spectra (5% damping) are used by the Methodology to 
characterize 
ground shaking demand. These spectra all have the same standard format defined 
by a 
PGA value (at zero period) and spectral response at a period of 0.3 second 
(acceleration 
domain) and spectral response at a period of 1.0 second (velocity domain). 
Ground 
shaking demand is also defined by peak ground velocity (PGV). 

 

4.1.1.2 Input Requirements and Output Information 

 

For computation of ground shaking demand, the following inputs are required: 

 

 Scenario Basis - The user must select the basis for determining ground shaking 
demand from one of three options: (1) a deterministic calculation, (2) 
probabilistic 
maps, supplied with the Methodology, or (3) user-supplied maps. For 
deterministic 
calculation of ground shaking, the user specifies a scenario earthquake 
magnitude and 
location. In some cases, the user may also need to specify certain source 
attributes 
required by the attenuation relationships supplied with the Methodology. 

 

 Attenuation Relationship - For deterministic calculation of ground shaking, 
the user 
selects an appropriate attenuation relationship from those supplied with the 
Methodology. Attenuation relationships are based on the geographic location of 
the 
study region (Western United States vs. Central Eastern United States) and on 
the 
type of fault for WUS sources. WUS regions include locations in, or west of, the 
Rocky Mountains, Hawaii and Alaska. Figure 4-1 shows the regional separation of 
WUS and CEUS locations as defined by USGS in the development of the National 
Seismic Hazard Maps. For WUS sources, the attenuation functions predict ground 
shaking based on source type, including: (1) strike-slip (SS) faults, (2) 
reverse-slip 
(R) faults, (3) normal (N) faults (4) Interface (IF) events and (5) Interslab 
(IS) events. 
The Methodology provides combinations of attenuation functions for the WUS and 
CEUS, respectively, based on the work by the USGS (Frankel et al., 2002, 
Frankel, 
1997, Klein et al., 1998). 

 




WUSCEUS 


 

Figure 4.1 Boundaries Between WUS and CEUS Locations. 

 

 Soil Map - The user may supply a detailed soil map to account for local site 
conditions. This map must identify soil type using a scheme that is based on, or 
can 
be related to, the site class definitions of the 1997 NEHRP Provisions (Section 
4.1.2.4), and must be in pre-defined digital format (as specified in the Users 
Manual). In the absence of a soil map, HAZUS-MH will amplify the ground motion 
demand assuming Site Class D soil at all sites. The user can also modify the 
assumed 
Site Class soil type for all sites by modifying the analysis parameters in 
HAZUS-MH 
(i.e. change the Site Class from D to A,B,C or E). 

 

4.1.2 Description of Methods 

 

The description of the methods for calculating ground shaking is divided into 
five 
separate areas: 

 
Basis for ground shaking (Section 4.1.2.1) 


 
Standard shape of response spectra (Section 4.1.2.2) 


 
Attenuation of ground shaking (Section 4.1.2.3) 


 
Distance measurement used with attenuation relationships (Section 4.1.2.4) 


 
Amplification of ground shaking - local site conditions (Section 4.1.2.5) 







4.1.2.1 Basis for Ground Shaking 

 

The methodology supports three options as the basis for ground shaking: 

 

 Deterministic calculation of scenario earthquake ground shaking 

 Probabilistic seismic hazard maps (USGS) 

 User-supplied seismic hazard maps 

 

Deterministic Calculation of Scenario Earthquake Ground Shaking 

For deterministic calculation of the scenario event, the user specifies the 
location (e.g., 
epicenter) and magnitude of the scenario earthquake. The Methodology provides 
three 
options for selection of an appropriate scenario earthquake location. The user 
can either: 
(1) specify an event based on a database of WUS seismic sources (faults), (2) 
specify an 
event based on a database of historical earthquake epicenters, or (3) specify an 
event 
based on an arbitrary choice of the epicenter. These options are described 
below. 

 

Seismic Source Database (WUS Fault Map) 

For the WUS, the Methodology provides a database of seismic sources (fault 
segments) 
developed by the USGS, the California Department of Mines and Geology (CDMG) and 
the Nevada Bureau of Mines and Geology (NBMG). The user accesses the database 
map 
(using HAZUS-MH) and selects a magnitude and epicenter on one of the identified 
fault 
segments. The database includes information on fault segment type, location, 
orientation 
and geometry (e.g., depth, width and dip angle), as well as on each fault 
segments 
seismic potential (e.g., maximum moment). 

 

The Methodology computes the expected values of surface and subsurface fault 
rupture 
length. Fault rupture length is based on the relationship of Wells and 
Coppersmith 
(1994) given below: 

 

()Mb + a = Llog10 (4-1) 

 

where: L is the rupture length (km) 

 M is the moment magnitude of the earthquake 

 

Table 4.1 Regression Coefficients of Fault Rupture Relationship of Wells and 
Coppersmith (1994) 

Rupture Type 

Fault Type 

a 

b 

Surface 

Strike Slip 

-3.55 

0.74 

 

Reverse 

-2.86 

0.63 

 

All 

-3.22 

0.69 

Subsurface 

Strike Slip 

-2.57 

0.62 

 

Reverse 

-2.42 

0.58 

 

All 

-2.44 

0.59 



 




Fault rupture is assumed to be of equal length on each side of the epicenter, 
provided the 
calculated rupture length is available in both directions along the specified 
fault segment. 
If the epicenter location is less than one-half of the rupture length from an 
end point of 
the fault segment (e.g., the epicenter is located at or near an end of the fault 
segment), 
then fault rupture length is truncated so that rupture does not extend past the 
end of the 
fault segment. If the calculated rupture length exceeds the length of the fault 
segment, 
then the entire fault segment is assumed to rupture between its end points. 

 

Historical Earthquake Database (Epicenter Map) 

The Methodology software provides a database of historical earthquakes developed 
from 
three sources (Composite Earthquake Catalog, 2002, Earthquake Data Base, 2002, 
Earthquake Seismicity Catalog, 1996) and contains over 8,000 records. The 
database has 
been sorted to remove historical earthquakes with magnitudes less than 5.0. The 
user 
accesses the database via HAZUS-MH and selects a historical earthquake epicenter 
which includes location, depth and magnitude information. 

 

For the WUS, the attenuation relationships require the user to specify the type, 
dip angle 
and orientation of the fault associated with the selected epicenter. The 
Methodology 
computes the expected values of surface and subsurface fault rupture length 
using 
Equation (4-1). Fault rupture is assumed to be of equal length on each side of 
the 
epicenter. For the CEUS, the attenuation relationships utilize the epicenter 
location and 
depth. 

 

Arbitrary Event 

Under this option, the user specifies a scenario event magnitude and arbitrary 
epicenter 
(using HAZUS-MH). For the WUS, the user must also supply the type, dip angle and 
orientation of the fault associated with the arbitrary epicenter. The 
Methodology 
computes the fault rupture length based on Equation (4-1) and assumes fault 
rupture to be 
of equal length on each side of the epicenter. For the CEUS the user must supply 
the 
depth of the hypocenter. 

 

Probabilistic Seismic Hazard Maps (USGS) 

The Methodology includes probabilistic seismic hazard contour maps developed by 
the 
USGS for the 2002 update of the National Seismic Hazard Maps (Frankel et al., 
2002). 
The USGS maps provide estimates of PGA and spectral acceleration at periods of 
0.3 
second and 1.0 second, respectively. Ground shaking estimates are available for 
eight 
hazard levels: ranging from the ground shaking with a 39% probability of being 
exceeded 
in 50 years to ground shakeing with a 2% probability of being exceeded in 50 
years. In 
terms of mean return periods, the hazard levels range from 100 years to 2500 
years. 

 

User-Supplied Seismic Hazard Maps 

The Methodology allows the user to supply PGA and spectral acceleration contour 
maps 
of ground shaking in a pre-defined digital format (as specified in the HAZUS-MH 
Users Manual). This option permits the user to develop a scenario event that 
could not 
be described adequately by the available attenuation relationships, or to 
replicate 
historical earthquakes (e.g., 1994 Northridge Earthquake). The maps of PGA and 




spectral acceleration (periods of 0.3 and 1.0 second) must be provided. The 
Methodology software assumes these ground motion maps include soil 
amplification, 
thus no soil map is required. 

 

If only PGA contour maps are available, the user can develop the other required 
maps 
based on the spectral acceleration response factors given in Table 4.2. 

 

 

4.1.2.2 Standard Shape of the Response Spectra 

 

The Methodology characterizes ground shaking using a standardized response 
spectrum 
shape, as shown in Figure 4.2. The standardized shape consists of four parts: 
peak 
ground acceleration (PGA), a region of constant spectral acceleration at periods 
from 
zero seconds to TAV (seconds), a region of constant spectral velocity at periods 
from TAV 
to TVD (seconds) and a region of constant spectral displacement for periods of 
TVD and 
beyond. 

 

In Figure 4.2, spectral acceleration is plotted as a function of spectral 
displacement 
(rather than as a function of period). This is the format of response spectra 
used for 
evaluation of damage to buildings (Chapter 5) and essential facilities (Chapter 
6). 
Equation (4-2) may be used to convert spectral displacement (inches), to period 
(seconds) 
for a given value of spectral acceleration (units of g), and Equation (4-3) may 
be used to 
convert spectral acceleration (units of g) to spectral displacement (inches) for 
a given 
value of period. 

 

 
ADSS = T32.0 (4-2) 

 

 (4-3) 28.9TS = SAD

 

The region of constant spectral acceleration is defined by spectral acceleration 
at a period 
of 0.3 second. The constant spectral velocity region has spectral acceleration 
proportional to 1/T and is anchored to the spectral acceleration at a period of 
1 second. 
The period, TAV, is based on the intersection of the region of constant spectral 
acceleration and constant spectral velocity (spectral acceleration proportional 
to 1/T). 
The value of TAV varies depending on the values of spectral acceleration that 
define these 
two intersecting regions. The constant spectral displacement region has spectral 
acceleration proportional to 1/T2 and is anchored to spectral acceleration at 
the period, 
TVD, where constant spectral velocity transitions to constant spectral 
displacement. 

 




The period, TVD, is based on the reciprocal of the corner frequency, fc, which 
is 
proportional to stress drop and seismic moment. The corner frequency is 
estimated in 
Joyner and Boore (1988) as a function of moment magnitude (M). Using Joyner and 
Boores formulation, the period TVD, in seconds, is expressed in terms of the 
earthquakes 
moment magnitude as shown by the following Equation (4-4): 

 

25)(McVD10 = 1/fT-
= (4-4) 

 

When the moment magnitude of the scenario earthquake is not known (e.g., when 
using 
USGS maps or user-supplied maps), the period TVD is assumed to be 10 seconds 
(i.e., 
moment magnitude is assumed to be M = 7.0). 

 

 

Figure 4.2 Standardized Response Spectrum Shape 

 

Using a standard response spectrum shape simplifies calculation of response 
needed in 
estimating damage and loss. In reality, the shape of the spectrum will vary 
depending on 
whether the earthquake occurs in the WUS or CEUS, whether it is a large or 
moderate 
size event and whether the site is near or far from the earthquake source. 
However, the 
differences between the shape of an actual spectrum and the standard spectrum 
tend to be 
significant only at periods less than 0.3 second and at periods greater than 
TVD, which do 
not significantly affect the Methodologys estimation of damage and loss. 

 

The standard response spectrum shape (with adjustment for site amplification) 
represents 
all site/source conditions, except for site/source conditions that have strong 
amplification 
at periods beyond 1 second. Although relatively rare, strong amplification at 
periods 
beyond 1 second can occur. For example, strong amplification at a period of 
about 2 

00.10.20.30.40.50.60.70.80.911.11.21.31.41.50123456789101112131415Spectral 
Displacement (inches)
Spectral Acceleration (g's)
Standard Shape - Site Class B 
Typical Shape - Site Class B (WUS)
0.3 sec.
1.0 sec.
TAVTVDSA (Velocity Domain) a 1/T 
PGA



seconds caused extensive damage and loss to taller buildings in parts of Mexico 
City 
during the 1985 Michoacan earthquake. In this case, the standard response 
spectrum 
shape would tend to overestimate short-period spectral acceleration and to 
underestimate 
long-period (i.e., greater than 1-second) spectral acceleration. 

 

Inferred Ground Shaking Hazard Information 

Certain ground shaking hazard information is inferred from other ground shaking 
hazard 
information when complete hazard data is not available. Inferred data includes 
the 
following: 

 

 
Peak ground velocity (PGV) is inferred from 1-second spectral acceleration 
response 


 
Spectral acceleration response is inferred from the peak ground acceleration 
(PGA) 


 
0.3-second spectral acceleration response is inferred from 0.2-second response 




 

PGV Inferred from 1-Second Spectral Response 

Unless supplied by the user (i.e., as user-supplied PGV maps), peak ground 
velocity 
(inches per second) is inferred from 1-second spectral acceleration, SA1 (units 
of g), using 
Equation (4-5). 

 

65.1/
24.3861..
.
..
.=ASPGVp 
(4-5) 

 

The factor of 1.65 in the denominator of Equation (4-5) represents the 
amplification 
assumed to exist between peak spectral response and PGV. This factor is based on 
the 
median spectrum amplification, as given in Table 2 of Newmark and Hall (1982) 
for a 
5%-damped system whose period is within the velocity-domain region of the 
response 
spectrum. 

 

Spectral Acceleration Response Inferred from Peak Ground Acceleration (PGA) 

 

When a user has maps of PGA only, spectral acceleration for the short periods, 
SAS, 
maps are developed from PGA, and spectral acceleration for the long period, SAL, 
is 
inferred from short period spectral acceleration, SAS, based on the factors 
given in Table 
4.2 for WUS and CEUS rock (Site Class B) locations. 

 

The factors given in Table 4.2 are based on the combination attenuation 
functions for 
WUS and CEUS events (Section 4.1.2.3). These factors distinguish between small-
magnitude and large-magnitude events and between sites that are located at 
different 
distances (i.e. CUES: distance to hypocenter and WUS: distance to fault rupture 
plane). 
The ratios of SAS/SAL and SAS/PGA define the standard shape of the response 
spectrum 
for each of the magnitude/distance combinations of Table 4.2. 

 

Table 4.2 requires magnitude and distance information to determine spectrum 
amplification factors. This information would likely be available for maps of 
observed 
earthquake PGA, or scenario earthquake PGA, but is not available for 
probabilistic maps 




of PGA, since these maps are aggregated estimates of seismic hazard due to 
different 
event magnitudes and sources. 

 

 

Table 4.2 Spectral Acceleration Response Factors 

 

Western United States (WUS)  Rock (Site Class B) 

Distance (km) 

SAS/PGA given Magnitude, M: 

SAS/SAL given Magnitude, M: 

5 

6 

7 

7.5 

5 

6 

7 

7.5 

10 km 

1.5 

1.8 

1.9 

1.9 

4.5 

2.8 

1.9 

1.6 

25 km 

1.5 

1.8 

1.9 

1.9 

4.8 

3.1 

2.1 

1.8 

50 km 

1.4 

1.8 

1.9 

1.9 

4.5 

2.9 

2.0 

1.7 

75 km 

1.4 

1.8 

1.9 

1.8 

4.3 

2.8 

1.8 

1.6 

Central and Eastern United States (CEUS)  Rock (Site Class B) 

10 km 

0.8 

1.1 

1.4 

1.7 

7.7 

4.2 

3.0 

2.7 

25 km 

0.9 

1.2 

1.4 

1.5 

6.9 

4.0 

2.9 

2.6 

50 km 

1.0 

1.4 

1.6 

1.7 

5.2 

3.8 

2.7 

2.4 

75 km 

1.2 

1.5 

1.7 

1.8 

9.2 

3.5 

2.6 

2.4 



 

 

0.3-Second Spectral Acceleration Response Inferred from 0.2-Second Response 

Some of the probabilistic maps developed by the USGS (Frankel et al. 2002), 
estimate 
short-period spectral response for a period of 0.2 second. Spectral response at 
a period of 
0.3 second is calculated by dividing 0.2-second response by a factor of 1.1 for 
WUS 
locations and by dividing 0.2-second response by a factor of 1.4 for CEUS 
locations. 

 

The factors describing the ratio of 0.2-second and 0.3-second response are based 
on the 
default combinations of WUS and CEUS attenuation functions, described in the 
next 
section, and the assumption that large-magnitude events tend to dominate seismic 
hazard 
at most WUS locations and that small-magnitude events tend to dominate seismic 
hazard 
at most CEUS locations. 

 

 

4.1.2.3 Attenuation of Ground Shaking 

 

Ground shaking is attenuated with distance from the source using relationships 
provided 
with the Methodology. These relationships define ground shaking for rock (Site 
Class B) 
conditions based on earthquake magnitude, M, and other parameters. These 
relationships 
are used to estimate PGA and spectral demand at 0.3 and 1.0 seconds, and with 
the 
standard response spectrum shape (described in Section 4.1.2.2) fully define 5%-
damped 
demand spectra at a given location. 

 




The Methodology provides eight WUS and five CEUS attenuation functions (Tables 
4.3 
and 4.4). The attenuation functions provided with the Methodology are consistent 
with 
those utilized by the USGS in the 2002 update of the the National Seismic Hazard 
Maps 
(Frankel et al., 2002). A complete description for each attenuation function can 
be found 
in the referenced citation. 

 

The WUS relationships should be used for study regions located in, or west of, 
the Rocky 
Mountains, Hawaii and Alaska. The CEUS attenuation relationships should be used 
for 
the balance of the continental United States and Puerto Rico. 

 

 

Table 4.3 Western United States (WUS) Attenuation Functions 

 

Western US Attenuation Functions 

Mechanism 

Distance 

Abrahamson and Silva (1997) 

N,SS,R 

rrup 

Boore, Joyner and Fumal (1997) 

N,SS,R 

rjb 

Campbell & Bozorgnia (2003) 

N,SS,R 

rseis 

Sadigh,Chang, Egan, Makdisi, and Young (1997) 

N,SS,R 

rrup 

Spudich, Joyner, Lindh, Boore, Margaris and Fletcher (1999) 

N 

rjb 

Munson and Thurber (1997) 

N 

rjb 

Youngs, Chiou, Silva and Humphrey (1997) 

IF,IS 

rrup 

Atkinson and Boore (2002) 

IF,IS 

rrup 



 

 

Table 4.4 Central and Eastern United States (CEUS) Attenuation Functions 

 

Central and Eastern US Attenuation Functions 

Distance 

Atkinson and Boore (1995) 

rhypo 

Toro, Abrahamson and Schneider (1997) 

rjb 

Frankel, Mueller, Barnhard, Perkins, Leyendecker, Dickman, Hooper (1996) 

rhypo 

Campbell (2002) 

rrup 

Sommerville, Collins, Abrahamson, Braves, and Saikia (2002) 

rjb 



 

 

The source-to-site distance measure used with the attenuation function will be 
described 
in the following section (Section 4.1.2.4). 

 




Combination Attenuation Relationships (WUS and CEUS) 

 

The Methodology also provides a series of combination attenuation relationships 
(Table 
4.5). These relationships are based on the approach used by the USGS in the 2002 
update of the National Seismic Hazard Maps (Frankel, 2002). 

 

 

Table 4.5 Combination Attenuation Relationships for the WUS and CEUS 

 

Name 

Participation Attenuation Functions 

Weight 

WUS Shallow Crustal 
Event - Extensional 

Abrahamson and Silva (1997): Hanging Wall 

0.20 

Sadigh,Chang, Egan, Makdisi, and Young (1997) 

0.20 

Boore, Joyner and Fumal (1997) 

0.20 

Spudich et al. (1999) 

0.20 

Campbell & Bozorgnia (2003) 

0.20 

WUS Shallow Crustal 
Event  Non-Extensional 

Abrahamson and Silva (1997): Hanging Wall 

0.25 

Sadigh,Chang, Egan, Makdisi, and Young (1997) 

0.25 

Boore, Joyner and Fumal (1997) 

0.25 

Campbell & Bozorgnia (2003) 

0.25 

WUS Cascadia 
Subduction Event 

Youngs, Chiou, Silva and Humphrey (1997) 

0.50 

Sadigh,Chang, Egan, Makdisi, and Young (1997) 

0.50 

WUS Deep Event ( > 35 
km in depth) 

Youngs, Chiou, Silva and Humphrey (1997) 

0.50 

Atkinson and Boore (2002) - Global 

0.25 

Atkinson and Boore (2002) - Cascadia 

0.25 

WUS Hawaiian Event 

Munson and Thurber (1997) 

0.50 

Boore, Joyner and Fumal (1997) 

0.50 

CEUS Event 

Atkinson and Boore (1997) 

0.286 

Toro, Abrahamson and Schneider (1997) 

0.286 

Frankel, Mueller, Barnhard, Perkins et al. (1996) 

0.286 

Campbell (2002) 

0.142 

CEUS Characteristic 
Event (New Madrid and 
Charleston) 

Atkinson and Boore (1997) 

0.250 

Toro, Abrahamson and Schneider (1997) 

0.250 

Frankel, Mueller, Barnhard, Perkins et al. (1996) 

0.250 

Campbell (2002) 

0.125 

Sommerville, Collins, Abrahamson et al. (2002) 

0.125 



 

 




Comparison of CEUS and WUS Gound Shaking Estimates 

 

The combination CEUS attenuation function predict significantly stronger ground 
shaking than the combination of WUS attenuation functions for the same scenario 
earthquake (i.e., same moment magnitude and distance to source). For example, 
Table 
4.6 compares WUS and CEUS rock (Site Class B) response spectra (standard shape) 
for a 
series of magnitude and distance combinations. As shown in Table 4.6, CEUS 
spectral 
demand is about 1.5 to 2.5 times WUS demand for the short periods and between 
1.0 to 
1.7 times WUS demand for the long period (T = 1.0 seconds). 

 

Table 4.6 Comparison of Ground Shaking Estimates for the WUS and CEUS 

 

Western United States (WUS)  Rock (Site Class B) 

Distance (km) 

SAS(E)/SAS(W) for a given 
Magnitude, M: 

SAL(E)/SAL(W) for a given 
Magnitude, M: 

5 

6 

7 

7.5 

5 

6 

7 

7.5 

10 km 

1.2 

1.4 

1.8 

2.2 

0.7 

0.9 

1.2 

1.3 

25 km 

1.5 

1.7 

2.1 

2.5 

1.0 

1.4 

1.6 

1.7 

50 km 

1.4 

1.8 

2.2 

2.4 

1.2 

1.4 

1.6 

1.7 

75 km 

1.7 

1.8 

2.4 

2.5 

0.8 

1.5 

1.7 

1.7 



 

 

4.1.2.4 Source-to-Site Distance Measures for Attenuation Functions 

 

The source-to-site distance is an integral part of each attenuation relationship 
and 
characterizes the decrease in ground shaking intensity as the distance from the 
earthquake 
source increases. The distance measures used in the Methodology are described in 
Table 
4.7 and illustrated in Figures 4.3 and 4.4. Figure 4.3 illustrates the distance 
measures 
from a vertical fault plane while Figure 4.4 illustrates the same measure for a 
dipping 
fault. In the Methodology, all distances and fault dimensions are in kilometers. 

 

Table 4.7 Source-to-Site Distance Measures 

 

Distance 

Description 

repi 

Distance from the site to the earthquake epicenter 

rhypo 

Distance from the site to the earthquake hypocenter 

rjb 

Distance from the site to the vertical projection of the fault rupture plane 

rrup 

Distance from the site to the fault rupture plane. 

rseis 

Distance from the site to the seismogenic portion of the fault rupture plane. 
Campbell (1997) provides estimates for the depth to the seismogenic 
portion of the fault rupture plane. 






 

 

Figure 4.3 Source-to-Site Distances for Vertical Faults 

 

 

 

Figure 4.4 Source-to-Site Distances for Dipping Faults. 

Seismogenic depth 

rjb

rrup 

rseis 

rhypo

Hypocenter 

Depth (d) 

rjb 

rhypo 

Hypocenter

rseis & rrup 

rrup 

rseis 

rhypo 

rjb 

rjb = 0 

Seismogenic depth 

Fault Width (W)

Dip angle 

rhypo




4.1.2.5 Amplification of Ground Shaking - Local Site Conditions 

 

Amplification of ground shaking to account for local site conditions is based on 
the site 
classes and soil amplification factors proposed for the 1997 NEHRP Provisions 
(which 
are essentially the same as the 1994 NEHRP Provisions, FEMA 222A, 1995). The 
NEHRP Provisions define a standardized site geology classification scheme and 
specify 
soil amplification factors for most site classes. The classification scheme of 
the NEHRP 
Provisions is based, in part, on the average shear wave velocity of the upper 30 
meters of 
the local site geology, as shown in Table 4.8. Users (with geotechnical 
expertise) are 
required to relate the soil classification scheme of soil maps to the 
classification scheme 
shown in Table 4.8. 

 

Table 4.8 Site Classes (from the 1997 NEHRP Provisions) 

 

Site 

Site Class Description 

Shear Wave Velocity (m/sec) 

Class 

 

Minimum 

Maximum 

A 

HARD ROCK 

Eastern United States sites only 

1500 

 

B 

ROCK 

 

760 

1500 

C 

VERY DENSE SOIL AND SOFT ROCK 

Untrained shear strength us > 2000 psf (us > 100 
kPa) or N > 50 blows/ft 

360 

760 

D 

STIFF SOILS 

Stiff soil with undrained shear strength 1000 psf < 
us < 2000 psf (50 kPa < us < 100 kPa) or 15 < N 
< 50 blows/ft 

180 

360 

E 

SOFT SOILS 

Profile with more than 10 ft (3 m) of soft clay 
defined as soil with plasticity index PI > 20, 
moisture content w > 40% and undrained shear 
strength us < 1000 psf (50 kPa) (N < 15 blows/ft) 

 

180 

F 

SOILS REQUIRING SITE SPECIFIC 
EVALUATIONS 

1. Soils vulnerable to potential failure or collapse 
under seismic loading: 

 e.g. liquefiable soils, quick and highly sensitive 
clays, collapsible weakly cemented soils. 

2. Peats and/or highly organic clays 

(10 ft (3 m) or thicker layer) 

3. Very high plasticity clays: 

(25 ft (8 m) or thicker layer with plasticity index >75) 

4. Very thick soft/medium stiff clays: 

(120 ft (36 m) or thicker layer) 

 

 



 

Soil amplification factors are provided in Table 4.9 for Site Classes A, B, C, D 
and E. 
No amplification factors are available for Site Class F, which requires special 
site-
specific geotechnical evaluation and is not used in the Methodology. 




 

Table 4.9 Soil Amplification Factors 

 

Site Class B 

Site Class 

Spectral Acceleration 

A 

B 

C 

D 

E 

Short-Period, SAS (g) 

Short-Period Amplification Factor, FA 

= 0.25 

0.8 

1.0 

1.2 

1.6 

2.5 

 0.50 

0.8 

1.0 

1.2 

1.4 

1.7 

 0.75 

0.8 

1.0 

1.1 

1.2 

1.2 

 1.0 

0.8 

1.0 

1.0 

1.1 

0.9 

= 1.25 

0.8 

1.0 

1.0 

1.0 

 0.8* 

1-Second Period, SA1 (g) 

1.0-Second Period Amplification Factor, FV 

= 0.1 

0.8 

1.0 

1.7 

2.4 

3.5 

0.2 

0.8 

1.0 

1.6 

2.0 

3.2 

0.3 

0.8 

1.0 

1.5 

1.8 

2.8 

0.4 

0.8 

1.0 

1.4 

1.6 

2.4 

= 0.5 

0.8 

1.0 

1.3 

1.5 

 2.0* 



* Site Class E amplification factors are not provided in the NEHRP Provisions 
when SAS 
> 1.0 or SA1 > 0.4. Values shown with an asterisk are based on judgment. 

 

The NEHRP Provisions do not provide soil amplification factors for PGA or PGV. 
The 
Methodology amplifies rock (Site Class B) PGA by the same factor as that 
specified in 
Table 4.10 for short-period (0.3-second) spectral acceleration, as expressed in 
Equation 
(4-15), and amplifies rock (Site Class B) PGV by the same factor as that 
specified in 
Table 4.10 for 1.0-second spectral acceleration, as expressed in Equations (4-
16). 

 

AiiFPGAPGA= (4-15) 

 

ViiFPGVPGV= (4-16) 

 

where: PGAi is peak ground acceleration for Site Class i (in units of g) 

PGA is peak ground acceleration for Site Class B (in units of g) 

FAi is the short-period amplification factor for Site Class i, as 
specified in Table 4.10 for spectral acceleration, SAS 

PGVi is peak ground acceleration for Site Class i (in units of g) 

PGV is peak ground acceleration for Site Class B (in units of g) 

FVi is the 1-second period amplification factor for Site Class i, 
as specified in Table 4.10 for spectral acceleration, SA1 

 




Construction of Demand Spectra 

Demand spectra including soil amplification effects are constructed at short-
periods using 
Equation (4-17) and at long-periods using Equation (4-18). The period, TAV, 
which 
defines the transition period from constant spectral acceleration to constant 
spectral 
velocity is a function of site class, as given in Equation (4-19). The period, 
TVD, which 
defines the transition period from constant spectral velocity to constant 
spectral 
displacement is defined by Equation (4-4), and is not a function of site class. 

AiASASiFSS= (4-17) 

 

ViAiAFSS=11 (4-18) 

 

...
.
...
.
...
.
...
.
=
AiViASAAViFFSST1 (4-19) 

where: SASi is short-period spectral acceleration for Site Class i (in units of 
g) 

SAS is short-period spectral acceleration for Site Class B (in units of g) 

FAi is the short-period amplification factor for Site Class i, as specified in 
Table 4.10 for spectral acceleration, SAS 

SA1i is 1-second period spectral acceleration for Site Class i (in units of g) 

SA1 is 1-second period spectral acceleration for Site Class B (in units of g) 

FVi is the 1-second period amplification factor for Site Class i, as specified 
in Table 4.10 for spectral acceleration, SA1 

TAVi is the transition period between constant spectral acceleration and 
constant spectral velocity for Site Class i (sec). 

 

Figure 4.5 illustrates construction of response spectra for Site Class D (stiff 
soil) and E 
(soft soil) from Site Class B (rock) response spectra. These spectra represent 
response 
(of a 5%-damped, linear-elastic single-degree-of-freedom system) located at a 
WUS site, 
20 km from a magnitude M = 7.0 earthquake, as predicted by the default 
combination of 
WUS attenuation relationships. Figure 4.5 shows the significance of soil type on 
site 
response (i.e., increase in site response with decrease in shear wave velocity) 
and the 
increase in the value of the transition period, TAV, with decrease in shear wave 
velocity. 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4.5 Example Construction of Site Class B, C and D Spectra - WUS 

00.10.20.30.40.50.60.70.80.9101234567890Spectral Displacement (inches)
Spectral Acceleration (g's)
Site Class B (Rock)
Site Class D (Stiff Soil)
Site Class E (Soft Soil)
0.3 sec.
1.0 sec.
SA1 x FVESA1 x FVDSAS x FAESAS x FADSASSA1



4.1.3 Guidance for Expert-Generated Ground Motion Estimation 

Ground motion estimation is a sophisticated combination of earth science, 
engineering 
and probabilistic methods and should not be attempted by users, including local 
geotechnical engineers, who not have the proper expertise. 

 

For users who dont have the expertise to estimate ground motion and who need 
guidance 
on which existing attenuation function to use, the table below first summarizes 
the 34 
choices that currently exist within HAZUS. Note that the dependent attenuation 
functions are the cocktail-based ones in HAZUS. 

 

# 

Description 

Fault 

Mechanism 

East or 
West 

Note 

1 

Abrahamson and Silva (1997) Hanging Wall 

N 

W 

 

2 

Abrahamson and Silva (1997) Foot Wall 

N 

W 

 

3 

Abrahamson and Silva (1997) Hanging Wall 

S 

W 

 

4 

Abrahamson and Silva (1997) Foot Wall 

S 

W 

 

5 

Abrahamson and Silva (1997) Hanging Wall 

R 

W 

 

6 

Abrahamson and Silva (1997) Foot Wall 

R 

W 

 

7 

Atkinson and Boore (1995) 

E 

E 

 

8 

Toro et al. (1997) 

E 

E 

 

9 

Sommerville (2002) 

E 

E 

 

10 

Frankel (1996) 

E 

E 

 

11 

Campbell (2002) 

E 

E 

 

12 

Boore, Joyner and Fumal (1997) 

N 

W 

 

13 

Boore, Joyner and Fumal (1997) 

S 

W 

 

14 

Boore, Joyner and Fumal (1997) 

R 

W 

 

15 

Sadigh et al. (1997) 

N 

W 

 

16 

Sadigh et al. (1997) 

S 

W 

 

17 

Sadigh et al. (1997) 

R 

W 

 

18 

Campbell & Bozorgnia (2003) 

N 

W 

 

19 

Campbell & Bozorgnia (2003) 

S 

W 

 

20 

Spudich et al. (1999) 

N 

W 

 

21 

Youngs et al. (1997) 

F 

W 

 

22 

Youngs et al. (1997) 

I 

W 

 

23 

Munson and Thurber (1997) 

N 

W 

Hawaii-specific 

24 

CEUS Event 

E 

E 

DEPENDENT 

25 

WUS Shallow Crustal Event - Extensional 

S 

W 

DEPENDENT 

26 

WUS Hawaiian Event 

S 

W 

DEPENDENT 

27 

WUS Cascadia Subduction Event 

F 

W 

DEPENDENT 

28 

WUS Deep Event 

I 

W 

DEPENDENT 

29 

WUS Shallow Crustal Event - Non Extensional 

R 

W 

DEPENDENT 

30 

Atkinson and Boore (2002) 

F 

W 

 

31 

Atkinson and Boore (2002) - Global 

I 

W 

 

32 

Atkinson and Boore (2002) - Cascadia 

I 

W 

 

33 

WUS Shallow Crustal Event - Extensional 

N 

W 

DEPENDENT 

34 

CEUS Characteristic Event 

E 

E 

DEPENDENT 



 

When the user creates a study region, HAZUS will recognize whether this region 
is the 
east coast (E) or west-coast (W), and automatically filters from the table above 
the ones 
applicable for that region. 




 

The dependent (cocktail-based) attenuations are what would show up as a default 
in 
HAZUS. However, the user can choose a more specific attenuation for a variety of 
reasons that include the following: 

 

.. 
Understanding the effects of different attenuation functions on the results. 
This is 
in particular very important given that ground motion has the maximum impact 
possible on the results. 




 

.. 
Simulating and setting up upper bound and lower bound estimates due to ground 
motion. In this case, the user needs to know which of the attenuation functions 
provide the smallest shaking and which of the attenuation functions provide the 
largest shaking. The plots below show examples of ground shaking for the east-
coast attenuation functions. In this case and for a moment magnitude of 8, 
Toros 
attenuation function will yield the highest shaking resulting in higher losses 
and 
Atkinson and Boore will yield the lowest shaking resulting in the lowest losses 
for this magnitude. 




 

0.0100.1001.00001020304050607Distance [km]
Example PGA [g]
Atkinson and Boore (1995)
Toro et al. (1997)
Sommerville (2002) 
Frankel (1996)
Campbell (2002) 


Magnitude 5 

0.0100.1001.00010.000020406080Distance [km]Example PGA [g]
Atkinson and Boore (1995)
Toro et al. (1997)
Sommerville (2002) 
Frankel (1996)
Campbell (2002) 


Magnitude 6 

0.0100.1001.00010.0000204060801001Distance [km]
Example PGA [g]
Atkinson and Boore (1995)
Toro et al. (1997)
Sommerville (2002) 
Frankel (1996)
Campbell (2002) 


Magnitude 7 

0.0100.1001.00010.0000204060801001Distance [km]
Example PGA [g]
Atkinson and Boore (1995)
Toro et al. (1997)
Sommerville (2002) 
Frankel (1996)
Campbell (2002) 


Magnitude 8 



 

.. 
Similar comparisons of ground motion should be done for study regions in the 
western US. For example, the plot below shows example peak ground 







accelerations for strike-slip type of events with magnitude 7. It is worth 
noting in 
this plot that for this example event, the attenuation function that yields the 
highest shaking within 40 kilometers from the source of the earthquake also 
yields the lowest shaking for areas further than 40 kilometers from the source. 
The implication here would be when a user wants to choose a particular 
attenuation function he/she needs to consider the distance between the source 
and 
the community/study region for which upper and lower bound losses need to be 
determined. 




0.0100.1001.0000102030405060708Distance [km]
Example PGA [g]
Abrahamson & Silva (1997)
Boore, Joyner & Fumal (1997)
Sadigh et al. (1997)
Campbell & Bozorgnia (2003) 


 

For a scenario in Hawaii, the attenuation function that ought to be used is 
Munson and 
Thurber (1997). The plot below shows example PGA values for the different 
magnitudes 
and distances when this attenuation function is used. 

11.2911.3311.6812.3513.2814.4415.7617.2218.7720.4122.1023.846.577.588.50.000.100
.200.300.400.500.600.700.800.901.00Ground Acc. (g)
Distance from EQ Source (km)
EQ Magnitude 
at Source 
Munson & Thurber Function Plot0.900-1.0000.800-0.9000.700-0.8000.600-0.7000.500-
0.6000.400-0.5000.300-0.4000.200-0.3000.100-0.200Ground Acc. (g)INITIAL 
ZONERAMPED ZONECAPPED ZONE 


 




Finally, it is important for the user to understand that attenuation functions 
(e.g., Youngs 
et. al. attenuation) in subduction zones must not be used under the arbitrary 
option near 
areas of surface faults. 

 

 

4.2 Ground Failure 

4.2.1 Introduction 

 

Three types of ground failure are considered: liquefaction, landsliding and 
surface fault 
rupture. Each of these types of ground failure are quantified by permanent 
ground 
deformation (PGD). Methods and alternatives for determining PGD due to each mode 
of 
ground failure are discussed below. 

 

4.2.1.1 Scope 

 

The scope of this section is to provide methods for evaluating the ground 
failure hazards 
of: (a) liquefaction, (b) landsliding, and (c) surface fault rupture. The 
evaluation of the 
hazard includes the probability of the hazard occurring and the resulting ground 
displacement. 

 

4.2.1.2 Input Requirements and Output Information 

 

Input 

Liquefaction 

 A geologic map based on the age, depositional environment, and possibly the 
material characteristics of the geologic units will be used with Table 4.10 to 
create a 
liquefaction susceptibility map 

 Groundwater depth map is supplied with a default depth of 5 feet. 

 Earthquake Moment Magnitude (M) 

Landsliding 

 A geologic map, a topographic map, and a map with ground water conditions will 
be 
used with Table 4.15 to produce a landslide susceptibility map 

 Earthquake Moment Magnitude (M) 

Surface Fault Rupture 

 Location of the surface trace of a segment of an active fault that is 
postulated 
to rupture during the scenario earthquake 

 

Output 

Liquefaction, and Landsliding 

 Aerial depiction map depicting estimated permanent ground deformations. 

Surface Fault Rupture 

 No maps are generated, only site-specific demands are determined. 

 

4.2.2 Description of Methods 

 

4.2.2.1 L
iquefaction 







 

4.2.2.1.1 Background 

 

Liquefaction is a soil behavior phenomenon in which a saturated soil looses a 
substantial 
amount of strength due to high excess pore-water pressure generated by and 
accumulated 
during strong earthquake ground shaking. 

 

Youd and Perkins (1978) have addressed the liquefaction susceptibility of 
various types 
of soil deposits by assigning a qualitative susceptibility rating based upon 
general 
depositional environment and geologic age of the deposit. The relative 
susceptibility 
ratings of Youd and Perkins (1978) shown in Table 4.10 indicate that recently 
deposited 
relatively unconsolidated soils such as Holocene-age river channel, flood plain, 
and delta 
deposits and uncompacted artificial fills located below the groundwater table 
have high 
to very high liquefaction susceptibility. Sands and silty sands are particularly 
susceptible 
to liquefaction. Silts and gravels also are susceptible to liquefaction, and 
some sensitive 
clays have exhibited liquefaction-type strength losses (Updike, et. al., 1988). 

 

Permanent ground displacements due to lateral spreads or flow slides and 
differential 
settlement are commonly considered significant potential hazards associated with 
liquefaction. 

 

4.2.2.1.2 Liquefaction Susceptibility 

 

The initial step of the liquefaction hazard evaluation is to characterize the 
relative 
liquefaction susceptibility of the soil/geologic conditions of a region or 
subregion. 
Susceptibility is characterized utilizing geologic map information and the 
classification 
system presented by Youd and Perkins (1978) as summarized in Table 4.10. Large-
scale 
(e.g., 1:24,000 or greater) or smaller-scale (e.g., 1:250,000) geologic maps are 
generally 
available for many areas from geologists at regional U.S. Geological Survey 
offices, state 
geological agencies, or local government agencies. The geologic maps typically 
identify 
the age, depositional environment, and material type for a particular mapped 
geologic 
unit. Based on these characteristics, a relative liquefaction susceptibility 
rating (e.g., 
very low to very high) is assigned from Table 4.10 to each soil type. Mapped 
areas of 
geologic materials characterized as rock or rock-like are considered for the 
analysis to 
present no liquefaction hazard. 

 

Table 4.10 Liquefaction Susceptibility of Sedimentary Deposits (from Youd and 
Perkins, 1978) 

 

 

General 
Distribution of 
Cohesionless 

 

Likelihood that Cohesionless Sediments when 
Saturated would be Susceptible to Liquefaction (by 
Age of Deposit) 

Type of Deposit 

 

 

Sediments in 
Deposits 

 

 

< 500 yr 
Modern 

 

Holocene 

< 11 ka 

 

Pleistocene 

11 ka - 2 Ma 

Pre-
Pleistocene

> 2 Ma 

(a) Continental Deposits 

River channel 

Locally variable 

Very High 

High 

Low 

Very Low 






Flood plain 

Locally variable 

High 

Moderate 

Low 

Very Low 

Alluvial fan and plain 

Widespread 

Moderate 

Low 

Low 

Very Low 

Marine terraces and plains 

Widespread 

--- 

Low 

Very Low 

Very Low 

Delta and fan-delta 

Widespread 

High 

Moderate 

Low 

Very Low 

Lacustrine and playa 

Variable 

High 

Moderate 

Low 

Very Low 

Colluvium 

Variable 

High 

Moderate 

Low 

Very Low 

Talus 

Widespread 

Low 

Low 

Very Low 

Very Low 

Dunes 

Widespread 

High 

Moderate 

Low 

Very Low 

Loess 

Variable 

High 

High 

High 

Unknown 

Glacial till 

Variable 

Low 

Low 

Very Low 

Very Low 

Tuff 

Rare 

Low 

Low 

Very Low 

Very Low 

Tephra 

Widespread 

High 

High 

? 

? 

Residual soils 

Rare 

Low 

Low 

Very Low 

Very Low 

Sebka 

Locally variable 

High 

Moderate 

Low 

Very Low 

(b) Coastal Zone 

Delta 

Widespread 

Very High 

High 

Low 

Very Low 

Esturine 

Locally variable 

High 

Moderate 

Low 

Very Low 

Beach 

 

High Wave Energy 

Widespread 

Moderate 

Low 

Very Low 

Very Low 

Low Wave Energy 

Widespread 

High 

Moderate 

Low 

Very Low 

Lagoonal 

Locally variable 

High 

Moderate 

Low 

Very Low 

Fore shore 

Locally variable 

High 

Moderate 

Low 

Very Low 

(c) Artificial 

Uncompacted Fill 

Variable 

Very High 

--- 

--- 

--- 

Compacted Fill 

Variable 

Low 

--- 

--- 

--- 



 

Liquefaction susceptibility maps produced for certain regions [e.g., greater San 
Francisco 
region (ABAG, 1980); San Diego (Power, et. al., 1982); Los Angeles (Tinsley, et. 
al., 
1985); San Jose (Power, et. al., 1991); Seattle (Grant, et. al., 1991); among 
others] are 
also available and may alternatively be utilized in the hazard analysis. 

 

4.2.2.1.3 Probability of Liquefaction 

 

The likelihood of experiencing liquefaction at a specific location is primarily 
influenced 
by the susceptibility of the soil, the amplitude and duration of ground shaking 
and the 
depth of groundwater. The relative susceptibility of soils within a particular 
geologic 
unit is assigned as previously discussed. It is recognized that in reality, 
natural geologic 
deposits as well as man-placed fills encompass a range of liquefaction 
susceptibilities due 
to variations of soil type (i.e., grain size distribution), relative density, 
etc. Therefore, 
portions of a geologic map unit may not be susceptible to liquefaction, and this 
should be 
considered in assessing the probability of liquefaction at any given location 
within the 
unit. In general, we expect non-susceptible portions to be smaller for higher 
susceptibilities. This "reality" is incorporated by a probability factor that 
quantifies the 
proportion of a geologic map unit deemed susceptible to liquefaction (i.e., the 
likelihood 
of susceptible conditions existing at any given location within the unit). For 
the various 
susceptibility categories, suggested default values are provided in Table 4.11. 




 

Table 4.11 Proportion of Map Unit Susceptible to Liquefaction 

 

Mapped Relative Susceptibility 

Proportion of Map Unit 

Very High 

0.25 

High 

0.20 

Moderate 

0.10 

Low 

0.05 

Very Low 

0.02 

None 

0.00 



 

 

These values reflect judgments developed based on preliminary examination of 
soil 
properties data sets compiled for geologic map units characterized for various 
regional 
liquefaction studies (e.g., Power, et. al., 1982). 

 

As previously stated, the likelihood of liquefaction is significantly influenced 
by ground 
shaking amplitude (i.e., peak horizontal acceleration, PGA), ground shaking 
duration as 
reflected by earthquake magnitude, M, and groundwater depth. Thus, the 
probability of 
liquefaction for a given susceptibility category can be determined by the 
following 
relationship: 

 

 PLiquefactionPLiquefactionPGAaKKPMwmlSCSC=
=

 (4-20) 

where 

PLiquefactionPGAaSC= is the conditional liquefaction probability for a 
given susceptibility category at a specified level of peak ground 
acceleration (See Figure 4.8) 

KM is the moment magnitude (M) correction factor (Equation 4-21) 

Kw is the ground water correction factor (Equation 4-22) 

Pml proportion of map unit susceptible to liquefaction (Table 4.11) 

 

Relationships between liquefaction probability and peak horizontal ground 
acceleration 
(PGA) are defined for the given susceptibility categories in Table 4.12 and also 
represented graphically in Figure 4.6. These relationships have been defined 
based on 
the state-of-practice empirical procedures, as well as the statistical modeling 
of the 
empirical liquefaction catalog presented by Liao, et. al. (1988) for 
representative 
penetration resistance characteristics of soils within each susceptibility 
category (See 
Section 4.2.3.2.3) as gleaned from regional liquefaction studies cited 
previously. Note 
that the relationships given in Figure 4.6 are simplified representations of the 
relationships that would be obtained using Liao, et al. (1988) or empirical 
procedures. 

 

 




Peak Horizontal Ground Acceleration, PGA (g)
P[L|PGA=a]
00.250.50.75100.10.20.30.40.50.6Very HighHighModerateLowVery Low 


 

Figure 4.6 Conditional Liquefaction Probability Relationships for Liquefaction 
Susceptibility Categories (after Liao, et. al., 1988). 




 

Table 4.12 Conditional Probability Relationship for Liquefaction 

Susceptibility Categories 

Susceptibility Category 

[]PLiquefaction PGAa= 

Very High 

0 = 9.09 a - 0.82 = 1.0 

High 

0 = 7.67a - 0.92 = 1.0 

Moderate 

0 = 6.67a -1.0 = 1.0 

Low 

0 = 5.57a -1.18 = 1.0 

Very Low 

0 = 4.16a - 1.08 = 1.0 

None 

0.0 



 

The conditional liquefaction probability relationships presented in Figure 4.6 
were 
developed for a M =7.5 earthquake and an assumed groundwater depth of five feet 
Correction factors to account for other moment magnitudes (M) and groundwater 
depths 
are given by Equations 4-21 and 4-22 respectively. These modification factors 
are well 
recognized and have been explicitly incorporated in state-of-practice empirical 
procedures for evaluating the liquefaction potential (Seed and Idriss, 1982; 
Seed, et. al., 
1985; National Research Council, 1985). These relationships are also presented 
graphically in Figures 4.7 and 4.8. The magnitude and groundwater depth 
corrections are 
made automatically in the methodology. The modification factors can be computed 
using 
the following relationships: 

 (4-21) Km=--+0002700267020552918832....MMM

 

 K0.022dww=+093. (4-22) 

 

where: Km is the correction factor for moment magnitudes other than M=7.5; 

 Kw is the correction factor for groundwater depths other than five feet; 

 M represents the magnitude of the seismic event, and; 

 dw represents the depth to the groundwater in feet. 

 




Earthquake Magnitude, MKm 
0.01.02.045678 


 

Figure 4.7 Moment Magnitude (M) Correction Factor for Liquefaction Probability 
Relationships (after Seed and Idriss, 1982). 

 

Depth to Groundwater, dw (feet)
Kw 
0.01.02.001020304 


 

Figure 4.8 Ground Water Depth Correction Factor for Liquefaction Probability 
Relationships. 

 




4.2.2.1.4 Permanent Ground Displacements 

 

Lateral Spreading 

The expected permanent ground displacements due to lateral spreading can be 
determined using the following relationship: 

 

 []()[]EPGDKEPGDPGAPLSCSC a ==./ (4-23) 

where 

 ()[EPGDPGAPL/SCa = is the expected permanent ground displacement for a 
given susceptibility category under a specified level of 
normalized ground shaking (PGA/PGA(t)) (Figure 4.9) 

 PGA(t) is the threshold ground acceleration necessary to induce 
liquefaction (Table 4.13) 

 K. is the displacement correction factor given by Equation 
4-24 

 

This relationship for lateral spreading was developed by combining the 
Liquefaction 
Severity Index (LSI) relationship presented by Youd and Perkins (1987) with the 
ground 
motion attenuation relationship developed by Sadigh, et. al. (1986) as presented 
in Joyner 
and Boore (1988). The ground shaking level in Figure 4.9 has been normalized by 
the 
threshold peak ground acceleration PGA(t) corresponding to zero probability of 
liquefaction for each susceptibility category as shown on Figure 4.6. The PGA(t) 
values 
for different susceptibility categories are summarized in Table 4.13. 

 

The displacement term, ()[EPGDPGAPL/SCa =, in Equation 4-23 is based on M = 
7.5 earthquakes. Displacements for other magnitudes are determined by modifying 
this 
displacement term by the displacement correction factor given by Equation 4-24. 
This 
equation is based on work done by Seed & Idriss (1982). The displacement 
correction 
factor, K., is shown graphically in Figure 4.10. 

 

 (4-24) K.....=-+-00086009140469809835MMM32

 

where M is moment magnitude. 

 




020406080100012345PGA/PGA(t) 
Displacement (inches) 
12x - 12 for 1< PGA/PGA(t)< 218x - 24 for 2< PGA/PGA(t)< 370x - 180 for 3< 
PGA/PGA(t)< 4 


Figure 4.9 Lateral Spreading Displacement Relationship (after Youd and Perkins, 
1978; Sadigh, et. al., 1986). 

 

 

Table 4.13 Threshold Ground Acceleration (PGA(t)) Corresponding 

to Zero Probability of Liquefaction 

Susceptibility Category 

PGA(t) 

Very High 

0.09g 

High 

0.12g 

Moderate 

0.15g 

Low 

0.21g 

Very Low 

0.26g 

None 

N/A 



 

 




Earthquake Magnitude, MDisplacement Correction Factor 
01245678 


 

Figure 4.10 Displacement Correction Factor, K., for Lateral Spreading 

Displacement Relationships (after Seed & Idriss, 1982). 

 

 

Ground Settlement 

 

Ground settlement associated with liquefaction is assumed to be related to the 
susceptibility category assigned to an area. This assumption is consistent with 
relationship presented by Tokimatsu and Seed (1987) that indicate strong 
correlations 
between volumetric strain (settlement) and soil relative density (a measure of 
susceptibility). Additionally, experience has shown that deposits of higher 
susceptibility 
tend to have increased thicknesses of potentially liquefiable soils. Based on 
these 
considerations, the ground settlement amplitudes are given in Table 4.14 for the 
portion 
of a soil deposit estimated to experience liquefaction at a given ground motion 
level. The 
uncertainty associated with these settlement values is assumed to have a uniform 
probability distribution within bounds of one-half to two times the respective 
value. It is 
noted that the relationship presented by Tokimatsu and Seed (1987) demonstrate 
very 
little dependence of settlement on ground motion level given the occurrence of 
liquefaction. The expected settlement at a location, therefore, is the product 
of the 
probability of liquefaction (Equation 4-18) for a given ground motion level and 
the 
characteristic settlement amplitude appropriate to the susceptibility category 
(Table 
4.14). 

 




Table 4.14 Ground Settlement Amplitudes for Liquefaction 
Susceptibility Categories 

 

Relative Susceptibility 

Settlement (inches) 

Very High 

12 

High 

6 

Moderate 

2 

Low 

1 

Very Low 

0 

None 

0 



 

4.2.2.2 Landslide 

 

4.2.2.2.1 Background 

 

Earthquake-induced landsliding of a hillside slope occurs when the static plus 
inertia 
forces within the slide mass cause the factor of safety to temporarily drop 
below 1.0. The 
value of the peak ground acceleration within the slide mass required to just 
cause the 
factor of safety to drop to 1.0 is denoted by the critical or yield acceleration 
ac. This 
value of acceleration is determined based on pseudo-static slope stability 
analyses and/or 
empirically based on observations of slope behavior during past earthquakes. 

 

Deformations are calculated using the approach originally developed by Newmark 
(1965). The sliding mass is assumed to be a rigid block. Downslope deformations 
occur 
during the time periods when the induced peak ground acceleration within the 
slide mass 
ais exceeds the critical acceleration ac. The accumulation of displacement is 
illustrated in 
Figure 4.11. In general, the smaller the ratio (below 1.0) of ac to ais, the 
greater is the 
number and duration of times when downslope movement occurs, and thus the 
greater is 
the total amount of downslope movement. The amount of downslope movement also 
depends on the duration or number of cycles of ground shaking. Since duration 
and 
number of cycles increase with earthquake magnitude, deformation tends to 
increase with 
increasing magnitude for given values of ac and ais. 

 

4.2.2.2.2 Landslide Susceptibility 

 

The landslide hazard evaluation requires the characterization of the landslide 
susceptibility of the soil/geologic conditions of a region or subregion. 
Susceptibility is 




ky1ky2ky3t2t1tAccelerationtVelocitytt1t3t1t3DisplacementNote: Critical 
accelerations in figure are ky1, ky2 and ky3. In applications, critical 
acceleration is usually taken as a single value. 


Figure 4.11 Integration of Accelerograms to Determine Downslope Displacements 
(Goodman and Seed, 1966). 




characterized by the geologic group, slope angle and critical acceleration. The 
acceleration required to initiate slope movement is a complex function of slope 
geology, 
steepness, groundwater conditions, type of landsliding and history of previous 
slope 
performance. At the present time, a generally accepted relationship or 
simplified 
methodology for estimating ac has not been developed. 

 

The relationship proposed by Wilson and Keefer (1985) is utilized in the 
methodology. 
This relationship is shown in Figure 4.12. Landslide susceptibility is measured 
on a scale 
of I to X, with I being the least susceptible. The site condition is identified 
using three 
geologic groups and groundwater level. The description for each geologic group 
and its 
associated susceptibility is given in Table 4.15. The groundwater condition is 
divided 
into either dry condition (groundwater below level of the sliding) or wet 
condition 
(groundwater level at ground surface). The critical acceleration is then 
estimated for the 
respective geologic and groundwater conditions and the slope angle. To avoid 
calculating the occurrence of landsliding for very low or zero slope angles and 
critical 
accelerations, lower bounds for slope angles and critical accelerations are 
established. 
These bounds are shown in Table 4.16. Figure 4.12 shows the Wilson and Keefer 
relationships within these bounds. 

 

Slope Angle (degrees)
Ac - Critical Acceleration (g) 
00.10.20.30.40.50.60.70.80510152025303540455055C (Wet)
C (Dry)
B (Wet)
A (Wet)
B (Dry)
A (Dry) 


Figure 4.12 Critical Acceleration as a Function of Geologic Group and Slope 
Angle 
(Wilson and Keefer, 1985). 

 




Table 4.15 Landslide Susceptibility of Geologic Groups 

 

Geologic Group 

Slope Angle, degrees 

 

0-10 

10-15 

15-20 

20-30 

30-40 

>40 

(a) DRY (groundwater below level of sliding) 

A 

Strongly Cemented Rocks (crystalline 
rocks and well-cemented sandstone, 

c' =300 psf, f' = 35o) 

None 

None 

I 

II 

IV 

VI 

B 

Weakly Cemented Rocks and Soils (sandy 
soils and poorly cemented sandstone, 

c' =0, f' = 35o) 

None 

III 

IV 

V 

VI 

VII 

C 

Argillaceous Rocks (shales, clayey soil, 
existing landslides, poorly compacted fills, 
c' =0 f' = 20o) 

V 

VI 

VII 

IX 

IX 

IX 

(b) WET (groundwater level at ground surface) 

A 

Strongly Cemented Rocks (crystalline 
rocks and well-cemented sandstone, c' 
=300 psf, f' = 35o) 

None 

III 

VI 

VII 

VIII 

VIII 

B 

Weakly Cemented Rocks and Soils (sandy 
soils and poorly cemented sandstone, c' =0, 
f' = 35o) 

V 

VIII 

IX 

IX 

IX 

X 

C 

Argillaceous Rocks (shales, clayey soil, 
existing landslides, poorly compacted fills, 
c' =0 f' = 20o) 

VII 

IX 

X 

X 

X 

X 



 

 

Table 4.16 Lower Bounds for Slope Angles and Critical Accelerations for 
Landsliding Susceptibility 

 

 

Slope Angle, degrees 

Critical Acceleration (g) 

Group 

Dry Conditions 

Wet Conditions 

Dry Conditions 

Wet Conditions 

A 

15 

10 

0.20 

0.15 

B 

10 

5 

0.15 

0.10 

C 

5 

3 

0.10 

0.05 



 

As pointed out by Wieczorek et al. (1985), the relationships in Figure 4.12 are 
conservative representing the most landslide-susceptible geologic types likely 
to be found 
in the geologic group. Thus, in using this relationship further consideration 
must be 
given to evaluating the probability of slope failure as discussed in Section 
4.2.2.2.3. 

 

In Table 4.17, landslide susceptibility categories are defined as a function of 
critical 
acceleration. Then, using Wilson and Keefer's relationship in Figure 4.14 and 
the lower 
bound values in Table 4.16, the susceptibility categories are assigned as a 
function of 
geologic group, groundwater conditions, and slope angle in Table 4.15. Tables 
4.15 and 
4.17 thus define the landslide susceptibility. 

 




Table 4.17 Critical Accelerations (ac) for Susceptibility Categories 

 

Susceptibility 
Category 

None 

I 

II 

III 

IV 

V 

VI 

VII 

VIII 

IX 

X 

Critical 
Accelerations (g) 

None 

0.60 

0.50 

0.40 

0.35 

0.30 

0.25 

0.20 

0.15 

0.10 

0.05 



 

4.2.2.2.3 Probability of Having a Landslide-Susceptible Deposit 

 

Because of the conservative nature of the Wilson and Keefer (1985) correlation, 
an 
assessment is made of the percentage of a landslide susceptibility category that 
is 
expected to be susceptible to landslide. Based on Wieczorek et al. (1985), this 
percentage is selected from Table 4.18 as a function of the susceptibility 
categories. 
Thus, at any given location, there is a specified probability of having a 
landslide-
susceptible deposit, and landsliding either occurs or does not occur within 
susceptible 
deposits depending on whether the induced peak ground acceleration ais exceeds 
the 
critical acceleration ac. 

 

Table 4.18 Percentage of Map Area Having a Landslide-Susceptible Deposit 

 

Susceptibility 
Category 

None 

I 

II 

III 

IV 

V 

VI 

VII 

VIII 

IX 

X 

Map Area 

0.00 

0.01 

0.02 

0.03 

0.05 

0.08 

0.10 

0.15 

0.20 

0.25 

0.30 



 

 

4.2.2.2.4 Permanent Ground Displacements 

 

The permanent ground displacements are determined using the following 
expression: 

 

 (4-25) [][]EPGDEdaan isis=/

 

where 

 is the expected displacement factor (Figure 4.14) [Edais/

 ais is the induced acceleration (in decimal fraction of g's) 

 n is the number of cycles (Equation 4-26). 

 

A relationship between number of cycles and earthquake moment magnitude (M) 
based 
on Seed and Idriss (1982) is shown in Figure 4.13 and can be expressed as 
follows. 

 

 n....=-+-034195521433615470769232MMM (4-26) 

 

The induced peak ground acceleration within the slide mass, ais, represents the 
average 
peak acceleration within the entire slide mass. For relatively shallow and 
laterally small 
slides, ais is not significantly different than the induced peak ground surface 
acceleration 
ai. For deep and large slide masses ais is less than ai. For many applications 
ais may be 




assumed equal to the accelerations predicted by the peak ground acceleration 
attenuation 
relationships being used for the loss estimation study. Considering also that 
topographic 
amplification of ground motion may also occur on hillside slopes (which is not 
explicitly 
incorporated in the attenuation relationships), the assumption of ais equal to 
ai may be 
prudent. The user may specify a ratio ais/ai less than 1.0. The default value is 
1.0. 

 

MagnitudeNumber of Cycles 
05101520253055.566.577.588.5 


 

Figure 4.13 Relationship between Earthquake Moment Magnitude 
and Number of Cycles. 

 

A relationship derived from the results of Makdisi and Seed (1978) is used to 
calculate 
downslope displacements. In this relationship, shown in Figure 4.14, the 
displacement 
factor d/ais is calculated as a function of the ratio ac/ais. For the 
relationship shown in 
Figure 4.14, the range in estimated displacement factor is shown and it is 
assumed that 
there is a uniform probability distribution of displacement factors between the 
upper and 
lower bounds. 

 




ac/aisDisplacement Factor, d/ais (cm/cycle) 
0.010.111010000.20.40.60.81Upper BoundLower Bound 


Figure 4.14 Relationship between Displacement Factor and Ratio of 
Critical Acceleration and Induced Acceleration. 

 

 

4.2.2.3 Surface Fault Rupture 

 

4.2.2.3.1 Permanent Ground Displacements 

 

The correlation between surface fault displacement and earthquake moment 
magnitude 
(M) developed by Wells and Coppersmith (1994) is used. The maximum displacement 
is 
given by the relationship shown in Figure 4.16. It is assumed that the maximum 
displacement can potentially occur at any location along the fault, although at 
the ends of 
the fault, displacements must drop to zero. The relationship developed by Wells 
and 
Coppersmith based on their empirical data set for all types of faulting (strike 
slip, reverse 
and normal) is used. It is considered that this relationship provides reasonable 
estimates 
for any type of faulting for general loss estimation purposes. The uncertainty 
in the 
maximum displacement estimate is incorporated in the loss estimation analysis. 
The log 
of the standard deviation of estimate is equal to 0.35 which is equivalent to a 
factor of 
about 2 in the displacement estimate at the plus-or-minus one standard deviation 
level. 

 

The median maximum displacement (MD) is given by the following relationship: 

 

 (4-27) log()..()MD=-+526079M

 

where M is moment magnitude. 

 

 




Moment MagnitudeMax Displacement in m 
0.010.111010055.566.577.58Median+ Std. Dev.
-Std. Dev. 


 

Figure 4.15 Relationship for Estimating Maximum Surface Fault Displacement. 

 

It has been observed that displacements along a fault vary considerably in 
amplitude from 
zero to the maximum value. Wells and Coppersmith found that the average 
displacement 
along the fault rupture segment was approximately equal to one-half the maximum 
displacement. This is equivalent to a uniform probability distribution for 
values of 
displacement ranging from zero to the maximum displacement. As a conservative 
estimate, a uniform probability distribution from one-half of the maximum fault 
displacement to the maximum fault displacement is incorporated in the loss 
estimation 
methodology for any location along the fault rupture. 

 

 

4.2.3 Guidance for Expert-Generated Ground Failure Estimation 

 

This section provides guidance for users who wish to use more refined methods 
and data 
to prepare improved estimates of ground failure. It is assumed that such users 
would be 
geotechnical experts with sufficient expertise in ground failure prediction to 
develop site-
specific estimates of PGD based on regional/local data. 

 

4.2.3.1 Input Requirements and Output Information 

 

4.2.3.1.1 Liquefaction 

 

Input 

 A map delineating areas of equal susceptibility (i.e., similar age, 
deposition, material 
properties, and ground water depth) 

 Probability distribution of susceptibility variation within each area 




 Relationships between liquefaction probability and ground acceleration for 
each 
susceptible area 

 Maps delineating topographic conditions (i.e., slope gradients and/or free-
face 
locations) and susceptible unit thicknesses 

 Relationships between ground displacements (i.e., lateral spreading and 
settlement), 
and ground acceleration for each susceptible unit, including probability 
distribution 
for displacement; they may vary within a given susceptible unit depending on 
topographic and liquefied zone thickness conditions 

 

Output 

 Contour maps depicting liquefaction hazard and associated potential ground 
displacements 

 

4.2.3.1.2 Landsliding 

 

Input 

 

 A map depicting areas of equal critical or yield acceleration ac (i.e., the 
values of 
peak ground acceleration within the slide mass required to just initiate 
landsliding, 
that is, reduce the factor of safety to 1.0 at the instant of time ac occurs) 

 The probability distribution for ac within each area 

 The ratio between induced peak ground surface acceleration, ai, and the peak 
ground 
acceleration within the slide mass ais (note: could be a constant ratio or could 
vary 
for different areas). The value ais/ai = 1. The default ratio is 1.0 

 Relationships between landslide displacement d induced acceleration aic and 
initial 
or yield acceleration ac including the probability distribution for d. Different 
relationships can be specified for different areas. The default relationship 
between 
the displacement factor d/ais and ac/ais is shown in Figure 4.14. 

 

Output 

 Contour maps depicting landsliding hazard and permanent ground displacements 

 

4.2.3.1.3 Surface Fault Rupture 

 

Input 

 Predictive relationship for the maximum amount of fault displacement 

 Specification of regions of the fault having lower maximum displacements 

 Specifying other than the default relationship for the probability 
distribution between 
minimum and maximum amounts of fault rupture displacement 

 

Output 

 Amount of fault displacement at locations along the fault trace 

 




4.2.3.2 Liquefaction 

 

4.2.3.2.1 Background 

 

The key for the user in defining analysis inputs is understanding the 
interrelationship 
among factors that significantly influence occurrence of liquefaction and 
associated 
ground displacement phenomena. 

 

During earthquake ground shaking, induced cyclic shear creates a tendency in 
most soils 
to change volume by rearrangement of the soil-particle structure. In loose 
soils, this 
volume change tendency is to compact or densify the soil structure. For soils 
such as fine 
sands, silts and clays, permeability is sufficiently low such that undrained 
conditions 
prevail and no or insignificant volume change can occur during the ground 
shaking. To 
accommodate the volume decrease tendency, the soil responds by increases of 
pore-water 
pressure and corresponding decreases of intergranular effective stress. The 
relationship 
between volume change tendency and pore-water increase is described by Martin, 
et al. 
(1975). Egan and Sangrey (1978) discuss the relationship among compressibility 
characteristics, the potential amount of pore-water pressure generation and the 
subsequent loss of strength in various soil materials. In general, more 
compressible soils 
such as plastic silts or clays do not generate excess pore-water pressure as 
quickly or to 
as large an extent as less compressible soils such as sands. Therefore, silty 
and clayey 
soils tend to be less susceptible than sandy soils to liquefaction-type 
behaviors. Even 
within sandy soils, the presence of finer-grained materials affects 
susceptibility as is 
reflected in the correlations illustrated in Figure 4.16 prepared by Seed, et 
al. (1985) for 
use in simplified empirical procedures for evaluating liquefaction potential. 

 

Excess pore-water pressure generation and strength loss potential are also 
highly 
dependent on the density of the soil, as may also be inferred from Figure 4.16. 
Density 
characteristics of soils in a deposit, notably sandy and silty soils, are 
reflected in 
penetration resistance measured, for example, during drilling and sampling an 
exploratory boring. Using penetration resistance data to help assess 
liquefaction hazard 
due to an earthquake is considered a reasonable engineering approach (Seed and 
Idriss, 
1982; Seed, et. al., 1985; National Research Council, 1985), because many of the 
factors 
affecting penetration resistance affect the liquefaction resistance of sandy and 
silty soils 
in a similar way and because state-of-practice liquefaction evaluation 
procedures are 
based on actual performance of soil deposits during worldwide historical 
earthquakes 
(e.g., Figure 4.16). 

 

 

 




 

 

Figure 4.16 Relationship between Cyclic Stress Ratio causing Liquefaction and 
(N1)60 values (M=7.5) (Seed et al., 1985). 

 

These displacement hazards are direct products of the soil behavior phenomena 
(i.e., high 
pore water pressure and significant strength reduction) produced by the 
liquefaction 
process. Lateral spreads are ground failure phenomena that occur near abrupt 
topographic features (i.e., free-faces) and on gently sloping ground underlain 
by liquefied 
soil. Earthquake ground-shaking affects the stability of sloping ground 
containing 
liquefiable materials by causing seismic inertia forces to be added to 
gravitational forces 
within the slope and by shaking-induced strength reductions in the liquefiable 
materials. 
Lateral spreading movements may be on the order of inches to several feet or 
more and 
are typically accompanied by surface fissures and slumping. Flow slides 
generally occur 




in liquefied materials found on steeper slopes and may involve ground movements 
of 
hundreds of feet. As a result, flowslides can be the most catastrophic of the 
liquefaction-
related ground-failure phenomena. Fortunately, flow slides are much less common 
occurrences than lateral spreads. 

 

Settlement is a result of the dissipation of excess pore pressure generated by 
the 
rearrangement of loosely compacted saturated soils into a denser configuration 
during 
shaking. Such dissipation will produce volume decreases (termed consolidation or 
compaction) within the soil that are manifested at the ground surface as 
settlement. 
Volume changes may occur in both liquefied and non-liquefied zones with 
significantly 
larger contributions to settlement expected to result from liquefied soil. 
Densification 
may also occur in loose unsaturated materials above the ground water table. 
Spatial 
variations in material characteristics may cause such settlements to occur 
differentially. 
Differential ground settlement may also occur near sand boil manifestations due 
to 
liquefied materials being removed from the depths of liquefaction and brought to 
the 
ground surface. 

 

These factors have been discussed briefly in preceding sections and incorporated 
to the 
extent possible in characterizing relationships of Section 4.2.2.1. The 
challenge to the 
user is to translate regional/local data, experience and judgment into defining 
site-
specific relationships. The following paragraphs offer additional comments 
regarding 
various aspects of that process. 

 

4.2.3.2.2 Susceptibility 

 

Fundamental soil characteristics and physical processes that affect liquefaction 
susceptibility have been identified through case histories and laboratory 
studies. 
Depositional environments of sediments and their geologic ages control these 
characteristics and processes, as discussed by Youd and Perkins (1978). 

 

The depositional environments of sediments control grain size distribution and, 
in part, 
the relative density and structural arrangement of grains. Grain size 
characteristics of a 
soil influence its susceptibility to liquefaction. Fine sands tend to be more 
susceptible 
than silts and gravels. All cohesionless soils, however, may be considered 
potentially 
liquefiable as the influence of particle size distribution is not thoroughly 
understood. In 
general, cohesive soils that contain more than about 20 percent clay may be 
considered 
nonliquefiable (Seed and Idriss, 1982, present criteria for classifying a soil 
as 
nonliquefiable). 

 

Relative density and structural arrangement of grains (soil structure) greatly 
influence 
liquefaction susceptibility of a cohesionless soil. Soils that have higher 
relative densities 
and more stable soil structure have a lower susceptibility to liquefaction. 
These factors 
may be related to both depositional environment and age. Sediments undisturbed 
after 
deposition (e.g., lagoon or bay deposits) tend to have lower densities and less 
stable 
structures than sediments subjected to wave or current action. With increasing 
age of a 
deposit, relative density may increase as particles gradually work closer 
together. The 




soil structure also may become more stable with age through slight particle 
reorientation 
or cementation. Also, the thickness of overburden sediments may increase with 
age, and 
the increased pressures associated with a thicker overburden will tend to 
increase the 
density of the soil deposit. 

 

An increase in the ratio of effective lateral earth pressure to effective 
vertical or 
overburden earth pressure in a soil has been shown to reduce its liquefaction 
susceptibility. Such an increase will occur when overburden is removed by 
erosion. 

 

In general, it is thought that the soil characteristics and processes that 
result in a lower 
liquefaction susceptibility also result in higher penetration resistance when a 
soil sampler 
is driven into a soil deposit. Therefore, blow count values, which measure 
penetration 
resistance of a soil sampler in a boring, are a useful indicator of liquefaction 
susceptibility. Similarly, the resistance from pushing a cone penetrometer into 
the soil is 
a useful indicator of liquefaction susceptibility. An understanding of the 
depositional 
environments and ages of soil units together with penetration resistance data 
enables 
assessment of liquefaction susceptibility. 

 

Additional information helpful to enhancing/refining the susceptibility 
characterization is 
observation of liquefaction and related phenomena during historical earthquakes, 
as well 
as evidence of paleoliquefaction. Although such information does not exist for 
all 
locations and its absence does not preclude liquefaction susceptibility, it is 
available for 
numerous locations throughout the country; for example, in Northern California 
(Youd 
and Hoose, 1978; Tinsley, et. al., 1994). Incorporation of such historical 
information has 
been shown to significantly enhance liquefaction-related loss estimation. 

 

4.2.3.2.3 Liquefaction Probability 

 

As described previously, simplified procedures for evaluating liquefaction 
potential 
presented by Seed, et. al. (1985), as well as probabilistic approach presented 
by Liao, et 
al. (1988), are useful tools for helping to characterize the relationships among 
liquefaction probability, peak ground acceleration, duration of shaking 
(magnitude), and 
groundwater depth, etc. A parameter commonly utilized in these procedures is 
penetration resistance, which was previously discussed relative to 
susceptibility. Within 
a given geologic unit, experience indicates that subsurface investigations may 
obtain a 
certain scatter in penetration resistance without necessarily any observable 
trend for 
variation horizontally or vertically within that unit. In such cases, a single 
representative 
penetration resistance value is often selected for evaluating the liquefaction 
potential at 
the site. The representative value is very much site-specific and depends on the 
particular distribution of penetration resistance values measured. For example, 
if most of 
the values are very close to each other, with a few much higher or lower values, 
the 
representative value might be selected as the value that is close to the mean of 
the 
predominant population of values that are close to each other. On the other 
hand, if the 
penetration resistance values appear to be widely scattered over a fairly broad 
range of 
values, a value near the 33rd percentile might be more appropriate to select (H. 
B. Seed, 




personal communication, 1984). A typical distribution of penetration resistance 
(N1) for 
a Holocene alluvial fan deposit (i.e., moderate susceptibility) is shown in 
Figure 4.17. 

 

The user may elect to eliminate the probabilistic factor that quantifies the 
proportion of a 
geologic map unit deemed susceptible to liquefaction (i.e., the likelihood of 
susceptible 
conditions existing at any given location within the unit) if regional 
geotechnical data 
enables microzonation of susceptibility areas, or define this factor as a 
probabilistic 
distribution, or incorporate the susceptibility uncertainty in defining other 
liquefaction 
probability relationships. 

 

 

Normalized Blow Count, N1 (blows/foot)
Percentage of Data Points 
Equal to or Less than N1 
01020304050607080901000102030405060708090Note: Based on 178 data points 


 

Figure 4.17 Typical Cumulative Distribution Curve of Penetration Resistance for 
Holocene Alluvial Fan Deposits (after Power, et. al., 1992). 

 

 

4.2.3.2.4 Permanent Ground Displacement 

 

Lateral Spreading 

 

Various relationships for estimating lateral spreading displacement have been 
proposed, 
including the previously utilized Liquefaction Severity Index (LSI) by Youd and 
Perkins 
(1978), and a relationship by Bartlet and Youd (1992), in which they 
characterize 
displacement potential as a function of global earthquake and local site 
characteristics 
(e.g., slope, liquefaction thickness, and grain size distribution). 
Relationships that are 
more site-specific may be developed based on simple stability and deformation 
analysis 
for lateral spreading conditions using undrained residual strengths for 
liquefied sand 
(Seed and Harder, 1990) along with Newmark-type (1965) and Makdisi and Seed 
(1978) 
displacement approaches. To reasonably represent the lateral spreading hazard by 
either 




published relationships or area-specific analyses, generalized information 
regarding 
stratigraphic conditions (i.e., depth to and thickness of the liquefied zone) 
and 
topographic conditions (i.e., ground slope and free-face situations) are 
required. 

 

Ground Settlement 

 

Relationships for assessing ground settlement are available (e.g., Tokimatsu and 
Seed, 
1978) and are suggested to the user for guidance. In addition, test results 
presented by 
Lee and Albaisa (1974) suggest that the magnitude of volumetric strain following 
liquefaction may be dependent on grain-size distribution. Area-specific 
information 
required for developing settlement relationships is similar to that for lateral 
spreading. 

 

4.2.3.3 Landsliding 

 

4.2.3.3.1 Background 

 

The key assessment is the generation of a map denoting areas of equal landslide 
susceptibility and their corresponding values of critical acceleration. This 
should be 
accomplished considering the geographical distribution of facilities at risk in 
the region 
and the types of landsliding that could affect the facilities. 

 

4.2.3.3.2 Landslide Susceptibility 

 

Keefer (1984) and Wilson and Keefer (1985) have identified many different types 
of 
landsliding, ranging from rock falls to deep-seated coherent soil or rock slumps 
to soil 
lateral spreads and flows. For loss estimation purposes, the potential for 
lateral spreads 
and flows should be part of the liquefaction potential assessment rather than 
the landslide 
potential. The significance of other forms of downslope movement depends on the 
potential for such movements to damage facilities. The emphasis on 
characterizing 
landslide susceptibility should be on failure modes and locations that pose a 
significant 
risk to facilities. For example, if the potential for rock falls were high 
(because of steep 
terrain and weak rock) but could occur only in undeveloped areas, then it would 
not be 
important to characterize the critical acceleration for this mode of failure. As 
another 
example, in evaluating the probability of landsliding and the amount of 
displacements as 
part of a regional damage assessment for a utility district (Power et al., 
1994), it was 
assessed that two types of landsliding posed the major risk to the facilities 
and piping: 
activation of existing deep-seated landslide deposits that had been mapped in 
hillside 
areas and that had the potential for disrupting areas in which water lines were 
located 
(landslides often covering many square blocks); and local slumping of roadway 
sidehill 
fills in which water lines were embedded. 

 

Having identified the modes and geographic areas of potential landsliding of 
significance, critical acceleration can be evaluated for these modes and areas. 
It is not 
necessarily required to estimate ac as a function of slope angle. In some cases, 
it may be 
satisfactory to estimate ac and corresponding ranges of values for generalized 
types of 
landslides and subregions, for example, reactivation of existing landslides 
within a 




certain subregion or within the total region. However, it is usually necessary 
to 
distinguish between dry and wet conditions because ac is usually strongly 
dependent on 
groundwater conditions. 

 

In general, there are two approaches to estimating ac: an empirical approach 
utilizing 
observations of landsliding in past earthquakes and corresponding records or 
estimates of 
ground acceleration; and an analytical approach, in which values of ac are 
calculated by 
pseudo-static slope stability analysis methods. Often, both approaches may be 
utilized 
(Power, et. al., 1994). When using the analytical approach, the sensitivity of 
results to 
soil strength parameters must be recognized. In assessing strength parameter 
values and 
ranges, it is often useful to back-estimate values, which are operable during 
static 
conditions. Thus, for certain types of geology, slope angles, static performance 
observations during dry and wet seasons, and estimates of static factors of 
safety, it may 
be possible to infer reasonable ranges of strength parameters from static slope 
stability 
analyses. For earthquake loading conditions, an assessment should also be made 
as to 
whether the short-term dynamic, cyclic strength would differ from the static 
strength. If 
the soil or rock is not susceptible to strength degradation due to cyclic load 
applications 
or large deformations, then it may be appropriate to assign strength values 
higher than 
static values due to rate of loading effects. On the other hand, values even 
lower than 
static values may be appropriate if significant reduction in strength is 
expected (such as 
due to large-deformation-induced remolding of soil). 

 

4.2.3.3.3 Probability of Landsliding 

 

The probability of landsliding at any location is determined by comparing the 
induced 
peak ground acceleration (adjusted to the value of the peak acceleration in the 
landslide 
mass ais) with the assessed distribution for critical acceleration ac (Figure 
4.19). 

 

4.2.3.3.4 Permanent Ground Displacements 

 

In assessing soil deformations using relationships such as shown in Figure 4.14, 
it should 
be kept in mind that the relationships are applicable to slope masses that 
exhibit 
essentially constant critical accelerations. For cases where significant 
reduction in 
strength may occur during the slope deformation process, these relationships may 
significantly underestimate deformations if the peak strength values are used. 
For 
example, deformations cannot be adequately estimated using these simplified 
correlations 
in cases of sudden, brittle failure, such as rock falls or soil or rock 
avalanches on steep 
slopes. 

 

4.2.3.4 Surface Fault Rupture 

 

4.2.3.4.1 Permanent Ground Displacements 

 

Refinements or alternatives that an expert may wish to consider in assessing 
displacements associated with surface fault rupture include: a predictive 
relationship for 
maximum fault displacement different from the default relationship (Figure 
4.15), 




specification of regions of the fault rupture (near the ends) where the maximum 
fault 
displacement is constrained to lower values, and specification of other than the 
default 
relationship for the probability distribution of fault rupture between minimum 
and 
maximum values. 

 

 

Accelerationac1aisac2Probability Density for a c 


 

Figure 4.18 Evaluation of Probability of Landsliding. 

 




4.3 References 

 

Abrahamson, N.A. and W.J. Silva, 1997. "Empirical Response Spectal Attenuation 
Relationships for Shallow Crustal Earthquakes", Seismological Research Letters, 
v. 68, 
no. 1, pp. 94-127. 

 

Association of Bay Area Governments (ABAG), 1980. "Liquefaction Potential and 
Liquefaction Susceptibility Maps", San Francisco Bay region, original scale 
1:250,000. 

 

Atkinson G.M. and D.M. Boore, 2002. Empirical Ground-motion Relationships for 
Subduction Zone Earthquake and their Application to Cascadia and Other Regions, 
Bulletin of the Seismological Society of America, in review. 

 

Atkinson G.M. and D.M. Boore., 1995. Ground-Motion Relations for Eastern North 
America, Bulletin of the Seismological Society of America, vol. 85, No. 1, 
February, pp. 
17-30. 

 

Bartlett, S.F., and T.L. Youd, 1992. "Empirical Prediction of Lateral Spread 
Displacement", in Hamada, M., and O'Rourke, T. D. (eds.), Proceedings from the 
Fourth 
Japan-U.S. Workshop on Earthquake Resistant Design of Lifeline Facilities and 
Countermeasures for Soil Liquefaction, Technical Report NCEER-92-0019, v. I, pp. 
351-
365. 

 

Boore, D. M., Joyner, W.B., and T.E. Fumal, 1997. "Equations for Estimating 
Horizontal 
Response Spectra and Peak Acceleration from Western North American Earthquake: a 
Summary of Recent Work", Seismological Research Letters, v. 68, pp. 128-153. 

 

Borcherdt, R. D. 1994. "New Developments in Estimating Site Response Effects on 
Ground Motion", Proceeding of Seminar on New Developments in Earthquake Ground 
Motion Estimation and Implications for Engineering Design Practice ATC-35, 
Applied 
Technology Council, Redwood City, California. 

 

Campbell, K.W., 2002. Prediction of Strong Ground using the Hybrid Empricial 
Method: Example Application to Eastern North America, Bulletin of the 
Seismological 
Society of America, in review. 

 

Campbell, K.W., 1997. Empirical Near-Source Attenuation Relationships for 
Horizontal and Vertical Components of Peak Ground Acceleration, Peak Ground 
Velocity and Pueudo-absolute Acceleration Response Spectra, Seismological 
Research 
Letters, v. 68, no. 1, pp. 154-179. 

 

Campbell, K.W., and Y. Bozorgnia, 2003. Updated Near-Source Ground-Motion 
(Attenuation) Relations for the Horizontal and Vertical Components of Peak 
Ground 
Acceleration and Acceleration Response Spectra, Bulletin of the Seismological 
Society 
of America v. 93, no. 1, pp. 314-331, February. 

 




Composite Earthquake Catalog, 2002. Advanced National Seismic System, Northern 
California Earthquake Data Center, Berkeley, California. 

 

Earthquake Data Base, 2002. National Earthquake Information Center, United 
States 
Geological Survey, Golden, Colorado. 

 

Earthquake Seismicity Catalog, Volume 1, 1996. National Geophysical Data Center, 
National Oceanic & Atmospheric Administration, Boulder, Colorado. 

 

Egan, J.A., and D.A. Sangrey, 1978. "Critical State Model for Cyclic Load Pore 
Pressure", Proceedings, Earthquake Engineering and Soil Dynamics Conference and 
Exhibit, Pasadena, California, June 19-21. 

 

Federal Emergency Management Agency, 1992. "FEMA 178 - NEHRP Handbook for 
the Seismic Evaluation of Existing Buildings", Washington, D. C., Developed by 
the 
Building Seismic Safety Council (BSSC) for the Federal Emergency Management 
Agency (FEMA). 

 

Federal Emergency Management Agency, 1995. FEMA 222A and 223A - NEHRP 
Recommended Provisions for Seismic Regulations for New Buildings, 1994 Edition, 
Washington, D. C., Developed by the Building Seismic Safety Council (BSSC) for 
the 
Federal Emergency Management Agency (FEMA). 

 

Federal Emergency Management Agency, 1997. 1997 NEHRP Recommended Provisions 
for Seismic Regulations for New Buildings, (in press) Washington, D. C., 
Developed by 
the Building Seismic Safety Council (BSSC) for the Federal Emergency Management 
Agency (FEMA). 

 

Frankel, A.D., Peterson, M.D., Mueller, C.S., Haller, K.M., Wheleler, R.L., 
Leyendecker, 
E. V., Wesson, R.L., Harmsen, S.C., Cramer, C.H., Perkins, D.M. and K.S. 
Rukstales, 
2002. Documentation for the 2002 Update of the National Seismic Hazard Maps, 
USGS 
Open-File Report 02-420, 110pp. 

 

Frankel, A.D., 1997. Issues and Assumptions for Draft Alaska Seismic Hazard 
Maps, 
United States Geological Survey, Golden, CO. 

 

Goodman, R.E., and H.B. Seed, 1966. "Earthquake-Induced Displacements in Sand 
Embankments", Journal of the Soil Mechanics and Foundations Division, 
Proceedings of 
the American Society of Civil Engineers, vol. 92, no. SM2, pp. 125-146. 

 

Grant, W.P., Perkins, W.J. and T.L. Youd, 1991. Evaluation of Liquefaction 
Potential in 
Seattle, Washington - USGS Professional Paper 91-0441T: United States Geological 
Survey. 

 

Joyner, W.B., and D.M. Boore, 1988. "Measurement, Characterization, and 
Prediction of 
Strong Ground Motion", Proceedings of Earthquake Engineering & Soil Dynamics II, 
pp. 




43- 102. Park City, Utah, 27 June 1988. New York: Geotechnical Division of the 
American Society of Civil Engineers. 

 

Keefer, D. K.,1984. "Landslides Caused by Earthquakes", Geological Society of 
America 
Bulletin, vol. 95, pp. 406-421. 

 

Klein, F., Frankel, A.D., Mueller, C.S., Wesson, R.L., and P. Okubo, 1998. 
Documentation for Draft Seimic-Hazard Maps for the State of Hawaii, United 
States 
Geological Survey. 

 

Lee, K. L., and Albaisa, A., 1974. "Earthquake Induced Settlement in Saturated 
Sands", 
Journal of the Soil Mechanics and Foundation Division, ASCE 100(GTA), pp. 387-
406. 

 

Liao, S.S., Veneziano, D., and R.V. Whitman, 1988. "Regression Models for 
Evaluating 
Liquefaction Probability", Journal of Geotechnical Engineering, vol. 114, No. 4, 
April. 

 

Makdisi, F. I. and H.B. Seed, 1978. "Simplified Procedure for Estimating Dam and 
Embankment Earthquake-Induced Deformations", Journal of the Geotechnical 
Engineering Division, American Society of Civil Engineers, vol. 104, No. GT7, 
July, pp. 
849-867. 

 

Martin G.R., Finn, W.D., and H.B. Seed, 1975. "Fundamentals of Liquefaction 
Under 
Cyclic Loading", Journal of the Geotechnical Engineering Division, ASCE 
104(GT5), 
pp. 423-438. 

 

Munson, C.G., and C.H. Thurber, 1997. "Analysis of the Attenuation of Strong 
Ground 
Motion on the Island of Hawaii", Bulletin of the Seismological Society of 
America, vol. 
87, No. 4, August, pp. 945-960. 

 

National Research Council, 1985, "Liquefaction of Soils During Earthquakes", 
Committee on Earthquake Engineering, Commission on Engineering and Technical 
Systems, National Academy Press, Washington, DC. 

 

Newmark, N.M., 1965. "Effects of Earthquakes on Dams and Embankments", 
Geotechnique, vol. 15, no. 2, pp. 139-160. 

 

Newmark, N.M. and W.J. Hall, 1982. Earthquake Spectra and Design, EERI 
Monograph. 

 

Power, M. S., A.W. Dawson, D.W. Streiff, R.G. Perman, and S. C. Haley, 1982. 
Evaluation of Liquefaction Susceptibility in the San Diego, California Urban 
Area. 
Proceedings 3rd International Conference on Microzonation, II, pp. 957-968. 

 

Power, M. S., R. G. Perman, J. R. Wesling, R. R. Youngs, M. K. Shimamoto, 1991. 
Assessment of Liquefaction Potential in the San Jose, California Urban Area. 




Proceedings 4th International Conference on Microzonation, II, pp. 677-684, 
Stanford, 
CA. 

 

Power, M. S., Taylor, C. L., and Perman, R. C., 1994. "Evaluation of Regional 
Landsliding Hazard Potential for Utility District - Eastern San Francisco Bay 
Area", 
Proceedings of the U.S. Fifth National Conference on Earthquake Engineering, 
Chicago, 
Illinois, July . 

 

Sadigh, K., Chang C. Y., Egan, J., Makdisi, F. and R. Youngs, 1997. Attenuation 
Relationships for Shallow Crustal Earthquakes based on California Strong Motion 
Data, 
Seismological Research Letters, v. 68, no. 1, pp. 180-189. 

 

Sadigh, K., Egan, J. A., and Youngs, R. R., 1986. "Specification of Ground 
Motion for 
Seismic Design of Long Period Structures", Earthquake Notes, vol. 57, no. 1, p. 
13, 
relationships are tabulated in Joyner and Boore (1988) and Youngs and others 
(1987). 

 

Seed, R. B., and Harder, L. F., 1990. "SPT-Based Analysis of Cyclic Pore 
Pressure 
Generation and Undrained Residual Strength", Proceedings of the H. B. Seed 
Memorial 
Symposium, vol. 2, pp. 351-376. 

 

Seed, H. B., and Idriss, I. M., 1971. "Simplified Procedure for Evaluating Soil 
Liquefaction Potential", Journal of the Soil Mechanics and Foundations Division, 
ASCE, 
vol. 97, no. SM9, Proceedings Paper 8371, September, pp. 1249-1273. 

 

Seed, H. B., and Idriss, I. M. 1982. "Ground Motions and Soil Liquefaction 
During 
Earthquakes", Earthquake Engineering Research Institute, Oakland, California, 
Monograph Series, p. 13. 

 

Seed, H. B., Tokimatsu, K., Harder, L. F., and Chung, R. M., 1985. "Influence of 
SPT 
Procedures in Soil Liquefaction Resistance Evaluations, Journal of Geotechnical 
Engineering, American Society of Civil Engineers, , vol. 111, no. 12, p. 1425-
1445. 

 

Somerville, P., Collins, N., Abrahamson, R., and C. Saikia, 2001. Ground-motion 
Attenuation Relationships for the Central and Eastern United States, final 
report to the 
U.S. Geological Survey. 

 

Spudich, P., Joyner, W.B., Lindh, A.G., Boore, D.M., Margaris, B.M., and J.B. 
Flecher, 
1999. SEA99: A Revised Ground Motion Prediction Relation for use in Extensional 
Tectonic Regimes, Bulletin of the Seismological Society of America, vol. 89, pp. 
1156-
1170. 

 

Tinsley, J. C., T. L. Youd, D. M. Perkins, and A. T. F. Chen, 1985. Evaluating 
Liquefaction Potential, Evaluating Earthquake Hazards in the Los Angeles Region 
- 
USGS Professional Paper 1360. pp. 263-316. 

 




Tokimatsu, A. M., and Seed, H. B., 1987. "Evaluation of Settlements in Sands Due 
to 
Earthquake Shaking", Journal of the Geotechnical Division, American Society of 
Civil 
Engineers, vol. 113, no. 8, pp. 681-878. 

 

Toro, G. R., Abrahamson, N. A. and Schneider, J. F., 1997. Engineering Model of 
Strong Ground Motions from Earthquakes in the Central and Eastern United 
States, 
Seismological Research Letters, v. 68, pp.41-57. 

 

Toro, G. R. and McGuire, R. K., 1991. Evaluations of Seismic Hazard at the 
Paducah 
Gaseous Diffusion Plant, Third DOE Natural Phenomena Hazards Mitigation 
Conference, pp. 157-165. 

 

Updike, R. G., Egan, J. A., Moriwaki, Y., Idriss, I. M., and Moses, T. L., 1988, 
"A Model 
for Earthquake-Induced Translatory Landslides in Quaternary Sediments", 
Geological 
Society of America Bulletin, vol. 100, May, pp. 783-792. 

 

Wells, D. L. and Coppersmith, K. J., 1994. "New Empirical Relationships Among 
Magnitude, Rupture Length, Rupture Width, and Surface Displacement", Bulletin of 
the 
Seismological Society of America, v 84, pp. 974-1002. 

 

Wieczorek, G. F., Wilson, R. C. and Harp, E. L., 1985. "Map of Slope Stability 
During 
Earthquakes in San Mateo County, California", U.S. Geological Survey 
Miscellaneous 
Investigations Map I-1257-E, scale 1:62,500. 

 

Wilson, R. C., and Keefer D. K., 1985. "Predicting Areal Limits of Earthquake 
Induced 
Landsliding, Evaluating Earthquake Hazards in the Los Angeles Region", U.S. 
Geological Survey Professional Paper, Ziony, J. I., Editor, pp. 317-493. 

 

Youd, T. L., and Hoose, S. N., 1978. "Historic Ground Failures in Northern 
California 
Triggered by Earthquakes", U.S. Geological Survey Professional Paper 993, 177 p. 

 

Youd, T. L., and Perkins, D. M., 1978. "Mapping of Liquefaction Induced Ground 
Failure Potential", Journal of the Geotechnical Engineering Division, American 
Society 
of Civil Engineers, vol. 104, no. 4, pp. 433-446. 

 

Youd, T. L., and Perkins, D. M., 1987. "Mapping of Liquefaction Severity Index", 
Journal of the Geotechnical Engineering Division, American Society of Civil 
Engineers, 
vol. 118, no. 11, pp. 1374-1392. 

 

Youngs R. R., Chiou, S. J., Silva W. L., and Humphrey J. R., 1997. "Strong 
Ground 
Motion Attenuation Relationships for Subduction Zone Earthquakes", Seismological 
Research Letters, January/February. 

 




Chapter 5 

Direct Physical Damage - General Building Stock 

 

5.1 Introduction 

 

This chapter describes methods for determining the probability of Slight, 
Moderate, 
Extensive and Complete damage to general building stock. General building stock 
represents typical buildings of a given model building type designed to either 
High-Code, 
Moderate-Code, or Low-Code seismic standards, or not seismically designed 
(referred to 
as Pre-Code buildings). Chapter 6 describes methods for estimating earthquake 
damage 
to essential facilities that include Special buildings designed and constructed 
to standards 
above normal Code provisions. The flowchart of the overall methodology, 
highlighting 
the building damage component and showing its relationship to other components, 
is 
shown in Flowchart 5-1. 

 

5.1.1 Scope 

 

The scope of this chapter includes development of methods for estimation of 
earthquake 
damage to buildings given knowledge of the model building type and an estimate 
of the 
level of ground shaking (or degree of ground failure). Model building types are 
defined 
in Section 5.2. The extent and severity of damage to structural and 
nonstructural 
components of a building is described by one of five damage states: None, 
Slight, 
Moderate, Extensive, and Complete. Damage states are defined in Section 5.3 for 
each 
model building type by physical descriptions of damage to building elements. 

 

This chapter focuses on the development of functions for estimating building 
damage due 
to ground shaking. These building damage functions include: (1) fragility curves 
that 
describe the probability of reaching or exceeding different states of damage 
given peak 
building response, and (2) building capacity (push-over) curves that are used 
(with 
damping-modified demand spectra) to determine peak building response. For use in 
lifeline damage evaluation, a separate set of building fragility curves 
expresses the 
probability of structural damage in terms of peak ground acceleration (PGA). 
Building 
damage functions for ground shaking are described in Section 5.4 for each model 
building type. 

 

While ground shaking typically dominates damage to buildings, ground failure can 
also 
be a significant contributor to building damage. Ground failure is characterized 
by 
permanent ground deformation (PGD) and fragility curves are used to describe the 
probability of reaching different states of damage given PGD. These fragility 
curves are 
similar to, but less detailed than, those used to estimate damage due to ground 
shaking. 
Building damage functions for ground failure are described in Section 5.5. 

 




8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


Flowchart 5.1 Building Damage Relationship to Other Components of the 
Methodology 




Section 5.6 describes implementation of ground shaking damage functions 
(including 
development of damping-modified demand spectra) and the calculation of the 
probability 
of combined ground shaking and ground failure damage. 

 

The methods described in this chapter may also be used by seismic/structural 
engineering 
experts to modify default damage functions (based on improved knowledge of 
building 
types, their structural properties and design vintage). Guidance for expert 
users is 
provided in Section 5.7 

 

5.1.2 Input Requirements and Output Information 

 

Input required to estimate building damage using fragility and capacity curves 
includes 
the following two items: 

 

 model building type (including height) and seismic design level that 
represents the 
building (or group of buildings) of interest, and 

 

 response spectrum (or PGA, for lifeline buildings, and PGD for ground failure 
evaluation) at the buildings site or at the centroid of the census tract area 
where the 
building (or group of buildings) is located. 

 

Typically, the model building type is not known for each building and must be 
determined from the inventory of facilities using the relationship of building 
type and 
occupancy, described in Chapter 3. The response spectrum, PGA and PGD at the 
building site (or census tract centroid) are PESH outputs, described in Chapter 
4. 

 

The output of fragility curves is an estimate of the cumulative probability of 
being in, 
or exceeding, each damage state for the given level of ground shaking (or ground 
failure). 
Discrete damage state probabilities are created using cumulative damage 
probabilities, as 
described in Section 5.6. Discrete damage state probabilities for model building 
types 
and occupancy classes are the outputs of the building damage module. These 
outputs are 
used directly as inputs to induced physical damage and direct economic and 
social loss 
modules, as shown in Flowchart 5.1. While the fragility and capacity curves are 
applicable, in theory, to a single building as well as to all buildings of given 
type, they 
are more reliable as predictors of damage for large, rather than small, 
population groups. 
They should not be considered reliable for prediction of damage to a specific 
facility 
without confirmation by a seismic/structural engineering expert. 

 

5.1.3 Form of Damage Functions 

 

Building damage functions are in the form of lognormal fragility curves that 
relate the 
probability of being in, or exceeding, a building damage state to for a given 
PESH 
demand parameter (e.g., response spectrum displacement). Figure 5.1 provides an 
example of fragility curves for the four damage states used in this methodology. 

Each fragility curve is defined by a median value of the PESH demand parameter 
(i.e., 
either spectral displacement, spectral acceleration, PGA or PGD) that 
corresponds to the 




threshold of the damage state and by the variability associated with that damage 
state. 
For example, the spectral displacement, Sd, that defines the threshold of a 
particular 
damage state (ds) is assumed to be distributed by: 

 

 (5-1) 

where: is the median value of spectral displacement of damage 
state, ds, and 

eds is a lognormal random variable with unit median value and 
logarithmic standard deviation, ds. 

 

 

Figure 5.1 Example Fragility Curves for Slight, Moderate, Extensive and 
Complete Damage. 

In a more general formulation of fragility curves, the lognormal standard 
deviation, , 
has been expressed in terms of the randomness and uncertainty components of 
variability, 
R and U, [Kennedy, et. al., 1980]. Since it is not considered practical to 
separate 
uncertainty from randomness, the combined random variable term, , is used to 
develop a 
composite best-estimate fragility curve. This approach is similar to that used 
to 
develop fragility curves for the FEMA-sponsored study of consequences of large 
earthquakes on six cities of the Mississippi Valley region [Allen & Hoshall, et 
al., 1985]. 

 

The conditional probability of being in, or exceeding, a particular damage 
state, ds, given 
the spectral displacement, Sd, (or other PESH parameter) is defined by the 
function: 

 

 []PS1lnSSdd,
dsdsdds=
.
..
.
..
.
..
.
..
F 
(5-2) 




 

where: dsdS, is the median value of spectral displacement at which the building 
reaches the threshold of damage state, ds, 

ds is the standard deviation of the natural logarithm of spectral 
displacement for damage state, ds, and 

F is the standard normal cumulative distribution function. 

Median spectral displacement (or acceleration) values and the total variability 
are 
developed for each of the model building types and damage states of interest by 
the 
combination of performance data (from tests of building elements), earthquake 
experience data, expert opinion and judgment. 

 

In general, the total variability of each damage state, ds, is modeled by the 
combination 
of following three contributors to damage variability: 

 

 uncertainty in the damage state threshold, 

 variability in the capacity (response) properties of the model building 
type of interest, and 

 uncertainty in response due to the spatial variability of ground motion 
demand. 

Each of these three contributors to damage state variability is assumed to be 
lognormally 
distributed random variables. 

 

The fragility curves are driven by a PESH parameter. For ground failure, the 
PESH 
parameter used to drive fragility curves is permanent ground displacement (PGD). 
For 
ground shaking, the PESH parameter used to drive building fragility curves is 
peak 
spectral response (either displacement or acceleration). Peak ground 
acceleration (PGA), 
rather than peak spectral displacement, is used to evaluate ground shaking-
induced 
structural damage to buildings that are components of lifelines (see Section 
5.4.4). Peak 
spectral response varies significantly for buildings that have different 
response properties 
(e.g., tall, flexible buildings will displace more than short, stiff buildings). 
Therefore, 
determination of peak spectral displacement requires knowledge of the buildings 
response properties. 

 

Building response is characterized by building capacity curves. These curves 
describe 
the push-over displacement of each building type and seismic design level as a 
function 
of laterally-applied earthquake load. The Methodology uses a technique, similar 
to the 
capacity spectrum method [Mahaney, et. al., 1993], to estimate peak building 
response as 
the intersection of the building capacity curve and the response spectrum of 
PESH 
shaking demand at the buildings location (demand spectrum). The capacity 
spectrum 
method is one of the two nonlinear static analysis methods described in the 
NEHRP 
Guidelines for the Seismic Rehabilitation of Buildings [FEMA, 1996a] and 
developed 
more extensively in Seismic Evaluation and Retrofit of Concrete Buildings [SSC, 
1996]. 

 

The demand spectrum is the 5%-damped PESH input spectrum reduced for higher 
levels 
of effective damping (e.g., effective damping includes both elastic damping and 
hysteretic damping associated with post-yield cyclic response of the building). 
Figure 




5.2 illustrates the intersection of a typical building capacity curve and a 
typical demand 
spectrum (reduced for effective damping greater than 5% of critical). Design-, 
yield- and 
ultimate-capacity points define the shape of building capacity curves. Peak 
building 
response (either spectral displacement or spectral acceleration) at the point of 
intersection 
of the capacity curve and demand spectrum is the parameter used with fragility 
curves to 
estimate damage state probabilities (see also Section 5.6.2.2). 

 

00.10.20.30.40.50.60.70.80.91012345678910Spectral Displacement (inches)
Spectral Acceleration (g's)
PESH Input Spectrum 
(5% Damping)
Demand Spectrum 
(Damping > 5%)
Capacity 
CurveUltimate CapacityDesign CapacityYield CapacitySaSd 


Figure 5.2 Example Building Capacity Curve and Demand Spectrum. 

 

5.2 Description of Model Building Types 

 

Table 5.1 lists the 36 model building types that are used by the Methodology. 
These 
model building types are based on the classification system of FEMA 178, NEHRP 
Handbook for the Seismic Evaluation of Existing Buildings [FEMA, 1992]. In 
addition, 
the methodology breaks down FEMA 178 classes into height ranges, and also 
includes 
mobile homes. 

 

Table 5.1 Model Building Types 

 

 

 

Height 

No. 

Label 

Description 

Range 

Typical 

 

 

 

Name 

Stories 

Stories 

Feet 

1 

2 

W1 

W2 

Wood, Light Frame (= 5,000 sq. ft.) 
Wood, Commercial and Industrial (> 
5,000 sq. ft.) 

 

1 - 2 

All 

1 

2 

14 

24 

3 

S1L 

Steel Moment Frame 

Low-Rise 

1 - 3 

2 

24 






4 

5 

S1M 

S1H 

Mid-Rise 

High-Rise 

4 - 7 

8+ 

5 

13 

60 

156 

6 

7 

8 

S2L 

S2M 

S2H 

Steel Braced Frame 

Low-Rise 

Mid-Rise 

High-Rise 

1 - 3 

4 - 7 

8+ 

2 

5 

13 

24 

60 

156 

9 

S3 

Steel Light Frame 

 

All 

1 

15 

10 

11 

12 

S4L 

S4M 

S4H 

Steel Frame with Cast-in-Place Concrete 
Shear Walls 

Low-Rise 

Mid-Rise 

High-Rise 

1 - 3 

4 - 7 

8+ 

2 

5 

13 

24 

60 

156 

13 

14 

15 

S5L 

S5M 

S5H 

Steel Frame with Unreinforced Masonry 
Infill Walls 

Low-Rise 

Mid-Rise 

High-Rise 

1 - 3 

4 - 7 

8+ 

2 

5 

13 

24 

60 

156 

16 

17 

18 

C1L 

C1M 

C1H 

Concrete Moment Frame 

Low-Rise 

Mid-Rise 

High-Rise 

1  3 

4 - 7 

8+ 

2 

5 

12 

20 

50 

120 

19 

20 

21 

C2L 

C2M 

C2H 

Concrete Shear Walls 

Low-Rise 

Mid-Rise 

High-Rise 

1 - 3 

4 - 7 

8+ 

2 

5 

12 

20 

50 

120 

22 

23 

24 

C3L 

C3M 

C3H 

Concrete Frame with Unreinforced 
Masonry Infill Walls 

Low-Rise 

Mid-Rise 

High-Rise 

1 - 3 

4 - 7 

8+ 

2 

5 

12 

20 

50 

120 

25 

PC1 

Precast Concrete Tilt-Up Walls 

 

All 

1 

15 

26 

27 

28 

PC2L 

PC2M 

PC2H 

Precast Concrete Frames with Concrete 
Shear Walls 

Low-Rise 

Mid-Rise 

High-Rise 

1 - 3 

4 - 7 

8+ 

2 

5 

12 

20 

50 

120 

29 

30 

RM1L 

RM1M 

Reinforced Masonry Bearing Walls with 
Wood or Metal Deck Diaphragms 

Low-Rise 

Mid-Rise 

1-3 

4+ 

2 

5 

20 

50 

31 

32 

33 

RM2L 

RM2M 

RM2H 

Reinforced Masonry Bearing Walls with 
Precast Concrete Diaphragms 

Low-Rise 

Mid-Rise 

High-Rise 

1 - 3 

4 - 7 

8+ 

2 

5 

12 

20 

50 

120 

34 

35 

URML 

URMM 

Unreinforced Masonry Bearing Walls 

Low-Rise 

Mid-Rise 

1 - 2 

3+ 

1 

3 

15 

35 

36 

MH 

Mobile Homes 

 

All 

1 

10 



5.2.1 Structural Systems 

 

A general description of each of the 16 structural systems of model building 
types is 
given in the following sections. 

 

Wood, Light Frame (W1): 

These are typically single-family or small, multiple-family dwellings of not 
more than 
5,000 square feet of floor area. The essential structural feature of these 
buildings is 
repetitive framing by wood rafters or joists on wood stud walls. Loads are light 
and 
spans are small. These buildings may have relatively heavy masonry chimneys and 
may 
be partially or fully covered with masonry veneer. Most of these buildings, 
especially the 




single-family residences, are not engineered but constructed in accordance with 
conventional construction provisions of building codes. Hence, they usually 
have the 
components of a lateral-force-resisting system even though it may be incomplete. 
Lateral 
loads are transferred by diaphragms to shear walls. The diaphragms are roof 
panels and 
floors that may be sheathed with sawn lumber, plywood or fiberboard sheathing. 
Shear 
walls are sheathed with boards, stucco, plaster, plywood, gypsum board, particle 
board, 
or fiberboard, or interior partition walls sheathed with plaster or gypsum 
board. 

 

Wood, Greater than 5,000 Sq. Ft. (W2): 

These buildings are typically commercial or industrial buildings, or multi-
family 
residential buildings with a floor area greater than 5,000 square feet. These 
buildings 
include structural systems framed by beams or major horizontally spanning 
members 
over columns. These horizontal members may be glue-laminated (glu-lam) wood, 
solid-
sawn wood beams, or wood trusses, or steel beams or trusses. Lateral loads 
usually are 
resisted by wood diaphragms and exterior walls sheathed with plywood, stucco, 
plaster, 
or other paneling. The walls may have diagonal rod bracing. Large openings for 
stores 
and garages often require post-and-beam framing. Lateral load resistance on 
those lines 
may be achieved with steel rigid frames (moment frames) or diagonal bracing. 

 

Steel Moment Frame (S1): 

These buildings have a frame of steel columns and beams. In some cases, the 
beam-
column connections have very small moment resisting capacity but, in other 
cases, some 
of the beams and columns are fully developed as moment frames to resist lateral 
forces. 
Usually the structure is concealed on the outside by exterior nonstructural 
walls, which 
can be of almost any material (curtain walls, brick masonry, or precast concrete 
panels), 
and on the inside by ceilings and column furring. Diaphragms transfer lateral 
loads to 
moment-resisting frames. The diaphragms can be almost any material. The frames 
develop their stiffness by full or partial moment connections. The frames can be 
located 
almost anywhere in the building. Usually the columns have their strong 
directions 
oriented so that some columns act primarily in one direction while the others 
act in the 
other direction. Steel moment frame buildings are typically more flexible than 
shear wall 
buildings. This low stiffness can result in large interstory drifts that may 
lead to 
relatively greater nonstructural damage. 

 

Steel Braced Frame (S2): 

These buildings are similar to steel moment frame buildings except that the 
vertical 
components of the lateral-force-resisting system are braced frames rather than 
moment 
frames. 

 

Steel Light Frame (S3): 

These buildings are pre-engineered and prefabricated with transverse rigid 
frames. The 
roof and walls consist of lightweight panels, usually corrugated metal. The 
frames are 
designed for maximum efficiency, often with tapered beam and column sections 
built up 
of light steel plates. The frames are built in segments and assembled in the 
field with 
bolted joints. Lateral loads in the transverse direction are resisted by the 
rigid frames 




with loads distributed to them by diaphragm elements, typically rod-braced steel 
roof 
framing bays. Tension rod bracing typically resists loads in the longitudinal 
direction. 

 

Steel Frame with Cast-In-Place Concrete Shear Walls (S4): 

The shear walls in these buildings are cast-in-place concrete and may be bearing 
walls. 
The steel frame is designed for vertical loads only. Diaphragms of almost any 
material 
transfer lateral loads to the shear walls. The steel frame may provide a 
secondary lateral-
force-resisting system depending on the stiffness of the frame and the moment 
capacity 
of the beam-column connections. In modern dual systems, the steel moment 
frames 
are designed to work together with the concrete shear walls. 

 

Steel Frame with Unreinforced Masonry Infill Walls (S5): 

This is one of the older types of buildings. The infill walls usually are offset 
from the 
exterior frame members, wrap around them, and present a smooth masonry exterior 
with 
no indication of the frame. Solidly infilled masonry panels, when they fully 
engage the 
surrounding frame members (i.e. lie in the same plane), may provide stiffness 
and lateral 
load resistance to the structure. 

 

Reinforced Concrete Moment Resisting Frames (C1): 

These buildings are similar to steel moment frame buildings except that the 
frames are 
reinforced concrete. There are a large variety of frame systems. Some older 
concrete 
frames may be proportioned and detailed such that brittle failure of the frame 
members 
can occur in earthquakes leading to partial or full collapse of the buildings. 
Modern 
frames in zones of high seismicity are proportioned and detailed for ductile 
behavior and 
are likely to undergo large deformations during an earthquake without brittle 
failure of 
frame members and collapse. 

 

Concrete Shear Walls (C2): 

The vertical components of the lateral-force-resisting system in these buildings 
are 
concrete shear walls that are usually bearing walls. In older buildings, the 
walls often are 
quite extensive and the wall stresses are low but reinforcing is light. In newer 
buildings, 
the shear walls often are limited in extent, generating concerns about boundary 
members 
and overturning forces. 

 

Concrete Frame Buildings with Unreinforced Masonry Infill Walls (C3): 

These buildings are similar to steel frame buildings with unreinforced masonry 
infill 
walls except that the frame is of reinforced concrete. In these buildings, the 
shear 
strength of the columns, after cracking of the infill, may limit the semi-
ductile behavior 
of the system. 

 

Precast Concrete Tilt-Up Walls (PC1): 

These buildings have a wood or metal deck roof diaphragm, which often is very 
large, 
that distributes lateral forces to precast concrete shear walls. The walls are 
thin but 
relatively heavy while the roofs are relatively light. Older or non-seismic-code 
buildings 
often have inadequate connections for anchorage of the walls to the roof for 
out-of-plane 
forces, and the panel connections often are brittle. Tilt-up buildings usually 
are one or 




two stories in height. Walls can have numerous openings for doors and windows of 
such 
size that the wall looks more like a frame than a shear wall. 

 

Precast Concrete Frames with Concrete Shear Walls (PC2): 

These buildings contain floor and roof diaphragms typically composed of precast 
concrete elements with or without cast-in-place concrete topping slabs. Precast 
concrete 
girders and columns support the diaphragms. The girders often bear on column 
corbels. 
Closure strips between precast floor elements and beam-column joints usually are 
cast-
in-place concrete. Welded steel inserts often are used to interconnect precast 
elements. 
Precast or cast-in-place concrete shear walls resist lateral loads. For 
buildings with 
precast frames and concrete shear walls to perform well, the details used to 
connect the 
structural elements must have sufficient strength and displacement capacity; 
however, in 
some cases, the connection details between the precast elements have negligible 
ductility. 

 

Reinforced Masonry Bearing Walls with Wood or Metal Deck Diaphragms (RM1): 

These buildings have perimeter bearing walls of reinforced brick or concrete-
block 
masonry. These walls are the vertical elements in the lateral-force-resisting 
system. The 
floors and roofs are framed with wood joists and beams either with plywood or 
braced 
sheathing, the latter either straight or diagonally sheathed, or with steel 
beams with metal 
deck with or without concrete fill. Interior wood posts or steel columns support 
wood 
floor framing; steel columns support steel beams. 

 

Reinforced Masonry Bearing Walls with Precast Concrete Diaphragms (RM2): 

These buildings have bearing walls similar to those of reinforced masonry 
bearing wall 
structures with wood or metal deck diaphragms, but the roof and floors are 
composed of 
precast concrete elements such as planks or tee-beams and the precast roof and 
floor 
elements are supported on interior beams and columns of steel or concrete (cast-
in-place 
or precast). The precast horizontal elements often have a cast-in-place topping. 

 

Unreinforced Masonry Bearing Walls (URM): 

These buildings include structural elements that vary depending on the 
buildings age 
and, to a lesser extent, its geographic location. In buildings built before 
1900, the 
majority of floor and roof construction consists of wood sheathing supported by 
wood 
framing. In large multistory buildings, the floors are cast-in-place concrete 
supported by 
the unreinforced masonry walls and/or steel or concrete interior framing. In 
unreinforced 
masonry constructed after 1950 (outside California) wood floors usually have 
plywood 
rather than board sheathing. In regions of lower seismicity, buildings of this 
type 
constructed more recently can include floor and roof framing that consists of 
metal deck 
and concrete fill supported by steel framing elements. The perimeter walls, and 
possibly 
some interior walls, are unreinforced masonry. The walls may or may not be 
anchored to 
the diaphragms. Ties between the walls and diaphragms are more common for the 
bearing walls than for walls that are parallel to the floor framing. Roof ties 
usually are 
less common and more erratically spaced than those at the floor levels. Interior 
partitions 
that interconnect the floors and roof can reduce diaphragm displacements. 

 

Mobile Homes (MH): 




These are prefabricated housing units that are trucked to the site and then 
placed on 
isolated piers, jack stands, or masonry block foundations (usually without any 
positive 
anchorage). Floors and roofs of mobile homes usually are constructed with 
plywood and 
outside surfaces are covered with sheet metal. 

 

5.2.2 Nonstructural Components 

Nonstructural components include a large variety of different architectural, 
mechanical 
and electrical components (e.g., components listed in the NEHRP seismic design 
provisions for new buildings [FEMA, 1997a]). Contents of the buildings are 
treated as a 
separate category. Nonstructural components are grouped as either "drift-
sensitive" or 
"acceleration-sensitive" components, in order to assess their damage due to an 
earthquake. Damage to drift-sensitive nonstructural components is primarily a 
function 
of interstory drift; damage to acceleration-sensitive nonstructural components 
and 
building contents is primarily a function of floor acceleration. Table 5.2 lists 
typical 
nonstructural components and building contents, and identifies each item as 
drift-
sensitive or acceleration sensitive. 

 

Anchorage/bracing of nonstructural components improves earthquake performance of 
most components although routine or typical anchorage/bracing provides only 
limited 
damage protection. It is assumed that typical nonstructural components and 
building 
contents have limited anchorage/bracing. Exceptions, such as special 
anchorage/bracing 
requirements for nonstructural components and contents of hospitals are 
addressed in 
Chapter 6. Nonstructural damage evaluation is dependent upon the response and 
performance of structural components, as well as being influenced by 
characteristics of 
nonstructural components themselves. Nonstructural damage simplifying 
assumptions 
are outlined in the following sections. 




Table 5.2 List of Typical Nonstructural Components and Contents of Buildings 

 

Type 

Item 

Drift-
Sensitive* 

Acceleration-
Sensitive* 

Architectural 

Nonbearing Walls/Partitions 

 

 

 

Cantilever Elements and Parapets 

 

 

 

Exterior Wall Panels 

 

 

 

Veneer and Finishes 

 

 

 

Penthouses 

 

 

 

Racks and Cabinets 

 

 

 

Access Floors 

 

 

 

Appendages and Ornaments 

 

 

Mechanical 

General Mechanical (boilers, etc.) 

 

 

and 

Manufacturing and Process Machinery 

 

 

Electrical 

Piping Systems 

 

 

 

Storage Tanks and Spheres 

 

 

 

HVAC Systems (chillers, ductwork, etc.) 

 

 

 

Elevators 

 

 

 

Trussed Towers 

 

 

 

General Electrical (switchgear, ducts, etc.) 

 

 

 

Lighting Fixtures 

 

 

Contents 

File Cabinets, Bookcases, etc. 

 

 

 

Office Equipment and Furnishings 

 

 

 

Computer/Communication Equipment 

 

 

 

Nonpermanent Manufacturing Equipment 

 

 

 

Manufacturing/Storage Inventory 

 

 

 

Art and other Valuable Objects 

 

 



* Solid dots indicate primary cause of damage, open dots indicate secondary 
cause of damage 

 

5.3 Description of Building Damage States 

 

The results of damage estimation methods described in this chapter (i.e., damage 
predictions for model building types for a given level of ground shaking) are 
used in 
other modules of the methodology to estimate: (1) casualties due to structural 
damage, 
including fatalities, (2) monetary losses due to building damage (i.e. cost of 
repairing or 
replacing damaged buildings and their contents); (3) monetary losses resulting 
from 
building damage and closure (e.g., losses due to business interruption); (4) 
social impacts 
(e.g., loss of shelter); and, (5) other economic and social impacts. 

 

The building damage predictions may also be used to study expected damage 
patterns in 
a given region for different scenario earthquakes (e.g., to identify the most 
vulnerable 
building types, or the areas expected to have the most damaged buildings). 

 

In order to meet the needs of such broad purposes, damage predictions must allow 
the 
user to glean the nature and extent of the physical damage to a building type 
from the 
damage prediction output so that life-safety, societal functional and monetary 
losses 
which result from the damage can be estimated. Building damage can best be 
described 
in terms of its components (beams, columns, walls, ceilings, piping, HVAC 
equipment, 
etc.). For example, such component damage descriptions as shear walls are 
cracked, 




ceiling tiles fell, diagonal bracing buckled, wall panels fell out, etc. 
used together 
with such terms as some and most would be sufficient to describe the nature 
and 
extent of overall building damage. 

 

Damage to nonstructural components of buildings (i.e., architectural components, 
such as 
partition walls and ceilings, and building mechanical/electrical systems) 
primarily affects 
monetary and societal functional losses and generates numerous casualties of 
mostly 
light-to-moderate severity. Damage to structural components (i.e., the gravity 
and 
lateral-load-resisting systems) of buildings, Hazard mitigation measures are 
different for 
these two categories of building components as well. Hence, it is desirable to 
separately 
estimate structural and nonstructural damage. 

 

Building damage varies from none to complete as a continuous function of 
building 
deformations (building response). Wall cracks may vary from invisible or 
hairline 
cracks to cracks of several inches wide. Generalized ranges of damage are 
used by 
the Methodology to describe structural and nonstructural damage, since it is not 
practical 
to describe building damage as a continuous function. 

 

The Methodology predicts a structural and nonstructural damage state in terms of 
one of 
four ranges of damage or damage states: Slight, Moderate, Extensive, and 
Complete. 
For example, the Slight damage state extends from the threshold of Slight damage 
up to 
the threshold of Moderate damage. General descriptions of these damage states 
are 
provided for all model building types with reference to observable damage 
incurred by 
structural (Section 5.3.1) and nonstructural building components (Section 
5.3.2). 
Damage predictions resulting from this physical damage estimation method are 
then 
expressed in terms of the probability of a building being in any of these four 
damage 
states. 

 

5.3.1 Structural Damage 

 

Descriptions for Slight, Moderate, Extensive, and Complete structural damage 
states for 
the 16 basic model building types are provided below. For estimating casualties, 
the 
descriptions of Complete damage include the fraction of the total floor area of 
each 
model building type that is likely to collapse. Collapse fractions are based on 
judgment 
and limited earthquake data considering the material and construction of 
different model 
building types. 

 

It is noted that in some cases the structural damage is not directly observable 
because the 
structural elements are inaccessible or not visible due to architectural 
finishes or 
fireproofing. Hence, these structural damage states are described, when 
necessary, with 
reference to certain effects on nonstructural elements that may be indicative of 
the 
structural damage state of concern. Small cracks are assumed, throughout this 
section, to 
be visible cracks with a maximum width of less than 1/8. Cracks wider than 1/8 
are 
referred to as large cracks. 

 




Wood, Light Frame (W1): 

Slight Structural Damage: Small plaster or gypsum-board cracks at corners of 
door and 
window openings and wall-ceiling intersections; small cracks in masonry chimneys 
and 
masonry veneer. 

Moderate Structural Damage: Large plaster or gypsum-board cracks at corners of 
door 
and window openings; small diagonal cracks across shear wall panels exhibited by 
small 
cracks in stucco and gypsum wall panels; large cracks in brick chimneys; 
toppling of tall 
masonry chimneys. 

Extensive Structural Damage: Large diagonal cracks across shear wall panels or 
large 
cracks at plywood joints; permanent lateral movement of floors and roof; 
toppling of 
most brick chimneys; cracks in foundations; splitting of wood sill plates and/or 
slippage 
of structure over foundations; partial collapse of room-over-garage or other 
soft-
story configurations; small foundations cracks. 

Complete Structural Damage: Structure may have large permanent lateral 
displacement, may collapse, or be in imminent danger of collapse due to cripple 
wall 
failure or the failure of the lateral load resisting system; some structures may 
slip and fall 
off the foundations; large foundation cracks. Approximately 3% of the total area 
of W1 
buildings with Complete damage is expected to be collapsed. 

 

Wood, Commercial and Industrial (W2): 

Slight Structural Damage: Small cracks at corners of door and window openings 
and 
wall-ceiling intersections; small cracks on stucco and plaster walls. Some 
slippage may 
be observed at bolted connections. 

Moderate Structural Damage: Larger cracks at corners of door and window 
openings; 
small diagonal cracks across shear wall panels exhibited by cracks in stucco and 
gypsum 
wall panels; minor slack (less than 1/8 extension) in diagonal rod bracing 
requiring re-
tightening; minor lateral set at store fronts and other large openings; small 
cracks or 
wood splitting may be observed at bolted connections. 

Extensive Structural Damage: Large diagonal cracks across shear wall panels; 
large 
slack in diagonal rod braces and/or broken braces; permanent lateral movement of 
floors 
and roof; cracks in foundations; splitting of wood sill plates and/or slippage 
of structure 
over foundations; partial collapse of soft-story configurations; bolt slippage 
and wood 
splitting at bolted connections. 

Complete Structural Damage: Structure may have large permanent lateral 
displacement, may collapse or be in imminent danger of collapse due to failed 
shear 
walls, broken brace rods or failed framing connections; it may fall its 
foundations; large 
cracks in the foundations. Approximately 3% of the total area of W2 buildings 
with 
Complete damage is expected to be collapsed. 

 

Steel Moment Frame (S1): 

Slight Structural Damage: Minor deformations in connections or hairline cracks 
in few 
welds. 

Moderate Structural Damage: Some steel members have yielded exhibiting 
observable 
permanent rotations at connections; few welded connections may exhibit major 
cracks 
through welds or few bolted connections may exhibit broken bolts or enlarged 
bolt holes. 




Extensive Structural Damage: Most steel members have exceeded their yield 
capacity, 
resulting in significant permanent lateral deformation of the structure. Some of 
the 
structural members or connections may have exceeded their ultimate capacity 
exhibited 
by major permanent member rotations at connections, buckled flanges and failed 
connections. Partial collapse of portions of structure is possible due to failed 
critical 
elements and/or connections. 

Complete Structural Damage: Significant portion of the structural elements have 
exceeded their ultimate capacities or some critical structural elements or 
connections 
have failed resulting in dangerous permanent lateral displacement, partial 
collapse or 
collapse of the building. Approximately 8%(low-rise), 5%(mid-rise) or 3%(high-
rise) of 
the total area of S1 buildings with Complete damage is expected to be collapsed. 

 

Steel Braced Frame (S2): 

Slight Structural Damage: Few steel braces have yielded which may be indicated 
by 
minor stretching and/or buckling of slender brace members; minor cracks in 
welded 
connections; minor deformations in bolted brace connections. 

Moderate Structural Damage: Some steel braces have yielded exhibiting observable 
stretching and/or buckling of braces; few braces, other members or connections 
have 
indications of reaching their ultimate capacity exhibited by buckled braces, 
cracked 
welds, or failed bolted connections. 

Extensive Structural Damage: Most steel brace and other members have exceeded 
their 
yield capacity, resulting in significant permanent lateral deformation of the 
structure. 
Some structural members or connections have exceeded their ultimate capacity 
exhibited 
by buckled or broken braces, flange buckling, broken welds, or failed bolted 
connections. 
Anchor bolts at columns may be stretched. Partial collapse of portions of 
structure is 
possible due to failure of critical elements or connections. 

Complete Structural Damage: Most the structural elements have reached their 
ultimate 
capacities or some critical members or connections have failed resulting in 
dangerous 
permanent lateral deflection, partial collapse or collapse of the building. 
Approximately 
8%(low-rise), 5%(mid-rise) or 3%(high-rise) of the total area of S2 buildings 
with 
Complete damage is expected to be collapsed. 

 

Steel Light Frame (S3): 

These structures are mostly single story structures combining rod-braced frames 
in one 
direction and moment frames in the other. Due to repetitive nature of the 
structural 
systems, the type of damage to structural members is expected to be rather 
uniform 
throughout the structure. 

Slight Structural Damage: Few steel rod braces have yielded which may be 
indicated 
by minor sagging of rod braces. Minor cracking at welded connections or minor 
deformations at bolted connections of moment frames may be observed. 

Moderate Structural Damage: Most steel braces have yielded exhibiting observable 
significantly sagging rod braces; few brace connections may be broken. Some weld 
cracking may be observed in the moment frame connections. 

Extensive Structural Damage: Significant permanent lateral deformation of the 
structure due to broken brace rods, stretched anchor bolts and permanent 
deformations at 




moment frame members. Some screw or welded attachments of roof and wall siding 
to 
steel framing may be broken. Some purlin and girt connections may be broken. 

Complete Structural Damage: Structure is collapsed or in imminent danger of 
collapse 
due to broken rod bracing, failed anchor bolts or failed structural members or 
connections. Approximately 3% of the total area of S3 buildings with Complete 
damage 
is expected to be collapsed. 

 

Steel Frame with Cast-In-Place Concrete Shear Walls (S4): 

This is a composite structural system where primary lateral-force-resisting 
system is 
the concrete shear walls. Hence, slight, Moderate and Extensive damage states 
are likely 
to be determined by the shear walls while the collapse damage state would be 
determined 
by the failure of the structural frame. 

Slight Structural Damage: Diagonal hairline cracks on most concrete shear wall 
surfaces; minor concrete spalling at few locations. 

Moderate Structural Damage: Most shear wall surfaces exhibit diagonal cracks; 
some 
of the shear walls have exceeded their yield capacities exhibited by larger 
diagonal 
cracks and concrete spalling at wall ends. 

Extensive Structural Damage: Most concrete shear walls have exceeded their yield 
capacities; few walls have reached or exceeded their ultimate capacity exhibited 
by large 
through-the wall diagonal cracks, extensive spalling around the cracks and 
visibly 
buckled wall reinforcement. Partial collapse may occur due to failed connections 
of steel 
framing to concrete walls. Some damage may be observed in steel frame 
connections. 

Complete Structural Damage: Structure may be in danger of collapse or collapse 
due to 
total failure of shear walls and loss of stability of the steel frames. 
Approximately 
8%(low-rise), 5%(mid-rise) or 3%(high-rise) of the total area of S4 buildings 
with 
Complete damage is expected to be collapsed. 

 

Steel Frame with Unreinforced Masonry Infill Walls (S5): 

This is a composite structural system where the initial lateral resistance is 
provided by 
the infill walls. Upon cracking of the infills, further lateral resistance is 
provided by the 
steel frames braced by the infill walls acting as diagonal compression struts. 
Collapse 
of the structure results when the infill walls disintegrate (due to compression 
failure of 
the masonry struts) and the steel frame loses its stability. 

 

Slight Structural Damage: Diagonal (sometimes horizontal) hairline cracks on 
most 
infill walls; cracks at frame-infill interfaces. 

Moderate Structural Damage: Most infill wall surfaces exhibit larger diagonal or 
horizontal cracks; some walls exhibit crushing of brick around beam-column 
connections. 

Extensive Structural Damage: Most infill walls exhibit large cracks; some bricks 
may 
be dislodged and fall; some infill walls may bulge out-of-plane; few walls may 
fall off 
partially or fully; some steel frame connections may have failed. Structure may 
exhibit 
permanent lateral deformation or partial collapse due to failure of some 
critical members. 

Complete Structural Damage: Structure is collapsed or in danger of imminent 
collapse 
due to total failure of many infill walls and loss of stability of the steel 
frames. . 




Approximately 8%(low-rise), 5%(mid-rise) or 3%(high-rise) of the total area of 
S5 
buildings with Complete damage is expected to be collapsed. 

 

Reinforced Concrete Moment Resisting Frames (C1): 

Slight Structural Damage: Flexural or shear type hairline cracks in some beams 
and 
columns near joints or within joints. 

Moderate Structural Damage: Most beams and columns exhibit hairline cracks. In 
ductile frames some of the frame elements have reached yield capacity indicated 
by 
larger flexural cracks and some concrete spalling. Nonductile frames may exhibit 
larger 
shear cracks and spalling. 

Extensive Structural Damage: Some of the frame elements have reached their 
ultimate 
capacity indicated in ductile frames by large flexural cracks, spalled concrete 
and 
buckled main reinforcement; nonductile frame elements may have suffered shear 
failures 
or bond failures at reinforcement splices, or broken ties or buckled main 
reinforcement in 
columns which may result in partial collapse. 

Complete Structural Damage: Structure is collapsed or in imminent danger of 
collapse 
due to brittle failure of nonductile frame elements or loss of frame stability. 
Approximately 13%(low-rise), 10%(mid-rise) or 5%(high-rise) of the total area of 
C1 
buildings with Complete damage is expected to be collapsed. 

 

Concrete Shear Walls (C2): 

Slight Structural Damage: Diagonal hairline cracks on most concrete shear wall 
surfaces; minor concrete spalling at few locations. 

Moderate Structural Damage: Most shear wall surfaces exhibit diagonal cracks; 
some 
shear walls have exceeded yield capacity indicated by larger diagonal cracks and 
concrete spalling at wall ends. 

Extensive Structural Damage: Most concrete shear walls have exceeded their yield 
capacities; some walls have exceeded their ultimate capacities indicated by 
large, 
through-the-wall diagonal cracks, extensive spalling around the cracks and 
visibly 
buckled wall reinforcement or rotation of narrow walls with inadequate 
foundations. 
Partial collapse may occur due to failure of nonductile columns not designed to 
resist 
lateral loads. 

Complete Structural Damage: Structure has collapsed or is in imminent danger of 
collapse due to failure of most of the shear walls and failure of some critical 
beams or 
columns. Approximately 13%(low-rise), 10%(mid-rise) or 5%(high-rise) of the 
total area 
of C2 buildings with Complete damage is expected to be collapsed. 

 

Concrete Frame Buildings with Unreinforced Masonry Infill Walls (C3): 

This is a composite structural system where the initial lateral resistance is 
provided by 
the infill walls. Upon cracking of the infills, further lateral resistance is 
provided by the 
concrete frame braced by the infill acting as diagonal compression struts. 
Collapse of 
the structure results when the infill walls disintegrate (due to compression 
failure of the 
masonry struts) and the frame loses stability, or when the concrete columns 
suffer 
shear failures due to reduced effective height and the high shear forces imposed 
on them 
by the masonry compression struts. 

 




Slight Structural Damage: Diagonal (sometimes horizontal) hairline cracks on 
most 
infill walls; cracks at frame-infill interfaces. 

Moderate Structural Damage: Most infill wall surfaces exhibit larger diagonal or 
horizontal cracks; some walls exhibit crushing of brick around beam-column 
connections. Diagonal shear cracks may be observed in concrete beams or columns. 

Extensive Structural Damage: Most infill walls exhibit large cracks; some bricks 
may 
dislodge and fall; some infill walls may bulge out-of-plane; few walls may fall 
partially 
or fully; few concrete columns or beams may fail in shear resulting in partial 
collapse. 
Structure may exhibit permanent lateral deformation. 

Complete Structural Damage: Structure has collapsed or is in imminent danger of 
collapse due to a combination of total failure of the infill walls and 
nonductile failure of 
the concrete beams and columns. Approximately 15%(low-rise), 13%(mid-rise) or 
5%(high-rise) of the total area of C3 buildings with Complete damage is expected 
to be 
collapsed. 

 

Precast Concrete Tilt-Up Walls (PC1): 

Slight Structural Damage: Diagonal hairline cracks on concrete shear wall 
surfaces; 
larger cracks around door and window openings in walls with large proportion of 
openings; minor concrete spalling at few locations; minor separation of walls 
from the 
floor and roof diaphragms; hairline cracks around metal connectors between wall 
panels 
and at connections of beams to walls. 

Moderate Structural Damage: Most wall surfaces exhibit diagonal cracks; larger 
cracks 
in walls with door or window openings; few shear walls have exceeded their yield 
capacities indicated by larger diagonal cracks and concrete spalling. Cracks may 
appear 
at top of walls near panel intersections indicating chord yielding. Some walls 
may 
have visibly pulled away from the roof. Some welded panel connections may have 
been 
broken, indicated by spalled concrete around connections. Some spalling may be 
observed at the connections of beams to walls. 

Extensive Structural Damage: In buildings with relatively large area of wall 
openings 
most concrete shear walls have exceeded their yield capacities and some have 
exceeded 
their ultimate capacities indicated by large, through-the-wall diagonal cracks, 
extensive 
spalling around the cracks and visibly buckled wall reinforcement. The plywood 
diaphragms may exhibit cracking and separation along plywood joints. Partial 
collapse 
of the roof may result from the failure of the wall-to-diaphragm anchorages 
sometimes 
with falling of wall panels. 

Complete Structural Damage: Structure is collapsed or is in imminent danger of 
collapse due to failure of the wall-to-roof anchorages, splitting of ledgers, or 
failure of 
plywood-to-ledger nailing; failure of beams connections at walls; failure of 
roof or floor 
diaphragms; or, failure of the wall panels. Approximately 15% of the total area 
of PC1 
buildings with Complete damage is expected to be collapsed. 

 

Precast Concrete Frames with Concrete Shear Walls (PC2): 

Slight Structural Damage: Diagonal hairline cracks on most shear wall surfaces; 
minor 
concrete spalling at few connections of precast members. 

Moderate Structural Damage: Most shear wall surfaces exhibit diagonal cracks; 
some 
shear walls have exceeded their yield capacities indicated by larger cracks and 
concrete 




spalling at wall ends; observable distress or movement at connections of precast 
frame 
connections, some failures at metal inserts and welded connections. 

Extensive Structural Damage: Most concrete shear walls have exceeded their yield 
capacities; some walls may have reached their ultimate capacities indicated by 
large, 
through-the wall diagonal cracks, extensive spalling around the cracks and 
visibly 
buckled wall reinforcement. Some critical precast frame connections may have 
failed 
resulting partial collapse. 

Complete Structural Damage: Structure has collapsed or is in imminent danger of 
collapse due to failure of the shear walls and/or failures at precast frame 
connections. 
Approximately 15%(low-rise), 13%(mid-rise) or 10%(high-rise) of the total area 
of PC2 
buildings with Complete damage is expected to be collapsed. 

 

Reinforced Masonry Bearing Walls with Wood or Metal Deck Diaphragms (RM1): 

Slight Structural Damage: Diagonal hairline cracks on masonry wall surfaces; 
larger 
cracks around door and window openings in walls with large proportion of 
openings; 
minor separation of walls from the floor and roof diaphragms. 

Moderate Structural Damage: Most wall surfaces exhibit diagonal cracks; some of 
the 
shear walls have exceeded their yield capacities indicated by larger diagonal 
cracks. 
Some walls may have visibly pulled away from the roof. 

Extensive Structural Damage: In buildings with relatively large area of wall 
openings 
most shear walls have exceeded their yield capacities and some of the walls have 
exceeded their ultimate capacities indicated by large, through-the-wall diagonal 
cracks 
and visibly buckled wall reinforcement. The plywood diaphragms may exhibit 
cracking 
and separation along plywood joints. Partial collapse of the roof may result 
from failure 
of the wall-to-diaphragm anchorages or the connections of beams to walls. 

Complete Structural Damage: Structure has collapsed or is in imminent danger of 
collapse due to failure of the wall anchorages or due to failure of the wall 
panels. 
Approximately 13%(low-rise) or 10%(mid-rise) of the total area of RM1 buildings 
with 
Complete damage is expected to be collapsed. 

 

Reinforced Masonry Bearing Walls with Precast Concrete Diaphragms (RM2): 

Slight Structural Damage: Diagonal hairline cracks on masonry wall surfaces; 
larger 
cracks around door and window openings in walls with large proportion of 
openings. 

Moderate Structural Damage: Most wall surfaces exhibit diagonal cracks; some of 
the 
shear walls have exceeded their yield capacities indicated by larger cracks. 

Extensive Structural Damage: In buildings with relatively large area of wall 
openings 
most shear walls have exceeded their yield capacities and some of the walls have 
exceeded their ultimate capacities exhibited by large, through-the wall diagonal 
cracks 
and visibly buckled wall reinforcement. The diaphragms may also exhibit cracking 

Complete Structural Damage: Structure is collapsed or is in imminent danger of 
collapse due to failure of the walls. Approximately 13%(low-rise), 10%(mid-rise) 
or 
5%(high-rise) of the total area of RM2 buildings with Complete damage is 
expected to be 
collapsed. 

 

Unreinforced Masonry Bearing Walls (URM): 




Slight Structural Damage: Diagonal, stair-step hairline cracks on masonry wall 
surfaces; larger cracks around door and window openings in walls with large 
proportion 
of openings; movements of lintels; cracks at the base of parapets. 

Moderate Structural Damage: Most wall surfaces exhibit diagonal cracks; some of 
the 
walls exhibit larger diagonal cracks; masonry walls may have visible separation 
from 
diaphragms; significant cracking of parapets; some masonry may fall from walls 
or 
parapets. 

Extensive Structural Damage: In buildings with relatively large area of wall 
openings 
most walls have suffered extensive cracking. Some parapets and gable end walls 
have 
fallen. Beams or trusses may have moved relative to their supports. 

Complete Structural Damage: Structure has collapsed or is in imminent danger of 
collapse due to in-plane or out-of-plane failure of the walls. Approximately 15% 
of the 
total area of URM buildings with Complete damage is expected to be collapsed. 

 

Mobile Homes (MH): 

Slight Structural Damage: Damage to some porches, stairs or other attached 
components. 

Moderate Structural Damage: Major movement of the mobile home over its supports 
resulting in some damage to metal siding and stairs and requiring resetting of 
the mobile 
home on its supports. 

Extensive Structural Damage: Mobile home has fallen partially off its supports, 
often 
severing utility lines. 

Complete Structural Damage: Mobile home has totally fallen off its supports; 
usually 
severing utility lines, with steep jack stands penetrating through the floor. 
Approximately 
3% of the total area of MH buildings with Complete damage is expected to be 
collapsed. 

 

5.3.2 Nonstructural Damage 

 

Four damage states are used to describe nonstructural damage: Slight, Moderate, 
Extensive and Complete nonstructural damage. Nonstructural damage is considered 
to 
be independent of the structural model building type (i.e. partitions, ceilings, 
cladding, 
etc. are assumed to incur the same damage when subjected to the same interstory 
drift or 
floor acceleration whether they are in a steel frame building or in a concrete 
shear wall 
building), consequently, building-specific damage state descriptions are not 
meaningful. 
Instead, general descriptions of nonstructural damage states are provided for 
common 
nonstructural systems. 

 

Damage to drift-sensitive nonstructural components is primarily a function of 
interstory 
drift (e.g. full-height drywall partitions) while for acceleration-sensitive 
components (e.g. 
mechanical equipment) damage is a function of the floor acceleration. Developing 
fragility curves for each possible nonstructural component is not practicable 
for the 
purposes of regional loss estimation and there is insufficient data to develop 
such 
fragility curves. Hence, in this methodology nonstructural building components 
are 
grouped into drift-sensitive and acceleration-sensitive component groups, and 
the damage 
functions estimated for each group are assumed to be "typical" of it sub-
components. 
Note, however, that damage depends on the anchorage/bracing provided to the 




nonstructural components. Damageability characteristics of each group are 
described by 
a set of fragility curves (see Subsection 5.4.3.3). 

 

The type of nonstructural components in a given building is a function of the 
building 
occupancy-use classification. For example, single-family residences would not 
have 
curtain wall panels, suspended ceilings, elevators, etc. while these items would 
be found 
in an office building. Hence, the relative values of nonstructural components in 
relation 
to the overall building replacement value vary with type of occupancy. In 
Chapter 15, 
estimates of replacement cost breakdown between structural building components 
for 
different occupancy/use related classifications are provided; further breakdowns 
are 
provided by drift- and acceleration-sensitive nonstructural components. 

 

In the following, general descriptions of the four nonstructural damage states 
are 
described for common nonstructural building components: 

 

Partitions Walls 

Slight Nonstructural Damage: A few cracks are observed at intersections of walls 
and 
ceilings and at corners of door openings. 

Moderate Nonstructural Damage: Larger and more extensive cracks requiring repair 
and repainting; some partitions may require replacement of gypsum board or other 
finishes. 

Extensive Nonstructural Damage: Most of the partitions are cracked and a 
significant 
portion may require replacement of finishes; some door frames in the partitions 
are also 
damaged and require re-setting. 

Complete Nonstructural Damage: Most partition finish materials and framing may 
have to be removed and replaced; damaged studs repaired, and walls be 
refinished. Most 
door frames may also have to be repaired and replaced. 

 

Suspended Ceilings 

Slight Nonstructural Damage: A few ceiling tiles have moved or fallen down. 

Moderate Nonstructural Damage: Falling of tiles is more extensive; in addition 
the 
ceiling support framing (T-bars) has disconnected and/or buckled at few 
locations; lenses 
have fallen off of some light fixtures and a few fixtures have fallen; localized 
repairs are 
necessary. 

Extensive Nonstructural Damage: The ceiling system exhibits extensive buckling, 
disconnected t-bars and falling ceiling tiles; ceiling partially collapses at 
few locations 
and some light fixtures fall; repair typically involves removal of most or all 
ceiling tiles. 

Complete Nonstructural Damage: The ceiling system is buckled throughout and/or 
fallen and requires complete replacement; many light fixtures fall. 

 

Exterior Wall Panels 

Slight Nonstructural Damage: Slight movement of the panels, requiring 
realignment. 

Moderate Nonstructural Damage: The movements are more extensive; connections of 
panels to structural frame are damaged requiring further inspection and repairs; 
some 
window frames may need realignment 




Extensive Nonstructural Damage: Most of the panels are cracked or otherwise 
damaged and misaligned, and most panel connections to the structural frame are 
damaged 
requiring thorough review and repairs; few panels fall or are in imminent danger 
of 
falling; some window panes are broken and some pieces of glass have fallen. 

Complete Nonstructural Damage: Most panels are severely damaged, most 
connections are broken or severely damaged, some panels have fallen and most are 
in 
imminent danger of falling; extensive glass breakage and falling. 

 

Electrical-Mechanical Equipment, Piping, Ducts 

 

Slight Nonstructural Damage: The most vulnerable equipment (e.g. unanchored or 
on 
spring isolators) moves and damages attached piping or ducts. 

Moderate Nonstructural Damage: Movements are larger and damage is more 
extensive; piping leaks at few locations; elevator machinery and rails may 
require 
realignment 

Extensive Nonstructural Damage: Equipment on spring isolators topples and falls; 
other unanchored equipment slides or falls breaking connections to piping and 
ducts; 
leaks develop at many locations; anchored equipment indicate stretched bolts or 
strain at 
anchorages. 

Complete Nonstructural Damage: Equipment is damaged by sliding, overturning or 
failure of their supports and is not operable; piping is leaking at many 
locations; some 
pipe and duct supports have failed causing pipes and ducts to fall or hang down; 
elevator 
rails are buckled or have broken supports and/or counterweights have derailed. 

 

5.4 Building Damage Due to Ground Shaking 

 

5.4.1 Overview 

 

This section describes capacity and fragility curves used in the Methodology to 
estimate 
the probability of Slight, Moderate, Extensive and Complete damage to general 
building 
stocks. General building stock represents a population of a given model building 
type 
designed to either High-Code, Moderate-Code, or Low-Code seismic standards, or 
not 
seismically designed, referred to as to a Pre-Code buildings. Chapter 6 
describes Special 
building damage functions for estimating damage to hospitals and other essential 
facilities that are designed and constructed to above average seismic standards. 

 

Capacity curves and fragility curves for High-Code, Moderate-Code, Low-Code and 
Pre-
Code buildings are based on modern code (e.g., 1976 Uniform Building Code, 1985 
NEHRP Provisions, or later editions of these model codes). Design criteria for 
various 
seismic design zones, as shown in Table 5.3. Additional description of seismic 
levels 
may be found in Section 5.7. 

 

Table 5.3 Approximate Basis for Seismic Design Levels 

 




Seismic Design 
Level 

Seismic Zone 

(Uniform Building Code) 

Map Area 

(NEHRP Provisions) 

High-Code 

Moderate-Code 

Low-Code 

Pre-Code 

4 

2B 

1 

0 

7 

5 

3 

1 



 

The capacity and fragility curves represent buildings designed and constructed 
to modern 
seismic code provisions. Study areas (e.g., census tracts) of recent 
construction are 
appropriately modeled using building damage functions with a seismic design 
level that 
corresponds to the seismic zone or map area of the governing provisions. Older 
areas of 
construction, not conforming to modern standards, should be modeled using a 
lower level 
of seismic design. For example, in areas of high seismicity (e.g., coastal 
California), 
buildings of newer construction (e.g., post-1973) are best represented by High-
Code 
damage functions, while buildings of older construction would be best 
represented by 
Moderate-Code damage functions, if built after about 1940, or by Pre-Code damage 
functions, if built before about 1940 (i.e., before seismic codes existed). Pre-
Code 
damage functions are appropriate for modeling older buildings that were not 
designed for 
earthquake load, regardless of where they are located in the United States. 
Guidance is 
provided to expert users in Section 5.7 for selection of appropriate building 
damage 
functions 

 

5.4.2 Capacity Curves 

 

Most buildings are presently designed or evaluated using linear-elastic analysis 
methods, 
primarily due to the relative simplicity of these methods in comparison to more 
complex, 
nonlinear methods. Typically, building response is based on linear-elastic 
properties of 
the structure and forces corresponding to the design-basis earthquake. For 
design of 
building elements, linear-elastic (5%-damped) response is reduced by a factor 
(e.g. the 
R-Factor in 1994 NEHRP Provisions) that varies for different types of lateral 
force 
resisting systems. The reduction factor is based on empirical data and judgment 
that 
account for the inelastic deformation capability (ductility) of the structural 
system, 
redundancy, overstrength, increased damping (above 5% of critical) at large 
deformations, and other factors that influence building capacity. Although this 
force-
based approach is difficult to justify by rational engineering analysis, 
buildings designed 
using these methods have performed reasonably well in past earthquakes. Aspects 
of 
these methods found not to work well in earthquakes have been studied and 
improved. In 
most cases, building capacity has been increased by improvements to detailing 
practices 
(e.g., better confinement of steel reinforcement in concrete elements). 

 

Except for a few brittle systems and acceleration-sensitive elements, building 
damage is 
primarily a function of building displacement, rather than force. In the 
inelastic range of 
building response, increasingly larger damage would result from increased 
building 
displacement although lateral force would remain constant or decrease. Hence, 
successful prediction of earthquake damage to buildings requires reasonably 
accurate 




estimation of building displacement response in the inelastic range. This, 
however, can 
not be accomplished using linear-elastic methods, since the buildings respond 
inelastically to earthquake ground shaking of magnitudes of interest for damage 
prediction. Building capacity (push-over) curves, used with capacity spectrum 
method 
(CSM) techniques [Mahaney, et. al., 1993, Kircher, 1996], provide simple and 
reasonably 
accurate means of predicting inelastic building displacement response for damage 
estimation purposes. 

 

A building capacity curve (also known as a push-over curve) is a plot of a 
buildings 
lateral load resistance as a function of a characteristic lateral displacement 
(i.e., a force-
deflection plot). It is derived from a plot of static-equivalent base shear 
versus building 
(e.g., roof) displacement. In order to facilitate direct comparison with 
earthquake 
demand (i.e. overlaying the capacity curve with a response spectrum), the force 
(base 
shear) axis is converted to spectral acceleration and the displacement axis is 
converted to 
spectral displacement. Such a plot provides an estimate of the buildings true 
deflection (displacement response) for any given earthquake response spectrum. 

 

The building capacity curves developed for the Methodology are based on 
engineering 
design parameters and judgment. Three control points that define model building 
capacity describe each curve: 

 

 Design Capacity 

 Yield Capacity 

 Ultimate Capacity 

 

Design capacity represents the nominal building strength required by current 
model 
seismic code provisions (e.g., 1994 NEHRP Provisions) or an estimate of the 
nominal 
strength for buildings not designed for earthquake loads. Wind design is not 
considered 
in the estimation of design capacity, and certain buildings (e.g., tall 
buildings located in 
zones of low or moderate seismicity) may have a lateral design strength 
considerably 
greater than that based on seismic code provisions. 

 

Yield capacity represents the true lateral strength of the building considering 
redundancies in design, conservatism in code requirements and true (rather than 
nominal) 
strength of materials. Ultimate capacity represents the maximum strength of the 
building 
when the global structural system has reached a fully plastic state. Ultimate 
capacity 
implicitly accounts for loss of strength due to shear failure of brittle 
elements. Typically, 
buildings are assumed capable of deforming beyond their ultimate point without 
loss of 
stability, but their structural system provides no additional resistance to 
lateral 
earthquake force. 

 

Up to the yield point, the building capacity curve is assumed to be linear with 
stiffness 
based on an estimate of the true period of the building. The true period is 
typically 
longer than the code-specified period of the building due to flexing of 
diaphragms of 
short, stiff buildings, flexural cracking of elements of concrete and masonry 
structures, 
flexibility of foundations and other factors observed to affect building 
stiffness. From the 




yield point to the ultimate point, the capacity curve transitions in slope from 
an 
essentially elastic state to a fully plastic state. The capacity curve is 
assumed to remain 
plastic past the ultimate point. An example building capacity curve is shown in 
Figure 
5.3. 

 

SaSdDyDuDdAuAyAdUltimateCapacityYieldCapacityDesignCapacity 


 

Figure 5.3 Example Building Capacity Curve. 

 

The building capacity curves are constructed based on estimates of engineering 
properties that affect the design, yield and ultimate capacities of each model 
building 
type. These properties are defined by the following parameters: 

 

 Cs design strength coefficient (fraction of buildings weight), 

 Te true elastic fundamental-mode period of building (seconds), 

 a1 fraction of building weight effective in push-over mode, 

 a2 fraction of building height at location of push-over mode displacement, 

 . overstrength factor relating true yield strength to design strength, 

 . overstrength factor relating ultimate strength to yield strength, and 

  ductility factor relating ultimate displacement to . times the yield 
displacement (i.e., assumed point of significant yielding of the structure) 

 

The design strength, Cs, is approximately based, on the lateral-force design 
requirements 
of current seismic codes (e.g., 1994 NEHRP Provisions). These requirements are a 
function of the buildings seismic zone location and other factors including: 
site soil 
condition, type of lateral-force-resisting system and building period. For each 
of the four 
design levels (High-Code, Moderate-Code, Low-Code and Pre-Code), design capacity 
is 
based on the best estimate of typical design properties. Table 5.4 summarizes 
design 
capacity for each building type and design level. Building period, Te, push-over 
mode 
parameters a1 and a2, the ratio of yield to design strength, ., and the ratio of 
ultimate to 




yield strength, ., are assumed to be independent of design level. Values of 
these 
parameters are summarized in Table 5.5 for each building type. Values of the 
ductility 
factor, , are given in Table 5.6 for each building type and design level. Note 
that for the 
following tables, shaded boxes indicate types that are not permitted by current 
seismic 
codes. 




 

Table 5.4 Code Building Capacity Parameters - Design Strength (Cs) 

Building 

Seismic Design Level (Fraction of Building Weight) 

Type 

High-Code 

Moderate-Code 

Low-Code 

Pre-Code 

W1 

0.200 

0.150 

0.100 

0.100 

W2 

0.200 

0.100 

0.050 

0.050 

S1L 

0.133 

0.067 

0.033 

0.033 

S1M 

0.100 

0.050 

0.025 

0.025 

S1H 

0.067 

0.033 

0.017 

0.017 

S2L 

0.200 

0.100 

0.050 

0.050 

S2M 

0.200 

0.100 

0.050 

0.050 

S2H 

0.150 

0.075 

0.038 

0.038 

S3 

0.200 

0.100 

0.050 

0.050 

S4L 

0.160 

0.080 

0.040 

0.040 

S4M 

0.160 

0.080 

0.040 

0.040 

S4H 

0.120 

0.060 

0.030 

0.030 

S5L 

 

 

0.050 

0.050 

S5M 

 

 

0.050 

0.050 

S5H 

 

 

0.038 

0.038 

C1L 

0.133 

0.067 

0.033 

0.033 

C1M 

0.133 

0.067 

0.033 

0.033 

C1H 

0.067 

0.033 

0.017 

0.017 

C2L 

0.200 

0.100 

0.050 

0.050 

C2M 

0.200 

0.100 

0.050 

0.050 

C2H 

0.150 

0.075 

0.038 

0.038 

C3L 

 

 

0.050 

0.050 

C3M 

 

 

0.050 

0.050 

C3H 

 

 

0.038 

0.038 

PC1 

0.200 

0.100 

0.050 

0.050 

PC2L 

0.200 

0.100 

0.050 

0.050 

PC2M 

0.200 

0.100 

0.050 

0.050 

PC2H 

0.150 

0.075 

0.038 

0.038 

RM1L 

0.267 

0.133 

0.067 

0.067 

RM1M 

0.267 

0.133 

0.067 

0.067 

RM2L 

0.267 

0.133 

0.067 

0.067 

RM2M 

0.267 

0.133 

0.067 

0.067 

RM2H 

0.200 

0.100 

0.050 

0.050 

URML 

 

 

0.067 

0.067 

URMM 

 

 

0.067 

0.067 

MH 

0.100 

0.100 

0.100 

0.100 



 

 




 

Table 5.5 Code Building Capacity Parameters - Period (Te), Pushover Mode 

Response Factors (a1, a2) and Overstrength Ratios (., .) 

Building 

Height to 

Period, Te 

Modal Factors 

Overstrength Ratios 

Type 

Roof (Feet) 

(Seconds) 

Weight, a1 

Height, a2 

Yield, . 

Ultimate, . 

W1 

14.0 

0.35 

0.75 

0.75 

1.50 

3.00 

W2 

24.0 

0.40 

0.75 

0.75 

1.50 

2.50 

S1L 

24.0 

0.50 

0.80 

0.75 

1.50 

3.00 

S1M 

60.0 

1.08 

0.80 

0.75 

1.25 

3.00 

S1H 

156.0 

2.21 

0.75 

0.60 

1.10 

3.00 

S2L 

24.0 

0.40 

0.75 

0.75 

1.50 

2.00 

S2M 

60.0 

0.86 

0.75 

0.75 

1.25 

2.00 

S2H 

156.0 

1.77 

0.65 

0.60 

1.10 

2.00 

S3 

15.0 

0.40 

0.75 

0.75 

1.50 

2.00 

S4L 

24.0 

0.35 

0.75 

0.75 

1.50 

2.25 

S4M 

60.0 

0.65 

0.75 

0.75 

1.25 

2.25 

S4H 

156.0 

1.32 

0.65 

0.60 

1.10 

2.25 

S5L 

24.0 

0.35 

0.75 

0.75 

1.50 

2.00 

S5M 

60.0 

0.65 

0.75 

0.75 

1.25 

2.00 

S5H 

156.0 

1.32 

0.65 

0.60 

1.10 

2.00 

C1L 

20.0 

0.40 

0.80 

0.75 

1.50 

3.00 

C1M 

50.0 

0.75 

0.80 

0.75 

1.25 

3.00 

C1H 

120.0 

1.45 

0.75 

0.60 

1.10 

3.00 

C2L 

20.0 

0.35 

0.75 

0.75 

1.50 

2.50 

C2M 

50.0 

0.56 

0.75 

0.75 

1.25 

2.50 

C2H 

120.0 

1.09 

0.65 

0.60 

1.10 

2.50 

C3L 

20.0 

0.35 

0.75 

0.75 

1.50 

2.25 

C3M 

50.0 

0.56 

0.75 

0.75 

1.25 

2.25 

C3H 

120.0 

1.09 

0.65 

0.60 

1.10 

2.25 

PC1 

15.0 

0.35 

0.50 

0.75 

1.50 

2.00 

PC2L 

20.0 

0.35 

0.75 

0.75 

1.50 

2.00 

PC2M 

50.0 

0.56 

0.75 

0.75 

1.25 

2.00 

PC2H 

120.0 

1.09 

0.65 

0.60 

1.10 

2.00 

RM1L 

20.0 

0.35 

0.75 

0.75 

1.50 

2.00 

RM1M 

50.0 

0.56 

0.75 

0.75 

1.25 

2.00 

RM2L 

20.0 

0.35 

0.75 

0.75 

1.50 

2.00 

RM2M 

50.0 

0.56 

0.75 

0.75 

1.25 

2.00 

RM2H 

120.0 

1.09 

0.65 

0.60 

1.10 

2.00 

URML 

15.0 

0.35 

0.50 

0.75 

1.50 

2.00 

URMM 

35.0 

0.50 

0.75 

0.75 

1.25 

2.00 

MH 

10.0 

0.35 

1.00 

1.00 

1.50 

2.00 






 

Table 5.6 Code Building Capacity Parameter - Ductility () 

Building 

Seismic Design Level 

Type 

High-Code 

Moderate-Code 

Low-Code 

Pre-Code 

W1 

8.0 

6.0 

6.0 

6.0 

W2 

8.0 

6.0 

6.0 

6.0 

S1L 

8.0 

6.0 

5.0 

5.0 

S1M 

5.3 

4.0 

3.3 

3.3 

S1H 

4.0 

3.0 

2.5 

2.5 

S2L 

8.0 

6.0 

5.0 

5.0 

S2M 

5.3 

4.0 

3.3 

3.3 

S2H 

4.0 

3.0 

2.5 

2.5 

S3 

8.0 

6.0 

5.0 

5.0 

S4L 

8.0 

6.0 

5.0 

5.0 

S4M 

5.3 

4.0 

3.3 

3.3 

S4H 

4.0 

3.0 

2.5 

2.5 

S5L 

 

 

5.0 

5.0 

S5M 

 

 

3.3 

3.3 

S5H 

 

 

2.5 

2.5 

C1L 

8.0 

6.0 

5.0 

5.0 

C1M 

5.3 

4.0 

3.3 

3.3 

C1H 

4.0 

3.0 

2.5 

2.5 

C2L 

8.0 

6.0 

5.0 

5.0 

C2M 

5.3 

4.0 

3.3 

3.3 

C2H 

4.0 

3.0 

2.5 

2.5 

C3L 

 

 

5.0 

5.0 

C3M 

 

 

3.3 

3.3 

C3H 

 

 

2.5 

2.5 

PC1 

8.0 

6.0 

5.0 

5.0 

PC2L 

8.0 

6.0 

5.0 

5.0 

PC2M 

5.3 

4.0 

3.3 

3.3 

PC2H 

4.0 

3.0 

2.5 

2.5 

RM1L 

8.0 

6.0 

5.0 

5.0 

RM1M 

5.3 

4.0 

3.3 

3.3 

RM2L 

8.0 

6.0 

5.0 

5.0 

RM2M 

5.3 

4.0 

3.3 

3.3 

RM2H 

4.0 

3.0 

2.5 

2.5 

URML 

 

 

5.0 

5.0 

URMM 

 

 

3.3 

3.3 

MH 

6.0 

6.0 

6.0 

6.0 






Building capacity curves are assumed to have a range of possible properties that 
are 
lognormally distributed as a function of the ultimate strength (Au) of each 
capacity curve. 
Capacity curves described by the values of parameters given in Tables 5.4, 5.5 
and 5.6 
represent median estimates of building capacity. The variability of the capacity 
of each 
building type is assumed to be: (Au) = 0.25 for code-designed buildings (High-
Code, 
Moderate-Code and Low-Code seismic design levels) and (Au) = 0.30 for Pre-Code 
buildings. 

 

Example construction of median, 84th percentile (+1) and 16th percentile (-1) 
building 
capacity curves for a typical building is illustrated in Figure 5.4. Median 
capacity curves 
are intersected with demand spectra to estimate peak building response. The 
variability 
of the capacity curves is used, with other sources of variability and 
uncertainty, to define 
total fragility curve variability. 

 

Figure 5.4 Example Construction of Median, +1 and -1 Building Capacity 
Curves. 

 

Tables 5.7a, 5.7b, 5.7c and 5.7d summarize yield capacity and ultimate capacity 
control 
points for High-Code, Moderate-Code, Low-Code and Pre-Code seismic design 
levels, 
respectively. Note that for the following tables, shaded boxes indicate types 
that are not 
permitted by current seismic codes. 




 

Table 5.7a Code Building Capacity Curves - High-Code Seismic Design Level 

Building 

Yield Capacity Point 

Ultimate Capacity Point 

Type 

Dy (in.) 

Ay (g) 

Du (in.) 

Au (g) 

W1 

0.48 

0.400 

11.51 

1.200 

W2 

0.63 

0.400 

12.53 

1.000 

S1L 

0.61 

0.250 

14.67 

0.749 

S1M 

1.78 

0.156 

28.40 

0.468 

S1H 

4.66 

0.098 

55.88 

0.293 

S2L 

0.63 

0.400 

10.02 

0.800 

S2M 

2.43 

0.333 

25.88 

0.667 

S2H 

7.75 

0.254 

61.97 

0.508 

S3 

0.63 

0.400 

10.02 

0.800 

S4L 

0.38 

0.320 

6.91 

0.720 

S4M 

1.09 

0.267 

13.10 

0.600 

S4H 

3.49 

0.203 

31.37 

0.457 

S5L 

 

 

 

 

S5M 

 

 

 

 

S5H 

 

 

 

 

C1L 

0.39 

0.250 

9.39 

0.749 

C1M 

1.15 

0.208 

18.44 

0.624 

C1H 

2.01 

0.098 

24.13 

0.293 

C2L 

0.48 

0.400 

9.59 

1.000 

C2M 

1.04 

0.333 

13.84 

0.833 

C2H 

2.94 

0.254 

29.39 

0.635 

C3L 

 

 

 

 

C3M 

 

 

 

 

C3H 

 

 

 

 

PC1 

0.72 

0.600 

11.51 

1.200 

PC2L 

0.48 

0.400 

7.67 

0.800 

PC2M 

1.04 

0.333 

11.07 

0.667 

PC2H 

2.94 

0.254 

23.52 

0.508 

RM1L 

0.64 

0.533 

10.23 

1.066 

RM1M 

1.38 

0.444 

14.76 

0.889 

RM2L 

0.64 

0.533 

10.23 

1.066 

RM2M 

1.38 

0.444 

14.76 

0.889 

RM2H 

3.92 

0.338 

31.35 

0.677 

URML 

 

 

 

 

URMM 

 

 

 

 

MH 

0.18 

0.150 

2.16 

0.300 






 

Table 5.7b Code Building Capacity Curves - Moderate-Code Seismic Design Level 

Building 

Yield Capacity Point 

Ultimate Capacity Point 

Type 

Dy (in.) 

Ay (g) 

Du (in.) 

Au (g) 

W1 

0.36 

0.300 

6.48 

0.900 

W2 

0.31 

0.200 

4.70 

0.500 

S1L 

0.31 

0.125 

5.50 

0.375 

S1M 

0.89 

0.078 

10.65 

0.234 

S1H 

2.33 

0.049 

20.96 

0.147 

S2L 

0.31 

0.200 

3.76 

0.400 

S2M 

1.21 

0.167 

9.70 

0.333 

S2H 

3.87 

0.127 

23.24 

0.254 

S3 

0.31 

0.200 

3.76 

0.400 

S4L 

0.19 

0.160 

2.59 

0.360 

S4M 

0.55 

0.133 

4.91 

0.300 

S4H 

1.74 

0.102 

11.76 

0.228 

S5L 

 

 

 

 

S5M 

 

 

 

 

S5H 

 

 

 

 

C1L 

0.20 

0.125 

3.52 

0.375 

C1M 

0.58 

0.104 

6.91 

0.312 

C1H 

1.01 

0.049 

9.05 

0.147 

C2L 

0.24 

0.200 

3.60 

0.500 

C2M 

0.52 

0.167 

5.19 

0.417 

C2H 

1.47 

0.127 

11.02 

0.317 

C3L 

 

 

 

 

C3M 

 

 

 

 

C3H 

 

 

 

 

PC1 

0.36 

0.300 

4.32 

0.600 

PC2L 

0.24 

0.200 

2.88 

0.400 

PC2M 

0.52 

0.167 

4.15 

0.333 

PC2H 

1.47 

0.127 

8.82 

0.254 

RM1L 

0.32 

0.267 

3.84 

0.533 

RM1M 

0.69 

0.222 

5.54 

0.444 

RM2L 

0.32 

0.267 

3.84 

0.533 

RM2M 

0.69 

0.222 

5.54 

0.444 

RM2H 

1.96 

0.169 

11.76 

0.338 

URML 

 

 

 

 

URMM 

 

 

 

 

MH 

0.18 

0.150 

2.16 

0.300 






 

Table 5.7c Code Building Capacity Curves - Low-Code Seismic Design Level 

Building 

Yield Capacity Point 

Ultimate Capacity Point 

Type 

Dy (in.) 

Ay (g) 

Du (in.) 

Au (g) 

W1 

0.24 

0.200 

4.32 

0.600 

W2 

0.16 

0.100 

2.35 

0.250 

S1L 

0.15 

0.062 

2.29 

0.187 

S1M 

0.44 

0.039 

4.44 

0.117 

S1H 

1.16 

0.024 

8.73 

0.073 

S2L 

0.16 

0.100 

1.57 

0.200 

S2M 

0.61 

0.083 

4.04 

0.167 

S2H 

1.94 

0.063 

9.68 

0.127 

S3 

0.16 

0.100 

1.57 

0.200 

S4L 

0.10 

0.080 

1.08 

0.180 

S4M 

0.27 

0.067 

2.05 

0.150 

S4H 

0.87 

0.051 

4.90 

0.114 

S5L 

0.12 

0.100 

1.20 

0.200 

S5M 

0.34 

0.083 

2.27 

0.167 

S5H 

1.09 

0.063 

5.45 

0.127 

C1L 

0.10 

0.062 

1.47 

0.187 

C1M 

0.29 

0.052 

2.88 

0.156 

C1H 

0.50 

0.024 

3.77 

0.073 

C2L 

0.12 

0.100 

1.50 

0.250 

C2M 

0.26 

0.083 

2.16 

0.208 

C2H 

0.74 

0.063 

4.59 

0.159 

C3L 

0.12 

0.100 

1.35 

0.225 

C3M 

0.26 

0.083 

1.95 

0.188 

C3H 

0.74 

0.063 

4.13 

0.143 

PC1 

0.18 

0.150 

1.80 

0.300 

PC2L 

0.12 

0.100 

1.20 

0.200 

PC2M 

0.26 

0.083 

1.73 

0.167 

PC2H 

0.74 

0.063 

3.67 

0.127 

RM1L 

0.16 

0.133 

1.60 

0.267 

RM1M 

0.35 

0.111 

2.31 

0.222 

RM2L 

0.16 

0.133 

1.60 

0.267 

RM2M 

0.35 

0.111 

2.31 

0.222 

RM2H 

0.98 

0.085 

4.90 

0.169 

URML 

0.24 

0.200 

2.40 

0.400 

URMM 

0.27 

0.111 

1.81 

0.222 

MH 

0.18 

0.150 

2.16 

0.300 






 

Table 5.7d Building Capacity Curves - Pre-Code Seismic Design Level 

Building 

Yield Capacity Point 

Ultimate Capacity Point 

Type 

Dy (in.) 

Ay (g) 

Du (in.) 

Au (g) 

W1 

0.24 

0.200 

4.32 

0.600 

W2 

0.16 

0.100 

2.35 

0.250 

S1L 

0.15 

0.062 

2.75 

0.187 

S1M 

0.44 

0.039 

5.33 

0.117 

S1H 

1.16 

0.024 

10.48 

0.073 

S2L 

0.16 

0.100 

1.88 

0.200 

S2M 

0.61 

0.083 

4.85 

0.167 

S2H 

1.94 

0.063 

11.62 

0.127 

S3 

0.16 

0.100 

1.88 

0.200 

S4L 

0.10 

0.080 

1.30 

0.180 

S4M 

0.27 

0.067 

2.46 

0.150 

S4H 

0.87 

0.051 

5.88 

0.114 

S5L 

0.12 

0.100 

1.20 

0.200 

S5M 

0.34 

0.083 

2.27 

0.167 

S5H 

1.09 

0.063 

5.45 

0.127 

C1L 

0.10 

0.062 

1.76 

0.187 

C1M 

0.29 

0.052 

3.46 

0.156 

C1H 

0.50 

0.024 

4.52 

0.073 

C2L 

0.12 

0.100 

1.80 

0.250 

C2M 

0.26 

0.083 

2.60 

0.208 

C2H 

0.74 

0.063 

5.51 

0.159 

C3L 

0.12 

0.100 

1.35 

0.225 

C3M 

0.26 

0.083 

1.95 

0.188 

C3H 

0.74 

0.063 

4.13 

0.143 

PC1 

0.18 

0.150 

2.16 

0.300 

PC2L 

0.12 

0.100 

1.44 

0.200 

PC2M 

0.26 

0.083 

2.08 

0.167 

PC2H 

0.74 

0.063 

4.41 

0.127 

RM1L 

0.16 

0.133 

1.92 

0.267 

RM1M 

0.35 

0.111 

2.77 

0.222 

RM2L 

0.16 

0.133 

1.92 

0.267 

RM2M 

0.35 

0.111 

2.77 

0.222 

RM2H 

0.98 

0.085 

5.88 

0.169 

URML 

0.24 

0.200 

2.40 

0.400 

URMM 

0.27 

0.111 

1.81 

0.222 

MH 

0.18 

0.150 

2.16 

0.300 



 

 

5.4.3 Fragility Curves 

 




This section describes building fragility curves for Slight, Moderate, Extensive 
and 
Complete structural damage states and Slight, Moderate, Extensive and Complete 
nonstructural damage states. Each fragility curve is characterized by median and 
lognormal standard deviation () values of PESH demand. Spectral displacement is 
the 
PESH parameter used for structural damage and nonstructural damage to drift-
sensitive 
components. Spectral acceleration is the PESH parameter used for calculating 
nonstructural damage to acceleration-sensitive components. 

 

5.4.3.1 Background 

 

The probability of being in or exceeding a given damage state is modeled as a 
cumulative 
lognormal distribution. For structural damage, given the spectral displacement, 
Sd, the 
probability of being in or exceeding a damage state, ds, is modeled as: 

 

 []PS1lnSSdd,
dsdsdds=
.
..
.
..
.
..
.
..
F 
(5-3) 

 

where: Sd,ds is the median value of spectral displacement at which the 
building reaches the threshold of the damage state, ds, 

ds is the standard deviation of the natural logarithm of spectral 
displacement of damage state, ds, and 

F is the standard normal cumulative distribution function. 

 

For example, a mid-rise, concrete-frame building (C1M) of High-Code seismic 
design 
has Extensive structural damage defined by a median spectral displacement value 
(Sd,E) 
of 9.0 inches and a lognormal standard deviation value (E) of 0.68. The 
lognormal 
fragility curve for Extensive structural damage to this building is shown in 
Figure 5.5. 

 

In Figure 5.5, the symbol, S, indicates the median value of 9.0 inches. The 
symbol, S+, 
indicates the +1 lognormal standard deviation level of the fragility curve, 
which is 
evaluated as S+ = S x exp() = 17.8 inches. Similarly, the symbol, S-, indicates 
the -1 
lognormal standard deviation level of the fragility curve, which is evaluated as 
S- = 
S/exp() = 4.6 inches. The corresponding probabilities of being in or exceeding 
the 
Extensive damage state for this example are: 

 

[]
[]
[]
PExtensiveDamageExtensiveDamageSExtensiveDamage==
=
=
-
+
S S=4. inches 0.16PS =. inches=0.50PS S=. inches=0.84ddd690178 


 




Figure 5.5 Example Fragility Curve - Extensive Structural Damage, 

C1M Model Building Type, High-Code Seismic Design. 

 

5.4.3.2 Development of Damage State Medians 

 

Median values of fragility curves are developed for each damage states (i.e., 
Slight, 
Moderate, Extensive and Complete) and for each of the three types of building 
components: structural, nonstructural drift-sensitive and nonstructural 
acceleration-
sensitive components. Structural fragility is characterized in terms of spectral 
displacement and by equivalent-PGA fragility curves (for buildings that are 
components 
of lifelines). Section 5.4.4 describes development of median values of 
equivalent-PGA 
structural fragility curves based on the structural fragility curves of this 
section. 

 

Median values of structural component fragility are based on building drift 
ratios that 
describe the threshold of damage states. Damage-state drift ratios are converted 
to 
spectral displacement using Equation (5-4): 

 

SdSdsRSds,,=da2 (5-4) 

 

where: SdSds, is the median value of spectral displacement, in inches, of 

 structural components for damage state, ds, 

dR,Sds is the drift ratio at the threshold of structural damage state, ds, 

a2 is the fraction of the building (roof) height at the location of push-
over mode displacement, as specified in Table 5.5, and 

h is the typical roof height, in inches, of the model building type of 
interest (see Table 5.1 for typical building height). 




 

Values of damage-state drift ratios are included in the Methodology based, in 
part, on a 
study by OAK Engineering [OAK, 1994] that reviewed and synthesized available 
drift/damage information from a number of published sources, including Kustu et 
al. 
(1982), Ferritto (1982 and 1983), Czarnecki (1973), Hasselman et al. (1980), 
Whitman et 
al. (1977) and Wong (1975). 

 

Median values of nonstructural drift-sensitive component fragility are based on 
building 
drift ratios that describe the threshold of damage states. Nonstructural drift-
sensitive 
components are identified in Table 5.2. Damage state drift ratios for 
nonstructural drift-
sensitive components are converted to median values of spectral displacement 
using the 
same approach as that of Equation (5-4). Values of damage-state drift are based, 
in part, 
on the work of Ferrito (1982 and 1983) and on a recent update of this data 
included in a 
California Division of the State Architect report [DSA, 1996]. 

 

Median values of nonstructural acceleration-sensitive component fragility are 
based on 
peak floor (input) acceleration that describes the threshold of damage states. 
These 
values of acceleration are used directly as median values of spectral 
acceleration for 
nonstructural acceleration-sensitive component fragility curves. Values of 
damage-state 
acceleration are based, in part, on the work of Ferrito (1982 and 1983) and on a 
recent 
update of this data included in a California Division of the State Architect 
report [DSA, 
1996]. 

 

5.4.3.3 Development of Damage State Variability 

 

Lognormal standard deviation () values that describe the variability of 
fragility curves 
are developed for each damage states (i.e., Slight, Moderate, Extensive and 
Complete) 
and for each of the three types of building components: structural, 
nonstructural drift-
sensitive and nonstructural acceleration-sensitive components. Structural 
fragility is 
characterized in terms of spectral displacement and by equivalent-PGA fragility 
curves 
(for buildings that are components of lifelines). Section 5.4.4 describes 
development of 
variability values for equivalent-PGA structural fragility curves. 

 

The total variability of each structural damage state, Sds, is modeled by the 
combination 
of three contributors to structural damage variability, C, D and M(Sds), as 
described in 
Equation (5-5): 

 

[]()(SdsCDdSdsMSdsCONVS=,,,(
22 (5-5) 

 

where: Sds is the lognormal standard deviation that describes the total 

 variability for structural damage state, ds, 

C is the lognormal standard deviation parameter that 
describes the variability of the capacity curve, 




D is the lognormal standard deviation parameter that 
describes the variability of the demand spectrum, 

M(Sds) is the lognormal standard deviation parameter that 
describes the uncertainty in the estimate of the median 
value of the threshold of structural damage state, ds. 

 

The variability of building response depends jointly on demand and capacity 
(since 
capacity curves are nonlinear). The function CONV in Equation (5-5) implies a 
complex process of convolving probability distributions of the demand spectrum 
and the 
capacity curve, respectively. Demand spectra and capacity curves are described 
probabilistically by median properties and variability parameters, D and C, 
respectively. 
Capacity curves are defined for each building type, but the demand spectrum is 
based on 
the PESH input spectrum whose shape is a function of source/site conditions. For 
development of building fragility curves, the demand spectrum shape represented 
Moderate duration ground shaking of a large-magnitude WUS earthquake at a soil 
site. 

 

The convolution process produces a surface that describes the probability of 
each 
demand/capacity intersection point when the median demand spectrum is scaled to 
intersect the median capacity curve at a given amplitude of response. Discrete 
values of 
the probabilistic surface are summed along a line anchored to the damage state 
median of 
interest (e.g., Sd,Sds) to estimate the probability of reaching or exceeding the 
median value 
given building response at the intersection point. This process is repeated for 
other 
intersection points to form a cumulative description of the probability of 
reaching (or 
exceeding) the damage state of interest. A lognormal function is fit to this 
cumulative 
curve yielding an estimate of the lognormal standard deviation of the combined 
effect of 
demand and capacity variability on building fragility. 

 

The lognormal standard deviation parameter that describes the uncertainty in the 
estimate 
of the median value of the threshold of structural damage state ds, M(Sds), is 
assumed to 
be independent of capacity and demand, and is added by the square-root-sum-of-
the-
squares (SRSS) method to the lognormal standard deviation parameter representing 
the 
combined effects of demand and capacity variability. 

 

In the development of the damage state variability for implementation with the 
USGS 
probabilistic seismic hazard curves, the procedure was modified. The USGS 
explicitly 
incorporated the ground motion uncertainty in their Project 97 seismic hazard 
curves. 
(See Chapter 4) These hazard curves were the basis for the HAZUS PESH data used 
in 
the Methodologys probabilistic analysis procedure. To avoid overestimation of 
the 
damage state variability due to this double counting of ground motion 
uncertainty, the 
convolution process was modified and reanalyzed. Modified damage state 
variability 
parameters were developed for each probabilistic return period (a total of 8 
return 
periods) and used when the probabilistic analysis option is selected. Due to 
large amount 
of modified parameters, their values are not reproduced in this chapter. To 
review the 
modified parameters, the user can access them via the HAZUS software [Analysis-
Damage Functions-Buildings]. 

 




The process, described above for structural components, is the same approach 
used to 
estimate the lognormal standard deviation for nonstructural drift-sensitive 
components. 
Nonstructural acceleration-sensitive components are treated in a similar manner 
to 
nonstructural drift-sensitive components, except that cumulative descriptions of 
the 
probability of reaching (or exceeding) the damage state of interest are 
developed in terms 
of spectral acceleration (rather than spectra displacement). Also, nonstructural 
acceleration-sensitive components are divided into two sub-populations: (1) 
components 
at or near ground level and (2) components at upper floors or on the roof. PGA, 
rather 
than spectral acceleration, is a more appropriate PESH input for components at 
or near 
ground level. Fragility curves for nonstructural acceleration-sensitive 
components 
assume 50% (low-rise), 33% (mid-rise) or 20% (high-rise) of nonstructural 
components 
are located at, or near, the ground floor, and represent a weighted combination 
of the 
probability of damage to components located at, or near, ground level and 
components 
located at upper-floor levels of the building. 

 

5.4.3.4 Structural Damage 

 

Structural damage fragility curves for buildings are described by median values 
of drift 
that define the thresholds of Slight, Moderate, Extensive and Complete damage 
states. In 
general, these estimates of drift are different for each model building type 
(including 
height) and seismic design level. Table 5.8 summarizes the ranges of drift 
ratios used to 
define structural damage for various low-rise building types designed to current 
High-
Code seismic provisions. A complete listing of damage-state drift ratios for all 
building 
types and heights are provided for each seismic design level in Tables 5.9a, 
5.9b, 5.9c 
and 5.9d, respectively. 

 

Table 5.8 Typical Drift Ratios Used to Define Median Values of Structural Damage 

 

Seismic Design 

Building Type 

Drift Ratio at the Threshold of Structural Damage 

Level 

(Low-Rise) 

Slight 

Moderate 

Extensive 

Complete 

High-Code 

W1/W2 

C1L, S2L 

RM1L/RM2L, PC1/PC2L 

0.004 

0.005 

0.004 

0.012 

0.010 

0.008 

0.040 

0.030 

0.024 

0.100 

0.080 

0.070 

Moderate-Code 

 

 

W1/W2 

C1L, S2L 

RM1L/RM2L, PC1/PC2L 

0.004 

0.005 

0.004 

0.010 

0.009 

0.007 

0.031 

0.023 

0.019 

0.075 

0.060 

0.053 

Low-Code 

W1/W2 

C1L, S2L 

RM1L/RM2L,PC1/PC2L 
URML, C3L, S5L 

0.004 

0.005 

0.004 

0.003 

0.010 

0.008 

0.006 

0.006 

0.031 

0.020 

0.016 

0.015 

0.075 

0.050 

0.044 

0.035 

Pre-Code 

 

 

W1/W2 

C1L, S2L 

RM1L/RM2L,PC1/PC2L 
URML, C3L, S5L 

0.003 

0.004 

0.003 

0.002 

0.008 

0.006 

0.005 

0.005 

0.025 

0.016 

0.013 

0.012 

0.060 

0.040 

0.035 

0.028 



 

In general, values of the drift ratio that define Complete damage to Moderate-
Code 
buildings are assumed to be 75% of the drift ratio that define Complete damage 
to High-




Code buildings, and values of the drift ratio that define Complete damage to 
Low-Code 
buildings are assumed to be 63% of the drift ratios that define Complete damage 
to High-
Code buildings. These assumptions are based on the recognition that post-yield 
capacity 
is significantly less in buildings designed with limited ductile detailing. 
Values of the 
drift ratio that define Slight damage were assumed to be the same for High-Code, 
Moderate-Code and Low-Code buildings, since this damage state typically does not 
exceed the buildings elastic capacity. 

 

Values of drift ratios that define Moderate and Extensive damage to Moderate-
Code and 
Low-Code buildings are selected such that their distribution between Slight and 
Complete damage-state drift ratios is in proportion to the distribution of 
damage-state 
drift ratios for High-Code buildings. 

 

Values of Pre-Code building drift ratios are based on the drift ratios for Low-
Code 
buildings, reduced slightly to account for inferior performance anticipated for 
these older 
buildings. For each damage state, the drift ratio of a Pre-Code building is 
assumed to be 
80% of the drift ratio of the Low-Code building of the same building type. 

 

Drift ratios are reduced for taller buildings assuming that the deflected shape 
will not 
affect uniform distribution of drift over the buildings height. For all damage 
states, drift 
ratios for mid-rise buildings are assumed to be 67% of those of low-rise 
buildings of the 
same type, and drift ratios for high-rise buildings are assumed to be 50% of 
those of low-
rise buildings of the same type. Since mid-rise and high-rise buildings are much 
taller 
than low-rise buildings, median values of spectral displacement (i.e., drift 
ratio times 
height of building at the point of push-over mode displacement) are still much 
greater for 
mid-rise and high-rise buildings than for low-rise buildings. 

 

The total variability of each structural damage state, Sds, is modeled by the 
combination 
of following three contributors to damage variability: 

 uncertainty in the damage-state threshold of the structural system 
(M(Sds) = 0.4, for all structural damage states and building types) 

 variability in capacity (response) properties of the model building 
type/seismic design level of interest (C(Au) = 0.25 for Code buildings, 
C(Au) = 0.30 for Pre-Code buildings) and 

 variability in response due to the spatial variability of ground motion 

Each of these three contributors to damage state variability is assumed to be 
lognormally 
distributed random variables. Capacity and demand are dependent parameters and a 
convolution process is used to derive combined capacity/demand variability of 
each 
structural damage state. Capacity/demand variability is then combined with 
damage state 
uncertainty, as described in Section 5.4.3.3. 

 

Tables 5.9a, 5.9b, 5.9c and 5.9d summarize median and lognormal standard 
deviation 
(Sds) values for Slight, Moderate, Extensive and Complete structural damage 
states 
High-Code, Moderate-Code, Low-Code and Pre-Code buildings, respectively. Note 
that 




for the following tables, shaded boxes indicate types that are not permitted by 
current 
seismic codes. 

 




Table 5.9a Structural Fragility Curve Parameters - High-Code 
Seismic Design Level 

 

Building Properties Interstory Drift at Spectral Displacement (inches) 
Type Height (inches) Threshold of Damage State Slight Moderate Extensive 
Complete 
Roof Modal Slight Moderate Extensive Complete Median Beta Median Beta Median 
Beta Median Beta 
W1 168 126 0.0040 0.0120 0.0400 0.1000 0.50 0.80 1.51 0.81 5.04 0.85 12.60 0.97 
W2 288 216 0.0040 0.0120 0.0400 0.1000 0.86 0.81 2.59 0.88 8.64 0.90 21.60 0.83 
S1L 288 216 0.0060 0.0120 0.0300 0.0800 1.30 0.80 2.59 0.76 6.48 0.69 17.28 0.72 
S1M 720 540 0.0040 0.0080 0.0200 0.0533 2.16 0.65 4.32 0.66 10.80 0.67 28.80 
0.74 
S1H 1872 1123 0.0030 0.0060 0.0150 0.0400 3.37 0.64 6.74 0.64 16.85 0.65 44.93 
0.67 
S2L 288 216 0.0050 0.0100 0.0300 0.0800 1.08 0.81 2.16 0.89 6.48 0.94 17.28 0.83 
S2M 720 540 0.0033 0.0067 0.0200 0.0533 1.80 0.67 3.60 0.67 10.80 0.68 28.80 
0.79 
S2H 1872 1123 0.0025 0.0050 0.0150 0.0400 2.81 0.63 5.62 0.63 16.85 0.64 44.93 
0.71 
S3 180 135 0.0040 0.0080 0.0240 0.0700 0.54 0.81 1.08 0.82 3.24 0.91 9.45 0.90 
S4L 288 216 0.0040 0.0080 0.0240 0.0700 0.86 0.89 1.73 0.89 5.18 0.98 15.12 0.87 
S4M 720 540 0.0027 0.0053 0.0160 0.0467 1.44 0.77 2.88 0.72 8.64 0.70 25.20 0.89 
S4H 1872 1123 0.0020 0.0040 0.0120 0.0350 2.25 0.64 4.49 0.66 13.48 0.69 39.31 
0.77 
S5L 
S5M 
S5H 
C1L 240 180 0.0050 0.0100 0.0300 0.0800 0.90 0.81 1.80 0.84 5.40 0.86 14.40 0.81 
C1M 600 450 0.0033 0.0067 0.0200 0.0533 1.50 0.68 3.00 0.67 9.00 0.68 24.00 0.81 
C1H 1440 864 0.0025 0.0050 0.0150 0.0400 2.16 0.66 4.32 0.64 12.96 0.67 34.56 
0.78 
C2L 240 180 0.0040 0.0100 0.0300 0.0800 0.72 0.81 1.80 0.84 5.40 0.93 14.40 0.92 
C2M 600 450 0.0027 0.0067 0.0200 0.0533 1.20 0.74 3.00 0.77 9.00 0.68 24.00 0.77 
C2H 1440 864 0.0020 0.0050 0.0150 0.0400 1.73 0.68 4.32 0.65 12.96 0.66 34.56 
0.75 
C3L 
C3M 
C3H 
PC1 180 135 0.0040 0.0080 0.0240 0.0700 0.54 0.76 1.08 0.86 3.24 0.88 9.45 0.99 
PC2L 240 180 0.0040 0.0080 0.0240 0.0700 0.72 0.84 1.44 0.88 4.32 0.98 12.60 
0.94 
PC2M 600 450 0.0027 0.0053 0.0160 0.0467 1.20 0.77 2.40 0.81 7.20 0.70 21.00 
0.82 
PC2H 1440 864 0.0020 0.0040 0.0120 0.0350 1.73 0.64 3.46 0.66 10.37 0.68 30.24 
0.81 
RM1L 240 180 0.0040 0.0080 0.0240 0.0700 0.72 0.84 1.44 0.86 4.32 0.92 12.60 
1.01 
RM1M 600 450 0.0027 0.0053 0.0160 0.0467 1.20 0.71 2.40 0.81 7.20 0.76 21.00 
0.75 
RM2L 240 180 0.0040 0.0080 0.0240 0.0700 0.72 0.80 1.44 0.81 4.32 0.91 12.60 
0.98 
RM2M 600 450 0.0027 0.0053 0.0160 0.0467 1.20 0.71 2.40 0.79 7.20 0.70 21.00 
0.73 
RM2H 1440 864 0.0020 0.0040 0.0120 0.0350 1.73 0.66 3.46 0.65 10.37 0.66 30.24 
0.72 
URML 
URMM 
MH 120 120 0.0040 0.0080 0.0240 0.0700 0.48 0.91 0.96 1.00 2.88 1.03 8.40 0.92 



Table 5.9b Structural Fragility Curve Parameters  Moderate Code 
Seismic Design Level 

 

Building Properties Interstory Drift at Spectral Displacement (inches) 
Type Height (inches) Threshold of Damage State Slight Moderate Extensive 
Complete 
Roof Modal Slight Moderate Extensive Complete Median Beta Median Beta Median 
Beta Median Beta 
W1 168 126 0.0040 0.0099 0.0306 0.0750 0.50 0.84 1.25 0.86 3.86 0.89 9.45 1.04 
W2 288 216 0.0040 0.0099 0.0306 0.0750 0.86 0.89 2.14 0.95 6.62 0.95 16.20 0.92 
S1L 288 216 0.0060 0.0104 0.0235 0.0600 1.30 0.80 2.24 0.75 5.08 0.74 12.96 0.88 
S1M 720 540 0.0040 0.0069 0.0157 0.0400 2.16 0.65 3.74 0.68 8.46 0.69 21.60 0.87 
S1H 1872 1123 0.0030 0.0052 0.0118 0.0300 3.37 0.64 5.83 0.64 13.21 0.71 33.70 
0.83 
S2L 288 216 0.0050 0.0087 0.0233 0.0600 1.08 0.93 1.87 0.92 5.04 0.93 12.96 0.93 
S2M 720 540 0.0033 0.0058 0.0156 0.0400 1.80 0.70 3.12 0.69 8.40 0.69 21.60 0.89 
S2H 1872 1123 0.0025 0.0043 0.0117 0.0300 2.81 0.66 4.87 0.64 13.10 0.69 33.70 
0.80 
S3 180 135 0.0040 0.0070 0.0187 0.0525 0.54 0.88 0.94 0.92 2.52 0.97 7.09 0.89 
S4L 288 216 0.0040 0.0069 0.0187 0.0525 0.86 0.96 1.50 1.00 4.04 1.03 11.34 0.92 
S4M 720 540 0.0027 0.0046 0.0125 0.0350 1.44 0.75 2.50 0.72 6.73 0.72 18.90 0.94 
S4H 1872 1123 0.0020 0.0035 0.0093 0.0262 2.25 0.66 3.90 0.67 10.50 0.70 29.48 
0.90 
S5L 
S5M 
S5H 
C1L 240 180 0.0050 0.0087 0.0233 0.0600 0.90 0.89 1.56 0.90 4.20 0.90 10.80 0.89 
C1M 600 450 0.0033 0.0058 0.0156 0.0400 1.50 0.70 2.60 0.70 7.00 0.70 18.00 0.89 
C1H 1440 864 0.0025 0.0043 0.0117 0.0300 2.16 0.66 3.74 0.66 10.08 0.76 25.92 
0.91 
C2L 240 180 0.0040 0.0084 0.0232 0.0600 0.72 0.91 1.52 0.97 4.17 1.03 10.80 0.87 
C2M 600 450 0.0027 0.0056 0.0154 0.0400 1.20 0.81 2.53 0.77 6.95 0.73 18.00 0.91 
C2H 1440 864 0.0020 0.0042 0.0116 0.0300 1.73 0.66 3.64 0.68 10.00 0.70 25.92 
0.87 
C3L 
C3M 
C3H 
PC1 180 135 0.0040 0.0070 0.0187 0.0525 0.54 0.89 0.94 0.92 2.52 0.97 7.09 1.04 
PC2L 240 180 0.0040 0.0069 0.0187 0.0525 0.72 0.96 1.25 1.00 3.37 1.03 9.45 0.88 
PC2M 600 450 0.0027 0.0046 0.0125 0.0350 1.20 0.82 2.08 0.79 5.61 0.75 15.75 
0.93 
PC2H 1440 864 0.0020 0.0035 0.0094 0.0263 1.73 0.68 3.00 0.69 8.08 0.77 22.68 
0.89 
RM1L 240 180 0.0040 0.0069 0.0187 0.0525 0.72 0.96 1.25 0.99 3.37 1.05 9.45 0.94 
RM1M 600 450 0.0027 0.0046 0.0125 0.0350 1.20 0.81 2.08 0.82 5.61 0.80 15.75 
0.89 
RM2L 240 180 0.0040 0.0069 0.0187 0.0525 0.72 0.91 1.25 0.96 3.37 1.02 9.45 0.93 
RM2M 600 450 0.0027 0.0046 0.0125 0.0350 1.20 0.81 2.08 0.80 5.61 0.75 15.75 
0.88 
RM2H 1440 864 0.0020 0.0035 0.0094 0.0263 1.73 0.67 3.00 0.69 8.08 0.70 22.68 
0.86 
URML 
URMM 
MH 120 120 0.0040 0.0080 0.0240 0.0700 0.48 0.91 0.96 1.00 2.88 1.03 8.40 0.92 



Table 5.9c Structural Fragility Curve Parameters - Low-Code Seismic Design Level 

Building Properties Interstory Drift at Spectral Displacement (inches) 
Type Height (inches) Threshold of Damage State Slight Moderate Extensive 
Complete 
Roof Modal Slight Moderate Extensive Complete Median Beta Median Beta Median 
Beta Median Beta 
W1 168 126 0.0040 0.0099 0.0306 0.0750 0.50 0.93 1.25 0.98 3.86 1.02 9.45 0.99 
W2 288 216 0.0040 0.0099 0.0306 0.0750 0.86 0.97 2.14 0.90 6.62 0.89 16.20 0.99 
S1L 288 216 0.0060 0.0096 0.0203 0.0500 1.30 0.77 2.07 0.78 4.38 0.78 10.80 0.96 
S1M 720 540 0.0040 0.0064 0.0135 0.0333 2.16 0.68 3.44 0.78 7.30 0.85 18.00 0.98 
S1H 1872 1123 0.0030 0.0048 0.0101 0.0250 3.37 0.66 5.37 0.70 11.38 0.76 28.08 
0.92 
S2L 288 216 0.0050 0.0080 0.0200 0.0500 1.08 0.96 1.73 0.89 4.32 0.86 10.80 0.98 
S2M 720 540 0.0033 0.0053 0.0133 0.0333 1.80 0.70 2.88 0.73 7.20 0.85 18.00 0.98 
S2H 1872 1123 0.0025 0.0040 0.0100 0.0250 2.81 0.66 4.49 0.67 11.23 0.74 28.08 
0.92 
S3 180 135 0.0040 0.0064 0.0161 0.0438 0.54 0.98 0.87 0.99 2.17 1.01 5.91 0.90 
S4L 288 216 0.0040 0.0064 0.0161 0.0438 0.86 1.05 1.38 0.98 3.47 0.89 9.45 0.98 
S4M 720 540 0.0027 0.0043 0.0107 0.0292 1.44 0.76 2.31 0.78 5.78 0.90 15.75 0.99 
S4H 1872 1123 0.0020 0.0032 0.0080 0.0219 2.25 0.70 3.60 0.75 9.01 0.90 24.57 
0.98 
S5L 288 216 0.0030 0.0060 0.0150 0.0350 0.65 1.11 1.30 1.04 3.24 0.99 7.56 0.95 
S5M 720 540 0.0020 0.0040 0.0100 0.0233 1.08 0.77 2.16 0.79 5.40 0.87 12.60 0.98 
S5H 1872 1123 0.0015 0.0030 0.0075 0.0175 1.68 0.70 3.37 0.73 8.42 0.89 19.66 
0.97 
C1L 240 180 0.0050 0.0080 0.0200 0.0500 0.90 0.95 1.44 0.91 3.60 0.85 9.00 0.97 
C1M 600 450 0.0033 0.0053 0.0133 0.0333 1.50 0.70 2.40 0.74 6.00 0.86 15.00 0.98 
C1H 1440 864 0.0025 0.0040 0.0100 0.0250 2.16 0.70 3.46 0.81 8.64 0.89 21.60 
0.98 
C2L 240 180 0.0040 0.0076 0.0197 0.0500 0.72 1.04 1.37 1.02 3.55 0.99 9.00 0.95 
C2M 600 450 0.0027 0.0051 0.0132 0.0333 1.20 0.82 2.29 0.81 5.92 0.81 15.00 0.99 
C2H 1440 864 0.0020 0.0038 0.0099 0.0250 1.73 0.68 3.30 0.73 8.53 0.84 21.60 
0.95 
C3L 240 180 0.0030 0.0060 0.0150 0.0350 0.54 1.09 1.08 1.07 2.70 1.08 6.30 0.91 
C3M 600 450 0.0020 0.0040 0.0100 0.0233 0.90 0.85 1.80 0.83 4.50 0.79 10.50 0.98 
C3H 1440 864 0.0015 0.0030 0.0075 0.0175 1.30 0.71 2.59 0.74 6.48 0.90 15.12 
0.97 
PC1 180 135 0.0040 0.0064 0.0161 0.0438 0.54 1.00 0.87 1.05 2.17 1.12 5.91 0.89 
PC2L 240 180 0.0040 0.0064 0.0161 0.0438 0.72 1.08 1.15 1.03 2.89 0.98 7.88 0.96 
PC2M 600 450 0.0027 0.0043 0.0107 0.0292 1.20 0.81 1.92 0.79 4.81 0.84 13.12 
0.99 
PC2H 1440 864 0.0020 0.0032 0.0080 0.0219 1.73 0.71 2.77 0.75 6.93 0.89 18.90 
0.98 
RM1L 240 180 0.0040 0.0064 0.0161 0.0438 0.72 1.11 1.15 1.10 2.89 1.10 7.88 0.92 
RM1M 600 450 0.0027 0.0043 0.0107 0.0292 1.20 0.87 1.92 0.84 4.81 0.79 13.12 
0.96 
RM2L 240 180 0.0040 0.0064 0.0161 0.0438 0.72 1.05 1.15 1.07 2.89 1.09 7.88 0.91 
RM2M 600 450 0.0027 0.0043 0.0107 0.0292 1.20 0.84 1.92 0.81 4.81 0.77 13.12 
0.96 
RM2H 1440 864 0.0020 0.0032 0.0080 0.0219 1.73 0.69 2.77 0.72 6.93 0.87 18.90 
0.96 
URML 180 135 0.0030 0.0060 0.0150 0.0350 0.41 0.99 0.81 1.05 2.03 1.10 4.73 1.08 
URMM 420 315 0.0020 0.0040 0.0100 0.0233 0.63 0.91 1.26 0.92 3.15 0.87 7.35 0.91 
MH 120 120 0.0040 0.0080 0.0240 0.0700 0.48 0.91 0.96 1.00 2.88 1.03 8.40 0.92 



Table 5.9d Structural Fragility Curve Parameters - Pre-Code Seismic Design Level 

 

5.4.3.5 Nonstructural Damage - Drift-Sensitive Components 

 

Table 5.10 summarizes drift ratios used by the Methodology to define the median 
values 
of damage fragility curves for drift-sensitive nonstructural components of 
buildings. 
Nonstructural damage drift ratios are assumed to be the same for each building 
type and 
each seismic design level. 

 

Table 5.10 Drift Ratios Used to Define Median Values of Damage for 

Nonstructural Drift-Sensitive Components 

Drift Ratio at the Threshold of Nonstructural Damage 

Slight 

Moderate 

Extensive 

Complete 

0.004 

0.008 

0.025 

0.050 



 

Median values of drift-sensitive nonstructural fragility curves are based on 
global 
building displacement (in inches), calculated as the product of: (1) drift 
ratio, (2) 
building height and (3) the fraction of building height at the location of push-
over mode 
displacement (a2). 

 

The total variability of each nonstructural drift-sensitive damage state, 
NSDds, is modeled 
by the combination of following three contributors to damage variability: 

Building Properties Interstory Drift at Spectral Displacement (inches) 
Type Height (inches) Threshold of Damage State Slight Moderate Extensive 
Complete 
Roof Modal Slight Moderate Extensive Complete Median Beta Median Beta Median 
Beta Median Beta 
W1 168 126 0.0032 0.0079 0.0245 0.0600 0.40 1.01 1.00 1.05 3.09 1.07 7.56 1.06 
W2 288 216 0.0032 0.0079 0.0245 0.0600 0.69 1.04 1.71 0.97 5.29 0.90 12.96 0.99 
S1L 288 216 0.0048 0.0076 0.0162 0.0400 1.04 0.85 1.65 0.82 3.50 0.80 8.64 0.95 
S1M 720 540 0.0032 0.0051 0.0108 0.0267 1.73 0.70 2.76 0.75 5.84 0.81 14.40 0.98 
S1H 1872 1123 0.0024 0.0038 0.0081 0.0200 2.70 0.69 4.30 0.71 9.11 0.85 22.46 
0.93 
S2L 288 216 0.0040 0.0064 0.0160 0.0400 0.86 1.01 1.38 0.96 3.46 0.88 8.64 0.98 
S2M 720 540 0.0027 0.0043 0.0107 0.0267 1.44 0.73 2.30 0.75 5.76 0.80 14.40 0.98 
S2H 1872 1123 0.0020 0.0032 0.0080 0.0200 2.25 0.70 3.59 0.70 8.99 0.84 22.46 
0.91 
S3 180 135 0.0032 0.0051 0.0128 0.0350 0.43 1.06 0.69 1.03 1.73 1.07 4.73 0.89 
S4L 288 216 0.0032 0.0051 0.0128 0.0350 0.69 1.11 1.11 1.03 2.77 0.99 7.56 0.98 
S4M 720 540 0.0021 0.0034 0.0086 0.0233 1.15 0.81 1.85 0.80 4.62 0.94 12.60 1.00 
S4H 1872 1123 0.0016 0.0026 0.0064 0.0175 1.80 0.73 2.88 0.75 7.21 0.90 19.66 
0.97 
S5L 288 216 0.0024 0.0048 0.0120 0.0280 0.52 1.20 1.04 1.11 2.59 1.08 6.05 0.95 
S5M 720 540 0.0016 0.0032 0.0080 0.0187 0.86 0.85 1.73 0.83 4.32 0.94 10.08 0.99 
S5H 1872 1123 0.0012 0.0024 0.0060 0.0140 1.35 0.72 2.70 0.75 6.74 0.92 15.72 
0.96 
C1L 240 180 0.0040 0.0064 0.0160 0.0400 0.72 0.98 1.15 0.94 2.88 0.90 7.20 0.97 
C1M 600 450 0.0027 0.0043 0.0107 0.0267 1.20 0.73 1.92 0.77 4.80 0.83 12.00 0.98 
C1H 1440 864 0.0020 0.0032 0.0080 0.0200 1.73 0.71 2.76 0.80 6.91 0.94 17.28 
1.01 
C2L 240 180 0.0032 0.0061 0.0158 0.0400 0.58 1.11 1.10 1.09 2.84 1.07 7.20 0.93 
C2M 600 450 0.0021 0.0041 0.0105 0.0267 0.96 0.86 1.83 0.83 4.74 0.80 12.00 0.98 
C2H 1440 864 0.0016 0.0031 0.0079 0.0200 1.38 0.73 2.64 0.75 6.82 0.92 17.28 
0.97 
C3L 240 180 0.0024 0.0048 0.0120 0.0280 0.43 1.19 0.86 1.15 2.16 1.15 5.04 0.92 
C3M 600 450 0.0016 0.0032 0.0080 0.0187 0.72 0.90 1.44 0.86 3.60 0.90 8.40 0.96 
C3H 1440 864 0.0012 0.0024 0.0060 0.0140 1.04 0.73 2.07 0.75 5.18 0.90 12.10 
0.95 
PC1 180 135 0.0032 0.0051 0.0128 0.0350 0.43 1.14 0.69 1.14 1.73 1.17 4.73 0.98 
PC2L 240 180 0.0032 0.0051 0.0128 0.0350 0.58 1.14 0.92 1.10 2.31 1.10 6.30 0.93 
PC2M 600 450 0.0021 0.0034 0.0086 0.0233 0.96 0.87 1.54 0.83 3.85 0.91 10.50 
1.00 
PC2H 1440 864 0.0016 0.0026 0.0064 0.0175 1.38 0.74 2.21 0.75 5.55 0.91 15.12 
0.96 
RM1L 240 180 0.0032 0.0051 0.0128 0.0350 0.58 1.20 0.92 1.17 2.31 1.17 6.30 0.94 
RM1M 600 450 0.0021 0.0034 0.0086 0.0233 0.96 0.91 1.54 0.89 3.85 0.89 10.50 
0.96 
RM2L 240 180 0.0032 0.0051 0.0128 0.0350 0.58 1.14 0.92 1.10 2.31 1.15 6.30 0.92 
RM2M 600 450 0.0021 0.0034 0.0086 0.0233 0.96 0.89 1.54 0.87 3.85 0.87 10.50 
0.96 
RM2H 1440 864 0.0016 0.0026 0.0064 0.0175 1.38 0.75 2.21 0.75 5.55 0.84 15.12 
0.94 
URML 180 135 0.0024 0.0048 0.0120 0.0280 0.32 1.15 0.65 1.19 1.62 1.20 3.78 1.18 
URMM 420 315 0.0016 0.0032 0.0080 0.0187 0.50 0.99 1.01 0.97 2.52 0.90 5.88 0.88 
MH 120 120 0.0032 0.0064 0.0192 0.0560 0.38 1.11 0.77 1.10 2.30 0.95 6.72 0.97 



 

 uncertainty in the damage-state threshold of nonstructural components 
(M(NSDds) = 0.5, for all damage states and building types), 

 variability in capacity (response) properties of the model building type 
that contains the nonstructural components of interest (C(Au) = 0.25 
for Code buildings, C(Au) = 0.30 for Pre-Code buildings), and 

 variability in response of the model building type due to the spatial 
variability of ground motion demand (D(A) = 0.45 and C(V) = 0.50). 

 

Each of these three contributors to damage state variability is assumed to be 
lognormally 
distributed random variables. Capacity and demand are dependent parameters and a 
convolution process is used to derive combined capacity/demand variability of 
each 
nonstructural damage state. Capacity/demand variability is then combined with 
damage 
state uncertainty, as described in Section 5.4.3.3. 

 

Table 5.11a, 5.11b, 5.11c and 5.11d summarize median and lognormal standard 
deviation 
(NSDds) values for Slight, Moderate, Extensive and Complete nonstructural 
drift-sensitive 
damage states for High-Code, Moderate-Code, Low-Code and Pre-Code buildings, 
respectively. Median values are the same for all design levels. Lognormal 
standard 
deviation values are slightly different for each seismic design level. Note that 
for the 
following tables, shaded boxes indicate types that are not permitted by current 
seismic 
codes. 




 

Table 5.11 Nonstructural Drift-Sensitive Fragility Curve Parameters - 
High-Code Seismic Design Level 

Building 

Median Spectral Displacement (inches) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.50 

0.85 

1.01 

0.88 

3.15 

0.88 

6.30 

0.94 

W2 

0.86 

0.87 

1.73 

0.89 

5.40 

0.96 

10.80 

0.94 

S1L 

0.86 

0.81 

1.73 

0.85 

5.40 

0.77 

10.80 

0.77 

S1M 

2.16 

0.71 

4.32 

0.72 

13.50 

0.72 

27.00 

0.80 

S1H 

4.49 

0.72 

8.99 

0.71 

28.08 

0.74 

56.16 

0.77 

S2L 

0.86 

0.84 

1.73 

0.90 

5.40 

0.97 

10.80 

0.92 

S2M 

2.16 

0.71 

4.32 

0.74 

13.50 

0.74 

27.00 

0.84 

S2H 

4.49 

0.71 

8.99 

0.71 

28.08 

0.72 

56.16 

0.78 

S3 

0.54 

0.86 

1.08 

0.88 

3.38 

0.98 

6.75 

0.98 

S4L 

0.86 

0.93 

1.73 

0.94 

5.40 

1.01 

10.80 

0.99 

S4M 

2.16 

0.80 

4.32 

0.76 

13.50 

0.76 

27.00 

0.93 

S4H 

4.49 

0.72 

8.99 

0.72 

28.08 

0.79 

56.16 

0.91 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.72 

0.84 

1.44 

0.88 

4.50 

0.90 

9.00 

0.88 

C1M 

1.80 

0.72 

3.60 

0.73 

11.25 

0.74 

22.50 

0.84 

C1H 

3.46 

0.71 

6.91 

0.71 

21.60 

0.78 

43.20 

0.88 

C2L 

0.72 

0.87 

1.44 

0.88 

4.50 

0.97 

9.00 

0.99 

C2M 

1.80 

0.84 

3.60 

0.82 

11.25 

0.74 

22.50 

0.81 

C2H 

3.46 

0.71 

6.91 

0.72 

21.60 

0.74 

43.20 

0.85 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.54 

0.82 

1.08 

0.91 

3.38 

0.95 

6.75 

1.03 

PC2L 

0.72 

0.89 

1.44 

0.93 

4.50 

1.03 

9.00 

1.04 

PC2M 

1.80 

0.87 

3.60 

0.83 

11.25 

0.77 

22.50 

0.89 

PC2H 

3.46 

0.73 

6.91 

0.73 

21.60 

0.77 

43.20 

0.89 

RM1L 

0.72 

0.89 

1.44 

0.91 

4.50 

0.97 

9.00 

1.06 

RM1M 

1.80 

0.81 

3.60 

0.86 

11.25 

0.80 

22.50 

0.81 

RM2L 

0.72 

0.85 

1.44 

0.87 

4.50 

0.95 

9.00 

1.03 

RM2M 

1.80 

0.82 

3.60 

0.84 

11.25 

0.76 

22.50 

0.80 

RM2H 

3.46 

0.71 

6.91 

0.73 

21.60 

0.73 

43.20 

0.85 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.48 

0.96 

0.96 

1.05 

3.00 

1.07 

6.00 

0.93 






Table 5.11b Nonstructural Drift-Sensitive Fragility Curve Parameters - 
Moderate-Code Seismic Design Level 

Building 

Median Spectral Displacement (inches) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.50 

0.89 

1.01 

0.91 

3.15 

0.90 

6.30 

1.04 

W2 

0.86 

0.94 

1.73 

0.99 

5.40 

1.00 

10.80 

0.90 

S1L 

0.86 

0.84 

1.73 

0.83 

5.40 

0.79 

10.80 

0.87 

S1M 

2.16 

0.71 

4.32 

0.74 

13.50 

0.85 

27.00 

0.95 

S1H 

4.49 

0.71 

8.99 

0.74 

28.08 

0.84 

56.16 

0.95 

S2L 

0.86 

0.93 

1.73 

0.99 

5.40 

0.96 

10.80 

0.92 

S2M 

2.16 

0.74 

4.32 

0.74 

13.50 

0.85 

27.00 

0.96 

S2H 

4.49 

0.72 

8.99 

0.73 

28.08 

0.80 

56.16 

0.94 

S3 

0.54 

0.93 

1.08 

0.98 

3.38 

1.01 

6.75 

0.94 

S4L 

0.86 

1.00 

1.73 

1.06 

5.40 

0.99 

10.80 

0.96 

S4M 

2.16 

0.77 

4.32 

0.80 

13.50 

0.95 

27.00 

1.04 

S4H 

4.49 

0.73 

8.99 

0.82 

28.08 

0.93 

56.16 

1.01 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.72 

0.93 

1.44 

0.96 

4.50 

0.94 

9.00 

0.88 

C1M 

1.80 

0.77 

3.60 

0.76 

11.25 

0.87 

22.50 

0.98 

C1H 

3.46 

0.74 

6.91 

0.80 

21.60 

0.94 

43.20 

1.03 

C2L 

0.72 

0.96 

1.44 

1.00 

4.50 

1.06 

9.00 

0.95 

C2M 

1.80 

0.84 

3.60 

0.81 

11.25 

0.83 

22.50 

0.98 

C2H 

3.46 

0.73 

6.91 

0.76 

21.60 

0.89 

43.20 

0.99 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.54 

0.94 

1.08 

0.99 

3.38 

1.05 

6.75 

1.08 

PC2L 

0.72 

1.00 

1.44 

1.06 

4.50 

1.07 

9.00 

0.93 

PC2M 

1.80 

0.85 

3.60 

0.83 

11.25 

0.92 

22.50 

1.00 

PC2H 

3.46 

0.74 

6.91 

0.79 

21.60 

0.93 

43.20 

1.02 

RM1L 

0.72 

1.00 

1.44 

1.06 

4.50 

1.12 

9.00 

1.01 

RM1M 

1.80 

0.88 

3.60 

0.85 

11.25 

0.84 

22.50 

0.98 

RM2L 

0.72 

0.96 

1.44 

1.02 

4.50 

1.10 

9.00 

0.99 

RM2M 

1.80 

0.88 

3.60 

0.83 

11.25 

0.81 

22.50 

0.98 

RM2H 

3.46 

0.73 

6.91 

0.76 

21.60 

0.88 

43.20 

0.99 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.48 

0.96 

0.96 

1.05 

3.00 

1.07 

6.00 

0.93 






Table 5.11c Nonstructural Drift-Sensitive Fragility Curve Parameters - 
Low-Code Seismic Design Level 

Building 

Median Spectral Displacement (inches) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.50 

0.98 

1.01 

0.99 

3.15 

1.02 

6.30 

1.09 

W2 

0.86 

1.01 

1.73 

0.97 

5.40 

0.93 

10.80 

1.03 

S1L 

0.86 

0.86 

1.73 

0.84 

5.40 

0.88 

10.80 

1.00 

S1M 

2.16 

0.74 

4.32 

0.89 

13.50 

0.99 

27.00 

1.05 

S1H 

4.49 

0.75 

8.99 

0.87 

28.08 

0.97 

56.16 

1.04 

S2L 

0.86 

1.01 

1.73 

0.94 

5.40 

0.94 

10.80 

1.03 

S2M 

2.16 

0.77 

4.32 

0.87 

13.50 

0.99 

27.00 

1.05 

S2H 

4.49 

0.74 

8.99 

0.86 

28.08 

0.97 

56.16 

1.05 

S3 

0.54 

1.03 

1.08 

1.02 

3.38 

0.96 

6.75 

0.99 

S4L 

0.86 

1.09 

1.73 

0.99 

5.40 

0.96 

10.80 

1.03 

S4M 

2.16 

0.83 

4.32 

0.95 

13.50 

1.04 

27.00 

1.07 

S4H 

4.49 

0.84 

8.99 

0.95 

28.08 

1.05 

56.16 

1.07 

S5L 

0.86 

1.14 

1.73 

1.04 

5.40 

0.98 

10.80 

1.01 

S5M 

2.16 

0.84 

4.32 

0.95 

13.50 

1.03 

27.00 

1.07 

S5H 

4.49 

0.84 

8.99 

0.95 

28.08 

1.03 

56.16 

1.06 

C1L 

0.72 

0.99 

1.44 

0.96 

4.50 

0.90 

9.00 

1.01 

C1M 

1.80 

0.79 

3.60 

0.88 

11.25 

0.99 

22.50 

1.06 

C1H 

3.46 

0.87 

6.91 

0.96 

21.60 

1.02 

43.20 

1.06 

C2L 

0.72 

1.08 

1.44 

1.05 

4.50 

0.95 

9.00 

0.99 

C2M 

1.80 

0.84 

3.60 

0.87 

11.25 

1.00 

22.50 

1.06 

C2H 

3.46 

0.79 

6.91 

0.93 

21.60 

0.99 

43.20 

1.07 

C3L 

0.72 

1.13 

1.44 

1.08 

4.50 

0.95 

9.00 

1.00 

C3M 

1.80 

0.88 

3.60 

0.92 

11.25 

1.00 

22.50 

1.06 

C3H 

3.46 

0.83 

6.91 

0.96 

21.60 

1.02 

43.20 

1.06 

PC1 

0.54 

1.05 

1.08 

1.10 

3.38 

1.10 

6.75 

0.93 

PC2L 

0.72 

1.12 

1.44 

1.04 

4.50 

0.93 

9.00 

1.02 

PC2M 

1.80 

0.86 

3.60 

0.93 

11.25 

1.02 

22.50 

1.07 

PC2H 

3.46 

0.83 

6.91 

0.94 

21.60 

1.04 

43.20 

1.07 

RM1L 

0.72 

1.15 

1.44 

1.12 

4.50 

1.03 

9.00 

0.99 

RM1M 

1.80 

0.89 

3.60 

0.89 

11.25 

1.00 

22.50 

1.05 

RM2L 

0.72 

1.09 

1.44 

1.08 

4.50 

1.01 

9.00 

0.99 

RM2M 

1.80 

0.85 

3.60 

0.86 

11.25 

0.99 

22.50 

1.06 

RM2H 

3.46 

0.79 

6.91 

0.92 

21.60 

0.99 

43.20 

1.06 

URML 

0.54 

1.07 

1.08 

1.13 

3.38 

1.16 

6.75 

1.01 

URMM 

1.26 

0.97 

2.52 

0.91 

7.88 

0.98 

15.75 

1.04 

MH 

0.48 

0.96 

0.96 

1.05 

3.00 

1.07 

6.00 

0.93 






Table 5.11d Nonstructural Drift-Sensitive Fragility Curve Parameters - 
Pre-Code Seismic Design Level 

Building 

Median Spectral Displacement (inches) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.50 

1.07 

1.01 

1.11 

3.15 

1.11 

6.30 

1.14 

W2 

0.86 

1.06 

1.73 

1.00 

5.40 

0.93 

10.80 

1.01 

S1L 

0.86 

0.90 

1.73 

0.87 

5.40 

0.91 

10.80 

1.02 

S1M 

2.16 

0.80 

4.32 

0.92 

13.50 

0.99 

27.00 

1.06 

S1H 

4.49 

0.79 

8.99 

0.89 

28.08 

1.00 

56.16 

1.07 

S2L 

0.86 

1.06 

1.73 

0.97 

5.40 

0.96 

10.80 

1.04 

S2M 

2.16 

0.80 

4.32 

0.90 

13.50 

1.02 

27.00 

1.06 

S2H 

4.49 

0.79 

8.99 

0.89 

28.08 

0.99 

56.16 

1.06 

S3 

0.54 

1.11 

1.08 

1.05 

3.38 

0.96 

6.75 

1.00 

S4L 

0.86 

1.12 

1.73 

1.00 

5.40 

0.99 

10.80 

1.05 

S4M 

2.16 

0.86 

4.32 

0.99 

13.50 

1.06 

27.00 

1.10 

S4H 

4.49 

0.88 

8.99 

0.99 

28.08 

1.07 

56.16 

1.09 

S5L 

0.86 

1.18 

1.73 

1.06 

5.40 

0.98 

10.80 

1.03 

S5M 

2.16 

0.86 

4.32 

0.99 

13.50 

1.05 

27.00 

1.09 

S5H 

4.49 

0.88 

8.99 

0.91 

28.08 

1.05 

56.16 

1.09 

C1L 

0.72 

1.02 

1.44 

0.98 

4.50 

0.93 

9.00 

1.03 

C1M 

1.80 

0.81 

3.60 

0.91 

11.25 

1.02 

22.50 

1.06 

C1H 

3.46 

0.90 

6.91 

0.99 

21.60 

1.05 

43.20 

1.10 

C2L 

0.72 

1.14 

1.44 

1.08 

4.50 

0.97 

9.00 

1.00 

C2M 

1.80 

0.88 

3.60 

0.90 

11.25 

1.03 

22.50 

1.07 

C2H 

3.46 

0.83 

6.91 

0.97 

21.60 

1.05 

43.20 

1.07 

C3L 

0.72 

1.19 

1.44 

1.11 

4.50 

0.99 

9.00 

1.02 

C3M 

1.80 

0.92 

3.60 

0.95 

11.25 

1.03 

22.50 

1.09 

C3H 

3.46 

0.86 

6.91 

0.90 

21.60 

1.04 

43.20 

1.09 

PC1 

0.54 

1.18 

1.08 

1.16 

3.38 

1.12 

6.75 

0.95 

PC2L 

0.72 

1.16 

1.44 

1.06 

4.50 

0.96 

9.00 

1.02 

PC2M 

1.80 

0.87 

3.60 

0.95 

11.25 

1.04 

22.50 

1.07 

PC2H 

3.46 

0.87 

6.91 

0.99 

21.60 

1.06 

43.20 

1.08 

RM1L 

0.72 

1.22 

1.44 

1.14 

4.50 

1.03 

9.00 

0.99 

RM1M 

1.80 

0.93 

3.60 

0.92 

11.25 

1.02 

22.50 

1.07 

RM2L 

0.72 

1.17 

1.44 

1.12 

4.50 

1.01 

9.00 

0.99 

RM2M 

1.80 

0.89 

3.60 

0.90 

11.25 

1.01 

22.50 

1.07 

RM2H 

3.46 

0.82 

6.91 

0.96 

21.60 

1.04 

43.20 

1.07 

URML 

0.54 

1.21 

1.08 

1.23 

3.38 

1.23 

6.75 

1.03 

URMM 

1.26 

0.99 

2.52 

0.95 

7.88 

0.99 

15.75 

1.06 

MH 

0.48 

1.15 

0.96 

1.09 

3.00 

0.93 

6.00 

0.99 



 




 5.4.3.6 Nonstructural Damage - Acceleration-Sensitive Components 

 

Table 5.12 summarizes the peak floor acceleration values used by the Methodology 
to 
define the median values of fragility curves for acceleration-sensitive 
nonstructural 
components of buildings. Nonstructural damage acceleration values are assumed to 
be 
the same for each model building type, but to vary by seismic design level. 

 

Table 5.12 Peak Floor Accelerations Used to Define Median Values of Damage to 
Nonstructural Acceleration-Sensitive Components 

Seismic Design 

Floor Acceleration at the Threshold of Nonstructural Damage (g) 

Level 

Slight 

Moderate 

Extensive 

Complete 

High-Code 

0.30 

0.60 

1.20 

2.40 

Moderate-Code 

0.25 

0.50 

1.00 

2.00 

Low-Code 

0.20 

0.40 

0.80 

1.60 

Pre-Code 

0.20 

0.40 

0.80 

1.60 



 

The floor acceleration values are used directly as median values, assuming 
average 
upper-floor demand is represented by response at the point of the push-over mode 
displacement. 

 

The total variability of each damage state, NSAds, is modeled by the 
combination of 
following three contributors to nonstructural acceleration-sensitive damage 
variability: 

 

 uncertainty in the damage-state threshold of nonstructural components 
(M(NSAds) = 0.6, for all damage states and building types), 

 variability in capacity (response) properties of the model building type 
that contains the nonstructural components of interest (C(Au) = 0.25 
for Code buildings, C(Au) = 0.30 for Pre-Code buildings), and 

 variability in response of the model building type due to the spatial 
variability of ground motion demand (D(A) = 0.45 and C(V) = 0.50). 

Each of these three contributors to damage state variability is assumed to be 
lognormally 
distributed random variables. Capacity and demand are dependent parameters and a 
convolution process is used to derive combined capacity/demand variability of 
each 
nonstructural damage state. Capacity/demand variability is then combined with 
damage 
state uncertainty, as described in Section 5.4.3.3. 

 

Tables 5.13a, 5.13b, 5.13c and 5.13d summarize median and lognormal standard 
deviation (NSAds) values for Slight, Moderate, Extensive and Complete 
nonstructural 
acceleration-sensitive damage states for High-Code. Moderate-Code, Low-Code and 
Pre-
Code buildings, respectively. Median values are the same for all building types. 
Lognormal standard deviation values are slightly different for each building 
type. Note 
that for the following tables, shaded boxes indicate types that are not 
permitted by current 
seismic codes. 




 

Table 5.13a Nonstructural Acceleration-Sensitive Fragility Curve Parameters - 
High-Code Seismic Design Level 

Building 

Median Spectral Acceleration (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.30 

0.73 

0.60 

0.68 

1.20 

0.68 

2.40 

0.68 

W2 

0.30 

0.70 

0.60 

0.67 

1.20 

0.67 

2.40 

0.68 

S1L 

0.30 

0.67 

0.60 

0.67 

1.20 

0.68 

2.40 

0.67 

S1M 

0.30 

0.67 

0.60 

0.68 

1.20 

0.67 

2.40 

0.67 

S1H 

0.30 

0.68 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

S2L 

0.30 

0.67 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

S2M 

0.30 

0.69 

0.60 

0.66 

1.20 

0.66 

2.40 

0.66 

S2H 

0.30 

0.68 

0.60 

0.66 

1.20 

0.65 

2.40 

0.65 

S3 

0.30 

0.68 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

S4L 

0.30 

0.68 

0.60 

0.68 

1.20 

0.67 

2.40 

0.67 

S4M 

0.30 

0.67 

0.60 

0.65 

1.20 

0.66 

2.40 

0.66 

S4H 

0.30 

0.67 

0.60 

0.66 

1.20 

0.65 

2.40 

0.65 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.30 

0.68 

0.60 

0.68 

1.20 

0.67 

2.40 

0.67 

C1M 

0.30 

0.68 

0.60 

0.68 

1.20 

0.66 

2.40 

0.66 

C1H 

0.30 

0.66 

0.60 

0.66 

1.20 

0.66 

2.40 

0.66 

C2L 

0.30 

0.69 

0.60 

0.67 

1.20 

0.66 

2.40 

0.64 

C2M 

0.30 

0.70 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

C2H 

0.30 

0.68 

0.60 

0.66 

1.20 

0.65 

2.40 

0.65 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.30 

0.74 

0.60 

0.67 

1.20 

0.67 

2.40 

0.64 

PC2L 

0.30 

0.68 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

PC2M 

0.30 

0.68 

0.60 

0.65 

1.20 

0.66 

2.40 

0.66 

PC2H 

0.30 

0.67 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

RM1L 

0.30 

0.70 

0.60 

0.67 

1.20 

0.67 

2.40 

0.63 

RM1M 

0.30 

0.72 

0.60 

0.66 

1.20 

0.65 

2.40 

0.65 

RM2L 

0.30 

0.70 

0.60 

0.66 

1.20 

0.67 

2.40 

0.64 

RM2M 

0.30 

0.72 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

RM2H 

0.30 

0.70 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.30 

0.65 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 






 

Table 5.13b Nonstructural Acceleration-Sensitive Fragility Curve Parameters - 
Moderate-Code Seismic Design Level 

Building 

Median Spectral Acceleration (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.25 

0.73 

0.50 

0.68 

1.00 

0.67 

2.00 

0.64 

W2 

0.25 

0.68 

0.50 

0.67 

1.00 

0.68 

2.00 

0.68 

S1L 

0.25 

0.67 

0.50 

0.66 

1.00 

0.67 

2.00 

0.67 

S1M 

0.25 

0.66 

0.50 

0.67 

1.00 

0.67 

2.00 

0.67 

S1H 

0.25 

0.66 

0.50 

0.68 

1.00 

0.68 

2.00 

0.68 

S2L 

0.25 

0.66 

0.50 

0.66 

1.00 

0.68 

2.00 

0.68 

S2M 

0.25 

0.66 

0.50 

0.65 

1.00 

0.65 

2.00 

0.65 

S2H 

0.25 

0.65 

0.50 

0.65 

1.00 

0.65 

2.00 

0.65 

S3 

0.25 

0.67 

0.50 

0.66 

1.00 

0.65 

2.00 

0.65 

S4L 

0.25 

0.66 

0.50 

0.66 

1.00 

0.66 

2.00 

0.66 

S4M 

0.25 

0.65 

0.50 

0.65 

1.00 

0.65 

2.00 

0.65 

S4H 

0.25 

0.65 

0.50 

0.66 

1.00 

0.66 

2.00 

0.66 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.25 

0.67 

0.50 

0.66 

1.00 

0.66 

2.00 

0.66 

C1M 

0.25 

0.66 

0.50 

0.65 

1.00 

0.63 

2.00 

0.63 

C1H 

0.25 

0.65 

0.50 

0.67 

1.00 

0.67 

2.00 

0.67 

C2L 

0.25 

0.68 

0.50 

0.66 

1.00 

0.68 

2.00 

0.68 

C2M 

0.25 

0.67 

0.50 

0.64 

1.00 

0.67 

2.00 

0.67 

C2H 

0.25 

0.66 

0.50 

0.65 

1.00 

0.65 

2.00 

0.65 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.25 

0.68 

0.50 

0.67 

1.00 

0.66 

2.00 

0.66 

PC2L 

0.25 

0.66 

0.50 

0.66 

1.00 

0.65 

2.00 

0.65 

PC2M 

0.25 

0.65 

0.50 

0.65 

1.00 

0.65 

2.00 

0.65 

PC2H 

0.25 

0.64 

0.50 

0.65 

1.00 

0.65 

2.00 

0.65 

RM1L 

0.25 

0.68 

0.50 

0.67 

1.00 

0.67 

2.00 

0.67 

RM1M 

0.25 

0.67 

0.50 

0.64 

1.00 

0.67 

2.00 

0.67 

RM2L 

0.25 

0.68 

0.50 

0.66 

1.00 

0.67 

2.00 

0.67 

RM2M 

0.25 

0.67 

0.50 

0.64 

1.00 

0.67 

2.00 

0.67 

RM2H 

0.25 

0.66 

0.50 

0.64 

1.00 

0.64 

2.00 

0.64 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.25 

0.65 

0.50 

0.67 

1.00 

0.67 

2.00 

0.67 






 

Table 5.13c Nonstructural Acceleration-Sensitive Fragility Curve Parameters - 
Low-Code Seismic Design Level 

Building 

Median Spectral Acceleration (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.20 

0.71 

0.40 

0.68 

0.80 

0.66 

1.60 

0.66 

W2 

0.20 

0.67 

0.40 

0.67 

0.80 

0.70 

1.60 

0.70 

S1L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S1M 

0.20 

0.66 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S1H 

0.20 

0.67 

0.40 

0.65 

0.80 

0.65 

1.60 

0.65 

S2L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S2M 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S2H 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S3 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S4L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S4M 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S4H 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S5L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.67 

1.60 

0.67 

S5M 

0.20 

0.64 

0.40 

0.68 

0.80 

0.67 

1.60 

0.67 

S5H 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

C1L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

C1M 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

C1H 

0.20 

0.67 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

C2L 

0.20 

0.66 

0.40 

0.67 

0.80 

0.66 

1.60 

0.66 

C2M 

0.20 

0.64 

0.40 

0.66 

0.80 

0.65 

1.60 

0.65 

C2H 

0.20 

0.64 

0.40 

0.66 

0.80 

0.66 

1.60 

0.66 

C3L 

0.20 

0.65 

0.40 

0.67 

0.80 

0.66 

1.60 

0.66 

C3M 

0.20 

0.64 

0.40 

0.67 

0.80 

0.66 

1.60 

0.66 

C3H 

0.20 

0.64 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

PC1 

0.20 

0.66 

0.40 

0.66 

0.80 

0.66 

1.60 

0.66 

PC2L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

PC2M 

0.20 

0.64 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

PC2H 

0.20 

0.64 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

RM1L 

0.20 

0.66 

0.40 

0.67 

0.80 

0.64 

1.60 

0.64 

RM1M 

0.20 

0.64 

0.40 

0.66 

0.80 

0.64 

1.60 

0.64 

RM2L 

0.20 

0.66 

0.40 

0.67 

0.80 

0.64 

1.60 

0.64 

RM2M 

0.20 

0.64 

0.40 

0.66 

0.80 

0.65 

1.60 

0.65 

RM2H 

0.20 

0.64 

0.40 

0.66 

0.80 

0.66 

1.60 

0.66 

URML 

0.20 

0.68 

0.40 

0.65 

0.80 

0.65 

1.60 

0.65 

URMM 

0.20 

0.64 

0.40 

0.66 

0.80 

0.66 

1.60 

0.66 

MH 

0.20 

0.65 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 






 

Table 5.13d Nonstructural Acceleration-Sensitive Fragility Curve Parameters - 
Pre-Code Seismic Design Level 

Building 

Median Spectral Acceleration (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.20 

0.72 

0.40 

0.70 

0.80 

0.67 

1.60 

0.67 

W2 

0.20 

0.66 

0.40 

0.67 

0.80 

0.65 

1.60 

0.65 

S1L 

0.20 

0.66 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S1M 

0.20 

0.66 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S1H 

0.20 

0.68 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S2L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S2M 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S2H 

0.20 

0.65 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

S3 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S4L 

0.20 

0.66 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S4M 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S4H 

0.20 

0.66 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S5L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S5M 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

S5H 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

C1L 

0.20 

0.66 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

C1M 

0.20 

0.66 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

C1H 

0.20 

0.68 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

C2L 

0.20 

0.65 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

C2M 

0.20 

0.64 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

C2H 

0.20 

0.65 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

C3L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

C3M 

0.20 

0.64 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

C3H 

0.20 

0.65 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

PC1 

0.20 

0.67 

0.40 

0.66 

0.80 

0.66 

1.60 

0.66 

PC2L 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

PC2M 

0.20 

0.65 

0.40 

0.68 

0.80 

0.68 

1.60 

0.68 

PC2H 

0.20 

0.66 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

RM1L 

0.20 

0.66 

0.40 

0.67 

0.80 

0.66 

1.60 

0.66 

RM1M 

0.20 

0.64 

0.40 

0.66 

0.80 

0.65 

1.60 

0.65 

RM2L 

0.20 

0.66 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

RM2M 

0.20 

0.64 

0.40 

0.66 

0.80 

0.66 

1.60 

0.66 

RM2H 

0.20 

0.65 

0.40 

0.67 

0.80 

0.67 

1.60 

0.67 

URML 

0.20 

0.69 

0.40 

0.65 

0.80 

0.65 

1.60 

0.65 

URMM 

0.20 

0.64 

0.40 

0.66 

0.80 

0.66 

1.60 

0.66 

MH 

0.20 

0.67 

0.40 

0.65 

0.80 

0.65 

1.60 

0.65 



 

5.4.4 Structural Fragility Curves - Equivalent Peak Ground Acceleration 

 




Structural damage functions are expressed in terms of an equivalent value of PGA 
(rather 
than spectral displacement) for evaluation of buildings that are components of 
lifelines. 
Only structural damage functions are developed based on PGA, since structural 
damage 
is considered the most appropriate measure of damage for lifeline facilities. 
Similar 
methods could be used to develop nonstructural damage functions based on PGA. In 
this 
case, capacity curves are not necessary to estimate building response and PGA is 
used 
directly as the PESH input to building fragility curves. This section develops 
equivalent-
PGA fragility curves based on the structural damage functions of Tables 5.9a - 
5.9d and 
standard spectrum shape properties of Chapter 4. 

 

Median values of equivalent-PGA fragility curves are based on median values of 
spectral 
displacement of the damage state of interest and an assumed demand spectrum 
shape that 
relates spectral response to PGA. As such, median values of equivalent PGA are 
very 
sensitive to the shape assumed for the demand spectrum (i.e., PESH-input 
spectrum 
reduced for damping greater than 5% of critical as described in Section 
5.6.2.1). 
Spectrum shape is influenced by earthquake source (i.e., WUS vs. CEUS 
attenuation 
functions), earthquake magnitude (e.g., large vs. small magnitude events), 
distance from 
source to site, site conditions (e.g., soil vs. rock) and effective damping 
which varies 
based on building properties and earthquake duration (e.g., Short, Moderate or 
Long 
duration). 

 

It is not practical to create equivalent-PGA fragility curves for all possible 
factors that 
influence demand spectrum shape. Rather, equivalent-PGA fragility curves are 
developed for a single set of spectrum shape factors (reference spectrum), and a 
formula 
is provided for modifying damage state medians to approximate other spectrum 
shapes. 
The reference spectrum represents ground shaking of a large-magnitude (i.e., M . 
7.0) 
western United States (WUS) earthquake for soil sites (e.g., Site Class D) at 
site-to-
source distances of 15 km, or greater. The demand spectrum based on these 
assumptions 
is scaled uniformly at each period such that the spectrum intersects the 
building capacity 
curve at the spectral displacement of the median value of the damage state of 
interest. 
The PGA of the scaled demand spectrum defines the median value of equivalent-PGA 
fragility. Figure 5.6 illustrates this scaling and intersection process for a 
typical building 
capacity curve and Slight, Moderate, Extensive and Complete structural damage 
states. 

 

The total variability of each equivalent-PGA structural damage state, SPGA, is 
modeled 
by the combination of following two contributors to damage variability: 

 

 uncertainty in the damage-state threshold of the structural system 
(M(SPGA) = 0.4 for all building types and damage states), 

 variability in response due to the spatial variability of ground motion 
demand (D(V) = 0.5 for long-period spectral response). 

 




Figure 5.6 Development of Equivalent-PGA Median Damage Values. 

The two contributors to damage state variability are assumed to be lognormally 
distributed, independent random variables and the total variability is simply 
the square-
root-sum-of-the-squares combination of individual variability terms (i.e., SPGA 
= 0.64). 
Tables 5.16a, 5.16b, 5.16c and 5.16d summarize median and lognormal standard 
deviation (SPGA) values for Slight, Moderate, Extensive and Complete PGA-based 
structural damage states for High-Code, Moderate-Code, Low-Code and Pre-Code 
buildings, respectively. 

 

The values given in Tables 5.16a through 5.16d are appropriate for use in the 
evaluation 
of scenario earthquakes whose demand spectrum shape is based on, or similar to, 
large-
magnitude, WUS ground shaking at soil sites (reference spectrum shape). For 
evaluation 
of building damage due to scenario earthquakes whose spectra are not similar to 
the 
reference spectrum shape, damage-state median parameters may be adjusted to 
better 
represent equivalent-PGA structural fragility for the spectrum shape of 
interest. This 
adjustment is based on: (1) site condition (if different from Site Class D) and 
(2) the 
ratio of long-period spectral response (i.e., SA1) to PGA (if different from a 
value of 1.5, 
the ratio of SA1 to PGA of the reference spectrum shape). Damage-state 
variability is not 
adjusted assuming that the variability associated with ground shaking (although 
different 
for different source/site conditions) when combined with the uncertainty in 
damage-state 
threshold, is approximately the same for all demand spectrum shapes. 

 

Tables 4.2 and 4.3 provide spectral acceleration response factors for WUS rock 
(Site 
Class B) and CEUS rock (Site Class B) locations, respectively. These tables are 
based on 
the default WUS and CEUS attenuation functions and describe response ratios, 
SAS/PGA 
and SAS/SA1, as a function of distance and earthquake magnitude. Although both 
short-
period response (SAS) and long-period response (SA1) can influence building 
fragility, 

00.10.20.30.40.50.60.70.80.91012345678910Spectral Displacement (inches)
Spectral Acceleration (g's)
Capacity 
Curve 
Reference Demand Spectrum Shape 
Large-Magnitude (i.e., MW = 7.0) 
Western United States Earthquake 
Soil-Site Conditions (i.e., Site Class D) 
Site/Source Distance > 15 kmCSD[M]
PGA[S]
SMESD[C]SD[E]SD[S]
PGA[M]
PGA[E]
PGA[C]



long-period response typically dominates building fragility and is the parameter 
used to 
relate spectral demand to PGA. Spectral response factors given in Tables 4.2 and 
4.3 are 
combined to form ratios of PGA/SA1 as given in Table 5.14 an

fo

Spectrum Shape 

o, RPGA/SA1 - WUS Rock (Si

PGA

giv

gni

M: 

= 5 

6 

7 

= 8 

 10 km 

3.8 

2.1 

1.5 

0.85 

20 km 

3.3 

1.8 

1.2 

0.85 

40 km 

2.9 

1.6 

1.05 

0.80 



 

ctrum Shap

, RPGA/SA1 - CEUS Rock (S

giv

gni

M: 

Distance 

= 5 

6 

7 

= 8 

 10 km 

7.8 

3.5 

2.1 

1.1 

20 km 

8.1 

3.1 

2.1 

1.7 

40 km 

6.1 

2.6 

1.8 

1.6 



 
Equivalent-PGA medians specified in Tables 5.16a through 5.16d for the reference 
spectrum shape are converted to medians representing other spectrum shapes usin

ra

PGAPGARFdsRdsPGASAV=.
..
.
..,/
.115

PGAds 

is the median PGA of structural damage state, ds, 
PGARds, is the median PGA of structural damage state, ds, as given i

Tables 5-13a through 5-13d for the reference spectrum shape

RPGis the spectrum shape ratio, given in Tables 5.14 - 5

 
In general, implementation of Equation (5-6) requires information on earthquake 
magnitude and source-to-site distance to estimate the spectrum shape ratio for 
rock sites, 
and 1-second period spectral acceleration at the site (to estimate the soil 
amplification 
factor). Note that for Tables 5.16a through 5.16d, shaded boxes indicate types 
that are 




 

Table 5.16a Equivalent-PGA Structural Fragility - 
High-Code Seismic Design Level 

Building 

Median Equivalent-PGA (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.26 

0.64 

0.55 

0.64 

1.28 

0.64 

2.01 

0.64 

W2 

0.26 

0.64 

0.56 

0.64 

1.15 

0.64 

2.08 

0.64 

S1L 

0.19 

0.64 

0.31 

0.64 

0.64 

0.64 

1.49 

0.64 

S1M 

0.14 

0.64 

0.26 

0.64 

0.62 

0.64 

1.43 

0.64 

S1H 

0.10 

0.64 

0.21 

0.64 

0.52 

0.64 

1.31 

0.64 

S2L 

0.24 

0.64 

0.41 

0.64 

0.76 

0.64 

1.46 

0.64 

S2M 

0.14 

0.64 

0.27 

0.64 

0.73 

0.64 

1.62 

0.64 

S2H 

0.11 

0.64 

0.22 

0.64 

0.65 

0.64 

1.60 

0.64 

S3 

0.15 

0.64 

0.26 

0.64 

0.54 

0.64 

1.00 

0.64 

S4L 

0.24 

0.64 

0.39 

0.64 

0.71 

0.64 

1.33 

0.64 

S4M 

0.16 

0.64 

0.28 

0.64 

0.73 

0.64 

1.56 

0.64 

S4H 

0.13 

0.64 

0.25 

0.64 

0.69 

0.64 

1.63 

0.64 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.21 

0.64 

0.35 

0.64 

0.70 

0.64 

1.37 

0.64 

C1M 

0.15 

0.64 

0.27 

0.64 

0.73 

0.64 

1.61 

0.64 

C1H 

0.11 

0.64 

0.22 

0.64 

0.62 

0.64 

1.35 

0.64 

C2L 

0.24 

0.64 

0.45 

0.64 

0.90 

0.64 

1.55 

0.64 

C2M 

0.17 

0.64 

0.36 

0.64 

0.87 

0.64 

1.95 

0.64 

C2H 

0.12 

0.64 

0.29 

0.64 

0.82 

0.64 

1.87 

0.64 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.20 

0.64 

0.35 

0.64 

0.72 

0.64 

1.25 

0.64 

PC2L 

0.24 

0.64 

0.36 

0.64 

0.69 

0.64 

1.23 

0.64 

PC2M 

0.17 

0.64 

0.29 

0.64 

0.67 

0.64 

1.51 

0.64 

PC2H 

0.12 

0.64 

0.23 

0.64 

0.63 

0.64 

1.49 

0.64 

RM1L 

0.30 

0.64 

0.46 

0.64 

0.93 

0.64 

1.57 

0.64 

RM1M 

0.20 

0.64 

0.37 

0.64 

0.81 

0.64 

1.90 

0.64 

RM2L 

0.26 

0.64 

0.42 

0.64 

0.87 

0.64 

1.49 

0.64 

RM2M 

0.17 

0.64 

0.33 

0.64 

0.75 

0.64 

1.83 

0.64 

RM2H 

0.12 

0.64 

0.24 

0.64 

0.67 

0.64 

1.78 

0.64 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.11 

0.64 

0.18 

0.64 

0.31 

0.64 

0.60 

0.64 






 

Table 5.16b Equivalent-PGA Structural Fragility - 
Moderate-Code Seismic Design Level 

Building 

Median Equivalent-PGA (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.24 

0.64 

0.43 

0.64 

0.91 

0.64 

1.34 

0.64 

W2 

0.20 

0.64 

0.35 

0.64 

0.64 

0.64 

1.13 

0.64 

S1L 

0.15 

0.64 

0.22 

0.64 

0.42 

0.64 

0.80 

0.64 

S1M 

0.13 

0.64 

0.21 

0.64 

0.44 

0.64 

0.82 

0.64 

S1H 

0.10 

0.64 

0.18 

0.64 

0.39 

0.64 

0.78 

0.64 

S2L 

0.20 

0.64 

0.26 

0.64 

0.46 

0.64 

0.84 

0.64 

S2M 

0.14 

0.64 

0.22 

0.64 

0.53 

0.64 

0.97 

0.64 

S2H 

0.11 

0.64 

0.19 

0.64 

0.49 

0.64 

1.02 

0.64 

S3 

0.13 

0.64 

0.19 

0.64 

0.33 

0.64 

0.60 

0.64 

S4L 

0.19 

0.64 

0.26 

0.64 

0.41 

0.64 

0.78 

0.64 

S4M 

0.14 

0.64 

0.22 

0.64 

0.51 

0.64 

0.92 

0.64 

S4H 

0.12 

0.64 

0.21 

0.64 

0.51 

0.64 

0.97 

0.64 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.16 

0.64 

0.23 

0.64 

0.41 

0.64 

0.77 

0.64 

C1M 

0.13 

0.64 

0.21 

0.64 

0.49 

0.64 

0.89 

0.64 

C1H 

0.11 

0.64 

0.18 

0.64 

0.41 

0.64 

0.74 

0.64 

C2L 

0.18 

0.64 

0.30 

0.64 

0.49 

0.64 

0.87 

0.64 

C2M 

0.15 

0.64 

0.26 

0.64 

0.55 

0.64 

1.02 

0.64 

C2H 

0.12 

0.64 

0.23 

0.64 

0.57 

0.64 

1.07 

0.64 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.18 

0.64 

0.24 

0.64 

0.44 

0.64 

0.71 

0.64 

PC2L 

0.18 

0.64 

0.25 

0.64 

0.40 

0.64 

0.74 

0.64 

PC2M 

0.15 

0.64 

0.21 

0.64 

0.45 

0.64 

0.86 

0.64 

PC2H 

0.12 

0.64 

0.19 

0.64 

0.46 

0.64 

0.90 

0.64 

RM1L 

0.22 

0.64 

0.30 

0.64 

0.50 

0.64 

0.85 

0.64 

RM1M 

0.18 

0.64 

0.26 

0.64 

0.51 

0.64 

1.03 

0.64 

RM2L 

0.20 

0.64 

0.28 

0.64 

0.47 

0.64 

0.81 

0.64 

RM2M 

0.16 

0.64 

0.23 

0.64 

0.48 

0.64 

0.99 

0.64 

RM2H 

0.12 

0.64 

0.20 

0.64 

0.48 

0.64 

1.01 

0.64 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.11 

0.64 

0.18 

0.64 

0.31 

0.64 

0.60 

0.64 






 

Table 5.16c Equivalent-PGA Structural Fragility - 
Low-Code Seismic Design Level 

Building 

Median Equivalent-PGA (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.20 

0.64 

0.34 

0.64 

0.61 

0.64 

0.95 

0.64 

W2 

0.14 

0.64 

0.23 

0.64 

0.48 

0.64 

0.75 

0.64 

S1L 

0.12 

0.64 

0.17 

0.64 

0.30 

0.64 

0.48 

0.64 

S1M 

0.12 

0.64 

0.18 

0.64 

0.29 

0.64 

0.49 

0.64 

S1H 

0.10 

0.64 

0.15 

0.64 

0.28 

0.64 

0.48 

0.64 

S2L 

0.13 

0.64 

0.17 

0.64 

0.30 

0.64 

0.50 

0.64 

S2M 

0.12 

0.64 

0.18 

0.64 

0.35 

0.64 

0.58 

0.64 

S2H 

0.11 

0.64 

0.17 

0.64 

0.36 

0.64 

0.63 

0.64 

S3 

0.10 

0.64 

0.13 

0.64 

0.20 

0.64 

0.38 

0.64 

S4L 

0.13 

0.64 

0.16 

0.64 

0.26 

0.64 

0.46 

0.64 

S4M 

0.12 

0.64 

0.17 

0.64 

0.31 

0.64 

0.54 

0.64 

S4H 

0.12 

0.64 

0.17 

0.64 

0.33 

0.64 

0.59 

0.64 

S5L 

0.13 

0.64 

0.17 

0.64 

0.28 

0.64 

0.45 

0.64 

S5M 

0.11 

0.64 

0.18 

0.64 

0.34 

0.64 

0.53 

0.64 

S5H 

0.10 

0.64 

0.18 

0.64 

0.35 

0.64 

0.58 

0.64 

C1L 

0.12 

0.64 

0.15 

0.64 

0.27 

0.64 

0.45 

0.64 

C1M 

0.12 

0.64 

0.17 

0.64 

0.32 

0.64 

0.54 

0.64 

C1H 

0.10 

0.64 

0.15 

0.64 

0.27 

0.64 

0.44 

0.64 

C2L 

0.14 

0.64 

0.19 

0.64 

0.30 

0.64 

0.52 

0.64 

C2M 

0.12 

0.64 

0.19 

0.64 

0.38 

0.64 

0.63 

0.64 

C2H 

0.11 

0.64 

0.19 

0.64 

0.38 

0.64 

0.65 

0.64 

C3L 

0.12 

0.64 

0.17 

0.64 

0.26 

0.64 

0.44 

0.64 

C3M 

0.11 

0.64 

0.17 

0.64 

0.32 

0.64 

0.51 

0.64 

C3H 

0.09 

0.64 

0.16 

0.64 

0.33 

0.64 

0.53 

0.64 

PC1 

0.13 

0.64 

0.17 

0.64 

0.25 

0.64 

0.45 

0.64 

PC2L 

0.13 

0.64 

0.15 

0.64 

0.24 

0.64 

0.44 

0.64 

PC2M 

0.11 

0.64 

0.16 

0.64 

0.31 

0.64 

0.52 

0.64 

PC2H 

0.11 

0.64 

0.16 

0.64 

0.31 

0.64 

0.55 

0.64 

RM1L 

0.16 

0.64 

0.20 

0.64 

0.29 

0.64 

0.54 

0.64 

RM1M 

0.14 

0.64 

0.19 

0.64 

0.35 

0.64 

0.63 

0.64 

RM2L 

0.14 

0.64 

0.18 

0.64 

0.28 

0.64 

0.51 

0.64 

RM2M 

0.12 

0.64 

0.17 

0.64 

0.34 

0.64 

0.60 

0.64 

RM2H 

0.11 

0.64 

0.17 

0.64 

0.35 

0.64 

0.62 

0.64 

URML 

0.14 

0.64 

0.20 

0.64 

0.32 

0.64 

0.46 

0.64 

URMM 

0.10 

0.64 

0.16 

0.64 

0.27 

0.64 

0.46 

0.64 

MH 

0.11 

0.64 

0.18 

0.64 

0.31 

0.64 

0.60 

0.64 



 

Table 5.16d Equivalent-PGA Structural Fragility - 
Pre-Code Seismic Design Level 

Building 

Median Equivalent-PGA (g) and Logstandard Deviation (Beta) 






Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.18 

0.64 

0.29 

0.64 

0.51 

0.64 

0.77 

0.64 

W2 

0.12 

0.64 

0.19 

0.64 

0.37 

0.64 

0.60 

0.64 

S1L 

0.09 

0.64 

0.13 

0.64 

0.22 

0.64 

0.38 

0.64 

S1M 

0.09 

0.64 

0.14 

0.64 

0.23 

0.64 

0.39 

0.64 

S1H 

0.08 

0.64 

0.12 

0.64 

0.22 

0.64 

0.38 

0.64 

S2L 

0.11 

0.64 

0.14 

0.64 

0.23 

0.64 

0.39 

0.64 

S2M 

0.10 

0.64 

0.14 

0.64 

0.28 

0.64 

0.47 

0.64 

S2H 

0.09 

0.64 

0.13 

0.64 

0.29 

0.64 

0.50 

0.64 

S3 

0.08 

0.64 

0.10 

0.64 

0.16 

0.64 

0.30 

0.64 

S4L 

0.10 

0.64 

0.13 

0.64 

0.20 

0.64 

0.36 

0.64 

S4M 

0.09 

0.64 

0.13 

0.64 

0.25 

0.64 

0.43 

0.64 

S4H 

0.09 

0.64 

0.14 

0.64 

0.27 

0.64 

0.47 

0.64 

S5L 

0.11 

0.64 

0.14 

0.64 

0.22 

0.64 

0.37 

0.64 

S5M 

0.09 

0.64 

0.14 

0.64 

0.28 

0.64 

0.43 

0.64 

S5H 

0.08 

0.64 

0.14 

0.64 

0.29 

0.64 

0.46 

0.64 

C1L 

0.10 

0.64 

0.12 

0.64 

0.21 

0.64 

0.36 

0.64 

C1M 

0.09 

0.64 

0.13 

0.64 

0.26 

0.64 

0.43 

0.64 

C1H 

0.08 

0.64 

0.12 

0.64 

0.21 

0.64 

0.35 

0.64 

C2L 

0.11 

0.64 

0.15 

0.64 

0.24 

0.64 

0.42 

0.64 

C2M 

0.10 

0.64 

0.15 

0.64 

0.30 

0.64 

0.50 

0.64 

C2H 

0.09 

0.64 

0.15 

0.64 

0.31 

0.64 

0.52 

0.64 

C3L 

0.10 

0.64 

0.14 

0.64 

0.21 

0.64 

0.35 

0.64 

C3M 

0.09 

0.64 

0.14 

0.64 

0.25 

0.64 

0.41 

0.64 

C3H 

0.08 

0.64 

0.13 

0.64 

0.27 

0.64 

0.43 

0.64 

PC1 

0.11 

0.64 

0.14 

0.64 

0.21 

0.64 

0.35 

0.64 

PC2L 

0.10 

0.64 

0.13 

0.64 

0.19 

0.64 

0.35 

0.64 

PC2M 

0.09 

0.64 

0.13 

0.64 

0.24 

0.64 

0.42 

0.64 

PC2H 

0.09 

0.64 

0.13 

0.64 

0.25 

0.64 

0.43 

0.64 

RM1L 

0.13 

0.64 

0.16 

0.64 

0.24 

0.64 

0.43 

0.64 

RM1M 

0.11 

0.64 

0.15 

0.64 

0.28 

0.64 

0.50 

0.64 

RM2L 

0.12 

0.64 

0.15 

0.64 

0.22 

0.64 

0.41 

0.64 

RM2M 

0.10 

0.64 

0.14 

0.64 

0.26 

0.64 

0.47 

0.64 

RM2H 

0.09 

0.64 

0.13 

0.64 

0.27 

0.64 

0.50 

0.64 

URML 

0.13 

0.64 

0.17 

0.64 

0.26 

0.64 

0.37 

0.64 

URMM 

0.09 

0.64 

0.13 

0.64 

0.21 

0.64 

0.38 

0.64 

MH 

0.08 

0.64 

0.11 

0.64 

0.18 

0.64 

0.34 

0.64 






 

5.5 Building Damage Due to Ground Failure 

 

5.5.1 Overview 

 

Building damage is characterized by four damage states (i.e., Slight, Moderate, 
Extensive 
and Complete). These four states are simplified for ground failure to include 
only one 
combined Extensive/Complete damage state. In essence, buildings are assumed to 
be 
either undamaged or severely damaged due to ground failure. In fact, Slight or 
Moderate 
damage can occur due to ground failure, but the likelihood of this damage is 
considered 
to be small (relative to ground shaking damage) and tacitly included in 
predictions of 
Slight or Moderate damage due to ground shaking. 

 

Given the earthquake demand in terms of permanent ground deformation (PGD), the 
probability of being in the Extensive/Complete damage state is estimated using 
fragility 
curves of a form similar to those used to estimate shaking damage. Separate 
fragility 
curves distinguish between ground failure due to lateral spreading and ground 
failure due 
to ground settlement, and between shallow and deep foundations. 

 

5.5.2 Fragility Curves - Peak Ground Displacement 

 

There is no available relationship between the likelihood of Extensive/Complete 
damage 
of buildings and PGD. Engineering judgment is used to develop a set of 
assumptions, 
which define building fragility. These assumptions are shown in Table 5.17 for 
buildings 
with shallow foundations (e.g., spread footings). 

 

Table 5.17 Building Damage Relationship to PGD - Shallow Foundations 

PE or C|PGD 

Settlement PGD 
(inches) 

Lateral Spread PGD 
(inches) 

0.1 

2 

12 

0.5 (median) 

10 

60 



 

The above assumptions are based on the expectation that about 10 (i.e., 8 
Extensive 
damage, 2 Complete damage) out of 100 buildings on spread footings would be 
severely 
damaged for 2 inches of settlement PGD or 12 inches of lateral spread PGD, and 
that 
about 50 (i.e., 40 Extensive damage, 10 Complete damage) out of 100 buildings on 
spread footings would be severely damaged for 10 inches of settlement PGD or 60 
inches 
of lateral spread PGD. Lateral spread is judged to require significantly more 
PGD to 
effect severe damage than ground settlement. Many buildings in lateral spread 
areas are 
expected to move with the spread, but not to be severely damaged until the 
spread 
becomes quite significant. 

 

Median PGD values given in the Table 5.17 are used with a lognormal standard 
deviation 
value of PGD = 1.2 to estimate P[E or C|PGD] for buildings on shallow 
foundations or 




buildings of unknown foundation type. The value of PGD = 1.2 is based on the 
factor of 
5 between the PGD values at the 10 and 50 percentile levels. 

 

No attempt is made to distinguish damage based on building type, since model 
building 
descriptions do not include foundation type. Foundation type is critical to PGD 
performance and buildings on deep foundations (e.g., piles) perform much better 
than 
buildings on spread footings, if the ground settles. When the building is known 
to be 
supported by a deep foundation, the probability of Extensive or Complete damage 
is 
reduced by a factor of 10 from that predicted for settlement-induced damage of 
the same 
building on a shallow foundation. Deep foundations will improve building 
performance 
by only a limited amount, if ground spreads laterally. When the building is 
known to be 
supported by a deep foundation, the probability of Extensive or Complete damage 
is 
reduced by a factor of 2 from that predicted for spread-induced damage of the 
same 
building on a shallow foundation. 

 

5.6 Evaluation of Building Damage 

 

5.6.1 Overview 

During an earthquake, the building may be damaged either by ground shaking, 
ground 
failure, or both. Buildings are evaluated separately for the two modes of 
failure the 
resulting damage-state probabilities are combined for evaluation of loss. 

 

5.6.2 Damage Due to Ground Shaking 

 

This section describes the process of developing damage state probabilities 
based on 
structural and nonstructural fragility curves, model building capacity curves 
and a 
demand spectrum. Building response (e.g., peak displacement) is determined by 
the 
intersection of the demand spectrum and the building capacity curve. The demand 
spectrum is based on the PESH input spectrum reduced for effective damping (when 
effective damping exceeds the 5% damping level of the PESH input spectrum). 

 

5.6.2.1 Demand Spectrum Reduction for Effective Damping 

 

The elastic response spectra provided as a PESH input apply only to buildings 
that 
remain elastic during the entire ground shaking time history and have elastic 
damping 
values equal to 5% of critical. This is generally not true on both accounts. 
Therefore, 
two modifications are made to elastic response spectra: (a) demand spectra are 
modified 
for buildings with elastic damping not equal to 5%, and (b) demand spectra are 
modified 
for the hysteretic energy dissipated by buildings pushed beyond their elastic 
limits. 
Modifications are represented by reduction factors by which the spectral 
ordinates are 
divided to obtain the damped demand spectra. 

 

Extensive work has been published in the past two decades on the effect of 
damping 
and/or energy dissipation on spectral demand. The Methodology reduces demand 
spectra 
for effective damping greater than 5% based on statistically-based formulas of 
Newmark 
and Hall (1982). Other methods are available for estimating spectral reduction 
factors 




based on statistics relating reduction to ductility demand. It is believed that 
both methods 
yield the same results for most practical purposes (FEMA 273). Newmark and Hall 
provide formulas for construction of elastic response spectra at different 
damping ratios, 
. (expressed as a percentage). These formulas represent all site classes (soil 
types) 
distinguishing between domains of constant acceleration and constant velocity. 
Ratios of 
these formulas are used to develop an acceleration-domain (short-period) 
reduction 
factor, RA, and a velocity-domain (1-second spectral acceleration) reduction 
factor, RV, 
for modification of 5%-damped, elastic response spectra (PESH input). These 
reduction 
factors are based on effective damping, Beff, as given in Equations (5-7) and 
(5-8) below: 

 

()RAe=-212321068...ln(. (5-7) 

 

()RVe=-165231041...ln(. (5-8) 

 

for which effective damping is defined as the sum of elastic damping, BE, and 
hysteretic 
damping, BH: 

 

BBBeffEH=+ (5-9) 

 

Elastic damping, BE, is dependent on structure type and is based on the 
recommendations 
of Newmark & Hall for materials at or just below their yield point. Hysteretic 
damping, 
.H, is dependent on the amplitude of response and is based on the area enclosed 
by the 
hysteresis loop, considering potential degradation of energy-absorption capacity 
of the 
structure during cyclic earthquake load. Effective damping, Beff, is also a 
function of the 
amplitude of response (e.g., peak displacement), as expressed in Equation (5-
10): 

 

BBAreaDAeffE=+

...
...
.p2 
(5-10) 

 

where: BE is the elastic (pre-yield) damping of the model building type 

Area is the area enclosed by the hysteresis loop, as defined by a 
symmetrical push-pull of the building capacity curve up to peak 
positive and negative displacements,  D 

D is the peak displacement response of the push-over curve, 

A is the peak acceleration response at peak displacement, D 

. is a degradation factor that defines the effective amount of 
hysteretic damping as a function of earthquake duration, as 
specified in Table 5.18. 

 




Table 5.18 Degradation Factor (.) as a Function of Short, Moderate and Long 
Earthquake Duration 

 

 

The Methodology recognizes the importance of the duration of ground shaking on 
building response by reducing effective damping (i.e., . factors) as a function 
of shaking 
duration. Shaking duration is described qualitatively as either Short, Moderate 
or Long, 
and is assumed to be a function of earthquake magnitude (although proximity to 
fault 
rupture also influences the duration of ground shaking). For scenario 
earthquakes of 
magnitude M = 5.5, effective damping is based on the assumption of ground 
shaking of 
Short duration. For scenario earthquakes of magnitude M = 7.5, effective damping 
is 
based on the assumption of ground shaking of Long duration. Effective damping is 
based 
on the assumption of Moderate duration for all other earthquake magnitudes 
(including 
probabilistic, or other, analyses of unknown magnitude). 

 

 

Construction of Demand Spectra 

Demand spectral acceleration, SA[T], in units of acceleration (g) is defined by 
Equation 
(5-11a) at short periods (acceleration domain), Equation (5-11b) at long periods 
(velocity 
domain) and Equation (5-11c) at very long periods (displacement domain). 

 

Building Type High-Code Design Moderate-Code Design Low-Code Design Pre-Code 
Design 
No. Label Short Moderate Long Short Moderate Long Short Moderate Long Short 
Moderate Long 
1 W1 1.00 0.80 0.50 0.90 0.60 0.30 0.70 0.40 0.20 0.50 0.30 0.10 
2 W2 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
3 S1L 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
4 S1M 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
5 S1H 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
6 S2L 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
7 S2M 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
8 S2H 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
9 S3 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
10 S4L 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
11 S4M 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
12 S4H 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
13 S5L 0.50 0.30 0.10 0.50 0.30 0.10 0.50 0.30 0.10 0.40 0.20 0.00 
14 S5M 0.50 0.30 0.10 0.50 0.30 0.10 0.50 0.30 0.10 0.40 0.20 0.00 
15 S5H 0.50 0.30 0.10 0.50 0.30 0.10 0.50 0.30 0.10 0.40 0.20 0.00 
16 C1L 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
17 C1M 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
18 C1H 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
19 C2L 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
20 C2M 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
21 C2H 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
22 C3L 0.50 0.30 0.10 0.50 0.30 0.10 0.50 0.30 0.10 0.40 0.20 0.00 
23 C3M 0.50 0.30 0.10 0.50 0.30 0.10 0.50 0.30 0.10 0.40 0.20 0.00 
24 C3H 0.50 0.30 0.10 0.50 0.30 0.10 0.50 0.30 0.10 0.40 0.20 0.00 
25 PC1 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
26 PC2L 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
27 PC2M 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
28 PC2H 0.70 0.50 0.30 0.60 0.40 0.20 0.50 0.30 0.10 0.40 0.20 0.00 
29 RM1L 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
30 RM1M 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
31 RM2L 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
32 RM2M 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
33 RM2H 0.90 0.60 0.40 0.80 0.40 0.20 0.60 0.30 0.10 0.40 0.20 0.00 
34 URML 0.50 0.30 0.10 0.50 0.30 0.10 0.50 0.30 0.10 0.40 0.20 0.00 
35 URMM 0.50 0.30 0.10 0.50 0.30 0.10 0.50 0.30 0.10 0.40 0.20 0.00 
36 MH 0.80 0.40 0.20 0.80 0.40 0.20 0.80 0.40 0.20 0.60 0.30 0.10 



At short periods, 0 < T = TAV: 

 ()()()ST]SRBSBAASiAeffASieff[[]...ln==-212321068 (5-11a) 

At long periods, TAV < T = TVD: 

 ()((ST]STRBSTBAAiVeffAieff[[]...ln=...
...
=...
...
-11165231041 (5-11b) 

At very long periods, T > TVD: 

 (()()ST]STTRBSTTBAAiVDVTVDAiVDTVD[[]...l=
.
..
.
..
=
.
..
.
..
-1212165231041(5-11c) 

where: SASi is the 5%-damped, short-period spectral acceleration for Site Class 
i (in units of g), as defined by Equation (4-5), 

SA1i is the 5%-damped, 1-second-period spectral acceleration for Site 
Class i (units of g), as defined by Equation (4-6), times 1 second, 

TAVi is the transition period between 5%-damped constant spectral 
acceleration and 5%-damped constant spectral velocity for Site 
Class i (sec.), as defined by Equation (4-7), 

.TVD is the value of effective damping at the transition period, TVD, and 

.TAVB is the value of effective damping at the transition period, TAV.. 

The transition period, TAVB, between acceleration and velocity domains is a 
function of 
the effective damping at this period, as defined by Equation (5-12). The 
transition 
period, TVD, between velocity and displacement domains is independent of 
effective 
damping, as defined by Equation (4-4). 

 

[]
[]
()()
()(TTRBRBTBBAVBAViATAVBVTAVBAViTAVBTAVB=
.
..
.
..
=
-
-
.
...
.
...
212321068165231041...ln...ln 
(5-12) 

 

Demand spectral displacement, SD[T], in inches, is based on SA[T], in units of 
g, as given 
on Equation (5-13): 

 

ST]ST]TDA[.[=982 (5-13) 

 

Figure 5.7 shows typical demand spectra (spectral acceleration plotted as a 
function of 
spectral displacement) for three demand levels. These three demand levels 
represent 
Short (. = 0.80), Moderate (. = 0.40) and Long (. = 0.20) duration ground 
shaking, 
respectively. Also shown in the figure is the building capacity curve of a low-
rise 
building of Moderate-Code seismic design that was used to estimate effective 
damping. 
The intersection of the capacity curve with each of the three demand spectra 
illustrates 
the significance of duration (damping) on building response. 

 




Figure 5.7 Example Demand Spectra - Moderate-Code Building 

(M = 7.0 at 20 km, WUS, Site Class E). 

 

5.6.2.2 Damage State Probability 

 

Structural and nonstructural fragility curves are evaluated for spectral 
displacement and 
spectral acceleration defined by the intersection of the capacity and demand 
curves. Each 
of these curves describes the cumulative probability of being in, or exceeding, 
a 
particular damage state. Nonstructural components (both drift- and acceleration-
sensitive 
components) may, in some cases, be dependent on the structural damage state 
(e.g., 
Complete structural damage may cause Complete nonstructural damage). The 
Methodology assumes nonstructural damage states to be independent of structural 
damage states. Cumulative probabilities are differenced to obtain discrete 
probabilities 
of being in each of the five damage states. This process is shown schematically 
in Figure 
5.8. 

 

00.10.20.30.40.50.60.70.80.91012345678910Spectral Displacement (inches)
Spectral Acceleration (g's)
PESH Spectrum - 5% DampingDemand Spectrum - Long DurationDemand Spectrum - 
Moderate DurationDemand Spectrum - Short DurationBuilding Capacity Curve0.3 sec.
1.0 sec.



SCCSSCMEMEMEFigure 5.8 Example Building Damage Estimation Process. 

It is also meaningful to interpret damage probabilities as the fraction of all 
buildings (of 
the same type) that would be in the particular damage state of interest. For 
example, a 
30% probability of Moderate damage may also be thought of as 30 out of 100 
buildings 
(of the same type) being in the Moderate damage state. 

 

5.6.3 Combined Damage Due to Ground Failure and Ground Shaking 

This section describes the combination of damage state probabilities due to 
ground 
failure (Section 5.5.2) and ground shaking (Section 5.6.2.2). It is assumed that 
damage 
due to ground shaking (GS) is independent of damage due to ground failure (GF). 
Ground failure tends to cause severe damage to buildings and is assumed to 
contribute 
only to Extensive and Complete damage states (refer to Section 5.5.1). These 
assumptions are described by the following formulas: 

 

 

 [][]PDSSPDSEGFGF=== (5-14) 

 

 [][]PDSMPDSEGFGF=== (5-15) 




 

 [][]PDSC0.2PDSEGFGF=== (5-16) 

The damage state probability (probability of being in or exceeding a given 
damage state) 
for GF is assumed to be the maximum of the three types of ground failure 
(liquefaction, 
landsliding, and spread). Thus, the combined (due to occurrence of GF or GS) 
probabilities of being in or exceeding given damage states are: 

 

 

[][][][][]PDSSPDSSPDSSPDSSPDSSCOMBGFGSGFGS===+=-== (5-17) 

 

[][][][][PDSMPDSMPDSMPDSMPDSMCOMBGFGSGFGS===+=-== (5-18) 

 

[][][][][]PDSEPDSEPDSEPDSEPDSECOMBGFGSGFGS===+=-== (5-19) 

 

[][][][][]PDSCPDSCPDSCPDSCPDSCCOMBGFGSGFGS===+=-== (5-20) 

 

 

where DS is damage state, and the symbols: S, M, E, and C stand for Slight, 
Moderate, 
Extensive, and Complete damage, respectively. COMB indicates the combined 
probability for the damage state due to occurrence of ground failure or ground 
shaking. 
Note that the following condition must always be true: 

 

[][][][]1PDSSPDSMPDSEPDSCCOMBCOMBCOMBCOMB======= (5-21) 

 

 

The discrete probabilities (probabilities of being in a given damage state) are 
given as: 

 

 

 [][]PDSCPDSCOMBCOMB== (5-22) 

 

 [][][]PDSEPDSEPDSCOMBCOMBCOMB===-= (5-23) 

 

 [][][]PDSMPDSMPDSCOMBCOMBCOMB===-= (5-24) 

 

 [][][]PDSSPDSSPDSCOMBCOMBCOMB===-= (5-25) 

 

 [][]PDSNone1PDSSCOMBCOMB==-= (5-26) 

 

 

5.6.4 Combined Damage to Occupancy Classes 

 

The damage state probabilities for model building types (as estimated from 
Section 5.6.3) 
are combined to yield the damage state probabilities of the occupancy classes to 
which 




they belong. For each damage state, the probability of damage to each model 
building 
type is weighted according to the fraction of the total floor area of that model 
building 
type and summed over all building types. This is expressed in equation form: 

 

 POSTRPMBTSTRFAFAds,ids,ji,jij136=.
..
.
..
S= 
(5-27) 

 

where PMBTSTRds,j is the probability of the model building type j being in 
damage 
state ds. POSTRds,i is the probability of occupancy class i being in damage 
state ds. 
FAi,j indicates the floor area of model building type j in occupancy class i, 
and FAi 
denotes the total floor area of the occupancy class i (refer to Chapter 3 for 
floor area 
distributions of model building types by occupancy class). Similarly, the 
damage-state 
probabilities for nonstructural components can be estimated. 

 

 PONSDPMBTNSDFAFAds,ids,ji,jij136=.
..
.
..
S= 
(5-28) 

 

 

 PONSAPMBTNSAFAFAds,ids,ji,jij136=.
..
.
..
S= 
(5-29) 

 

where PMBTNSDds,j and PMBTNSAds,j refer to the probabilities of model building 
type j being in nonstructural drift- and acceleration-sensitive damage state ds, 
respectively; and PONSDds,i and PONSAds,i refer to the probabilities of the 
occupancy 
class i being the nonstructural drift-sensitive and acceleration-sensitive 
damage state, ds, 
respectively. These occupancy class probabilities are used in Chapter 15 to 
estimate 
direct economic loss. 

 

 

5.7 Guidance for Expert Users 

This section provides guidance for users who are seismic/structural experts 
interested in 
modifying the building damage functions supplied with the methodology. This 
section 
also provides the expert user with guidance regarding the selection of the 
appropriate mix 
of design levels for the region of interest. 

 

 

5.7.1 Selection of Representative Seismic Design Level 

The methodology permits the user to select the seismic design level considered 
appropriate for the study region and to define a mix of seismic design levels 
for each 
model building type. The building damage functions provided are based on 
current-Code 
provisions and represent buildings of modern design and construction. Most 
buildings in 
a study region will likely not be of modern design and construction (i.e., do 
not conform 
to 1976 UBC, 1985 NEHRP Provisions, or later editions of these model Codes). For 
many study regions, particularly those in the Central and Eastern United States, 
seismic 




provisions may not be enforced (or only adopted very recently). Building damage 
functions for new buildings designed and constructed to meet modern-Code 
provisions 
should not be used for older, non-complying buildings. 

 

The building damage functions represent specific cells of a three by three 
matrix that 
defines three seismic design levels (High, Moderate and Low) and, for each of 
these 
design levels, three seismic performance levels (Inferior, Ordinary and 
Superior), as 
shown in Table 5.19. For completeness, cells representing Special buildings of 
Chapter 6 
(Essential Facilities) are also included in the matrix. 

 

Table 5.19 Seismic Design and Performance Levels of Default Building Damage 
Functions (and Approximate Structural Strength and Ductility) 

 

Seismic Performance Level 

Seismic Design 
Level 

Superior1 

Ordinary 

Inferior 

High 

(UBC Zone 4) 

Special High-Code 

Maximum Strength 

Maximum Ductility 

High-Code 

High Strength 

High Ductility 

 

Moderate Strength 

Mod./Low Ductility 

Moderate 

(UBC Zone 2B) 

Special Moderate-Code 

High/Mod. Strength 

High Ductility 

Moderate-Code 

Moderate Strength 

Moderate Ductility 

 

Low Strength 

Low Ductility 

Low 

(UBC Zone 1) 

Special Low-Code 

Mod./Low Strength 

Moderate Ductility 

Low-Code 

Low Strength 

Low Ductility 

Pre-Code 

Minimal Strength 

Minimal Ductility 



1. 
See Chapter 6 for Special High-Code, Moderate-Code and Low-Code building damage 
functions. 




 

Table 5.19 also defines the approximate structural strength and ductility 
attributes of 
buildings occupying each of the nine cells. The design level is defined by 
Seismic Zones 
of the Uniform Building Code (UBC), since most buildings in the United States 
that have 
been designed for earthquakes used some version of the UBC. Table 5.20 relates 
UBC 
seismic zones to seismic design regions of the NEHRP Provisions. 

 

Expert users may tailor the damage functions to their study area of interest by 
determining the appropriate fraction of each building type that conforms 
essentially to 
modern-Code provisions (based on age of construction). Buildings deemed not to 
conform to modern-Code provisions should be assigned a lower seismic design 
level, or 
defined as Pre-Code buildings if not seismically designed. For instance, older 
buildings 
located in High-Code seismic design areas should be evaluated using damage 
functions 
for either Moderate-Code buildings or Pre-Code buildings, for buildings that 
pre-date 
seismic codes. Table 5.20 provides guidance for selecting appropriate building 
damage 
functions based on building location (i.e., seismic region) and building age. 
The years 
shown as break-off points should be considered very approximate and may not be 
appropriate for many seismic regions, particularly regions of low and moderate 
seismicity where seismic codes have not been routinely enforced. 

 




Table 5.20 Guidelines for Selection of Damage Functions for Typical Buildings 
Based on UBC Seismic Zone and Building Age 

UBC Seismic Zone 
(NEHRP Map Area) 

Post-1975 

1941 - 1975 

Pre-1941 

Zone 4 

(Map Area 7) 

High-Code 

Moderate-Code 

Pre-Code 

(W1 = Moderate-Code) 

Zone 3 

(Map Area 6) 

Moderate-Code 

Moderate-Code 

Pre-Code 

(W1 = Moderate-Code) 

Zone 2B 

(Map Area 5) 

Moderate-Code 

Low-Code 

Pre-Code 

(W1 = Low-Code) 

Zone 2A 

(Map Area 4) 

Low-Code 

Low-Code 

Pre-Code 

(W1 = Low-Code) 

Zone 1 

(Map Area 2/3) 

Low-Code 

Pre-Code 

(W1 = Low-Code) 

Pre-Code 

(W1 = Low-Code) 

Zone 0 

(Map Area 1) 

Pre-Code 

(W1 = Low-Code) 

Pre-Code 

(W1 = Low-Code) 

Pre-Code 

(W1 = Low-Code) 



 

The guidelines given in Table 5.20 assume that buildings in the study region are 
not 
designed for wind. The user should consider the possibility that mid-rise and 
high-rise 
buildings could be designed for wind and may have considerable lateral strength 
(though 
not ductility), even if not designed for earthquake. Users must be knowledgeable 
about 
the type and history of construction in the study region of interest and apply 
engineering 
judgment in assigning the fraction of each building type to a seismic design 
group. 

 

5.7.2 Development of Damage Functions for Other Buildings 

 

For a building type other than one of the 36 described in Table 5.1, expert 
users should 
select a set of building damage functions that best represents the type of 
construction, 
strength and ductility of the building type of interest. Such buildings include 
rehabilitated structures that have improved seismic capacity. For example, URM 
(Pre-
Code) buildings retrofitted in accordance with Division 88, the Los Angeles City 
Ordinance to reduce the risk of life loss, demonstrated significantly improved 
seismic 
performance during the 1994 Northridge earthquake [SSC, 1995]. Structural damage 
to 
these buildings would be better estimated using either essential facility damage 
functions 
of either Low-Code or Moderate-Code RM1 buildings. 

 

 

5.8 Building Damage References 

Allen and Hoshall, Jack R. Benjamin and Associates, and Systan Inc. 1985. An 
Assessment of Damage and Casualties for Six Cities in the Central United States 
Resulting from Earthquakes in the New Madrid Seismic Zone. Prepared for FEMA. 

Applied Technology Council (ATC). 1985. ATC-13 - Earthquake Damage Evaluation 
Data for California. Redwood City, California: Applied Technology Council. 

Central United States Earthquake Preparedness Project. 1990. Estimated Future 
Earthquake Losses for St. Louis City and County, Missouri. FEMA Report 192. 
Washington, D.C. 




Culver, Charles G., H.S. Lew, Gary C. Hart, and Clarkson W. Pinkham. 1975. 
Natural 
Hazards Evaluation of Existing Buildings. U.S. Department of Commerce Building 
Science Series 61. Washington, D.C. 

Czarnecki, Robert Martin, 1973. Earthquake Damage to Tall Buildings. Structures 
Publication No. 359, Dept. of Civil Engineering, M.I.T. 

Earthquake Engineering Research Institute (EERI). 1994. Expected Seismic 
Performance of Buildings. Oakland, California. 

Federal Emergency Management Agency (FEMA). 1996a. NEHRP Guidelines for the 
Seismic Rehabilitation of Buildings. FEMA 273. Washington, D.C. 

Federal Emergency Management Agency (FEMA). 1996b. NEHRP Commentary on the 
Guidelines for the Seismic Rehabilitation of Buildings. FEMA 274. Washington, 
D.C. 

Federal Emergency Management Agency (FEMA). 1997a. NEHRP Recommended 
Provisions for Seismic Regulations for New Buildings. Part 1 - Provisions. FEMA 
222A. 
Washington, D.C. 

Federal Emergency Management Agency (FEMA). 1995b. NEHRP Recommended 
Provisions for Seismic Regulations for New Buildings. Part 2 - Commentary. FEMA 
223A. Washington, D.C. 

Federal Emergency Management Agency (FEMA). 1992. NEHRP Handbook for the 
Seismic Evaluation of Existing Buildings. FEMA 178. Washington, D.C. 

Ferritto, J.M. 1980. An Economic Analysis of Seismic Upgrading for Existing 
Buildings. 
Naval Civil Engineering Laboratory, TM No. 51-80-17. Port Hueneme, California. 

Ferritto, J.M. 1982. An Economic Analysis of Earthquake Design Levels. Naval 
Civil 
Engineering Laboratory, TN No. N-1640. Port Hueneme, California. 

Ferritto, J.M. 1983. An Economic Analysis of Earthquake Design Levels for New 
Concrete Construction. Naval Civil Engineering Laboratory, TN No. N-1671. Port 
Hueneme, California. 

Hasselman, T.K., Ronald T. Eguchi, and John H. Wiggins. 1980. Assessment of 
Damageability for Existing Buildings in a Natural Hazards Environment. Volume I: 
Methodology. Redondo Beach, California. 

Kennedy, R. P., C. A. Cornell, R. L. Campbell, S. Kaplan, and H. F. Perla. 1980. 
Probabilistic Seismic Safety of an Existing Nuclear Power Plant. Nuclear 
Engineering 
and Design 59 (2): 315-38. 

Kustu, O., D. D. Miller, and S. T. Brokken. 1982. Development of Damage 
Functions 
for High-Rise Building Components. Prepared by URS/John A. Blume & Associates 
for 
the U.S. Department of Energy. San Francisco, California 




Kustu, O., D. D. Miller, and R. E. Scholl. 1983. A Computerized Method for 
Predicting 
Earthquake Losses In Urban Areas - Final Technical Report for Phase II. Prepared 
by 
URS/John A. Blume & Associates for the USGS. San Francisco, California. 

Lizundia, B., W. Dong, and W. T. Holmes. 1993. Analysis of Unreinforced Masonry 
Building Damage Patterns in the Loma Prieta Earthquake and Improvement of Loss 
Estimation Methodologies. Rutherford & Chekene, Inc. San Francisco, California. 

Mahaney, James A., Terrence F. Paret, Bryan E. Kehoe, and Sigmund A. Freeman. 
1993. 
The Capacity Spectrum Method for Evaluating Structural Response during the Loma 
Prieta Earthquake. Proceedings of the 1993 United States National Earthquake 
Conference, Memphis, Tennessee. Vol. 2, Pages 501-510. 

Newmark, N.M. and W.J. Hall. 1982. Earthquake Spectra and Design, Earthquake 
Engineering Research Institute Monograph. 

OAK Engineering Inc. (OAK), "Development of Damage Functions for Buildings," 
1994 

Rihal, Satwant S. 1980. Racking Tests of Nonstructural Building Partitions. 
School of 
Architecture and Environmental Design, California Polytechnic State University, 
San 
Luis Obispo, California. 

State of California, Division of the State Architect (DSA). 1996. Earthquake 
Hazard 
Mitigation Technology Application Guidelines, Sacramento, California. 

State of California, Seismic Safety Commission (SSC). 1996. Seismic Evaluation 
and 
Retrofit of Concrete Buildings. Report No. SSC 96-01, Sacramento, California. 

State of California, Seismic Safety Commission (SSC). 1995. Turning Loss to 
Gain. 
Report No. SSC 95-01, Sacramento, California. 

Steinbrugge, K.V., and S.T. Algermissen. 1990. Earthquake Losses to Single-
Family 
Dwellings: California Experience. USGS Bulletin 1939. United States Geological 
Survey. 

Whitman, Robert V., Sheu-Tien Hong and John W. Reed. 1973. Damage Statistics for 
High-Rise Buildings in the Vicinity of the San Fernando Earthquake. Structures 
Publication No. 363, Dept. of Civil Engineering, M.I.T. Boston, Massachusetts. 

Whitman, Robert V. 1973. Damage Probability Matrices for Prototype Buildings. 
Structures Publication No. 380, Dept. of Civil Engineering, M.I.T. Boston, 
Massachusetts. 

Whitman, Robert V., Tarek S. Aziz and Earl H. Wong. 1977. Preliminary 
Correlations 
Between Earthquake Damage and Strong Ground Motion. Structures Publication No. 
564, Dept. of Civil Engineering, M.I.T. Boston, Massachusetts. 

Wong, Earl Hom. 1975. Correlations Between Earthquake Damage and Strong Ground 
Motion. Dept. of Civil Engineering, M.I.T., Boston, Massachusetts. 

 




Chapter 6 

Direct Physical Damage - Essential and High Potential Loss Facilities 

 

 

6.1 Introduction 

 

This chapter describes methods for determining the probability of Slight, 
Moderate, 
Extensive and Complete damage to essential facilities. These methods are 
identical to 
those of Chapter 5 that describe damage to Code buildings, except that certain 
essential 
facilities are represented by Special building damage functions. Special 
building 
damage functions are appropriate for evaluation of essential facilities when the 
user 
anticipates above-Code seismic performance for these facilities. The flowchart 
of the 
methodology highlighting the essential and high potential loss facility damage 
components and showing its relationship to other components is shown in 
Flowchart 6.1. 

 

This chapter also provides guidance for high potential loss (HPL) facilities. 
The 
methodology highlighting the Direct Physical Damage is shown in Flowchart 6.1. 

 

6.1.1 Scope 

 

The scope of this chapter includes: (1) classification of essential facilities, 
(2) building 
damage functions for Special buildings, (3) methods for estimation of earthquake 
damage 
to essential facilities, given knowledge of the model building type and seismic 
design 
level, and an estimate of earthquake demand, and (3) guidance for expert users, 
including 
estimation of damage to high potential loss (HPL) facilities. 

 

Special buildings and their damage functions are described in Sections 6.2 
through 6.5. 
Evaluation of damage to essential facilities is given in Section 6.6 and 
guidance for 
expert users is given in Section 6.7. Typically, sections of Chapter 6 reference 
(rather 
than repeat) material of the corresponding section of Chapter 5. 

 

6.1.2 Essential Facility Classification 

 

Facilities that provide services to the community and those that should be 
functional 
following an earthquake are considered to be essential facilities. Examples of 
essential 
facilities include hospitals, police stations, fire stations, emergency 
operations centers 
(EOCs) and schools. The methodology adopted for damage assessment of such 
facilities 
is explained in this section. 

 

Essential facilities are classified on the basis of facility function and, in 
the case of 
hospitals, size. Table 6.1 lists the classes of essential facilities used in the 
Methodology. 
Hospitals are classified on the basis of number of beds, since the structural 
and 
nonstructural systems of a hospital are related to the size of the hospital 
(i.e., to the 
number of beds it contains). 




8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Flowchart 6.1: Essential and High Potential Loss Facility Component Relationship 
to 
other Components in the Methodology 

 




Table 6.1 Classification of Essential Facilities 

 

No. 

Label 

Occupancy Class 

Description 

 

 

Medical Care Facilities 

 

1 

EFHS 

Small Hospital 

Hospital with less than 50 Beds 

2 

EFHM 

Medium Hospital 

Hospital with beds between 50 & 150 

3 

EFHL 

Large Hospital 

Hospital with greater than 150 Beds 

4 

EFMC 

Medical Clinics 

Clinics, Labs, Blood Banks 

 

 

Emergency Response 

 

5 

EFFS 

Fire Station 

 

6 

EFPS 

Police Station 

 

7 

EFEO 

Emergency Operation Centers 

 

 

 

Schools 

 

8 

EFS1 

Schools 

Primary/ Secondary Schools (K-12) 

9 

EFS2 

Colleges/Universities 

Community and State Colleges, State 
and Private Universities 



 

 

It is the responsibility of the user to identify each essential facility as 
either a Code 
building or a Special building of a particular model building type and seismic 
design 
level. This chapter provides building damage functions for Special buildings 
that have 
significantly better than average seismic capacity. Chapter 5 provides building 
damage 
functions for Code-buildings. If the user is not able to determine that the 
essential 
facility is significantly better than average, then the facility should be 
modeled using 
Code building damage functions (i.e., the same methods as those developed in 
Chapter 5 
for general building stock). 

 

6.1.3 Input Requirements and Output Information 

 

Input required to estimate essential facility damage using fragility and 
capacity curves 
includes the following two items: 

 

 model building type (including height) and seismic design level that 
represents the 
essential facility (or type of essential facilities) of interest, and 

 

 response spectrum (or PGA, for lifeline buildings, and PGD for ground failure 
evaluation) at the essential facilitys site. 

 

The response spectrum, PGA and PGD at the essential facility site are PESH 
outputs, 
described in Chapter 4. 

 

The output of fragility curves is an estimate of the cumulative probability of 
being in or 
exceeding, each damage state for the given level of ground shaking (or ground 
failure). 
Cumulative damage probabilities are differenced to create discrete damage state 
probabilities, as described in Chapter 5 (Section 5.6). Discrete probabilities 
of damage 
are used directly as inputs to induced physical damage and direct economic and 
social 
loss modules, as shown in Flowchart 6.1. 




 

Typically, the model building type (including height) is not known for each 
essential 
facility and must be inferred from the inventory of essential facilities using 
the 
occupancy/building type relationships described in Chapter 3. In general, 
performance of 
essential facilities is not expected to be better than the typical building of 
the 
representative model building type. Exceptions to this generalization include 
California 
hospitals of recent (post-1973) construction. 

 

6.1.4 Form of Damage Functions 

 

Building damage functions for essential facilities are of the same form as those 
described 
in Chapter 5 for general building stock. For each damage state, a lognormal 
fragility 
curve relates the probability of damage to PGA, PGD or spectral demand 
determined by 
the intersection of the model building types capacity curve and the demand 
spectrum. 
Figure 6.1 provides an example of fragility curves for four damage states: 
Slight, 
Moderate, Extensive and Complete. 

 

 

Spectral Displacement (inches)
0.000.250.500.751.000.005.0010.0015.0020.0025.00SlightModerateExtensiveComplete 


 

Figure 6.1 Example Fragility Curves for Slight, Moderate, Extensive 

and Complete Damage. 

 

 

The fragility curves are driven by a PESH parameter. For ground failure, the 
PESH 
parameter used to drive fragility curves is permanent ground displacement (PGD). 
For 
ground shaking, the PESH parameter used to drive building fragility curves is 
peak 
spectral response (either displacement or acceleration), or peak ground 
acceleration 
(PGA) for essential lifeline facilities. Peak spectral response varies 
significantly for 




buildings that have different response properties and, therefore, requires 
knowledge of 
these properties. 

 

Building response is characterized by building capacity curves. These curves 
describe 
the push-over displacement of each building type and seismic design level as a 
function 
of laterally-applied earthquake load. Design-, yield- and ultimate-capacity 
points define 
the shape of each building capacity curve. The Methodology estimates peak 
building 
response as the intersection of the building capacity curve and the demand 
spectrum at 
the buildings location. 

 

The demand spectrum is the 5%-damped PESH input spectrum reduced for higher 
levels 
of effective damping (e.g., effective damping includes both elastic damping and 
hysteretic damping associated with post-yield cyclic response of the building). 
Figure 
6.2 illustrates the intersection of a typical building capacity curve and a 
typical demand 
spectrum (reduced for effective damping greater than 5% of critical). 

 

 

Figure 6.2 Example Building Capacity Curve and Demand Spectrum. 

 

 

6.2 Description of Model Building Types 

 

The model building types used for essential facilities are identical to those 
used for 
general building stock. These building types are described in Section 5.2 and 
listed in 
Table 5.1. Typical nonstructural components of essential facilities include 
those 
architectural, mechanical and electrical, and contents listed in Table 5.2 for 
general 
building stock. 

00.10.20.30.40.50.60.70.80.91012345678910Spectral Displacement (inches)
Spectral Acceleration (g's)
PESH Input Spectrum 
(5% Damping)
Demand Spectrum 
(Damping > 5%)
Capacity 
CurveUltimate CapacityDesign CapacityYield CapacitySaSd



 

Essential facilities also include certain special equipment, such as emergency 
generators, 
and certain special contents, such as those used to operate a hospital. Special 
equipment 
and contents of essential facilities are considered to be acceleration-sensitive 
nonstructural components of these facilities. 

 

6.3 Description of Building Damage States 

 

Building damage states for structural and nonstructural components of essential 
facilities 
are the same as those described in Section 5.3 for general building stock. 

 

6.4 Building Damage Due to Ground Shaking - Special Buildings 

 

6.4.1 Overview 

 

This section describes capacity and fragility curves used in the Methodology to 
estimate 
the probability of Slight, Moderate, Extensive and Complete damage to Special 
buildings 
of a given model building type designed to High-, Moderate-, or Low-Code seismic 
standards. Special building damage functions are appropriate for evaluation of 
essential 
facilities when the user anticipates above-Code seismic performance for these 
facilities. 

 

Capacity curves and fragility curves for Special buildings of High-Code, 
Moderate-Code, 
or Low-Code seismic design are based on modern code (e.g., 1976 Uniform Building 
Code, 1996 NEHRP Provisions, or later editions of these model codes) design 
criteria for 
various seismic design zones, as shown in Table 6.2. Additional description of 
seismic 
design levels may be found in Section 6.7. 

 

Table 6.2 Approximate Basis for Seismic Design Levels 

 

Seismic Design Level 

(I = 1.5) 

Seismic Zone 

(1994 Uniform Building Code) 

Map Area 

(1994 NEHRP Provisions) 

High-Code 

Moderate-Code 

Low-Code 

4 

2B 

1 

7 

5 

3 



 

 

The capacity and fragility curves represent buildings designed and constructed 
to modern 
seismic code provisions (e.g.,1994 UBC) using an importance factor of I = 1.5. 
Moderate-Code and Low-Code seismic design levels are included for completeness. 
Most essential facilities located in Seismic Zones O, T, 2A or 2B have not been 
designed 
for Special building code criteria. 




 

6.4.2 Capacity Curves - Special Buildings 

 

The building capacity curves for Special buildings are similar to those for the 
general 
building stock (Chapter 5), but with increased strength. Each curve is described 
by three 
control points that define model building capacity: 

 

 Design Capacity 

 Yield Capacity 

 Ultimate Capacity 

 

Design capacity represents the nominal building strength required by current 
model 
seismic code provisions (e.g., 1994 UBC) including an importance factor of I = 
1.5. 
Wind design is not considered in the estimation of design capacity and certain 
buildings 
(e.g., taller buildings located in zones of low or moderate seismicity) may have 
a lateral 
design strength considerably greater than based on seismic code provisions. 

 

Yield capacity represents the true lateral strength of the building considering 
redundancies in design, conservatism in code requirements and true (rather than 
nominal) 
strength of materials. Ultimate capacity represents the maximum strength of the 
building 
when the global structural system has reached a fully plastic state. An example 
building 
capacity curve is shown in Figure 6-3. 

 

 

SaSdDyDuDdAuAyAdUltimateCapacityYieldCapacityDesignCapacity 


 

Figure 6.3 Example Building Capacity Curve. 

 

The building capacity curves for Special buildings are constructed based on the 
same 
engineering properties (i.e., Cs, Te, a1, a2, ., ., ) as those used to describe 
capacity 
curves of Code buildings (i.e., Tables 5.4, 5.5 and 5.6), except for design 
strength, Cs, 
and ductility (). The design strength, Cs, is approximately based on the 
lateral-force 
design requirements of current seismic codes (e.g., 1994 NEHRP or 1994 UBC) 
using an 
importance factor of I = 1.5. Values of the ductility factor, , for Special 
buildings are 




based on Code-building ductility increased by 1.33 for Moderate-Code buildings 
and by 
1.2 for Low-Code buildings. The ductility parameter defines the displacement 
value of 
capacity curve at the point where the curve reaches a fully plastic state. 

 

Building capacity curves are assumed to have a range of possible properties that 
are 
lognormally distributed as a function of the ultimate strength (Au) of each 
capacity curve. 
Special building capacity curves represent median estimates of building 
capacity. The 
variability of the capacity of each building type is assumed to be: (Au) = 0.15 
for 
Special buildings. An example construction of median, 84th percentile (+1) and 
16th 
percentile (-1) building capacity curves for a typical building is illustrated 
in Figure 
6.4. Median capacity curves are intersected with demand spectra to estimate peak 
building response. The variability of the capacity curves is used, with other 
sources of 
variability and uncertainty, to define total fragility curve variability. 

 

Figure 6.4 Example Construction of Median, +1 and -1 Building Capacity 
Curves. 

 

Tables 6.3a, 6.3b and 6.3c summarize yield capacity and ultimate capacity 
control points 
for Special buildings of High-Code, Moderate-Code and Low-Code seismic design 
levels, respectively. Note that for the following tables, shaded boxes indicate 
types that 
are not permitted by current seismic codes. 




 

 

Table 6.3a Special Building Capacity Curves - High-Code 

Seismic Design Level 

 

Building 

Yield Capacity Point 

Ultimate Capacity Point 

Type 

Dy (in.) 

Ay (g) 

Du (in.) 

Au (g) 

W1 

0.72 

0.600 

17.27 

1.800 

W2 

0.94 

0.600 

18.79 

1.500 

S1L 

0.92 

0.375 

22.00 

1.124 

S1M 

2.66 

0.234 

42.60 

0.702 

S1H 

6.99 

0.147 

83.83 

0.440 

S2L 

0.94 

0.600 

15.03 

1.200 

S2M 

3.64 

0.500 

38.82 

1.000 

S2H 

11.62 

0.381 

92.95 

0.762 

S3 

0.94 

0.600 

15.03 

1.200 

S4L 

0.58 

0.480 

10.36 

1.080 

S4M 

1.64 

0.400 

19.65 

0.900 

S4H 

5.23 

0.305 

47.05 

0.685 

S5L 

 

 

 

 

S5M 

 

 

 

 

S5H 

 

 

 

 

C1L 

0.59 

0.375 

14.08 

1.124 

C1M 

1.73 

0.312 

27.65 

0.937 

C1H 

3.02 

0.147 

36.20 

0.440 

C2L 

0.72 

0.600 

14.39 

1.500 

C2M 

1.56 

0.500 

20.76 

1.250 

C2H 

4.41 

0.381 

44.09 

0.952 

C3L 

 

 

 

 

C3M 

 

 

 

 

C3H 

 

 

 

 

PC1 

1.08 

0.900 

17.27 

1.800 

PC2L 

0.72 

0.600 

11.51 

1.200 

PC2M 

1.56 

0.500 

16.61 

1.000 

PC2H 

4.41 

0.381 

35.27 

0.762 

RM1L 

0.96 

0.800 

15.34 

1.600 

RM1M 

2.08 

0.667 

22.14 

1.333 

RM2L 

0.96 

0.800 

15.34 

1.600 

RM2M 

2.08 

0.667 

22.14 

1.333 

RM2H 

5.88 

0.508 

47.02 

1.015 

URML 

 

 

 

 

URMM 

 

 

 

 

MH 

0.27 

0.225 

4.32 

0.450 



 




 

Table 6.3b Special Building Capacity Curves - Moderate-Code 

Seismic Design Level 

 

Building 

Yield Capacity Point 

Ultimate Capacity Point 

Type 

Dy (in.) 

Ay (g) 

Du (in.) 

Au (g) 

W1 

0.54 

0.450 

12.95 

1.350 

W2 

0.47 

0.300 

9.40 

0.750 

S1L 

0.46 

0.187 

11.00 

0.562 

S1M 

1.33 

0.117 

21.30 

0.351 

S1H 

3.49 

0.073 

41.91 

0.220 

S2L 

0.47 

0.300 

7.52 

0.600 

S2M 

1.82 

0.250 

19.41 

0.500 

S2H 

5.81 

0.190 

46.47 

0.381 

S3 

0.47 

0.300 

7.52 

0.600 

S4L 

0.29 

0.240 

5.18 

0.540 

S4M 

0.82 

0.200 

9.83 

0.450 

S4H 

2.61 

0.152 

23.53 

0.343 

S5L 

 

 

 

 

S5M 

 

 

 

 

S5H 

 

 

 

 

C1L 

0.29 

0.187 

7.04 

0.562 

C1M 

0.86 

0.156 

13.83 

0.468 

C1H 

1.51 

0.073 

18.10 

0.220 

C2L 

0.36 

0.300 

7.19 

0.750 

C2M 

0.78 

0.250 

10.38 

0.625 

C2H 

2.21 

0.190 

22.05 

0.476 

C3L 

 

 

 

 

C3M 

 

 

 

 

C3H 

 

 

 

 

PC1 

0.54 

0.450 

8.63 

0.900 

PC2L 

0.36 

0.300 

5.76 

0.600 

PC2M 

0.78 

0.250 

8.31 

0.500 

PC2H 

2.21 

0.190 

17.64 

0.381 

RM1L 

0.48 

0.400 

7.67 

0.800 

RM1M 

1.04 

0.333 

11.07 

0.667 

RM2L 

0.48 

0.400 

7.67 

0.800 

RM2M 

1.04 

0.333 

11.07 

0.667 

RM2H 

2.94 

0.254 

23.51 

0.508 

URML 

 

 

 

 

URMM 

 

 

 

 

MH 

0.27 

0.225 

4.32 

0.450 



 




 

Table 6.3c Special Building Capacity Curves - Low-Code 

Seismic Design Level 

 

Building 

Yield Capacity Point 

Ultimate Capacity Point 

Type 

Dy (in.) 

Ay (g) 

Du (in.) 

Au (g) 

W1 

0.36 

0.300 

6.48 

0.900 

W2 

0.24 

0.150 

3.52 

0.375 

S1L 

0.23 

0.094 

4.13 

0.281 

S1M 

0.67 

0.059 

7.99 

0.176 

S1H 

1.75 

0.037 

15.72 

0.110 

S2L 

0.24 

0.150 

2.82 

0.300 

S2M 

0.91 

0.125 

7.28 

0.250 

S2H 

2.91 

0.095 

17.43 

0.190 

S3 

0.24 

0.150 

2.82 

0.300 

S4L 

0.14 

0.120 

1.94 

0.270 

S4M 

0.41 

0.100 

3.69 

0.225 

S4H 

1.31 

0.076 

8.82 

0.171 

S5L 

0.18 

0.150 

2.16 

0.300 

S5M 

0.51 

0.125 

4.09 

0.250 

S5H 

1.63 

0.095 

9.80 

0.190 

C1L 

0.15 

0.094 

2.64 

0.281 

C1M 

0.43 

0.078 

5.19 

0.234 

C1H 

0.75 

0.037 

6.79 

0.110 

C2L 

0.18 

0.150 

2.70 

0.375 

C2M 

0.39 

0.125 

3.89 

0.313 

C2H 

1.10 

0.095 

8.27 

0.238 

C3L 

0.18 

0.150 

2.43 

0.338 

C3M 

0.39 

0.125 

3.50 

0.281 

C3H 

1.10 

0.095 

7.44 

0.214 

PC1 

0.27 

0.225 

3.24 

0.450 

PC2L 

0.18 

0.150 

2.16 

0.300 

PC2M 

0.39 

0.125 

3.11 

0.250 

PC2H 

1.10 

0.095 

6.61 

0.190 

RM1L 

0.24 

0.200 

2.88 

0.400 

RM1M 

0.52 

0.167 

4.15 

0.333 

RM2L 

0.24 

0.200 

2.88 

0.400 

RM2M 

0.52 

0.167 

4.15 

0.333 

RM2H 

1.47 

0.127 

8.82 

0.254 

URML 

0.36 

0.300 

4.32 

0.600 

URMM 

0.41 

0.167 

3.26 

0.333 

MH 

0.27 

0.225 

4.32 

0.450 



 

 

 




6.4.3 Fragility Curves - Special Buildings 

 

This section describes Special building fragility curves for Slight, Moderate, 
Extensive 
and Complete structural damage states and Slight, Moderate, Extensive and 
Complete 
nonstructural damage states. Each fragility curve is characterized by a median 
and a 
lognormal standard deviation () value of PESH demand. Spectral displacement is 
the 
PESH parameter used for structural damage and nonstructural damage to drift-
sensitive 
components. Spectral acceleration is the PESH parameter used for nonstructural 
damage 
to acceleration-sensitive components. 

 

Special building fragility curves for ground failure are the same as those of 
Code 
buildings (Section 5.5). 

 

6.4.3.1 Background 

 

The form of the fragility curves for Special buildings is the same as that used 
for Code 
buildings. The probability of being in, or exceeding, a given damage state is 
modeled as 
a cumulative lognormal distribution. Given the appropriate PESH parameter (e.g., 
spectral displacement, Sd, for structural damage), the probability of being in 
or exceeding 
a damage state, ds, is modeled as follows: 

 

[]..
.
..
.
...
.
...
.
F=
dsddsds,
ddSSln1SP 
(6-1) 

 

where: Sd,ds is the median value of spectral displacement at which the building 
reaches the threshold of the damage state, ds, 

ds is the standard deviation of the natural logarithm of spectral 
displacement of damage state, ds, and 

F is the standard normal cumulative distribution function. 

 

6.4.3.2 Structural Damage - Special Buildings 

 

Structural damage states for Special buildings are based on drift ratios that 
are assumed 
to be slightly higher than those of Code buildings of the same model building 
type and 
seismic design level. It is difficult to quantify this improvement in 
displacement capacity 
since it is a function not just of building type and design parameters, but also 
design 
review and construction inspection. It is assumed that the improvement in 
displacement 
capacity results in a 1.25 increase in drift capacity of each damage state for 
all Special 
building types and seismic design levels. Special buildings perform better than 
Code 
buildings due to increased structure strength (of the capacity curves) and 
increased 
displacement capacity (of the fragility curves). In general, increased strength 
tends to 
most improve building performance near yield and improved displacement capacity 
tends 
to most improve the ultimate capacity of the building. 

 




Median values of Special building structural fragility are based on drift ratios 
(that 
describe the threshold of damage states and the height of the building to point 
of push-
over mode displacement using the same approach as that of Code buildings 
(Section 
5.4.3.2). 

 

The variability of Special building structural damage is based on the same 
approach as 
that of Code buildings (Section 5.4.3.3). The total variability of each 
structural damage 
state, Sds, is modeled by the combination of following three contributors to 
damage 
variability: 

 

 uncertainty in the damage state threshold of the structural system 

(M(Sds) = 0.4, for all structural damage states and building types) 

 

 variability in capacity (response) properties of the model building 
type/seismic design level of interest (C(Au) = 0.15 for Special 
buildings), and 

 

 variability in response due to the spatial variability of ground motion 
demand (D(A) = 0.45 and C(V) = 0.50).is based on the dispersion 
factor typical of the attenuation of large-magnitude earthquakes as in 
the WUS (Chapter 4). 

 

Each of these three contributors to damage state variability are assumed to be 
lognormally distributed random variables. Capacity and demand are dependent 
parameters and a convolution process is used to derive combined capacity/demand 
variability of each structural damage state. Capacity/demand variability is then 
combined 
with damage state uncertainty, as described in Section 5.4.3.3. 

 

Tables 6.4a, 6.4b and 6.4c summarize median and lognormal standard deviation 
(Sds) 
values for Slight, Moderate, Extensive and Complete structural damage states of 
Special 
buildings for High-Code, Moderate-Code and Low-Code seismic design levels, 
respectively. Note that for the following tables, shaded boxes indicate types 
that are not 
permitted by current seismic codes. 




Table 6.4a Building Structural Fragility - High-Code Seismic Design Level 

 

Table 6.4b Building Structural Fragility - Moderate-Code Seismic Design Level 

 

Building Properties Interstory Drift at Spectral Displacement (inches) 
Type Height (inches) Threshold of Damage State Slight Moderate Extensive 
Complete 
Roof Modal Slight Moderate Extensive Complete Median Beta Median Beta Median 
Beta Median Beta 
W1 168 126 0.0050 0.0124 0.0383 0.0937 0.63 0.76 1.56 0.77 4.82 0.78 11.81 0.96 
W2 288 216 0.0050 0.0124 0.0383 0.0938 1.08 0.79 2.68 0.86 8.27 0.88 20.25 0.84 
S1L 288 216 0.0075 0.0130 0.0294 0.0750 1.62 0.73 2.80 0.71 6.35 0.70 16.20 0.77 
S1M 720 540 0.0050 0.0086 0.0196 0.0500 2.70 0.64 4.67 0.65 10.58 0.66 27.00 
0.75 
S1H 1872 1123 0.0037 0.0065 0.0147 0.0375 4.21 0.62 7.29 0.62 16.51 0.66 42.12 
0.70 
S2L 288 216 0.0063 0.0108 0.0292 0.0750 1.35 0.82 2.34 0.85 6.30 0.89 16.20 0.85 
S2M 720 540 0.0042 0.0072 0.0194 0.0500 2.25 0.66 3.90 0.66 10.50 0.68 27.00 
0.81 
S2H 1872 1123 0.0031 0.0054 0.0146 0.0375 3.51 0.62 6.08 0.63 16.38 0.65 42.12 
0.71 
S3 180 135 0.0050 0.0087 0.0234 0.0656 0.68 0.77 1.17 0.81 3.16 0.89 8.86 0.89 
S4L 288 216 0.0050 0.0087 0.0234 0.0656 1.08 0.88 1.87 0.92 5.05 0.98 14.18 0.87 
S4M 720 540 0.0033 0.0058 0.0156 0.0437 1.80 0.70 3.12 0.67 8.41 0.70 23.62 0.90 
S4H 1872 1123 0.0025 0.0043 0.0117 0.0328 2.81 0.66 4.87 0.66 13.13 0.70 36.86 
0.81 
S5L 
S5M 
S5H 
C1L 240 180 0.0063 0.0108 0.0292 0.0750 1.13 0.80 1.95 0.82 5.25 0.84 13.50 0.81 
C1M 600 450 0.0042 0.0072 0.0194 0.0500 1.87 0.66 3.25 0.67 8.75 0.66 22.50 0.84 
C1H 1440 864 0.0031 0.0054 0.0146 0.0375 2.70 0.64 4.68 0.64 12.60 0.68 32.40 
0.81 
C2L 240 180 0.0050 0.0105 0.0289 0.0750 0.90 0.77 1.89 0.86 5.21 0.91 13.50 0.89 
C2M 600 450 0.0033 0.0070 0.0193 0.0500 1.50 0.71 3.16 0.70 8.68 0.69 22.50 0.83 
C2H 1440 864 0.0025 0.0053 0.0145 0.0375 2.16 0.64 4.55 0.65 12.51 0.66 32.40 
0.79 
C3L 
C3M 
C3H 
PC1 180 135 0.0050 0.0087 0.0234 0.0656 0.68 0.79 1.17 0.81 3.16 0.86 8.86 1.00 
PC2L 240 180 0.0050 0.0087 0.0234 0.0656 0.90 0.83 1.56 0.89 4.21 0.97 11.81 
0.89 
PC2M 600 450 0.0033 0.0058 0.0156 0.0438 1.50 0.76 2.60 0.74 7.01 0.73 19.69 
0.88 
PC2H 1440 864 0.0025 0.0043 0.0117 0.0328 2.16 0.65 3.75 0.66 10.10 0.70 28.35 
0.81 
RM1L 240 180 0.0050 0.0087 0.0234 0.0656 0.90 0.80 1.56 0.85 4.21 0.92 11.81 
0.97 
RM1M 600 450 0.0033 0.0058 0.0156 0.0438 1.50 0.73 2.60 0.75 7.01 0.75 19.69 
0.80 
RM2L 240 180 0.0050 0.0087 0.0234 0.0656 0.90 0.77 1.56 0.81 4.21 0.92 11.81 
0.96 
RM2M 600 450 0.0033 0.0058 0.0156 0.0438 1.50 0.72 2.60 0.72 7.01 0.72 19.69 
0.77 
RM2H 1440 864 0.0025 0.0043 0.0117 0.0328 2.16 0.63 3.75 0.65 10.10 0.66 28.35 
0.76 
URML 
URMM 
MH 120 120 0.0050 0.0100 0.0300 0.0875 0.60 0.81 1.20 0.89 3.60 0.97 10.50 0.86 
Building Properties Interstory Drift at Spectral Displacement (inches) 
Type Height (inches) Threshold of Damage State Slight Moderate Extensive 
Complete 
Roof Modal Slight Moderate Extensive Complete Median Beta Median Beta Median 
Beta Median Beta 
W1 168 126 0.0050 0.0150 0.0500 0.1250 0.63 0.66 1.89 0.72 6.30 0.72 15.75 0.91 
W2 288 216 0.0050 0.0150 0.0500 0.1250 1.08 0.69 3.24 0.77 10.80 0.89 27.00 0.85 
S1L 288 216 0.0075 0.0150 0.0375 0.1000 1.62 0.67 3.24 0.70 8.10 0.71 21.60 0.68 
S1M 720 540 0.0050 0.0100 0.0250 0.0667 2.70 0.62 5.40 0.62 13.50 0.63 36.00 
0.71 
S1H 1872 1123 0.0037 0.0075 0.0188 0.0500 4.21 0.63 8.42 0.62 21.06 0.62 56.16 
0.63 
S2L 288 216 0.0063 0.0125 0.0375 0.1000 1.35 0.69 2.70 0.80 8.10 0.89 21.60 0.84 
S2M 720 540 0.0042 0.0083 0.0250 0.0667 2.25 0.62 4.50 0.66 13.50 0.66 36.00 
0.71 
S2H 1872 1123 0.0031 0.0063 0.0188 0.0500 3.51 0.62 7.02 0.63 21.06 0.63 56.16 
0.66 
S3 180 135 0.0050 0.0100 0.0300 0.0875 0.68 0.66 1.35 0.71 4.05 0.80 11.81 0.90 
S4L 288 216 0.0050 0.0100 0.0300 0.0875 1.08 0.77 2.16 0.82 6.48 0.92 18.90 0.91 
S4M 720 540 0.0033 0.0067 0.0200 0.0583 1.80 0.69 3.60 0.67 10.80 0.68 31.50 
0.82 
S4H 1872 1123 0.0025 0.0050 0.0150 0.0438 2.81 0.62 5.62 0.63 16.85 0.65 49.14 
0.73 
S5L 
S5M 
S5H 
C1L 240 180 0.0063 0.0125 0.0375 0.1000 1.13 0.69 2.25 0.74 6.75 0.82 18.00 0.81 
C1M 600 450 0.0042 0.0083 0.0250 0.0667 1.87 0.63 3.75 0.65 11.25 0.66 30.00 
0.71 
C1H 1440 864 0.0031 0.0063 0.0188 0.0500 2.70 0.63 5.40 0.63 16.20 0.63 43.20 
0.69 
C2L 240 180 0.0050 0.0125 0.0375 0.1000 0.90 0.69 2.25 0.72 6.75 0.82 18.00 0.95 
C2M 600 450 0.0033 0.0083 0.0250 0.0667 1.50 0.65 3.75 0.69 11.25 0.66 30.00 
0.70 
C2H 1440 864 0.0025 0.0063 0.0188 0.0500 2.16 0.62 5.40 0.63 16.20 0.64 43.20 
0.69 
C3L 
C3M 
C3H 
PC1 180 135 0.0050 0.0100 0.0300 0.0875 0.68 0.63 1.35 0.74 4.05 0.79 11.81 0.96 
PC2L 240 180 0.0050 0.0100 0.0300 0.0875 0.90 0.76 1.80 0.80 5.40 0.87 15.75 
0.97 
PC2M 600 450 0.0033 0.0067 0.0200 0.0583 1.50 0.66 3.00 0.73 9.00 0.72 26.25 
0.73 
PC2H 1440 864 0.0025 0.0050 0.0150 0.0438 2.16 0.62 4.32 0.64 12.96 0.65 37.80 
0.74 
RM1L 240 180 0.0050 0.0100 0.0300 0.0875 0.90 0.70 1.80 0.74 5.40 0.76 15.75 
0.98 
RM1M 600 450 0.0033 0.0067 0.0200 0.0583 1.50 0.63 3.00 0.68 9.00 0.70 26.25 
0.70 
RM2L 240 180 0.0050 0.0100 0.0300 0.0875 0.90 0.66 1.80 0.70 5.40 0.76 15.75 
0.97 
RM2M 600 450 0.0033 0.0067 0.0200 0.0583 1.50 0.63 3.00 0.70 9.00 0.69 26.25 
0.68 
RM2H 1440 864 0.0025 0.0050 0.0150 0.0438 2.16 0.63 4.32 0.63 12.96 0.63 37.80 
0.65 
URML 
URMM 
MH 120 120 0.0050 0.0100 0.0300 0.0875 0.60 0.81 1.20 0.89 3.60 0.97 10.50 0.86 



Table 6.4c Special Building Structural Fragility - Low-Code Seismic Design Level 

 

6.4.3.3 Nonstructural Damage - Drift-Sensitive 

 

Damage states of nonstructural drift-sensitive components of Special buildings 
are based 
on the same drift ratios as those of Code buildings (Table 5.10). Even for 
essential 
facilities, nonstructural components are typically not designed or detailed for 
special 
earthquake displacements. Improvement in the performance of drift-sensitive 
components of Special buildings is assumed to be entirely a function of drift 
reduction 
due to the increased stiffness and strength of the structures of these 
buildings. 

 

Median values of drift-sensitive nonstructural fragility curves are based on 
global 
building displacement (in inches), calculated as the product of: (1) drift 
ratio, (2) 
building height and (3) the fraction of building height at the location of push-
over mode 
displacement (a2). 

 

The total variability of each nonstructural drift-sensitive damage state, 
NSDds, is modeled 
by the combination of following three contributors to damage variability: 

 

 uncertainty in the damage state threshold of nonstructural components 
(M(NSDds) = 0.5, for all structural damage states and building types, 

 

 variability in capacity (response) properties of the model building type 
that contains the nonstructural components of interest (C(Au) = 0.15 
for Special buildings, and 

 

Building Properties Interstory Drift at Spectral Displacement (inches) 
Type Height (inches) Threshold of Damage State Slight Moderate Extensive 
Complete 
Roof Modal Slight Moderate Extensive Complete Median Beta Median Beta Median 
Beta Median Beta 
W1 168 126 0.0050 0.0124 0.0383 0.0937 0.63 0.80 1.56 0.81 4.82 0.88 11.81 1.01 
W2 288 216 0.0050 0.0124 0.0383 0.0938 1.08 0.89 2.68 0.89 8.27 0.86 20.25 0.97 
S1L 288 216 0.0075 0.0119 0.0253 0.0625 1.62 0.73 2.58 0.73 5.47 0.75 13.50 0.93 
S1M 720 540 0.0050 0.0080 0.0169 0.0417 2.70 0.66 4.30 0.70 9.12 0.78 22.50 0.91 
S1H 1872 1123 0.0037 0.0060 0.0127 0.0313 4.21 0.64 6.72 0.66 14.23 0.68 35.10 
0.86 
S2L 288 216 0.0063 0.0100 0.0250 0.0625 1.35 0.89 2.16 0.89 5.40 0.88 13.50 0.97 
S2M 720 540 0.0042 0.0067 0.0167 0.0417 2.25 0.67 3.60 0.68 9.00 0.74 22.50 0.92 
S2H 1872 1123 0.0031 0.0050 0.0125 0.0313 3.51 0.62 5.62 0.63 14.04 0.68 35.10 
0.84 
S3 180 135 0.0050 0.0080 0.0201 0.0547 0.68 0.89 1.08 0.90 2.71 0.98 7.38 0.85 
S4L 288 216 0.0050 0.0080 0.0200 0.0547 1.08 0.98 1.73 0.95 4.33 0.97 11.81 0.98 
S4M 720 540 0.0033 0.0053 0.0134 0.0364 1.80 0.69 2.88 0.72 7.22 0.81 19.68 0.98 
S4H 1872 1123 0.0025 0.0040 0.0100 0.0273 2.81 0.66 4.50 0.67 11.26 0.78 30.71 
0.93 
S5L 288 216 0.0038 0.0075 0.0188 0.0438 0.81 1.00 1.62 1.00 4.05 1.03 9.45 0.91 
S5M 720 540 0.0025 0.0050 0.0125 0.0292 1.35 0.74 2.70 0.72 6.75 0.78 15.75 0.94 
S5H 1872 1123 0.0019 0.0037 0.0094 0.0219 2.11 0.67 4.21 0.69 10.53 0.74 24.57 
0.90 
C1L 240 180 0.0063 0.0100 0.0250 0.0625 1.13 0.85 1.80 0.85 4.50 0.88 11.25 0.95 
C1M 600 450 0.0042 0.0067 0.0167 0.0417 1.87 0.70 3.00 0.69 7.50 0.75 18.75 0.95 
C1H 1440 864 0.0031 0.0050 0.0125 0.0313 2.70 0.66 4.32 0.71 10.80 0.79 27.00 
0.95 
C2L 240 180 0.0050 0.0096 0.0247 0.0625 0.90 0.91 1.72 0.94 4.44 1.01 11.25 0.90 
C2M 600 450 0.0033 0.0064 0.0164 0.0417 1.50 0.76 2.86 0.74 7.40 0.74 18.75 0.94 
C2H 1440 864 0.0025 0.0048 0.0123 0.0313 2.16 0.66 4.12 0.67 10.66 0.74 27.00 
0.91 
C3L 240 180 0.0038 0.0075 0.0188 0.0438 0.68 0.92 1.35 0.99 3.38 1.04 7.88 0.88 
C3M 600 450 0.0025 0.0050 0.0125 0.0292 1.12 0.77 2.25 0.79 5.62 0.78 13.12 0.93 
C3H 1440 864 0.0019 0.0038 0.0094 0.0219 1.62 0.68 3.24 0.69 8.10 0.70 18.90 
0.88 
PC1 180 135 0.0050 0.0080 0.0201 0.0547 0.68 0.89 1.08 0.95 2.71 1.00 7.38 0.96 
PC2L 240 180 0.0050 0.0080 0.0201 0.0547 0.90 0.98 1.44 0.98 3.61 1.02 9.84 0.91 
PC2M 600 450 0.0033 0.0053 0.0134 0.0364 1.50 0.76 2.40 0.75 6.02 0.75 16.40 
0.94 
PC2H 1440 864 0.0025 0.0040 0.0100 0.0273 2.16 0.66 3.46 0.68 8.66 0.73 23.63 
0.92 
RM1L 240 180 0.0050 0.0080 0.0201 0.0547 0.90 0.97 1.44 1.01 3.61 1.07 9.84 0.88 
RM1M 600 450 0.0033 0.0053 0.0134 0.0364 1.50 0.78 2.40 0.78 6.02 0.78 16.40 
0.94 
RM2L 240 180 0.0050 0.0080 0.0201 0.0547 0.90 0.94 1.44 0.98 3.61 1.05 9.84 0.89 
RM2M 600 450 0.0033 0.0053 0.0134 0.0364 1.50 0.76 2.40 0.75 6.02 0.75 16.40 
0.92 
RM2H 1440 864 0.0025 0.0040 0.0100 0.0273 2.16 0.66 3.46 0.67 8.66 0.80 23.63 
0.89 
URML 180 135 0.0038 0.0075 0.0187 0.0438 0.51 0.89 1.01 0.91 2.53 0.96 5.91 1.09 
URMM 420 315 0.0025 0.0050 0.0125 0.0292 0.79 0.81 1.57 0.84 3.94 0.87 9.19 0.82 
MH 120 120 0.0050 0.0100 0.0300 0.0875 0.60 0.81 1.20 0.89 3.60 0.97 10.50 0.86 



 variability in response of the model building type due to the spatial 
variability of ground motion demand (D(A) = 0.45 and C(V) = 0.50). 

 

Each of these three contributors to damage state variability are assumed to be 
lognormally distributed random variables. Capacity and demand are dependent 
parameters and a convolution process is used to derive combined capacity/demand 
variability of each nonstructural damage state. Capacity/demand variability is 
then 
combined with damage state uncertainty, as described in Section 5.4.3.3. 

 

Tables 6.5a, 6.5b and 6.5c summarize median and lognormal standard deviation 
(NSDds) 
values for Slight, Moderate, Extensive and Complete damage states of 
nonstructural 
drift-sensitive components of Special buildings for High-Code, Moderate-Code and 
Low-
Code seismic design levels, respectively. Note that for the following tables, 
shaded 
boxes indicate types that are not permitted by current seismic codes. 




 

 

Table 6.5a Special Building Nonstructural Drift-Sensitive Fragility - High-Code 
Seismic Design Level 

 

Building 

Median Spectral Displacement (inches) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.50 

0.74 

1.01 

0.77 

3.15 

0.79 

6.30 

0.78 

W2 

0.86 

0.76 

1.73 

0.77 

5.40 

0.88 

10.80 

0.93 

S1L 

0.86 

0.72 

1.73 

0.76 

5.40 

0.75 

10.80 

0.74 

S1M 

2.16 

0.68 

4.32 

0.68 

13.50 

0.70 

27.00 

0.73 

S1H 

4.49 

0.70 

8.99 

0.69 

28.08 

0.69 

56.16 

0.70 

S2L 

0.86 

0.74 

1.73 

0.77 

5.40 

0.90 

10.80 

0.95 

S2M 

2.16 

0.70 

4.32 

0.72 

13.50 

0.73 

27.00 

0.72 

S2H 

4.49 

0.71 

8.99 

0.69 

28.08 

0.70 

56.16 

0.73 

S3 

0.54 

0.70 

1.08 

0.76 

3.38 

0.83 

6.75 

0.93 

S4L 

0.86 

0.81 

1.73 

0.84 

5.40 

0.93 

10.80 

1.00 

S4M 

2.16 

0.76 

4.32 

0.74 

13.50 

0.75 

27.00 

0.82 

S4H 

4.49 

0.70 

8.99 

0.71 

28.08 

0.72 

56.16 

0.80 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.72 

0.77 

1.44 

0.76 

4.50 

0.84 

9.00 

0.88 

C1M 

1.80 

0.71 

3.60 

0.71 

11.25 

0.72 

22.50 

0.71 

C1H 

3.46 

0.70 

6.91 

0.69 

21.60 

0.71 

43.20 

0.75 

C2L 

0.72 

0.76 

1.44 

0.76 

4.50 

0.80 

9.00 

0.94 

C2M 

1.80 

0.74 

3.60 

0.76 

11.25 

0.73 

22.50 

0.74 

C2H 

3.46 

0.69 

6.91 

0.69 

21.60 

0.71 

43.20 

0.75 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.54 

0.69 

1.08 

0.78 

3.38 

0.85 

6.75 

0.88 

PC2L 

0.72 

0.80 

1.44 

0.83 

4.50 

0.90 

9.00 

1.03 

PC2M 

1.80 

0.75 

3.60 

0.80 

11.25 

0.77 

22.50 

0.77 

PC2H 

3.46 

0.70 

6.91 

0.71 

21.60 

0.73 

43.20 

0.82 

RM1L 

0.72 

0.74 

1.44 

0.80 

4.50 

0.80 

9.00 

0.94 

RM1M 

1.80 

0.70 

3.60 

0.77 

11.25 

0.77 

22.50 

0.77 

RM2L 

0.72 

0.74 

1.44 

0.76 

4.50 

0.78 

9.00 

0.96 

RM2M 

1.80 

0.71 

3.60 

0.78 

11.25 

0.74 

22.50 

0.74 

RM2H 

3.46 

0.69 

6.91 

0.69 

21.60 

0.71 

43.20 

0.74 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.48 

0.85 

0.96 

0.92 

3.00 

0.98 

6.00 

0.99 






 

Table 6.5b Special Building Nonstructural Drift-Sensitive Fragility - Moderate-
Code Seismic Design Level 

 

Building 

Median Spectral Displacement (inches) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.50 

0.77 

1.01 

0.82 

3.15 

0.84 

6.30 

0.87 

W2 

0.86 

0.84 

1.73 

0.88 

5.40 

0.93 

10.80 

0.93 

S1L 

0.86 

0.78 

1.73 

0.78 

5.40 

0.78 

10.80 

0.76 

S1M 

2.16 

0.71 

4.32 

0.71 

13.50 

0.73 

27.00 

0.81 

S1H 

4.49 

0.69 

8.99 

0.69 

28.08 

0.72 

56.16 

0.82 

S2L 

0.86 

0.81 

1.73 

0.91 

5.40 

0.96 

10.80 

0.89 

S2M 

2.16 

0.73 

4.32 

0.74 

13.50 

0.73 

27.00 

0.87 

S2H 

4.49 

0.69 

8.99 

0.70 

28.08 

0.74 

56.16 

0.84 

S3 

0.54 

0.82 

1.08 

0.86 

3.38 

0.97 

6.75 

0.95 

S4L 

0.86 

0.89 

1.73 

0.97 

5.40 

1.02 

10.80 

0.94 

S4M 

2.16 

0.76 

4.32 

0.74 

13.50 

0.84 

27.00 

0.97 

S4H 

4.49 

0.71 

8.99 

0.73 

28.08 

0.83 

56.16 

0.94 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.72 

0.80 

1.44 

0.86 

4.50 

0.88 

9.00 

0.88 

C1M 

1.80 

0.73 

3.60 

0.72 

11.25 

0.74 

22.50 

0.89 

C1H 

3.46 

0.71 

6.91 

0.71 

21.60 

0.79 

43.20 

0.93 

C2L 

0.72 

0.84 

1.44 

0.87 

4.50 

0.95 

9.00 

1.00 

C2M 

1.80 

0.79 

3.60 

0.76 

11.25 

0.76 

22.50 

0.88 

C2H 

3.46 

0.70 

6.91 

0.71 

21.60 

0.77 

43.20 

0.87 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.54 

0.82 

1.08 

0.87 

3.38 

0.93 

6.75 

1.02 

PC2L 

0.72 

0.88 

1.44 

0.95 

4.50 

1.03 

9.00 

0.99 

PC2M 

1.80 

0.84 

3.60 

0.77 

11.25 

0.79 

22.50 

0.95 

PC2H 

3.46 

0.72 

6.91 

0.74 

21.60 

0.84 

43.20 

0.94 

RM1L 

0.72 

0.86 

1.44 

0.88 

4.50 

0.99 

9.00 

1.04 

RM1M 

1.80 

0.80 

3.60 

0.79 

11.25 

0.79 

22.50 

0.88 

RM2L 

0.72 

0.81 

1.44 

0.86 

4.50 

0.97 

9.00 

1.03 

RM2M 

1.80 

0.78 

3.60 

0.77 

11.25 

0.77 

22.50 

0.88 

RM2H 

3.46 

0.71 

6.91 

0.71 

21.60 

0.74 

43.20 

0.87 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.48 

0.85 

0.96 

0.92 

3.00 

0.98 

6.00 

0.99 



 




 

Table 6.5c Special Building Nonstructural Drift-Sensitive Fragility - Low-Code 
Seismic Design Level 

 

Building 

Median Spectral Displacement (inches) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.50 

0.83 

1.01 

0.86 

3.15 

0.88 

6.30 

1.00 

W2 

0.86 

0.93 

1.73 

0.94 

5.40 

0.99 

10.80 

0.93 

S1L 

0.86 

0.81 

1.73 

0.80 

5.40 

0.80 

10.80 

0.94 

S1M 

2.16 

0.73 

4.32 

0.76 

13.50 

0.86 

27.00 

0.98 

S1H 

4.49 

0.71 

8.99 

0.74 

28.08 

0.87 

56.16 

0.98 

S2L 

0.86 

0.94 

1.73 

0.93 

5.40 

0.93 

10.80 

0.98 

S2M 

2.16 

0.73 

4.32 

0.76 

13.50 

0.91 

27.00 

0.99 

S2H 

4.49 

0.71 

8.99 

0.74 

28.08 

0.85 

56.16 

0.96 

S3 

0.54 

0.89 

1.08 

0.96 

3.38 

1.01 

6.75 

0.90 

S4L 

0.86 

1.02 

1.73 

0.99 

5.40 

0.95 

10.80 

1.01 

S4M 

2.16 

0.76 

4.32 

0.84 

13.50 

0.95 

27.00 

1.04 

S4H 

4.49 

0.74 

8.99 

0.87 

28.08 

0.96 

56.16 

1.03 

S5L 

0.86 

1.04 

1.73 

1.04 

5.40 

1.00 

10.80 

0.99 

S5M 

2.16 

0.78 

4.32 

0.84 

13.50 

0.97 

27.00 

1.04 

S5H 

4.49 

0.76 

8.99 

0.87 

28.08 

0.96 

56.16 

1.03 

C1L 

0.72 

0.90 

1.44 

0.92 

4.50 

0.93 

9.00 

0.93 

C1M 

1.80 

0.74 

3.60 

0.77 

11.25 

0.94 

22.50 

1.00 

C1H 

3.46 

0.75 

6.91 

0.86 

21.60 

0.97 

43.20 

1.03 

C2L 

0.72 

0.93 

1.44 

0.99 

4.50 

1.06 

9.00 

0.92 

C2M 

1.80 

0.80 

3.60 

0.80 

11.25 

0.91 

22.50 

1.00 

C2H 

3.46 

0.73 

6.91 

0.80 

21.60 

0.93 

43.20 

1.01 

C3L 

0.72 

0.99 

1.44 

1.05 

4.50 

1.06 

9.00 

0.93 

C3M 

1.80 

0.84 

3.60 

0.83 

11.25 

0.95 

22.50 

1.01 

C3H 

3.46 

0.76 

6.91 

0.84 

21.60 

0.96 

43.20 

1.03 

PC1 

0.54 

0.92 

1.08 

0.99 

3.38 

1.07 

6.75 

1.02 

PC2L 

0.72 

0.99 

1.44 

1.02 

4.50 

1.02 

9.00 

0.95 

PC2M 

1.80 

0.81 

3.60 

0.82 

11.25 

0.95 

22.50 

1.02 

PC2H 

3.46 

0.74 

6.91 

0.86 

21.60 

0.96 

43.20 

1.02 

RM1L 

0.72 

0.98 

1.44 

1.06 

4.50 

1.08 

9.00 

0.94 

RM1M 

1.80 

0.83 

3.60 

0.84 

11.25 

0.91 

22.50 

0.99 

RM2L 

0.72 

0.94 

1.44 

1.03 

4.50 

1.07 

9.00 

0.92 

RM2M 

1.80 

0.81 

3.60 

0.80 

11.25 

0.91 

22.50 

0.99 

RM2H 

3.46 

0.74 

6.91 

0.79 

21.60 

0.92 

43.20 

1.01 

URML 

0.54 

0.93 

1.08 

0.98 

3.38 

1.05 

6.75 

1.11 

URMM 

1.26 

0.89 

2.52 

0.88 

7.88 

0.87 

15.75 

0.99 

MH 

0.48 

0.85 

0.96 

0.92 

3.00 

0.98 

6.00 

0.99 



 




6.4.3.4 Nonstructural Damage - Acceleration-Sensitive Components 

 

Damage states of nonstructural acceleration-sensitive components of Special 
buildings 
are based on the peak floor accelerations of Code buildings of seismic design 
level (Table 
5.12) increased by a factor of 1.5. A factor of 1.5 on damage-state acceleration 
reflects 
increased anchorage strength of nonstructural acceleration-sensitive components 
of 
Special buildings. 

 

The floor acceleration values are used directly as median values, assuming 
average 
upper-floor demand is represented by response at the point of the push-over mode 
displacement. 

 

The total variability of each damage state, NSAds, is modeled by the 
combination of 
following three contributors to nonstructural acceleration-sensitive damage 
variability: 

 

 uncertainty in the damage state threshold of nonstructural components 
(M(NSAds) = 0.6, for all structural damage states and building types, 

 

 variability in capacity (response) properties of the model building type 
that contains the nonstructural components of interest (C(Au) = 0.15 
for Special buildings, and 

 

 variability in response of the model building type due to the spatial 
variability of ground motion demand (D(A) = 0.45 and C(V) = 0.50). 

 

Each of these three contributors to damage state variability are assumed to be 
lognormally distributed random variables. Capacity and demand are dependent 
parameters and a convolution process is used to derive combined capacity/demand 
variability of each nonstructural damage state. Capacity/demand variability is 
then 
combined with damage state uncertainty, as described in Section 5.4.3.3. 

 

Tables 6.6a, 6.6b and 6.6c summarize median and lognormal standard deviation 
(NSAds) 
values for Slight, Moderate, Extensive and Complete damage states of 
nonstructural 
acceleration-sensitive components of Special buildings for High-Code, Moderate-
Code 
and Low-Code seismic design levels, respectively. 

 




 

Table 6.6a Special Building Nonstructural Acceleration-Sensitive Fragility - 
High-
Code Seismic Design Level 

 

Building 

Median Spectral Acceleration (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.45 

0.72 

0.90 

0.68 

1.80 

0.68 

3.60 

0.68 

W2 

0.45 

0.69 

0.90 

0.67 

1.80 

0.68 

3.60 

0.68 

S1L 

0.45 

0.66 

0.90 

0.67 

1.80 

0.67 

3.60 

0.67 

S1M 

0.45 

0.66 

0.90 

0.67 

1.80 

0.68 

3.60 

0.68 

S1H 

0.45 

0.67 

0.90 

0.66 

1.80 

0.66 

3.60 

0.66 

S2L 

0.45 

0.66 

0.90 

0.67 

1.80 

0.66 

3.60 

0.66 

S2M 

0.45 

0.68 

0.90 

0.65 

1.80 

0.65 

3.60 

0.65 

S2H 

0.45 

0.67 

0.90 

0.65 

1.80 

0.65 

3.60 

0.65 

S3 

0.45 

0.68 

0.90 

0.67 

1.80 

0.66 

3.60 

0.66 

S4L 

0.45 

0.67 

0.90 

0.67 

1.80 

0.67 

3.60 

0.67 

S4M 

0.45 

0.66 

0.90 

0.65 

1.80 

0.66 

3.60 

0.66 

S4H 

0.45 

0.66 

0.90 

0.65 

1.80 

0.63 

3.60 

0.63 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.45 

0.67 

0.90 

0.68 

1.80 

0.67 

3.60 

0.67 

C1M 

0.45 

0.66 

0.90 

0.66 

1.80 

0.66 

3.60 

0.66 

C1H 

0.45 

0.67 

0.90 

0.65 

1.80 

0.65 

3.60 

0.65 

C2L 

0.45 

0.68 

0.90 

0.67 

1.80 

0.67 

3.60 

0.63 

C2M 

0.45 

0.68 

0.90 

0.65 

1.80 

0.64 

3.60 

0.64 

C2H 

0.45 

0.68 

0.90 

0.65 

1.80 

0.64 

3.60 

0.64 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.45 

0.72 

0.90 

0.66 

1.80 

0.67 

3.60 

0.63 

PC2L 

0.45 

0.68 

0.90 

0.67 

1.80 

0.66 

3.60 

0.66 

PC2M 

0.45 

0.67 

0.90 

0.64 

1.80 

0.65 

3.60 

0.65 

PC2H 

0.45 

0.66 

0.90 

0.64 

1.80 

0.63 

3.60 

0.63 

RM1L 

0.45 

0.73 

0.90 

0.66 

1.80 

0.68 

3.60 

0.64 

RM1M 

0.45 

0.69 

0.90 

0.65 

1.80 

0.64 

3.60 

0.64 

RM2L 

0.45 

0.71 

0.90 

0.66 

1.80 

0.67 

3.60 

0.63 

RM2M 

0.45 

0.70 

0.90 

0.65 

1.80 

0.64 

3.60 

0.64 

RM2H 

0.45 

0.69 

0.90 

0.65 

1.80 

0.64 

3.60 

0.64 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.38 

0.66 

0.75 

0.67 

1.50 

0.67 

3.00 

0.67 



 




 

Table 6.6b Special Building Nonstructural Acceleration-Sensitive Fragility - 
Moderate-Code Seismic Design Level 

 

Building 

Median Spectral Acceleration (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.38 

0.71 

0.75 

0.68 

1.50 

0.68 

3.00 

0.65 

W2 

0.38 

0.67 

0.75 

0.68 

1.50 

0.68 

3.00 

0.68 

S1L 

0.38 

0.67 

0.75 

0.67 

1.50 

0.68 

3.00 

0.68 

S1M 

0.38 

0.67 

0.75 

0.67 

1.50 

0.67 

3.00 

0.67 

S1H 

0.38 

0.67 

0.75 

0.66 

1.50 

0.66 

3.00 

0.66 

S2L 

0.38 

0.66 

0.75 

0.66 

1.50 

0.68 

3.00 

0.68 

S2M 

0.38 

0.65 

0.75 

0.65 

1.50 

0.64 

3.00 

0.64 

S2H 

0.38 

0.65 

0.75 

0.65 

1.50 

0.65 

3.00 

0.65 

S3 

0.38 

0.66 

0.75 

0.66 

1.50 

0.66 

3.00 

0.66 

S4L 

0.38 

0.67 

0.75 

0.66 

1.50 

0.65 

3.00 

0.65 

S4M 

0.38 

0.65 

0.75 

0.65 

1.50 

0.65 

3.00 

0.65 

S4H 

0.38 

0.65 

0.75 

0.65 

1.50 

0.65 

3.00 

0.65 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.38 

0.68 

0.75 

0.66 

1.50 

0.68 

3.00 

0.68 

C1M 

0.38 

0.66 

0.75 

0.65 

1.50 

0.65 

3.00 

0.65 

C1H 

0.38 

0.65 

0.75 

0.65 

1.50 

0.65 

3.00 

0.65 

C2L 

0.38 

0.67 

0.75 

0.67 

1.50 

0.67 

3.00 

0.67 

C2M 

0.38 

0.65 

0.75 

0.64 

1.50 

0.66 

3.00 

0.66 

C2H 

0.38 

0.65 

0.75 

0.64 

1.50 

0.64 

3.00 

0.64 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.38 

0.67 

0.75 

0.67 

1.50 

0.65 

3.00 

0.65 

PC2L 

0.38 

0.66 

0.75 

0.66 

1.50 

0.64 

3.00 

0.64 

PC2M 

0.38 

0.64 

0.75 

0.64 

1.50 

0.64 

3.00 

0.64 

PC2H 

0.38 

0.64 

0.75 

0.65 

1.50 

0.65 

3.00 

0.65 

RM1L 

0.38 

0.67 

0.75 

0.67 

1.50 

0.67 

3.00 

0.67 

RM1M 

0.38 

0.65 

0.75 

0.64 

1.50 

0.66 

3.00 

0.66 

RM2L 

0.38 

0.67 

0.75 

0.67 

1.50 

0.67 

3.00 

0.67 

RM2M 

0.38 

0.65 

0.75 

0.64 

1.50 

0.66 

3.00 

0.66 

RM2H 

0.38 

0.65 

0.75 

0.64 

1.50 

0.64 

3.00 

0.64 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.38 

0.66 

0.75 

0.67 

1.50 

0.67 

3.00 

0.67 



 




 

Table 6.6c Special Building Nonstructural Acceleration-Sensitive Fragility - 
Low-
Code Seismic Design Level 

 

Building 

Median Spectral Acceleration (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.30 

0.71 

0.60 

0.68 

1.20 

0.66 

2.40 

0.65 

W2 

0.30 

0.66 

0.60 

0.66 

1.20 

0.69 

2.40 

0.69 

S1L 

0.30 

0.66 

0.60 

0.68 

1.20 

0.68 

2.40 

0.68 

S1M 

0.30 

0.66 

0.60 

0.68 

1.20 

0.68 

2.40 

0.68 

S1H 

0.30 

0.67 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

S2L 

0.30 

0.65 

0.60 

0.68 

1.20 

0.68 

2.40 

0.68 

S2M 

0.30 

0.65 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

S2H 

0.30 

0.64 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

S3 

0.30 

0.65 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

S4L 

0.30 

0.65 

0.60 

0.68 

1.20 

0.68 

2.40 

0.68 

S4M 

0.30 

0.64 

0.60 

0.68 

1.20 

0.68 

2.40 

0.68 

S4H 

0.30 

0.64 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

S5L 

0.30 

0.65 

0.60 

0.68 

1.20 

0.68 

2.40 

0.68 

S5M 

0.30 

0.64 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

S5H 

0.30 

0.64 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

C1L 

0.30 

0.65 

0.60 

0.68 

1.20 

0.68 

2.40 

0.68 

C1M 

0.30 

0.64 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

C1H 

0.30 

0.67 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

C2L 

0.30 

0.66 

0.60 

0.66 

1.20 

0.65 

2.40 

0.65 

C2M 

0.30 

0.63 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

C2H 

0.30 

0.63 

0.60 

0.66 

1.20 

0.66 

2.40 

0.66 

C3L 

0.30 

0.65 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

C3M 

0.30 

0.63 

0.60 

0.66 

1.20 

0.66 

2.40 

0.66 

C3H 

0.30 

0.63 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

PC1 

0.30 

0.66 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

PC2L 

0.30 

0.65 

0.60 

0.68 

1.20 

0.68 

2.40 

0.68 

PC2M 

0.30 

0.63 

0.60 

0.67 

1.20 

0.67 

2.40 

0.67 

PC2H 

0.30 

0.64 

0.60 

0.66 

1.20 

0.66 

2.40 

0.66 

RM1L 

0.30 

0.66 

0.60 

0.66 

1.20 

0.65 

2.40 

0.65 

RM1M 

0.30 

0.64 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

RM2L 

0.30 

0.66 

0.60 

0.66 

1.20 

0.66 

2.40 

0.66 

RM2M 

0.30 

0.64 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

RM2H 

0.30 

0.63 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

URML 

0.30 

0.68 

0.60 

0.66 

1.20 

0.64 

2.40 

0.64 

URMM 

0.30 

0.64 

0.60 

0.65 

1.20 

0.65 

2.40 

0.65 

MH 

0.38 

0.66 

0.75 

0.67 

1.50 

0.67 

3.00 

0.67 



 

 

 




6.4.4 Structural Fragility Curves - Equivalent Peak Ground Acceleration 

 

Structural damage fragility curves are expressed in terms of an equivalent value 
of PGA 
(rather than spectral displacement) for evaluation of Special buildings that are 
components of lifelines. Only structural damage functions are developed based on 
PGA, 
since structural damage is considered the most appropriate measure of damage for 
lifeline 
facilities. Similar methods could be used to develop nonstructural damage 
functions 
based on PGA. In this case, capacity curves are not necessary to estimate 
building 
response and PGA is used directly as the PESH input to building fragility 
curves. 

 

This section provides equivalent-PGA fragility curves for Special buildings 
based on the 
structural damage functions of Tables 6.4a - 6.4c and standard spectrum shape 
properties 
of Chapter 4. These functions have the same format and are based on the same 
approach 
and assumptions as those described in Section 5.4.4 for development equivalent-
PGA 
fragility curves for Code buildings. 

 

The values given in Tables 6.7a through 6.7c are appropriate for use in the 
evaluation of 
scenario earthquakes whose demand spectrum shape is based on, or similar to, 
large-
magnitude, WUS ground shaking at soil sites (reference spectrum shape). For 
evaluation 
of building damage due to scenario earthquakes whose spectra are not similar to 
the 
reference spectrum shape, damage-state median parameters may be adjusted to 
better 
represent equivalent-PGA structural fragility for the spectrum shape of 
interest. 

 

Median values of equivalent-PGA are adjusted for: (1) the site condition (if 
different 
from Site Class D) and (2) the ratio of long-period spectral response (i.e., 
SA1) to PGA (if 
different from a value of 1.5, the ratio of SA1 to PGA of the reference spectrum 
shape). 
Damage-state variability is not adjusted assuming that the variability 
associated with 
ground shaking (although different for different source/site conditions) when 
combined 
with the uncertainty in damage-state threshold, is approximately the same for 
all demand 
spectrum shapes. 

 

Equivalent-PGA medians, given in Tables 6.7a through 6.7c for the reference 
spectrum 
shape, are adjusted to represent other spectrum shapes using the spectrum shape 
ratios of 
Tables 5.14 and 5.15, the soil amplification factor, FV, and Equation (5-6). In 
general, 
implementation of Equation (5-6) requires information on earthquake magnitude 
and 
source-to-site distance to estimate the spectrum shape ratio for rock sites, and 
1-second 
period spectral acceleration at the site (to estimate the soil amplification 
factor). Note 
that for the following tables, shaded boxes indicate types that are not 
permitted by current 
seismic codes. 

 




 

Table 6.7a Equivalent-PGA Structural Fragility - Special High-Code 

Seismic Design Level 

 

Building 

Median Equivalent-PGA (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.32 

0.64 

0.78 

0.64 

2.00 

0.64 

3.22 

0.64 

W2 

0.35 

0.64 

0.82 

0.64 

1.76 

0.64 

3.13 

0.64 

S1L 

0.25 

0.64 

0.44 

0.64 

0.92 

0.64 

2.17 

0.64 

S1M 

0.17 

0.64 

0.34 

0.64 

0.85 

0.64 

2.10 

0.64 

S1H 

0.13 

0.64 

0.26 

0.64 

0.65 

0.64 

1.73 

0.64 

S2L 

0.33 

0.64 

0.58 

0.64 

1.10 

0.64 

2.07 

0.64 

S2M 

0.18 

0.64 

0.35 

0.64 

0.97 

0.64 

2.34 

0.64 

S2H 

0.14 

0.64 

0.27 

0.64 

0.81 

0.64 

2.13 

0.64 

S3 

0.19 

0.64 

0.36 

0.64 

0.79 

0.64 

1.44 

0.64 

S4L 

0.34 

0.64 

0.54 

0.64 

1.04 

0.64 

1.91 

0.64 

S4M 

0.21 

0.64 

0.37 

0.64 

0.98 

0.64 

2.27 

0.64 

S4H 

0.16 

0.64 

0.32 

0.64 

0.90 

0.64 

2.29 

0.64 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.29 

0.64 

0.51 

0.64 

1.07 

0.64 

2.06 

0.64 

C1M 

0.19 

0.64 

0.36 

0.64 

1.02 

0.64 

2.48 

0.64 

C1H 

0.14 

0.64 

0.28 

0.64 

0.83 

0.64 

2.03 

0.64 

C2L 

0.33 

0.64 

0.66 

0.64 

1.42 

0.64 

2.40 

0.64 

C2M 

0.22 

0.64 

0.49 

0.64 

1.24 

0.64 

2.97 

0.64 

C2H 

0.15 

0.64 

0.37 

0.64 

1.11 

0.64 

2.80 

0.64 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.25 

0.64 

0.48 

0.64 

1.02 

0.64 

1.86 

0.64 

PC2L 

0.32 

0.64 

0.51 

0.64 

1.03 

0.64 

1.78 

0.64 

PC2M 

0.22 

0.64 

0.40 

0.64 

0.92 

0.64 

2.25 

0.64 

PC2H 

0.15 

0.64 

0.30 

0.64 

0.83 

0.64 

2.13 

0.64 

RM1L 

0.39 

0.64 

0.65 

0.64 

1.52 

0.64 

2.53 

0.64 

RM1M 

0.25 

0.64 

0.50 

0.64 

1.15 

0.64 

2.76 

0.64 

RM2L 

0.34 

0.64 

0.59 

0.64 

1.41 

0.64 

2.36 

0.64 

RM2M 

0.22 

0.64 

0.43 

0.64 

1.05 

0.64 

2.65 

0.64 

RM2H 

0.15 

0.64 

0.30 

0.64 

0.89 

0.64 

2.58 

0.64 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.16 

0.64 

0.26 

0.64 

0.45 

0.64 

0.88 

0.64 



 




 

Table 6.7b Equivalent-PGA Structural Fragility - Special Moderate-Code 

Seismic Design Level 

 

Building 

Median Equivalent-PGA (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.32 

0.64 

0.59 

0.64 

1.32 

0.64 

2.08 

0.64 

W2 

0.28 

0.64 

0.51 

0.64 

1.00 

0.64 

1.83 

0.64 

S1L 

0.20 

0.64 

0.31 

0.64 

0.60 

0.64 

1.29 

0.64 

S1M 

0.16 

0.64 

0.28 

0.64 

0.60 

0.64 

1.27 

0.64 

S1H 

0.13 

0.64 

0.22 

0.64 

0.51 

0.64 

1.17 

0.64 

S2L 

0.27 

0.64 

0.37 

0.64 

0.67 

0.64 

1.27 

0.64 

S2M 

0.17 

0.64 

0.28 

0.64 

0.69 

0.64 

1.40 

0.64 

S2H 

0.14 

0.64 

0.23 

0.64 

0.63 

0.64 

1.44 

0.64 

S3 

0.18 

0.64 

0.26 

0.64 

0.46 

0.64 

0.86 

0.64 

S4L 

0.26 

0.64 

0.36 

0.64 

0.61 

0.64 

1.17 

0.64 

S4M 

0.18 

0.64 

0.29 

0.64 

0.69 

0.64 

1.33 

0.64 

S4H 

0.16 

0.64 

0.26 

0.64 

0.66 

0.64 

1.42 

0.64 

S5L 

 

 

 

 

 

 

 

 

S5M 

 

 

 

 

 

 

 

 

S5H 

 

 

 

 

 

 

 

 

C1L 

0.23 

0.64 

0.33 

0.64 

0.63 

0.64 

1.22 

0.64 

C1M 

0.17 

0.64 

0.28 

0.64 

0.70 

0.64 

1.38 

0.64 

C1H 

0.14 

0.64 

0.23 

0.64 

0.59 

0.64 

1.15 

0.64 

C2L 

0.26 

0.64 

0.44 

0.64 

0.77 

0.64 

1.34 

0.64 

C2M 

0.20 

0.64 

0.35 

0.64 

0.81 

0.64 

1.63 

0.64 

C2H 

0.15 

0.64 

0.30 

0.64 

0.78 

0.64 

1.63 

0.64 

C3L 

 

 

 

 

 

 

 

 

C3M 

 

 

 

 

 

 

 

 

C3H 

 

 

 

 

 

 

 

 

PC1 

0.24 

0.64 

0.33 

0.64 

0.63 

0.64 

1.05 

0.64 

PC2L 

0.24 

0.64 

0.35 

0.64 

0.59 

0.64 

1.06 

0.64 

PC2M 

0.19 

0.64 

0.29 

0.64 

0.62 

0.64 

1.27 

0.64 

PC2H 

0.15 

0.64 

0.25 

0.64 

0.60 

0.64 

1.30 

0.64 

RM1L 

0.31 

0.64 

0.44 

0.64 

0.79 

0.64 

1.33 

0.64 

RM1M 

0.24 

0.64 

0.36 

0.64 

0.74 

0.64 

1.65 

0.64 

RM2L 

0.28 

0.64 

0.41 

0.64 

0.74 

0.64 

1.27 

0.64 

RM2M 

0.21 

0.64 

0.32 

0.64 

0.69 

0.64 

1.58 

0.64 

RM2H 

0.15 

0.64 

0.25 

0.64 

0.64 

0.64 

1.53 

0.64 

URML 

 

 

 

 

 

 

 

 

URMM 

 

 

 

 

 

 

 

 

MH 

0.16 

0.64 

0.26 

0.64 

0.45 

0.64 

0.88 

0.64 






 

Table 6.7c Equivalent-PGA Structural Fragility - Special Low-Code 

Seismic Design Level 

 

Building 

Median Equivalent-PGA (g) and Logstandard Deviation (Beta) 

Type 

Slight 

Moderate 

Extensive 

Complete 

 

Median 

Beta 

Median 

Beta 

Median 

Beta 

Median 

Beta 

W1 

0.28 

0.64 

0.50 

0.64 

1.00 

0.64 

1.51 

0.64 

W2 

0.21 

0.64 

0.34 

0.64 

0.68 

0.64 

1.10 

0.64 

S1L 

0.16 

0.64 

0.23 

0.64 

0.42 

0.64 

0.71 

0.64 

S1M 

0.15 

0.64 

0.23 

0.64 

0.42 

0.64 

0.73 

0.64 

S1H 

0.13 

0.64 

0.20 

0.64 

0.40 

0.64 

0.71 

0.64 

S2L 

0.19 

0.64 

0.25 

0.64 

0.44 

0.64 

0.74 

0.64 

S2M 

0.16 

0.64 

0.24 

0.64 

0.52 

0.64 

0.88 

0.64 

S2H 

0.14 

0.64 

0.21 

0.64 

0.50 

0.64 

0.93 

0.64 

S3 

0.14 

0.64 

0.18 

0.64 

0.30 

0.64 

0.57 

0.64 

S4L 

0.19 

0.64 

0.23 

0.64 

0.38 

0.64 

0.68 

0.64 

S4M 

0.16 

0.64 

0.23 

0.64 

0.47 

0.64 

0.81 

0.64 

S4H 

0.15 

0.64 

0.23 

0.64 

0.48 

0.64 

0.87 

0.64 

S5L 

0.18 

0.64 

0.26 

0.64 

0.41 

0.64 

0.68 

0.64 

S5M 

0.14 

0.64 

0.24 

0.64 

0.50 

0.64 

0.80 

0.64 

S5H 

0.13 

0.64 

0.24 

0.64 

0.51 

0.64 

0.84 

0.64 

C1L 

0.17 

0.64 

0.22 

0.64 

0.39 

0.64 

0.67 

0.64 

C1M 

0.15 

0.64 

0.23 

0.64 

0.48 

0.64 

0.80 

0.64 

C1H 

0.13 

0.64 

0.20 

0.64 

0.39 

0.64 

0.66 

0.64 

C2L 

0.19 

0.64 

0.27 

0.64 

0.44 

0.64 

0.79 

0.64 

C2M 

0.16 

0.64 

0.26 

0.64 

0.56 

0.64 

0.93 

0.64 

C2H 

0.14 

0.64 

0.25 

0.64 

0.56 

0.64 

0.96 

0.64 

C3L 

0.17 

0.64 

0.25 

0.64 

0.39 

0.64 

0.65 

0.64 

C3M 

0.14 

0.64 

0.23 

0.64 

0.46 

0.64 

0.75 

0.64 

C3H 

0.12 

0.64 

0.22 

0.64 

0.48 

0.64 

0.79 

0.64 

PC1 

0.18 

0.64 

0.24 

0.64 

0.38 

0.64 

0.65 

0.64 

PC2L 

0.18 

0.64 

0.23 

0.64 

0.36 

0.64 

0.66 

0.64 

PC2M 

0.16 

0.64 

0.22 

0.64 

0.45 

0.64 

0.79 

0.64 

PC2H 

0.14 

0.64 

0.21 

0.64 

0.45 

0.64 

0.81 

0.64 

RM1L 

0.22 

0.64 

0.29 

0.64 

0.44 

0.64 

0.80 

0.64 

RM1M 

0.19 

0.64 

0.26 

0.64 

0.50 

0.64 

0.92 

0.64 

RM2L 

0.20 

0.64 

0.27 

0.64 

0.41 

0.64 

0.77 

0.64 

RM2M 

0.17 

0.64 

0.24 

0.64 

0.47 

0.64 

0.88 

0.64 

RM2H 

0.14 

0.64 

0.22 

0.64 

0.49 

0.64 

0.92 

0.64 

URML 

0.19 

0.64 

0.28 

0.64 

0.47 

0.64 

0.68 

0.64 

URMM 

0.14 

0.64 

0.22 

0.64 

0.38 

0.64 

0.70 

0.64 

MH 

0.16 

0.64 

0.26 

0.64 

0.45 

0.64 

0.88 

0.64 



 




 

6.5 Damage Due to Ground Failure - Special Buildings 

 

Damage to Special buildings due to ground failure is assumed to be the same as 
the 
damage to Code buildings for the same amount of permanent ground deformation 
(PGD). 
Fragility curves developed in Section 5.5 for Code buildings are also 
appropriate for 
prediction of damage to Special buildings due to ground failure. 

 

6.6 Evaluation of Building Damage - Essential Facilities 

 

6.6.1 Overview 

 

Special building capacity and fragility curves for structural and nonstructural 
systems are 
used to predict essential facility damage when the user is able to determine 
that the 
essential facility is superior to a typical building of the model building type 
and design 
level of interest. If such a determination cannot be made by the user, then the 
Code 
building functions of Chapter 5 are used to evaluate essential building damage. 
These 
criteria are summarized in Table 6.8. 

 

Table 6.8 Criteria for Evaluating Essential Facility Damage 

 

Evaluate Essential Facility Using: 

User Deems Essential Facility to be: 

Code building damage functions 
(High-Code, Moderate-Code, Low-
Code and Pre-Code functions of 
Chapter 5) 

Typical of the model building type and 
seismic design level of interest (i.e., no 
special seismic protection of components) 

Special building damage functions 
(High-Code, Moderate-Code and 
Low-Code functions of Chapter 6) 

Superior to the model building type and 
seismic design level of interest (e.g., 50 
percent stronger lateral-force-resisting 
structural system, and special anchorage 
and bracing of nonstructural components) 



 

 

During an earthquake, the essential facilities may be damaged either by ground 
shaking, 
ground failure, or both. Essential facilities are evaluated separately for the 
two modes of, 
ground shaking and ground failure, and the resulting damage-state probabilities 
combined for evaluation of loss. 

 

6.6.2 Damage Due to Ground Shaking 

 

Damage to essential facilities due to ground shaking uses the same methods as 
those 
described in Section 5.6.2 for Code buildings, with the exception that Special 
buildings 
are assumed to have less degradation and greater effective damping than Code 
buildings. 




 

6.6.2.1 Demand Spectrum Reduction for Effective Damping - Special Buildings 

 

Demand spectra for evaluation of damage to Special buildings are constructed 
using the 
same approach, assumptions and formulas as those described in Section 5.6.2.1 
for Code 
buildings, except values of the degradation factor, ., that defines the 
effective amount of 
hysteretic damping as a function of duration are different for Special 
buildings. 
Degradation factors for Special buildings are given in Table 6.9. 

 

Figure 6.5 shows typical demand spectra (spectral acceleration plotted as a 
function of 
spectral displacement) for three demand levels. These three demand levels 
represent 
Short (. = 0.90), Moderate (. = 0.60) and Long (. = 0.40) duration ground 
shaking, 
respectively. Also shown in the figure is the building capacity curve of a low-
rise 
Special building (Moderate-Code seismic design) that was used to estimate 
effective 
damping. The intersection of the capacity curve with each of the three demand 
spectra 
illustrates the significance of duration (damping) on building response. 

00.10.20.30.40.50.60.70.80.91012345678910Spectral Displacement (inches)
Spectral Acceleration (g's)
PESH Spectrum - 5% DampingDemand Spectrum - Long DurationDemand Spectrum - 
Moderate DurationDemand Spectrum - Short DurationBuilding Capacity Curve0.3 sec.
1.0 sec. 


Figure 6.5 Example Demand Spectra - Special Building 

(M = 7.0 at 20 km, WUS, Site Class E). 

 

Comparison of Figure 6.5 with Figure 5.7 (same example building and PESH demand, 
except capacity curve and damping represents Code building properties) 
illustrates the 
significance of increased strength and damping (reduced degradation) of Special 
buildings on the reduction of building displacement. In this case, the Special 
building 
displaces only about one-half as much as a comparable Code building for the same 
level 
of PESH demand. Forces on nonstructural acceleration-sensitive components are 
not 
reduced, but are slightly increased due to the higher strength of the Special 
building. 




 

Table 6.9 Special Building Degradation Factor (.) as a Function of Short, 

Moderate and Long Earthquake Duration 

 

Building Type 

High-Code Design 

Moderate-Code Design 

Low-Code Design 

No. 

Label 

Short 

Moderate 

Long 

Short 

Moderate 

Long 

Short 

Moderate 

Long 

1 

W1 

1.0 

1.0 

0.7 

1.0 

0.8 

0.5 

0.9 

0.6 

0.3 

2 

W2 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

3 

S1L 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

4 

S1M 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

5 

S1H 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

6 

S2L 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

7 

S2M 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

8 

S2H 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

9 

S3 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

10 

S4L 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

11 

S4M 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

12 

S4H 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

13 

S5L 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

14 

S5M 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

15 

S5H 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

16 

C1L 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

17 

C1M 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

18 

C1H 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

19 

C2L 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

20 

C2M 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

21 

C2H 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

22 

C3L 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

23 

C3M 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

24 

C3H 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

25 

PC1 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

26 

PC2L 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

27 

PC2M 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

28 

PC2H 

0.8 

0.6 

0.4 

0.7 

0.5 

0.3 

0.6 

0.4 

0.2 

29 

RM1L 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

30 

RM1M 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

31 

RM2L 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

32 

RM2M 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

33 

RM2H 

1.0 

0.8 

0.6 

0.9 

0.6 

0.4 

0.8 

0.4 

0.2 

34 

URML 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

35 

URMM 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

0.6 

0.4 

0.2 

36 

MH 

0.9 

0.6 

0.4 

0.9 

0.6 

0.4 

0.9 

0.6 

0.4 



 

 

6.6.2.2 Damage State Probability 

 

Structural and nonstructural fragility curves of essential facilities are 
evaluated for 
spectral displacement and spectral acceleration defined by the intersection of 
the capacity 
and demand curves. Each of these curves describe the cumulative probability of 
being in 
or exceeding, a particular damage state. Nonstructural components (both drift- 
and 
acceleration-sensitive components) may, in some cases, be dependent on the 
structural 




damage state (e.g., Complete structural damage may cause Complete nonstructural 
damage). The Methodology assumes nonstructural damage states to be independent 
of 
structural damage states. Cumulative probabilities are differenced to obtain 
discrete 
probabilities of being in each of the five damage states. This process is shown 
schematically in Figure 6.6. 

 

SCCSSCMEMEME 


Figure 6.6 Example Essential Facility Damage Estimation Process. 

 

 

 

6.6.3 Combined Damage Due to Ground Failure and Ground Shaking 

 

Damage to essential facilities is based either on Code building damage functions 
or 
Special building damage functions. Code building damage due to ground shaking is 
combined with damage due to ground shaking as specified in Section 5.6.3. 
Special 
building damage due to ground failure (Section 6.5.2) is combined with damage 
due to 
ground shaking (Section 6.6.2.2) using the same approach, assumptions and 
formulas as 
those given in Section 5.6.3 for Code buildings. 




 

6.6.4 Combined Damage to Essential Facilities 

 

Combined ground shaking/ground failure damage to the model building type and 
design 
level of interest (either a Special or a Code building) represents combined 
damage to the 
essential facility. 

 

6.7 Guidance for Expert Users 

 

This section provides guidance for users who are seismic/structural experts 
interested in 
modifying essential facility damage functions supplied with the methodology. 
This 
section also provides the expert user with guidance regarding the selection of 
the 
appropriate mix of design levels for the region of interest. 

 

6.7.1 Selection of Representative Seismic Design Level 

 

The methodology permits the user to select the seismic design level considered 
appropriate for each essential facility and to designate the facility as a 
Special building, 
when designed and constructed to above-Code standards. In general, performance 
of 
essential facilities is not expected to be better than the typical (Code) 
building of the 
representative model building type. Exceptions to this generalization include 
California 
hospitals of recent (post-1973) construction. If the user is not able to 
determine that the 
essential facility is significantly better than average, then the facility 
should be modeled 
using Code building damage functions (i.e., same methods as those developed in 
Chapter 
5 for general building stock). 

 

Table 6.10 provides guidance for selecting appropriate building damage functions 
for 
essential facilities based on design vintage. These guidelines are applicable to 
the 
following facilities: 

 

1. hospitals and other medical facilities having surgery or emergency treatment 
areas, 

2. fire and police stations, and 

3. municipal government disaster operation and communication centers deemed (for 
design) to be vital in emergencies, 

 

provided that seismic codes (e.g., Uniform Building Code) were adopted and 
enforced in 
the study area of interest. Such adoption and enforcement is generally true for 
jurisdictions of California, but may not be true other areas. 

 

 




Table 6.10 Guidelines for Selection of Damage Functions for Essential Facilities 
Based on UBC Seismic Zone and Building Age 

 

UBC Seismic Zone 
(NEHRP Map Area) 

Post-1973 

1941 - 1973 

Pre-1941 

Zone 4 

(Map Area 7) 

Special High-Code 

Moderate-Code 

Pre-Code 

(W1 = Moderate-Code) 

Zone 3 

(Map Area 6) 

Special Moderate-Code 

Moderate-Code 

Pre-Code 

(W1 = Moderate-Code) 

Zone 2B 

(Map Area 5) 

Moderate-Code 

Low-Code 

Pre-Code 

 (W1 = Low-Code) 

Zone 2A 

(Map Area 4) 

Low-Code 

Low-Code 

Pre-Code 

(W1 = Low-Code) 

Zone 1 

Map Area 2/3) 

Low-Code 

Pre-Code 

(W1 = Low-Code) 

Pre-Code 

(W1 = Low-Code) 

Zone 0 

(Map Area 1) 

Pre-Code 

(W1 = Low-Code) 

Pre-Code 

(W1 = Low-Code) 

Pre-Code 

(W1 = Low-Code) 



 

The guidelines given in Table 6.1 assume that essential buildings in the study 
region are 
not designed for wind. The user should consider the possibility that mid-rise 
and high-
rise facilities could be designed for wind and may have considerable lateral 
strength, 
even if not designed for earthquake. Users must be knowledgeable about the type 
and 
history of construction in the study region of interest and apply engineering 
judgment in 
assigning essential facilities to a building type and seismic design level. 

 

6.7.2 High Potential Loss Facilities 

 

6.7.2.1 Introduction 

 

This section describes damage evaluation of high potential loss (HPL) 
facilities. HPL 
facilities are likely to cause heavy earthquake losses, if significantly 
damaged. Examples 
of such facilities include nuclear power plants, certain military and industrial 
facilities, 
dams, etc. 

 

6.7.2.2 Input Requirements and Output Information 

 

The importance of these facilities (in terms of potential earthquake losses) 
suggests that 
damage assessment be done in a special way as compared to ordinary buildings. 
Each 
HPL facility should be treated on an individual basis by users who have 
sufficient 
expertise to evaluate damage to such facilities. Required input to the damage 
evaluation 
module includes the following items: 

 

 capacity curves that represents median (typical) properties of the HPL 
facility 
structure, or a related set of engineering parameters, such as period, yield 
strength, and ultimate capacity, that may be used by seismic/structural 
engineering experts with the methods of Chapter 5 to select representative 
damage functions, 




 

 fragility curves for the HPL facility under consideration, or related set 
engineering 
parameters, that can be used by seismic/structural engineering experts with the 
methods of Chapter 5 to select appropriate damage functions. 

 

The direct output (damage estimate) from implementation of the fragility curves 
is an 
estimate of the probability of being in, or exceeding, each damage state for the 
given 
level of ground shaking. This output is used directly as an input to other 
damage or loss 
estimation methods or combined with inventory information to predict the 
distribution of 
damage as a function of facility type, and geographical location. In the latter 
case, the 
number and geographical location of facilities of interest would be a required 
input to the 
damage estimation method. 

 

6.7.2.3 Form of Damage Functions and Damage Evaluation 

The form of user-supplied HPL facility damage functions should be the same as 
that of 
buildings (Chapter 5) and their use in the methodology would be similar to that 
of 
essential facilities. 

 

6.8 Essential Facility and HPL Damage References 

Refer to Section 5.8 for building damage references. 

 

6.9 Restoration Curves 

Restoration curves are based on generic ATC-13 data for the social function 
classifications of interest and are approximated as normal curves characterized 
by a mean 
and a standard deviation. The parameters of these restoration curves are given 
in Table 
6.11 and are fully user-editable 

 

 Table 6.11 Generic Restoration Functions for Essential Facilities 

 

EF 
Class 

Description 

Slight 
Mean 

Slight 
Sigma 

Moderate 
Mean 

Moderate 
Sigma 

Extensive 
Mean 

Extensive 
Sigma 

Complete 
Mean 

Complete 
Sigma 

EDFLT 

Default for 
Emergency 
Response Facility 

5 

1 

20 

2 

90 

10 

180 

20 

EFEO 

Emergency 
Operation Centers 

5 

1 

20 

2 

90 

10 

180 

20 

EFFS 

Fire Station 

5 

1 

20 

2 

90 

10 

180 

20 

EFHL 

Large Hospital 
(greater than 150 
beds) 

5 

1 

20 

2 

90 

10 

180 

20 

EFHM 

Medium Hospital (50 
to 150 Beds) 

5 

1 

20 

2 

90 

10 

180 

20 

EFHS 

Small Hospital (less 
than 50 Beds) 

5 

1 

20 

2 

90 

10 

180 

20 

EFMC 

Medical Clinics and 
Labs 

5 

1 

20 

2 

90 

10 

180 

20 

EFPS 

Police Station 

5 

1 

20 

2 

90 

10 

180 

20 

EFS1 

Grade Schools 
(Primary and High 
Schools) 

5 

1 

20 

2 

90 

10 

180 

20 

EFS2 

Colleges/Universities 

5 

1 

20 

2 

90 

10 

180 

20 

FDFLT 

Default for Fire 
Station 

5 

1 

20 

2 

90 

10 

180 

20 






MDFLT 

Default for Medical 

5 

1 

20 

2 

90 

10 

180 

20 

PDFLT 

Default for Police 

5 

1 

20 

2 

90 

10 

180 

20 

SDFLT 

Default for School 

5 

1 

20 

2 

90 

10 

180 

20 



 




Chapter 7 

Direct Physical Damage to Lifelines - Transportation Systems 

 

This chapter describes the methodology for estimating direct physical damage to 
Transportation Systems, which include the following seven systems: 

 

 Highway 

 Railway 

 Light Rail 

 Bus 

 Port 

 Ferry 

 Airport 

 

The flowchart of the overall methodology, highlighting the transportation system 
module 
and its relationship to other modules, is shown in Flowchart 7.1. 

 

7.1 Highway Transportation System 

 

7.1.1 Introduction 

 

This section presents an earthquake loss estimation methodology for a highway 
transportation system. This system consists of roadways, bridges and tunnels. 
Roads 
located on soft soil or fill or which cross a surface fault rupture can 
experience failure 
resulting in loss of functionality. Bridges that fail usually result in 
significant disruption 
to the transportation network, especially bridges that cross waterways. 
Likewise, tunnels 
are often not redundant, and major disruption to the transportation system is 
likely to 
occur should a tunnel become non-functional. Past earthquake damage reveals that 
bridges and tunnels are vulnerable to both ground shaking and ground failure, 
while 
roads are significantly affected by ground failure alone. 

 

7.1.2 Scope 

 

The scope of this section includes development of methods for estimation of 
earthquake 
damage to a highway transportation system given knowledge of the system's 
components 
(i.e., roadways, bridges, or tunnels), the classification of each component 
(e.g., for 
roadways, whether the road is a major road or urban road), and the ground motion 
(i.e. 
peak ground acceleration and/or permanent ground deformation). 

 

Damage states describing the level of damage to each highway system component 
are 
defined (i.e. slight/minor, moderate, extensive or complete). Damage states are 
related to 
a damage ratio defined as the ratio of repair to replacement cost for evaluation 
of direct 
economic loss. Component restoration curves are provided for each damage state 
to 
evaluate loss of function. Restoration curves describe the fraction or 
percentage of the 
component that is expected to be open or operational as a function of time 
following the 




earthquake. For example, an extensively damaged roadway link might be closed (0% 
functional) immediately following the earthquake, but 100% functional after 30 
days. 

 

 

8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

 

Flowchart 7.1 Transportation System Damage Relationship to Other Modules of the 
Earthquake Loss Estimation Methodology 




Fragility curves are developed for each type of highway system component. These 
curves describe the probability of reaching or exceeding each damage state given 
the 
level of ground motion. 

 

7.1.3 Input Requirements and Output Information 

 

Descriptions of required input to estimate damages to each highway system are 
given 
below. 

 

Roadways 

 

 Geographical location of roadway links (longitude and latitude of end nodes) 

 Permanent ground deformation (PGD) at roadway link 

 Roadway classification 

 

Bridges 

 

 Geographical location of bridge [longitude and latitude] 

 Bridge classification 

 Spectral accelerations at 0.3 sec and 1.0 sec, and PGD at bridge 

 Peak Ground Acceleration (for PGD-related computations) 

 

Tunnels 

 

 Geographical location of tunnels [longitude and latitude] 

 PGA and PGD at tunnel 

 Tunnel Classification 

 

Direct damage output for highway systems includes probability estimates of (1) 
component functionality and (2) physical damage expressed in terms of the 
component's 
damage ratio. Note that damage ratios, which are input to direct economic loss 
methods, 
are described in Chapter 15. 

 

Component functionality is described by the probability of damage state 
(immediately 
following the earthquake) and by the associated fraction or percentage of the 
component 
that is expected to be functional after a specified period of time. For example, 
a roadway 
link might be found to have a 0.50 probability of extensive damage and on this 
basis 
would have a 0.50 probability that the road would be: (1) closed immediately, 
(2) 
partially open after a 3-day restoration period and (3) fully open after a 1-
month 
restoration period. 

 

Interdependence of components on overall system functionality is not addressed 
by the 
methodology. Such considerations require a network system analysis that would be 
performed separately by a highway system expert. 

 




 7.1.4 Form of Damage Functions 

 

Damage functions or fragility curves for all three highway system components 
mentioned 
above are modeled as lognormally-distributed functions that give the probability 
of 
reaching or exceeding different damage states for a given level of ground motion 
or 
ground failure. Each fragility curve is characterized by a median value of 
ground motion 
or ground failure and an associated dispersion factor (lognormal standard 
deviation). 
Ground motion is quantified in terms of peak ground acceleration (PGA) and 
spectral 
acceleration (Sa), and ground failure is quantified in terms of permanent ground 
displacement (PGD). 

 

 For roadways, fragility curves are defined in terms of PGD. 

 For bridges, fragility curves are defined in terms of Sa (0.3 sec), Sa(1.0 
sec) and 
PGD. 

 For tunnels, fragility curves are defined in terms of PGA and PGD. 

 

Definitions of various damage states and the methodology used in deriving all 
these 
fragility curves are presented in the following sections. 

 

 7.1.5 Description of Highway Components 

 

As mentioned previously, a highway system is composed of three components: 
roadways, 
bridges and tunnels. In this section, a brief description of each is given. 

 

Roadways 

 

Roadways are classified as major roads and urban roads. Major roads include 
interstate and state highways and other roads with four lanes or more. Parkways 
are 
also classified as major roads. Urban roads include intercity roads and other 
roads 
with two lanes. 

 

Bridges 

Bridges are classified based on the following structural characteristics: 

 
Seismic Design 


 
Number of spans: single vs. multiple span bridges 


 
Structure type: concrete, steel, others 


 
Pier type: multiple column bents, single column bents and pier walls 


 
Abutment type and bearing type: monolithic vs. non-monolithic; high rocker 
bearings, 
low steel bearings and neoprene rubber bearings 


 
Span continuity: continuous, discontinuous (in-span hinges), and simply 
supported. 




 




The seismic design of a bridge is taken into account in terms of the (i) 
spectrum 
modification factor, (ii) strength reduction factor due to cyclic motion, (iii) 
drift limits, 
and (iv) the longitudinal reinforcement ratio. 

This classification scheme incorporates various parameters that affect damage 
into 
fragility analysis and provides a means to obtain better fragility curves when 
data become 
available. A total of 28 classes (HWB1 through HWB28) are defined this way. 
These 
classes differentiate between the different bridge characteristics found in the 
National 
Bridge Inventory (NBI). 

Tables 7.1.a and 7.1.b summarize the key NBI characteristics used, while Table 
7.2 
presents the 28 classes derived for HAZUS. Please refer to Table 3.6 in Chapter 
3 for the 
full definitions of these bridges. 

Table 7.1.a Bridge material Classes in NBI [NBI, 1988] 

Code 

Description 

1 

Concrete 

2 

Concrete continuous 

3 

Steel 

4 

Steel continuous 

5 

Prestressed concrete 

6 

Prestressed concrete continuous 

7 

Timber 

8 

Masonry 

9 

Aluminium, Wrought Iron, or Cast Iron 

0 

Other 



Table 7.1.b Bridge Types in NBI [NBI, 1988] 

Code 

Description 

01 

Slab 

02 

Stringer/Multi-beam or Girder 

03 

Girder and Floor beam System 

04 

Tee Beam 

05 

Box Beam or Girders - Multiple 

06 

Box Beam or Girders  single or Spread 

07 

Frame 

08 

Orthotropic 

09 

Truss  Deck 

10 

Truss  Thru 

11 

Arch  Deck 

12 

Arch  Thru 

13 

Suspension 

14 

Stayed Girder 

15 

Movable  Lift 

16 

Movable  Bascule 

17 

Movable  Swing 

18 

Tunnel 

19 

Culvert 

20 

Mixed Types (applicable only to approach spans) 

21 

Segmental Box Girder 

22 

Channel Beam 

00 

Other 






 

Table 7.2 HAZUS Bridge Classification Scheme 

CLASS 

NBI Class 

State 

Year Built 

# 
Spans 

Length of 
Max. Span 
(meter) 

Length 
less than 
20 m 

K3D 

(See note 
below) 

Ishape 

(See note 
below) 

Design 

Description 

HWB1 

All 

Non-CA 

< 1990 

 

> 150 

N/A 

EQ1 

0 

Conventional 

Major Bridge - Length > 150m 

HWB1 

 All 

CA 

< 1975 

 

> 150 

N/A 

EQ1 

0 

Conventional 

Major Bridge - Length > 150m 

HWB2 

 All 

Non-CA 

>= 1990 

 

> 150 

N/A 

EQ1 

0 

Seismic 

Major Bridge - Length > 150m 

HWB2 

 All 

CA 

>= 1975 

 

> 150 

N/A 

EQ1 

0 

Seismic 

Major Bridge - Length > 150m 

HWB3 

 All 

Non-CA 

< 1990 

1 

 

N/A 

EQ1 

1 

Conventional 

Single Span 

HWB3 

All 

CA 

< 1975 

1 

 

N/A 

EQ1 

1 

Conventional 

 Single Span 

HWB4 

 All 

Non-CA 

>= 1990 

1 

 

N/A 

EQ1 

1 

Seismic 

 Single Span 

HWB4 

 All 

CA 

>= 1975 

1 

 

N/A 

EQ1 

1 

Seismic 

 Single Span 

HWB5 

101-106 

Non-CA 

< 1990 

 

 

N/A 

EQ1 

0 

Conventional 

Multi-Col. Bent, Simple Support -
Concrete 

HWB6 

101-106 

CA 

< 1975 

 

 

N/A 

EQ1 

0 

Conventional 

 Multi-Col. Bent, Simple Support 
- Concrete 

HWB7 

101-106 

Non-CA 

>= 1990 

 

 

N/A 

EQ1 

0 

Seismic 

 Multi-Col. Bent, Simple Support 
- Concrete 

HWB7 

101-106 

CA 

>= 1975 

 

 

N/A 

EQ1 

0 

Seismic 

 Multi-Col. Bent, Simple Support 
- Concrete 

HWB8 

205-206 

CA 

< 1975 

 

 

N/A 

EQ2 

0 

Conventional 

Single Col., Box Girder - 
Continuous Concrete 

HWB9 

205-206 

CA 

>= 1975 

 

 

N/A 

EQ3 

0 

Seismic 

 Single Col., Box Girder - 
Continuous Concrete 

HWB10 

201-206 

Non-CA 

< 1990 

 

 

N/A 

EQ2 

1 

Conventional 

Continuous Concrete 

HWB10 

201-206 

CA 

< 1975 

 

 

N/A 

EQ2 

1 

Conventional 

 Continuous Concrete 

HWB11 

201-206 

Non-CA 

>= 1990 

 

 

N/A 

EQ3 

1 

Seismic 

 Continuous Concrete 

HWB11 

201-206 

CA 

>= 1975 

 

 

N/A 

EQ3 

1 

Seismic 

 Continuous Concrete 

HWB12 

301-306 

Non-CA 

< 1990 

 

 

No 

EQ4 

0 

Conventional 

Multi-Col. Bent, Simple Support -
Steel 

HWB13 

301-306 

CA 

< 1975 

 

 

No 

EQ4 

0 

Conventional 

 Multi-Col. Bent, Simple Support 
- Steel 

HWB14 

301-306 

Non-CA 

>= 1990 

 

 

N/A 

EQ1 

0 

Seismic 

 Multi-Col. Bent, Simple Support 
- Steel 

HWB14 

301-306 

CA 

>= 1975 

 

 

N/A 

EQ1 

0 

Seismic 

 Multi-Col. Bent, Simple Support 
- Steel 

HWB15 

402-410 

Non-CA 

< 1990 

 

 

No 

EQ5 

1 

Conventional 

Continuous Steel 

HWB15 

402-410 

CA 

< 1975 

 

 

No 

EQ5 

1 

Conventional 

 Continuous Steel 

HWB16 

402-410 

Non-CA 

>= 1990 

 

 

N/A 

EQ3 

1 

Seismic 

 Continuous Steel 

HWB16 

402-410 

CA 

>= 1975 

 

 

N/A 

EQ3 

1 

Seismic 

 Continuous Steel 






Table 7.2 HAZUS Bridge Classification Scheme (Continued) 

CLASS 

NBI 
Class 

State 

Year 
Built 

# Spans 

Length of 
Max. Span 
(meter) 

Length 
less than 
20 m 

K3D 

(notes 
below) 

Ishape 

(notes 
below) 

Design 

Description 

HWB17 

501-506 

Non-CA 

< 1990 

 

 

N/A 

EQ1 

0 

Conventional 

Multi-Col. Bent, Simple 
Support - Prestressed 
Concrete 

HWB18 

501-506 

CA 

< 1975 

 

 

N/A 

EQ1 

0 

Conventional 

 Multi-Col. Bent, Simple 
Support - Prestressed 
Concrete 

HWB19 

501-506 

Non-CA 

>= 1990 

 

 

N/A 

EQ1 

0 

Seismic 

 Multi-Col. Bent, Simple 
Support - Prestressed 
Concrete 

HWB19 

501-506 

CA 

>= 1975 

 

 

N/A 

EQ1 

0 

Seismic 

 Multi-Col. Bent, Simple 
Support - Prestressed 
Concrete 

HWB20 

605-606 

CA 

< 1975 

 

 

N/A 

EQ2 

0 

Conventional 

Single Col., Box Girder - 
Prestressed Continuous 
Concrete 

HWB21 

605-606 

CA 

 >= 1975 

 

 

N/A 

EQ3 

0 

Seismic 

Single Col., Box Girder - 
Prestressed Continuous 
Concrete 

HWB22 

601-607 

Non-CA 

< 1990 

 

 

N/A 

EQ2 

1 

Conventional 

Continuous Concrete 

HWB22 

601-607 

CA 

< 1975 

 

 

N/A 

EQ2 

1 

Conventional 

 Continuous Concrete 

HWB23 

601-607 

Non-CA 

>= 1990 

 

 

N/A 

EQ3 

1 

Seismic 

Continuous Concrete 

HWB23 

601-607 

CA 

>= 1975 

 

 

N/A 

EQ3 

1 

Seismic 

 Continuous Concrete 

HWB24 

301-306 

Non-CA 

< 1990 

 

 

Yes 

EQ6 

0 

Conventional 

Multi-Col. Bent, Simple 
Support - Steel 

HWB25 

301-306 

CA 

< 1975 

 

 

Yes 

EQ6 

0 

Conventional 

 Multi-Col. Bent, Simple 
Support - Steel 

HWB26 

402-410 

Non-CA 

< 1990 

 

 

Yes 

EQ7 

1 

Conventional 

Continuous Steel 

HWB27 

402-410 

CA 

< 1975 

 

 

Yes 

EQ7 

1 

Conventional 

 Continuous Steel 

HWB28 

 

 

 

 

 

 

 

 

 

All other bridges that are not 
classified 



 

EQ1 through EQ7 in Table 7.2 are equations for evaluating K3D. K3D is a factor 
that 
modifies the piers 2-dimensional capacity to allow for the 3-dimensional arch 
action in 
the deck. All of the equations have the same functional form; K3D = 1 + A / (N  
B), 
where N is the number of spans and the parameters A and B are given in table 
7.3. 

 

The Ishape term (given in table 7.2) is a Boolean indicator. The Kshape factor 
is the 
modifier that converts cases for short periods to an equivalent spectral 
amplitude at T=1.0 
second. When Ishape = 0, the Kshape factor does not apply. When Ishape = 1, the 
Kshape factor 
applies. Later in this section, the use of the Kshape factor will be illustrated 
through an 
example. 

 

The 28 bridge classes in Table 7.2 (HWB1 through HWB28) reflect the maximum 
number of combinations for standard bridge classes. Attributes such as the 
skeweness 
and number of spans are accounted for in the evaluation of damage potential 
through a 
modification scheme that is presented later in this section. 




 

Table 7.3 Coefficients for Evaluating K3D 

Equation 

A 

B 

K3D 

EQ1 

0.25 

1 

1 + 0.25 / (N  1) 

EQ2 

0.33 

0 

1 + 0.33 / (N) 

EQ3 

0.33 

1 

1 + 0.33 / (N  1) 

EQ4 

0.09 

1 

1 + 0.09 / (N  1) 

EQ5 

0.05 

0 

1 + 0.05 / (N) 

EQ6 

0.20 

1 

1 + 0.20 / (N  1) 

EQ7 

0.10 

0 

1 + 0.10 / (N) 



 

 

Tunnels 

 

Tunnels are classified as bored/drilled or cut & cover. 

 

7.1.6 Definitions of Damage States 

 

A total of five damage states are defined for highway system components. These 
are 
none (ds1), slight/minor (ds2), moderate (ds3), extensive (ds4) and complete 
(ds5). 

 

Slight/Minor Damage (ds2) 

 

 For roadways, ds2 is defined by slight settlement (few inches) or offset of 
the 
ground. 

 

 For bridges, ds2 is defined by minor cracking and spalling to the abutment, 
cracks in shear keys at abutments, minor spalling and cracks at hinges, minor 
spalling at the column (damage requires no more than cosmetic repair) or 
minor cracking to the deck 

 

 For tunnels, ds2 is defined by minor cracking of the tunnel liner (damage 
requires no more than cosmetic repair) and some rock falling, or by slight 
settlement of the ground at a tunnel portal. 

 

 

Moderate Damage (ds3) 

 For roadways, ds3 is defined by moderate settlement (several inches) or offset 
of the ground. 

 




 For bridges, ds3 is defined by any column experiencing moderate (shear cracks) 
cracking and spalling (column structurally still sound), moderate movement of 
the 
abutment (<2"), extensive cracking and spalling of shear keys, any connection 
having cracked shear keys or bent bolts, keeper bar failure without unseating, 
rocker bearing failure or moderate settlement of the approach. 

 

 For tunnels, ds3 is defined by moderate cracking of the tunnel liner and rock 
falling. 

 

Extensive Damage (ds4) 

 For roadways, ds4 is defined by major settlement of the ground (few feet). 

 

 For bridges, ds4 is defined by any column degrading without collapse  shear 
failure - (column structurally unsafe), significant residual movement at 
connections, or major settlement approach, vertical offset of the abutment, 
differential settlement at connections, shear key failure at abutments. 

 

 For tunnels, ds4 is characterized by major ground settlement at a tunnel 
portal 
and extensive cracking of the tunnel liner. 

 

Complete Damage (ds5) 

 For roadways, ds5 is defined by major settlement of the ground (i.e., same as 
ds4). 

 

 For bridges, ds5 is defined by any column collapsing and connection losing all 
bearing support, which may lead to imminent deck collapse, tilting of 
substructure 
due to foundation failure. 

 

 For tunnels, ds5 is characterized by major cracking of the tunnel liner, which 
may include possible collapse. 

 

7.1.7 Component Restoration Curves 

 

Restoration curves are developed based on a best fit to ATC-13 data for the 
social 
function classifications of interest (SF 25a through SF 25e) consistent with 
damage states 
defined in the previous section (first four classes in ATC-13). Figure 7.1 shows 
restoration curves for urban and major roads, Figure 7.2 represents restoration 
curves for 
highway bridges, while Figure 7.3 shows restoration curves for highway tunnels. 
The 
smooth curves shown in these figures are normal curves characterized by a mean 
and a 
standard deviation. The parameters of these restoration curves are given in 
Tables 7.4 
and 7.5. The former table gives means and standard deviations for each 
restoration curve 
(i.e., smooth continuous curve), while the second table gives approximate 
discrete 
functions for the restoration curves developed. 

 




Table 7.4 Continuous Restoration Functions for Highways (after ATC-13, 1985) 

 

 Roadways 

Highway Bridges 

Highway Tunnels 

Damage State 

Mean 

(Days) 

s 

(days) 

Mean 

(Days) 

s 

(days) 

Mean 

(Days) 

s 

(days) 

Slight/Minor 

0.9 

0.05 

0.6 

0.6 

0.5 

0.3 

Moderate 

2.2 

1.8 

2.5 

2.7 

2.4 

2.0 

Extensive 

21 

16 

75.0 

42.0 

45.0 

30.0 

Complete 

 

 

230.0 

110.0 

210.0 

110.0 



 

The values shown in Table 7.5 below represent distributions on functionality for 
each 
restoration period based on damage state immediately after the earthquake. 

Table 7.5 Discrete Restoration Functions for Highways 

Roadways 

Restoration 

Functional Percentage 

Period 

Slight 

Moderate 

Extensive/Complete 

1 day 

90 

25 

10 

3 days 

100 

65 

14 

7 days 

100 

100 

20 

30 days 

100 

100 

70 

90 days 

100 

100 

100 

Bridges 

Restoration 

Functional Percentage 

Period 

Slight 

Moderate 

Extensive 

Complete 

1 day 

70 

30 

2 

0 

3 days 

100 

60 

5 

2 

7 days 

100 

95 

6 

2 

30 days 

100 

100 

15 

4 

90 days 

100 

100 

65 

10 

Tunnels 

Restoration 

Functional Percentage 

Period 

Slight 

Moderate 

Extensive 

Complete 

1 day 

90 

25 

5 

0 

3 days 

100 

65 

8 

3 

7 days 

100 

100 

10 

3 

30 days 

100 

100 

30 

5 

90 days 

100 

100 

95 

15 



 




7.1.8 Development of Damage Functions 

 

Fragility curves for highway system components are defined with respect to 
classification 
and ground motion parameter. 

 

Damage functions for Roadways 

 

Fragility curves for major roads and urban roads are shown in Figures 7.4. and 
7.5, respectively. The medians and dispersions of these curves are presented in 
Table 7.6. 

Table 7.6 Damage Algorithms for Roadways 

Permanent Ground Deformation 

Components 

Damage State 

Median (in) 

 

 

slight/minor 

12 

0.7 

Major Road 

moderate 

24 

0.7 

(Hrd1) 

extensive/complete 

60 

0.7 

 

slight/minor 

6 

0.7 

Urban Roads 

moderate 

12 

0.7 

(Hrd2) 

extensive/complete 

24 

0.7 



 

Damage Functions for Bridges 

 

There are 28 primary bridge types for which all four damage states are 
identified 
and described. For other bridges, fragility curves of the 28 primary bridge 
types 
are adjusted to reflect the expected performance of a specific bridge which may 
be better or worse than the corresponding primary bridge type. 

 

A total of 224 bridge damage functions are obtained, 112 due to ground shaking 
and 112 due to ground failure. For a complete description on the theoretical 
background of the damage functions, see Basoz and Mander (1999). This 
document is referenced at the end of this section and can be obtained from NIBS 
or FEMA. 

 

Medians of these damage functions are given in Table 7.7. The dispersion is set 
to 0.6 for the ground shaking damage algorithm and 0.2 for the ground failure 
damage algorithm. Only incipient unseating and collapse (i.e., which correspond 
to extensive and complete damage states) are considered as possible types of 
damage due to ground failure. Initial damage to bearings (i.e., which would 
correspond to slight and/or moderate damage states) from ground failure is not 
considered. 

 

Figures 7.6 and 7.7 show example fragility curves for major bridges. 




Table 7.7 Damage Algorithms for Bridges 

 

Sa [1.0 sec in gs] for Damage Functions 

due to Ground Shaking 

PGD [inches] for Damage Functions 

due to Ground Failure 

CLASS 

Slight 

Moderate 

Extensive 

Complete 

Slight 

Moderate 

Extensive 

Complete 

HWB1 

0.40 

0.50 

0.70 

0.90 

3.9 

3.9 

3.9 

13.8 

HWB2 

0.60 

0.90 

1.10 

1.70 

3.9 

3.9 

3.9 

13.8 

HWB3 

0.80 

1.00 

1.20 

1.70 

3.9 

3.9 

3.9 

13.8 

HWB4 

0.80 

1.00 

1.20 

1.70 

3.9 

3.9 

3.9 

13.8 

HWB5 

0.25 

0.35 

0.45 

0.70 

3.9 

3.9 

3.9 

13.8 

HWB6 

0.30 

0.50 

0.60 

0.90 

3.9 

3.9 

3.9 

13.8 

HWB7 

0.50 

0.80 

1.10 

1.70 

3.9 

3.9 

3.9 

13.8 

HWB8 

0.35 

0.45 

0.55 

0.80 

3.9 

3.9 

3.9 

13.8 

HWB9 

0.60 

0.90 

1.30 

1.60 

3.9 

3.9 

3.9 

13.8 

HWB10 

0.60 

0.90 

1.10 

1.50 

3.9 

3.9 

3.9 

13.8 

HWB11 

0.90 

0.90 

1.10 

1.50 

3.9 

3.9 

3.9 

13.8 

HWB12 

0.25 

0.35 

0.45 

0.70 

3.9 

3.9 

3.9 

13.8 

HWB13 

0.30 

0.50 

0.60 

0.90 

3.9 

3.9 

3.9 

13.8 

HWB14 

0.50 

0.80 

1.10 

1.70 

3.9 

3.9 

3.9 

13.8 

HWB15 

0.75 

0.75 

0.75 

1.10 

3.9 

3.9 

3.9 

13.8 

HWB16 

0.90 

0.90 

1.10 

1.50 

3.9 

3.9 

3.9 

13.8 

HWB17 

0.25 

0.35 

0.45 

0.70 

3.9 

3.9 

3.9 

13.8 

HWB18 

0.30 

0.50 

0.60 

0.90 

3.9 

3.9 

3.9 

13.8 

HWB19 

0.50 

0.80 

1.10 

1.70 

3.9 

3.9 

3.9 

13.8 

HWB20 

0.35 

0.45 

0.55 

0.80 

3.9 

3.9 

3.9 

13.8 

HWB21 

0.60 

0.90 

1.30 

1.60 

3.9 

3.9 

3.9 

13.8 

HWB22 

0.60 

0.90 

1.10 

1.50 

3.9 

3.9 

3.9 

13.8 

HWB23 

0.90 

0.90 

1.10 

1.50 

3.9 

3.9 

3.9 

13.8 

HWB24 

0.25 

0.35 

0.45 

0.70 

3.9 

3.9 

3.9 

13.8 

HWB25 

0.30 

0.50 

0.60 

0.90 

3.9 

3.9 

3.9 

13.8 

HWB26 

0.75 

0.75 

0.75 

1.10 

3.9 

3.9 

3.9 

13.8 

HWB27 

0.75 

0.75 

0.75 

1.10 

3.9 

3.9 

3.9 

13.8 

HWB28 

0.80 

1.00 

1.20 

1.70 

3.9 

3.9 

3.9 

13.8 






 

 The damage algorithm for bridges can be broken into eight steps: 

 

Step 1: 

Get the bridge location (longitude and latitude), class (HWB1 through HWB28), 
number 
of spans (N), skew angle (a), span width (W), bridge length (L), and maximum 
span 
length (Lmax). Note that the skew angle is defined as the angle between the 
centerline of 
a pier and a line normal to the roadway centerline. 

 

Step 2: 

 Evaluate the soil-amplified shaking at the bridge site. That is, get the peak 
ground 
acceleration (PGA), spectral accelerations (Sa[0.3 sec] and Sa[1.0 sec] ) and 
the 
permanent ground deformation (PGD). 

 

Step 3: 

Evaluate the following three modification factors: 

 

Kskew = sqrt[sin(90-a)] 

 

Kshape = 2.5 x Sa(1.0 sec) / Sa(0.3 sec) 

 

K3D = 1 + A / (N  B) A and B are read from Table 7.3 

 

Step 4: 

Modify the ground shaking medians for the standard fragility curves in Table 
7.7 as 
follows: 

 

 New Median [for slight] = Old Median [for slight] x Factorslight 

 

Where 

 Factorslight = 1 if Ishape = 0 (Ishape is read from Table 7.2) 

 or 

 Factorslight = minimum of (1, Kshape ) if Ishape = 1 

 

 New median [moderate] = Old median [for moderate] * ( Kskew ) * ( K3D ) 

 

 New median [extensive] = Old median [for extensive] * ( Kskew ) * ( K3D ) 

 

 New median [complete] = Old median [for complete] * ( Kskew ) * ( K3D ) 

 

Step 5: 

Use the new medians along with the dispersion  = 0.6 to evaluate the ground 
shaking-
related damage state probabilities. Note that Sa(1.0 sec) (listed in Table 7.7) 
is the 
parameter to use in this evaluation. 

 




Step 6: 

Modify the PGD medians for the standard fragility curves listed in Table 7.7 
as 
follows: 

 

 New PGD median [for slight] = Table7.7 PGD median [for slight] x f1 

 New PGD median [moderate] = Table7.7 PGD median [for moderate] x f1 

 New PGD median [extensive] = Table7.7 PGD median [for extensive] x f1 

 New PGD median [complete] = Table7.7 median [for complete] x f2 

 

Where f1 and f2 are modification factors that are functions of the number of 
spans (N), 
width of the span (W), length of the bridge (L), and the skewness (a) and can be 
computed using the equations in Table 7.8 below. 

Table 7.8 Modifiers for PGD Medians 

CLASS 

f1 

f2 


HWB1 

1 

1 

HWB2 

1 

1 

HWB3 

1 

1 

HWB4 

1 

1 

HWB5 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB6 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB7 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB8 

1 

sin (a) 

HWB9 

1 

sin (a) 

HWB10 

1 

sin (a) 

HWB11 

1 

sin (a) 

HWB12 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB13 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB14 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB15 

1 

sin (a) 

HWB16 

1 

sin (a) 

HWB17 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB18 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB19 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB20 

1 

sin (a) 

HWB21 

1 

sin (a) 

HWB22 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB23 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB24 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB25 

0.5 * L / [ N . W . sin (a) ] 

0.5 * L / [ N . W . sin (a) ] 

HWB26 

1 

sin (a) 

HWB27 

1 

sin (a) 

HWB28 

1 

1 



 

 




 

Step 7: 

Use the new medians along with the dispersion  = 0.2 to evaluate ground 
failure-related 
damage state probabilities. 

 

Step 8: 

Combine the damage state probabilities and evaluate functionality of bridge. 

 

Example of bridge damage evaluation: 

Consider a three-span simply supported prestressed concrete bridge seated on 
neoprene 
bearings located in the Memphis area. The table below lists the data for this 
bridge 
obtained from NBI. For the scenario earthquake, assume that the ground motion 
for rock 
conditions (NEHRP class B) is defined by the following parameters: 
Sa(0.3 sec) = 2.1g, Sa(1.0 sec) = 0.24g PGA = 0.38g 

 

Also, assume that the bridge is located in soil type D. 

 

The median spectral acceleration ordinates for different damage states are 
determined as 
follows: 


Step 1: Ground motion data is amplified for soil conditions (Table 4.10 in 
Chapter 4): 

 

Sa(0.3 sec) = 2.1g (1 x 2.1g), 

Sa(1.0 sec) = 0.43g (1.8 x 0.24g) 

PGA = 0.53g (1.4 x 0.38g ) 

 

Step 2: The bridge class is determined. 

Bridge Data Required for the Analysis 

NBI field 

Data 

Remarks 

27 

1968 

Year built 

34 

32 

Angle of skew 

43 

501 

Prestressed concrete, simple span 

45 

3 

Number of spans 

48 

23 

Maximum span length (m) 

49 

56 

Total bridge length (m) 



 

Based on the information in the table above, HWB17 is determined to be the 
bridge 
class. 

 

Step 3: Parameters needed in evaluating the median spectral accelerations are 
computed: 

 




Kskew = sqrt[sin(90-a)] =sqrt [sin (90  32) ] = 0.91 

 

Kshape = 2.5 x Sa(1.0 sec) / Sa(0.3 sec) = 0.50 

 

K3D = 1 + A / (N  B) = 1 + 0.25 / (3-1) = 1.125 (See Tables 7.2 and 7.3) 

 

Step 4: 

 

From Table 7.2, Ishape is 0 for HWB17, therefore long periods governs, and 
Factorslight is 1. Therefore: 

 

New Sa[1.0 sec] [for slight] = Old Sa[1.0 sec] [for slight] *Factorslight 

 = 0.25g * 1.00 = 0.25g 

New Sa[1.0 sec] [moderate] = Old Sa[1.0 sec] [for moderate] * ( Kskew ) * ( K3D 
) 

 = 0.35g * 0.91 * 1.125 = 0.36g 

New Sa[1.0 sec] [extensive] = Old Sa[1.0 sec] [for extensive] * ( Kskew ) * ( 
K3D ) 

= 0.45g * 0.91 * 1.125 = 0.46g 

New Sa[1.0 sec] [complete] = Old Sa[1.0 sec] [for complete] * ( Kskew ) * ( K3D 
) 

= 0.70g * 0.91 * 1.125 = 0.72g 

 

Step 5: 

With these new medians, the shaking-related discrete damage state probabilities 
are 
(using lognormal functions with the above medians and with betas equal to 0.6): 

 

P[No damage] = 1  0.82 = 0.18 

P[Slight damage] = 0.82  0.62 = 0.20 

P[Moderate damage] = 0.62  0.46 = 0.16 

P[Extensive damage] = 0.46  0.20 = 0.26 

P[Complete damage] = 0.20 

 

Damage Functions for Tunnels 

 

The tunnel damage functions developed based on the damage potential of their 
subcomponents, namely the liner and the portal (G&E, 1994). G&E findings are 
based 
partly on earthquake experience data reported by Dowding et. al. (1978) and Owen 
et. al 
(1981). The subcomponent damage functions are given in Tables A.7.1 and A.7.2. 

 

Ten tunnel damage functions were developed, four damage functions for ground 
shaking 
(PGA) and six damage functions for ground failure (PGD). Medians and dispersion 
factors of these damage functions are given in Table 7.9. Graphical 
representations of 
these damage functions are also provided. Figures 7.8 and Figure 7.9 plot 
fragility 
curves due to PGA for bored/drilled and cut & cover tunnels, respectively, while 
Figure 
7.10 presents fragility curves for tunnels due to PGD. 

 




Table 7.9 Damage Algorithms for Tunnels 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Bored/Drilled 
(HTU1) 

slight/minor 

moderate 

0.6 

0.8 

0.6 

0.6 

Cut & Cover 
(HTU2) 

slight/minor 

moderate 

0.5 

0.7 

0.6 

0.6 

Permanent Ground Deformation 

Classification 

Damage State 

Median (in) 

 

Bored/Drilled 
(HTU1) 

slight/moderate 

extensive 

complete 

6.0 

12.0 

60.0 

0.7 

0.5 

0.5 

Cut & Cover 
(HTU2) 

slight/moderate 

extensive 

complete 

6.0 

12.0 

60.0 

0.7 

0.5 

0.5 



 

7.1.9 Guidance for Loss Estimation Using Advanced Data and Models Analysis 

 

For an advanced analysis, experts can use the methodology developed with the 
flexibility 
to (1) include a more refined inventory of the transportation system pertaining 
to the 
study area, and (2) include component-specific and system-specific fragility 
data. User-
supplied damage algorithms can be modified to incorporate improved information 
about 
key components of the highway system. Similarly, improved restoration curves can 
be 
developed, given knowledge of available resources and a more accurate layout of 
the 
transportation network within the local topographic (i.e., if the redundancy and 
importance of highway components of the network are known). 

 

7.1.10 References 

(1) Applied Technology Council, "Earthquake Damage Evaluation Data for 
California", ATC-13, 
Redwood City, CA, 1985. 

(2) Dowding, C.H. and Rozen, A., "Damage to Rock Tunnels from Earthquake 
Shaking", Journal 
of the Geotechnical Engineering Division, American Society of Civil Engineers, 
New York, NY, 
February 1978. 

(3) National Institute of Building Sciences, Enhancement of the Highway 
Transportation 
Lifeline Module in HAZUS, prepared by Nesrin Basoz and John Mander, January 
1999. 

(4) Kim, S.H., "A GIS-Based Regional Risk Analysis Approach for Bridges against 
Natural 
Hazards", a dissertation submitted to the faculty of the graduate school of the 
State University of 
New York at Buffalo, September 1993. 

(5) Owen, G.N. and Scholl, R.E., "Earthquake Engineering Analysis of a Large 
Underground 
Structures", Federal Highway Administration and National Science Foundation, 
FHWA/RD-
80/195, January 1981. 

(6) G&E Engineering Systems, Inc. (G&E), "NIBS Earthquake Loss Estimation 
Methods, 
Technical Manual, Transportation Systems (Highway Systems)", May 1994. 




 

 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensive/CompleteATC-13 Data 


Figure 7.1 Restoration Curves for Urban and Major Roads (after ATC-13, 1985). 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


 

Figure 7.2 Restoration Curves for Highway Bridges (after ATC-13, 1985). 

 

 




 

 

 

 

 

 

 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


 

Figure 7.3 Restoration Curves for Highway Tunnels (after ATC-13, 1985). 

 

 

 

 

 

 

 

 

 

 

 

 




 

Permanent Ground Deformation (inches)
[ Probability Ds > ds | PGD ]
0.000.250.500.751.000.006.0012.0018.0024.0030.0036.0042.0048.00MinorModerateExte
nsive/Complete 


Highways. 


 

Permanent Ground Deformation (inches)
[ Probability Ds > ds | PGD ]
0.000.250.500.751.000.006.0012.0018.0024.0030.0036.0042.0048.00MinorModerateExte
nsive/Complete 





1.0

0.00.10.20.30.40.50.60.70.80.9

 

1.0

0.00.10.20.30.40.50.60.70.80.9

 

 

 

 




Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
te 


Figure 7.8 Fragility Curves at Various Damage States for Bored/Drilled Tunnels 
Subject to Peak Ground Acceleration. 

 

Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
te 


Figure 7.9 Fragility Curves at Various Damage States for Cut & Cover Tunnels 
Subject to Peak Ground Acceleration. 

 

 

 

 

 

 

 




 

 

 

 

 

 

 

Permanent Ground Deformation (inches)
Probability [ Ds > ds | PGD ] 
00.250.50.7510.006.0012.0018.0024.0030.0036.0042.0048.00Slight/MinorModerateExte
nsive / Complete 


 

 

 

Figure 7.10 Fragility Curves at Various Damage States for All Types of Tunnels 
Subject to Permanent Ground deformation. 




7.2 Railway Transportation System 

 

7.2.1 Introduction 

 

This section presents an earthquake loss estimation methodology for a railway 
transportation system. This system consists of tracks/roadbeds, bridges, 
tunnels, urban 
stations, maintenance facilities, fuel facilities, and dispatch facilities. Past 
earthquake 
damage reveals that bridges, tunnels, urban stations, maintenance facilities, 
fuel facilities, 
and dispatch facilities are vulnerable to both ground shaking and ground 
failure, while 
railway tracks/roadbeds are significantly affected by ground failure alone. 
Railway 
tracks located on soft soil or fill or which cross a surface fault rupture can 
experience 
failure resulting in loss of functionality. Railway bridges that fail usually 
result in 
significant disruption to the transportation network, especially bridges that 
cross 
waterways. Likewise, railway tunnels are often not redundant, and major 
disruption to 
the transportation system is likely to occur should a tunnel become non-
functional. 

 

7.2.2 Scope 

 

The scope of this section includes development of methods for estimation of 
earthquake 
damage to a railway transportation system given knowledge of the system's 
components 
(i.e., tracks, bridges, tunnels, stations, maintenance facilities, fuel 
facilities, or dispatch 
facilities), the classification of each component (e.g., for fuel facilities, 
whether the 
equipment within the facility is anchored or not), and the ground motion (i.e. 
peak 
ground acceleration and permanent ground deformation). 

 

Damage states describing the level of damage to each railway system component 
are 
defined (i.e. slight/minor, moderate, extensive or complete). Damage states are 
related to 
damage ratio (defined as ratio of repair to replacement cost) for evaluation of 
direct 
economic loss. Component restoration curves are provided for each damage state 
to 
evaluate loss of function. Restoration curves describe the fraction or 
percentage of the 
component that is expected to be open or operational as a function of time 
following the 
earthquake. For example, an extensively damaged railway facility might be closed 
(0% 
functional) immediately following the earthquake, but 100% functional after 30 
days. 

 

Fragility curves are developed for each type of railway system component. These 
curves 
describe the probability of reaching or exceeding each damage state given the 
level of 
ground motion. 

 

Evaluation of component functionality is done similar to the way it was done for 
highway 
components. 

 

Interdependence of components on the overall system functionality is not 
addressed by 
the methodology. Such considerations require a system (network) analysis. 

 

 

 




7.2.3 Input Requirements and Output Information 

 

Required input to estimate damage to railway systems includes the following 
items: 

 

Track and Roadbeds 

 

 Geographical location of railway links [longitude and latitude of end nodes] 

 Permanent ground deformation (PGD) at trackbed link 

 

Railway Bridges 

 

 Geographical location of bridge (longitude and latitude) 

 Spectral Acceleration at 0.3 and 1.0 seconds and PGD at bridge 

 Bridge classification 

 

Railway Tunnels 

 

 Geographical location of tunnels (longitude and latitude) 

 PGA and PGD at tunnel 

 Tunnel classification 

 

Railway System Facilities 

 

 Geographical location of facilities (longitude and latitude) 

 PGA and PGD at facility 

 Facility classification 

 

Direct damage output for railway systems includes probability estimates of (1) 
component functionality and (2) physical damage, expressed in terms of the 
component's 
damage ratio. Damage ratios, used as inputs to the direct economic loss module, 
are 
presented in section 15.3 of Chapter 15. 

 

Component functionality is described similar to highway system components, that 
is, by 
the probability of being in a damage state (immediately following the 
earthquake) and by 
the associated fraction or percentage of the component that is expected to be 
functional 
after a specified period of time. 

 

7.2.4 Form of Damage Functions 

 

Damage functions or fragility curves for all railway system components described 
below 
are modeled as lognormal functions that give the probability of reaching or 
exceeding 
different levels of damage for a given level of ground motion. Each fragility 
curve is 
characterized by a median value of ground motion (or failure) and an associated 
dispersion factor (lognormal standard deviation). Ground motion is quantified in 
terms 
of peak ground acceleration (PGA) and ground failure is quantified in terms of 
permanent 
ground displacement (PGD). 




 

 For tracks/roadbeds, fragility curves are defined in terms of PGD. 

 For railway bridges, fragility curves are defined similar to those for highway 
bridges 

 For tunnels, fragility curves are the same as defined for highway systems (in 
terms of 
PGA and PGD) 

 For railway system facilities, fragility curves are defined in terms of PGA or 
Sd and 
PGD. 

 

Definitions of various damage states and the methodology used in deriving all 
these 
fragility curves are presented in the following sections. 

 

7.2.5 Description of Railway System Components 

 

A railway system consists of four components: tracks/roadbeds, bridges, tunnels, 
and 
facilities. This section provides a brief description of each. 

 

Tracks/Roadbeds 

 

Tracks/roadbeds refers to the assembly of rails, ties, and fastenings, and the 
ground 
on which they rest. Only one classification is adopted for these components. 
This 
classification is analogous to that of urban roads in highway systems. 

 

Bridges 

 

Railway bridges are classified similar to highway steel and concrete bridges. 

 

Tunnels 

 

Railway tunnels follow the same classification as highway tunnels. That is, they 
are 
classified either as bored/drilled tunnels, or cut & cover tunnels. 

 

Railway System Facilities 

 

Railway system facilities include urban and suburban stations, maintenance 
facilities, 
fuel facilities, and dispatch facilities. 

 

Urban and Suburban stations: are generally key connecting hubs that are 
important for system functionality. In western US, these buildings are mostly 
made of reinforced concrete shear walls or moment resisting steel frames, while 
in the eastern US, the small stations are mostly wood and the large ones are 
mostly masonry or braced steel frames.. 

 

Maintenance facilities are housed in large structures that are not usually 
critical 
for system functionality as maintenance activities can be delayed or performed 
elsewhere. These building structures are often made of steel braced frames. 

 




Fuel facilities include buildings, tanks (anchored, unanchored, or buried), 
backup 
power systems (if available, anchored or unanchored diesel generators), pumps, 
and other equipment (anchored or unanchored). It should be mentioned that 
anchored equipment in general refers to equipment designed with special seismic 
tiedowns or tiebacks, while unanchored equipment refers to equipment designed 
with no special considerations other than the manufacturer's normal 
requirements. 
While some vibrating components, such as pumps, are bolted down regardless of 
concern for earthquakes, as used here anchored means all components have 
been engineered to meet seismic criteria which may include bracing (e.g., pipe 
or 
stack bracing) or flexibility requirements (e.g., flexible connections across 
separation joints) as well as anchorage. These definitions of anchored and 
unanchored apply to all lifeline components. The fuel facility functionality is 
determined with a fault tree analysis considering redundancies and subcomponent 
behavior. Note that generic building damage functions are used in this fault 
tree 
analysis for developing the overall fragility curve of fuel facilities. Above 
ground 
tanks are typically made of steel with roofs also made of steel. Buried tanks 
are 
typically concrete wall construction with concrete roofs. In total, five types 
of 
fuel facilities are considered. These are: fuel facilities with or without 
anchored 
equipment and with or without backup power (all combinations), and fuel 
facilities with buried tanks. 

 

Dispatch facilities consist of buildings, backup power supplies (if available, 
anchored or unanchored diesel generators), and electrical equipment (anchored or 
unanchored). Generic reinforced concrete building with shear walls damage 
functions, are used in this fault tree analysis for developing the overall 
fragility 
curves for dispatch facilities. In total, four types of dispatch facilities are 
considered. These are dispatch facilities with or without anchored equipment and 
with or without backup power (all combinations). 

 

7.2.6 Definitions of Damage States 

 

A total of five damage states are defined for railway system components. These 
are none 
(ds1), slight/minor (ds2), moderate (ds3), extensive (ds4) and complete (ds5). 

 

Slight/Minor Damage (ds2) 

 

 For tracks and roadbeds, ds2 is defined by minor (localized) derailment due to 
slight differential settlement of embankment or offset of the ground. 

 

 For railway bridges, ds2 is defined similar to highway bridges. 

 

 For railway tunnels, ds2 is defined similar to highway tunnels. 

 

 For railway system facilities, 

 




 . for urban stations and maintenance facilities, ds2 is defined by slight 
building 
damage (check building module for full description of potential damage). 

 

 . for fuel facilities with anchored equipment, ds2 is defined by slight damage 
to 
pump building, minor damage to anchor of tanks, or loss of off-site power (check 
electric power systems for more on this) for a very short period and minor 
damage to backup power (i.e. to diesel generators, if available). 

 

. for fuel facilities with unanchored equipment, ds2 is defined by elephant foot 
buckling of tanks with no leakage or loss of contents, slight damage to pump 
building, or loss of commercial power for a very short period and minor damage 
to backup power (i.e to diesel generators, if available). 

 

. for fuel facilities with buried tanks (PGD related damage), ds2 is defined by 
minor uplift (few inches) of the buried tanks or minor cracking of concrete 
walls. 

 

 . for dispatch facilities with anchored equipment, ds2 is defined by minor 
anchor 
damage, slight damage to building, or loss of commercial power for a very short 
period and minor damage to backup power (i.e. diesel generators, if available). 

 

 . for dispatch facilities with unanchored equipment, ds2 is defined by loss of 
off-
site power for a very short period and minor damage to backup power (i.e. to 
diesel generators, if available), or slight damage to building. 

 

Moderate Damage (ds3) 

 

 For railway tracks and roadbeds, ds3 is defined by considerable derailment due 
to 
differential settlement or offset of the ground. Rail repair is required. 

 

 For railway bridges, ds3 is defined as for highway bridges. 

 

 For railway tunnels, ds3 is defined as for highway tunnels 

 

 For railway system facilities, 

 

 . for urban stations and maintenance facilities, ds3 is defined by moderate 
building damage (check building module for description of potential damage). 

 

 . for fuel facilities with anchored equipment, ds3 is defined by elephant foot 
buckling of tanks with no leakage or loss of contents, considerable damage to 
equipment, moderate damage to pump building, or loss of commercial power for 
few days and malfunction of backup power (i.e., diesel generators, if 
available). 

 

 . for fuel facilities with unanchored equipment, ds3 is defined by elephant 
foot 
buckling of tanks with partial loss of contents, moderate damage to pump 




building, loss of commercial power for few days and malfunction of backup 
power (i.e., diesel generators, if available). 

 

. for fuel facilities with buried tanks, ds3 is defined by damage to roof 
supporting 
columns, and considerable cracking of walls. 

 

 . for dispatch facilities with anchored equipment, ds3 is defined by 
considerable 
anchor damage, moderate damage to building, or loss of commercial power for 
few days and malfunction of backup power (i.e., diesel generators, if 
available). 

 

 . for dispatch facilities with unanchored equipment, ds3 is defined by moderate 
damage to building, or loss of off-site power for few days and malfunction of 
backup power (i.e., diesel generators, if available).. 

 

Extensive Damage (ds4) 

 

 For railway tracks/roadbeds, ds4 is defined by major differential settlement 
of the 
ground resulting in potential derailment over extended length. 

 

 For railway bridges, ds4 is defined as for highway bridges. 

 

 For railway tunnels, ds4 is defined as for highway tunnels. 

 

 For railway system facilities, 

 

 . for urban stations and maintenance facilities, ds4 is defined by extensive 
building damage (check building module for description of potential damage). 

 

 . for fuel facilities with anchored equipment, ds4 is defined by elephant foot 
buckling of tanks with loss of contents, extensive damage to pumps 
(cracked/sheared shafts), or extensive damage to pump building. 

 

 . for fuel facilities with unanchored equipment, ds4 is defined by weld failure 
at 
base of tank with loss of contents, extensive damage to pump building, or 
extensive damage to pumps (cracked/sheared shafts). 

 

. for fuel facilities with buried tanks, ds4 is defined by considerable uplift 
(more 
than a foot) of the tanks and rupture of the attached piping. 

 

. For dispatch facilities with unanchored or anchored equipment, ds4 is defined 
by extensive building damage. 

 

Complete Damage (ds5) 

 

 For railway tracks/roadbeds, ds5 is the same as ds4. 




 

 For railway bridges, ds5 is defined as for highway bridges. 

 

 For railway tunnels, ds5 is defined as for highway tunnels. 

 

 For railway system facilities, 

 

 . For urban stations and maintenance facilities, ds5 is defined by extensive to 
complete building damage (check building module for description of potential 
damage). 

 

 . For fuel facilities with anchored equipment, ds5 is defined by weld failure 
at 
base of tank with loss of contents, or extensive to complete damage to pump 
building. 

 

 . For fuel facilities with unanchored equipment, ds5 is defined by tearing of 
tank 
wall or implosion of tank (with total loss of content), or extensive/complete 
damage to pump building. 

 

. For fuel facilities with buried tanks, ds5 is same as ds4. 

 

. For dispatch facilities with unanchored or anchored equipment, ds5 is defined 
by complete damage to building. 

 

 

7.2.7 Component Restoration Curves 

 

Restoration curves are developed based in part on ATC-13 damage data for the 
social 
function classifications of interest (SF 26a through SF 26d) consistent with 
damage states 
defined in the previous section. Normally distributed functions are used to 
approximate 
these restoration curves, as was done for highway systems. Means and dispersions 
(standard deviations) of these restoration functions are given in Table 7.10.a. 
Table 
7.10.b gives approximate discrete functions for these developed restoration 
functions. 
Figures 7.11 through 7.14 show restoration functions for railway tracks/roadbed, 
bridges, 
tunnels and facilities, respectively. ATC-13 restoration data for railway 
terminal stations 
are used to generically represent all other railway facilities. 

 




Table 7.10.a Continuous Restoration Functions for Railway System Components 

(after ATC-13, 1985) 

Classification 

Damage State 

Mean (Days) 

s (days) 

 

Railway Tracks 

slight/minor 

moderate 

extensive 

complete 

0.9 

3.3 

15.0 

65.0 

0.07 

3.0 

13.0 

45.0 

 

Railway Bridges 

slight/minor 

moderate 

extensive 

complete 

0.9 

2.8 

31.0 

110.0 

0.06 

1.8 

22.0 

73.0 

 

Railway Tunnels 

slight/minor 

moderate 

extensive 

complete 

0.9 

4.0 

37.0 

150.0 

0.05 

3.0 

30.0 

80.0 

 

Railway Facilities 

slight/minor 

moderate 

extensive 

complete 

0.9 

1.5 

15.0 

65.0 

0.05 

1.5 

15.0 

50.0 



 

 

 

 

Table 7.10.b Discretized Restoration Functions for Railway System Components 

 

Classification 

Damage State 

1 day 

3 days 

7 days 

30 days 

90 days 

 

 

Functional Percentage 

 

Railway Tracks 

slight/minor 

moderate 

extensive 

complete 

90 

22 

14 

6 

100 

46 

18 

8 

100 

90 

28 

10 

100 

100 

87 

22 

100 

100 

100 

70 

 

Railway Bridges 

slight/minor 

moderate 

extensive 

complete 

80 

15 

9 

7 

100 

55 

10 

7 

100 

100 

14 

8 

100 

100 

50 

14 

100 

100 

100 

40 

 

Railway Tunnels 

slight/minor 

moderate 

extensive 

complete 

95 

16 

11 

3 

100 

38 

13 

4 

100 

85 

16 

4 

100 

100 

40 

7 

100 

100 

97 

22 

 

Railway Facilities 

slight/minor 

moderate 

extensive 

complete 

95 

37 

15 

10 

100 

85 

20 

11 

100 

100 

29 

12 

100 

100 

83 

25 

100 

100 

100 

70 



 

 




7.2.8 Development of Damage Functions 

 

Fragility curves for railway system components are defined with respect to 
classification 
and ground motion parameter. 

 

Damage functions for Railway Tracks/Roadbeds 

 

Damage functions for tracks/roadbeds are similar to those of major roads. The 
medians and dispersions of these curves were given in Table 7.6 (see highway 
system 
section). 

 

Damage Functions for Railway Bridges 

 

Fragility curves for bridges are the same ones presented in section 7.1.8 for 
highway 
bridges. 

 

Damage Functions for Tunnels 

 

Tunnel damage functions are the same as those derived for highways. These were 
given in Table 7.9 and plotted in Figures 7.9 and 7.10 of the "highway systems" 
section. 

 

 

Damage Functions for Railway System Facilities 

 

Damage functions for railway system facilities are defined in terms of spectral 
values and PGD. Note that, unless it is specified otherwise, ground failure 
(PGD) related damage functions for these facilities are assumed to be similar to 
those described for buildings. These are: 

 

- For lateral spreading, a lognormal damage function with a median of 60 inches 
and a dispersion of 1.2 is assumed for the damage state of "at least extensive". 
20% of this damage is assumed to be complete. That is, for a PGD of 10" due to 
lateral spreading, there is a 7% probability of "at least extensive" damage. 

 

- For vertical settlement, a lognormal curve with a median of 10 inches and a 
dispersion of 1.2 is assumed for the damage state of "at least extensive". 20% 
of 
this damage is assumed to be complete. That is, for a PGD of 10" due to vertical 
settlement, there is a 50% chance of "at least extensive" damage. 

 

- For fault movement or landslide, a lognormal curve with a median of 10 inches 
and a dispersion of 0.5 is assumed for "complete" damage state. That is, for 10 
inches of PGD due to fault movement or landslide, there is a 50% chance of 
"complete" damage. 

 




An example of how to combine multiple PGD algorithms with a PGA algorithm is 
presented later in this section. 

 

Damage Functions for Urban Stations and Maintenance Facilities 

 

These damage functions are similar to the building fragility curves developed in 
Chapter 5. 

 

 

Damage Functions for Fuel Facilities 

 

Fragility curves are developed for the five types of fuel facilities mentioned 
before, 
namely, fuel facilities with anchored equipment and backup power, fuel 
facilities with 
anchored equipment but no backup power, fuel facilities with unanchored 
equipment 
and backup power, fuel facilities with unanchored equipment and no backup power, 
and fuel facilities with buried tanks. Medians and dispersions of damage 
functions to 
fuel facility subcomponents are summarized in Tables B.7.3 and B.7.4 of Appendix 
7B. A generic building type is used in developing fragility curves for fuel 
facilities in 
the specified fault tree logic (see Table B.7.3 of Appendix 7B). Note that the 
interaction effects, specifically that of the electric power module, are 
considered in 
this fault tree logic for the slight/minor and moderate damage states (refer to 
section 
8.5.8 of Chapter 8 for more details on loss of commercial power effects on other 
lifelines). 

 

Component fragility curves are obtained using the same methodology as used for 
bridges wherein a lognormal curve that best fits the results of the Boolean 
combination is determined numerically. It should be mentioned that the Boolean 
logic is implicitly presented within the definition of a particular damage 
state. 

 

The fault tree shown in Figure 7.19a presents the Boolean logic for the case of 
moderate damage to fuel facilities with anchored equipment and backup power, 
while 
Figure 7.19b compares the fragility curve resulting from the Boolean combination 
to 
the fitted lognormal fragility curve. The dotted line in Figure 7.19 represents 
the 
overall fuel facility fragility curve. 

 

The medians and dispersions of the damage functions for anchored and unanchored 
fuel facilities are shown in Table 7.11. These damage functions are also shown 
as 
fragility curves in Figures 7.20.a through 7.20e. 

 




Table 7.11 Damage Algorithms for Fuel Facilities 

 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Facility with 
Anchored 
Components w/ 

Backup Power 

slight/minor 

moderate 

extensive 

complete 

0.23 

0.43 

0.64 

1.10 

0.50 

0.45 

0.60 

0.60 

Facility with 
Anchored 
Components w/o 

Backup Power 

slight/minor 

moderate 

extensive 

complete 

0.12 

0.27 

0.64 

1.10 

0.55 

0.50 

0.60 

0.60 

Facility with 
Unnchored 
Components w/ 
Backup Power 

slight/minor 

moderate 

extensive 

complete 

0.10 

0.23 

0.48 

0.80 

0.55 

0.50 

0.60 

0.60 

Facility with 
Unnchored 
Components w/o 
Backup Power 

slight/minor 

moderate 

extensive 

complete 

0.09 

0.20 

0.48 

0.80 

0.50 

0.45 

0.60 

0.60 



 

Permanent Ground Deformation 

Classification 

Damage State 

Median (in) 

 

Fuel facility w/ 
buried tanks 

slight/minor 

moderate 

extensive/ 

Complete 

4 

8 

24 

0.5 

0.5 

0.5 



 

PGA Related Damage Functions for Dispatch Facilities 

 

As with fuel facilities, the same generic building type is used in developing 
the PGA 
related fragility curves for dispatch facilities in the fault tree logic. The 
medians and 
dispersions of the PGA related damage functions for anchored and unanchored 
dispatch facilities are given in Table 7.12 and plotted in Figures 7.21.c 
through 
7.21.d. Note that the medians and dispersions of the damage functions for 
dispatch 
facility subcomponents are summarized in Tables B.7.5 and B.7.6 of Appendix 7B. 

 




Table 7.12 Damage Algorithms for Dispatch Facilities 

 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Facility with 
Anchored 
Components w/ 

Backup Power 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.35 

0.8 

1.50 

0.75 

0.65 

0.80 

0.80 

Facility with 
Anchored 
Components w/o 

Backup Power 

slight/minor 

moderate 

extensive 

complete 

0.12 

0.27 

0.80 

1.50 

0.50 

0.45 

0.80 

0.80 

Facility with 
Unnchored 
Components w/ 
Backup Power 

slight/minor 

moderate 

extensive 

complete 

0.13 

0.28 

0.80 

1.50 

0.55 

0.50 

0.80 

0.80 

Facility with 
Unnchored 
Components w/o 
Backup Power 

slight/minor 

moderate 

extensive 

complete 

0.11 

0.23 

0.80 

1.50 

0.45 

0.40 

0.80 

0.80 



 

Note that the values of Table 7.12 indicate that the damage functions of 
dispatch facilities 
are mostly dominated by the building behavior. 

 

Multiple Hazards Analysis for Railway System Facilities 

 

In this section, a hypothetical example illustrating the methodology for 
combining 
multiple hazards for nodal facilities is presented. 

 

Assume that due to some earthquake, a railway fuel facility with anchored 
components 
and backup power is subject to a PGA level of 0.3g, a lateral spreading 
displacement of 
12 inches, a vertical settlement of 3 inches, and a potential landslide 
displacement of 15 
inches. Assume also that the probability of liquefaction is 0.6, and that the 
probability of 
landslide is 0.7. 

 

 Due to ground shaking, the following probabilities of exceedence are obtained: 

 

P[ Ds = ds2 | PGA = 0.3g ] = 0.70 

P[ Ds = ds3 | PGA = 0.3g ] = 0.21 

P[ Ds = ds4 | PGA = 0.3g ] = 0.10 

P[ Ds = ds5 | PGA = 0.3g ] = 0.02 

 

 Due to vertical settlement, the following probabilities of exceedence are 
obtained: 

 

P[ Ds = ds2 | PGD = 3 inches ] = 0.16 

P[ Ds = ds3 | PGD = 3 inches ] = 0.16 

P[ Ds = ds4 | PGD = 3 inches] = 0.16 




P[ Ds = ds5 | PGD = 3 inches ] = 20% * 0.16 = 0.03 

 

 Due to lateral spreading, the following probabilities of exceedence are 
obtained: 

 

P[ Ds = ds2 | PGD = 12 inches ] = 0.09 

P[ Ds = ds3 | PGD = 12 inches ] = 0.09 

P[ Ds = ds4 | PGD = 12 inches] = 0.09 

P[ Ds = ds5 | PGD = 12 inches ] = 20% * 0.09 = 0.02 

 

Therefore, for liquefaction, vertical settlement controls 

 

 Due to landslide, the following probabilities of exceedence are obtained: 

 

P[ Ds = ds2 | PGD = 15 inches ] = 0.64 

P[ Ds = ds3 | PGD = 15 inches ] = 0.64 

P[ Ds = ds4 | PGD = 15 inches] = 0.64 

P[ Ds = ds5 | PGD = 15 inches ] = 0.64 

 

Next, we compute the combined probabilities of exceedence (from complete to 
slight/minor): 

 

P[ Ds = ds5 ] = 0.02 + 0.6x0.03 + 0.7x0.64 

 - 0.02x0.6x0.03 - 0.02x0.7x0.64 - 0.6x0.03x0.7x0.64 

+ 0.02x0.6x0.03x0.7x0.64 

= 0.47 

P[ Ds = ds4 ] = 0.10 + 0.6x0.16 + 0.7x0.64 

 - 0.10x0.6x0.16 - 0.10x0.7x0.64 - 0.6x0.16x0.7x0.64 

+ 0.10x0.6x0.16x0.7x0.64 

= 0.55 

P[ Ds = ds3 ] = 0.21 + 0.6x0.16 + 0.7x0.64 

 - 0.21x0.6x0.16 - 0.21x0.7x0.64 - 0.6x0.16x0.7x0.64 

+ 0.21x0.6x0.16x0.7x0.64 

= 0.61 

P[ Ds = ds2 ] = 0.70 + 0.6x0.16 + 0.7x0.64 

 - 0.70x0.6x0.16 - 0.16x0.7x0.64 - 0.6x0.16x0.7x0.64 

+ 0.70x0.6x0.16x0.7x0.64 

= 0.85 

 

Therefore, the combined discrete damage states probabilities are: 

 

P[ Ds = ds1 ] = 1 - 0.85 = 0.15 

P[ Ds = ds2 ] = 0.85 - 0.61 = 0.24 

P[ Ds = ds3 ] = 0.61 - 0.55 = 0.06 

P[ Ds = ds4 ] = 0.55 - 0.47 = 0.08 

P[ Ds = ds5 ] = 0.47 




 

These discrete values will then be used in the evalution of functionality and 
economic 
losses. 

 

7.2.9 Guidance for Loss Estimation Using Advanced Data and Models Analysis 

 

For this advanced level of analysis, the expert can take advantage of the 
methodologys 
flexibility to (1) include a more refined inventory of the railway system 
pertaining to the 
area of study, and (2) include component-specific and system-specific fragility 
data. 
Default User-Supplied Data Analysis damage algorithms can be modified, or 
replaced, to 
incorporate improved information about key components of a railway system, such 
as 
urban stations. Similarly, better restoration curves can be developed, given 
knowledge of 
available resources and a more accurate layout of the railway network within the 
local 
topographic and geological conditions (i.e., if the redundancy and importance of 
railway 
components of the network are known). 

 

7.2.10 References 

 

Applied Technology Council, "Earthquake Damage Evaluation Data for California", 
ATC-13, Redwood City, CA, 1985. 

 

G&E Engineering Systems, Inc. (G&E), "NIBS Earthquake Loss Estimation Methods, 
Technical Manual, Transportation Systems (Railway Systems)", May 1994. 

 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 7.11 Restoration Curves for Railway Tracks/Roadbeds. 

 




 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 7.12 Restoration Curves for Railway Bridges. 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 7.13 Restoration Curves for Railway Tunnels. 

 

 




Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 7.14 Restoration Curves for Railway Facilities. 

 

Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 7.15 Fragility Curves at Various Damage States for Seismically Designed 
Railway Bridges Subject to Peak Ground Acceleration. 

 




Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 7.16 Fragility Curves at Various Damage States for Conventionally 
Designed 
Railway Bridges Subject to Peak Ground Acceleration. 

 

 

 

 

Permanent Ground Deformation (inches)
Probability [ Ds > ds | PGD ] 
00.250.50.7510.006.0012.0018.0024.0030.00Slight/MinorModerateExtensiveComplete 


Figure 7.17 Fragility Curves at Various Damage States for Seismically-Designed 
Railway Bridges Subject to Permanent Ground Deformation. 

 

 




Permanent Ground Deformation (inches)
Probability [ Ds > ds | PGD ] 
00.250.50.7510.006.0012.0018.0024.0030.00Slight/MinorModerateExtensiveComplete 


Figure 7.18 Fragility Curves at Various Damage States for Conventionally-
Designed 
Railway Bridges Subject to PGD. 

 

 

 

 

 

 

 

Top event occurs if anyof the bottom events occur 
Sub-
ComponentFuel FacilityComponentEquipmentPump BuildingTanksTop event occurs if 
allof the bottom events occurOff-Site PowerBackup Power 


 

Figure 7.19a Fault Tree for Moderate Damage to Fuel Facilities with Anchored 
Equipment and Backup Power. 

 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.000.250.500.751.000.000.200.400.600.801.001.20Off-Site PowerTankBackup Power 
Fragility Curve For aFuel Facility(Component)
SubcomponentsEquipmentPump Building 


Facility Fragility Curve (dotted line). 


 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.000.250.500.751.000.000.200.400.600.801.001.201.40MinorModerateExtensiveComple
te 





 

at

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.000.250.500.751.000.000.200.400.600.801.001.201.40MinorModer 


 

at

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.000.250.500.751.000.000.200.400.600.801.001.201.40MinorModer




Unanchored Components and no Backup Power. 


 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.000.250.500.751.000.000.200.400.600.801.001.201.40MinorModerateExtensiveComple
te 





od

Permanent Ground Deformation (inches)
[ Probability Ds > ds | PGD ] 
00.250.50.7510.006.0012.0018.0024.0030.0036.0042.0048.00MinorM 


 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 7.21.a Fragility Curves at Various Damage States for Dispatch Facility 
with 
Anchored Components and Backup Power. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 7.21.b Fragility Curves at Various Damage States for Dispatch Facility 
with 
Anchored Components but no Backup Power. 

 

 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 7.21.c Fragility Curves at Various Damage States for Dispatch Facility 
with 
Unanchored Components and Backup Power. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 7.21.d Fragility Curves at Various Damage States for Dispatch Facility 
with 
Unanchored Components and no Backup Power. 

 

 




7.3 Light Rail Transportation System 

 

7.3.1 Introduction 

 

This section presents an earthquake loss estimation methodology for a light rail 
transportation system. Like railway systems, light rail systems consist of 
railway 
tracks/roadbeds, bridges, tunnels, maintenance facilities, dispatch facilities 
and DC 
power substations. Therefore, the only difference in the case of light rail 
systems is in 
the fuel facilities, which are DC power substations. 

 

7.3.2 Scope 

 

The scope of this section includes development of methods for estimation of 
earthquake 
damage to a light rail transportation system given knowledge of the system's 
components, 
the classification of each component (e.g., for dispatch facilities, whether the 
facility's 
equipment is anchored or not), and the ground motion (i.e. peak ground 
acceleration 
and/or permanent ground deformation). 

 

Damage states describing the level of damage to each light rail system component 
are 
defined (i.e. slight, moderate, extensive or complete). Damage states are 
related to 
damage ratio (defined as ratio of repair to replacement cost) for evaluation of 
direct 
economic loss. Component restoration curves are provided for each damage state 
to 
evaluate loss of function. 

 

Fragility curves are developed for each type of light rail system component. 
These 
curves describe the probability of reaching or exceeding each damage state given 
the 
level of ground motion. 

 

Interdependence of components on overall system functionality is not addressed 
by the 
methodology. Such considerations require a system (network) analysis that would 
be 
performed separately by a light rail system expert as an advanced study. 

 

7.3.3 Input Requirements and Output Information 

 

Required input to estimate damage to light rail systems includes the following 
items: 

 

Light Rail Tracks/Roadbeds 

 

 Geographical location of railway links [longitude and latitude of end nodes] 

 Permanent ground deformation (PGD) at roadbed link 

 

Light Rail Bridges 

 

 Geographical location of bridge [longitude and latitude] 

 Spectral values and PGD at bridge 

 Bridge classification 




 

Light Rail Tunnels 

 

 Geographical location of tunnels [longitude and latitude] 

 PGA and PGD at tunnel 

 Tunnel Classification 

 

Light Rail Facilities (DC substations, maintenance and dispatch facilities) 

 

 Geographical location of facilities [longitude and latitude] 

 PGA and PGD at facility 

 Classification 

 

Direct damage output for light rail systems includes probability estimates of 
(1) 
component functionality and (2) physical damage, expressed in terms of the 
component's 
damage ratio. Note that damage ratios, which are the inputs to direct economic 
loss 
methods, are described in section 15.3 of Chapter 15. 

 

Component functionality is described by the probability of being in a damage 
state 
(immediately following the earthquake) and by the associated fraction or 
percentage of 
the component that is expected to be functional after a specified period of 
time. 

 

7.3.4 Form of Damage Functions 

 

Damage functions or fragility curves for all light rail system components 
mentioned 
above are modeled as lognormal functions that give the probability of reaching 
or 
exceeding different levels of damage for a given level of ground motion. Each 
fragility 
curve is characterized by a median value of ground motion (or failure) and an 
associated 
dispersion factor (lognormal standard deviation). Ground motion is quantified in 
terms 
of peak ground acceleration (PGA) and ground failure is quantified in terms of 
permanent 
ground displacement (PGD). 

 

 Fragility curves for tracks/roadbeds are the same as for railway 
tracks/roadbeds. 

 Fragility curves for bridges are the same as for railway bridges. 

 Fragility curves for tunnels are the same as for railway tunnels. 

 Fragility curves for maintenance and dispatch facilities are the same as for 
railway 
maintenance and dispatch facilities. 

 Fragility curves for DC power substations are defined in terms of PGA and PGD. 

 

7.3.5 Description of Light Railway System Components 

 

A light rail system consists mainly of six components: tracks/roadbeds, bridges, 
tunnels, 
maintenance facilities, dispatch facilities, and DC power substations. The first 
five are 
the same as for railway systems and are already described in Section 7.2. 
Therefore, only 
DC substations will be described in this subsection. 

 




DC Power Substations 

 

Light rail systems use electric power and have low voltage DC power substations. 
DC power is used by the light rail system's electrical distribution system. The 
DC 
power substations consist of electrical equipment, which convert the local 
electric 
utility AC power to DC power. Two types of DC power stations are considered. 
These are: (1) DC power stations with anchored (seismically designed) components 
and (2) DC power stations with unanchored (which are not seismically designed) 
components. 

 

7.3.6 Definitions of Damage States 

 

A total of five damage states are defined for light rail system components. 
These are 
none (ds1), slight/minor (ds2), moderate (ds3), extensive (ds4) and complete 
(ds5). 

 

 

Slight or Minor Damage (ds2) 

 

 For tracks/roadbeds, ds2 is defined similar to railway tracks. 

 

 For light rail bridges, ds2 is defined similar to railway bridges. 

 

 For light rail tunnels, ds2 is defined similar to highway tunnels. 

 

 For light rail system facilities, 

 

 . For maintenance facilities, ds2 is defined similar to railway maintenance 
facilities. 

 

 . For dispatch facilities, ds2 is defined similar to railway dispatch 
facilities. 

 

 . For DC power substations with anchored or unanchored components, ds2 is 
defined by loss of off-site power for a very short period, or slight damage to 
building. 

 

Moderate Damage (ds3) 

 

 For tracks/roadbeds, ds3 is defined similar to railway tracks. 

 

 For light rail bridges, ds3 is defined similar to railway bridges. 

 

 For light rail tunnels, ds3 is defined similar to highway tunnels. 

 

 For light rail system facilities, 

 




 . For maintenance facilities, ds3 is defined similar to railway maintenance 
facilities. 

 

 . For dispatch facilities, ds3 is defined similar to railway dispatch 
facilities. 

 

 . For DC power substations with anchored or unanchored components, ds3 is 
defined by loss of off-site power for few days, considerable damage to 
equipment, 
or moderate damage to building. 

 

Extensive Damage (ds4) 

 

 For tracks/roadbeds, ds4 is defined similar to railway tracks. 

 

 For light rail bridges, ds4 is defined similar to railway bridges. 

 

 For light rail tunnels, ds4 is defined similar to highway tunnels. 

 

 For light rail system facilities, 

 

 . For maintenance facilities, ds4 is defined similar to railway maintenance 
facilities. 

 

 . For dispatch facilities, ds4 is defined similar to railway dispatch 
facilities. 

 

 . For DC power substations with anchored or unanchored components, ds4 is 
defined by extensive building damage. 

 

Complete Damage (ds5) 

 

 For tracks/roadbeds, ds5 is defined similar to railway tracks. 

 

 For light rail bridges, ds5 is defined similar to railway bridges. 

 

 For light rail tunnels, ds5 is defined similar to highway tunnels. 

 

 For light rail system facilities, 

 

 . For maintenance facilities, ds5 is defined similar to railway maintenance 
facilities. 

 

 . For dispatch facilities, ds5 is defined similar to railway dispatch 
facilities. 

 

 . For DC power substations with anchored or unanchored components, ds5 is 
defined by complete building damage. 

 




7.3.7 Component Restoration Curves 

 

The restoration curves for light rail tracks/roadbeds, bridges, tunnels, and 
facilities are 
assumed to be the same as those for railway system components. 

 

7.3.8 Development of Damage Functions 

 

Fragility curves for light rail system components are defined with respect to 
classification 
and ground motion parameter. Again, except for DC power stations, damage 
functions of 
the other light rail system components have been already established in either 
section 7.1 
(highway systems) or section 7.2 (railway systems). 

 

Damage functions for Light Rail Tracks/Roadbeds 

 

See damage functions for railway tracks/roadbeds. 

 

Damage Functions for Light Rail Bridges 

 

See damage functions for railway bridges. 

 

Damage Functions for Light Rail Tunnels 

 

See damage functions for highway tunnels. 

 

Damage Functions for Light Rail System Facilities 

 

Damage functions for light rail system facilities are defined in terms of PGA 
and 
PGD. Note that ground failure (PGD) related damage functions for these 
facilities are assumed to be similar to those described for railway system 
facilities 
in section 7.2.8. 

 

PGA Related Damage Functions for Maintenance Facilities 

 

Maintenance facilities for light rail systems are mostly made of steel braced 
frames. 
Since no default inventory is provided for these facilities, the user will be 
expected to 
provide the appropriate mapping between these facilities whose damage functions 
are 
listed in Table 7.7 of section 7.2.8 and their model building types. 

 

PGA Related Damage Functions for Dispatch Facilities 

 

See damage functions for railway dispatch facilities. 

 

PGA Related Damage Functions for DC Power Substations 

Fragility curves for the two types of DC power substations are developed based 
on 
the type of damage incurred by the DC power substation subcomponents (building, 
equipment, and off-site power for interaction effects). These two types are DC 
power 




substations with unanchored equipment, and DC power substations with unanchored 
equipment. Medians and dispersions of damage functions to DC power substations 
subcomponents are summarized in Tables C.7.1 and C.7.2 of Appendix 7C. 
Component fragility curves are obtained using the same methodology as used 
before. 
That is, each fragility curve is determined by a lognormal curve that best fits 
the 
results of the Boolean combination. It should be mentioned that the Boolean 
logic is 
implicitly presented within the definition of a particular damage state. The 
medians 
and dispersions of the damage functions for anchored and unanchored DC power 
substations are shown in Table 7.13 and plotted in Figures 7.22.a and 7.22.b. 

 

Table 7.13 Damage Algorithms for DC Power Substations 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Substation with 
Anchored 
Components 

slight 

moderate 

extensive 

complete 

0.12 

0.27 

0.80 

1.50 

0.55 

0.45 

0.80 

0.80 

Substation with 
Unnchored 
Components 

slight 

moderate 

extensive 

complete 

0.11 

0.23 

0.80 

1.50 

0.50 

0.40 

0.80 

0.80 



 

7.3.9 Guidance for Loss Estimation Using Advanced Data and Models Analysis 

 

For this type of analysis, the expert can use the methodology developed with the 
flexibility to (1) include a refined inventory of the light rail system 
pertaining to the area 
of study, and (2) include component specific and system specific fragility data. 
Default 
User-Supplied Data Analysis damage algorithms can be modified or replaced to 
accommodate any specified key component of a light railway system, such as a 
bridge. 
Similarly, better restoration curves could be developed given knowledge of 
available 
resources and a more accurate layout of the light rail network within the local 
topographic and geological conditions (i.e. redundancy and importance of a light 
railway 
component in the network are known). 

 

7.3.10 References 

 

Applied Technology Council, "Earthquake Damage Evaluation Data for California", 
ATC-13, Redwood City, CA, 1985. 

 

G & E Engineering Systems, Inc. (G & E), "NIBS Earthquake Loss Estimation 
Methods, 
Technical Manual, Transportation Systems", May 1994. 




 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 7.22.a Fragility Curves at Various Damage States for DC Power Substations 
with Anchored Components. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 7.22.b Fragility Curves at Various Damage States for DC Power Substations 
with Unanchored Components. 

 




7.4 Bus Transportation System 

 

7.4.1 Introduction 

 

This section presents a loss estimation methodology for a bus transportation 
system 
during earthquakes. Bus facilities consist of maintenance, fuel, and dispatch 
facilities. 
The facilities may sustain damage due to ground shaking or ground failure. Major 
losses 
can occur if bus maintenance buildings collapse, and operational problems may 
arise if a 
dispatch facility is damaged. 

 

7.4.2 Scope 

 

The scope of this section includes development of methods for estimation of 
earthquake 
damage to a bus transportation system given knowledge of components (i.e., fuel, 
maintenance, and dispatch facilities with or without backup power), 
classification (i.e. for 
fuel facilities, anchored or unanchored components), and the ground motion (i.e. 
peak 
ground acceleration and/or permanent ground deformation). 

 

Damage states describing the level of damage to each of the bus system 
components are 
defined (i.e. slight, moderate, extensive or complete). Damage states are 
related to 
damage ratio (defined as ratio of repair to replacement cost) for evaluation of 
direct 
economic loss. Component restoration curves are provided for each damage state 
to 
evaluate loss of function. Restoration curves describe the fraction or 
percentage of the 
component that is expected to be open or operational as a function of time 
following the 
earthquake. For bus systems, the restoration is dependent upon the extent of 
damage to 
the fuel, maintenance, and dispatch facilities. 

 

Fragility curves are developed for each class of bus system component. These 
curves 
describe the probability of reaching or exceeding each damage state given the 
level of 
ground motion. Based on these fragility curves, a method for assessing 
functionality of 
each of the three bus system components is presented. 

 

Interdependence of components on overall system functionality is not addressed 
by the 
methodology. Such considerations require a system (network) analysis that would 
be 
performed separately by a bus system expert as an advanced study. 

 

7.4.3 Input Requirements and Output Information 

 

Required input to estimate damage to bus systems includes the following items: 

 

Urban Stations 

 

 Geographical location of site 

 Spectral values and PGD at station 

 Classification 

 




Fuel Facilities 

 

 Geographical location of site 

 PGA and PGD at facility 

 Classification (i.e. with or without anchored equipment and backup power) 

 

Maintenance Facilities 

 

 Geographical location of site 

 Spectral values and PGD at facility 

 Classification (i.e. building type) 

 

Dispatch Facilities 

 

 Geographical location of each warehouse 

 PGA and PGD at facility 

 Classification (i.e. with or without anchored equipment and backup power) 

 

Direct damage output for bus systems includes probability estimates of (1) 
component 
functionality and (2) physical damage, expressed in terms of the component's 
damage 
ratio. 

 

Component functionality is described by the probability of being in a damage 
state 
(immediately following the earthquake) and by the associated fraction or 
percentage of 
the component that is expected to be functional after a specified period of 
time. 

 

7.4.4 Form of Damage Functions 

 

Damage functions or fragility curves for all three bus system components, 
mentioned 
above, are lognormal functions that give the probability of reaching or 
exceeding 
different levels of damage for a given level of ground motion. Each fragility 
curve is 
characterized by a median value of ground motion (or failure) and an associated 
dispersion factor (lognormal standard deviation). Ground motion is quantified in 
terms 
of peak ground acceleration (PGA) and ground failure is quantified in terms of 
permanent 
ground displacement (PGD). 

 For urban stations, the fragility curves are defined in terms of Spectral 
values and 
PGD. 

 

 For fuel facilities, the fragility curves are defined in terms of PGA and PGD. 

 

 For maintenance facilities, the fragility curves are defined in terms of 
Spectral values 
and PGD. 

 

 For dispatch facilities, the fragility curves are defined in terms of PGA and 
PGD. 

Definitions of various damage states and the methodology used in deriving all 
these 
fragility curves are presented in the following section. 




 

7.4.5 Description of Bus System Components 

 

A bus system consists mainly of four components: urban stations, fuel 
facilities, 
maintenance facilities, and dispatch facilities. This section provides a brief 
description of 
each. 

 

Urban Stations 

 

These are mainly buildings structures. 

 

Bus System Fuel Facilities 

 

Fuel facility consists of fuel storage tanks, buildings, pump equipment and 
buried 
pipe, and, sometimes, backup power. The fuel facility functionality is 
determined 
with a fault tree analysis considering redundancies and sub-component behavior. 
The 
same classes assumed for railway fuel facilities are assumed here. These are 
listed in 
Table 3.9. 

 

Bus System Maintenance Facilities 

 

Maintenance facilities for bus systems are mostly made of steel braced frames. 
The 
same classes assumed for railway maintenance facilities are assumed here. 

 

Bus System Dispatch Facilities 

 

 The same classes assumed for railway dispatch facilities are assumed here. 
These 
are listed in Table 3.9. 

 

7.4.6 Definitions of Damage States 

 

A total of five damage states are defined for highway system components. These 
are 
none (ds1), slight/minor (ds2), moderate (ds3), extensive (ds4) and complete 
(ds5). 

 

Slight Damage (ds2) 

 

. For urban stations, ds2 is defined similar to railway urban stations. 

 

. For fuel facilities, ds2 is defined similar to railway fuel facilities. 

 

. For maintenance facilities, ds2 is defined similar to railway maintenance 
facilities. 

 

. For dispatch facilities, ds2 is defined similar to railway dispatch 
facilities. 

 

Moderate Damage (ds3) 




 

. For urban stations, ds3 is defined similar to railway urban stations. 

 

. For fuel facilities, ds3 is defined similar to railway fuel facilities. 

 

. For maintenance facilities, ds3 is defined similar to railway maintenance 
facilities. 

 

. For dispatch facilities, ds3 is defined similar to railway dispatch 
facilities. 

 

Extensive Damage (ds4) 

 

. For urban stations, ds4 is defined similar to railway urban stations. 

 

. For fuel facilities, ds4 is defined similar to railway fuel facilities. 

 

. For maintenance facilities, ds4 is defined similar to railway maintenance 
facilities. 

 

. For dispatch facilities, ds4 is defined similar to railway dispatch 
facilities. 

 

Complete Damage (ds5) 

 

. For urban stations, ds5 is defined similar to railway urban stations. 

 

. For fuel facilities, ds5 is defined similar to railway fuel facilities. 

 

. For maintenance facilities, ds5 is defined similar to railway maintenance 
facilities. 

 

. For dispatch facilities, ds5 is defined similar to railway dispatch 
facilities. 

 

7.4.7 Component Restoration Curves 

 

Restoration Curves are developed based on a best fit to ATC-13 damage data for 
the 
social functions SF 26a through SF 26d, consistent with damage states defined in 
the 
previous section. Normal distribution functions are developed using the ATC-13 
data for 
the mean time for 30%, 60% and 100% restoration of different sub-components in 
different damage states. The restoration curves for bus transportation systems 
are similar 
to those of railway transportation systems. Means and dispersions of these 
restoration 
functions are given in Tables 7.10.a. Discretized restoration functions are 
shown in 
Table 7.10.b, where the percentage restoration is shown at discrete times. 

 

7.4.8 Development of Damage Functions 

 




Fragility curves for bus system components are defined with respect to 
classification and 
ground motion parameter. 

 

Damage Functions for Bus System Urban Stations 

 

Urban stations are classified based on the building structural type. Damage 
functions 
for bus system urban stations are similar to those for the railway 
transportation 
system (see Section 7.2.8). 

 

Damage Functions for Bus System Fuel Facilities 

 

Fuel facilities are classified based on two criteria: (1) whether the sub-
components 
comprising the fuel facilities are anchored or unanchored and (2) whether backup 
power exists in the facility. Damage functions for bus system fuel facilities 
are 
similar to those for the railway transportation system (see Section 7.2.8). 

 

Damage Functions for Bus System Maintenance Facilities 

 

The PGA and PGD median values for the damage states of maintenance facilities 
are 
similar to those of light rail maintenance facilities presented in Section 
7.3.8. 

 

Damage Functions for Bus System Dispatch Facility 

 

The PGA and PGD median values for the damage states of dispatch facilities are 
similar to those of railway dispatch facilities given in Section 7.2.8. 

 

7.4.9 Guidance for Loss Estimation using Advanced Data and Models Analysis 

 

For this level of analysis, the expert can use the methodology developed with 
the 
flexibility to: (1) include a refined inventory of the bus system pertaining to 
the area of 
study, and (2) include component specific and system specific fragility data. 
Default 
User-Supplied Data Analysis damage algorithms can be modified or replaced to 
accommodate any specified key component of a bus system, such as a warehouse. 
Similarly, better restoration curves could be developed given knowledge of 
available 
resources and a more accurate layout of the bus transportation network within 
the local 
topographic and geological conditions (i.e., redundancy and importance of a bus 
system 
component in the network are known). 

 

 

 

 

 

 




7.4.10 References 

 

Applied Technology Council, "Earthquake Damage Evaluation Data for California", 
ATC-13, Redwood City, CA, 1985. 

 

G & E Engineering Systems, Inc. (G & E), "NIBS Earthquake Loss Estimation 
Methods, 
Technical Manual, Transportation Systems", May 1994. 

 




7.5 Port Transportation System 

 

7.5.1 Introduction 

 

This section presents a loss estimation methodology for a port transportation 
system. 
Port facilities consist of waterfront structures (e.g., wharfs, piers and 
seawalls); cranes 
and cargo handling equipment; fuel facilities; and warehouses. In many cases, 
these 
facilities were constructed prior to widespread use of engineered fills; 
consequently, the 
wharf, pier, and seawall structures are prone to damage due to soil failures 
such as 
liquefaction. Other components may be damaged due to ground shaking as well as 
ground failure. 

 

7.5.2 Scope 

 

The scope of this section includes developing methods for estimating earthquake 
damage 
to a port transportation system given knowledge of components (i.e., waterfront 
structures, cranes and cargo handling equipment, fuel facilities, and 
warehouses), 
classification (i.e. for fuel facilities, anchored or unanchored components, 
with or without 
back-up power), and the ground motion (i.e. peak ground acceleration and/or 
permanent 
ground deformation). 

 

Damage states describing the level of damage to each of the port system 
components are 
defined (i.e. slight, moderate, extensive or complete). Damage states are 
related to 
damage ratio (defined as ratio of repair to replacement cost) for evaluation of 
direct 
economic loss. Component restoration curves are provided for each damage state 
to 
evaluate loss of function. Restoration curves describe the fraction or 
percentage of the 
component that is expected to be open or operational as a function of time 
following the 
earthquake. For ports the restoration is dependent upon the extent of damage to 
the 
waterfront structures, cranes/cargo handling equipment, fuel facilities, and 
warehouses. 
From the standpoint of functionality of the port, the user should consider the 
restoration 
of only the waterfront structures and cranes since the fuel facilities and 
warehouses are 
not as critical to the functionality of the port. 

 

Fragility curves are developed for each class of port system component. These 
curves 
describe the probability of reaching or exceeding a certain damage state given 
the level of 
ground motion. Based on these fragility curves, a method for assessing 
functionality of 
each of the four port system components is presented. 

 

Interdependence of components on overall system functionality is not addressed 
by the 
methodology. Such considerations require a system (network) analysis that would 
be 
performed separately by a port system expert as an advanced study. 

 

7.5.3 Input Requirements and Output Information 

 

Required input to estimate damage to port systems includes the following items: 

 




Waterfront Structures 

 

 Geographic location of port (longitude and latitude) 

 PGA & PGD 

 Classification 

 

Cranes/Cargo Handling Equipment 

 

 Geographic location of port (longitude and latitude) 

 PGA and PGD 

 Classification (i.e. stationary or rail mounted) 

 

Fuel Facilities 

 

 Geographical location of facility [longitude and latitude] 

 PGA and PGD 

 Classification 

 

Warehouses 

 

 Geographical location of warehouse [longitude and latitude] 

 PGA and PGD 

 Classification (i.e. building type) 

 

Direct damage output for port systems includes probability estimates of (1) 
component 
functionality and (2) physical damage, expressed in terms of the component's 
damage 
ratio. Damage ratios are used as inputs to direct economic loss methods, as 
described in 
section 15.3 of Chapter 15. 

 

7.5.4 Form of Damage Functions 

 

Damage functions or fragility curves for all four port system components, 
mentioned 
above, are lognormally distributed functions that give the probability of 
reaching or 
exceeding different levels of damage for a given level of ground motion. Each 
fragility 
curve is characterized by a median value of ground motion (or failure) and an 
associated 
dispersion factor (lognormal standard deviation). Ground motion is quantified in 
terms 
of peak ground acceleration (PGA) and ground failure is quantified in terms of 
permanent 
ground displacement (PGD). 

 

 For waterfront structures, the fragility curves are defined in terms of PGD 
and PGA. 

 

 For cranes/cargo handling equipment, the fragility curves are defined in terms 
of 
PGA and PGD. 

 

 For fuel facilities, the fragility curves are defined in terms of PGA and PGD. 

 




 For warehouses, the fragility curves are defined in terms of PGA and PGD. 

 

Definitions of various damage states and the methodology used in deriving all 
these 
fragility curves are presented in the following section. 

 

7.5.5 Description of Port Components 

 

A port system consists of four components: waterfront structures, cranes/cargo 
handling 
equipment, fuel facilities, and warehouses. This section provides a brief 
description of 
each. 

 

Waterfront Structures 

 

This component includes wharves (port embankments), seawalls (protective walls 
from erosion), and piers (break-water structures which form harbors) that exist 
in the 
port system. Waterfront structures typically are supported by wood, steel or 
concrete 
piles. Many also have batter piles to resist lateral loads from wave action and 
impact 
of vessels. Seawalls are caisson walls retaining earth fill material. 

 

Cranes and Cargo Handling Equipment 

 

These are large equipment items used to load and unload freight from vessels. 
These 
are can be stationary or mounted on rails. 

 

Port Fuel Facilities 

 

The fuel facility consists mainly of fuel storage tanks, buildings, pump 
equipment, 
piping, and, sometimes, backup power. These are the same as those for railway 
systems presented in Section 7.2. The functionality of fuel systems is 
determined 
with a fault tree analysis, which considers redundancies and sub-component 
behavior, 
as it can be seen in Figures 7.18 and 7.19 of Section 7.2. Note that five types 
of fuel 
facilities in total are defined. 

 

Warehouses 

 

Warehouses are large buildings usually constructed of structural steel. In some 
cases, 
warehouses may be several hundred feet from the shoreline, while in other 
instances; 
they may be located on the wharf itself. 

 

7.5.6 Definition of Damage States 

 

A total of five damage states are defined for port system components. These are 
none 
(ds1), slight/minor (ds2), moderate (ds3), extensive (ds4) and complete (ds5). 

 

Slight/Minor Damage (ds2) 

 




 For waterfront structures, ds2 is defined by minor ground settlement resulting 
in 
few piles (for piers/seawalls) getting broken and damaged. Cracks are formed on 
the surface of the wharf. Repair may be needed. 

 

 For cranes/cargo handling equipment, ds2 is defined by slight damage to 
structural members with no loss of function for the stationary equipment, while 
for the unanchored or rail mounted equipment, ds1 is defined as minor derailment 
or misalignment without any major structural damage to the rail mount. Minor 
repair and adjustments may be required before the crane becomes operable. 

 

 For fuel facilities, ds2 is defined the same as for railway facilities. 

 

 For warehouses, ds2 is defined by slight damage to the warehouse building. 

 

Moderate Damage (ds3) 

 

 For waterfront structures, ds3 is defined as considerable ground settlement 
with 
several piles (for piers/seawalls) getting broken and damaged. 

 

 For cranes/cargo handling equipment, ds3 is defined as derailment due to 
differential displacement of parallel track. Rail repair and some repair to 
structural members is required. 

 

 For fuel facilities, ds3 is defined the same as for railway facilities. 

 

 For warehouses, ds3 is defined by moderate damage to the warehouse building. 

 

Extensive Damage (ds4) 

 

 For waterfront structures, ds4 is defined by failure of many piles, extensive 
sliding 
of piers, and significant ground settlement causing extensive cracking of 
pavements. 

 

 For cranes/cargo handling equipment, ds4 is defined by considerabe damage to 
equipment. Toppled or totally derailed cranes are likely to occcur. Replacement 
of structural members is required. 

 

 For fuel facilities, ds4 is defined same as for railway facilities. 

 

 For warehouses, ds4 is defined by extensive damage to warehouse building. 

 

Complete Damage (ds5) 

 

 For waterfront structures, ds5 is defined as failure of most piles due to 
significant 
ground settlement. Extensive damage is widespread at the port facility. 

 

 For cranes/cargo handling equipment, ds5 is the same as ds4. 




 

 For fuel facilities with buried tanks, ds5 is the same as for railway 
facilities. 

 

 For warehouses, ds5 is defined by total damage to the warehouse building. 

 

7.5.7 Component Restoration Curves 

 

Restoration Curves are developed based on a best fit to ATC-13 damage data for 
social 
functions SF 28.a and SF 29.b, consistent with damage states defined in the 
previous 
section. Normal distribution functions are developed using the ATC-13 data for 
the 
mean time for 30%, 60% and 100% restoration of different sub-components in 
different 
damage states. Means and dispersions of these restoration functions are given in 
Table 
7.14.a. The discretized restoration functions are given in Table 7.14.b, where 
the 
percentage restoration is shown at some specified time intervals. These 
restoration 
functions are shown in Figures 7.23 and 7.24. Figure 7.23 represents restoration 
curves 
for waterfront structures, while Figure 7.24 shows restorations curve for cranes 
and cargo 
handling equipment. 

 

Table 7.14.a Restoration Functions for Port Sub-Components 

Restoration Functions (All Normal Distributions) 

Classification 

Damage State 

Mean (Days) 

s 

Buildings, 
Waterfront 
Structures 

slight/minor 

moderate 

extensive 

complete 

0.6 

3.5 

22 

85 

0.2 

3.5 

22 

73 

Cranes/Cargo 
Handling 
Equipment 

slight/minor 

moderate 

extensive 

complete 

0.4 

6 

30 

75 

0.35 

6 

30 

55 



 

Table 7.14.b Discretized Restoration Functions for Port Sub-Components 

Discretized Restoration Functions 

Classification 

Damage State 

1 day 

3 days 

7 days 

30 days 

90 days 

Buildings, 
Waterfront 
Structures 

slight/minor 

moderate 

extensive 

complete 

96 

24 

17 

12 

100 

43 

19 

13 

100 

84 

25 

14 

100 

100 

63 

22 

100 

100 

100 

53 

Cranes/Cargo 
Handling 
Equipment 

slight/minor 

moderate 

extensive 

complete 

96 

20 

17 

9 

100 

31 

18 

10 

100 

57 

22 

11 

100 

100 

50 

21 

100 

100 

100 

62 



 




7.5.8 Development of Damage Functions 

 

Damage functions for port system facilities are defined in terms of PGA and PGD. 
Note 
that, unless it is specified otherwise, ground failure (PGD) related damage 
functions for 
these facilities are assumed to be similar to those described for railroad 
system facilities 
in section 7.2.8. 

 

An example of how to combine PGD and PGA algorithms is presented in section 
7.2.8. 

 

Damage functions for Waterfront Structures 

 

Damage functions for waterfront structures were established based on 
damagability 
of subcomponents, namely, piers, seawalls, and wharf. Fault tree logic and the 
lognormal best fitting technique were used in developing these fragility curves. 
The 
fault tree is implicitly described in the description of the damage state. The 
obtained 
damage functions are shown in Figure 7.25. Their medians and dispersions are 
presented in Table 7.15a. Subcomponent damage functions are given in Table 7.D.1 
of Appendix 7D. 

 

 Table 7.15.a Damage Algorithms for Waterfront Structures 

Permanent Ground Deformation 

Components 

Damage State 

Median (in) 

 

 

slight/minor 

5 

0.50 

Waterfront 

moderate 

12 

0.50 

Structures 

 extensive 

 17 

0.50 

(PWS1) 

complete 

43 

0.50 



 

Damage Functions for Cranes and Cargo Handling Equipment 

 

For cranes, a distinction is made between stationary and rail-mounted cranes. 
The 
medians and dispersions of damage functions are presented in Tables 7.15.b, 
while the 
fragility curves are shown in Figures 7.26 through 7.29. 

 




Table 7.15.b Damage Algorithms for Cranes/Cargo Handling Equipment 

 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Anchored/ 
Stationary 
(PEQ1) 

slight/minor 

moderate 

extensive/complete 

0.3 

0.5 

1.0 

0.6 

0.6 

0.7 

Unanchored/Rail 
mounted (PEQ2) 

slight/minor 

moderate 

extensive/complete 

0.15 

0.35 

0.8 

0.6 

0.6 

0.7 



 

Permanent Ground Deformation 

Classification 

Damage State 

Median (in) 

 

Anchored/ 
Stationary 
(PEQ1) 

slight/minor 

moderate 

extensive/complete 

3 

6 

12.0 

0.6 

0.7 

0.7 

Unanchored/Rail 
mounted (PEQ2) 

slight/minor 

moderate 

extensive/complete 

2 

4.0 

10 

0.6 

0.6 

0.7 



 

 Damage Functions for Port System Fuel Facilities 

 

Damage funcitons for fuel facilities are similar to those developed for railway 
fuel 
facilities in Section 7.2.8. 

 

 

PGA Related Damage Functions for Warehouses 

 

Since no default inventory is provided for these facilities, the user will be 
expected to provide the appropriate mapping bewteen these facilities and the 
building types which are assumed to be the same as for railway maintenance 
facilities whose damage functions are listed in Table 7.7 of section 7.2.8. 

 

7.5.9 Guidance for Loss Estimation using Advanced Data and Models Analysis 

 

For this type of analysis, the expert can use the methodology developed with the 
flexibility to: (1) include a refined inventory of the port transportation 
system pertaining 
to the area of study, and (2) include component specific and system specific 
fragility 
data. Default User-Supplied Data Analysis damage algorithms can be modified or 
replaced to accommodate any specified key component of a port system, such as a 
warehouse. Similarly, better restoration curves could be developed given 
knowledge of 
available resources and a more accurate layout of the port network within the 
local 
topographic and geological conditions (i.e., redundancy and importance of a port 
system 
component in the network are known). 

 




7.5.10 References 

 

Applied Technology Council, "Earthquake Damage Evaluation Data for California", 
ATC-13, Redwood City, CA, 1985. 

 

G & E Engineering Systems, Inc. (G & E), "NIBS Earthquake Loss Estimation 
Methods, 
Technical Manual, Transportation Systems", May 1994. 

 




Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 7.23 Restoration Curves for Port Waterfront Structures. 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 7.24 Restoration Curves for Cranes/Cargo Handling Equipment. 

 

 

 

 

 

 




 

Permanent Ground Deformation (inches)
Probability [ Ds > ds | PGD ] 
00.250.50.7510.006.0012.0018.0024.0030.00Slight/MinorModerateExtensiveComplete 


 

Figure 7.25 Fragility Curves for Waterfront Structures. 

 




 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.000.250.500.751.000.000.200.400.600.801.001.201.40MinorModerateExtensive/Compl
ete 


Subject to Peak Ground Acceleration. 


 

Permanent Ground Deformation (inches)
[ Probability Ds > ds | PGD ] 
00.250.50.7510.005.0010.0015.0020.0025.0030.00MinorModerateExtensive/Complete 





MinorModerateExten

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.000.250.500.751.000.000.200.400.600.801.001.201.40

Permanent Ground Deformation (inches)
[ Probability Ds > ds | PGD ] 
00.250.50.7510.005.0010.0015.0020.0025.0030.00MinorModerateExtensive/Complete 





The scope of this section includes development of methods for estimation of 
earthquake 
ortation system given knowledge of components (i.e., waterfront 

 or unanchored com

performed separately by a transportation system expert as an advanced study. 




7




 

 

 

 

fragility curves are defined in terms of PGA and PGD. 
Definitions of various damage states and the methodology u

c

 
7.6.5 Description of Ferry System Components 
A ferry system consists of the five components mentioned above: waterfront 
structures, 
fuel facilities, maintenance facilities, dispatch facilities, and pa

se

 
Waterfront Structures 
These are the same as those for port systems described in Section 7.5.5. 

Fuel Fac

 
Maintenance Facilities 
These are often steel braced frame structures, but other building types are 
possible. 
Dispatch Facilities 
These are similar to

Passenger Terminals 
These are often moment resisting steel frames, but other building types are 
possible. 
7.6.6 Definitions of Damage States 
A total of five damage states are defined for ferry system components. These are 
none 
(ds1), slight/minor (ds2), moderate (ds3), extensive (ds4) and complete (ds5). 


2 
 For waterfront structures, ds2 is the same as that for waterfront structures 
in the 
port module. 


2 
 For maintenance facilities, ds2 is defined by slight damage to building. 





 

 

M

3 


 For waterfront structures, ds3 is

port module. 

 

 For fuel facilities, d

 

 For maintenance

 

F

module. 

 

 For passenger terminals, d

 

E

 

 For waterfront structures, ds4 is t

port module. 

 

 

 

 For maintenance facilities, ds4 is de

 

 For dispatch facilities, ds4 is the s

 
 For passenger terminals, ds4 is defined by extensive damage to building. 
Complete Damage (ds5

 For waterfront structures, ds

 
 For fuel facilities, ds5 is the same as that for fuel facilities in the port 
module. 
 For maintenance facilities, ds5 is defined by complete damage to building. 
 For dispatch facilities, ds5 is the same as that for dispatch facilities in 
the railway 
module. 




7

 

F

waterfront structures, fuel facilities), or those in railway systems (i.e. 
dispatch facilities, 
ntenance facilities, passenger terminals). Therefore,

sy

 
7.6.8 Development of Damage Functions 

 

Similar to restoration curves, damage functions 

fo

 
7.6.9 Guidance for Loss Estimation Using Advanced Data and Models Analysis 


y, and (2) include component specific and system specific fragility d

ommodate any 

lity. Similarly, better restoration curves could be developed given knowled

lied Technology Council, "Earthquake Damage Evaluation Data f

 E Engineering Systems, Inc. (G & E), "NIBS Earthquake Loss Estimation Methods

T

 

 




po transportation system consists of runways, control tower, fuel facilities, 
terminal 
gs, maintenan

ntance facilities are often constructed of structural steel or reinforced concr

a

was, control tower, fuel, and maintenance facilities, terminal buildings, and

manent ground deformation

ae states describing the level of damage to each of the airport system 
components 

a

noic loss. Component restoration curves are provided for each damage stat

ponent that is expected to be open or operational as a function of time followi

or terminals, buildings, storage tanks (for fuel facilities), control tower, and 
runways. 

vesdescribe the probability of reaching or exceeding each damage state g

.3 Iput Requirements and Output Information 

 

R

 

 

 PGD 


C

 

 




 Classification (i.e. building type)

Fuel Facilities 
 Geographical location of facility [longitude and latitude] 
 PGA and PGD 

 

 
 Geographical location of airport [lo

 

 Classification (i.e. building type) 

Maintenance and Hangar Facilities 
 Geographical location of facility [longitude and latitude] 
 Spectral values and PGD 
 Classification (i.e. building type) 
Parking Structures 


 Spectral valu

 
Direct damage output for airport syst

fu

ratio. Damage ratios are used as inputs to direct economic loss methods, as 
described in 
section 15.3 of Chapter 15. 

 

7




 

 

PGD. 

 

Definitions of various damage states and the methodology used in deriving these 
fragility 
curves are presented in the following section. 
7.7.5 Description of Airport Components 
An airport system consists of the six components mentioned abov

to

provides a br

Runways 
This component consists of well-paved "flat and wide surfaces". 
Control Tower 
Control tower consists of a bu

 
Fuel Facilities 
These have been previously defined in Section 7.2.5 of railway systems. 
Terminal Buildings 
These are similar to urban stations of railway systems from the classification 
standpoint (as well as services provided to passengers). 


Classification of maintenance facilities is the same as for those in railway 
systems. 
Hangar facilities and parking structures are mainly composed of buildings. 
7.7.6 Definitions of Damage States 

A total of five damage states are defined for airport sy

(d

12345 


Slight/Min

 

 For runways, ds2 is defined as minor ground settlem

surface. 

 For control t

 

 For fuel facilitie




 

 

section 5.3. 

 For maintenance and hangar facilities, ds2 is defined a

building as give

 

F

section 5.3. 

 

Moderate Damage (ds3) 

 

 For runways, ds3 is defined same a

 

 For control tower, ds3 is defined a

se

 

 For fuel facilities, ds3 is th

 

 

in section 5.3. 

 For maintenance and hangar facilities, ds3 is defined as m

building as given in sectio

 

 

in section 5.3. 
Extensive Damage (ds4) 


4heaving of runway surface. 

 For control tower, ds4 is defined as extensive damage to the building as given 
in 
section 5.3. 
 For fuel facilities, ds4 is the same as that for fuel facilities in the 
railway module. 
 For terminal buildings, ds4 is defined as extensive damage to the building as 
given in section 5.3. 

4building as given in section 5.3. 

 For parking structures, ds4 is defined as extensive damage to the building as 
given 
in s




Complete Damage (ds5) 


5of runway surface. 


5section 5.3. 

 For fuel facilities, ds5 is the same as that for fuel facilities in the 
railway module. 
 For terminal buildings, ds5 is 

 

 For maintenance and hangar facilities, ds5 is defined as com

 

 For parking structures, ds5 is defined as complete damage to the building as 
given 
in section

 

mal distribution functions are developed using this ATC-13 data for the mean 
time 
30%, 60% and 100% restoration. Means and disp

oration functions are presented in Table 7.16.b, where the percentage

wn at selected time intervals. 

Classification Damage State Mean (Days) s 
Control Towers, slight 0 0 

Terminal Building 
slig

Runways extensive 
complete 
35 
85 
35 
65 




Table 7.16.b Discretiz

Classification Damage State 1 day 3 days 7 days 30 days 90 days 
ontrol Towers, 
rking Structures, 
slight 
moderate 
100 100 100 100 100 

Terminal Building complete 11 11 12 16 31 
Runways 
slight/moderate 27 57 100 100 100 

co

aunctions for airport system facilities are defined in terms of PGA and PGD 
epfor runways (PGD only). Note that, unless it is specified otherwise, ground 

 facilities in section 7.2.8. 

ple of how to combine PGD and PGA algorithms is presented in section 7.2.8. 

Damage Functions for Runw 


The earthquake hazard for airport runways is ground failure. Little damage is 

attributed to ground shaking; therefore,
fa

re as the hazard. All runays are assumed to be paved. The median values and 
dispersion for the damage states for runways are given in Table 7.17. These 
damage 

functions are also shown in Figure 7.32. 


Table 7.1

Permanent Ground Deformation 
Classification 

 
Runways 
slight/moderate 
extensive 
1 
4 
0.6 
0.6 

 

Damage Functions for Rest of Airport System Components 

 

In section 7.7.5, these components were defined by "one to one" correspondence 
with 

those for railway systemc

oponents (i.e. fuel facilities, maintenance facilities, and other buildings) can 
be 
found in Section 7.2.8. 




ibility to: (1) include a refined inventory of the airport system pertaining to 
the area 

odate any specified key component of a airport system, such as a control tower. 

ogphic and geological conditions. 

.10References 

C-13, Redwood City, CA, 1985. 

hncal Manual, Transportation Systems (Airport Systems)", May 1994. 




 

Minor

Moderate

Ext

ive

Comple

ATC-13 Da

100



 Percent Functio50

025

Time (days)

Figure 7.30 Restorati

 
100MinorModerateExtensiveCompleteATC-13 Data

Time (days)
1101001000 
Figure 7.31 Restoratio








 

 
Slight/ModerateExtensiveComplete

1

0.75

0.25

Permanent Ground Deformation (inches)
Probability [ Ds > ds | PGD ] 
0




 

Liner extensive 
complete 
12 
60 
0.5 
0.5 

e 

Portal extensive 
complete 
12 
60 
0.5 
0.5 





 

 

 

 

 

e 

Abutment moderatextensive 
15 
30 
0.7 
0.7 
nnection complete 30 0

e 




 

Peak Ground Acceleration 
Subcomponents Damage State Median (g)  

Column 
slight 
extensive 
0.3 
0.8 
0.55 
0.7 

complete 1

 moderate 0.8 

Connection 

moderate 
extensive 

0.7 
1.0 

Deck 

slight 

0.5 

0.55 



onents Damage State Median (in) 

Column 

extensive 

10 

butment

omplete

moderate 

extensive 

21 

10 

21 

0.7 

0.7 

0.7 

omplete

slight 
moderate 

21 

2 
12 

0.7 

0.5 
0.7 



e

 

 


 


 






Electric Power slight 0.80 0.60 

(Backup) moderate 1.00 0.80 
Electric Power slight 0.15 0.6 

moderate 0.25slight 0.30

Tank 

moderate 
e

0.70 

xtensive 

complete 

slight 
me 

1.25 
1 

.60

0.15 

0.65 
0 

.60

0.80 

oderatxtensive 

ecomplete 

0.40 
0.80 

1.50 

0.80 
0.80 

0.80 

Horizontal 
Pump 

extensive 

1.60 

0.60 

uipmen

oderate

1.00 

0.60 



 

Table B.7.4 Su

component D

amage Algo

m

ity with 

hored Com

nts

G&

 Ground A

on 

 

(Backup) 

slight 

moderate 

slight 

0.40 

0.15 

0.60 

0.80 

0.6 



e 

e 

e 

e 




h Facility with Anchored Components (after G&E, 

Damage State Median (g) 

Electric Power 

slight 

0.80 

oderate

slight 

moderate 

1.00 

0.15 

0.25 

0.80 

0.6 

0.5 

slight 

moderate 

0.15 

0.40 

0.80 

0.80 

extensive 

complete 

0.80 

1.50 

0.80 

0.80 

uipme

oderate

1.00 

0.60 



 

i

 Ground A

on 

slight 

moderate 

0.20 

0.40 

 

0.60 

0.80 



e 

e 
e 

e 




 

APPENDIX 7C 

 


 

 

Substatio

h Anc

omp

 

round 

amage Stat

on 

dian (

 

Building 

slight 
oderate

m 
extensive 

complete 
moderate 

0.15 
0.40 

0.80 

1.50 
1.00 

0.80 
0.80 

0.80 

0.80 
0.60 

Equipment 

slight 

0.15 

0.6 

moderate 


0.25 

0.5 



 

 


ith Unanchored Components 

round 

amat

on 

Subcomponents 

age St

slight 

dian (

0.15 

 

0.80 

oderate

extensive 

complete 

moderate 

0.40 

0.80 

1.50 

0.60 

0.80 

0.80 

0.80 

0.60 

slight 

moderate 

 

 

0.15 

0.25 

0.6 

0.5 

 






 

APPENDIX 7D 

 

slight 

8 

 

0.6 

slight 

8 

0.6 



Seawalls 
extensive 24 0.6 

complete 60 0.6 

 

 






 Chapter 8 

Direct Damage to Lifelines - Utility Systems 

 

This chapter describes and presents the methodology for estimating direct damage 
to 
Utility Systems. The Utility Module is composed of the following six systems: 

 

 Potable Water 

 Waste Water 

 Oil (crude and refined) 

 Natural Gas 

 Electric Power 

 Communication 

 

The flowchart of the overall methodology, highlighting the utility system module 
and its 
relationship to other modules, is shown in Flowchart 8.1. 

 

8.1 Potable Water Systems 

 

8.1.1 Introduction 

 

This section presents a loss estimation methodology for a water system during 
earthquakes. This system consists of supply, storage, transmission, and 
distribution 
components. All of these components are vulnerable to damage during earthquakes, 
which may result in a significant disrutpion to the water utility network. 

 

8.1.2 Scope 

 

The scope of this section includes development of methods for estimation of 
earthquake 
damage to a potable water system given knowledge of the system's components 
(i.e., 
tanks, aqueducts, water treatment plants, wells, pumping stations, conveyance 
pipes, 
junctions, hydrants, and valves), classification (i.e., for water treatment 
plants, small, 
medium or large), and the ground motion (i.e. peak ground velocity, peak ground 
acceleration and/or permanent ground deformation). Damage states describing the 
level 
of damage to each of the water system components are defined (i.e., 
slight/minor, 
moderate, extensive, or complete), while for pipelines, the number of repairs/km 
is the 
key paramter. Fragility curves are developed for each classification of the 
water system 
component. These curves describe the probability of reaching or exceeding each 
damage 
state given the level of ground motion or ground failure. 

 

Based on these fragility curves, a method for assessing functionality of each 
component 
of the water system is presented. A simplified approach for evaluating the 
overall water 
system network performance is also provided. 

 

 

 




 

 

8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

 

Flowchart 8.1 Utility System Damage Relationship to Other Modules of the 
Earthquake Loss Estimation Methodology 

 

 

 




8.1.3 Input Requirements and Output Information 

 

Depending on the desired level of analysis, the input required for analyzing 
water 
systems varies. In total, three levels of analysis are enabled in HAZUS. 

 

Level One: 

The default inventory in HAZUS contains estimate of potable water pipelines 
aggregated 
at the census tract level. This pipeline data was developed using the US Census 
TIGER 
street file datasets. For the level one analysis, eighty (80) percent of the 
pipes are 
assumed to be brittle with the remaining pipes assumed to be ductile. In 
addition, peak 
ground velocity and permanent ground deformation (PGV and PGD) for each census 
tract is needed for the analysis. 

 

The results from a level one analysis include the expected number of leaks and 
breaks per 
census tract and a simplified evaluation of the potable water system network 
performance 
(i.e. number of households without water). 

 

Level Two: 

For this level, the input required to estimate damage to potable water systems 
includes 
the following items: 

 

Transmission Aqueducts and Distribution Pipelines 

 

 Geographical location of aqueduct/pipe links (longitude and latitude of end 
nodes) 

 Peak ground velocity and permanent ground deformation (PGV and PGD) 

 Classification (ductile pipe or brittle pipe) 

 

Reservoirs, Water Treatment Plants, Wells, Pumping Stations and Storage Tanks 

 

 Geographical location of facility (longitude and latitude) 

 PGA and PGD 

 Classification (e.g., capacity and anchorage) 

 

Direct damage output from level 2 analysis includes probability estimates of (1) 
component functionality and (2) damage, expressed in terms of the component's 
damage 
ratio (repair cost to replacement cost). Note that damage ratios for each of the 
potable 
water system components are presented in section 15.3 of Chapter 15. In 
addition, a 
simplified evaluation of the potable water system network performance is also 
provided. 
This is based on network analyses done for Oakland, San Francisco and Tokyo. The 
output from this simplified version of network analysis consists of an estimate 
of the flow 
reduction to the areas served by the water system being evaluated. Details of 
this 
methodology are presented in subsection 8.1.9. 




 

Level Two Enhanced: 

This level of analysis essentially relies on the same type of information 
provided in the 
previous level with four main differences: 

 

.. 
Three additional components are considered. These are: junctions, hydrants, and 
valves. 


.. 
Connectivity of the components is maintained (i.e., what facilities are 
connected 
to which pipeline links or valves). 


.. 
Serviceability in the system considered (i.e., the demand pressures and flow 
demands at the different distribution nodes). 


.. 
Input data for the water system need to be in one of the following three 
commercially available formats: KYPIPE, EPANET, or CYBERNET. 




 

Recent work by Khater and Waisman (EQE, 1999) elaborates in great details on the 
level 
two enhanced analysis model implemented in HAZUS. In particular, this work 
provides a comprehensive theoretical background on the governing equations for a 
water 
system and explains how the commercial data need to be formatted in order to be 
able to 
import it into HAZUS. This work is available in a separate document entitled 
Potable 
Water System Analysis Model (POWSAM) that can be acquired directly from NIBS. 

 

Results from the level two enhanced analysis are similar to the level two. That 
is, 
probability estimates of (1) component functionality and (2) damage, expressed 
in terms 
of the component's damage ratio (repair cost to replacement cost). The main 
difference is 
in the evaluation of the potable water system network performance, which is in 
this case 
based on a more comprehensive approach. Note that in either case, the 
performance is 
expressed in terms of an estimate of the flow reduction to the areas served by 
the water 
system being evaluated and the number of households expected to be deprived from 
water. 

 

 

8.1.4 Form of Damage Functions 

 

Damage functions or fragility curves for water system components other than 
pipelines 
are modeled as lognormally-distributed functions that give the probability of 
reaching or 
exceeding different damage states for a given level of ground motion (quantified 
in terms 
of PGA) and ground failure (quantified in terms of PGD). Each of these fragility 
curves 
is characterized by a median value of ground motion (or failure) and an 
associated 
dispersion factor (lognormal standard deviation). For pipelines, empirical 
relations that 
give the expected repair rates due to ground motion (quantified in terms of PGV) 
or 
ground failure (quantified in terms of PGD) are provided. Definitions of various 
damage 
states and the methodology used in deriving all these fragility curves are 
presented in the 
next section. 

 




8.1.5 Description of Potable Water System Components 

 

A potable water system typically consists of terminal reservoirs, water 
treatment plants, 
wells, pumping plants, storage tanks and transmision and distribution pipelines. 
In this 
subsection, a brief description of each of these components is presented. 

 

Terminal Reservoirs 

 

Terminal reservoirs are typically lakes (man made or natural) and are usually 
located 
nearby and upstream of the water treatment plant. Vulnerability of terminal 
reservoirs 
and associated dams is marginally assessed in the loss estimation methodology. 
Therefore, even though reservoirs are an essential part of a potable water 
system, it is 
assumed in the analysis of water systems that the amount of water flowing into 
water 
treatment plants from reservoirs right after an earthquake is essentially the 
same as before 
the earthquake. 

 

Transmission Aqueducts 

 

These transmission conduits are typically large size pipes (more than 20 inches 
in 
diameter) or channels (canals) that convey water from its source (reservoirs, 
lakes, rivers) 
to the treatment plant. 

 

Transmission pipelines are commonly made of concrete, ductile iron, cast iron, 
or steel. 
These could be elevated/at grade or buried. Elevated or at grade pipes are 
typically made 
of steel (welded or riveted), and they can run in single or multiple lines. 

 

Canals are typically lined with concrete, mainly to avoid excessive loss of 
water by 
seepage and to control erosion. In addition to concrete lining, expansion joints 
are 
usually used to account for swelling and shrinkage under varying temperature and 
moisture conditions. Damageability of channels has occurred in some earthquake, 
but is 
outside the scope of the scope of the methodology. 

 

Supply Facilities- Water Treatment Plants (WTP) 

 

Water treatment plants are generally composed of a number of physical and 
chemical unit 
processes connected in series, for the purpose of improving the water quality. A 
conventional WTP consists of a coagulation process, followed by a sedimentation 
process, and finally a filtration process. Alternately, a WTP can be regarded as 
a system 
of interconnected pipes, basins, and channels through which the water moves, and 
where 
the flow is governed by hydraulic principles. WTP are categorized as follows: 

 

Small water treatment plants, with capacity ranging from 10 mgd to 50 mgd, are 
assumed 
to consist of a filter gallery with flocculation tanks (composed of paddles and 
baffles) and 
settling (or sedimentation) basins as main components, chemical tanks (needed in 
the 
coagulation and other destabilization processes), chlorination tanks, electrical 
and 
mechanical equipment, and elevated pipes. 




 

Medium water treatment plants, with capacity ranging from 50 mgd to 200 mgd, are 
simulated by adding more redundancy to small treatment plants (i.e. twice as 
many 
flocculation, sedimentation, chemical and chlorination tanks). 

 

Large water treatment plants, with capacity above 200 mgd, are simulated by 
adding even 
more redundancy to small treatment plants (i.e., three times as many 
flocculation, 
sedimentation, chemical and chlorination tanks/basins). 

 

Water treatment plants are also classified based on whether the subcomponents 
(equipment and backup power) are anchored or not as defined in section 7.2.5. 

 

Pumping Plants (PP) 

 

Pumping plants are usually composed of a building, one or more pumps, electrical 
equipment, and in some cases, backup power systems. Pumping plants are 
classified as 
either small PP (less than 10 mgd capacity) or medium/large PP (more than 10 mgd 
capacity). Pumping plants are also classified with respect to whether the 
subcomponents 
(equipment and backup power) are anchored or not. As noted in Chapter 7, 
anchored 
means equipment designed with special seismic tie downs and tiebacks while 
unanchored 
means equipment with manufactures normal requirements. 

 

Wells (WE) 

 

Wells typically have a capacity between 1 and 5 mgd. Wells are used in many 
cities as 
a primary or supplementary source of water supply. Wells include a shaft from 
the 
surface down to the aquifer, a pump to bring the water up to the surface, 
equipment used 
to treat the water, and sometimes a building, which encloses the well and 
equipment. 

 

Water Storage Tanks (ST) 

 

Water storage tanks can be elevated steel, on ground steel 
(anchored/unanchored), on 
ground concrete (anchored/unanchored), buried concrete, or on ground wood tanks. 
Typical capacity of storage tanks is in the range of 0.5 mgd to 2 mgd. 

 

Distribution Facilities and Distribution Pipes 

 

Distribution of water can be accomplished by gravity, or by pumps in conjunction 
with 
on-line storage. Except for storage reservoirs located at a much higher altitude 
than the 
area being served, distribution of water would necessitate, at least, some 
pumping along 
the way. Typically, water is pumped at a relatively constant rate, with flow in 
excess of 
consumption being stored in elevated storage tanks. The stored water provides a 
reserve 
for fire flow and may be used for general-purpose flow should the electric power 
fail, or 
in case of pumping capacity loss. 

 




Distribution pipelines are commonly made of concrete (prestressed or 
reinforced), 
asbestos cement, ductile iron, cast iron, steel, or plastic. The selection of 
material type 
and pipe size are based on the desired carrying capacity, availability of 
material, 
durability, and cost. Distribution pipes represent the network that delivers 
water to 
consumption areas. Distribution pipes may be further subdivided into primary 
lines, 
secondary lines, and small distribution mains. The primary or arterial mains 
carry flow 
from the pumping station to and from elevated storage tanks, and to the 
consumption 
areas, whether residential, industrial, commercial, or public. These lines are 
typically 
laid out in interlocking loops, and all smaller lines connecting to them are 
typically 
valved so that failure in smaller lines does not require shutting off the 
larger. Primary 
lines can be up to 36 inches in diameter. Secondary lines are smaller loops 
within the 
primary mains and run from one primary line to another. They serve primarily to 
provide 
a large amount of water for fire fighting without excessive pressure loss. Small 
distribution lines represent the mains that supply water to the user and to the 
fire 
hydrants. 

 

In this earthquake loss estimation study, the simplified method for water system 
network 
performance evaluation applies to a distribution pipe network digitized at the 
primary 
level. 

 

8.1.6 Definition of Damage States 

 

Potable water systems are susceptible to earthquake damage. Facilities such as 
water 
treatment plants; wells, pumping plants and storage tanks are most vulnerable to 
PGA, 
and sometimes PGD, if located in liquefiable or landslide zones. Therefore, the 
damage 
states for these components are defined and associated with PGA and PGD. 
Aqueducts 
and pipelines, on the other hand, are vulnerable to PGV and PGD. Therefore, the 
damage 
states for these components are associated with these two ground motion 
parameters. 

 

8.1.6.1 Damage State Defintions for Components Other than Pipelines 

 

A total of five damage states for potable water system components are defined. 
These 
are none (ds1), slight/minor (ds2), moderate (ds3), extensive (ds4), and 
complete (ds5). 




Slight/Minor Damage (ds2) 

 

 For water treatment plants, ds2 is defined by malfunction of plant for a short 
time 
(less than three days) due to loss of electric power and backup power if any, 
considerable damage to various equipment, light damage to sedimentation basins, 
light damage to chlorination tanks, or light damage to chemical tanks. Loss of 
water 
quality may occur. 

 

 For pumping plants, ds2 is defined by malfunction of plant for a short time 
(less 
than three days) due to loss of electric power and backup power if any, or 
slight 
damage to buidings. 

 

 For wells, ds2 is defined by malfunction of well pump and motor for a short 
time 
(less than three days) due to loss of electric power and backup power if any, or 
light 
damage to buidings. 

 

 For Storage Tanks, ds2 is defined by the tank suffering minor damage without 
loss 
of its contents or functionality. Minor damage to the tank roof due to water 
sloshing, 
minor cracks in concrete tanks, or localized wrinkles in steel tanks fits the 
description 
of this damage state. 

 

Moderate Damage (ds3) 

 

 For water treatment plants, ds3 is defined by malfunction of plant for about a 
week 
due to loss of electric power and backup power if any, extensive damage to 
various 
equipment, considerable damage to sedimentation basins, considerable damage to 
chlorination tanks with no loss of contents, or considerable damage to chemical 
tanks. 
Loss of water quality is imminent. 

 

 For pumping plants, ds3 is defined by the loss of electric power for about a 
week, 
considerable damage to mechanical and electrical equipment, or moderate damage 
to 
buildings. 

 

 For wells, ds3 is defined by malfunction of well pump and motor for about a 
week 
due to loss of electric power and backup power if any, considerable damage to 
mechanical and electrical equipment, or moderate damage to buildings. 

 

 For Storage Tanks, ds3 is defined by the tank being considerably damaged, but 
only 
minor loss of content. Elephant foot buckling for steel tanks without loss of 
content, 
or moderate cracking of concrete tanks with minor loss of content fits the 
description 
of this damage state. 

 




Extensive Damage (ds4) 

 

 For water treatment plants, ds4 is defined by the pipes connecting the 
different 
basins and chemical units being extensively damaged. This type of damage will 
likely result in the shutdown of the plant. 

 

 For pumping plants, ds4 is defined by the building being extensively damaged, 
or 
the pumps being badly damaged beyond repair. 

 

 For wells, ds4 is defined by the building being extensively damaged or the 
well pump 
and vertical shaft being badly distorted and nonfunctional. 

 

 For Storage Tanks, ds4 is defined by the tank being severely damaged and going 
out 
of service. Elephant foot buckling for steel tanks with loss of content, 
stretching of 
bars for wood tanks, or shearing of wall for concrete tanks fits the description 
of this 
damage state. 

 

Complete Damage (ds5) 

 

 For water treatment plants, ds5 is defined by the complete failure of all 
pipings, or 
extensive damage to the filter gallery. 

 

 For pumping plants, ds5 is defined by the building collapsing. 

 

 For wells, ds5 is defined by the building collapsing. 

 

 For Storage Tanks, ds5 is defined by the tank collapsing and losing all of its 
content. 

 

8.1.6.2 Defintion of Damage States for Pipelines 

 

For pipelines, two damage states are considered. These are leaks and breaks. 
Generally, 
when a pipe is damaged due to ground failure (PGD), the type of damage is likely 
to be a 
break, while when a pipe is damaged due to seismic wave propagation (PGV), the 
type of 
damage is likely to be joint pull-out or crushing at the bell. In the loss 
methodology, it is 
assumed that damage due to seismic waves will consist of 80% leaks and 20% 
breaks, 
while damage due to ground failure will consist of 20% leaks and 80% breaks. The 
user 
can override these default percentages. 

 

8.1.7 Component Restoration Curves 

 

Restoration functions for potable water system components, namely, water 
treatment 
plants, wells, pumping plants, and storage tanks are based on SF-30a, SF-30b and 
SF-30d 
of ATC-13 consistent with damage states defined in the previous section. That 
is, 
restoration functions for ds2, ds3, ds4, and ds5 defined herein are assumed to 
correspond 
to ds2, ds3, ds4, and ds5 of ATC-13. The parameters of these restoration curves 
are given 
in Tables 8.1.a and 8.1.b, and 8.1.c. 




 

Table 8.1.a: Continuous Restoration Functions for Potable Water Systems 

(After ATC-13, 1985) 

Restoration Functions (All Normal Distributions) 

Classification 

Damage State 

Mean (Days) 

s (days) 

Water Treatment 
Plants 

slight/minor 

moderate 

extensive 

complete 

0.9 

1.9 

32.0 

95.0 

0.3 

1.2 

31.0 

65.0 

 

Pumping Plants 

slight/minor 

moderate 

extensive 

complete 

0.9 

3.1 

13.5 

35.0 

0.3 

2.7 

10.0 

18.0 

 

Wells 

slight/minor 

moderate 

extensive 

complete 

0.8 

1.5 

10.5 

26.0 

0.2 

1.2 

7.5 

14.0 

Water Storage 
Tanks 

slight/minor 

moderate 

extensive 

complete 

1.2 

3.1 

93.0 

155.0 

0.4 

2.7 

85.0 

120.0 



 

Table 8.1.a gives means and standard deviations for each restoration curve 
(i.e., smooth 
continuous curve), while Table 8.1.b gives approximate discrete functions for 
the 
restoration curves developed. These restoration functions are also shown in 
Figures 8.1 
through 8.4. 

 

 

Table 8.1.b: Discretized Restoration Functions for Potable Water System 
Components 

Discretized Restoration Functions 

Classification 

Damage State 

1 day 

3 days 

7 days 

30 days 

90 days 

Water Treatment 
Plants 

slight/minor 

moderate 

extensive 

complete 

65 

23 

16 

7 

100 

82 

18 

8 

100 

100 

21 

9 

100 

100 

48 

16 

100 

100 

97 

47 

 

Pumping Plants 

slight/minor 

moderate 

extensive 

complete 

65 

22 

10 

3 

100 

50 

15 

4 

100 

93 

25 

6 

100 

100 

95 

40 

100 

100 

100 

100 






Table 8.1.b: Discretized Restoration Functions for Potable Water System 
Components (continued) 

Discretized Restoration Functions 

Classification 

Damage State 

1 day 

3 days 

7 days 

30 days 

90 days 

 

Wells 

slight/minor 

moderate 

extensive 

complete 

85 

34 

11 

4 

100 

90 

16 

6 

100 

100 

33 

9 

100 

100 

100 

62 

100 

100 

100 

100 

Water Storage 
Tanks 

slight/minor 

moderate 

extensive 

complete 

30 

20 

13 

10 

100 

49 

15 

11 

100 

93 

16 

12 

100 

100 

23 

15 

100 

100 

49 

30 



 

The restoration functions for pipelines are expressed in terms of number of days 
needed 
to fix the leaks and breaks. These restoration functions are given in Table 
8.1.c 

 

Table 8.1.c: Restoration Functions for Potable Water Pipelines 

Class 

 

Diameter 
from: [in] 

 

Diameter to: 
[in] 

# Fixed 
Breaks per 
Day per 
Worker 

# Fixed 
Leaks per 
Day per 
Worker 

 

# Available 
Workers 

Priority 

a 

60 

300 

0.33 

0.66 

User-
specified 

1 (Highest) 

b 

36 

60 

0.33 

0.66 

User-
specified 

2 

c 

20 

36 

0.33 

0.66 

User-
specified 

3 

d 

12 

20 

0.50 

1.0 

User-
specified 

4 

e 

8 

12 

0.50 

1.0 

User-
specified 

5 (Lowest) 

u 

Unknown 

diameter 

or for Default 
Data Analysis 

0.50 

1.0 

User-
specified 

6 (lowest) 



 

Where the total number of available workers can be specified by the user. It 
should be 
noted that the values in Table 8.1.c are based on the following 4 assumptions: 

 
(1) Pipes that are less than 20 in diameter are defined as small, while pipes 
with 
diameter greater than 20 are defined as large. 

 (2) For both small and large pipes a 16 hour day shift is assumed. 

 (3) For small pipes, a 4-person crew needs 4 hours to fix a leak, while the 
same 4-person 
crew needs 8 hours to fix a break. (Mathematically, this is equivalent to saying 
it takes 16 
people to fix a leak in one hour and it takes 32 people to fix a break in one 
hour). 




(4) For large pipes, a 4-person crew needs 6 hours to fix a leak, while the same 
4-person 
crew needs 12 hours to fix a break. (Mathematically, this is equivalent to say 
it takes 24 
people to fix a leak in one hour and 48 people to fix a break in one hour). 

With this algorithm for potable water pipelines, the total number of days needed 
to finish 
repairs is calculated as: 

 

Days needed to finish all repairs = (1/available work) * [(# small pipe 
leaks/1.0) + (# 
small pipe breaks/0.5) +(# large pipe leaks/0.66) + (# large pipe breaks/0.33)] 

 

The percentage of repairs finished at Day1, Day3, Day7, Day30, and Day90 are 
then 
computed using linear interpolation. 

 

8.1.8 Development of Damage Functions 

 

In this subsection, damage functions for the various components of a potable 
water 
system are presented. In cases where the components are made of subcomponents 
(i.e., 
water treatment plants, pumping plants, and wells), fragility curves for these 
components 
are based on the probabilistic combination of subcomponent damage functions 
using 
Boolean expressions to describe the relationship of subcomponents to the 
components. It 
should be mentioned that the Boolean logic is implicitly presented within the 
definition 
of a particular damage state. For example, slight/minor damage for a water 
treatment 
plant was defined by malfunction for a short time due to loss of electric power 
AND 
backup power (if any), considerable damage to various equipment, light damage to 
sedimentation basins, light damage to chlorination tanks, OR light damage to 
chemical 
tanks. Therefore, the fault tree for slight/minor damage has FIVE primary OR 
branches: 
electric power, equipment, sedimentation basins, chlorination tanks, and 
chemical tanks, 
and TWO secondary AND branches under electric power: commercial power and backup 
power. The Boolean approach involves evaluation of the probability of each 
component 
reaching or exceeding different damage states, as defined by the damage level of 
its 
subcomponents. These evaluations produce component probabilities at various 
levels of 
ground motion. In general, the Boolean combinations do not produce a lognormal 
distribution, so a lognormal curve that best fits this probability distribution 
is determined 
numerically. It should be mentioned that damage functions due to ground failure 
(i.e., 
PGD) for all potable water systems components except pipelines (i.e., water 
treatment 
plants, pumping plants, wells, and storage tanks) are assumed to be similar to 
those 
described for buildings, unless specified otherwise. These are: 

 

- For lateral spreading, a lognormal damage function with a median of 60 inches 
and a 
dispersion of 1.2 is assumed for the damage state of "at least extensive". 20% 
of this 
damage is assumed to be complete. For a PGD of 10 inches due to lateral 
spreading, 
there is a 7% probability of "at least extensive" damage. 

 

For vertical settlement, a lognormal curve with a median of 10 inches and a 
dispersion of 
1.2 is assumed for the damage state of "at least extensive". 20% of this damage 
is 




assumed to be complete. For a PGD of 10" due to vertical settlement, there is a 
50% 
chance of "at least extensive" damage. 

 

- For fault movement or landslide, a lognormal curve with a median of 10 inches 
and a 
dispersion of 0.5 is assumed for "complete" damage state. That is, for 10 inches 
of PGD 
due to fault movement or landslide, there is a 50% chance of "complete" damage. 

 

An example of how to combine a PGD algorithm with a PGA algorithm for lifeline 
components was presented in section 7.2.8 of Chapter 7. 

 

Damage Functions for Water Treatment Plants (due to Ground Shaking) 

 

PGA related damage functions for water treatment plants are developed with 
respect to 
their classification. A total of 24 damage functions are presented. Half of 
these damage 
functions correspond to water treatment plants with anchored subcomponents, 
while the 
other half correspond to water treatment plants with unanchored subcomponents 
(see 
section 7.2.5 for the definition of anchored and unanchored subcomponents). 
Medians 
and dispersions of these damage functions are given in Tables 8.3 through 8.5. 

 

Medians and dispersions of damage functions for the water treatment plant 
subcomponents are summarized in Tables A.8.6 and A.8.7 of Appendix 8A. The 
medians for elevated pipe damage functions in these tables are based on ATC-13 
data 
(FC-32) for "at grade pipe" using the following MMI to PGA conversion (after 
G&E, 
1994), along with a best-fit lognormal curve. 

 

Table 8.2: MMI to PGA Conversion (after G&E, 1994) 

MMI 

VI 

VII 

VIII 

IX 

X 

XI 

XII 

PGA 

0.12 

0.21 

0.36 

0.53 

0.71 

0.86 

1.15 



 

Graphical representations of water treatment plant damage functions are also 
provided. 
Figures 8.5 through 8.10 are fragility curves for the different classes of water 
treatment 
plants. 

 




Table 8.3: Damage Algorithms for Small Water Treatment Plants 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
subcomponents 

( PWT1) 

slight/minor 

moderate 

extensive 

complete 

0.25 

0.38 

0.53 

0.83 

0.50 

0.50 

0.60 

0.60 

Plants with 
unanchored 
subcomponents 

(PWT2) 

slight/minor 

moderate 

extensive 

complete 

0.16 

0.27 

0.53 

0.83 

0.40 

0.40 

0.60 

0.60 



 

Table 8.4: Damage Algorithms for Medium Water Treatment Plants 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
subcomponents 

(PWT3) 

slight/minor 

moderate 

extensive 

complete 

0.37 

0.52 

0.73 

1.28 

0.40 

0.40 

0.50 

0.50 

Plants with 
unanchored 
subcomponents 

(PWT4) 

slight/minor 

moderate 

extensive 

complete 

0.20 

0.35 

0.75 

1.28 

0.40 

0.40 

0.50 

0.50 



 

Table 8.5: Damage Algorithms for Large Water Treatment Plants 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
subcomponents 

(PWT5) 

slight/minor 

moderate 

extensive 

complete 

0.44 

0.58 

0.87 

1.57 

0.40 

0.40 

0.45 

0.45 

Plants with 
unanchored 
subcomponents 

(PWT6) 

slight/minor 

moderate 

extensive 

complete 

0.22 

0.35 

0.87 

1.57 

0.40 

0.40 

0.45 

0.45 



 

Damage Functions for Pumping Plants (due to Ground Shaking) 

 

PGA related damage functions for pumping plants are developed with respect to 
their 
classification. A total of 16 damage functions are presented. Half of these 
damage 
functions correspond to pumping plants with anchored subcomponents, while the 
other half correspond to pumping plants with unanchored subcomponents. Medians 
and dispersions of these damage functions are given in Tables 8.6 and 8.7. 
Graphical 
representations of damage functions for the different classes of pumping plants 
are 
presented in Figures 8.11 through 8.14. Note that medians and dispersions of 
damage 
functions for pumping plants' subcomponents are summarized in Appendix 8A. 




 

Table 8.6: Damage Algorithms for Small Pumping Plants 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
subcomponents 

(PPP1) 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.36 

0.66 

1.50 

0.70 

0.65 

0.65 

0.80 

Plants with 
unanchored 
subcomponents 

(PPP2) 

slight/minor 

moderate 

extensive 

complete 

0.13 

0.28 

0.66 

1.50 

0.60 

0.50 

0.65 

0.80 



 

Table 8.7: Damage Algorithms for Medium/Large Pumping Plants 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
subcomponents 

(PPP3) 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.36 

0.77 

1.50 

0.75 

0.65 

0.65 

0.80 

Plants with 
unanchored 
subcomponents 

(PPP4) 

slight/minor 

moderate 

extensive 

complete 

0.13 

0.28 

0.77 

1.50 

0.60 

0.50 

0.65 

0.80 



 

Damage Functions for Wells (due to Ground Shaking) 

 

A total of four PGA-related damage functions are presented. In developing these 
damage functions, it is assumed that equipment in wells is anchored. Medians and 
dispersions of these damage functions are given in Table 8.8. Graphical 
representations of well damage functions are also shown in Figure 8.15. Note 
that 
medians and dispersions of damage functions for well subcomponents are 
summarized in Appendix 8A. 

 

Table 8.8: Damage Algorithms for Wells 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

 

Wells (PWE1) 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.36 

0.72 

1.50 

0.75 

0.65 

0.65 

0.80 



 




Damage Functions for Water Storage tanks 

 

A total of 24 PGA related damage functions are developed. These correspond to 
on-
ground concrete (anchored and unanchored), on ground steel (anchored and 
unanchored), 
elevated steel, and on-ground wood tanks. For tanks, anchored and unanchored 
refers to 
positive connection, or a lack thereof, between the tank wall and the supporting 
concrete 
ring wall. The PGD algorithm associated with these water storage tanks is 
described at 
the beginning of section 8.1.8. For buried storage tanks a separate PGD 
algorithm is 
presented. Medians and dispersions of the PGA related damage functions are given 
in 
Table 8.9. Graphical representations of water storage tank damage functions are 
also 
provided. Figures 8.16 through 8.21 are fragility curves for the different 
classes of water 
storage tanks. 

 

Table 8.9: Damage Algorithms for Water Storage Tanks 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

On-Ground 
Anchored 
Concrete Tank 

(PST1) 

slight/minor 

moderate 

extensive 

complete 

0.25 

0.52 

0.95 

1.64 

0.55 

0.70 

0.60 

0.70 

On-Ground 
Unanchored 
Concrete Tank 

(PST2) 

slight/minor 

moderate 

extensive 

complete 

0.18 

0.42 

0.70 

1.04 

0.60 

0.70 

0.55 

0.60 

On-Ground 
Anchored Steel 
Tank 

(PST3) 

slight/minor 

moderate 

extensive 

complete 

0.30 

0.70 

1.25 

1.60 

0.60 

0.60 

0.65 

0.60 

On-Ground 
Unanchored 
Steel Tank 

(PST4) 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.35 

0.68 

0.95 

0.70 

0.75 

0.75 

0.70 

Above-Ground 
Steel Tank 
(PST5) 

slight/minor 

moderate 

extensive 

complete 

0.18 

0.55 

1.15 

1.50 

0.50 

0.50 

0.60 

0.60 

On-Ground 
Wood Tank 
(PST6) 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.70 

0.90 

0.60 

0.60 

0.70 

0.70 

Permanent Ground Deformation 

Classification 

Damage State 

Median (in) 

 

 

Buried Concrete 
Tank (PST7) 

slight/minor 

moderate 

extensive 

complete 

2 

4 

8 

12 

0.50 

0.50 

0.50 

0.50 



 




Damage Functions for Buried Pipelines 

 

Two damage algorithms are used for buried pipelines. The first algorithm is 
associated with peak ground velocity (PGV) while the second algorithm is 
associated with permanent ground deformation (PGD). Note that in both of these 
algorithms the diameter of pipe is not considered to be a factor. 

 

The PGV algorithm is based on the empirical data presented in a work done by 
O'Rourke and Ayala (1993). The data correspond to actual pipeline damage 
observed in four US and two Mexican earthquakes. This data is plotted in Figure 
8.22.a. The following relation represents a good fit for this empircal data: 

 

Repair Rate [Repairs/Km] . 0.0001 x (PGV)(2.25) 

 

With PGV expressed in cm/sec. Note that the data plotted in Figure 8.22.a 
correspond to asbestos cement, concrete and cast iron pipes; therefore, the 
above 
(RR to PGV) relation is assumed to apply for brittle pipelines. For ductile 
pipelines (steel, ductile iron and PVC), the above relation is multiplied by 
0.3. 
That is, ductile pipelines have 30% of the vulnerability of brittle pipelines. 
Note 
that welded steel pipes with arc-welded joints are classified as ductile, and 
that 
welded steel pipes with gas-welded joints are classified as brittle. It is 
conceivable that the only other information available to the user regarding 
steel 
pipes is the year of installation. In this case, the user should classify pre-
1935 
steel pipes as brittle pipes. 

 

The damage algorithm for buried pipelines due to ground failure is based on work 
conducted by Honegger and Eguchi (1992) for the San Diego County Water 
Authority (SDCWA). Figure 8.22.b shows the base fragility curve for cast iron 
pipes. The best-fit function to this curve is given by: 

 

Repair Rate [Repairs/Km] . Prob [liq] x PGD(0.56) 

 

With PGD expressed in inches. This RR to PGD relation is assumed to apply for 
brittle pipelines. For ductile pipelines, the same multiplier as the PGV 
algorithm 
is assumed (i.e., 0.3). 

 

To summarize, the pipeline damage algorithms that are used in the current loss 
estimation methodology are presented in Table 8.10 

 




Table 8.10: Damage Algorithms for Water Pipelines 

 

PGV Algorithm 

PGD Algorithm 

 

R. R. . 0.0001 x PGV(2.25) 

R. R. . Prob[liq]xPGD(0.56) 

Pipe Type 

Multiplier 

Example of 
Pipe 

Multiplier 

Example of 
Pipe 

Brittle Pipes 
(PWP1) 

1 

CI, AC, RCC 

1 

CI, AC, RCC 

Ductile Pipes 
(PWP2) 

0.3 

DI, S, PVC 

0.3 

DI, S, PVC 



 

8.1.9 System Performance 

 

In the previous section, damage algorithms for the various components of a water 
system 
were presented. For the level 2 enhanced analysis (i.e., assuming the commercial 
data 
was readily available and processed as described in the Potable Water System 
Analysis 
Model manual), this information is combined and a system network analysis is 
performed. 

 

This section, however, outlines the simplified methodology that is used in the 
level 1 and 
level 2 analyses and which allows for a quick evaluation of the system 
performance in the 
aftermath of an earthquake. 

 

This approach is based on system performance studies done for water networks in 
Oakland, Tokyo, and San Francisco. In the Tokyo study (Isoyama and Katayama, 
1982), 
water system network performance evaluations following an earthquake were 
simulated 
for two different supply strategies: (1) supply priority to nodes with larger 
demands, and 
(2) supply priority to nodes with lowest demands. The "best" and "worst" node 
performances are approximately reproduced in a different format in Figure 8.23. 
The 
probability of pipeline failure, which was assumed to follow a Poisson process 
in the 
original paper, was substituded with the average break rate which was 
backcalculated 
based on a pipeline link length of about 5 kilometers (i.e., in the trunk 
network of the 
water supply system of Tokyo, the average link length is about 5 kilometers). 
Note that 
in this figure, serviceability index is considered as a measure of the reduced 
flow. 

 

Recently, researchers at Cornell University (Markov, Grigoriu and O'Rourke, 
1994) 
evaluated the San Francisco auxiliary (fire fighting) water supply system 
(AWSS). Some 
of their results are reproduced and shown also in Figure 8.23. 

 

G&E (1994) also did a similar study for the EBMUD (East Bay Municipal District) 
water 
supply system. Their results are shown as well in Figure 8.23. 

 

Based on these results, the damage algorithm proposed in this earthquake loss 
estimation 
for the simplified system performance evaluation is defined by a "conjugate" 
lognormal 
function (i.e., 1 - lognormal function). This damage function has a median of 
0.1 
repairs/km and a beta of 0.85, and it is shown in Figure 8.23. Hence, given 
knowledge of 




the pipe classification and length, one can estimate the system performance. 
That is, 
damage algorithms provided in the previous section give repair rates and 
therefore the 
expected total number of repairs (i.e., by multiplying the expected repair rate 
for each 
pipe type in the network by its length and summing up over all pipes in the 
network). 
The average repair rate is then computed as the ratio of the expected total 
number of 
repairs to the total length of pipes in the network. 

 

Example 

Assume we have a pipeline network of total length equal to 500 kilometers, and 
that this 
network is mainly composed of 16" diameter brittle pipes with each segment being 
20 
feet in length. Assume also that this pipeline is subject to both ground shaking 
and 
ground failure as detailed in Table 8.11. Note that the repair rates (R.R.) in 
this table are 
computed based on the equations provided in section 8.1.8. 

 

Table 8.11: Example of System Performance Evaluation 

PGV 
(cm/sec) 

R.R. 
(Re/km) 

Length 
(km) 

# 
Repairs 

PGD 
(inches) 

Probab. 

of Lique 

R.R. 
(Re/km) 

Length 
(km) 

# 
Repairs 

35 

0.2980 

50 

~ 15 

18 

1.0 

5.0461 

1 

~ 5 

30 

0.2106 

50 

~ 11 

12 

1.0 

4.0211 

1 

~ 4 

25 

0.1398 

50 

~ 7 

6 

0.80 

2.7275 

5 

~ 11 

20 

0.0846 

50 

~ 4 

2 

0.65 

1.4743 

53 

~ 51 

15 

0.0443 

100 

~ 4 

1 

0.60 

1 

20 

12 

10 

0.0178 

100 

~ 2 

0.5 

0.40 

0.6783 

20 

~ 6 

5 

0.0038 

100 

0 

0 

0.10 

0 

400 

0 

 

Total 

500 

43 

 

 

Total 

500 

89 



 

Therefore, due to PGV, the estimated number of leaks is 80% x 43 = 34, and the 
estimated number of breaks is 9, while due to PGD, the estimated number of leaks 
is 20% 
x 89 = 18 and the estimated number of breaks is 71. 

 

When we apply the "conjugate" lognormal damage function, which has a median of 
0.1 
repairs/km and a beta of 0.85, first we compute conservatively the average break 
rate as: 

 

 Average break rate = (9 + 71) / 500 = 0.16 repairs/km 

 

Hence, the serviceability index right after the earthquake is: 

 

 Serviceability Index = 1 - Lognormal(0.16, 0.1, 0.85) = 0.29 or 29 % 

 

8.1.10 Guidance for Loss Estimation Using Advanced Data and Models Analysis 

 

For this type of analysis, the expert can use the methodology developed with the 
flexibility to (1) include a more refined inventory of the water system 
pertaining to the 
area of study, (2) include component-specific and system-specific fragility 
data, and (3) 
utilize a commercial model to estimate overall system functionality. Default 
damage 
algorithms can be modified or replaced to incorporate improved information about 
key 




components of a water system. Similarly, better restoration curves can be 
developed, 
given knowledge of available resources and a more accurate layout of the water 
network 
within the local topographic and geological conditions. 

 

8.1.11 References 

 

(1) ATC-13, "Earthquake Damage Evaluation Data for California", Applied 
Technology Council, 
Redwood City, CA, 1985. 

 

(2) G&E Engineering Systems, Inc. (G&E), "NIBS Earthquake Loss Estimation 
Methods, 
Technical Manual, (Water Systems)", May 1994. 

 

(3) Eguchi R.T., Taylor C., and Hasselman T.K., "Seismic Component Vulnerability 
Models for 
Lifeline Risk Analysis", February 1983. 

 

(4) Velon J.P. and Johnson T.J., "Water Distribution and Treatment (Davis 
Handbook of Applied 
Hydraulics)", 1993. 

 

(5) Honegger D.G. and Eguchi R.T., "Determination of Relative Vulnerabilities to 
Seismic 
Damage for San Diego County Water Authority (SDCWA) Water Transmission 
Pipelines", 
October 1992. 

 

(6) McGhee T.J., " Water Supply and Sewerage, 6th Edition", 1993. 

 

(7) K/J/C and Dames & Moore, "Earthquake Loss Estimation for the City of 
Everett, Washington 
Lifelines", May 1991. 

 

(8) K/J/C and Dames & Moore, "Earthquake Loss Estimation Modeling of the Seattle 
Water 
System", October 1990. 

 

(9) TCLEE, "Seismic Loss Estimates for a Hypothetical Water System", August 
1991. 

 

(10) O'Rourke M.J and Ayala G., "Pipeline Damage due to Wave Propagation 
Journal of 
Geotechnical Engineering, ASCE Vol 119, No.9, Sept. 1993. 

 

(11) Markov I., Grigoriu M., and O'Rourke T., "An evaluation of Seismic 
Serviceability of Water 
Supply Networks with Application to San Francisco Auxiliary Water Supply 
System", NCEER 
Report No. 94-0001, 1994. 

 

(12) Isoyama R. and Katayama T., "Reliability Evaluation of Water Supply Systems 
During 
Earthquakes", February 1982. 

 

(13) Khater M and Waisman F., "Potable Water System Analysis Model (POWSAM)", 
September 1999. NIBS technical report. 

 

 




Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 8.1: Restoration Curves for Water Treatment Plants (after ATC-13, 1985). 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 8.2: Restoration Curves for Pumping Plants (after ATC-13, 1985). 

 




Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 8.3: Restoration Curves for Wells (after ATC-13, 1985). 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 8.4: Restoration Curves for Water Storage Tanks (after ATC-13, 1985). 

 

 

 

 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.5: Fragility Curves for Small Water Treatment Plants with Anchored 
Components. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.6: Fragility Curves for Small Water Treatment Plants with Unanchored 
Components. 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.7: Fragility Curves for Medium Water Treatment Plants with Anchored 
Components. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.8: Fragility Curves for Medium Water Treatment Plants with Unanchored 
Components. 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.9: Fragility Curves for Large Water Treatment Plants with Anchored 
Components. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.10: Fragility Curves for Large Water Treatment Plants with Unanchored 
Components. 

 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.11: Fragility Curves for Small Pumping Plants with Anchored 
Components. 

 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.12: Fragility Curves for Small Pumping Plants with Unanchored 
Components. 

 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.13: Fragility Curves for Medium/Large Pumping Plants with Anchored 
Components. 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.14: Fragility Curves for Medium/Large Pumping Plants with Anchored 
Components. 

 

 

 

 

 




 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


 

Figure 8.15: Fragility Curves for Wells 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


 

Figure 8.16: Fragility Curves for Anchored On Ground Concrete Tank. 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.17: Fragility Curves for Unanchored On Ground Concrete Tank. 

 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


 

Figure 8.18: Fragility Curves for Anchored On Ground Steel Tank. 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.19: Fragility Curves for Unanchored On Ground Steel Tank. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.20: Fragility Curves for Above Ground Steel Tank. 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.21: Fragility Curves for On Ground Wood Tank. 

 

 

 




 

 

Peak Ground Velocity (cm/sec)
Repair Rate [ Repairs/Km ] 
0.00010.0010.010.1110110 


Figure 8.22.a: Ground Shaking (Wave Propagation) Damage Model for Brittle 
Pipes (Specifically CI, AC, RCC, and PCCP) Based on Four U.S. and Two Mexican 
Earthquakes (after O'Rourke and Ayala, 1993). 

 

Permanent Ground Deformation (inches)
Repair Rate [Repairs/km] 
0481216020406080100Honegger & Eguchi (1992)
NIBS 


Honegger and Eguchi, 1992). 





 

 

 

 

 

 

 

 

 

Average Break Rate [Breaks/km] 
Serviceability Index [Percentage] 
0204060801000.010.11NIBSAWSS (simulation results atCornell)
AWSS (average)
EBMUD(G&E) [Large Pipes]
EBMUD (G&E) [Small Pipes]
Isoyama & Katayama (lowerrange)
Isoyama & Katayama (upperrange) 


Figure 8.23: Damage Index Versus Average Break Rate for Post-Earthquake 
System Performance Evaluation. 

 

 




8.2 Waste Water Systems 

 

8.2.1 Introduction 

 

This section presents a loss estimation methodology for a waste water system 
during 
earthquakes. This system consists of transmission, and treatment components. 
These 
components are vulnerable to damage during earthquakes, which may result in 
significant 
disruption to the utility network. 

 

8.2.2 Scope 

 

The scope of this section includes development of methods for estimation of 
earthquake 
damage to a waste water system given knowledge of components (i.e., underground 
sewers and interceptors, waste water treatment plants, and lift stations), 
classification 
(i.e., for waste water treatment plants, small, medium or large), and the ground 
motion 
(i.e., peak ground velocity, peak ground acceleration and/or permanent ground 
deformation). Damage states describing the level of damage to each of the waste 
water 
system components are defined (i.e., minor, moderate, extensive or complete for 
facilities 
plus #repairs/km for sewers/interceptors). Fragility curves are developed for 
each 
classification of water system component. These curves describe the probability 
of 
reaching or exceeding each damage state given the level of ground motion. Based 
on 
these fragility curves, a method for assessing functionality of each component 
of the 
waste water system is presented. 

 

8.2.3 Input Requirements and Output Information 

 

Required input to estimate damage to waste water systems is listed below. 

 

Sewers and Interceptors 

 

 Longitude and latitude of end nodes of links 

 Peak ground velocity and permanent ground deformation (PGV and PGD) 

 Classification 

 

Waste Water Treatment Plant and Lift Stations 

 

 Longitude and latitude of facility 

 PGA and PGD 

 Classification (small, medium or large, with anchored or unanchored 
components) 

 

Direct damage output for waste water systems includes probability estimates of 
(1) 
component functionality and (2) damage, expressed in terms of the component's 
damage 
ratio (repair cost to replacement cost). Note that damage ratios for each of the 
waste 
water system components are presented in section 15.3 of Chapter 15. 

 

8.2.4 Form of Damage Functions 




 

Damage functions or fragility curves for waste water system components other 
than 
sewers and interceptors are modeled as lognormally-distributed functions that 
give 
the probability of reaching or exceeding different damage states for a given 
level of 
ground motion (quantified in terms of PGA) and ground failure (quantified in 
terms 
of PGD). Each of these fragility curves is characterized by a median value of 
ground 
motion (or failure) and an associated dispersion factor (lognormal standard 
deviation). For sewers and interceptors, empirical relations that give the 
expected 
repair rates due to ground motion (quantified in terms of PGV) or ground failure 
(quantified in terms of PGD) are provided. 

 

Definitions of various damage states and the methodology used in deriving all 
these 
fragility curves are presented in the next section. 

 

8.2.5 Description of Waste Water System Components 

 

As mentioned before, a waste water system typically consists of collection 
sewers, 
interceptors, lift stations, and wastewater treatment plants. In this section, a 
brief 
description of each of these components is given. 

 

Collection Sewers 

 

Collection sewers are generally closed conduits that carry normally sewage with 
a 
partial flow. Collection sewers could be sanitary sewers, storm sewers, or 
combined sewers. Pipe materials that are used for potable water transportation 
may also be used for wastewater collection. The most commonly used sewer 
material is clay pipe manufactored with integral bell and spigot end. These 
pipes 
range in size from 4 to 42 inches in diameter. Concrete pipes are mostly used 
for 
storm drains and for sanitary sewers carrying noncorrosive sewage (i.e. with 
organic materials). For the smaller diameter range, plastic pipes are also used. 

 

Interceptors 

 

Interceptors are large diameter sewer mains. They are usually located at the 
lowest elevation areas. Pipe materials that are used for interceptor sewers are 
similar to those used for collection sewers. 

 

Lift Stations (LS) 

 

Lift stations are important parts of the waste water system. Lift stations serve 
to 
raise sewage over topographical rises. If the lift station is out of service for 
more 
than a short time, untreated sewage will either spill out near the lift station, 
or 
back up into the collection sewer system. 

 

In this study, lift stations are classified as either small LS (capacity less 
than 10 
mgd) or medium/large LS (capacity greater than 10 mgd). Lift stations are also 




classified as having either anchored or unanchored subcomponents (see section 
7.2.5 for the definition of anchored and unanchored subcomponents) 

 

Waste Water Treatment Plants (WWTP) 

 

Three sizes of wastewater treatment plants are considered: small (capacity less 
than 50 mgd), medium (capacity between 50 and 200 mgd), and large (capacity 
greater than 200 mgd). WWTP has the same processes existing in WTP with the 
addition of secondary treatment subcomponents. 

 

8.2.6 Definitions of Damage States 

 

Waste water systems are susceptible to earthquake damage. Facilities such as 
waste water 
treatment plants and lift stations are mostly vulnerable to PGA, and sometimes 
PGD, if 
located in liquefiable or landslide zones. Therefore, the damage states for 
these 
components are defined and associated with PGA and PGD. Sewers, on the other 
hand, 
are vulnerable to PGV and PGD. Therefore, the damage algorithms for these 
components are associated with those two ground motion parameters. 

 

8.2.6.1 Damage States Definitions for Components other than Sewers/Interceptors 

 

A total of five damage states are defined for waste water system components 
other than 
sewers and interceptors. These are none (ds1), slight/minor (ds2), moderate 
(ds3), 
extensive (ds4) and complete (ds5). 

 

Slight/Minor Damage (ds2) 

 

 For waste water treatment plants, ds2 is defined as for WTP in 
potable water systems. 

 

 For lift stations, ds2 is defined as for pumping plants in potable water 
systems. 

 

Moderate Damage (ds3) 

 

 For waste water treatment plants, ds3 is defined as for WTP in 
potable water systems. 

 

 For lift stations, ds3 is defined as for pumping plants in potable water 
systems. 

 

Extensive Damage (ds4) 

 

 For waste water treatment plants, ds4 is defined as for WTP in 
potable water systems. 

 




 For lift stations, ds4 is defined as for pumping plants in potable water 
systems. 

 

Complete Damage (ds5) 

 

 For waste water treatment plants, ds5 is defined as for WTP in 
potable water systems. 

 

 For lift stations, ds5 is defined as for pumping plants in potable water 
systems. 

 

8.2.6.2 Damage States Definitions for Sewers/Interceptors 

 

For sewers/interceptors, two damage states are considered. These are leaks and 
breaks. Generally, when a sewer/interceptor is damaged due to ground failure, 
the type of damage is likely to be a break, while when a sewer/interceptor is 
damaged due to seismic wave propagation; the type of damage is likely to be 
joint 
pullout or crushing at the bell. In the loss methodology, it is assumed that 
damage due to seismic waves will consist of 80% leaks and 20% breaks, while 
damage due to ground failure will consist of 20% leaks and 80% breaks. The user 
can override these default percentages. 

 

8.2.7 Component Restoration Curves 

 

The restoration curves for waste water system components are based on ATC-13 
expert 
data (SF-31.a through SF-331.c). Restoration data for lift stations, and 
wastewater 
treatment plants, in the form of dispersions of the restoration functions, are 
given in 
Table 8.12.a. The restoration functions are shown in Figures 8.24 and 8.25. 
Figure 8.24 
represents the restoration functions for lift stations and Figure 8.25 
represents the 
restoration curves for wastewater treatment plants. The discretized restoration 
functions 
are presented in Table 8.12.b, where the restoration percentage is shown at 
discretized 
times. Restoration for sewers follows the same approach for potable water 
pipelines, 
presented in section 8.1.7 . 

 

 




Table 8.12.a: Restoration Functions for Waste Water System Components 

Restoration Functions (All Normal Distributions) 

Classification 

Damage State 

Mean (Days) 

s 

 

Lift Stations 

slight/minor 

moderate 

extensive 

complete 

1.3 

3.0 

21.0 

65.0 

0.7 

1.5 

12.0 

25.0 

Waste Water 
Treatment Plants 

slight/minor 

moderate 

extensive 

complete 

1.5 

3.6 

55.0 

160.0 

1.0 

2.5 

25.0 

60.0 

Sewers/Interceptors 

See Section 8.1.7 



 

 

Table 8.12.b: Discretized Restoration Functions for Waste Water System 
Components 

Discretized Restoration Functions 

Classification 

Damage State 

1 day 

3 days 

7 days 

30 days 

90 days 

 

Lift Stations 

slight/minor 

moderate 

extensive 

complete 

34 

10 

5 

0 

100 

50 

7 

1 

100 

100 

13 

2 

100 

100 

78 

9 

100 

100 

100 

85 

Waste Water 
Treatment Plants 

slight/minor 

moderate 

extensive 

complete 

31 

15 

2 

1 

94 

40 

2 

1 

100 

92 

3 

1 

100 

100 

16 

2 

100 

100 

92 

13 

Sewers/Interceptors 

See Section 8.1.7 



 

8.2.8 Development of Damage Functions 

 

In this subsection, damage functions for the various components of a waste water 
system 
are presented. In cases where the components are made of subcomponents (i.e., 
waste 
water treatment plants and lift stations), fragility curves for these components 
are based 
on the probabilistic combination of subcomponent damage functions using Boolean 
expressions to describe the relationship of subcomponents. The Boolean logic is 
implicitly presented within the definition of a particular damage state (see 
section 8.1.8 
for an example). 

 

Damage functions due to ground failure (i.e., PGD) for waste water treatment 
plants and 
lift stations are assumed to be similar to those described for potable water 
system 
facilities in section 8.1.8. 

 




Damage Functions for Lift Stations 

 

Damage functions for lift stations are similar to those of pumping plants in 
potable water systems described in Section 8.1.8. 

 

Damage Functions for Waste Water Treatment Plants (due to Ground 
Shaking) 

 

Tables 8.13 through 8.15 present damage functions for small, medium and large 
wastewater treatment plants, respectively. Graphical representations of 
wastewater treatment plant damage functions are shown in Figures 8.26 through 
8.31. The medians and dispersions of damage functions to waste water treatment 
plants subcomponents are summarized in Tables B.8.1 and B.8.2 of Appendix 8B. 

 

 

Table 8.13: Damage Algorithms for Small Waste Water Treatment Plants 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
components 

(WWT1) 

slight/minor 

moderate 

extensive 

complete 

0.23 

0.35 

0.48 

0.80 

0.40 

0.40 

0.50 

0.55 

Plants with 
unanchored 
components 

(WWT2) 

slight/minor 

moderate 

extensive 

complete 

0.16 

0.26 

0.48 

0.80 

0.40 

0.40 

0.50 

0.55 



 

 

Table 8.14: Damage Algorithms for Medium Waste Water Treatment Plants 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
components 

(WWT3) 

slight/minor 

moderate 

extensive 

complete 

0.33 

0.49 

0.70 

1.23 

0.40 

0.40 

0.45 

0.55 

Plants with 
unanchored 
components 

(WWT4) 

slight/minor 

moderate 

extensive 

complete 

0.20 

0.33 

0.70 

1.23 

0.40 

0.40 

0.45 

0.55 



 




Table 8.15: Damage Algorithms for Large Waste Water Treatment Plants 

 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
components 

(WWT5) 

slight/minor 

moderate 

extensive 

complete 

0.40 

0.56 

0.84 

1.50 

0.40 

0.40 

0.40 

0.40 

Plants with 
unanchored 
components 

(WWT6) 

slight/minor 

moderate 

extensive 

complete 

0.22 

0.35 

0.84 

1.50 

0.40 

0.40 

0.40 

0.40 



 

Damage Functions for Sewers and Interceptors 

 

The same two damage algorithms proposed for buried pipelines in potable water 
systems are assumed to apply for sewers and interceptors. These are listed again 
in Table 8.16. Note that R.R. stands for repair rates or number of repairs per 
kilometer, PGV stands for peak ground velocity in cm/sec, and PGD stands for 
permanent ground deformation in inches. 

 

Table 8.16: Damage Algorithms for Sewers/Interceptors 

 

PGV Algorithm 

PGD Algorithm 

 

R. R. . 0.0001 x PGV(2.25) 

R. R. . Prob[liq]xPGD(0.56) 

Pipe Type 

Multiplier 

Example of 
Pipe 

Multiplier 

Example of 
Pipe 

Brittle Sewers/Interceptors 
(WWP1) 

1 

Clay, 
Concrete 

1 

Clay, 
Concrete 

Ductile Sewers/Interceptors 
(WWP2) 

0.3 

Plastic 

0.3 

Plastic 



 

8.2.9 Guidance for Loss Estimation with Advanced Data and Models Analysis 

 

For this type of analysis, the expert can use the methodology developed with the 
flexibility to (1) include a more refined inventory of the waste water system 
pertaining to 
the area of study, and (2) include component-specific and system-specific 
fragility data. 
Default damage algorithms for User-Supplied Data Analysis, can be modified or 
replaced 
to incorporate improved information about key components of a waste water 
system. 
Similarly, better restoration curves can be developed, given knowledge of 
available 
resources and a more accurate layout of the wastewater network within the local 
topographic and geological conditions. 

 




8.2.10 References 

 

(1) ATC-13, "Earthquake Damage Evaluation Data for California", Applied 
Technology 
Council, Redwood City, CA, 1985. 

 

(2) G&E Engineering Systems, Inc. (G&E), "NIBS Earthquake Loss Estimation 
Methods, Technical Manual, (Waste Water Systems)", June 1994. 

 

 

 




 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 8.24: Restoration Curves for Lift Stations. 

 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 8.25: Restoration Curves for Waste Water Treatment Plants. 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.26: Fragility Curves for Small Waste Water Treatment Plants with 

Anchored Components. 

 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.27: Fragility Curves for Small Waste Water Treatment Plants with 
Unanchored Components. 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.28: Fragility Curves for Medium Waste Water Treatment Plants with 
Anchored Components. 

 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.29: Fragility Curves for Medium Waste Water Treatment Plants with 
Unanchored Components. 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.30: Fragility Curves for Large Waste Water Treatment Plants with 

Anchored Components. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.31: Fragility Curves for Large Waste Water Treatment Plants with 
Unanchored Components. 

 

 




8.3 Oil Systems 

 

8.3.1 Introduction 

 

This section presents a loss estimation methodology for an oil system during 
earthquakes. 
This system consists of refineries and transmission components. These components 
are 
vulnerable to damage during earthquakes, which may result in significant 
disruption to 
this utility network. 

 

8.3.2 Scope 

 

The scope of this section includes development of methods for estimation of 
earthquake 
damage to an oil system given knowledge of components (i.e. refineries, pumping 
plants, 
and tank farms), classification (i.e. for refineries, with anchored or 
unanchored 
components), and the ground motion (i.e. peak ground velocity, peak ground 
acceleration 
and/or permanent ground deformation). Damage states describing the level of 
damage to 
each of the oil system components are defined (i.e. minor, moderate, extensive 
or 
complete, plus # repairs/km for pipelines). Fragility curves are developed for 
each 
classification of the oil system component. These curves describe the 
probability of 
reaching or exceeding each damage state given the level of ground motion. 

 

Based on these fragility curves, a method for assessing functionality of each 
component 
of the oil system is presented. 

 

8.3.3 Input Requirements and Output Information 

 

Required input to estimate damage to oil described are listed below. 

 

Refineries, Pumping Plants and Tank Farms 

 

 Longitude and latitude of facility 

 PGA and PGD 

 Classification (small, medium/large, with anchored or unanchored components) 

 

Oil Pipelines 

 

 Geographical location of pipe links (longitude and latitude of end nodes) 

 Peak ground velocity and permanent ground deformation (PGV and PGD) 

 Classification 

 

Direct damage output for oil systems includes probability estimates of (1) 
component 
functionality and (2) damage, expressed in terms of the component's damage ratio 
(repair 
cost to replacement cost). Note that damage ratios for each of the oil system 
components 
are presented in section 15.3 of Chapter 15. 

 




8.3.4 Form of Damage Functions 

 

Damage functions or fragility curves for oil system components other than 
pipelines 
are modeled as lognormally-distributed functions that give the probability of 
reaching 
or exceeding different damage states for a given level of ground motion 
(quantified in 
terms of PGA) and ground failure (quantified in terms of PGD). Each of these 
fragility curves is characterized by a median value of ground motion (or 
failure) and 
an associated dispersion factor (lognormal standard deviation). For oil 
piplines, 
empirical relations that give the expected repair rates due to ground motion 
(quantified in terms of PGV) or ground failure (quantified in terms of PGD) are 
provided. 

 

Definitions of various damage states and the methodology used in deriving all 
these 
fragility curves are presented in the next section. 

 

8.3.5 Description of Oil System Components 

 

As mentioned before, an oil system typically consists of refineries, pumping 
plants, tank 
farms, and pipelines. In this section, a brief description of each of these 
components is 
given. 

 

Refineries (RF) 

 

Refineries are an important part of an oil system. They are used for processing 
crude oil before it can be used. Although supply of water is critical to the 
functioning of refinery, it is assumed in the methodology that an uninterrupted 
supply of water is available to the refinery. Two sizes of refineries are 
considered: small, and medium/large. 

 

Small refineries (capacity less than 100,000 barrels per day), are 
assumed to consist of steel tanks on grade, stacks, other electrical and 
mechanical equipment, and elevated pipes. Stacks are essentially tall 
cylindrical chimneys. 

 

Medium/Large refineries (capacity more than 100,000 barrels per day), are 
simulated by adding more redundancy to small refineries (i.e. twice as 
many tanks, stacks, elevated pipes). 

 

Oil Pipelines 

 

Oil pipelines are used for the transportation of oil over long distances. About 
seventy-five percent of the crude oil is transported throughout the United 
States 
by pipelines. A large segment of industry and millions of people could be 
severely affected by disruption of crude oil supplies. Rupture of crude oil 
pipelines could lead to pollution of land and rivers. Pipelines are typically 
made 
of mild steel with submerged arc welded joints, although older gas welded steel 




pipe may be present in some systems. In this sudy, buried pipelines are 
considered to be vulnerable to PGV and PGD. 

 

Pumping Plants (PP) 

 

Pumping plants serve to maintain the flow of oil in cross-country pipelines. 
Pumping plants usually use two or more pumps. Pumps can be of either 
centrifugal or reciprocating type. However, no differentiation is made between 
these two types of pumps in the analysis of oil systems. Pumping plants are 
classified as having either anchored or unanchored subcomponents, as defined in 
7.2.5. 

 

Tank Farms (TF) 

 

Tank farms are facilities that store fuel products. They include tanks, pipes 
and 
electric components. Tank farms are classified as having either anchored or 
unanchored subcomponents, as defined in 7.2.5. 

 

8.3.6 Definitions of Damage States 

 

Oil systems are susceptible to earthquake damage. Facilities such as refineries, 
pumping 
plants and tank farms are mostly vulnerable to PGA, and sometimes PGD, if 
located in 
liquefiable or landslide zones. Therefore, the damage states for these 
components are 
defined and associated with PGA and PGD. Pipelines, on the other hand, are 
vulnerable 
to PGV and PGD. Therefore, the damage states for these components are associated 
with 
these two ground motion parameters. 

 

8.3.6.1 Damage States Definitions for Components other than Pipelines 

 

A total of five damage states are defined for oil system components other than 
pipelines. 
These are none (ds1), slight/minor (ds2), moderate (ds3), extensive (ds4) and 
complete 
(ds5). 

 

Slight/Minor Damage (ds2) 

 

 For refineries, ds2 is defined by malfunction of plant for a short time (few 
days) due to loss of electric power and backup power, if any, or light damage 
to tanks. 

 

 For pumping plants, ds2 is defined by light damage to building. 

 

 For tank farms, ds2 is defined by malfunction of plant for a short time (less 
than three days) due to loss of backup power or light damage to tanks. 

 




Moderate Damage (ds3) 

 

 For refineries, ds3 is defined by malfunction of plant for a week or so due to 
loss of electric power and backup power if any, extensive damage to various 
equipment, or considerable damage to tanks. 

 

 For pumping plants, ds3 is defined by considerable damage to mechanical 
and electrical equipment, or considerable damage to building. 

 

 For tank farms, ds3 is defined by malfunction of tank farm for a week or so 
due to loss of backup power, extensive damage to various equipment, or 
considerable damage to tanks. 

 

Extensive Damage (ds4) 

 

 For refineries, ds4 is defined by the tanks being extensively damaged, or 
stacks collapsing. 

 

 For pumping plants, ds4 is defined by the building being extensively 
damaged, or pumps badly damaged. 

 

 For tank farms, ds4 is defined by the tanks being extensively damaged, or 
extensive damage to elevated pipes. 

 

Complete Damage (ds5) 

 

 For refineries, ds5 is defined by the complete failure of all elevated pipes, 
or 
collapse of tanks. 

 

 For pumping plants, ds5 is defined by the building being in complete damage 
state. 

 

 For tank farms, ds5 is defined by the complete failure of all elevated pipes, 
or collapse of tanks. 

 

8.3.6.2 Damage State Definitions for Pipelines 

 

For pipelines, two damage states are considered. These are leaks and breaks. 
Generally, 
when a pipe is damaged due to ground failure, the type of damage is likely to be 
a break, 
while when a pipe is damaged due to seismic wave propagation; the type of damage 
is 
likely to be local buckling of the pipe walll. In the loss methodology, it is 
assumed that 
damage due to seismic waves will consist of 80% leaks and 20% breaks, while 
damage 
due to ground failure will consist of 20% leaks and 80% breaks. The user can 
override 
these default percentages. 

 




8.3.7 Component Restoration Curves 

 

The restoration curves for the oil system are obtained using the data for mean 
restoration 
time from ATC-13. The restoration functions for pumping plants are similar to 
those of 
pumping plants in potable water system. The data for refineries and tank farms 
are based 
on SF-18b and SF-18d of ATC-13. Means and standard deviations of the restoration 
functions are given in Table 8.17.a. The restoration functions are shown in 
Figures 8.32 
through 8.34. Figure 8.32 represents the restoration functions for refineries, 
Figure 8.33 
represents the restoration curves for tank farms, and Figure 8.34 represents the 
restoration 
curves for buried pipes. The discretized restoration functions are presented in 
Table 
8.17.b, where the restoration percentage is given at discretized times. 
Restoration for oil 
pipelines follows the same approach for potable water pipelines, presented in 
section 
8.1.7 . 

 

Table 8.17.a: Restoration Functions for Oil System Components 

Restoration Functions (All Normal Distributions) 

Classification 

Damage State 

Mean (Days) 

s 

Refineries 

slight/minor 

moderate 

extensive 

complete 

0.4 

3.0 

14.0 

190.0 

0.1 

2.2 

12.0 

80.0 

Tank Farms 

slight/minor 

moderate 

extensive 

complete 

0.9 

7.0 

28.0 

70.0 

0.5 

7.0 

26.0 

55.0 

Pipelines 

See section 8.1.7 



 

Table 8.17.b: Discretized Restoration Functions for Oil System Components 

Discretized Restoration Functions 

Classification 

Damage State 

1 day 

3 days 

7 days 

30 days 

90 days 

 

Refineries 

slight/minor 

moderate 

extensive 

complete 

100 

19 

14 

0 

100 

50 

18 

1 

100 

97 

28 

2 

100 

100 

91 

3 

100 

100 

100 

11 

 

Tank Farms 

slight/minor 

moderate 

extensive 

complete 

58 

20 

15 

11 

100 

29 

17 

12 

100 

50 

21 

13 

100 

100 

54 

24 

100 

100 

100 

65 

Pipelines 

See section 8.1.7 



 

8.3.8 Development of Damage Functions 

 

In this subsection, damage functions for the various components of a refined or 
a crude 
oil system are presented. In cases where the components are made of 
subcomponents 
(i.e., refineries, tank farms and pumping plants), fragility curves for these 
components are 




based on the probabilistic combination of subcomponent damage functions using 
Boolean expressions to describe the relationship of subcomponents. It should be 
mentioned that the Boolean logic is implicitly presented within the definition 
of a 
particular damage state (see section 8.1.8 for an example). 

 

It should be mentioned that damage functions due to ground failure (i.e., PGD) 
for 
refineries, tank farms and pumping plants are assumed to be similar to those 
described for 
potable water system facilities in section 8.1.8. 

 

Damage Functions for Refineries (due to Ground Shaking) 

 

PGA related damage functions for refineries are developed with respect to 
classification. Tables 8.18.a and 8.18.b present damage functions for small and 
medium/large refineries, respectively. These fragility curves are also plotted 
in 
Figures 8.35 through 8.38. The medians and dispersions of damage functions to 
refinery subcomponents are summarized in Tables C.8.1 and C.8.2 of Appendix 
8C. 

 

Table 8.18.a: Damage Algorithms for Small Refineries 

 (Capacity < 100,000 barrels/day) 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Refineries with 
anchored 
components 
(ORF1) 

slight/minor 

moderate 

extensive 

complete 

0.29 

0.52 

0.64 

0.86 

0.55 

0.50 

0.60 

0.55 

Refineries with 
unanchored 
components 
(ORF2) 

slight/minor 

moderate 

extensive 

complete 

0.13 

0.27 

0.43 

0.68 

0.50 

0.50 

0.60 

0.55 



 




Table 8.18.b: Damage Algorithms for Medium/Large Refineries 

 (Capacity = 100,000 barrels/day) 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Refineries with 
anchored 
components 
(ORF3) 

slight/minor 

moderate 

extensive 

complete 

0.38 

0.60 

0.98 

1.26 

0.45 

0.45 

0.50 

0.45 

Refineries with 
unanchored 
components 
(ORF4) 

slight/minor 

moderate 

extensive 

complete 

0.17 

0.32 

0.68 

1.04 

0.40 

0.45 

0.50 

0.45 



 

Damage Functions for Pumping Plants (due to Ground Shaking) 

 

PGA related damage functions for pumping plants are also developed with respect 
to classification and ground motion parameter and are presented in Table 8.19. 
These damage functions are also plotted in Figures 8.39 and 8.40. The medians 
and dispersions of pumping plants subcomponent damage functions are 
summarized in Tables C.8.3 and C.8.4 of Appendix 8C. 

 

Table 8.19: Damage Algorithms for Pumping Plants 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
components 
(OPP1) 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.34 

0.77 

1.50 

0.75 

0.65 

0.65 

0.80 

Plants with 
unanchored 
components 
(OPP2) 

slight/minor 

moderate 

extensive 

complete 

0.12 

0.24 

0.77 

1.50 

0.60 

0.60 

0.65 

0.80 



 

Damage Functions for Tank Farms (due to Ground Shaking) 

 

PGA related damage functions for tank farms are developed with respect to 
classification and ground motion parameter. These damage functions are given in 
terms of median values and dispersions corresponding each damage state in Table 
8.20. The fragility curves are plotted in Figures 8.41 and 8.42. The medians and 
dispersions of tank farms subcomponent damage functions are presnted in 
Appendix 8C. 

 




Table 8.20: Damage Algorithms for Tank Farms 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Plants with 
anchored 
components 
(OTF1) 

slight/minor 

moderate 

extensive 

complete 

0.29 

0.50 

0.87 

0.55 

0.55 

0.50 

Plants with 
unanchored 
components 
(OTF2) 

slight/minor 

moderate 

extensive 

complete 

0.12 

0.23 

0.41 

0.68 

0.55 

0.55 

0.55 

0.55 



 

Damage Functions for Oil Pipelines 

The same two damage algorithms proposed for potable water pipelines are 
assumed to apply for crude and refined oil pipelines. These are listed again in 
Table 8.21. Note that mild steel pipelines with submerged arc welded joints are 
classfied as ductile pipes, while the older gas welded steel pipelines, if any, 
are 
classifed as brittle pipes. In Table 8.21, R.R. stands for repair rates or 
number of 
repairs per kilometer, PGV stands for peak ground velocity in cm/sec, and PGD 
stands for permanent ground deformation in inches. 

 

Table 8.21: Damage Algorithms for Oil Pipelines 

 

PGV Algorithm 

PGD Algorithm 

 

R. R. . 0.0001 x PGV(2.25) 

R. R. . Prob[liq]xPGD(0.56) 

Pipe Type 

Multiplier 

Example of 
Pipe 

Multiplier 

Example of 
Pipe 

Brittle Oil Pipelines (OIP1) 

1 

Steel Pipe 
w/ GasWJ 

1 

Steel Pipe 
w/ GasWJ 

Ductile Oil Pipelines (OIP2) 

0.3 

Steel Pipe 
w/ ArcWJ 

0.3 

Steel Pipe 
w/ ArcWJ 



 

8.3.9 Guidance for Loss Estimation with Advanced Data and Models 

For this type of analysis, the expert can use the methodology developed with the 
flexibility to (1) include a more refined inventory of the oil system pertaining 
to the area 
of study, and (2) include component-specific and system-specific fragility data. 
Default 
damage algorithms for User-Supplied Data Analysis, can be modified or replaced 
to 
incorporate improved information about key components of an oil system. 
Similarly, 
better restoration curves can be developed, given knowledge of available 
resources. 

 

8.3.10 References 

 (1) ATC-13, "Earthquake Damage Evaluation Data for California", Applied 
Technology 
Council, Redwood City, CA, 1985. 

 (2) G&E Engineering Systems, Inc. (G&E), "NIBS Earthquake Loss Estimation 
Methods, 
Technical Manual, (Fuel Systems)", June 1994. 




Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 8.32: Restoration Curves for Refineries. 

 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


 

Figure 8.33: Restoration Curves for Tank Farms. 

 

 

 

 




 

Time [ days ]
Percent Functional [ % ]
0255075100110LeaksBreaks 


Figure 8.34: Restoration Curves for Oil Pipelines. 




 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.35: Fragility Curves for Small Refineries with Anchored Components. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.36: Fragility Curves for Small Refineries with Unanchored Components. 

 

 

 

 

 




 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.37: Fragility Curves for Medium/Large Refineries with Anchored 
Components. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.38: Fragility Curves for Medium/Large Refineries with Unanchored 
Components. 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.39: Fragility Curves for Pumping Plants with Anchored Components. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.40: Fragility Curves for Pumping Plants with Unanchored Components. 

 

 

 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerate/Extensive
Complete 


 

Figure 8.41: Fragility Curves for Tank Farms with Anchored Components. 

 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.42: Fragility Curves for Tank Farms with Unanchored Components. 

 

 




8.4 Natural Gas Systems 

 

8.4.1 Introduction 

 

A natural gas system consists of compressor stations and buried/elevated 
pipelines. Both 
of these components are vulnerable to damage during earthquakes. In addition to 
economic losses, failure of natural gas systems can also cause fires. 

 

8.4.2 Scope 

 

The scope of this section includes development of methods for estimation of 
earthquake 
damage to a natural gas system given knowledge of components (i.e. compressor 
stations), classification (i.e. for compressor stations, with anchored or 
unanchored 
components), and ground motion (i.e. peak ground velocity, peak ground 
acceleration 
and/or permanent ground deformation). Damage states describing the level of 
damage to 
each of the natural gas system components are defined (i.e., minor, moderate, 
extensive 
or complete for facilities and number of repairs/km for pipelines). Fragility 
curves are 
developed for each classification of the natural gas system component. These 
curves 
describe the probability of reaching or exceeding each damage state given the 
level of 
ground motion (or ground failure). Based on these fragility curves, 
functionality of each 
component of the natural gas system can be assessed. 

 

8.4.3 Input Requirements and Output Information 

 

Required input to estimate damage to natural gas systems are described below. 

 

Compressor Stations 

 

 Geographic location of facility (longitude and latitude) 

 PGA and PGD 

 Classification (w/ or w/o anchored components) 

 

Natural Gas Pipelines 

 

 Geographic location of pipeline links (longitude and latitude of end nodes) 

 PGV and PGD 

 Classification 

 

Direct damage output for natural gas systems includes probability estimates of 
(1) 
component functionality and (2) damage, expressed in terms of the component's 
damage 
ratio (repair cost to replacement cost). Note that damage ratios for each of the 
natural gas 
system components are presented in section 15.3 of Chapter 15. 

 




8.4.4 Form of Damage Functions 

 

Damage functions or fragility curves for natural gas system components mentioned 
above 
are lognormally distributed functions that give the probability of reaching or 
exceeding 
different levels of damage for a given level of ground motion. 

 

 For compressor stations, these fragility curves are defined by a median 
PGA/PGD 
and a dispersion. 

 

 For natural gas pipelines, these fragility curves are defined by a median 
PGV/PGD 
and dispersion. 

 

Definitions of various damage states and the methodology used in deriving all 
these 
fragility curves are presented in the next section. 

 

8.4.5 Description of Natural Gas System Components 

 

As mentioned before, a natural gas system typically consists of compressor 
stations and 
pipelines. In this section, a brief description of each of these components is 
given. 

 

Compressor Stations 

 

Compressor stations serve to maintain the flow of gas in cross-country 
pipelines. 
Compressor stations consist of either centrifugal or reciprocating compressors. 
However, no differentiation is made between these two types of compressors in 
the analysis of natural gas systems. Compressor stations are categorized as 
having 
either anchored or unanchored subcomponents, as defined in 7.2.5. The 
compressor stations are similar to pumping plants in oil systems discussed in 
Section 8.3. 

 

Natural Gas Pipelines 

 

Pipelines are typically made of mild steel with submerged arc welded joints, 
although older lines may have gas-welded joints. These are used for the 
transportation of natural gas over long distances. Many industries and residents 
could be severely affected should disruption of natural gas supplies occur. 

 

8.4.6 Definitions of Damage States 

 

Facilities such as compressor stations are mostly vulnerable to PGA, sometimes 
PGD, if 
located in liquefiable or landslide zones. Therefore, damage states for these 
components 
are defined and associated with either PGA or PGD. Pipelines, on the other hand, 
are 
vulnerable to PGV and PGD; therefore, damage states for these components are 
associated with these two ground motion parameters. 

 

8.4.6.1 Damage States Definitions for Compressor Stations 




 

A total of five damage states are defined for gas system components. These are 
none 
(ds1), slight/minor (ds2), moderate (ds3), extensive (ds4) and complete (ds5). 

 

Slight/Minor Damage (ds2) 

 

 ds2 is defined by slight damage to building. 

 

Moderate Damage (ds3) 

 

 ds3 is defined by considerable damage to mechanical and electrical 
equipment, or considerable damage to building. 

 

Extensive Damage (ds4) 

 

 ds4 is defined by the building being extensively damaged, or the pumps 
badly damaged beyond repair. 

 

Complete Damage (ds5) 

 

 ds5 is defined by the building in complete damage state. 

 

8.4.6.2 Damage States Defintions for Pipelines 

 

For pipelines, two damage states are considered. These are leaks and breaks. 
Generally, 
when a pipe is damaged due to ground failure, the type of damage is likely to be 
a break, 
while when a pipe is damaged due to seismic wave propagation; the type of damage 
is 
likely to be local bucking of the pipe wall. In the loss methodology, it is 
assumed that 
damage due to seismic waves will consist of 80% leaks and 20% breaks, while 
damage 
due to ground failure will consist of 20% leaks and 80% breaks. The user can 
override 
these default percentages. 

 

8.4.7 Component Restoration Curves 

 

The restoration curves for natural gas system components are similar to those of 
the oil 
system discussed in Section 8.3.7. Compressor stations in natural gas systems 
are 
analogous to pumping plants in oil systems. 

 

8.4.8 Development of Damage Functions 

 

Fragility curves for natural gas system components are defined with respect to 
classification and ground motion parameter. 

 




Damage Functions for Compressor Stations 

 

Damage functions for compressor stations are taken as identical to those of 
pumping plants in oil systems discussed in Section 8.3.8. 

 

Damage Functions for Pipelines 

 

Damage functions for natural gas pipelines are taken as identical to those for 
oil 
pipelines discussed in Section 8.3.8. 

 

8.4.9 Guidance for Loss Estimation with Advanced Data and Models Analysis 

 

For this type of analysis, the expert can use the methodology developed with the 
flexibility to (1) include a more refined inventory of the natural gas system 
pertaining to 
the area of study, and (2) include component-specific and system-specific 
fragility data. 
Default damage algorithms for User-Supplied Data Analysis can be modified or 
replaced 
to incorporate improved information about key components of a natural system. 
Similarly, better restoration curves can be developed, given knowledge of 
available 
resources. 

 

8.4.10 References 

 

(1) ATC-13, "Earthquake Damage Evaluation Data for California", Applied 
Technology 
Council, Redwood City, CA, 1985. 

 

(2) G&E Engineering Systems, Inc. (G&E), "NIBS Earthquake Loss Estimation 
Methods, Technical Manual, (Fuel Systems)", June 1994. 

 




8.5 Electric Power Systems 

 

8.5.1 Introduction 

 

This section presents the earthquake loss estimation methodology for an electric 
power 
system. This system consists of generation facilities, substations, and 
distribution 
circuits. All of these components are vulnerable to damage during earthquakes, 
which 
may result in significant disruption of power supply. 

 

8.5.2 Scope 

 

The scope of this section includes development of methods for estimating 
earthquake 
damage to an electric power system given knowledge of components (i.e. 
generation 
facilities, substations, and distribution circuits), classification (i.e., for 
substations, low 
voltage, medium voltage, or high voltage), and the ground motion (i.e. peak 
ground 
acceleration and permanent ground deformation). Damage states describing the 
level of 
damage to each of the electric power system components are defined (i.e., minor, 
moderate, extensive or complete). Fragility curves are developed for each 
classification 
of the electric power system component. These curves describe the probability of 
reaching or exceeding each damage state given the level of ground motion. 

 

Based on these fragility curves, the method for assessing functionality of each 
component 
of the electric power system is presented. 

 

8.5.3 Input Requirements and Output Information 

 

Required input to estimate damage to electric power systems includes the 
following 
items: 

 

Substations 

 

 Longitude and latitude of facility 

 PGA and PGD 

 Classification (low, medium, or high voltage; with anchored or standard 
components) 

 

Distribution Circuits 

 

 Longitude and latitude of facility 

 PGA 

 Classification (seismically designed or standard components) 

 

Generation Plants 

 

 Longitude and latitude of facility 

 PGA 

 Classification (small or medium/large, with anchored or unanchored components) 




 

Direct damage output for an electric power system includes probability estimates 
of (1) 
component functionality and (2) damage, expressed in terms of the component's 
damage 
ratio. Damage ratios for electric power systems components are presented in 
section 15.3 
of Chapter 15. A simplified system performance evaluation methodology is also 
provided. The output from this simplified version of system analysis consists of 
a 
probabilistic estimate for the power outage. 

 

8.5.4 Form of Damage Functions 

 

Damage functions or fragility curves for all electric power system components 
mentioned 
above are modeled as lognormally-distributed functions that give the probability 
of 
reaching or exceeding different levels of damage for a given level of ground 
motion. 
These fragility curves are defined by a median ground motion parameter and a 
dispersion. 

 

8.5.5 Description of Electric Power System Components 

 

As mentioned before, the components of an electric power system considered in 
the loss 
estimation methodology are substations, distribution circuits, and generation 
plants. In 
this section a brief description of each of these components is presented. 

 

Substations 

 

An electric substation is a facility that serves as a source of energy supply 
for the 
local distribution area in which it is located, and has the following main 
functions: 

 

- Change or switch voltage from one level to another. 

- Provide points where safety devices such as disconnect switches, circuit 
breakers, and other equipment can be installed. 

- Regulate voltage to compensate for system voltage changes. 

- Eliminate lightnning and switching surges from the system. 

- Convert AC to DC and DC to AC, as needed. 

- Change frequency, as needed. 

 

Substations can be entirely enclosed in buildings where all the equipment is 
assembled into one metal clad unit. Other substations have step-down 
transformers, high voltage switches, oil circuit breakers, and lightning 
arrestors 
located outside the substation building. In the current loss estimation 
methodology, only transmission (138 kV to 765 kV or higher) and 
subtransmission (34.5 kV to 161 kV) substations are considered. These will be 
classified as high voltage (350 kV and above), medium voltage (150 kV to 350 
kV) and low voltage (34.5 kV to 150 kV), and will be referred to as 500 kV 
substations, 230kV substations, and 115kV substations, respectively. The 
classification is also a function of whether the subcomponents are anchored or 
typical (unanchored), as defined in 7.2.5. 




 

Distribution Circuits 

 

The distribution system is divided into a number of circuits. A distribution 
circuit 
includes poles, wires, in-line equipment and utility-owned equipment at customer 
sites. A distribution circuit also includes above ground and underground 
conductors. Distribution circuits either consist of anchored or unanchored 
components. 

 

Generation Plants 

 

These plants produce alternating current (AC) and may be any of the following 
types: 

 

- Hydroelectric 

- Steam turbine (fossil fuel fired or nuclear) 

- Combustion turbine (fossil fuel fired) 

- Geothermal 

- Solar 

- Wind 

- Compressed air 

Fossil fuels are either coal, oil, or natural gas. 

Generation plant subcomponents include diesel generators, turbines, racks and 
panels, boilers and pressure vessels, and the building in which these are 
housed. 

 

The size of the generation plant is determined from the number of Megawatts of 
electric power that the plant can produce under normal operations. Small 
generation plants have a generation capacity of less than 200 Megawatts. 
Medium/Large generation plants have a capacity greater than 200 Megawatts. 
Fragility curves for generation plants with anchored versus unanchored 
subcomponents are presented. 

 

8.5.6 Definitions of Damage States 

 

Electric power systems are susceptible to earthquake damage. Facilities such as 
substations, generation plants, and distribution circuits are mostly vulnerable 
to PGA, and 
sometimes PGD, if located in liquefiable or landslide zones. Therefore, the 
damage 
states for these components are defined in terms of PGA and PGD. 

 

A total of five damage states are defined for electric power system components. 
These 
are none (ds1), slight/minor (ds2), moderate (ds3), extensive (ds4) and complete 
(ds5). 

 

Note that for power systems, in particular for substations and distribution 
circuits, these 
damage states are defined with respect to the percentage of subcomponents being 
damaged. That is, for a substation with n1 transformers, n2 disconnect switches, 
n3 
circuit breakers, and n4 current transformers, the substation is said to be in a 
slight or 




minor damage state if 5% of n2 or 5% of n3 are damaged, and it is in the 
extensive 
damage state if 70% of n1, 70% of n2, or 70% of n3 are damaged, or if the 
building is in 
extensive damage state. A parametric study on n1, n2, n3, and n4 values shows 
that the 
medians of the damage states defined in this manner don't change appreciably 
(less than 3 
%) as the ni's vary, while the corresponding dispersions get smaller as the ni's 
increase. 
Therefore, we used dispersions obtained from the small sample numbers along with 
the 
relatively constant median values. 

 

Slight/Minor Damage (ds2) 

 

 For substations, ds2 is defined as the failure of 5% of the disconnect 
switches 
(i.e., misalignment), or the failure of 5 % of the circuit breakers (i.e., 
circuit 
breaker phase sliding off its pad, circuit breaker tipping over, or interrupter-
head falling to the ground), or by the building being in minor damage state. 

 

 For distribution circuits, ds2 is defined by the failure of 4 % of all 
circuits. 

 

 For generation plants, ds2 is defined by turbine tripping, or light damage to 
diesel generator, or by the building being in minor damage state. 

 

Moderate Damage (ds3) 

 

 For substations, ds3 is defined as the failure of 40% of disconnect switches 
(e.g., misalignment), or 40% of circuit breakers (e.g., circuit breaker phase 
sliding off its pad, circuit breaker tipping over, or interrupter-head falling 
to 
the ground), or failure of 40% of current transformers (e.g., oil leaking from 
transformers, porcelain cracked), or by the building being in moderate damage 
state. 

 

 For distribution circuits, ds3 is defined by the failure of 12% of circuits. 

 

 For generation plants, ds3 is defined some by the chattering of instrument 
panels and racks, considerable damage to boilers and pressure vessels, or by 
the building being in moderate damage state. 

 

Extensive Damage (ds4) 

 

 For substations, ds4 is defined as the failure of 70% of disconnect switches 
(e.g., misalignment), 70% of circuit breakers, 70% of current transformers 
(e.g., oil leaking from transformers, porcelain cracked), or by failure of 70% 
of transformers (e.g., leakage of transformer radiators), or by the building 
being in extensive damage state. 

 

 For distribution circuits, ds4 is defined by the failure of 50% of all 
circuits. 

 




 For generation plants, ds4 is defined by considerable damage to motor driven 
pumps, or considerable damage to large vertical pumps, or by the building 
being in extensive damage state. 

 

Complete Damage (ds5) 

 

 For substations, ds5 is defined as the failure of all disconnect switches, all 
circuit breakers, all transformers, or all current transformers, or by the 
building being in complete damage state. 

 For distribution circuits, ds5 is defined by the failure of 80% of all 
circuits. 

 For generation plants, ds5 is defined by extensive damage to large horizontal 
vessels beyond repair, extensive damage to large motor operated valves, or by 
the building being in complete damage state. 

 

8.5.7 Component Restoration Curves 

 

Restoration curves for electric substations and distribution circuits are based 
on a G&E 
report (1994), while restoration curves for generation facilities are obtained 
using the 
data for mean restoration times from ATC-13 social function SF-29.a (the first 
four 
damage states). These functions are presented in Tables 8.22.a and 8.22.b. The 
first 
table gives means and standard deviations for each restoration curve (i.e., 
smooth 
continuous curve), while the second table gives approximate discrete functions 
for the 
restoration curves developed. These restoration functions are also shown in 
Figures 8.43 
through 8.45. 

 

Table 8.22.a: Restoration Functions for Electric Power System Components 

Restoration Functions (All Normal Distributions) 

Classification 

Damage State 

Mean (Days) 

 

Electric Sub-
Stations 

slight/minor 

moderate 

extensive 

complete 

1.0 

3.0 

7.0 

30.0 

0.5 

1.5 

3.5 

15.0 

Distribution Circuits 

slight/minor 

moderate 

extensive 

complete 

0.3 

1.0 

3.0 

7.0 

0.2 

0.5 

1.5 

3.0 

Generation 
Facilities 

slight/minor 

moderate 

extensive 

complete 

0.5 

3.6 

22.0 

65.0 

0.1 

3.6 

21.0 

30.0 



 




 

Table 8.22.b: Discretized Restoration Functions for Electric Power 
Components 

Discretized Restoration Functions 

Classification 

Damage State 

1 day 

3 days 

7 days 

30 days 

90 days 

 

Electric Sub-
Stations 

slight/minor 

moderate 

extensive 

complete 

50 

9 

4 

3 

100 

50 

13 

4 

100 

100 

50 

7 

100 

100 

100 

50 

100 

100 

100 

100 

 

Distribution Circuits 

slight/minor 

moderate 

extensive 

complete 

100 

50 

9 

2 

100 

100 

50 

10 

100 

100 

100 

50 

100 

100 

100 

100 

100 

100 

100 

100 

 

Generation 
Facilities 

slight/minor 

moderate 

extensive 

complete 

100 

24 

16 

2 

100 

44 

19 

2 

100 

83 

24 

3 

100 

100 

65 

13 

100 

100 

100 

80 



 

8.5.8 Development of Damage Functions 

 

Fragility curves for electric power system components are defined with respect 
to 
classification and ground motion parameters. These curves are based on the 
probabilistic 
combination of subcomponent damage functions using Boolean expressions to 
describe 
the relationship of subcomponents. The Boolean approach involves evaluation of 
the 
probability of each component reaching or exceeding different damage states, as 
defined 
by the damage level of its subcomponents. It should be mentioned that the 
Boolean logic 
is implicitly presented within the definition of a particular damage state. For 
example, 
the moderate damage state for substations is defined as the failure of 40% of 
disconnect 
switches, OR the failure of 40% of circuit breakers, OR the failure of 40% of 
transformers, OR by the building being in moderate damage state. Therefore, the 
fault 
tree for moderate damage for substations has FOUR primary OR branches: 
disconnect 
switches, circuit breakers, transformers, and building. Within the first 3 OR 
branches 
(i.e., disconnect switches, circuit breakers, and transformers) the multiple 
possible 
combinations are considered. These evaluations produce component probabilities 
at 
various levels of ground motion. In general, the Boolean combinations do not 
produce a 
lognormal distribution, so a lognormal curve that best fits this probability 
distribution is 
determined numerically. 

 

Damage functions due to ground failure (i.e., PGD) for substations and 
generation plants 
are assumed to be similar to those described for potable water system facilities 
in section 
8.1.8. 

 

PGA Related Damage Functions for Electric Power Substations 

 

A total of 24 sub-station damage functions are used in the methodology. Half of 
these damage functions correspond to substations with anchored components, 
while the other half correspond to substations with unanchored components. 




Medians and dispersions of these damage functions are given in Tables 8.23 and 
8.24. These damage functions are also presented in the form of fragility curves 
in 
Figures 8.46 through 8.51. Note that each figure contains four damage functions. 

 

Table 8.23: Damage Algorithms for Substations 

(Anchored / Seismic Components) 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Low voltage 
(ESS1) 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.29 

0.45 

0.90 

0.70 

0.55 

0.45 

0.45 

Medium voltage 
(ESS3) 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.25 

0.35 

0.70 

0.60 

0.50 

0.40 

0.40 

High voltage 
(ESS5) 

slight/minor 

moderate 

extensive 

complete 

0.11 

0.15 

0.20 

0.47 

0.50 

0.45 

0.35 

0.40 



 

Table 8.24: Damage Algorithms for Substations 

(Unanchored / Standard Components) 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Low voltage 
(ESS2) 

slight/minor 

moderate 

extensive 

complete 

0.13 

0.26 

0.34 

0.74 

0.65 

0.50 

0.40 

0.40 

Medium voltage 
(ESS4) 

slight/minor 

moderate 

extensive 

complete 

0.10 

0.20 

0.30 

0.50 

0.60 

0.50 

0.40 

0.40 

High voltage 
(ESS6) 

slight/minor 

moderate 

extensive 

complete 

0.09 

0.13 

0.17 

0.38 

0.50 

0.40 

0.35 

0.35 



 

PGA Related Damage Functions for Distribution Circuits 

 

A total of 8 distribution circuits damage functions are obtained. Four of these 
damage functions correspond to distribution circuits with seismically designed 
components, while the other four correspond to distribution circuits with 
standard 
components. Medians and dispersions of these damage functions are presented in 
Table 8.25 and plotted in Figures 8.52 and 8.53. Note that subcomponent damage 
functions of a distribution circuit are presented in Table D.8.7 of Appendix 8D. 




Table 8.25: Damage Algorithms for Distribution Circuits 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Seismic 
Components 
(EDC1) 

slight/minor 

moderate 

extensive 

complete 

0.28 

0.40 

0.72 

1.10 

0.30 

0.20 

0.15 

0.15 

Standard 
Components 
(EDC2) 

slight/minor 

moderate 

extensive 

complete 

0.24 

0.33 

0.58 

0.89 

0.25 

0.20 

0.15 

0.15 



 

PGA Related Damage Functions for Generation Plants 

 

A total of 16 damage functions for generation plants are developed. Eight of 
these damage functions correspond to small generation plants (less than 200 
MW), while the other eight correspond to medium/large plants (more than 200 
MW). Medians and dispersions of these damage functions are given in Tables 
8.26 and 8.27. These damage functions are also shown as fragility curves in 
Figures 8.54 through 8.57. Note that subcomponent damage functions of a 
generation plant are presented in Tables D.8.8 and D.8.9 of Appendix 8D. 

 

Table 8.26: Damage Algorithms for Small Generation Facilities 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Facility with 
Anchored 
Components 
(EPP1) 

slight/minor 

moderate 

extensive 

complete 

0.10 

0.21 

0.48 

0.78 

0.55 

0.55 

0.50 

0.50 

Facility with 
Unanchored 
Components 
(EPP2) 

slight/minor 

moderate 

extensive 

complete 

0.10 

0.17 

0.42 

0.58 

0.50 

0.50 

0.50 

0.55 



 

Table 8.27: Damage Algorithms for Medium/Large Generation Facilities 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

Facility with 
Anchored 
Components 
(EPP3) 

slight/minor 

moderate 

extensive 

complete 

0.10 

0.25 

0.52 

0.92 

0.60 

0.60 

0.55 

0.55 

Facility with 
Unanchored 
Components 
(EPP4) 

slight/minor 

moderate 

extensive 

complete 

0.10 

0.22 

0.49 

0.79 

0.60 

0.55 

0.50 

0.50 



 




8.5.9 Power Outage and Performance Evaluation for Electric Power Systems 

 

For electric power systems, power service outages for the study region are 
assumed to be 
dependent on the nonfunctionality of substations servicing the region. This 
component is 
in fact among one of the more vulnerable electric power component to earthquake, 
and 
damage to this facility affects wide areas. 

 

Example 

Assume that in a study region, in the Western US, there are 2 medium voltage 
substations, both with anchored designed components. At one facility the PGA is 
0.15g 
while at the other facility the PGA is 0.3g. We want to evaluate the electric 
power 
system performance. The damage and restoration algorithms for medium voltage 
substations are reproduced in Table 8.28. 

 




Table 8.28: Electric Power System Performance Example Parameters 

Medium Voltage Substations with Seismic Components 

Damage State 

Median (g) 

 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.25 

0.35 

0.7 

0.6 

0.5 

0.4 

0.4 

Continuous Restoration Functions 

(All Normal Distributions) 

Damage State 

Mean (days) 

s (days) 

slight/minor 

moderate 

extensive 

complete 

1.0 

3.0 

7.0 

30 

0.5 

1.5 

3.5 

15 

Discretized Restoration Functions 

Damage 
State 

3 days 

7 days 

30 days 

90 days 

slight/minor 

moderate 

extensive 

complete 

100 

50 

13 

4 

100 

100 

50 

7 

100 

100 

100 

50 

100 

100 

100 

100 



 

The discrete probabilities for different damage states are then determined at 
these two 
substations: 

 

 At Substation 1, 

P[ Ds = ds1 | PGA = 0.15g ] = 0.50 

P[ Ds = ds2 | PGA = 0.15g ] = 0.35 

P[ Ds = ds3 | PGA = 0.15g ] = 0.13 

P[ Ds = ds4 | PGA = 0.15g ] = 0.02 

P[ Ds = ds5 | PGA = 0.15g ] = 0.00 

At substation 2, 

P[ Ds = ds1 | PGA = 0.3g ] = 0.12 

P[ Ds = ds2 | PGA = 0.3g ] = 0.24 

P[ Ds = ds3 | PGA = 0.3g ] = 0.29 

P[ Ds = ds4 | PGA = 0.3g ] = 0.33 

P[ Ds = ds5 | PGA = 0.3g ] = 0.02 

 

 The best estimate of functionality for each restoration period is estimated by 
the 
weighted combination: 

FPc = FRi x P[dsi] 
i=1i=5S

 

In this example, the weighted combination after 3 days would be: 

 

At substation # 1, 

FPc [3 days] = 0.5 x 100% + 0.35 x 100% + 0.13 x 50% + 0.02 x 13% + 0.0 x 4% 




= 91.8 % 

 

At substation # 2, 

FPc [3 days] = 0.12 x 100 %+ 0.24 x 100% + 0.29 x 50% + 0.33 x 13% + 0.02 x 
4% 

 = 54.9 % 

 

Therefore, in the study region and 3 days after the earthquake, about 8% of the 
area 
serviced by substation # 1 will be still suffering power outage while 45% of the 
area 
serviced by substation # 2 will be still out of power, or in average 23% of the 
whole 
study region will be out of power. 

 

Note that the expected number of customers without power after each restoration 
period 
is estimated by multiplying the probability of power outage with the number of 
households (housing units) in each census tract. 

 

Finally, it should be mentioned that the interaction between electric power and 
other 
lifeline systems was considered marginally through a fault tree analysis. Loss 
of electric 
power is assumed to affect only the slight/minor and moderate damage states of 
other 
lifeline systems that depend on power. This assumption is based on the fact that 
if a 
water treatment plant, for example, is in the extensive damage state that the 
availability 
of power becomes of secondary importance. The fault tree analysis also assumes 
that the 
substation serving the other lifeline components it interacts with will be 
subject to a 
comparable level of ground motion. The following generic electric power damage 
functions (based largely on medium voltage substations damage functions) are 
considered for lifeline interaction: 

 

Table 8.29: Generic Damage Algorithm for Electric Power System 

Peak Ground Acceleration 

Subcomponents 

Damage State 

Median (g) 

 

Loss of 
Commercial 
Power 

slight/minor 

moderate 

0.15 

0.30 

0.40 

0.40 



 

 

8.5.10 Guidance for Loss Estimation with Advanced Data and Models Analysis 

 

For this type of analysis, the expert can use the methodology developed for 
User-
Supplied Data Analysis with the flexibility to (1) include a refined inventory 
of the 
electric power system pertaining to the area of study, and (2) include 
component-specific 
and system-specific fragility data, and (3) perform a network analysis of actual 
circuits to 
better estimate the overall system functionality. Default damage algorithms for 
User-
Supplied Data Analysis can be modified or replaced to accommodate any specified 
key 
component of an electric power system. Similarly, better restoration curves 
could be 




developed given knowledge of available resources and a more accurate layout of 
the 
network within the local topographic and geological conditions. 

 

8.5.11 References 

 

(1) ATC-13, "Earthquake Damage Evaluation Data for California", Applied 
Technology Council, 
Redwood City, CA, 1985. 

 (2) G&E Engineering Systems, Inc. (G&E), "NIBS Earthquake Loss Estimation 
Methods, 
Technical Manual, (Electric Power Systems)", June 1994. 

 (3) Schiff A., "Seismic Design Practices for Power Systems: Evolution, 
Evaluation, and Needs", 
TCLEE Monograph No. 4 August, 1991. 

 (4) Matsuda et al., "Earthquake Evaluation of a Substation Network", TCLEE 
Monograph No. 4 
August, 1991. 




Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveComplete 


Figure 8.43: Restoration Curves for Electric Substations. 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveComplete 


Figure 8.44: Restoration Curves for Distribution Circuits. 

 

 

 

 

 

 




 

 

 

 

 

 

 

 

Time (days) 
Percent Functional 
02550751001101001000MinorModerateExtensiveCompleteATC-13 Data 


Figure 8.45: Restoration Curves for Generation Facilities. 

 

 

 

 

 

 

 

 

 

 

 

 

 




Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveCompleteFigure 8.46: Fragility Curves for Low voltage Substations 
with Seismic 
Components. 

 

 

Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 8.47: Fragility Curves for Medium Voltage Substations with Seismic 
Components. 

 




Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 8.48: Fragility Curves for High Voltage Substations with Seismic 
Components. 

 

 

 

Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 8.49: Fragility Curves for Low Voltage Substations with Standard 
Components. 

 




Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 8.50: Fragility Curves for Medium Voltage Substations with Standard 
Components. 

 

 

Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 8.51: Fragility Curves for High Voltage Substations with Standard 
Components. 

 

 

 

 




Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.000.250.500.751.000.000.200.400.600.801.001.201.40Slight/MinorModerateExtensiv
eComplete 


Figure 8.52: Fragility Curves for Seismic Distribution Circuits. 

 

 

Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.000.250.500.751.000.000.200.400.600.801.001.201.40Slight/MinorModerateExtensiv
eComplete 


Figure 8.53: Fragility Curves for Standard Distribution Circuits. 

 

 




Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


 

Figure 8.54: Fragility Curves for Small Generation Facilities with Anchored 
Components. 

 

 

Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 8.55: Fragility Curves for Small Generation Facilities with Unanchored 
Components. 

 




Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 8.56: Fragility Curves for Medium/Large Generation Facilities with 
Anchored Components. 

 

 

Peak Ground Acceleration (g)
Probability [ Ds > ds | PGA ] 
0.00000.25000.50000.75001.00000.000.200.400.600.801.001.201.40Slight/MinorModera
teExtensiveComplete 


Figure 8.57: Fragility Curves for Medium/Large Generation Facilities with 
Unanchored Components. 

 




 

8

 

T

earthquakes. The major components of a communication system are: 


 

central switching equipment) 
Transmission lines (these incl

connect central office to end users) 
Cabling (low capacity links) 

 

C

system considered in this section. Therefore, fragility curves are presented for 
these 
components only. Other components, such as cables and other lines, usually have 
enough 
slack to accomodate ground shaking and even moderate amounts of permanent ground 
deformations. 


8

 

T

damage to a communication facility given knowledge of its subcomponents (i.e., 
building 
type, switching equipment, backup power and off-site power), classification 
(i.e., for 
equipment, anchored versus unanchored components), and the ground motion (i.e., 
peak 
ground acceleration and/or permanent ground deformation). 


D

(i.e. slight, moderate, extensive or complete). Fragility curves are developed 
for each 
classification of the communication system component. These curves describe the 
probability of reaching or exceeding each damage state given the level of ground 
motion 
or ground failure. Restoration curves are also provided to evaluate the loss of 
function. 


8

 

R

items: 


 PGA 

 Classi

 

D

component (i.e. central office / broadcasting station) functionality and (2) 
damage, 
expressed in terms of the component's damage ratio. Damage ratios for a 
communication 
facility are presented in section 15.3 of Chapter 15. 





8

 

D

lognormally-distributed functions that give the probability of reaching or 
exceeding 
different damage states for a given level of ground motion (quantified in terms 
of PGA) 
and ground failure (quantified in terms of PGD). Each of these fragility curves 
is 
characterized by a median value of ground motion and an associated dispersion 
factor 
(lognormal standard deviation). Definitions of various damage states and the 
methodology used in deriving all these fragility curves are presented in the 
following 
section. 


8

 

A

consists of a building (generic type is assumed in the methodology), central 
switching 
equipment (i.e., digital switches, anchored or unanchored), and back-up power 
supply 
(i.e. diesel generators or battery generators, anchored or unanchored) that may 
be needed 
to supply the requisite power to the center in case of loss of off-site power. 


8

 

C

states are defined for these components. These are none (ds1), slight/minor 
(ds2), 
moderate (ds3), extensive (ds4) and complete (ds5). 


Sl

 

S

building, or inability of the center to provide services during a short period 
(few days) due to loss of electric power and backup power, if available. 


 

M

facility building, few digital switching boards being dislodged, or the central 
office being out of service for a few days due to loss of electric power (i.e., 
power failure) and backup power (typically due to overload), if available. 





Extensive Damage (ds4) 

 

 Extensive damage, ds4 is defined by severe damage to the communication 
facility building resulting in limited access to facility, or by many digital 
switching boards being dislodged, resulting in malfunction. 

 

Complete Damage (ds5) 

 

 Complete damage, ds5 is defined by complete damage to the communication 
facility building, or damage beyond repair to digital switching boards. 

 

8.6.7 Component Restoration Curves 

 

Restoration functions are shown in Figures 8.58, 8.59 and 8.60. Figure 8.58 is 
based on 
ATC-13 social function SF-33a (first four damage states). The curves in this 
figure are 
obtained in a similar manner to the restoration curves for other lifeline 
systems. The 
parameters of these restoration curves are given in Table 8.30.a and 8.30.b. The 
best-fit 
normal distribution to the data shown in Figure 8.59 has a mean of 3 days and a 
standard 
deviation of 3 days. This restoration curve corresponds to the case where (1) 
the 
communication facility building does not suffer extensive damage (major 
structural 
damage would require extended period of time to repair), and (2) the 
communication 
network did not suffer extensive damage. In essence, the plotted restoration 
curve in 
Figure 8.59 corresponds to the communication facility being in moderate to 
extensive 
damage state, according to the definitions of damage states presented herein. 

 

 

Table 8.30.a: Continuous Restoration Functions for Communication Facilities 

(After ATC-13, 1985) 

Restoration Functions (All Normal Distributions) 

Classification 

Damage State 

Mean (Days) 

s 

 

Communication 
facility 

slight/minor 

moderate 

extensive 

complete 

0.5 

1 

7 

40 

0.2 

1.0 

7.0 

40.0 



 

 

Table 8.30.b: Discretized Restoration Functions for Communication Facilities 

Discretized Restoration Functions 

Classification 

Damage State 

1 day 

3 days 

7 days 

30 days 

90 days 

 

Communication 
facility 

slight/minor 

moderate 

extensive 

complete 

99 

50 

20 

16 

100 

98 

28 

18 

100 

100 

50 

20 

100 

100 

100 

40 

100 

100 

100 

89 






A recently published paper by Tang and Wong (1994) on the performance of 
telecommunication systems in the Northridge Earthquake of January 17, 1994 
indicates 
that within three days the system stabilized. Table 8.31 shows the system 
performance 
during the three days following that quake. 

 

Table 8.31: Daily Call Attempts as Recorded in a Central Office in the Afflicted 
Area 

(Tang and Wong, 1994) 

 

Daily Call Attempts in 1,000s 

 

Jan 17 

Jan 18 

Jan 19 

Jan 20 

1993 

Average 

Call Attemtps 

5,455 

4,237 

3,240 

2,860 

1,500 

Performance 

86.9% 

95.2% 

96.0% 

97.6% 

99.3% 



 

8.6.8 Development of Damage Functions 

 

In this subsection, damage functions for the central offices are presented. 
Fragility 
curves for these components are based on the probabilistic combination of 
subcomponent 
damage functions using Boolean expressions to describe the relationship of 
subcomponents to the component. It should be mentioned that the Boolean logic is 
implicitly presented within the definition of the damage state (see section 
8.1.8 for an 
example). Note also that damage functions due to ground failure (i.e., PGD) for 
central 
offices are assumed to be similar to those described for potable water system 
facilities. 

 

PGA related damage functions are given in terms of median values and dispersions 
for 
each damage state in Table 8.32. These are also plotted in Figures 8.61.a and 
8.61.b. 

 

Table 8.32: Damage Algorithms for Communication Facilities 

Peak Ground Acceleration 

Classification 

Damage State 

Median (g) 

 

 

Facilities with 
anchored components 

slight/minor 

moderate 

extensive 

complete 

0.15 

0.32 

0.60 

1.25 

0.75 

0.60 

0.62 

0.65 

Facilities with 
unanchored 
components 

slight/minor 

moderate 

extensive 

complete 

0.13 

0.26 

0.46 

1.03 

0.55 

0.50 

0.62 

0.62 



 

 

8.6.9 Guidance for Loss Estimation Using Advanced Data and Models Analysis 

 

For this type of analysis, the expert can use the methodology developed for the 
User-
Supplied Data Analysis with the flexibility to: (1) include a refined inventory 
of the 
communication system pertaining to the area of study, and (2) include specific 
and 
system specific fragility data. Default damage algorithms for User-Supplied Data 




Analysis, can be modified or replaced to accommodate any specified key component 
of a 
communication system, such as switching equipment. Similarly, better restoration 
curves 
could be developed given knowledge of the redundancy importance of a 
communication 
system components in the network, the availability of resources and a more 
accurate 
layout of the communication network within the local topographic and geological 
conditions. 

 

8.6.10 References 

 

(1) Tang A. and Wong F., "Observation on Telecommunications Lifeline Performance 
in the 
Northridge Earthquake of January 17, 1994, Magnitude 6.6", 1994. 

(2) Tang A., "Two Decades of Communications Systems Seismic Protection 
Improvements", 
TCLEE Monograph No. 4 August, 1991. 

(3) G&E Engineering Systems, Inc. (G&E), "NIBS Earthquake Loss Estimation 
Methods, 
Technical Manual, (Communication Systems)", June 1994. 

(4) ATC-13, "Earthquake Damage Evaluation Data for California", Applied 
Technology Council, 
Redwood City, CA, 1985. 

 




 

Time [ Days ]
Percent Functional [ % ] 
0.0025.0050.0075.00100.001101001000MinorModerateExtensiveCompleteATC-13 Data 


 

 

Figure 8.58: Restoration Curves for Central Offices (after ATC-13, 1985). 

 

Restoration Time (days)
Percentage of Normal025507510002468 


 

Figure 8.59: Restoration Curve for Communication System Service: Normal 
Service (After G&E, 1994). 

 




Time After Earthquake - DaysPercentage of 
Normal05010015020025030035001234567Hardware CapacityCall AttemptsNormal Service 


Figure 8.60: Communication System Service Restoration (after G&E, 1994). 

 

Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.61.a: Fragility Curves for Communication Systems with Anchored 
Components. 

 

 

 




Peak Ground Acceleration (g)
[ Probability Ds > ds | PGA ] 
0.0000.2500.5000.7501.0000.000.200.400.600.801.001.201.40MinorModerateExtensiveC
omplete 


Figure 8.61.b: Fragility Curves for Communication Systems with Unanchored 
Components. 

 

 




 

Appendix 8A 

Subcomponent Damage Functions for Potable Water Systems 

 

 

Any given subcomponent in the lifeline methodology can experience all five 
damage 
states; however, the only damage states listed in the appendices of Chapters 7 
and 8 are 
the ones used in the fault tree logic of the damage state of interest of the 
component. 

 

 

Table A.8.1: Subcomponent Damage Algorithms for Pumping Plants 

With Anchored Components (after G&E, 1994) 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median 
(g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.80 

1.00 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Vertical/ Horizontal 
Pump* 

extensive 

1.25/1.60 

0.60 

Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 

Equipment 

moderate 

1.00 

0.60 



* Difference in median values has little effect on the fault tree analysis 

 




Table A.8.2: Subcomponent Damage Algorithms for Pumping Plants 

with Unanchored Components (after G&E, 1994) 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median 
(g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.20 

0.40 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Vertical/ 
Horizontal Pump* 

extensive 

1.25/1.60 

0.60 

Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 

Equipment 

moderate 

0.60 

0.60 



* Difference in median values has little effect on the fault tree analysis 

 

Table A.8.3: Subcomponent Damage Algorithms for Wells 

with Anchored Components (after G&E 1994) 

Peak Ground Acceleration 

Subcomponents

Damage 
State 

Median 
(g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.80 

1.00 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Well Pump 

extensive 

1.00 

0.60 

Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 

Electric 
Equipment 

moderate 

1.00 

0.60 



 

 

 




Table A.8.4: Subcomponent Damage Algorithms for Wells 

with Unanchored Components (after G&E 1994) 

Peak Ground Acceleration 

Subcomponents

Damage 
State 

Median 
(g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.20 

0.40 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Well Pump 

extensive 

 

1.00 

0.60 

Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 

Electric 
Equipment 

moderate 

0.60 

0.60 



 

 




Table A.8.5: Subcomponent Damage Algorithms for 

Sedimentation/Flocculation System (after G&E 1994) 

Peak Ground Acceleration 

Subcomponents

Damage 
State 

Median 
(g) 

 

Basins 

minor 

0.40 

0.60 

Baffles 

minor 

0.70 

0.60 

Paddles 

moderate 

0.80 

0.60 

Scrapers 

moderate 

0.90 

0.60 



 

 

Table A.8.6: Subcomponent Damage Algorithms for Water Treatment 

Plants with Anchored Components (after G&E 1994) 

Peak Ground Acceleration 

Subcomponents

Damage 
State 

Median 
(g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.80 

1.00 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Chlorination 

Equipment 

minor 

moderate 

0.65 

1.00 

0.60 

0.70 

Sediment 
Flocculation 

minor 

moderate 

0.36 

0.60 

0.50 

0.50 

Chemical 

Tanks 

minor 

moderate 

0.40 

0.65 

0.70 

0.70 

Electric 
Equipment 

moderate 

1.00 

0.60 

Elevated Pipe 

extensive 

complete 

0.53 

1.00 

0.60 

0.60 

Filter Gallery 

complete 

2.00 

1.00 



 

 

 

 

 

 

 




Table A.8.7: Subcomponent Damage Algorithms for Water Treatment 

Plants with Unanchored Components (after G&E 1994) 

Peak Ground Acceleration 

Subcomponents

Damage 
State 

Median 
(g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.20 

0.40 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Chlorination 

Equipment 

minor 

moderate 

0.35 

0.70 

0.60 

0.70 

Sediment 
Flocculation 

minor 

moderate 

0.36 

0.60 

0.50 

0.50 

Chemical 

Tanks 

minor 

moderate 

0.25 

0.40 

0.60 

0.60 

Electric 
Equipment 

moderate 

0.60 

0.60 

Elevated Pipe 

extensive 

complete 

0.53 

1.00 

0.60 

0.60 

Filter Gallery 

complete 

2.00 

1.00 



 




 

APPENDIX 8B 

Subcomponent Damage Functions for Waste Water Systems 

 

 

Table B.8.1: Subcomponent Damage Algorithms for Waste Water Treatment 

Plants with Anchored Components (after G&E, 1994) 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median 
(g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.80 

1.00 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Chlorination 

Equipment 

minor 

moderate 

0.65 

1.00 

0.60 

0.70 

Sediment 
Flocculation 

minor 

moderate 

extensive 

0.36 

0.60 

1.20 

0.50 

0.50 

0.60 

Chemical 

Tanks 

minor 

moderate 

0.40 

0.65 

0.70 

0.70 

Electrical/ 
Mechanical 
Equipment 

moderate 

1.00 

0.60 

Elevated Pipe 

extensive 

complete 

0.53 

1.00 

0.60 

0.60 

Buildings 

complete 

1.50 

0.80 



 

 

 




 

Table B.8.2: Subcomponent Damage Algorithms for Waste Water Treatment 

Plants with Unanchored Components (after G&E, 1994) 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median 
(g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.20 

0.40 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Chlorination 

Equipment 

minor 

moderate 

0.35 

0.70 

0.60 

0.70 

Sediment 
Flocculation 

minor 

moderate 

extensive 

0.36 

0.60 

1.20 

0.50 

0.50 

0.60 

Chemical 

Tanks 

minor 

moderate 

0.25 

0.40 

0.60 

0.60 

Electrical/ 
Mechanical 
Equipment 

moderate 

0.60 

0.60 

Elevated Pipe 

extensive 

complete 

0.53 

1.00 

0.60 

0.60 

Buildings 

complete 

1.50 

0.80 



 

 

 




 

APPENDIX 8C 

Subcomponent Damage Functions for Oil Systems 

 

 

Table C.8.1: Subcomponent Damage Algorithms for Refineries with 

Anchored Components (after G&E, 1994) 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median (g) 

 

ElectricPower 
(Backup) 

minor 

moderate 

0.80 

1.00 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Electrical/ 
Mechanical 
Equipment 

moderate 

1.00 

0.60 

Tanks 

minor 

moderate 

extensive 

complete 

0.30 

0.70 

1.25 

1.60 

0.60 

0.60 

0.65 

0.60 

Stacks 

extensive 

0.75 

0.70 

Elevated Pipe 

complete 

1.00 

0.60 



 

 

Table C.8.2: Subcomponent Damage Algorithms for Refineries with 

Unanchored Components (after G&E, 1994) 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median (g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.20 

0.40 

0.60 

0.80 

Loss of com-mercial 
Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Electrical/ 
Mechanical 
Equipment 

moderate 

0.60 

0.60 

Tanks 

minor 

moderate 

extensive 

complete 

0.15 

0.35 

0.68 

0.95 

0.70 

0.75 

0.75 

0.70 

Stacks 

extensive 

0.60 

0.70 

Elevated Pipe 

complete 

1.00 

0.60 



 




 

 

Table C.8.3: Subcomponent Damage Algorithms for Pumping Plants 

 with Anchored Components (after G&E, 1994) 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median (g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.80 

1.00 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Vertical/ 

Horiz. Pump* 

extensive 

1.25/1.60 

0.60 

Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 

Electrical/ 
Mechanical 
Equipment 

moderate 

1.00 

0.60 



* Difference in median values has little effect on the fault tree analysis 

 

 

 

Table C.8.4: Subcomponent Damage Algorithms for Pumping Plants 

 with Unanchored Components (after G&E, 1994) 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median (g) 

 

Electric Power 
(Backup) 

minor 

moderate 

0.20 

0.40 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Vertical/ 

Horizontal Pump* 

extensive 

1.25/1.60 

0.60 

Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 

Electrical/ 
Mechanical 
Equipment 

moderate 

0.60 

0.60 



 
Difference in median values has little effect on the fault tree analysis 




 

 

 




 

Table C.8.5: Subcomponent Damage Algorithms for Tank Farms 

with Anchored Components (after G&E, 1994) 

 

Peak Ground Acceleration 

Subcomponents 

Damage State 

Median (g) 

 

ElectricPower 
(Backup) 

minor 

moderate 

0.80 

1.00 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Electrical/ 
Mechanical 
Equipment 

moderate 

1.00 

0.60 

Tanks 

minor 

moderate 

extensive 

complete 

0.30 

0.70 

1.25 

1.60 

0.60 

0.60 

0.65 

0.60 

Elevated Pipes 

extensive 

complete 

0.53 

1.00 

0.60 

0.60 



 

 

Table C.8.6: Subcomponent Damage Algorithms for Tank Farms 

 with Unanchored Components (after G&E, 1994) 

 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median (g) 

 

ElectricPower 
(Backup) 

minor 

moderate 

0.20 

0.40 

0.60 

0.80 

Loss of Com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Electrical/ 
Mechanical 
Equipment 

moderate 

0.60 

0.60 

Tanks 

minor 

moderate 

extensive 

complete 

0.15 

0.35 

0.68 

0.95 

0.70 

0.75 

0.75 

0.70 

Elevated Pipes 

extensive 

complete 

0.53 

1.00 

0.60 

0.60 



 

 




APPENDIX 8D 

Subcomponent Damage Functions for Electric Power Systems 

 

 

Table D.8.1: Damage Algorithms for Subcomponents of Low Voltage 

Substations with Anchored Subcomponents (after G&E, 1994) 

Peak Ground Acceleration 

Classification 

Damage 
State 

Median 
(g) 

 

Transformer 

All* 

0.75 

0.70 

Disconnect 
Switches 

All* 

1.20 

0.70 

Live Tank 
Circuit 
Breaker 

All* 

1.0 

0.70 

Current 
Transformer 

All* 

0.75 

0.70 



* Damage state depends on the percentage of the subcomponents failing 

 

 

Table D.8.2: Damage Algorithms for Subcomponents of Low Voltage 

Substations with Unanchored Subcomponents (after G&E, 1994) 

Peak Ground Acceleration 

Classification 

Damage 
State 

Median 
(g) 

 

Transformer 

All* 

0.50 

0.70 

Disconnect 
Switches 

All* 

0.90 

0.70 

Live Tank 
Circuit 
Breaker 

All* 

0.60 

0.70 

Current 
Transformer 

All* 

0.75 

0.70 



* Damage state depends on the percentage of the subcomponents failing 

 

 

 




Table D.8.3: Damage Algorithms for Subcomponents of Medium Voltage 

Substations with Anchored Subcomponents (after G&E, 1994) 

Peak Ground Acceleration 

Classification 

Damage 
State 

Median 
(g) 

 

Transformer 

All* 

0.60 

0.70 

Disconnect 
Switches 

All* 

0.75 

0.70 

Live Tank 
Circuit 
Breaker 

All* 

0.70 

0.70 

Current 
Transformer 

All* 

0.50 

0.70 



* Damage state depends on the percentage of the subcomponents failing 

 

 

 

Table D.8.4: Damage Algorithms for Subcomponents of Medium Voltage 

Substations with Unnchored Subcomponents (after G&E, 1994) 

Peak Ground Acceleration 

Classification 

Damage 
State 

Median 
(g) 

 

Transformer 

All* 

0.30 

0.70 

Disconnect 
Switches 

All* 

0.50 

0.70 

Live Tank 
Circuit 
Breaker 

All* 

0.50 

0.70 

Current 
Transformer 

All* 

0.50 

0.70 



* Damage state depends on the percentage of the subcomponents failing 

 

 

 




Table D.8.5: Damage Algorithms for Subcomponents of High Voltage 

Substations with Anchored Subcomponents (after G&E, 1994) 

Peak Ground Acceleration 

Classification 

Damage 
State 

Median 
(g) 

 

Transformer 

All* 

0.40 

0.70 

Disconnect 
Switches 

All* 

0.60 

0.70 

Live Tank 
Circuit 
Breaker 

All* 

0.40 

0.70 

Current 
Transformer 

All* 

0.30 

0.70 



* Damage state depends on the percentage of the subcomponents failing 

 

 

Table D.8.6: Damage Algorithms for Subcomponents of High Voltage 

Substations with Unnchored Subcomponents 

Peak Ground Acceleration 

Classification 

Damage 
State 

Median 
(g) 

 

Transformer 

All* 

0.25 

0.70 

Disconnect 
Switches 

All* 

0.40 

0.70 

Live Tank 
Circuit 
Breaker 

All* 

0.30 

0.70 

Current 
Transformer 

All* 

0.30 

0.70 



* Damage state depends on the percentage of the subcomponents failing 

 

 

Table D.8.7: Damage Algorithms for Distribution Circuits (after G&E, 1994) 

Peak Ground Acceleration 

Classification 

Damage 
State 

Median 
(g) 

 

Seismic 

All* 

0.75 

0.50 

Standard 

All* 

0.60 

0.50 



* Damage state depends on the percentage of the subcomponents failing 




 

 

Table D.8.8: Damage Algorithms for Subcomponents of Generation 

Facilities with Anchored Subcomponents (after G&E, 1994) 

Peak Ground Acceleration 

Classification 

Damage 
State 

Median 
(g) 

 

Electrical 
Equipment 

minor 

moderate 

0.30 

0.50 

0.40 

0.60 

Boilers & 
Pressure 
vessels 

Moderate 

0.52 

0.70 

Large vertical 
vessels with 
formed heads 

Moderate 

Extensive 

0.60 

0.88 

0.40 

0.39 

Motor Driven 
Pumps 

Extensive 

1.28 

0.34 

Large 
horizontal 
vessels 

Complete 

1.56 

0.61 

Large motor 
operated 
valves 

Complete 

1.93 

0.65 

Boiler 
Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 

Turbine 
Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 



 

 

 




Table D.8.9: Damage Algorithms for Subcomponents of Generation 

Facilities with Unanchored Subcomponents (after G&E, 1994) 

Peak Ground Acceleration 

Classification 

Damage 
State 

Median 
(g) 

 

Electrical 
Equipment 

minor 

moderate 

0.22 

0.35 

0.50 

0.70 

Boilers & 
Pressure 
vessels 

Moderate 

0.36 

0.70 

Large vertical 
vessels with 
formed heads 

Moderate 

Extensive 

0.46 

0.68 

0.50 

0.48 

Motor Driven 
Pumps 

Extensive 

1.00 

0.43 

Large 
horizontal 
vessels 

Complete 

1.05 

0.75 

Large motor 
operated 
valves 

Complete 

1.23 

0.80 

Boiler 
Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 

Turbine 
Building 

minor 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 



 

 

 




APPENDIX 8E 

Subcomponent Damage Functions for Communication Systems 

 

 

Table E.8.1: Subcomponent Damage Algorithms for 

Communication Systems with Anchored Components 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median (g) 

 

Electric Power 

(Backup) 

slight 

moderate 

0.80 

1.00 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Switching 
Equipment 

moderate 

extensive 

complete 

0.70 

1.00 

2.53 

0.70 

0.70 

0.70 

Building 

slight 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 



 

Table E.8.2: Subcomponent Damage Algorithms for 

Communication Systems with Unanchored Components 

Peak Ground Acceleration 

Subcomponents 

Damage 
State 

Median (g) 

 

Electric Power 

(Backup) 

slight 

moderate 

0.20 

0.40 

0.60 

0.80 

Loss of com-
mercial Power 

minor 

moderate 

0.15 

0.30 

0.40 

0.40 

Switching 
Equipment 

moderate 

extensive 

complete 

0.45 

0.62 

1.58 

0.70 

0.70 

0.70 

Building 

slight 

moderate 

extensive 

complete 

0.15 

0.40 

0.80 

1.50 

0.80 

0.80 

0.80 

0.80 



 




Chapter 9 

Induced Damage Models - Inundation 

 

 

9.1 Introduction 

 

Flood-induced damage in an earthquake can result from tsunamis (seismic sea 
waves), 
seiches (sloshing effects in lakes and bays) or dam or levee failure. Especially 
in the case 
of dams and levees, a single structures failure could result in large losses, 
which implies 
that a site-specific analysis should be done rather than using the methodology, 
which is 
designed to estimate losses based on probabilities of performance across large 
inventories. Therefore, the potential exposure to earthquake-caused inundation 
is 
computed in the methodology, while prediction of losses or the likelihood of 
losses is 
excluded. Figure 9.1 illustrates the relationship of the inundation module to 
other 
modules in the methodology. 

 

9.1.1 Scope 

 

The purpose of this module provides the methods for assessing inundation loss 
potential 
due to dam and levee failure, tsunami and seiche. For each of these hazards, 
various 
levels of results can be obtained according to the complexity of the evaluation, 
data 
requirements, and the use of expert assistance to perform the assessment. 

 

The purpose of this module is to identify the potential sources of flooding in a 
study area 
and overlays existing inundation maps with other data to identify the potential 
exposure. 
If existing inundation maps are not available, creating inundation maps will 
require the 
involvement of experts to perform sophisticated evaluations. 

 

9.1.2 Form of Inundation Estimate 

 

In using existing inundation maps care must be taken in interpreting the 
results. These 
maps usually are based on worst-case assumptions, such as a dam being completely 
full 
and failing catastrophically, and rarely is such a scenario tied to a specific 
earthquake 
scenario. 

 

In general, a complete characterization of flood hazard includes an assessment 
of: 

 

.. 
Area of inundation 


.. 
Depth and velocity of flooding 


.. 
Arrival time of the flood following the occurrence of the earthquake, such as in 
the 
case of a dam or levee failure or tsunami 


.. 
Probability of the above described event 




 

 




8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Flowchart 9.1: Relationship of inundation Module to other Modules in the 
Earthquake Loss Estimation Methodology 

 




 

The information on inundation that is reported will vary from analysis to 
analysis. Only 
in a detailed engineering analysis, as described above, is a complete 
characterization of 
the inundation provided. 

 

For each source of flooding (dam or levee failure, tsunami and seiche), the 
primary 
format for the presentation of the hazard will be an inundation map. An 
inundation map 
identifies the bounds of the area that will be inundated. The bounds can be used 
to 
evaluate the population and economic values in the affected area. When digitized 
for 
entry into a GIS system, the area of inundation could be overlaid with a 
topographic map 
to infer the depth of flooding. However, in the current methodology, this 
capability does 
not exist. Figure 9.1 provides an example of an inundation map. 

 

9.1.3 Input Requirements and Output Information 

 

This subsection defines the input requirements and output information for the 
induced 
damage inundation module. Subsection 9.1.3.1 describes the input requirements, 
followed by subsection 9.1.3.2 providing the output information. 

 

9.1.3.1 Input Requirements 

 

9.1.3.1.1 Dam Failure 

 

The input information comes from a default database developed from the National 
Inventory of Dams database (NATDAM) [FEMA, 1993]. The database identifies all 
dams in the United States that satisfy the minimum size or hazard criteria given 
in Table 
9.1. For each dam, the database contains multiple fields of information related 
to the 
dam and the body of water impounded by the dam. Hazard classifications are found 
in 
Section 9.1.3.2.1. Where they exist, inundation maps can be collected. The 
availability 
of inundation maps can be determined by contacting the following organizations: 

 

 State or federal dam safety or water resources regulatory agencies 

 State office of emergency services 

 Local emergency services, law enforcement, or fire protection agencies 

 Dam owner (which may be a private individual or organization or public 
agency such as the U.S. Army Corps of Engineers or Bureau of Reclamation). 

 




Table 9.1 National Inventory of Dam - Size and Hazard Criteria 

Category 

Criterion 

Excluded 

Dam 
Height 

Structural Height (H) > 25 ft. 

C < 15 acre-feet maximum capacity 
regardless of dam height 

Reservoir 
Size 

Reservoir Impoundment 

Capacity (C) > 50 acre-feet 

H < 6 feet 

regardless of reservoir capacity 

Hazard 

Any dam that poses a "significant" 
threat to human life or property in the 
event of its failure. 

 



 

9.1.3.1.2 Levee Failure 

 

Unlike dams, a national inventory for levees does not exist. The user must 
contact local 
sources to identify levees in the study region. Possible sources include United 
States 
Army Corps of Engineers district offices, local flood, reclamation, or levee 
maintenance 
control districts, the United States Soil Conservation Service, and municipal or 
county 
authorities. The user must provide the geographical location of the levees 
(represented in 
the methodology software as polylines). Additional information that should be 
included 
in the levee inventory includes the levee design basis (e.g., 100 year flood), 
the levee 
crest elevations, normal water level elevation, and levee owner/operator. 

 

Since most levees and in some locations floodwalls are designed to provide 
protection 
during periods of flooding, they are typically dry (i.e., do not impound/retain 
water) at 
the majority of the time. As a result, seismic failure of a levee during non-
flood 
conditions does not pose an inundation hazard. As part of the process of 
identifying 
levees in the study region, the user should also obtain information as to 
whether the levee 
is dry the majority of the year (e.g. greater than 75% of the time). If this is 
the case, the 
levee might be screened out from further consideration, unless a study of the 
worst-case 
scenario is desired 

 

Existing levee inundation maps are used to identify areas that may be flooded in 
the case 
of a failure. It is unlikely that an existing levee inundation study will be 
available. If a 
study is available, it should be reviewed to determine whether the water level 
used is 
consistent with the level that can be expected when an earthquake occurs. 

 




 

 

 

Figure 9.1 Dam failure inundation map. 





 

9.1.3.1.3 Input Requirements - Tsunami 

 

The first objective in the analysis of tsunami is to simply identify whether a 
tsunami 
hazard exists. To accomplish this, the following information is needed. 

 

.. Location of the earthquake (on-shore or off-shore event) 

.. Type of faulting 

 

If the earthquake source is on-shore there is no tsunami hazard. The same is 
true if an 
offshore event occurs that involves primarily strike-slip movement. 
Alternatively, if the 
earthquake occurs offshore and significant vertical displacement of the seafloor 
occurs 
and a tsunami exists. The assessment of tsunami inundation in the methodology is 
for 
nearby seismic events only. Tsunami inundation maps based on distant events 
should not 
be combined with the study region scenarios. For example, a tsunami affecting 
the West 
Coast generated by and earthquake in Alaska should not be combined with the 
study of 
losses occurring from an earthquake in Los Angeles. 

 

The user should determine the size and location of the earthquake that was 
assumed to 
estimate the tsunami inundation or, if specified, the mean return period of the 
tsunami. 
This will provide a basis to judge whether the existing inundation map 
conservatively or 
un-conservatively estimates the inundation that would be produced by the study 
earthquake. In cases where a scenario earthquake would generate a tsunami, the 
probability basis of the tsunami inundation map should match that of the 
scenario 
earthquake. For example, if an existing tsunami inundation map based on wave 
run-ups 
caused by local earthquake that have a mean return period of 500 years for a 
study region 
in Alaska, then the scenario earthquake selected for use with the methodology 
should 
also have a 500 year return period. Otherwise, the tsunami and the earthquake 
loss 
outputs should not be combined because this would describe different events. 

 

9.1.3.1.4 Input Requirements - Seiche 

 

To first step in seiche analysis is to identify natural or man-made bodies of 
water where a 
seiche may be generated. The default database of dams can be used to identify 
the man-
made bodies of water (see Section 9.1.3.1.1) while the user must generate an 
inventory of 
natural water bodies in the study region. The following criteria can be used to 
identify 
bodies of water that should be considered in the assessment: 

 

 The lake volume must be greater than 500,000 acre-feet 

 There must be an existing population and/or property located in proximity to 
the lake 
shore that could be inundated 

 

If these criteria are not met, lakes should not be considered for assessment. 
Existing 
seiche inundation maps are used to identify areas subject to flooding. Sources 
of existing 
seiche inundation studies include state and federal agencies that regulate dams, 
dam or 




lake owners, and state office of emergency services. The availability of such 
studies is 
very limited. 

 

 

9.1.3.2 Output Information 

 

The output of the dam failure inundation module consists of an inventory of the 
dams 
located in the study region divided into three groups corresponding to the 
hazard 
classifications provided in the database. The hazard classification system is 
shown in the 
Table 9.2 below. 

 

Table 9.2 Dam Hazard Classifications 

Hazard 

Urban Development 

Economic Loss 

Low 

No permanent structures for human 
habitation 

Minimal (undeveloped to occasional 
structures or agriculture) 

Significant 

Urban development and no more than a 
small number of inhabitable structures 

Appreciable (notable agriculture, 
industry) 

High 

Urban development with more than a 
small number of inhabitable structures 

Serious (extensive community, industry 
or agriculture) 



 

 

In addition to the inventory of dams located in the study region, the analysis 
will utilize 
existing digital dam inundation maps to identify the population and property at 
risk due 
to the dam failure. 

 

The output of levees analysis is an inventory of the levees in the study region 
whose 
failure could lead to flooding. In addition to the inventory of levees located 
in the study 
region, analysis can use existing digital levee, tsunami, and seiche inundation 
maps 
(limited availability) to quantify the population and property at risk due to 
the failure of 
levees. 

 

9.2 Description of Methodology 

 

9.2.1 Dam Failure 

 

This subsection describes the approach used to perform analyses for inundation 
due to 
dam failure. To start the analysis of dams, the dams that are located in the 
study region 
have to be identified. To do this, a geographic search through the default dam 
database is 
conducted. Based on the dam hazard classification, a list of the Low, 
Significant and 
High Hazard dams can generated. Note that hazard here means the danger posed 
if the 
dam fails, and is not a description of the probability of such failure. Next, an 
analysis 
using existing digital inundation maps is conducted to estimate the potential 
population 
and economic value impacted by a dam failure. 

 




9.2.2 Levee Failure 

 

The tasks and analysis tools are similar to those required for dam failure. An 
inventory 
of levees located in the study region is generated by contacting local, state 
and federal 
agencies. The inventory should typically include levees that act as water 
barriers greater 
than 10 percent of the time. This excludes from the inventory levees that remain 
dry 
except during short periods of flooding, because of the small probability the 
earthquake 
will coincide with a time of high water level. Existing levee failure inundation 
studies 
are used to identify areas that may be impacted by levee failure. When using 
existing 
inundation studies, the following should be considered: 

 

 Existing inundation studies must be reviewed to determine assumptions 
regarding 
water levels 

 The analyst should identify areas where levee failure will have the most 
severe 
impact; existing studies may not have used this approach 

 

9.2.3 Tsunami 

 

This subsection describes the approach to perform evaluations for inundation due 
to 
tsunami. Existing tsunami studies may include inundation maps for the scenario 
earthquake. However, they should be reviewed to verify the assumptions on which 
the 
tsunami was based. As explained above, tsunami inundation maps developed for 
distant 
earthquakes should not be used in combination with a local scenario event. 
However, the 
methodology can be used to independently estimate the population and building 
value at 
risk from a distant event tsunami simply by using a representative inundation 
map in 
which case these results would not be combined with those of a local earthquake 
scenario. 

 

9.2.4 Seiche 

 

This subsection describes the approach to perform evaluations for inundation due 
to 
seiche. Existing seiche inundation studies are used to identify the areas where 
flooding 
may occur. However, in most cases such studies do not exist. In some cases the 
results 
of a seiche analysis may be available that did not produce an inundation map. In 
this 
case, the user could transfer the results to a topographic map of the lakeshore 
area to 
determine the bounds of inundation. 

 

9.3 Guidance for Expert-Generated Estimates 

 

Losses that might be caused by earthquake-caused flooding are not calculated 
within the 
methodology, because of the facility-specific evaluation by experts that is 
necessary. The 
information in this section is not intended to supplant the need for experts 
when a loss 
study is extended into these induced hazards, but rather to provide these civil 
engineering, hydrological, and geotechnical experts guidance to standardize 
their 
analyses. 

 




9.3.1 Dam Failure 

 

The greatest uncertainty lies in the likely cause, mode, degree and time 
sequence of 
failure. Another uncertainty involves flood routing and limits of inundation 
downstream 
of the failed dams. Although several historical dam failures have been 
documented, very 
few have provided an exact description of the hydraulics of the failure flood. 

 

The hydraulic characteristics of a surge released from a dam failure depends on 
the size, 
shape and position of the breach, volume of water stored behind the dam, the dam 
height, 
width and length of the reservoir, and the reservoir inflow and tailwater 
condition at the 
time of the failure. To provide uniformity in the evaluation of the effects of 
dam failure 
during a seismic event, the following guidelines are provided. These guidelines 
should 
be followed unless deviations are appropriate in the opinion of an expert 
analyst. 

 

Antecedent Conditions - Reservoir levels generally predictably related to the 
purpose of 
the reservoir. Whereas a seismic event can occur anytime during the year, the 
following 
guidance is provided: 

 

1. Reservoir Conditions - It should be assumed that the reservoir is at the 
average 
operational level for the season when water levels are highest. If the average 
operational level is not known, the maximum normal depth of water should be 
used. 

2. Antecedent Flow - Unless a dam has failed due to failure of an upstream dam, 
the 
antecedent stream flow into the reservoir is assumed equivalent to the mean 
monthly 
flow for the season assumed for the scenario. If the failure is assumed to occur 
during the flood season, then the mean annual flood for the month is assumed. 
This 
antecedent flow can also be applied as the base flow downstream of the dam. 

 

Tailwater Condition - No assumption on the varying tailwater condition is 
necessary 
when using DAMBRK, a program developed by the National Weather Service (NWS), 
because the model automatically calculates the tailwater elevation based on the 
base flow 
and outflow from the spillway or breach formation. The model does appropriate 
correction for submergence automatically. 

 

River Cross-Section - For the purpose of representing the river channel in the 
DAMBRK model (see Figure 9.2), cross-sections of the river at selected critical 
stations 
are normally taken from U.S.G.S. 7 1/2 minute topographic maps. Since only 8 
elevation-top-widths data points can be accepted by DAMBRK, care should be used 
in 
selecting cross-section data for the stations along the river or valleys to 
assure accurate 
estimates of flood elevations. 

 

Mode of Failure - A conservative estimate of flooding due to a dam failure would 
assume complete disappearance of the dam. For small concrete dams, such an 
assumption may be reasonable. However, for large concrete gravity dams, it is 
more 
reasonable to assume partial breach with some parts of the dam remaining intact. 
For 
example, embankment dams will generally fail by erosion. 

 




Shape and Size of Failure - Breach shapes are assumed to follow regular 
geometrical 
shapes such as a triangle, rectangle, trapezoid, or parabolic figure. Failure 
depth is 
always assumed equal to the total height of the dam unless there is a high 
tailwater. 
Table 9.3 gives guidance on the various parameters that could be assumed for a 
given 
breach shape and size. 

 

Time to Maximum Failure - This is one of the most unpredictable parameters in 
dam 
break modeling. To facilitate the adoption of reasonable values of time to 
maximum 
failure, Table 9.3 gives recommended values for various types of dams. 

 

Expansion and Contraction Coefficients - The manual for DAMBRK recommends 
values of cross-section contraction/expansion coefficients for the contraction 
or 
expansion of the downstream reach's cross-sectional geometry. Contraction values 
generally vary from 0.1 to 0.3 while expansion values usually vary from -1.0 to 
-0.1. If 
contraction-expansion effects are negligible, a value of 0.1 is used. 

 

Table 9.3 Suggested Breach Characteristics (see Figure 9.3) 

(Fread, 1982) 

Parameter 

Value 

Type of Dam 

Average Breach Width (BR) 

W = Crest Length 

H = Dam Height 

BR = Width of 1 or more 
monoliths, usually BR < 0.50W 

 

HD < BR < 5HD 

(usually between 2HD and 4HD) 

 

BR > 0.8 Crest Length 

Arch 

 

Masonry, Gravity 

 

 

Earthen, Rockfill, Timber Crib 

 

 

Slag, Refuse 

Horizontal Component of the 
Side Slope of Breach (Z) 

0<Z< Slope of the Valley Walls 

 

Z = 0 

 

1/4 < Z < 1 

 

1 < Z < 2 

Arch 

 

Masonry, Gravity, Timber Crib 

 

Earthen (engineered compacted) 

 

Slag, Refuse (non-engineered) 

Time to Failure (TFH) (hours) 

TFH < 0.10 

 

0.1 < TFH < 0.3 

 

0.1 < TFH < 1.0 

 

0.1 < TFH < 0.5 

 

0.1 < TFH < 0.3 

Arch 

 

Masonry, Gravity 

 

Earthen (engineered compacted), 
Timber Crib 

Earthen (non-engineered, poor 
construction) 

Slag, Refuse 



 

 




Wetted Perimeter (P)
Area (A)Depth 


 

 

HDamWater Surface ofReservoirW (Crest Length)
1Z1ZWater Surface ofTailraceBRBottom of BreachBreach 


 

 

channel is the most indeterminate variable in dam break modeling. Calibrated 
values 
from high-water marks cannot really be used to represent those expected under a 
dam 
failure flood. Published data such as those from the U.S.G.S. can only be used 
to 
approximate the expected value from the hypothetical flooding. Therefore, it is 
necessary that relatively reasonable values be assumed or considered before a 
flood plain 
analysis is started. In most cases, these assumed values are varied through the 
modeling 




 Table 9.4 Recommended Values of Manning's n

Channel Type 

 no brush 

 
0.30-0.035 

b. Dense growth of wee

c. Some weeds, light brush on banks 

d. Some weeds, heavy brush on banks 
ks 

e. Some weeds, dense willows on ban

f. For trees within channel, with branch

above values by 

2. Irregular sections, with pools, slight channel meander; increase values given 
ababout 

0.35-0.05 

0.05-0.07 

0.06-0.08 

 

0.01-0.02 

 

0.01-0.02 

3. Mountain streams, no vegetation in channel, banks usually steep, trees and 
brush 
along banks submerged at high stage: 

 

 
0.04-0.05 

a. Bottom of gravel, cobbles, and few boulders 
b. Bottom of cobbles, with large boulders 



R

ro

human life or property. The results of the routing should be plotted on 
inundation maps 
with the dam break flood wave travel time and flood depths indicated at critical 
downstream locations. 
9.3.2 Levee Failure 

 

The guidance for exper

a

flooding due to levee failure. However, in the case of levee failure the analyst 
should 
consider multiple locations for levee failure based on a consideration of the 
locations 
where the levee may be most vulnerable and where the impact of flooding in the 
study 
area would be greatest. 
9.3.3 Tsunami 

 

The most detailed

fo

Coast. Therefore, most guidelines refer to the work in this state. However, it 
should be 
noted that even though the following guidelines have been applied to Hawaii, the 
same 
procedures and assumptions could be adapted to other coastlines of the country 
that 
would be subject to tsunami flooding. 
Tsunami inundation maps that have be

a




is recommended for modeling of tsunami for inundation studies. The available 
two-
dimensional models solve the non-linear shallow water long wave equation using 
different methods of finite difference solution. A complete description of the 
available 
and verified models in the United Sates is provided in Bernard and Gonzalez, 
1994. 
Numeric models are used to make scenario specific tsunami assessments. Inputs 
required 
for this assessment include detailed information on the location of earthquake 
and fault 
movement that is expected to occur on the ocean floor. In addition, information 
is 
needed regarding the bathymetry of the ocean floor, shoreline geometry, 
topographic data 
and tide information. Good quality bathymetric and topographic data are 
essential for 
accurate inundation model results. 
9.3.4 Seiche 


A

made bodies of

e

landslide potential and wave run-up. The length and width of the lake or 
reservoir 
correspond to the average dimensions of the body of water where wave generation 
is 
evaluated. The user may have to consider a number of different wave geometries 
to 
determine the critical dimensions that generate the largest estimated wave 
height. At a 
minimum, geologic maps of the lake or reservoir rim or landslide potential maps 
should 
be obtained. In addition, for earthquakes that occur on faults along or within 
bodies of 
water, the location of the event and the magnitude of vertical fault 
displacement is 
required. 
A simple calculation is performed to determine the maximum wave height that 
would be 
generated 

w

 
H = A(9-1) 

 

w

 

 f 

 L = Wavelength = 5.12 / f2 

 

The above approach is a simplified meth

generated by seismic motion at the l

c

period of the lake is based on its shape, the period will be different on 
different axes. 
Oscillations of water bodies above and below their mean level have a natural 
period 
depending upon the physical features of the water body. A disturbing force with




sa

energy dissipated by friction equals the rate of application of energy. When the 
force 
causing the displacement ceases or changes in intensity, a series of pulsations 
follow at 
the natural frequency until damped by frictional forces. Standing waves of large 
amplitude are likely to be generated when the causative forces which sets the 
water basin 
in motion is periodic in character, especially if the period of these forces is 
the same as, 
or is in resonance with, the natural or free oscillation period of the basin. 
The period of the seiche is dependent on the geometry of the basin. This period 
can be 
estimated with Merian's equation. 

 

 Tlngdnb=2 (9-2) 

 

w

 

 n 

 g = gravitational accele

 d = depth of water 

 

For the fundamental and maximum perio

 

 Tgd1b= 

 

However, the preceding equation is based on the assumption of uniform and 
constant 

in

 
T2dxgdlb=. 

where dx = finite increment of lb. 

 

 

9

 

B

Report, November 16-18, 1993

P

Administration, Seattle, Washington, January. 





Bretschneider, C. L. and Wybro, P. G., 1976. "Tsunami Inundation Prediction", 
Proceedings of the 15th Conference on Coasta

E

 
Curtis, G. D., 1990. "Tsunami Inundation/Evacuation Line and Zone Selection 
Procedure", Mem

In

9, 1988, revised May 15. 
FEMA, 1993. "Water Control Infrastructure, National Inventory of Dams 1992," 
FEMA 
246, Federal Emergency

W

 
Fread, D. L., 1982. NWS DAMBRK Model, National Weather Service, Silver Spring, 
MD. 

 

Houston, J. R., et al., 1977. "Tsunami-Wave Elevation Frequency of Occurrence 
for the 
Hawa

E

 
Liu, P.L.-F. and Yoon, S. B., 1991. "Estimation of Tsunami Wave Heights Along 
South 
Eastern Korea Shoreline", S

C

 
Mader, C. L. and Curtis, G., 1991. "Modeling Hilo, Hawaii, Tsunami Inundation", 
Science of Tsunami Hazards, 9, pp.85-9

 

Mader, C. L., Curtis, G., and Nabeshima, G., 1993. "Modeling Tsunami Flooding of 
Hilo, 
Hawaii", Recent Advances in Marine Scien

 

U.S. Army Corps of Engineers, 1982. National Program for Inspection of Non-
Federal 
Dams - Final Report to Congress, Washington, DC. 

 

U.S. Department of Transportation, 1980. "Hydraulic Charts for the Selection of 
Highway Culverts," Federal Highway Administratio

 




Chapter 10 

Induced Damage Models - Fire Following Earthquake 

 

 

10.1 Introduction 

 

Fires following earthquakes can cause severe losses. These losses can sometimes 
outweigh the total losses from the direct damage caused by the earthquake, such 
as 
collapse of buildings and disruption of lifelines. Many factors affect the 
severity of the 
fires following an earthquake, including but not limited to: ignition sources, 
types and 
density of fuel, weather conditions, functionality of water systems, and the 
ability of fire 
fighters to suppress the fires. 

 

It should be recognized that a complete fire following earthquake model requires 
extensive input with respect to the level of readiness of local fire departments 
and the 
types and availability (functionality) of water systems. To reduce the input 
requirements 
and to account for simplifications in the lifeline module, the fire following 
earthquake 
model presented in this report is also simplified. In addition, while building 
upon past 
efforts, the model is still to be considered a technology which is in its 
maturing process. 
With better understanding of fires that will be garnered after future 
earthquakes, there 
will undoubtedly be room for improvement in our forecasting capability. The 
methodology, highlighting the Fire Following Earthquake component, is shown in 
Flowchart 10.1 

 

10.1.1 Scope 

 

A complete fire following earthquake (FFE) model encompasses the three phases of 
a 
fire: 

 

 ignition 

 spread 

 suppression 

 

This methodology provides the user with the following estimates: 

 

 
Number of ignitions 


 
Total burned area 


 
Population exposed to the fires 


 
Building value consumed by the fire 




 

Using Default and User-Supplied Data Analysis information will provide an 
estimate of 
the magnitude of the FFE problem, that could be used to plan for and estimate 
demands 
on local fire fighting resources. 

 

 




 

 

 

 

8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Flowchart 10.1: Fire Following Earthquake Component Relationship to other 
Modules in the Earthquake Loss Estimation Methodology 




 

10.1.2 Form of Damage Estimates 

 

The FFE methodology provides the following: 

 

 an estimate of the number of serious fire ignitions that require fire 
department 
response after a scenario earthquake 

 an estimate of the total burned area 

 an estimate of the population and building exposure affected by the fire 

 

By applying the FFE module for several scenario earthquakes, representing 
different 
potential earthquakes for the study area, with different recurrence intervals, 
the user can 
examine the efficacy of certain pre-earthquake actions that can be used to 
mitigate the 
potential losses from fires in future earthquakes. For example, the user could 
study the 
effect of building more fire stations; adding more fire apparatus; improving 
immediate 
post-earthquake response to detect fires and suppress fires before they spread 
or 
seismically upgrading the water system. Since all these activities cost money, 
the user 
could study which combination of activities is most effective for their 
communities. 

 

10.1.3 Input Requirements 

 

This section describes the inputs required and output provided by the FFE 
module. 

 

Input for Analysis: 

Provided as general building stock inventory data: 

 
Square footage of residential single family dwellings (SFD) 


 
Square footage of residential non-SFD 


 
Square footage of commercial buildings 


 
Square footage of industrial buildings 




Provided as essential facility inventory data: 

 
Number of fire stations 


 
Number of engines at each fire stations 


 
Geographical location of each station 




Provided by the PESH module: 

 
PGA 




Analysis options input by the user: 

 
Wind speed 


 
Wind direction 


 
Speed of the fire engine truck (after earthquake) 


 
Number of Simulations 


 
Maximum Simulation Time 


 
Simulation Time Increment 




 




Multiple estimates for the same scenario earthquake are calculated by simulating 
fire 
following earthquakes several times. Hence, the user needs to provide the number 
of 
simulations that should be performed in order to come up with average estimates 
from 
independent simulations. It is suggested that the user try 6 to 10 simulations. 
The 
maximum time after the earthquake for which the simulation should be performed 
and 
the time increment for each simulation are also user inputs. For example, a 
resonable 
maximum time could be 10,000 minutes when all the fires could possibly be 
suppressed. 
It is suggested that a time increment of 1 to 15 minutes be provided for 
sufficiently 
accurate simulations. 

 

 

10.2 Description of Methodology 

 

10.2.1 Ignition 

 

The first step in evaluating the potential losses due to fires following 
earthquake is to 
estimate the number of fires that actually occur after the earthquake. The 
ignition model 
is based on the number of serious FFEs that have occurred after past earthquakes 
in the 
United States. 

 

The term "ignition" refers to each individual fire that starts (ignites) after 
an earthquake 
that ultimately requires fire department response to suppress. Thus, a fire that 
starts after 
an earthquake but which is put out by the occupants of the building without fire 
department response is not considered an ignition for purposes of this model. 
Fires that 
are put out by building occupants are usually those discovered very early and 
are put out 
before they can do substantial damage. These ignitions do not lead to 
significant losses. 

 

In a fire ignition model previously developed by Scawthorn (1987), the number of 
FFEs 
was established by counting the actual FFEs versus the inventory exposed to 
equal levels 
of MMI (Modified Mercalli Intensity). The model did not include fire data from 
more 
recent and well documented earthquakes, such as the 1989 Loma Prieta event. For 
this 
methodology, the model has been re-calibrated. The prediction parameter (MMI) 
and 
output parameter (number of ignitions per thousand Single Family Equivalent 
Dwellings 
(SFEDs)), have not been carried forward in this project. (One SFED is defined to 
be 
1,500 square feet of floor area.) 

 

The calibration process has been performed in three steps: 

 
The database of actual earthquake experience was expanded by incorporating new 
data points representing the fire ignitions from the 1989 Loma Prieta 
earthquake. 


 
The ignition per SFED scale was changed to ignitions per 1,000,000 square feet 
of structure inventory. 


 
The MMI scale was converted to the PGA scale as shown in Table 10.1. 




 




Table 10.1: MMI to PGA Conversion Table 

MMI 

VI 

VII 

VIII 

IX 

X 

XI 

XII 

PGA 

0.12 

0.21 

0.36 

0.53 

0.71 

0.86 

1.15 



 

Table 10.2 provides the results after performing the calibration. This table 
provides the 
database of fire ignitions from past United States earthquakes, calibrated to 
ignitions per 
1,000,000 square feet, and as predicted using PGA. 

 

Figure 10.1 is a plot of the information found in Table 10.2. As can be seen 
from the 
plot, there is considerable scatter in the empirical evidence. The reasons for 
this scatter 
include the following: 

 

 The horizontal axis is based upon historical interpretations of MMI scale 
value 
processed through an MMI to PGA conversion. Different investigators will 
sometimes rate a specific area with different MMI values, sometimes differing by 
one 
or two intensities. This introduces large uncertainties. Also, the MMI to PGA 
conversion process builds in more uncertainty. For example, the same PGA values 
at 
rock and at soft soil sites can produce different levels of damage, particularly 
if 
liquefaction or landslides occur. 

 

 The quantification of the actual number of fire ignitions in past earthquakes 
is most 
often based on conflicting data sources. The usual sources base some estimates 
on 
journals and newspaper accounts, which often conflict. More recent efforts have 
tracked down each fire ignition using fire incident reports from fire 
departments, and 
these data are more reliable. 

 

 Fire ignitions are probably not related to a single input parameter, whether 
it be MMI 
or PGA. Actual fire ignitions start for a number of reasons, including: 

- Toppling over of unanchored items (this is PGA-related), causing short 
circuits or 
fuel spills. This causes fires if an ignition source (spark) is present. 

- Breakage of underground utilities (such as gas lines) which provides a fuel 
source 
for the ignition. This is PGD-related. 

- Interstory drift of structures, which may cause short circuits in electrical 
wiring. 
This is related to PGA and age of structure / wiring condition. 

- Time of day. During meal times, more electrical and gas appliances are in use. 
This would allow for more potential for ignitions than if the earthquake 
occurred 
during night-time hours. Similarly, time of year is important in that many gas 
or 
oil appliances are used in winter for home heating. [Note: time of year is an 
important factor for fire spread given an ignition, in that fire growth is 
dependent 
upon heat.] 

 

 

A second order fit of the data provides the following ignition model: 

 

()()Ignitions0.592*PGAPGA=-+-002502892.. (10-1) 




 

Table 10.2 Fires Following United States Earthquakes (1906 - 1989) 

 

City, Year of 
Earthquake 

PGA (g) 

Intensity 
(MMI) 

Ignitions 

Ignitions per 
1,000,000 Sq. Feet 

Coalinga 1983 

Daly City 1989 

Anchorage 1964 

Berkeley 1906 

Berkeley 1989 

Burbank 1971 

Glendale 1971 

Los Angeles 1971 

Los Angeles 1933 

Long Beach 1933 

Marin Co. 1989 

Morgan Hill 1984 

Mountain View 1989 

Norwalk 1933 

Oakland 1906 

Oakland 1989 

Pasadena 1971 

San Francisco 1989 

San Francisco 1906 

San Francisco 1957 

San Fernando 1971 

San Jose 1984 

San Jose 1906 

Santa Clara 1906 

Santa Cruz 1989 

Santa Cruz Co. 1989 

San Mateo Co. 1906 

Santa Rosa 1969 

Santa Rosa 1906 

Whittier 1987 

0.36 

0.12 

0.71 

0.44 

0.07 

0.21 

0.15 

0.15 

0.15 

0.53 

0.12 

0.21 

0.21 

0.28 

0.44 

0.07 

0.21 

0.21 

0.44 

0.12 

0.53 

0.36 

0.36 

0.44 

0.36 

0.28 

0.36 

0.36 

0.71 

0.28 

VIII 

VI 

X 

VIII-IX 

 

VII 

VI-VII 

VI-VII 

VI-VII 

IX 

VI 

VII 

VII 

VII-VIII 

VII-IX 

 

VII 

VII 

VII-X 

VI 

IX 

VIII 

VIII 

VIII-IX 

VIII 

VII-VIII 

VIII 

VIII 

X 

VII-VIII 

1 

3 

7 

1 

1 

7 

9 

128 

3 

19 

2 

4 

1 

1 

2 

0 

2 

27 

52 

0 

3 

5 

1 

1 

1 

24 

1 

1 

1 

6 

0.30 

0.05 

0.24 

0.16 

0.013 

0.16 

0.13 

0.09 

0.01 

0.26 

0.02 

0.40 

0.02 

0.05 

0.06 

0.00 

0.04 

0.08 

0.26 

0.00 

0.37 

0.02 

0.08 

0.22 

0.04 

0.03 

0.14 

0.06 

0.18 

0.10 



 

 

Figure 10.1 also shows the best fit curve using equation 10-1. The correlation 
between 
PGA and number of ignitions in the fitting is quite low. This confirms that PGA 
is by 
itself not a perfect indicator of fire ignitions. This result is not too 
surprising, given the 
uncertainties involved in the collection of the empirical data and in the ways 
fires start. 

 

Timing of Ignitions 

 

The number of ignitions that are predicted using the above ignition model are 
based on 
empirical results, and include fires attributed to the earthquake, both starting 
immediately 
after the earthquake and starting some time after the earthquake. 

 




Based upon the empirical record, and using judgment, it is estimated that about 
70 
percent of all fire ignitions start immediately after the earthquake. 
"Immediately" means 
that the fire ignition is discovered within a few minutes after the earthquake. 

 

Peak Ground Acceleration (g)
Ignitions per 1,000,000 Sq Ft Bldg Floor 
Area00.050.10.150.20.250.30.350.400.10.20.30.40.50.60.70.8Best Fit, Second 
OrderData 


Figure 10.1 Fire Ignitions in United States Earthquakes (1906-1989). 

 

The remaining ignitions start sometime after the earthquake, ranging from an 
hour to 
possibly a day or so after the earthquake. A typical cause of these later 
ignitions is the 
restoration of electric power. When power is restored, short circuits that 
occurred due to 
the earthquake become energized and can ignite fires. Similarly, when power is 
restored, 
items which have overturned, fallen onto range tops, etc., can ignite. If no one 
is present 
at the time electric power is restored, fire ignitions requiring fire department 
response can 
occur. 

 

10.2.2 Spread 

 

The second step in performing the FFE analysis is to estimate the spread of the 
initial fire 
ignition. The following description of fire spread in urban areas is based on a 
model 
developed by Hamada (1975). Hamada developed a model for fire spreading for 
urban 
Japan. His model is described as follows: 

 

 (NaK*KKtV2sd=
15.
*
d 
(10-2) 

 




where: 

N 
t V = Number of structures fully burned 

t = time, in minutes after initial ignition 

V = wind velocity, in meters per second 

d = "Built-upness" factor, dimensionless, described below 

a = average structure plan dimension, in meters 

d = average building separation, in meters 

Ks = half the width of fire from flank to flank, in meters 

Kd = length of fire in downwind direction, from the initial ignition location, 
in 
meters 

Ku = length of fire in upwind (rear) direction, from the initial ignition 
location, 
in meters 

 

 

d==SaTract Areai2i1n 
(10-3a) 

 

where: 

ai = plan dimension of building i 

n = number of structures 

 

 

()
KadT*tdd=
+ 
(10-3b) 

()()Ka2dadTtT ; Kssss=+
...
...
+
+
-0 (10-3c) 

 

()()Ka2dadTtT ; Kuuuu=+
...
...
+
+
-0 (10-3d) 

 

()()TVVfa8dV 
fa16dVdbb=
++
-+++
...
...
.
..
++++
...
...
.
..
116101000713037525255062525252...
.
.
.
. 
(10-3e) 

 

()TVfa8dVfa16dVsbb=
+
-+++
...
...
++++
...
...
.
..
.
..
11000513037550255062550252.
.
.
.
. 


 

 (10-3f) 

 




()TVfa8dVfa16dVubb=
+
-+++
...
...
++++
...
...
.
..
.
..
110002130375502506255022.
.
.
.
. 


 

 (10-3g) 

where: 

 fNumber of fire resistant buildingsAll buildingsb= 

 

 

A discussion of the Hamada model follows. 

 

 It is assumed that an urban area is represented by a series of equal square 
(plan area) 
structures, with equal spacing between structures. The plan dimension of the 
average 
structure is denoted "a", and hence the plan area is a2. 

 

 It is assumed that the spaces between structures in a subdivision can be 
represented 
by an average separation distance, d. For purposes of this model, the separation 
distance represents the typical distance between structures within a single 
block. This 
distance accounts for side yards, backyards and front yards, but does not 
include 
streets and sidewalks. 

 

 The "built-upness", or building density ratio d is defined by equation 10-3a. 
To put 
this building density ratio in context, a value of 0.35 represents a densely 
built area, 
and a value of 0.10 represents an area which is not very densely built. 

 

 Figure 10.2 shows the fire spread in terms of ovals, which is the usual case 
of fires 
burning through an evenly distributed fuel load, with constant wind velocity. In 
the 
actual urban conflagrations, fires exhibit this trend initially, but the final 
shape of the 
fire spread differs, as different fuel loads are experienced, as wind shifts, 
and as 
different fire suppression actions take place. The fire burn area is 
approximated as 
the product of the downwind fire spread plus the upwind fire spread (Kd + Ku) 
times 
the width of the fire spread (2Ks). 

 

 




Initial IgnitionKuKdKsWINDTime t1Time t2Time t3Structure fully burnt at time 
t3Structure partially burnt at time t3Structure barely burnt at time t3Structure 
not burnt at time t3 


Figure 10.2 Fire Spread Process. 

 

 The fire spread model accounts for the speed of advance of the fire 
considering the 
following variables: 

 

- Direction of spread. The speed of advance of the fire is highest in the 
downwind 
direction, slower in the side wind direction, and slowest in the upwind 
direction. 

- Wind velocity. The speed of advance of the fire increases as the square of the 
wind velocity. 

- Fire resistance of structures. The speed of advance through wood structures is 
about twice the speed of advance through fire resistant structures. 

 

It should be noted that the Hamada model results in different fire spreading 
rates in the 
downwind, sidewind, and upwind directions even for zero wind speed. To correct 
this 
problem, a linear interpolation function is introduced which forces the fire 
spreading 
rates to be equal in all directions as the wind speed approaches zero. For wind 
speeds 
less than 10 m/sec, the adjusted fire spreading rates (Kd', Ku' and Ks') are 
given as 
follows: 

 

KKV10KK2K1V10d'ddus=
...
...
+
+.
..
.
..
-
...
... 
(10-4a) 




 

KKV10KK2K1V10u'udus=
...
...
+
+.
..
.
..
-
...
... 
(10-4b) 

 

KKV10KK2K1V10s'sdus=
...
...
+
+.
..
.
..
-
...
... 
(10-4c) 

 

10.2.3 Suppression 

 

The term suppression is defined as all the work of extinguishing a fire, 
beginning with its 
discovery. The steps in the suppression activity are defined as follows: 

 

 Discovery Time. Elapsed time from the start of the fire until the time of the 
first 
discovery which results directly in subsequent suppression action. 

 Report Time. Elapsed time from discovery of a fire until it is reported to a 
fire 
agency that will respond with personnel, supplies and equipment to the fire. 

 Arrival Time. Elapsed time from the report time until the beginning of 
effective 
work on a fire. 

 Control Time. Elapsed time from the beginning of effective work on a fire to 
when 
the fire is controlled. 

 Mop-up Time. Elapsed time from completion of the controlling process until 
enough 
mop-up has been done to ensure that the fire will not break out and the 
structure is 
safe to re-occupy. 

 

 

 

10.2.3.1 Discovery Time 

 

The time to discover a fire is usually on the order of a few minutes if anyone 
is present to 
observe the fire. In modern urban areas, many structures have smoke detectors, 
and these 
will alert occupants or perhaps people nearby the structure that a fire has 
ignited. The 
following discovery model is used: 

 

 85 percent of structures are occupied at the time of the earthquake. In these 
structures, fires are discovered randomly between 0 and 5 minutes. 

 15 percent of structures are not occupied at the time of the earthquake. In 
these 
structures, fires are discovered randomly between 3 and 10 minutes. 

 

10.2.3.2 Report Time 

 

The time to report a fire is usually less than one minute under non-earthquake 
conditions. 
Most people report a fire directly to the fire department or call 911. The 911 
dispatchers 
determines the degree of the emergency and notify the fire department. 

 




After an earthquake, this usual method to report fires will be hampered, either 
due to 
phone system overload (inability to get a dial tone) or due to physical damage 
to various 
parts of the phone system. In theory, the fire model could account for the 
various levels 
of phone system damage from outputs from the communications module. However, for 
simplification the report time aspects are based on the following methods. 

 

Five different methods are considered in determining how the fire will actually 
be 
reported to the fire department after an earthquake. 

 

 Cellular phone: The model assumes that 15 percent of all fires can be reported 
by 
cellular phone taking 1 minute. 

 Regular phone: The model assumes that 25 percent of all fires can be reported 
by 
regular phone taking 1 minute; 50 percent of all fires can be reported by 
regular 
phone, taking anywhere from 1 to 5 minutes; and 25 percent of all fires cannot 
be 
reported by regular phone. 

 Citizen alert: In all fires, one option to report fires is for the resident to 
walk or drive 
to the nearest fire station and report the fire. This method of reporting is 
available for 
all fire ignitions. The time to report such a fire is anywhere from 1 to 11 
minutes. 

 Roving Fire Vehicle: A fire department practice for fire response after 
earthquakes is 
to immediately get fire apparatus onto the streets, looking for fires. The model 
assumes that a roving vehicle can detect a fire somewhere between 3 and 14 
minutes 
after the earthquake. 

 Aircraft: In many post-earthquake responses, helicopters and other aircraft 
will be 
flying over the affected areas. Often by the time a fire is spotted at height, 
it has 
already grown to significant proportions. The model assumes that fires can be 
detected by aircraft anywhere from 6 minutes to 20 minutes after the earthquake. 

 

The model considers all five methods to report fires. The method which results 
in the 
earliest detection is the one which is used in the subsequent analysis. 

 

10.2.3.3 Arrival Time 

 

The arrival time is the time it takes after the fire is reported for the first 
fire suppression 
personnel and apparatus to arrive at a fire ignition. Under non-earthquake 
conditions, fire 
engines respond to fires by driving at about 30 miles per hour on average. After 
an 
earthquake, it is expected that fire engines will have a somewhat more difficult 
time in 
arriving at a fire due to damage to the road network, debris in the streets due 
to fallen 
power poles or damaged structures, traffic jam caused by signal outages, and the 
like. 

 

The model accounts for this slowdown in arrival time as follows: 

 

 If the fire was detected by a roving fire engine, arrival time is 0 minutes 
(the 
engine is already at the fire). 

 If the fire is called in or reported by citizens, the time for the first 
engine from a 
local fire department to arrive at the fire is between 2 and 12 minutes. (Under 




non-earthquake conditions, arrival time is usually about 1 - 6 minutes, so the 
model assumes that the fire engines drive at 50 percent of normal speed). 

 

10.2.3.4 Control Time 

 

The time and resources needed to control the fire will depend upon the status of 
the fire 
at first arrival of the first fire engine. The model accounts for different 
control times 
considering the status of the fire. Since the status of a fire can vary over 
time, the model 
continues to check fire status every minute. 

 

10.2.3.4.1 Room and Contents Fires 

 

If the total time from ignition to arrival is short, then the fire may be still 
a "room and 
contents" fire. These fires are small, and most fire engines carry enough water 
in the 
truck to control them. (Typical water carried in a pumper truck is 500 gallons 
to 1000 
gallons). If this is the case, the model assumes that the first responding fire 
engine can 
control the fire. The engine is held at the location of the fire for 10 minutes. 
Thereafter, 
the engine is released for response to other fires that may be ongoing. 

 

10.2.3.4.2 Structure Fires - Engines Needed 

 

If the fire has spread to beyond a room and contents fire, then suppression 
activities 
require two resources: an adequate number of fire apparatus (engine trucks, 
ladder trucks, 
hose trucks) and personnel, and an adequate amount of water. 

 

Most fire apparatus today are engine trucks, and the model does not 
differentiate between 
the capabilities of a ladder truck and an engine truck. (The user should input 
to the 
model the sum of fire department apparatus which can pump water at a rate of 
about 
1,000 gpm to 2,000 gpm. Hose tenders without pumps, search and rescue trucks, 
and 
automobiles are not counted as available apparatus in the model). 

 

The model determines the number of required trucks as follows: 

 Single Family Residential Fires. Figure 10.3 shows the number of fire trucks 
needed 
to suppress a fire, versus the number of structures already burned. 

 Other Fires. Figure 10.4 shows the number of fire trucks needed to suppress a 
fire, 
versus the number of structures already burned, for the case when the original 
ignition was at a structure other than a single family building. These ignitions 
include fires at apartment, commercial, wholesale and industrial structures. 
From 
Figure 10.4, it is shown that a minimum of two trucks are needed if the burnt 
structures range from zero to four. Since only one truck is sent to each fire, 
this leads 
to all fires becoming a conflagration, regardless of size. A modification is 
introduced 
by modifying the requirement to: 

 

 
One truck is needed if the burnt structures are less than 2. 


 
Two trucks are needed if the burnt structures are between 2 and 4. 




 




This modification will reduce the total burnt area since all fires close to the 
fire stations 
will be controlled and putout by only one engine. 

 

10.2.3.4.3 Structure Fires - Water Needed 

 

Except in the case of room and content fires, urban fire suppression usually 
requires large 
quantities of water in order to gain control. (The issue of firebreaks in urban 
areas is 
described later). The amount of water needed is usually expressed in two terms: 

 

 Required Flow: This is the amount of water needed to fight a fire from one or 
more 
fire hydrants, usually expressed in gallons per minute, gpm. 

 Required duration: This is the length of time the fire flow is needed, in 
hours (or 
minutes). 

 

Number of Structures BurnedNumber of Fire Engines Needed 
0204060801001201401601802000-22-55-1010-2020-5050-100100-200200-500500-10001000-
30003000+
1235812205075150200 


Figure 10.3 Ignitions That Start in Single Family Structures. 

 

 

 




Number of Structures BurnedNumber of Fire Engines Needed 
0204060801001201401601802000-44-88-1212-2020-4040-5050-100100-200200-500500-
10001000-30003000+
234681020507515020035 


Figure 10.4 Ignitions That Start in Non-Single Family Structures. 

 

A term often used in describing water needs is pressure. In the usual fire 
fighting 
terminology, the fire flows are required at the hydrant outlet at a minimum of 
20 psi 
residual pressure while the hydrant is flowing. 

 

Most cities use a water distribution system that delivers water for customer 
needs 
(drinking, sanitary, and other uses) and water for fire flow needs through a 
single set of 
pipes. Water pressures are usually kept in the mains at around 40 psi - 60 psi 
to meet 
normal customer needs. When a hydrant is opened, flows through the water mains 
increase. In areas of the city where mains are not highly interconnected (such 
as in 
hillside communities) or where mains have small diameters (2", 4" and some 6" 
pipes), 
the high velocities of water needed to deliver the water to the fire hydrant 
cause 
significant pressure drops. If the water pressure drops below about 20 psi, then 
fire 
engines have a difficult time drafting the water out of the hydrant. 

 

The water needed to fight a fire at any given time t (Wt in gallons), depends 
upon the 
extent of the fire. The following equations are used to calculate the water 
needed: 

 

()WN ; NttVtV=<12500300004. (10-5) 

where NtV = Number of structures burned at time t, at wind velocity V 

 

Equation (10-5) is based upon the Uniform Fire Code (1991) for single structure 
fires 
(NtV = 1) and modified for large conflagration fires. 

 




For apartment fires, the amount of water needed is somewhat higher than the 
water 
needed for a single family residence, and is expressed in equations 10-6 and 10-
7: 

 

()WN ; NttVtV=15000405. (10-6) 

or, 

()WN ; NttVtV=+-<=3000125044300004. (10-7) 

 

For commercial, wholesale and industrial fires, the amount of water needed is 
higher than 
the water needed for a small apartment building, and is expressed in equations 
10-8 and 
10-9: 

 

()WN ; NttVtV=25000405. (10-8) 

or, 

()WN ; NttVtV=+-<=5000125044300004. (10-9) 

 

For petroleum fires, the amount of water needed is higher than the water needed 
for other 
types of fires, and is expressed in equations 10-10 and 10-11: 

 

()WN ; NttVtV=40000405. (10-10) 

or, 

()WN ; NttVtV=+-<=8000125044300004. (10-11) 

 

 For all types of fires, the duration of flow is determined by equation 10-12: 

 

(Dengines needed=0504.*. (10-12) 

 

where D = duration of flow needed, in hours 

 ()engines needed = taken from Figure 10.3 or 10.4 

 

10.2.3.4.4 Engines Available 

 

The number of fire apparatus (engines and ladders) available in the study area 
is supplied 
by the user as input to the model. The following information is needed: 

 

 The number of pumper apparatus engines in every jurisdiction within the study 
area. 
The user must select the level of refinement of the jurisdiction within the 
study area. 
A jurisdiction can be set at either the fire station level, the battalion level, 
or the city 
level. 

- Jurisdictions can be set as a city if the city has population of about 400,000 
people 
or less. 




- Jurisdictions should be set as a battalion (or more refined) if the city has 
population greater than about 400,000. 

 

 The number of pumper apparatus available from mutual aid, from jurisdictions 
outside the study area. Mutual aid jurisdictions can usually be set in terms of 
the 
number of pumper apparatus available within a county. The geographic extent of 
the 
earthquake should be considered to decide what proportion of mutual aid that can 
be 
normally counted on will be delivered. 

 

The model tracks the order of detection of the fires. Fire engines will serve 
fires which 
have been discovered first and are nearest to the fire stations. An insufficient 
number of 
fire trucks will result in the fire spreading faster which will be addressed 
later. 

 

10.2.3.4.5 Water Available 

 

The water available to fight a fire depends upon the capacity of the water 
distribution 
system, taking into account the level of damage to the system. Parameters that 
determine 
the amount of water available in a cell to suppress fires include: 

 

 Available water flow 

 Duration of water flow for pumped water system 

 

10.2.3.4.6 Fire Spread with Partially Effective Suppression 

 

For each fire, at each time step of the analysis, the model checks to see what 
is the available flow for fire suppression activities and what number of fire 
trucks are at the scene of the fire. Based upon the size of the fire at that 
time, the model calculates the 
number of fire trucks needed and the amount of water normally needed to control 
the fire. 
From these values, two ratios are calculated: 

 

R 
truck = trucks at fire 
trucks needed at fire , but Rtruck should not exceed 1.0 

 

R 
water = , but Rwater should not exceed 1.0 available flow at fire 
flow needed

 

where, 

 

available flow = (reduction factor) * (typical discharge from hydrant) * (number 
of hydrants to fight fire) 

 

The reduction factor is set to the serviceability index obtained from Chapter 8. 
The 
typical discharge from a hydrant is around 1750 gallons/min. Finally, the number 
of 
hydrants available at the scene of the fire is estimated as follows: 

 

No. of Hydrants = 1.5 * (Kd+Ku)(2Ks)/(100*100) 




 

Where Kd, Ku, and Ks are previously defined. Note that 100 is the average 
spacing in 
meters between fire hydrants (typically, the spacing is in the range 60 m to 150 
m). The 
coefficient 1.5 reflects the assumption of 50% of additional fire hydrants from 
adjacent 
blocks or equivalent will be available to fight the fire. 

 

Based on the calculated values of Rtruck and Rwater, the fire suppression 
effectiveness is: 

 

()PR*RReffectivetruckwatertruck==07033.. (10-13) 

 

This equation reflects the following logic. If the available trucks and water 
are much less 
than required, then there is good chance that the fire will spread. Conversely, 
if most of 
the trucks and water needed are available, then the fire suppression 
effectiveness is much 
better. 

 

Due to fire suppression, the rate of fire spread will be slowed down and the 
reduced rate 
will be 

 

Spread Rate = ()SpreadPnon-suppressedeffective0.7-1 (10-14) 

 

The Spread Rate is the key variable used in determining the spread of the fire. 
Equations 
10-13 and 10-14 together provide the prediction as to the effectiveness of 
partial fire 
suppression in stopping urban conflagration. 

 

10.2.3.4.7 Fire Spread at Natural Fire Breaks 

 

Fire breaks are one of the ways to stop fires from spreading. Fire breaks abound 
in an 
urban area and include streets, highways, parks, and lakes. The model accounts 
for fire 
breaks as follows: 

 

 Fires can spread within a city block following equation 10-3 as modified by 
equation 
10-14. The model keeps track of the spread. 

 

 The average city block is assumed to have two rows of houses, and there are 15 
houses down a single side of a block. The average length of a city block is 
taken as 
the average of the width and length of the block. If the user does not supply 
the 
average width of a city block street, including sidewalks, then the model will 
use 
default width of 25 meters. 

 

 The model assumes that every fifth fire break is three times wider than the 
average 
city street fire break. These wide fire breaks account for the presence of wide 
boulevards, interstate highways, parks and lakes. 

 

 If the fire spread just reaches a fire break, then there is a probability that 
the fire break 
will control the fire, even with no active suppression or partial suppression 
ongoing. 




The probability of the fire jumping the fire break increases with the wind 
velocity, 
decreases with the width of the fire break, and decreases if there is active 
fire 
suppression as shown in Figure 10.5. Figure 10.5 is adapted from Dames and 
Moore, 
1987, and combined with judgment. 

 

Firebreak Width (meters)
Probability of Cross Firebreak00.10.20.30.40.50.60.70.80.910102030405060Calm 
Winds withSuppressionCalm Winds withSuppressionLight Winds withSuppressionLight 
Winds with noSuppressionHigh Winds withSuppressionHigh Winds with 
noSuppressionno 


Figure 10.5 Probability of Crossing Firebreak. 

 

10.3 Guidance for Expert-Generated Estimates 

 

As described in Section 10.2, the FFE model makes several simplifying 
assumptions 
about the study area. Any or all of these assumptions can be relaxed, and the 
resulting 
FFE model will be more refined. The reader may adjust the model by relaxing the 
following assumptions: 

 

 Analyze the actual water system, for each pressure zone. Many water systems 
are 
made up of dozens of pressure zones, many interdependent upon each other. With 
zone-by-zone information, the analysis can much better identify which parts of 
the 
study area are most prone to conflagration. 

 

 Adjust the model for urban intermix fuels, if these conditions are applicable 
to the 
study area. Fire spreads are much higher in these areas than in urban areas. The 
analysis will have to digitize in the fuel mix for each cell of the model, and 
adjust the 
fire spread model accordingly. 

 




 Add high flow water system boundaries to the model. In some areas of the city, 
the 
water system may be designed to provide very high flows: 24" diameter (or 
larger) 
transmission pipes (with hydrants) which carry flows on the order of 20,000 gpm 
or 
higher. If there are adequate fire department resources available, then almost 
any fire 
can be stopped at these locations, even under relatively high winds. Of course, 
the 
Water System Lifeline module will have to also be analyzed to determine if these 
pipes break under the earthquake. 

 

 

10.4 References 

 

Dames and Moore Report, March 1987. "Fire Following Earthquake, Estimates of the 
Conflagration Risk to Insured Property in Greater Los Angeles and San 
Francisco," 
Report to All-Industry Research Advisory Council, C. Scawthorn. 

 

G&E Engineering Systems Inc., 1994. "Earthquake Loss Estimation, Technical 
Manual, 
Fire Following Earthquake", Prepared for the National Institute of Building 
Sciences, 
Principal Investigator: John M. Eidinger. 

 

Hamada, M., 1975. "Architectural Fire Resistant Themes", No 21., Kenchikugaku 
Taikei, 
Shokokusha, Tokyo. 

 

International Conference of Building Officials, Uniform Fire Code, 1991. 




Chapter 11 

Induced Damage Models - Hazardous Materials Release 

 

 

11.1 Introduction 

 

Hazardous materials are those chemicals, reagents or substances that exhibit 
physical or 
health hazards, whether the materials are in a usable or waste state. The scale, 
and hence 
the consequences, of hazardous materials releases can vary from very small, such 
as a 
gallon of paint falling off of shelves, to regional, such as release of toxic 
chemicals from 
a processing plant. Most hazardous materials incidents have immediately led to 
human 
casualties only in cases where explosions have occurred. Non-explosive hazardous 
materials incidents, which comprise the vast majority, typically have led to 
contamination of the environment and temporary health consequences to human 
beings. 
Hazardous materials releases can also lead to fires. With specific reference to 
earthquake 
caused hazardous materials incidents, the data thus far indicate that there have 
been no 
human casualties. The consequences of these incidents have been fires and 
contamination 
of the environment, and have led to economic impacts because of the response and 
clean-
up requirements. The methodology highlighting the Hazardous Materials Release 
component is shown in Flowchart 11.1. 

 

 

11.1.1 Scope 

 

This loss estimation methodology has been restricted to identifying the location 
of 
facilities that contain hazardous material which could lead to a significant 
immediate 
demand on health care and emergency response facilities. These types of 
incidents would 
include large toxic releases, fires or explosions. Thus, the default database of 
hazardous 
material facilites is limited to facilities where large quantities of chemicals 
that are 
considered highly toxic, flammable or highly explosive are stored. Estimates of 
releases 
that could cause pollution of the environment and the need for long-term clean-
up effects 
are beyond the scope of this methodology. 

 

An exhaustive search of the existing literature for models that can be utilized 
to predict 
the likelihood of occurrence of hazardous materials releases during earthquakes 
was 
conducted at the beginning of this study. Unfortunately, no directly usable 
models were 
found. There were three attempts at modeling that had been made previously 
(Tierney, et 
al., 1990, Ravindra, 1992, Los Angeles County Fire Department, 1992). The model 
developed by Tierney et al. focused on the likelihood of gaseous releases, and 
its 
potential effect on surrounding populations. However, it was not found to be 
suitable for 
risk assessment efforts by local jurisdiction personnel due to the level of 
detailed analysis 
required. The study conducted by Ravindra is in essence identical to the effort 
by the 
Los Angeles County Fire Department. This effort is really intended for seismic 
vulnerability analysis of individual facilities, and requires significant expert 
input, 

 




8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Flowchart 11.1: Hazardous Materials Release Relationship to other Modules in the 
Earthquake Loss Estimation Methodology 




including a walk-through inspection. Furthermore, this effort is aimed at large 
complexes similar to petrochemical facilities, and is not suitable for a more 
general 
application. There is, therefore, the need for a more general model that can be 
used by 
emergency preparedness officials at the local jurisdiction level so that they 
can determine 
the potential for hazardous materials incidents occurring during earthquakes. 

 

Due to the limitations of state-of-the-art hazardous materials release models, 
this module 
is restricted to establishing a standardized approach for classifying materials 
and 
developing a good database that can be used by local planners to identify those 
facilities 
that may be most likely to have significant releases in future earthquakes. A 
default 
database of potential sites is provided from an EPA database of hazardous 
materials sites. 
This database can be supplemented by the user with local information. A more 
detailed 
vulnerability assessment would involve going to individual facilities to 
determine how 
chemicals are stored, the vulnerability of buildings and storage tanks and other 
relevant 
information. 

 

11.1.2 Classification of Hazardous Materials 

 

The most widely used detailed classification scheme is the one that has been 
developed 
by the National Fire Protection Association, and is presented in the 1991 
Uniform Fire 
Code, among other documents. This classification scheme is shown in Table 11.1. 
The 
hazards posed by the various materials are divided into two major categories: 
Physical 
Hazards and Health Hazards. Depending upon the exact nature of the hazard, these 
two 
major categories are divided into subcategories. These subcategories of hazards, 
with 
their definitions, and examples of materials that fall within each category, are 
contained 
in Appendix 11A and 11B. A more detailed description of these categories, with 
more 
extensive examples can be found in Appendix VI-A of the 1991 Uniform Fire Code. 
Table 11.1 also contains minimum quantities of the materials that must be on 
site to 
require permitting according to the Uniform Fire Code. It should be noted that 
the 
minimum permit quantities might vary depending upon whether the chemical is 
stored 
inside or outside of a building. 

 

11.1.3 Input Requirements and Output Information 

 

The input to this module is essentially a listing of the locations of facilities 
storing 
hazardous materials and the types/amounts of the materials stored at the 
facility. 
Facilities need only be identified if they use, store or handle quantities of 
hazardous 
materials in excess of the quantities listed in Table 11.1. Other facilities 
that may have 
hazardous materials, but in quantities less than those listed in Table 11.1 
should not be 
included in the database because it is anticipated that releases of these small 
quantities 
will not put significant immediate demands on health and emergency services. 
However, 
the user may choose to modify threshold amounts in building the database. 

 

 

 




Table 11.1: Classification of Hazardous Materials and Permit Amounts 

 

Label 

Material Type 

Permit Amount 

Hazard Type & 

 

 

Inside Building 

Outside Building 

Remarks 

HM01 

Carcinogens 

10 lbs 

10 lbs 

Health 

HM02 

Cellulose nitrate 

25 lbs 

25 lbs 

Physical 

HM03 

Combustible fibers 

100 cubic ft 

100 cubic ft 

Physical 

 

HM04 

HM05 

HM06 

Combustible liquids 

Class I 

Class II 

Class III-A 

 

5 gallons 

25 gallons 

25 gallons 

 

10 gallons 

60 gallons 

60 gallons 

Physical 

HM07 

Corrosive gases 

 

Any amount 

Any amount 

Health [1] 

 

HM08 

Corrosive liquids 

55 gallons 

55 gallons 

Physical; Health 

 

HM09 

HM10 

HM11 

HM12 

HM13 

Cryogens 

Corrosive 

Flammable 

Highly toxic 

Nonflammable 

Oxidizer (including 
oxygen) 

 

1 gallon 

1 gallon 

1 gallon 

60 gallons 

50 gallons 

 

1 gallon 

60 gallons 

1 gallon 

500 gallons 

50 gallons 

 

Health 

Physical 

Health 

Physical 

Physical 

HM14 

Highly toxic gases 

Any amount 

Any amount 

Health; [1] 

HM15 

Highly toxic liquids & 
solids 

Any amount 

Any amount 

Health 

HM16 

Inert 

6,000 cubic ft 

6,000 cubic ft 

Physical; [1] 

HM17 

Irritant liquids 

55 gallons 

55 gallons 

Health 

HM18 

Irritant solids 

500 lbs 

500 lbs 

Health 

HM19 

Liquefied petroleum gases 

> 125 gallons 

> 125 gallons 

Physical 

HM20 

Magnesium 

10 lbs 

10 lbs 

Physical 

HM21 

Nitrate film 

(Unclear) 

(Unclear) 

Health 

HM22 

Oxidizing gases (including 
oxygen) 

500 cubic feet 

500 cubic feet 

Physical [1] 

 

HM23 

HM24 

HM25 

HM26 

Oxidizing liquids 

 Class 4 

 Class 3 

 Class 2 

 Class 1 

 

Any amount 

1 gallon 

10 gallons 

55 gallons 

 

Any amount 

1 gallon 

10 gallons 

55 gallons 

Physical 

 

HM27 

HM28 

HM29 

HM30 

Oxidizing solids 

 Class 4 

 Class 3 

 Class 2 

 Class 1 

 

Any amount 

10 lbs 

100 lbs 

500 lbs 

 

Any amount 

10 lbs 

100 lbs 

500 lbs 

Physical 

 

 

HM31 

HM32 

HM33 

HM34 

Organic peroxide liquids 
and solids 

 Class I 

 Class II 

 Class III 

 Class IV 

 

 

Any amount 

Any amount 

10 lbs 

20 lbs 

 

 

Any amount 

Any amount 

10 lbs 

20 lbs 

Physical 

 

HM35 

HM36 

Other health hazards 

 Liquids 

 Solids 

 

55 gallons 

500 lbs 

 

55 gallons 

500 lbs 

Health 



 




Table 11.1: Classification of Hazardous Materials and Permit Amounts (cont.) 

 

Label 

Material Type 

Permit Amount 

Hazard Type & 

 

 

Inside Building 

Outside Building 

Remarks 

HM37 

Pyrophoric gases 

Any amount 

Any amount 

Physical [1] 

HM38 

Pyrophoric liquids 

Any amount 

Any amount 

Physical 

HM39 

Pyrophoric solids 

Any amount 

Any amount 

Physical 

HM40 

Radioactive materials 

1 m Curie in 
unsealed source 

1 m Curie in 
sealed source 

Health [1] 

HM41 

Sensitizer, liquids 

55 gallons 

55 gallons 

Health 

HM42 

Sensitizer, solids 

500 lbs 

500 lbs 

Health 

HM43 

Toxic gases 

Any amount 

Any amount 

Health [1] 

HM44 

Toxic liquids 

50 gallons 

50 gallons 

Health 

HM45 

Toxic solids 

500 lbs 

500 lbs 

Health 

HM46 

Unstable gases (reactive) 

Any amount 

Any amount 

Physical [1] 

 

HM47 

HM48 

HM49 

HM50 

Unstable liquids (reactive) 

 Class 4 

 Class 3 

 Class 2 

 Class 1 

 

Any amount 

Any amount 

5 gallons 

10 gallons 

 

Any amount 

Any amount 

5 gallons 

10 gallons 

Physical 

 

HM51 

HM52 

HM53 

HM54 

Unstable solids (reactive) 

 Class 4 

 Class 3 

 Class 2 

 Class 1 

 

Any amount 

Any amount 

50 lbs 

100 lbs 

 

Any amount 

Any amount 

50 lbs 

100 lbs 

Physical 

 

HM55 

HM56 

HM57 

Water-reactive liquids 

 Class 3 

 Class 2 

 Class 1 

 

Any amount 

5 gallons 

10 gallons 

 

Any amount 

5 gallons 

10 gallons 

Physical 

 

HM58 

HM59 

HM60 

Water-reactive solids 

 Class 3 

 Class 2 

 Class 1 

 

Any amount 

50 pounds 

100 pounds 

 

Any amount 

50 pounds 

100 pounds 

Physical 



[1] Includes compressed gases 

 

To build the hazardous materials database for a selected region, the user should 
attempt 
to gather the following information: 

 

 Name of Facility or Name of Company 

 Street Address 

 City 

 County 

 State 

 Zip Code 

 Name of Contact in Company 

 Phone Number of Contact in Company 

 Standard Industrial Classification (SIC) Code 

 Chemical Abstracts Service (CAS) Registry Number 

 Chemical Name 




 Chemical Quantity 

 Hazardous Material Class (From Table 11.1) 

 Latitude and Longitude of Facility 

 

The Chemical Abstracts Service (CAS) registry number is a numeric designation 
assigned by the American Chemical Societys Chemical Abstracts Service and 
uniquely 
identifies a specific chemical compound. This entry allows one to conclusively 
identify a 
material regardless of the name or naming system used. To obtain this data the 
user must 
identify the local agency with which users of hazardous materials must file for 
permits. 
Based upon current understanding of the process, this local agency would be the 
Fire 
Department for incorporated areas, and the County Health Department for 
unincorporated 
areas. The user may opt to use only the information contained in a modified 
version of 
the EPA-TRI Database that is provided in the methodology. This database, 
however, is 
limited and the user is urged to collect additional inventory. 

 

The output of this module is essentially a database that can be sorted according 
to any of 
the fields listed above. It can be displayed on a map and overlaid with other 
maps. 

 

 

11.2 Description of Methodology 

 

The analysis here is divided into three levels, as described below: 

 

 
Default Analysis: Listing of all facilities housing hazardous materials that are 
contained in the default hazardous materials database. 


 
User-Supplied Data Analysis: Listing of all facilities housing hazardous 
materials that are contained in the default hazardous materials database and 
refined by the user with locally available information. 


 
Advanced Data and Models Analysis: Detailed risk assessment for individual 
facilities, including expert-generated estimates. 




 

11.3 Guidance for Expert-Generated Estimates 

 

A detailed analysis is quite involved and is intended to provide the user with a 
relatively 
good estimate of the likelihood of a hazardous materials incident occurring at 
individual 
facilities during an earthquake. The detailed analysis therefore provides 
vulnerabilities 
of individual facilities. While the model were based primarily on location of 
facilities 
and type(s) and quantities of hazardous materials on site, a more detailed 
analysis is 
intended to take into account a number of other factors including the level of 
preparedness of individual facilities and the type of structure within which the 
hazardous 
materials are located. To do this detailed analysis, it is necessary to have an 
expert 
conduct a detailed analysis of individual facilities. 

 

The level of sophistication to be attained in an analysis can vary 
significantly, depending 
upon how the analysis is defined. It is recommended very strongly that the user 
clearly 
identify the purpose and scope of the analysis first before engaging an expert 
to conduct 




the analysis. Based on the level of analysis expected, the user then has to 
identify and 
select an expert, or several experts, to conduct the analysis. In any case, it 
will be 
necessary for the expert(s) to conduct a thorough survey and inspection of the 
facilities. 
The areas that need to be covered include the following: structures, building 
contents 
including equipment, storage areas, tanks, and emergency preparedness. Depending 
upon the level of the analysis, the experts required could cover the following: 
a 
hazardous materials expert, a structural engineer, an emergency planner, and a 
mechanical engineer. The role(s) each of these experts would play is explained 
below. 

 

Input Requirements 

 

The most elementary form of detailed analysis would consist of a hazardous 
materials 
expert doing a walk through to identify target hazard areas. In most 
jurisdictions, the fire 
department personnel are the best trained in issues pertaining to hazardous 
materials. 
Many fire departments are also willing to meet with major users of hazardous 
materials 
to do what is termed pre-planning. In this effort, fire departments visit the 
facilities of 
users, identify areas that they think are particularly vulnerable, and suggest 
improvements. If there were code violations, the fire department personnel would 
point 
this out. In highly industrialized areas, there are consulting firms that are 
capable of 
conducting this assessment. The smaller consulting firms tend to be comprised 
only of 
individuals with expertise in hazardous materials issues. 

 

It must be borne in mind that when assessing the potential for hazardous 
materials 
releases during earthquakes, the performance of the structure and the 
performance of 
nonstructural items are both important. Another very important factor is the 
level of 
preparedness, especially where it pertains to the ability to contain an incident 
and prevent 
it from spreading or enlarging. 

 

The structural and nonstructural vulnerability of a hazardous materials facility 
are 
assessed by a qualified structural engineer. For example, the integrity of an 
above 
ground storage tank, containing 100,000 gallons of petroleum, should be 
evaluated by a 
structural engineer. 

 

A large number of hazardous materials incidents during earthquakes have occurred 
at 
locations where the structure itself suffered no damage. This has been due to 
the manner 
in which the hazardous materials are stored and used within the buildings or 
structures. 
Generally, it is the extent to which nonstructural hazard mitigation measures 
have been 
implemented that determines the vulnerability of the contents. At the present 
time there 
is no profession that specializes in nonstructural engineering. A reference on 
nonstructural hazard mitigation measures has been written by Reitherman (1983). 
A 
more specific paper discussing hazard prevention techniques in the laboratory 
has been 
written by Selvaduray (1989). Though not directly pertaining to industrial 
facilities, 
FEMA has developed a guide for nonstructural hazard mitigation in hospitals 
(FEMA, 
1989). Hazard mitigation strategies, particularly where they pertain to 
preventing toxic 
gas releases during earthquakes, have been studied by ABAG, and are contained in 
a 
special report prepared by ABAG (1991). 




 

In conducting a detailed analysis, it is important not only to assess the 
potential for 
occurrence of incidents, but it is also important to assess the capability of 
containing 
incidents and preventing them from spreading or becoming enlarged. The level of 
preparedness of the individual facilities generally determines this. There have 
been a 
number of cases where the incidents would have been smaller than they actually 
were, 
had the organization/facility had the capability to respond in a timely manner. 
The type 
of expert needed here is an Emergency Planner. Unfortunately, it is not easy 
to find an 
emergency planner who specializes in assessing individual facilities. Here 
again, perhaps 
the most qualified and educated personnel are fire department personnel. In most 
cases, 
hazardous materials consultants also address issues pertaining to response. In 
the case 
when an expert is not available, the document by the U.S. Environmental 
Protection 
Agency (EPA, 1987), which provides technical guidance for hazards analysis and 
emergency planning for extremely hazardous substances is an excellent guide. 
Another 
useful guide is the Hazardous Materials Emergency Response Guide published by 
the 
National Response Team (1987). The user should keep in mind that both of these 
documents are quite general in nature, and do not address earthquake concerns 
specifically. Nevertheless, in the absence of more specific information, these 
guides are 
definitely useful in getting the user started towards assessing the risks. 

 

Depending upon the type of facility, there could also be a large number of 
mechanical 
systems, including piping that either utilize or carry hazardous materials. 
Examples of 
such facilities include petroleum refineries, semiconductor processing 
facilities, and 
polymer resin synthesis facilities. In such cases, the type of expert capable of 
conducting 
an adequate vulnerability analysis of the mechanical and piping systems would be 
a 
mechanical engineer. It should be pointed out that mechanical engineering is a 
very 
broad field, and the particular type of mechanical engineer who would be 
suitable for a 
task such as the one posed here would be one with a very strong background in 
plant 
safety, and preferably also in structural analysis. A number of hazardous 
materials 
releases during past earthquakes have occurred in mechanical and piping systems. 
This 
component should therefore not be ignored. A book on assessing the earthquake 
vulnerability of building equipment has been written by McGavin (1983). This 
book 
provides particularly valuable information on anchoring of equipment. One 
approach to 
assessing the vulnerability of hazardous materials piping systems has been 
developed and 
presented by Kircher (1990), and can potentially be utilized by mechanical 
engineers 
having the capability to conduct particularly sophisticated analysis. 

 

There are two documents that provide a general methodology for assessing the 
earthquake vulnerability of entire facilities, particularly those that contain 
hazardous 
materials. One such document is the Proposed Guidance for RMPP Seismic 
Assessments contained within the Los Angeles County Fire Departments Risk 
Management and Prevention Program Guidelines. This document provides guidelines 
for assessing the earthquake vulnerability of facilities that use hazardous 
materials, 
especially Acutely Hazardous Materials (AHM). However, the methodology provided 
does require a structural engineer. On the positive side, there are relatively 
detailed 
guidelines for assessing the vulnerability of piping systems. Ravindra (1992) 
has 




presented an approach, that is very similar to the one developed by the Los 
Angeles 
County Fire Department, for seismic evaluation of hazardous materials 
facilities. 

 

 

Output Information 

 

Ideally, upon completion of a detailed analysis, the user will have a very good 
idea of the 
vulnerability (ies) contained within each facility. The user will have a 
relatively good 
grasp of the potential for occurrence of hazardous materials releases, during 
earthquakes, 
at each of the facilities analyzed. While this might not be a quantified 
probability 
number, the results of the analysis should provide sufficient information to 
categorize the 
likelihood in terms of high, medium, or low. In addition to the overall 
likelihood, the 
user should also be able to identify the locations within each facility where 
hazardous 
materials releases might occur. This can be particularly important for larger 
facilities that 
cover several acres. It is only by identifying specific locations within the 
larger facilities 
that adequate response can be planned for. Another piece of information that the 
user 
should obtain from an expert-assisted analysis is the likely consequence of a 
hazardous 
materials release. Particularly important here is the scope of the release, and 
the manner 
in which it would affect the surrounding area. It is expected that this can be 
determined 
by combining the analysis data with other data such as hazard, type of the 
material, phase 
of the material (solid, liquid or gas), prevailing weather conditions, and 
demographics of 
the surrounding region. 

 

The analysis should also provide the user with the ability to assess the 
response 
capability of each facility inspected. Depending upon the response capability 
that each 
facility has, the user would need to adjust his/her response capability to 
account for this. 
In general, the larger industrial facilities, such as petroleum refineries, tend 
to have 
relatively extensive response capability in-house. As such, they would be able 
to be the 
first responders, with the local jurisdictions providing the necessary backup 
capabilities. On the other hand, if the larger industrial facilities do not have 
sufficient 
capabilities to respond to hazardous materials releases, the analysis would 
provide the 
local emergency preparedness officials with the opportunity to require such 
facilities to 
increase their response capability. 

 

11.4 References 

 

Association of Bay Area Governments 1991, Toxic Gas Releases in Earthquakes: 
Existing Programs, Sources, and Mitigation Strategies, November. 

 

Federal Emergency Management Agency 1989, Non Structural Earthquake Hazard 
Mitigation for Hospitals and other Health Care Facilities, FEMA IG 370, May. 

 

International Conference of Building Officials, Uniform Fire Code, 1991. 

 

Kircher, C. A. & Lashkari, B., 1990, Seismic Restraint of Hazardous-Materials 
Piping, 
ATC-29 Seminar: Seismic Design and Performance of Equipment and Nonstructural 




Elements in Buildings and Industrial Structures, Applied Technology Council, 
Irvine, 
California, October. 

 

Kircher, C. A., et al., 1987, Seismic Restraint of Hazardous Piping Systems in 
Industrial 
Buildings (Phase I), Jack R. Benjamin Associates, Mt. View, California, April. 

 

Los Angeles County Fire Department 1992, Hazardous Materials Division, Risk 
Management and Prevention Program Guidelines, July. 

 

McGavin, A., 1983, Protection of Essential Building Equipment, McGraw-Hill. 

 

National Response Team 1987, Hazardous Materials Emergency Planning Guide, 
March. 

 

Ravindra, M., 1992. Seismic Assessment of Chemical Facilities under California 
Risk 
Management and Prevention Program, Proceedings International Conference on 
Hazard 
Identification and Risk Analysis, Human Factors and Human Reliability in Process 
Safety, Orlando, Florida, Proceedings published by Center for Chemical Process 
Safety, 
American Institute of Chemical Engineers, January, 1992. 

 

Reitherman, R. K., 1982, Earthquake-Caused Hazardous Material Releases, 1982 
Hazardous Material Spills Conference Proceedings, April, 1982. 

 

Reitherman, R., 1983, Reducing the Risks of Nonstructural Earthquake Damage: A 
Practical Guide, Scientific Service, Inc., Redwood City, Feb 1983. 

 

Selvaduray, G., Wyatt, E. & Perkins, J., 1993, Analysis of Hazardous Materials 
Releases 
during the Loma Prieta Earthquake, Proceedings 3rd US-Japan Workshop Urban 
Earthquake Hazard Reduction, EERI Publication No. 93-B, February 1993 

 

Selvaduray, G., 1989, Earthquake Hazard Reduction in the Laboratory, Proc. 
HAZMACON 89, Association of Bay Area Governments, April. 

 

Tierney, K. J., Seligson, H. A. & Eguchi, R. T., 1990, Chemical Hazards, 
Mitigation, 
and Preparedness in Areas of High Seismic Risk: A Methodology for Estimating the 
Risk of Post-Earthquake Hazardous Materials Releases, A Report prepared for The 
National Science Foundation and The National Center for Earthquake Engineering 
Research. January 8. 

 

U.S. Environmental Protection Agency 1987, FEMA, U.S. Department of 
Transportation, 
Technical Guidance for Hazards Analysis - Emergency Planning for Extremely 
Hazardous Substances, U.S. Environmental Protection Agency December. 

 




 

Appendix 11A 

Listing of Chemicals contained in SARA Title III, including their CAS Numbers, 
Hazards and Treshold Planning Quantities 

 

CAS 
Number 

Chemical Name 

Hazard 

Treshold Planning 

Quantity (pounds) 

00075-86-5 

Acetone cyanohydrin 

Poison 

1,000 

01752-30-3 

Acetone thiosemicarbazide 

Poison 

1,000 + 

00107-02-8 

Acrolein 

Flammable liquid & poison 

500 

00079-06-1 

Acrylamide 

Poison 

1,000 + 

00107-13-1 

Acrylonitrile 

Flammable liquid & poison 

10,000 

00814-68-6 

Acrylyl chloride 

Poison 

100 

00111-69-3 

Adiponitrile 

Poison 

1,000 

00116-06-3 

Aldicarb 

Deadly poison 

100 + 

00309-00-2 

Aldrin 

Poison 

500 + 

00107-18-6 

Allyl alcohol1 

Flammable liquid & poison 

1,000 

00107-11-9 

Allylamine 

Flammable liquid & poison 

500 

20859-73-8 

Aluminum phosphide 

Flammable solid & poison 

500 

00054-62-6 

Aminopterin 

Poison 

500 + 

00078-53-5 

Amiton 

Deadly poison 

500 

03734-97-2 

Amiton oxalate 

Deadly poison 

100 + 

07664-41-7 

Ammonia, anhydrous 

Poison 

500 

00300-62-9 

Amphetamine 

Deadly poison 

1,000 

00062-53-3 

Aniline 

Poison 

1,000 

00088-05-1 

Aniline, 2,4,6-trimethyl 

Poison 

500 

07783-70-2 

Antimony pentafluoride 

Corrosive to skin, eyes, 
mucuous membranes 

500 

01397-94-0 

Antimycin A 

Poison 

1,000 + 

00086-88-4 

Antu 

Poison 

500 + 

01303-28-2 

Arsenic pentoxide 

Poison 

100 + 

01327-53-3 

Arsenous oxide 

Poison 

100 

07784-34-1 

Arsenous trichloride 

Poison 

500 

07784-42-1 

Arsine 

Poison gas & flammable gas 

100 

02642-71-9 

Azinphos-ethyl 

Poison 

100 + 

00086-50-0 

Azinphos-methyl 

Poison 

10 + 

00098-87-3 

Benzal chloride 

Moderately toxic 

500 

00098-16-8 

Benzehamine,3-(trifluoromethyl)- 

Poison 

500 

00100-14-1 

Benzene, 1-(chloromethyl)-4-nitro- 

Poison 

500 + 

00098-05-5 

Benzenearsonic acid 

Deadly poison 

10 + 

03615-21-2 

Benzimidazole, 4,5-dichloro-2-(trifluoromethyl) 

Poison 

500 + 

00098-07-7 

Benzotrichloride (benzoic trichloride) 

Corrosive & poison 

100 

00100-44-7 

Benzyl chloride 

Corrosive & poison 

500 

00140-29-4 

Benzyl cynaide 

Poison 

500 

15271-41-7 

Bicyclo [2,2,1]heptane-2-carbonitrile,5-chloro-
6((((methylamino)carbonyl)oxy)imino)-(1S-(1-alpha,2-
beta,4-alpha,5-alpha,6E))- 

Poison 

500 + 

00111-44-4 

Bis(2chloroethyl)ether 

Poison 

10,000 

00542-88-1 

Bis(chloromethyl)ether 

Poison & carcinogen 

100 

00534-07-6 

Bis(chloromethyl)ketone 

Poison 

10 + 

04044-65-9 

Bitoscanate 

Poison 

500 + 

10294-34-5 

Boron trichloride 

Corrosive, poison, irritant & 
reactive with water 

500 

07637-07-2 

Boron trifluoride 

Poison & strong irritant 

500 

00353-42-4 

Borontrifluoride compound with methyl ether (1:1) 

Flammable, corrosive & 
poison 

1,000 

28772-56-7 

Bromadiolone 

Deadly poison 

100 + 

07726-95-6 

Bromine 

Corrosive & poison 

500 

01306-19-0 

Cadmium oxide 

Poison 

100 + 

02223-93-0 

Cadmium stearate 

Poison 

1,000 + 

07778-44-1 

Calcium arsenate 

Poison & carcinogen 

500 + 

00056-25-7 

Cantharidin 

Deadly poison 

100 + 

00051-83-2 

Carbachol chloride 

Deadly poison 

500 + 



 




CAS 
Number 

Chemical Name 

Hazard 

Treshold Planning 

Quantity (pounds) 

26419-73-8 

Carbamic acid, methyl-O-(((2,4-dimethyl-1,3-
dithiolan-2-yl)methylene)amino)- 

Poison 

100 + 

01563-66-2 

Carbofuran 

Poison 

10 + 

00075-15-0 

Carbon disulfide 

Flammable liquid & poison 

10,000 

000786-19-6 

Carbophenothion 

Poison 

500 

00057-74-9 

Chlordane 

Flammable liquid & poison 

1,000 

00470-90-6 

Chlorfenvinfos 

Poison 

500 

07782-50-5 

Chlorine (not muratic acid or bleach) 

Poison gas 

100 

24934-91-6 

Chlormephos 

Poison 

500 

00999-81-5 

Chlormequat chloride 

 

100 + 

00079-11-8 

Chloroacetic acid 

Corrosive & poison 

100 + 

00107-07-3 

Chloroethanol 

Flammable liquid & poison 

500 

00627-11-2 

Chloroethyl chloroformate 

Poison 

1,000 

00555-77-1 

Tris(2-chloroethyl)amine 

Moderately toxic 

100 

00067-66-3 

Chloroform 

Poison 

10,000 

00107-30-2 

Chloromethyl methyl ether 

Flammable liquid & poison 

100 

03691-35-8 

Chlorophacinone 

Poison 

100 + 

01982-47-4 

Chloroxuron 

Poison 

500 + 

21923-23-9 

Chlorthiophos 

Poison 

500 

10025-73-7 

Chromic chloride 

Poison 

1 + 

10210-68-1 

Cobalt carbonyl 

Poison 

10 + 

62207-76-5 

Cobalt,((2,2-(1,2-
ethanediylbis(nitrilomethylidyne))bis(6-
fluorophenolato))(2-)-N,N,O,O)- 

Poison 

100+ 

00064-86-6 

Colchicine 

Poison 

10 + 

00056-72-4 

Coumaphos 

Poison 

100 + 

05836-29-3 

Coumatetralyl 

Poison 

500 + 

00095-48-7 

Othro-cresol 

Poison 

1,000 + 

00535-89-7 

Crimidine 

Deadly poison 

100 + 

00123-73-9 

Crotonaldehyde 

Poison 

1,000 

04170-30-3 

E-crotonaldehyde 

Flammable liquid & poison 

1,000 

00506-68-3 

Cyanogen bromide 

Poison 

500 + 

00506-78-5 

Cyanogen iodide 

Poison 

1,000 + 

02636-26-2 

Cyanophos 

Poison 

1,000 

00675-14-9 

Cyanuric fluoride 

Poison 

1000 

00066-81-9 

Cycloheximide 

Poison 

100 + 

000108-91-8 

Cyclohexylamine 

Flammable liquid & poison 

10,000 

17702-41-9 

Decaborane (14) 

 

500 + 

08065-48-3 

Demeton 

Deadly poison 

500 

00919-86-8 

Demeton-s-methyl 

Poison 

500 

10311-84-9 

Dialifor 

Poison 

100 + 

19287-45-7 

Diborane 

Flammable gas & poison 

100 

00110-57-6 

Trans-1,4-dichlorobutene 

Poison 

500 

00149-74-6 

Dichloromethylphenylsilane 

Flammable liquid & poison 

1,000 

00062-73-7 

Dichlorvos 

Poison 

1,000 

00141-66-2 

Dicrotophos 

Poison 

100 

01464-53-5 

Diepoxybutane 

Poison 

500 

00814-49-3 

Diethyl chlorophosphate 

Deadly poison 

500 

01642-54-2 

Diethylcarbamazine citrate 

Poison 

100+ 

00071-63-6 

Digitoxin 

Deadly poison 

100+ 

02238-07-5 

Diglycidyl ether 

Poison 

1,000 

20830-75-5 

Digoxin 

Deadly poison 

10+ 

00115-26-4 

Dimefox 

Poison 

500 

00060-51-5 

Dimethiate 

Poison 

500+ 

06923-22-4 

3-(Dimethoxy phosphinyloxy)-N-methyl-cis croton-
amide(monocrotophos) 

Poison 

10 

00075-78-5 

Dimethyldichlorosilane 

Poison & irritant 

500 

00057-14-7 

Dimethylhydrazine 

Flammable liquid & poison 

1,000 

00099-98-9 

Dimethyl-p-phenylenediamine 

Poison 

10+ 

02524-03-0 

Dimethyl phosphochloridothioate 

Corrosive & poison 

500 

00077-78-1 

Dimethyl sulfate 

Corrosive & poison 

500 

00644-64-4 

Dimetilan 

Poison 

500+ 

00534-52-1 

4,6-Dinitro-o-cresol 

Poison 

10+ 



 




CAS 
Number 

Chemical Name 

Hazard 

Treshold Planning 

Quantity (pounds) 

00088-85-7 

Dinoseb 

Poison 

100+ 

01420-07-1 

Dinoterb 

Poison 

500+ 

00078-34-2 

Dioxathion 

Poison 

500 

00082-66-6 

Diphacinone 

Poison 

10+ 

00152-16-9 

Diphosphoramide, octamethyl 

Poison 

100 

00298-04-4 

Disulfoton 

Poison 

500 

00514-73-8 

Dithiazamine iodide 

Poison 

500+ 

00541-53-7 

Dithiobiuret 

Poison 

100+ 

00316-42-7 

Emetine, dihydrochloride 

Poison 

1+ 

00115-29-7 

Endosulfan 

Poison 

10+ 

02778-04-3 

Endothion 

Poison 

500+ 

00072-20-8 

Endrin 

Poison 

500+ 

00106-89-8 

Epichlorohydrin 

Flammable liquid & poison 

1,000 

02104-64-5 

EPN 

Poison 

100+ 

00050-14-6 

Ergocalciferol 

Poison 

1,000+ 

00379-79-3 

Ergotamine tartate 

Poison 

500+ 

01622-32-8 

Ethanesulfonyl chloride,2-chloro 

Poison 

500 

10140-87-1 

Ethanol,1,2-dichloroacetate 

Combustible & poison 

1,000 

00563-12-2 

Ethion 

Poison 

1,000 

13194-48-4 

Ethoprophos 

Poison 

1,000 

00538-07-8 

Ethylbis(2-chloroethyl)amine 

Deadly poison 

500 

00107-15-3 

Ethylenediamine 

Corrosive, flammable liquid, 
irritant 

10,000 

00371-62-0 

Ethylene fluorohydrin 

Poison 

10 

00151-56-4 

Ethyleneimine 

Flammable liquid & poison 

500 

00075-21-8 

Ethylene oxide 

Flammable gas & poison 

1,000 

00542-90-5 

Ethylthiocyanate 

Poison 

10,000 

22224-92-6 

Fenamiphos 

Poison 

10+ 

00122-14-5 

Fenitrothion 

Poison 

500 

00115-90-2 

Fensulfothion 

Poison 

500 

04301-50-2 

Fluenetil 

Poison 

100+ 

07782-41-4 

Fluorine 

Oxidizer & poison 

500 

00640-19-7 

Fluoroacetamide (1061) 

Poison 

100+ 

00144-49-0 

Fluoroacetic acid 

Poison 

10+ 

00359-06-8 

Fluoroacetyl chloride 

Poison 

10 

00051-21-8 

Fluorouracil 

Poison 

500+ 

00944-22-9 

Fonofos 

Poison 

500 

00050-00-0 

Formaldehyde 

Combustible liquid & poison 

500 

00107-16-4 

Formaldehyde cyanohydrin 

Poison 

1,000 

23422-53-9 

Formetanate hydrochloride 

Poison 

500+ 

02540-82-1 

Formothion 

Poison 

100 

17702-57-7 

Formparanate 

Poison 

100+ 

21548-32-3 

Fosthientan 

Poison 

500 

03878-19-1 

Fuberidazole 

Poison 

100+ 

00110-00-9 

Furan 

Flammable liquid & poison 

500 

13450-90-3 

Gallium trichloride 

Poison 

500+ 

00077-47-4 

Hexachlorocyclopentadiene 

Corrosive & deadly poison 

100 

04835-11-4 

Hexamethylenediamine,N,N-dibutyl 

Poison 

500 

00302-01-2 

Hydrazine 

Flammable liquid, corrosive 
& poison 

1,000 

00074-90-8 

Hydrocyanic acid 

Deadly poison 

100 

07647-01-0 

Hydrogen chloride (gas only) 

Highly corrosive irritant 

500 

07664-39-3 

Hydrogen fluoride 

Corrosive & poison 

100 

07722-84-1 

Hydrogen peroxide (conc. >52%) 

Oxidizer, moderately toxic 

1,000 

07783-07-5 

Hydrogen selenide 

Flammable gas & deadly 
poison 

10 

07783-06-4 

Hydrogen sulfide 

Flammable gas & poison 

500 

00123-31-9 

Hydroquinone 

Poison 

500+ 

13463-40-6 

Iron pentacarbonyl 

Poison 

100 

00297-78-9 

Isobenzan 

Poison 

100+ 

00078-82-0 

Isobutyronitrile 

Flammable liquid & poison 

1,000 

00102-36-3 

Isocyanic aicd,3,4-dichlorophenyl ester 

Poison 

500+ 

00465-73-6 

Isodrin 

Poison 

100+ 

CAS 
Number 

Chemical Name 

Hazard 

Treshold Planning 

Quantity (pounds) 






00055-91-4 

Isofluorphate 

Poison 

100 

04098-71-9 

Isophorone diisocyanate 

Poison 

100 

00108-23-6 

Isopropyl chloroformate 

Flammable liquid & poison 

1,000 

00119-38-0 

Isopropylmethylpyrazolyl dimethylcarbamate 

Poison 

500 

00078-97-7 

Lactonitrile 

Poison 

1,000 

21609-90-5 

Leptophos 

Poison 

500+ 

00541-25-3 

Lewisite 

Poison 

10 

00058-89-9 

Lindane 

Poison 

1,000+ 

07580-67-8 

Lithium hydride 

Flammable solid & poison 

100 

00109-77-3 

Malononitrile 

Poison 

500+ 

12108-13-3 

Mangenese tricarbonyl methylcyclopentadienyl 

Poison 

100 

00950-10-7 

Mephosfolan 

Poison 

500 

01600-27-7 

Mercuric acetate 

Poison 

500+ 

07487-94-7 

Mercuric chloride 

Poison 

500+ 

21908-53-2 

Mercuric oxide 

Powerful oxidant 

500+ 

10476-95-6 

Methacrolein diacetate 

Poison 

1,000 

00760-93-0 

Methacrylic anhydride 

Poison 

500 

00126-98-7 

Methylacrylonitrile 

Poison 

500 

00920-46-7 

Methacryloyl chloride 

Poison 

100 

30674-80-7 

Methacryloyloxyethylisocyanate 

Poison 

100 

10265-92-6 

Methamidophos 

Poison 

100+ 

00558-25-8 

Methanesulfonyl fluoride 

Poison 

1,000 

00950-37-8 

Methidathion 

Poison 

500+ 

02032-65-7 

Methiocarb 

Poison 

500+ 

16752-77-5 

Methomyl 

Poison 

500+ 

00151-38-2 

Methoxyethylmercuric acetate 

Poison 

500+ 

00074-83-9 

Methyl bromide 

Poison gas 

1,000 

00080-63-7 

Methyl 2-chloroacrylate 

Moderately toxic 

500 

00079-22-1 

Methyl chloroformate 

Flammable liquid, corrosive 
& poison 

500 

00060-34-4 

Methyl hydrazine 

Flammable liquid, corrosive, 
poison 

500 

00624-83-9 

Methyl isocyanate 

Flammable liquid & poison 

500 

00556-61-6 

Methyl isothiocyanate 

Flammable liquid & poison 

500 

00074-93-1 

Methyl mercaptan 

Flammable gas & poison 

500 

00502-39-6 

Methylmercuric dicyanamide 

Poison 

500+ 

03735-23-7 

Methyl phenkapton 

Poison 

500 

00676-97-1 

Methyl phosphonic dichloride 

Corrosive & poison 

100 

00556-64-9 

Methyl thiocyanate 

Poison 

10,000 

00075-79-6 

Methyl trichlorosilane 

Flammable liquid, corrosive 
& poison 

500 

00079-84-4 

Methyl vinyl ketone 

 

10 

01129-41-5 

Metolcarb 

Poison 

100+ 

07786-34-7 

Mevinphos 

Poison 

500 

00315-18-4 

Mexacarbate 

Poison 

500+ 

00050-07-7 

Mitomycin C 

Poison 

500+ 

06923-22-4 

Monocrotophos 

Poison 

10+ 

02763-96-4 

Muscinol 

Poison 

10,000 

00505-60-2 

Mustard gas 

Poison 

500 

13463-39-3 

Nickel carbonyl 

Flammable liquid & poison 

1 

00054-11-5 

Nicotine 

Poison 

100 

00065-30-5 

Nicotine sulfate 

Poison 

100+ 

07697-37-2 

Nitric acid (.40% pure) 

Corrosive, oxidizer & poison 

1,000 

10102-43-9 

Nitric oxide 

Poison gas 

100 

00098-95-3 

Nitrobenzene 

Poison 

10,000 

01122-60-7 

Nitrocyclohexane 

Poison 

500 

10102-44-0 

Nitrogen dioxide 

Oxidizer & moderately toxic 

100 

00051-75-2 

Nitrogen mostard 

Deadly poison 

10 

00062-75-9 

N-Nitrosodimethylamine 

Poison 

1,000 

00991-42-4 

Norbormide 

Poison 

100+ 

PMN-82-147 

Organorhodium complex 

Flammable & toxic 

10+ 

00630-60-4 

Ouabain 

Poison 

100+ 

23135-22-0 

Oxamyl 

Poison 

100+ 

CAS 
Number 

Chemical Name 

Hazard 

Treshold Planning 

Quantity (pounds) 

00078-71-7 

Oxetane,3,3,-bis(chloromethyl)- 

Poison 

500 

02497-07-6 

Oxydisulfoton 

Poison 

500 

10028-15-6 

Ozone 

Poison 

100 






01910-42-5 

Paraquat 

Poison 

10+ 

02074-50-2 

Paraquat methosulfate 

Poison 

10+ 

00056-38-2 

Parathion 

Poison 

100 

00298-00-0 

Parathion-methyl 

Poison 

100+ 

13002-03-8 

Paris green 

Poison 

500+ 

19624-22-7 

Pentaborane 

Flammable liquid & poison 

500 

02570-26-5 

Pentadecylamine 

Poison 

100+ 

00079-21-0 

Peracetic acid 

Corrosive & poison 

500 

00594-42-3 

Perchloromethylmercaptan 

Poison 

500 

00108-95-2 

Phenol 

Poison 

500+ 

04418-66-0 

Phenol,2,2-thiobis(4-chloro-6-methyl) 

Poison 

100+ 

00064-00-6 

Phenol,3-(1-methylethyl)-methylcarbamate 

Poison 

500+ 

00058-36-6 

Phenoarsazine 10,10-oxydi- 

Poison 

500+ 

00696-28-6 

Phenyl dichloroarsine 

Poison 

500 

00059-88-1 

Phenylhydrazine hydrochloride 

Poison 

1,000+ 

00062-38-4 

Phenylmercury acetate 

Poison 

500+ 

02097-19-0 

Phenylsilatrane 

Poison 

100+ 

00103-85-5 

Phenylthiourea 

Poison 

100+ 

00298-02-2 

Phorate 

Poison 

10 

04104-14-7 

Phosacetim 

Poison 

100+ 

00947-02-4 

Phosfolan 

Poison 

100+ 

00075-44-5 

Phosgene 

Poison gas 

10 

00732-11-6 

Phosmet 

Poison 

10+ 

13171-21-6 

Phosphamidon 

Poison 

100 

07803-51-2 

Phosphine 

Flammable & poison gas 

500 

02665-30-7 

Phosphonothioic acid, methyl-o-(4-nitrophenol)o-
phenyl ester 

Poison 

500 

50782-69-9 

Phosphonothioic acid, methyl-s-(2-(bis(1-
methylethyl)amino)o-ethyl ester` 

Poison 

100 

02703-13-1 

Phosphonothioic acid methyl,-o-ethyl-o-4-
(methylthio)phenyl ester 

Deadly poison 

500 

03254-63-5 

Phosphoric acid, dimethyl,4-(mehtylthio)phenyl ester 

Poison 

500 

02587-90-8 

Phosphorothioic aicd,o,o-dimethyl-s-(2-methyl-thio-
ethyl ester 

Poison 

500 

07723-14-0 

Phosphorus 

Flammable solid & poison 

100 

10025-87-3 

Phosphorus oxychloride 

Corrosive, irritant & poison 

500 

10026-13-8 

Phosphorus pentachloride 

Corrosive & poison 

500 

01314-56-3 

Phosphorus pentoxide 

Corrosive & poison 

10 

07719-12-2 

Phosphorus trichloride 

Corrosive & poison 

1,000 

00057-47-6 

Physostigmine 

Poison 

100+ 

00057-64-7 

Physostigmine, salicylate (1:1) 

Poison 

100+ 

00124-87-8 

Picrotoxin 

Poison 

500+ 

00110-89-4 

Piperidine 

Poison 

1,000 

23505-41-1 

Pirimifos-ethyl 

Poison 

1,000 

10124-50-2 

Potassium arsenite 

Poison 

500+ 

00151-50-8 

Potassium cyanide 

Deadly poison 

100 

00506-61-6 

Potassium silver cyanide 

Poison & irritant 

500 

02631-37-0 

Promecarb 

Poison 

500+ 

00106-96-7 

Propagyl bromide 

Flammable liquid & deadly 
poison 

10 

00057-57-8 

beta-Propiolactone 

Poison 

500 

00107-12-0 

Propionitrile 

Flammable liquid & poison 

500 

00542-76-7 

Propionitrile, 3-chloro 

Poison 

1,000 

00070-69-9 

Propiophenone,4-amino 

Poison 

100+ 

00109-61-5 

Propyl chloroformate 

Flammable liquid, corrosive 
& poison 

500 

00075-56-9 

Propylene oxide 

Flammable liquid & poison 

10,000 

00075-55-8 

Propyleneimene 

Flammable liquid & poison 

10,000 

02275-18-5 

Prothoate 

Poison 

100+ 

00129-00-0 

Pyrene 

Poison 

1,000+ 

CAS 
Number 

Chemical Name 

Hazard 

Treshold Planning 

Quantity (pounds) 

00140-76-1 

Pyridine,2-methyl-5-vinyl 

Poison 

500 

00504-24-5 

Pyridine,4-amino 

Poison 

500+ 

01124-33-0 

Pyridine,4-nitro-,1-oxide 

Poison 

500+ 

53558-25-1 

Pyriminil 

Poison 

100+ 

14167-18-1 

Salcomine 

Poison 

500+ 

00107-44-8 

Sarin 

Deadly poison 

10 






07783-00-8 

Selenous acid 

Poison 

1,000+ 

07791-23-3 

Selenium oxychloride 

Poison 

500 

00563-41-7 

Semicarbazide hydrochloride 

Poison 

1,000+ 

03037-72-7 

Silane, (4-aminobutyl)diethoxymethyl 

Poison 

1,000 

07631-89-2 

Sodium arsenate 

Poison 

1,000+ 

07784-46-5 

Sodium arsenite 

Deadly poison 

500+ 

26628-22-8 

Sodium azide 

Poison 

500 

00124-65-2 

Sodium cacodylate 

Poison 

100+ 

00143-33-9 

Sodium cyanide 

Deadly poison 

100 

00062-74-8 

Sodium fluoroacetate 

Deadly poison 

10+ 

13410-01-0 

Sodium selenate 

Poison 

100+ 

10102-18-8 

Sodium selenite 

Poison 

100+ 

10102-20-2 

Sodium tellurite 

Poison 

500+ 

00900-95-8 

Stannane, acetoxytriphenyl 

Poison 

500+ 

00057-24-9 

Strychnine 

Poison 

100+ 

00060-41-3 

Strychnine, sulfate 

Poison 

100+ 

03689-24-5 

Sulfotep 

Poison 

500 

03569-57-1 

Sulfoxide,3-chloropropyloctyl 

Poison 

500 

07446-09-5 

Sulfur dioxide 

Poison gas 

500 

07783-60-0 

Sulfur tetrafluoride 

Poison gas 

100 

07446-11-9 

Sulfur trioxide 

Corrosive & poison 

100 

07664-93-9 

Sulfuric acid (>93%) 

Corrosive & poison 

1,000 

00077-81-6 

Tabun 

Poison 

10 

13494-80-9 

Tellurium 

Poison 

500+ 

07783-80-4 

Tellarium hexafluoride 

Poison gas 

100 

00107-49-3 

TEPP 

Poison 

100 

13071-79-9 

Terbufos 

Deadly poison 

100 

00078-00-2 

Teraethyllead 

Flammable liquid & poison 

100 

00597-64-8 

Tetraethyltin 

Poison 

100 

00075-74-1 

Tetramethyllead 

Poison 

100 

00509-14-8 

Tetranitromethane 

Oxidizer & poison 

500 

10031-59-1 

Thallium sulfate 

Poison 

100+ 

06533-73-9 

Thallous carbonate 

Poison 

100+ 

07791-12-0 

Thallous chloride 

Poison 

100+ 

02757-18-8 

Thallous malonate 

Poison 

100+ 

07446-18-6 

Thallous sulfate 

Poison 

100+ 

02231-57-4 

Thiocarbazide 

Poison 

1,000+ 

39196-18-4 

Thiofanox 

Poison 

100+ 

00297-97-2 

Thioazin 

Poison 

500 

00108-98-5 

Thiophenol 

Flammable liquid & poison 

500 

00079-19-6 

Thiosemicarbazide 

Poison 

100+ 

05344-82-1 

Thiourea, (2-chlorophenyl) 

Poison 

100+ 

00614-78-8 

Thiourea (2-methylphenyl) 

Poison 

500+ 

07550-45-0 

Titanium tetrachloride 

Corrosive & poison 

100 

00584-84-9 

Toluene 2,4-diisocyanate 

Poison 

500 

00091-08-7 

Toluene 2,6-diisocyanate 

Poison 

100 

08001-35-2 

Toxaphene 

Poison 

500+ 

01031-47-6 

Triamiphos 

Poison 

500+ 

24017-47-8 

Triazofos 

Poison 

500 

00076-02-8 

Trichloroacetyl chloride 

Corrosive & moderately 
toxic 

500 

01558-25-4 

Trichloro(chloromethyl)silane 

Poison 

100 

27137-85-5 

Trichloro(chlorophenyl)silane 

Corrosive & poison 

500 

00115-21-9 

Trichloroethylsilane 

Flammable liquid & poison 

500 

00327-98-0 

Trichloronate 

Poison 

500 

00098-13-5 

Trichlorophenylsilane 

Corrosive & poison 

500 

00998-30-1 

Triethoxysilane 

Poison 

500 






 

CAS 
Number 

Chemical Name 

Hazard 

Treshold Planning 

Quantity (pounds) 

00075-77-4 

Trimethylchlorosilane 

Flammable liquid, corrosive 
& moderately toxic 

1,000 

00824-11-3 

Trimethylolpropane phosphate 

Poison 

100+ 

01066-45-1 

Trimethyltin chloride 

Deadly poison 

500+ 

00639-58-7 

Triphenyltin chloride 

Poison 

500+ 

02001-95-8 

Valinomycin 

Poison 

1,000+ 

01314-62-1 

Vanadium pentoxide 

Poison 

100+ 

00108-05-4 

Vinyl acetate monomer 

Flammable liquid & 
moderately toxic 

1,000 

00081-81-2 

Warfarin 

Poison 

500+ 

00129-06-6 

Warfarin sodium 

Poison 

100+ 

28347-13-9 

Xylene dichloride 

Poison 

100+ 

58270-08-9 

Zinc, dichloro(4,4-dimethyl-
5((((methylamino)carbonyl)oxino)pentanenitrile)-,(T-
4) 

Poison 

100+ 

01314-84-7 

Zinc phosphide 

Flammable solid & poison 

500 



Note: For the Treshold Planning Quantities marked with a +, the quantity 
listed applies only if in 
powdered form and with a particle size of less than 100 microns, or is handled 
in solution or molten form, 
or has a NFPA rating for reactivity of 2, 3 or 4. Otherwise the Treshold 
Planning Quantity is 10,000 lbs. 
The material is still required to be reported on an annual inventory at the 
Treshold Planning Quantity or 
500 lbs, whichever is less. 

 

Source of hazard information: N. Irving San and Richard J. Lews, Sr., Dangerous 
Properties of Industrial 
Materials, Seventh Edition, Volumes I - III, Van Nostrand Reinhold, New York, 
(1989). 

 




 

Appendix 11B 

Listing of Chemicals contained in the TRI Database, including their CAS Numbers 
and Hazards 

CAS NUMBER 

CHEMICAL NAME 

HAZARDS 

75-07-0 

Acetaldehyde 

Poison 

60-35-5 

Acetamide 

Experimental carcinogen 

67-64-1 

Acetone 

Moderately toxic 

75-05-8 

Acetonitrile 

Poison 

53-96-3 

2-Acetylaminofluorene 

Moderately toxic 

107-02-8 

Acrolein 

Poison 

79-06-1 

Acrylamide 

Poison 

79-10-7 

Acrylic acid 

Poison 

107-13-1 

Acrylonitrile 

Poison 

309-00-2 

Aldrin 

Poison 

107-05-1 

Allyl chloride 

Poison 

7429-90-5 

Aluminum (fume or dust) 

Not considered a industrial poison 

1344-28-1 

Aluminum oxide 

Experimental tumorigen 

117-79-3 

2-Aminoanthraquinone 

Experimental carcinogen 

60-09-3 

4-Aminoazobenzene 

Poison 

92-67-1 

4-Aminobiphenyl 

Poison 

82-28-0 

1-Amino-2-methylanthraquinone 

Experimental neoplastigen 

7664-41-7 

Ammonia 

Poison 

6484-52-2 

Ammonium nitrate (solution) 

Powerful oxidizer & an allergen 

7783-20-2 

Ammonium sulfate (solution) 

Moderately toxic 

62-53-3 

Aniline 

Poison 

90-04-0 

o-Anisidine 

Moderately toxic 

109-94-9 

p-Anisidine 

Moderately toxic 

134-29-2 

o-Anisidine hydrochloride 

Experimental carcinogen 

120-12-7 

Anthracene 

Experimental tumorigen 

7440-36-0 

Antimony 

Poison 

7440-38-2 

Arsenic 

Carcinogen 

1332-21-4 

Asbestos (friable) 

Carcinogen 

7440-39-3 

Barium 

Poison 

98-87-3 

Benzal chloride 

Poison 

55-21-0 

Benzamide 

Moderately toxic 

71-43-2 

Benzene 

Poison 

92-87-5 

Benzidine 

Poison 

98-07-7 

Benzoic trichloride (Benzotrichloride) 

Poison 

98-88-4 

Benzoyl chloride 

Carcinogen 

94-36-0 

Benzoyl peroxide 

Poison 

100-44-7 

Benzyl chloride 

Poison 

7440-41-7 

Beryllium 

Deadly poison 

92-52-4 

Biphenyl 

Poison 

111-44-4 

Bis(2-chloroethyl) ether 

Poison 

542-88-1 

Bis(chloromethyl) ether 

Poison 

108-60-1 

Bis(2-chloro-1-methyulethyl) ether 

Poison 

103-23-1 

Bis(2-ethylhexyl) adipate 

Experimental carcinogen 

75-25-2 

Bromoform (Tribromomethane) 

Poison 

74-83-9 

Bromomethane (methyl bromide) 

Poison 

106-99-0 

1,3-Butadiene 

Experimental carcinogen 

141-32-2 

Butyl acrylate 

Moderately toxic 

71-36-3 

n-Butyl alcohol 

Poison 

78-92-2 

sec-Butyl alcohol 

Poison 

75-65-0 

tert-Butyl alcohol 

Moderately toxic 

85-68-7 

Butyl benzyl phthalate 

Moderately toxic 

106-88-7 

1,2-Butylene oxide 

Moderately toxic 

123-72-8 

Butyraldehyde 

Moderately toxic 

2650-18-2 

C.I. Acid Blue 9, diammonium salt 

Poison 

3844-45-9 

C.I. Acid Blue, disodium salt 

Experimental neoplastigen 

4680-78-8 

C.I. Acid Green 3 

Experimental tumorigen 

569-64-2 

C.I. Basic Green 4 

Poison 

989-38-8 

C.I. Basic Red 1 

Poison 

1937-37-7 

C.I. Direct black 38 

Experimental tumorigen 

2602-46-2 

C.I. Direct Blue 6 

Experimental carcinogen 






 

CAS NUMBER 

CHEMICAL NAME 

HAZARDS 

16071-86-6 

C.I. Direct Brown 95 

Experimental carcinogen 

2832-40-8 

C.I. Disperse Yellow 3 

Experimental tumorigen 

3761-53-3 

C.I. Food Red 5 

 

81-88-9 

C.I. Food Red 15 

Poison 

3118-97-6 

C.I. Solvent Orange 7 

Experimental carcinogen 

97-56-3 

C.I. Solvent Yellow 3 

Experimental carcinogen 

842-07-9 

C.I. Solvent Yellow 14 

Experimental carcinogen 

492-80-8 

C.I. Solvent Yellow 34 (Auramine) 

Poison 

128-66-5 

C.I. Vat Yellow 4 

Experimental carcinogen 

7440-43-9 

Cadmiun 

Poison 

156-62-7 

Calcium cyanamide 

Poison 

133-06-2 

Captan 

Moderately toxic 

63-25-2 

Carbaryl 

Poison 

75-15-0 

Carbon disulfide 

Poison 

56-23-5 

Carbon tetrachloride 

Poison 

463-58-1 

Carbonyl sulfide 

Poison 

120-80-9 

Catechol 

Moderately toxic 

133-90-4 

Chloramben 

Experimental carcinogen 

57-74-9 

Chlordane 

Poison 

7782-50-5 

Chlorine 

Moderately toxic 

10049-04-4 

Chlorine dioxide 

Moderately toxic 

79-11-8 

Chloroacetic acid 

Poison 

532-27-4 

2-Chloroacetophenone 

Poison 

108-90-7 

Chlorobenzene 

Poison 

510-15-6 

Chlorobenzilate 

Experimental carcinogen 

75-00-3 

Chloroethane 

Mildly toxic 

67-66-3 

Chloroform 

Poison 

74-87-3 

Chloromethane (Methyl chloride) 

Mildly toxic 

107-30-2 

Chloromethyl methyl ether 

Poison 

126-99-8 

Chloroprene 

Poison 

1897-45-6 

Chlorothalonil 

Moderately toxic 

7740-47-3 

Chromium 

Poison 

7440-48-4 

Cobalt 

Poison 

7440-50-8 

Copper 

Experimental tumorigen 

120-71-8 

p-Cresidine 

Moderately toxic 

1319-77-3 

Cresol (mixed isomers) 

Moderately toxic 

108-39-4 

m-Cresol 

Poison 

95-48-7 

o-Cresol 

Poison 

106-44-5 

p-Cresol 

Poison 

98-82-8 

Cumene 

Moderately toxic 

80-15-9 

Cumene hydroperoxide 

Moderately toxic 

135-20-6 

Cupferron 

Poison 

110-82-7 

Cyclohexane 

Poison 

94-75-7 

2,4-D (Acetic acid,(2,4-dichlore-phenoxy)) 

Poison 

1163-19-5 

Decabromodiphenyl oxide 

Experimental neoplastigen 

2303-16-4 

Diallate 

Poison 

615-05-4 

2,4-Diaminoanisole 

Poison 

39156-41-7 

2,4-Diaminoanisole sulfate 

Poison 

101-80-4 

4,4-Diaminophenyl ether 

Poison 

25376-45-8 

Diaminotoluane (mixed isomers) 

Poison 

95-80-7 

2,4-Diaminotoluene 

Poison 

334-80-3 

Diazomethane 

Experimental tumorigen 

132-64-9 

Dibenzofuran 

 

96-12-8 

1,2-Dibromo-3-chloropropane (DBCP) 

Poison 

106-93-4 

1,2-Dibromoethane (Ethylene dibromide) 

Poison 

84-74-2 

Dibutyl phthalate 

Moderately toxic 

25321-22-6 

Dichlorobenzene (mixed isomers) 

Poison 

95-50-1 

1,2-Dichlorobenzene 

Poison 

541-73-1 

1,3-Dichlorobenzene 

Poison 

106-46-7 

1,4-Dichlorobenzene 

Poison 

91-94-1 

3,3-Dichlorobenzidine 

Experimental carcinogen 

75-27-4 

Dichlorobromomethane 

Moderately toxic 

107-06-2 

1,2-Dichloroethane 

Poison 






 

CAS NUMBER 

CHEMICAL NAME 

HAZARDS 

540-59-0 

1,2-Dichloroethylene 

Poison 

75-09-2 

Dichloromethane (Methylene chloride) 

Poison 

120-83-2 

2,4-Dichlorophenol 

Poison 

78-87-5 

1,2-Dichloropropane 

Moderately toxic 

542-75-6 

1,3-Dichloropropylene 

Poison 

62-73-7 

Dichlorvos 

Poison 

115-32-2 

Dicofol 

Poison 

1464-53-5 

Diepoxybutane 

Poison 

111-42-2 

Diethanolamine 

Moderately toxic 

117-81-7 

di-(2-ethylhexyl) phthalate (DEHP) 

 Poison 

84-66-2 

Diethyl phthalate 

Poison 

64-67-5 

Diethyl sulfate 

Poison 

119-90-4 

3,3-Dimethoxybenzidine 

Moderately toxic 

60-11-7 

4-Dimethylaminoazobenzene 

Poison 

119-93-7 

3,3-Dimethylbenzidine (o-Tolidine) 

Poison 

79-44-7 

Dimethylcarbamyl chloride 

Poison 

57-14-7 

1,1-Dimethyl hydrazine 

Poison 

105-67-9 

2,4-Dimethylphenol 

Poison 

131-11-3 

Dimethyl phthalate 

Moderately toxic 

77-78-1 

Dimethyl sulfate 

Poison 

534-52-1 

4,6-Dinitro-o-cresol 

Poison 

51-28-5 

2,4-Dinitrophenol 

Deadly poison 

121-14-2 

2,4-Dinitrotoluene 

Poison 

606-20-2 

2,5-Dinitrotoluene 

Moderately toxic 

117-84-0 

n-Dioctyl phthalate 

Mildly toxic 

123-91-1 

1,4-Dioxane 

Poison 

122-66-7 

1,2-Diphenylhydrazine (Hydrazobenzene) 

Poison 

106-89-8 

Epichlorohydrin 

Poison 

110-80-5 

2-Ethoxyethanol 

Moderately toxic 

140-88-5 

Ethyl acrylate 

Poison 

100-41-4 

Ethylbenzene 

Moderately toxic 

541-41-3 

Ethyl chloroformate 

Poison 

74-85-1 

Ethylene 

Simple asphyxiant 

107-21-1 

Ethylene glycol 

Poison 

151-56-4 

Ethyleneimine (Aziridine) 

Poison 

75-21-8 

Ethylene oxide 

Poison 

96-45-7 

Ethylene thiourea 

Poison 

2164-17-2 

Fluometuron 

Poison 

50-00-0 

Formaldehyde 

Poison 

76-13-1 

Freon 113 

Mildly toxic 

76-44-8 

Heptachlor (1,4,5,6,7,8,8,-Heptachloro-3a,4,7,7a-tetrahydro-4,7-
methano-1H-indene) 

Poison 

118-74-1 

Hexachlorobenzene 

Poison 

87-68-3 

Hexachloro-1,3-butadiene 

Poison 

77-47-4 

Hexachlorocyclopentadiene 

Deadly poison 

67-72-1 

Hexachloroethane 

Poison 

13355-87-1 

Hexachloronaphthalene 

Poison 

680-31-9 

Hexamethylphosphoramide 

Experimental carcinogen 

302-01-2 

Hydrazine 

Poison 

10034-93-2 

Hydrazine sulfate 

Poison 

7647-01-0 

Hydrochloric acid 

Poison 

74-90-8 

Hydrogen cyanide 

Deadly poison 

7664-39-3 

Hydrogen fluoride 

Poison 

123-31-9 

Hydroquinone 

Poison 

78-84-2 

Isobutyraldehyde 

Moderately toxic 

67-63-0 

Isopropyl alcohol 

Poison 

80-05-7 

4,4-Isopropylidenediphenol 

Poison 

7439-92-1 

Lead 

Poison 

58-89-9 

Lindene 

Poison 

108-31-6 

Maleic acid 

Poison 

12427-38-2 

Maneb 

Experimental carcinogen 

7439-96-5 

Manganese 

Experimental tumorigen 

108-78-1 

Melamine 

Experimental carcinogen 






 

CAS NUMBER 

CHEMICAL NAME 

HAZARDS 

7439-97-6 

Mercury 

Poison 

67-56-1 

Methanol 

Poison 

72-43-5 

Methoxychlor (Benzene-1,1-(2,2,2,-trichloroethylidene)bis(4-
methoxy) 

Moderately toxic 

109-86-4 

2-Methoxyethanol 

Moderately toxic 

96-33-3 

Methyl acrylate 

Poison 

1634-04-4 

Methyl tert-butyl ether 

Flammable 

101-14-4 

4,4-Methylenebis(2-chloro aniline) 

Poison 

101-61-1 

4,4-Methylenebis (N,N-dimethyl)benzenamine 

Moderately toxic 

101-68-8 

Methylenebis(phenylisocyanate) 

Poison 

74-95-3 

Methylene bromide 

Poison 

101-77-9 

4,4-Methylenedianiline 

Poison 

78-93-3 

Methyl ethyl ketone 

Moderately toxic 

60-34-4 

Methyl hydrazine 

Poison 

74-88-4 

Methyl iodide 

Poison 

108-10-1 

Methyl isobutyl ketone 

Poison 

624-83-9 

Methyl isocyanate 

Poison 

80-62-6 

Mehtyl methacrylate 

Moderately toxic 

90-94-8 

Michlers ketone 

Poison 

1313-27-5 

Molybdenum trioxide 

Poison 

505-60-2 

Mustard gas 

Poison 

91-20-3 

Naphthalene 

Poison 

134-32-7 

alpha-Naphthylamine 

Poison 

91-59-8 

beta-Naphthylamine 

Poison 

7440-02-0 

Nickel 

Poison 

7697-37-2 

Nitric acid 

Poison 

139-13-9 

Nitrilotriacetic acid 

Poison 

99-59-2 

5-Nitro-o-anisidine 

Moderately toxic 

98-95-3 

Nitrobenzene 

Poison 

92-93-3 

4-Nitrobephenyl 

Poison 

1836-75-5 

Nitrofen 

Poison 

51-75-2 

Nitrogen mustard 

Deadly poison 

55-63-0 

Nitroglycerin 

Poison 

88-75-5 

2-Nitrophenol 

Poison 

100-02-7 

4-Nitrophenol 

Poison 

79-46-9 

2-Nitropropane 

Poison 

156-10-5 

p-Nitrosodiphenylamine 

Poison 

121-69-7 

N,N,-Dimethylaniline 

Poison 

924-16-3 

N-Nitrosodi-n-butylamine 

Moderately toxic 

55-18-5 

N-Nitrosodiethylamine 

Poison 

62-75-9 

N-Nitrosodimethylamine 

Poison 

86-30-6 

N-Nitrosodiohenylamine 

Moderately toxic 

621-64-7 

N-Nitrosodi-n-propylamine 

Moderately toxic 

4549-40-0 

N-Nitrosomethylvinylamine 

Poison 

59-89-2 

N-Nitrosomorpholine 

Poison 

759-73-9 

N-Nitroso-N-ethylurea 

Poison 

684-93-5 

N-Nitroso-N-methylurea 

Poison 

16543-55-8 

N-Nitrosonorrnicotine 

Experimental carcinogen 

100-75-4 

N-Nitrosopiperidine 

Poison 

2234-13-1 

Octachloronaphthlene 

Poison 

20816-12-0 

Osmiun tetroxide 

Poison 

56-38-2 

Parathion 

Deadly poison 

87-86-5 

Pentachlorophenol 

Poison 

79-21-0 

Peracetic acid 

Poison 

108-95-2 

Phenol 

Poison 

106-50-3 

p-Phenylenediamine 

Poison 

90-43-7 

2-Phenylphenol 

Poison 

75-44-5 

Phosgene 

Poison 

7664-38-2 

Phosphoric acid 

Poison 

7723-14-0 

Phosphorus 

Poison 

85-44-9 

Phthalic anhydride 

Poison 

88-89-1 

Picric acid 

Poison 



 

CAS NUMBER 

CHEMICAL NAME 

HAZARDS 






1336-36-3 

Polychlorinated biphenyls (PCBs) 

Moderately toxic 

1120-71-4 

Propane sultone 

Poison 

57-57-8 

beta-Propiolactone 

Poison 

123-38-6 

Propionaldehyde 

Moderately toxic 

114-26-1 

Propoxur 

Poison 

115-07-1 

Propylene (propene) 

Simple asphyxiant 

75-55-8 

Propyleneimine 

Poison 

75-56-9 

Propylene oxide 

Poison 

110-86-1 

Pyridine 

Poison 

91-22-5 

Quinoline 

Poison 

106-51-4 

Quinone 

Poison 

82-68-8 

Quintozene (Pentachloronitrobenzene) 

Experimental carcinogen 

81-07-2 

Saccharin 

Moderately toxic 

94-59-7 

Safrole 

Poison 

7782-49-2 

Selenium 

Poison 

7440-22-4 

Silver 

Experimental tumorigen 

1310-73-2 

Sodium hydroxide (solution) 

Poison 

7757-82-6 

Sodium sulfate (solution) 

Moderately toxic 

100-42-5 

Styrene 

Experimental poison 

96-09-3 

Styrene oxide 

Moderately toxic 

7664-93-9 

Sulfuric acid 

Poison 

100-21-0 

Terephthalic acid 

Moderately toxic 

79-34-5 

1,1,2,2,-Tetrachloroethane 

Poison 

127-18-4 

Tetrachloroethylene 

Experimental poison 

961-11-5 

Tetrachlorovinphos 

Poison 

7440-28-0 

Thallium 

Poison 

62-55-5 

Thioacetamide 

Poison 

139-65-1 

4,4-Thiodianiline 

Poison 

62-56-6 

Thiourea 

Poison 

1314-20-1 

Thorium dioxide 

Carcinogen 

7550-45-0 

Titanium tetrachloride 

Poison 

108-88-3 

Toluene 

Poison 

584-84-9 

Toulene-2,4-diisocyanate 

Poison 

91-08-7 

Toluene-2,6-diisocyanate 

Poison 

95-53-4 

o-Toluidine 

Poison 

636-21-5 

o-Toluidine hydrochloride 

Poison 

8001-35-2 

Toxaphene 

Poison 

68-76-8 

Triaziquone 

Poison 

52-68-6 

Trichlorfon (Phosphoric acid (2,2,2-trichloro-1-hydroxyethyl)-
dimethyl ester 

Poison 

120-82-1 

1,2,4-Trichlorobenzene 

Poison 

71-55-6 

1,1,1-Trichloroethane (methyl chloroform) 

Poison 

79-00-5 

1,1,2-Trichloroethane 

Poison 

79-01-6 

Trichloroethylene 

Experimental poison 

95-95-4 

2,4,5-Trichlorophenol 

Poison 

88-06-2 

2,4,6-Trichlorophenol 

Poison 

1582-09-8 

Trifluralin 

Moderately toxic 

95-63-6 

1,2,4-Trimethylbenzene 

Moderately toxic 

126-72-7 

Tris(2,3-dibromopropyl) phosphate 

Poison 

51-79-6 

Urethane (Ethyl carbamate) 

Moderately toxic 

7440-62-2 

Vanadium (fume or dust) 

Poison 

108-05-4 

Vinyl acetate 

Moderately toxic 

593-60-2 

Vinyl bromide 

Moderately toxic 

75-01-4 

Vinyl chloride 

Poison 

75-35-4 

Vinylidene chloride 

Poison 

1330-20-7 

Xylene (mixed isomers) 

Moderately toxic 

108-38-3 

m-Xylene 

Moderately toxic 

95-47-6 

o-Xylene 

Moderately toxic 

106-42-3 

p-Xylene 

Moderately toxic 

87-62-7 

2,6-Xylidine 

Moderately toxic 

7440-66-6 

Zinc (fume or dust) 

Skin & systemic irritant 

12122-67-7 

Zineb 

Moderately toxic 



 




Chapter 12 

Induced Damage Methods - Debris 

 

 

12.1 Introduction 

 

Very little has been done in the area of estimating debris from earthquakes. 
Some of the 
early regional loss estimation studies (e.g., Algermissen, et al., 1973; Rogers, 
et al., 1976) 
included some simplified models for estimating the amount of debris from shaking 
damage to unreinforced masonry structures. This methodology adopts a similar 
empirical approach to estimate two different types of debris. The first is 
debris that falls 
in large pieces, such as steel members or reinforced concrete elements. These 
require 
special treatment to break into smaller pieces before they are hauled away. The 
second 
type of debris is smaller and more easily moved with bulldozers and other 
machinery and 
tools. This includes brick, wood, glass, building contents and other materials. 
The 
methodology highlighting the Debris component is shown in Flowchart 12.1. 

 

 

12.1.1 Scope 

 

The module will estimate debris from building damage during earthquakes. No 
debris 
estimates are made for bridges or other lifelines. 

 

 

12.1.2 Form of Damage Estimate 

 

The module will determine the expected amounts of debris to be generated for 
each 
census tract. Output from this module will be the weight (tons) of debris. The 
classes of 
debris are defined as follows: 

 

 Brick, wood and other 

 Reinforced concrete and steel members 

 

 

12.1.3 Input Requirements and Output Information 

 

Input to this module includes the following items: 

 

 Probabilities of structural and nonstructural damage states for model building 
types 
for each census tract provided from the direct physical damage module 

 Square footage by occupancy class for each census tract provided from the 
inventory 

 The occupancy to model building type relationship for each census tract 

 

 




8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Flowchart 12.1: Debris Component Relationship to other Modules of the 
Earthquake Loss Estimation Methodology 




12.2 Description of Methodology 

 

The methodology for debris estimation is an empirical approach. That is, given 
the 
damage states for structural and nonstructural components, debris estimates are 
based on 
observations of damage that has occurred in past earthquakes and estimates of 
the 
weights of structural and nonstructural elements. The estimation can be made 
considering model building type, general occupancy class or specific occupancy 
class. In 
this section, the methodology described is based on model building types. Tables 
have 
been compiled to estimate generated debris from different structural and 
nonstructural 
damage states for each model building type. Given the distribution of different 
building 
types in square footage in each occupancy class, similar tables can also be 
compiled to 
estimate debris based on occupancy class. 

 

12.2.1 Debris Generated From Damaged Buildings 

 

Debris generated from damaged buildings (in tons) is based on the following 
factors: 

 

 Unit weight of structural and nonstructural elements (tons per 1000 sq. ft. of 
floor 
area) for each of the model building types 

 Probabilities of damage states for both structural and drift-sensitive 
nonstructural 
elements by census tract 

 Square footage of each of the model building types by census tract 

 Debris generated from different damage states of structural and nonstructural 
elements (% of unit weight of element) 

 

The recommended values for unit weights of structural and nonstructural elements 
and 
debris generated per model building type are given in Tables 12.1, 12.2 and 
12.3. 




Table 12.1 Unit Weight (tons per 1000 ft2) for Structural and Nonstructural 
Elements for the Model Building Types 

 

 

Model 

Brick, Wood and Other 

Reinforced Concrete and Steel 

# 

Building Type 

Structural 

Nonstructural 

Structural 

Nonstructural 

1 

W1 

6.5 

12.1 

15.0 

0.0 

2 

W2 

4.0 

8.1 

15.0 

1.0 

3 

S1L 

0.0 

5.3 

44.0 

5.0 

4 

S1M 

0.0 

5.3 

44.0 

5.0 

5 

S1H 

0.0 

5.3 

44.0 

5.0 

6 

S2L 

0.0 

5.3 

44.0 

5.0 

7 

S2M 

0.0 

5.3 

44.0 

5.0 

8 

S2H 

0.0 

5.3 

44.0 

5.0 

9 

S3 

0.0 

0.0 

67.0 

1.5 

10 

S4L 

0.0 

5.3 

65.0 

4.0 

11 

S4M 

0.0 

5.3 

65.0 

4.0 

12 

S4H 

0.0 

5.3 

65.0 

4.0 

13 

S5L 

20.0 

5.3 

45.0 

4.0 

14 

S5M 

20.0 

5.3 

45.0 

4.0 

15 

S5H 

20.0 

5.3 

45.0 

4.0 

16 

C1L 

0.0 

5.3 

98.0 

4.0 

17 

C1M 

0.0 

5.3 

98.0 

4.0 

18 

C1H 

0.0 

5.3 

98.0 

4.0 

19 

C2L 

0.0 

5.3 

112.0 

4.0 

20 

C2M 

0.0 

5.3 

112.0 

4.0 

21 

C2H 

0.0 

5.3 

112.0 

4.0 

22 

C3L 

20.0 

5.3 

90.0 

4.0 

23 

C3M 

20.0 

5.3 

90.0 

4.0 

24 

C3H 

20.0 

5.3 

90.0 

4.0 

25 

PC1 

5.5 

5.3 

40.0 

1.5 

26 

PC2L 

0.0 

5.3 

100.0 

4.0 

27 

PC2M 

0.0 

5.3 

100.0 

4.0 

28 

PC2H 

0.0 

5.3 

100.0 

4.0 

29 

RM1L 

17.5 

5.3 

28.0 

4.0 

30 

RM1M 

17.5 

5.3 

28.0 

4.0 

31 

RM2L 

17.5 

5.3 

78.0 

4.0 

32 

RM2M 

24.5 

5.3 

78.0 

4.0 

33 

RM2H 

24.5 

5.3 

78.0 

4.0 

34 

URML 

35.0 

10.5 

41.0 

4.0 

35 

URMM 

35.0 

10.5 

41.0 

4.0 

36 

MH 

10.0 

18.0 

22.0 

0.0 






Table 12.2 Brick, Wood, and Other Debris Generated from Damaged Structural 
and Nonstructural Elements (in Fraction of Weight, %) 

 

 

Building 

Structural Damage State 

Nonstructural Damage State 

# 

Type 

Slight 

Moder 

Exten 

Comp 

Slight 

Moder 

Exten 

Comp 

1 

W1 

0.0 

5.0 

34.0 

100.0 

2.0 

8.0 

35.0 

100.0 

2 

W2 

0.0 

6.0 

33.0 

100.0 

2.0 

10.0 

40.0 

100.0 

3 

S1L 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

4 

S1M 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

5 

S1H 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

6 

S2L 

0.0 

0.0 

0.0 

100.0 

0.0 

0.0 

0.0 

100.0 

7 

S2M 

0.0 

0.0 

0.0 

100.0 

0.0 

0.0 

0.0 

100.0 

8 

S2H 

0.0 

0.0 

0.0 

100.0 

0.0 

0.0 

0.0 

100.0 

9 

S3 

0.0 

0.0 

0.0 

100.0 

0.0 

0.0 

0.0 

100.0 

10 

S4L 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

11 

S4M 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

12 

S4H 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

13 

S5L 

5.0 

25.0 

60.0 

100.0 

1.0 

7.0 

35.0 

100.0 

14 

S5M 

5.0 

25.0 

60.0 

100.0 

1.0 

7.0 

35.0 

100.0 

15 

S5H 

5.0 

25.0 

60.0 

100.0 

1.0 

7.0 

35.0 

100.0 

16 

C1L 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

17 

C1M 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

18 

C1H 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

19 

C2L 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

20 

C2M 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

21 

C2H 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

22 

C3L 

5.0 

25.0 

60.0 

100.0 

1.0 

7.0 

35.0 

100.0 

23 

C3M 

5.0 

25.0 

60.0 

100.0 

1.0 

7.0 

35.0 

100.0 

24 

C3H 

5.0 

25.0 

60.0 

100.0 

1.0 

7.0 

35.0 

100.0 

25 

PC1 

0.0 

6.0 

32.0 

100.0 

2.0 

11.0 

42.0 

100.0 

26 

PC2L 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

27 

PC2M 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

28 

PC2H 

0.0 

0.0 

0.0 

100.0 

1.0 

7.0 

35.0 

100.0 

29 

RM1L 

3.5 

20.0 

50.0 

100.0 

2.0 

10.0 

40.0 

100.0 

30 

RM1M 

3.5 

20.0 

50.0 

100.0 

2.0 

10.0 

40.0 

100.0 

31 

RM2L 

5.0 

25.0 

60.0 

100.0 

1.0 

7.0 

35.0 

100.0 

32 

RM2M 

5.0 

25.0 

60.0 

100.0 

1.0 

7.0 

35.0 

100.0 

33 

RM2H 

5.0 

25.0 

60.0 

100.0 

1.0 

7.0 

35.0 

100.0 

34 

URML 

5.0 

25.0 

55.0 

100.0 

2.0 

12.0 

45.0 

100.0 

35 

URMM 

5.0 

25.0 

55.0 

100.0 

2.0 

12.0 

45.0 

100.0 

36 

MH 

0.0 

5.0 

33.0 

100.0 

2.0 

8.0 

35.0 

100.0 



 




Table 12.3 Reinforced Concrete and Wrecked Steel Generated from Damaged 
Structural and Nonstructural Elements (in Percentage of Weight) 

 

 

Building 

Structural Damage State 

Nonstructural Damage State 

# 

Type 

Slight 

Moder 

Exten 

Comp 

Slight 

Moder 

Exten 

Comp 

1 

W1 

0.0 

3.0 

27.0 

100.0 

0.0 

0.0 

0.0 

100.0 

2 

W2 

0.0 

2.0 

25.0 

100.0 

0.0 

10.0 

28.0 

100.0 

3 

S1L 

0.0 

4.0 

30.0 

100.0 

0.1 

8.0 

28.0 

100.0 

4 

S1M 

0.0 

4.0 

30.0 

100.0 

0.1 

8.0 

28.0 

100.0 

5 

S1H 

0.0 

4.0 

30.0 

100.0 

0.1 

8.0 

28.0 

100.0 

6 

S2L 

0.0 

4.0 

30.0 

100.0 

0.1 

8.0 

28.0 

100.0 

7 

S2M 

0.0 

4.0 

30.0 

100.0 

0.1 

8.0 

28.0 

100.0 

8 

S2H 

0.0 

4.0 

30.0 

100.0 

0.1 

8.0 

28.0 

100.0 

9 

S3 

0.0 

5.0 

30.0 

100.0 

0.0 

10.0 

30.0 

100.0 

10 

S4L 

2.0 

10.0 

40.0 

100.0 

0.1 

10.0 

30.0 

100.0 

11 

S4M 

2.0 

10.0 

40.0 

100.0 

0.1 

10.0 

30.0 

100.0 

12 

S4H 

2.0 

10.0 

40.0 

100.0 

0.1 

10.0 

30.0 

100.0 

13 

S5L 

0.0 

4.0 

30.0 

100.0 

0.1 

10.0 

30.0 

100.0 

14 

S5M 

0.0 

4.0 

30.0 

100.0 

0.1 

10.0 

30.0 

100.0 

15 

S5H 

0.0 

4.0 

30.0 

100.0 

0.1 

10.0 

30.0 

100.0 

16 

C1L 

0.0 

5.0 

33.0 

100.0 

0.1 

8.0 

28.0 

100.0 

17 

C1M 

0.0 

5.0 

33.0 

100.0 

0.1 

8.0 

28.0 

100.0 

18 

C1H 

0.0 

5.0 

33.0 

100.0 

0.1 

8.0 

28.0 

100.0 

19 

C2L 

1.0 

8.0 

35.0 

100.0 

0.1 

10.0 

30.0 

100.0 

20 

C2M 

1.0 

8.0 

35.0 

100.0 

0.1 

10.0 

30.0 

100.0 

21 

C2H 

1.0 

8.0 

35.0 

100.0 

0.1 

10.0 

30.0 

100.0 

22 

C3L 

0.0 

4.0 

32.0 

100.0 

0.1 

10.0 

30.0 

100.0 

23 

C3M 

0.0 

4.0 

32.0 

100.0 

0.1 

10.0 

30.0 

100.0 

24 

C3H 

0.0 

4.0 

32.0 

100.0 

0.1 

10.0 

30.0 

100.0 

25 

PC1 

2.0 

10.0 

35.0 

100.0 

0.1 

10.0 

30.0 

100.0 

26 

PC2L 

2.0 

7.0 

35.0 

100.0 

0.1 

9.0 

30.0 

100.0 

27 

PC2M 

2.0 

7.0 

35.0 

100.0 

0.1 

9.0 

30.0 

100.0 

28 

PC2H 

2.0 

7.0 

35.0 

100.0 

0.1 

9.0 

30.0 

100.0 

29 

RM1L 

0.0 

3.0 

25.0 

100.0 

0.1 

10.0 

30.0 

100.0 

30 

RM1M 

0.0 

3.0 

25.5 

100.0 

0.1 

10.0 

31.0 

100.0 

31 

RM2L 

0.0 

3.0 

30.5 

100.0 

0.1 

9.0 

30.0 

100.0 

32 

RM2M 

0.0 

3.0 

30.5 

100.0 

0.1 

9.0 

30.0 

100.0 

33 

RM2H 

0.0 

3.0 

30.5 

100.0 

0.1 

9.0 

30.0 

100.0 

34 

URML 

0.0 

2.0 

25.0 

100.0 

0.0 

10.0 

29.0 

100.0 

35 

URMM 

0.0 

2.0 

25.0 

100.0 

0.0 

10.0 

29.0 

100.0 

36 

MH 

0.0 

3.0 

27.0 

100.0 

0.0 

0.0 

0.0 

100.0 



 




The following notation is used throughout the chapter. 

 

i - the iteration variable for the types of debris, i = 1 to 2 

 where: 1- brick, wood and other 

 2- reinforced concrete and steel components 

j - the iteration variable for the damage states, j=1 to 5, 

 where: 1- none, 2- slight; 3- moderate; 4- extensive; 5- complete 

k - the iteration variable for the model building types, k=1 to 36 

 

The inputs provided from direct physical damage module are the probabilities of 
different 
structural and nonstructural damage states. Thus, the first step in the debris 
calculation is 
to combine the debris fraction generated from the different damage states into 
the 
expected debris fraction for each model building type. The expected debris 
fraction for 
model building type k and debris type i due to structural damage is given by: 

 

 (12-1) EDF(i,k)P(j,k)DF(i,j,k)sssj2=*
=S5

 

where: 

 EDF(i,k)s - the expected debris fraction of debris type i due to structural 
damage 
for model building type k 

 P(j,k)s - the probability of structural damage state j for model building type 
k 
at the location being considered 

 - the debris fraction of debris type i for model building type k in 
structural damage state j (from Tables 12.2 and 12.3) 
DF(i,j,k)s

 

The expected debris fraction of debris type i due to nonstructural damage is 
given by: 

 

 (12-2) EDF(i,k)P(j,k)DF(i,j,k)nsnsnsj25=*
=S

 

where: 

EDF(i,k)ns - the expected debris fraction of debris type i due to nonstructural 
damage for model building type k 

P(j,k)ns - the probability of drift sensitive nonstructural damage state j for 
model building type k at the location being considered 

DF(i,j,k)ns - the debris fraction of debris type i for model building type k in 
drift 
sensitive nonstructural damage state j (from Tables 12.2 and 12.3) 

 

These values indicate the expected percentage of debris type i generated due to 
structural 
or nonstructural damage to model building type k. If we know the square footage 
of each 
model building type (by census tract), SQ(k), and weights of debris type i per 
1000 ft2 of 




building, W(i,k)s and W(i,k)ns, then the amount of debris for this particular 
location can 
be obtained as follows: 

 

[SQ(k)k)(i,Wk)(i,EDFk)(i,Wk)(i,EDFDB(i)
361knsnsss**+*=S= 
(12-3) 

 

where: 

W(i,k)s - the weight of debris type i per 1000 ft2 of floor area for structural 
elements of model building type k (From Table 12.1) 

W(i,k)ns - the weight of debris type i per 1000 ft2 of floor area for 
nonstructural 
elements of model building type k; (From Table 12.1) 

SQ(k) - the census tract square footage for model building type k in 
thousands of square feet 

DB(i) - the amount of debris type i (in tons) 

 

12.3 Guidance for Expert-Generated Estimates 

 

There is no difference in the methodology for Advanced Data and Models Analysis 
except more accurate input. 

 

 

12.4 References 

 

Algermissen, S. T., M. Hopper, K. Campbell, W. A. Rinehart, D. Perkins, K. V. 
Steinbrugge, H. J. Lagorio, D. F. Moran, F. S. Cluff, H. J. Degenkolb, C. M. 
Duke, G. O. 
Gates, N. N. Jacobson, R. A. Olson, and C. R. Allen. 1973. "A Study of 
Earthquake 
Losses in the Los Angeles, California Area." Washington, D.C.: National Oceanic 
and 
Atmospheric Administration (NOAA). 

 

Rogers, A. M., S. T. Algermissen, W. W. Hays, D. M. Perkins, D. O. Van Strien, 
H. C. 
Hughes, R. C. Hughes, H. J. Lagorio, and K. V. Steinbrugge. 1976. "A Study of 
Earthquake Losses in the Salt Lake City, Utah Area" - USGS OFR 76-89. 
Washington, 
D.C.: United States Geological Survey. 

 




Chapter 13 

Direct Social Losses - Casualties 

 

 

13.1 Introduction 

 

This chapter describes and develops the methodology for the estimation of 
casualties, 
describes the form of output, and defines the required input. The methodology is 
based 
on the assumption that there is a strong correlation between building damage 
(both 
structural and nonstructural) and the number and severity of casualties. In 
smaller 
earthquakes, nonstructural damage will most likely control the casualty 
estimates. In 
severe earthquakes where there will be a large number of collapses and partial 
collapses, 
there will be a proportionately larger number of fatalities. Data regarding 
earthquake 
related injuries are not of the best quality. Data are not available across all 
model 
building types. Available data often have insufficient information about the 
type of 
structure in which the casualties occurred and the casualty generating 
mechanism. Thus 
an attempt to develop very sophisticated models based on such data is neither 
feasible nor 
reliable. The methodology highlighting the Casualty component is shown in 
Flowchart 
13.1. 

 

13.1.1 Scope 

 

This module provides a methodology for estimating casualties caused only by 
building 
and bridge damage. The model estimates casualties directly caused by structural 
or non-
structural damage although non-structural casualties are not directly derived 
from non-
structutral damage but instead are derived from structural damage output. The 
method 
excludes casualties caused by heart attacks, car accidents, falls, power failure 
which 
causes failure of a respirator, incidents during post-earthquake search and 
rescue or post-
earthquake clean-up and construction activities, electrocution, tsunami, 
landslides, 
liquefaction, fault rupture, dam failures, fires or hazardous materials 
releases. 
Psychological impacts of the earthquake on the exposed population are not 
modeled. A 
study by Aroni and Durkin (1985) suggests that falls would add to the injuries 
estimate. 
Studies by Durkin (1992, 1995) suggest that falls, heart attacks, car accidents, 
fire and 
other causes not directly attributable to structural or nonstructural damage 
would increase 
estimates of deaths. 

 

Although fire following earthquakes has been the cause of significant casualties 
(notably 
in the firestorm following the 1923 Kanto, Japan, earthquake), such cases have 
involved 
the combination of a number of conditions, which are of low probability of 
occurrence in 
U.S. earthquakes. More typical of fires in the U.S is the catastrophic Oakland 
Hills fire 
of 1990, in which over 3500 residences were destroyed, yet casualties were low. 
Similarly, there is the possibility (but low probability) of a large number of 
casualties due 
to tsunami, landslides, sudden failure of a critical dam, or a massive release 
of toxic 
substances. If the particular characteristics of the study region give the user 
cause for 
concern about the possibility of casualties from fire, tsunami, landslides, 
liquefaction, 




dam failure, or hazardous materials, it would be advisable to initiate specific 
studies 
directed towards the problem. 

 

8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Flowchart 13.1: Direct Social Loss (Casualties) Relationship to other Components 
of the Earthquake Loss Estimation Methodology 

 




The scope of this module is to provide a simple and consistent framework for 
earthquake 
casualty estimation and formats for data collection and data sharing across the 
disciplines 
that are involved in casualty estimation. Many recognized relevant issues in 
casualty 
estimation such as occupancy potential, collapse and non-collapse vulnerability 
of the 
building stock, time of the earthquake occurrence, and spatial distribution of 
the damage, 
are included in the methodology. The methodology is flexible enough to handle: 

 
United States-specific casualty data when available 




 Data based on interpretation of worldwide casualty data for casualty 
estimations in 
the United States 

 Multidisciplinary inputs from engineering, medical, social science, and other 
disciplines involved with earthquake related casualty estimation. 

 

Data formats are flexible enough to handle currently available data, to re-
evaluate 
previously collected data, and to accept new data as they become available. 

 

 

13.1.2 Form of Casualty Estimate 

 

The output from the module consists of a casualty breakdown by injury severity 
level, 
defined by a four level injury severity scale (Durkin and Thiel, 1991; Coburn, 
1992; 
Cheu, 1994). Casualties are calculated at the census tract level. The output is 
at the 
census tract level and aggregated to the study region. Table 13.1 defines the 
injury 
classification scale used in the methodology. 

 

Table 13.1: Injury Classification Scale 

 

Injury Severity 
Level 

Injury Description 

Severity 1 

Injuries requiring basic medical aid that could be administered by 
paraprofessionals. These types of injuries would require bandages or 
observation. 
Some examples are: a sprain, a severe cut requiring stitches, a minor burn 
(first 
degree or second degree on a small part of the body), or a bump on the head 
without loss of consciousness. Injuries of lesser severity that could be self 
treated 
are not estimated by HAZUS. 

Severity 2 

Injuries requiring a greater degree of medical care and use of medical 
technology 
such as x-rays or surgery, but not expected to progress to a life threatening 
status. 
Some examples are third degree burns or second degree burns over large parts of 
the body, a bump on the head that causes loss of consciousness, fractured bone, 
dehydration or exposure. 

Severity 3 

Injuries that pose an immediate life threatening condition if not treated 
adequately and expeditiously. Some examples are: uncontrolled bleeding, 
punctured organ, other internal injuries, spinal column injuries, or crush 
syndrome. 

Severity 4 

Instantaneously killed or mortally injured 



 




Other, more elaborate casualty scales exist. They are based on quantifiable 
medical 
parameters such as medical injury severity scores, coded physiologic variables, 
and other 
factors. The selected four-level injury scale represents an achievable 
compromise 
between the demands of the medical community (in order to plan their response), 
and the 
ability of the engineering community to provide the required data. For example, 
medical 
professionals would like to have the classification in terms of 
"Injuries/Illnesses" to 
account for worsened medical conditions caused by an earthquake (e.g., heart 
attack). 
However, currently available casualty assessment methodologies do not allow for 
a finer 
resolution in the casualty scale definition. 

 

 

13.1.3 Input Requirements 

 

There are three types of data used by the casualty module: 

 

 Scenario time definition 

 Data supplied by other modules 

 Data specific to the casualty module 

 

Scenario Time Definition 

 

The methodology provides information necessary to produce casualty estimates for 
three 
times of day. The following time options are provided: 

 

 Earthquake striking at 2:00 a.m. (night time scenario) 

 Earthquake striking at 2:00 p.m. (day time scenario) 

 Earthquake striking at 5:00 p.m. (commute time scenario) 

 

These scenarios are expected to generate the highest casualties for the 
population at 
home, the population at work/school and the population during rush hour, 
respectively. 

 

Data Supplied by Other Modules 

 

Other modules supply population distribution data, inventory (building stock 
distribution) data, and damage state probabilities. These data are provided at 
the census 
tract level. The default values provided in the methodology are best estimates, 
made from 
available data. However, it is fully expected that the user will modify the 
default 
database contingent on the availability of improved information. 

 




Population Distribution Data 

 

The population for each census tract is distributed into six basic groups: 

 
Residential population 


 
Commercial population 


 
Educational population 


 
Industrial population 


 
Commuting population 


 
Hotel population 




The default population distribution is calculated for the three times of day for 
each census 
tract. Table 13.2 provides the relationships used to determine the default 
distribution. 
There are two multipliers associated with each entry in the table. The second 
multiplier 
indicates the fraction of a population component present in an occupancy for a 
particular 
scenario time. The first multiplier then divides that population component into 
indoors 
and outdoors. For example at 2 AM, the default is that 99% (0.99) of the 
nighttime 
residential population will be in a residential occupancy and 99.9% (0.999) of 
those 
people will be indoors. These factors should be changed if better information is 
available. 

 

The factor of 0.80 that is multiplied by the number of children aged 16 and 
under, used to 
calculate educational population, is intended to represent the fact that 
children under the 
age of five are too young to go to school and that on any given day a certain 
number of 
students will not be attending school due to illness or other factors. Average 
attendance 
figures for public and private schools should be used when modifying the 
educational 
occupancy values in Table 13.2. 

 

The population distribution is inferred from Bureau of the Census data and Dun 
and 
Bradstreet data and has an inherent error associated with the distribution. For 
example, 
the number of people in any given census tract at 5 PM is inferred from 
knowledge of 
where people work, where they live and travel times. Similarly, it is assumed 
that the 
children ages 16 and under are attending school in the census tract where they 
live. In 
many cases the user has a better understanding of the distribution of the 
working and 
school populations among census tracts. In this case, modifications to the 
default 
information should be made to reflect the improved knowledge. It is likely that 
improved 
information on the number of hotel visitors can be obtained from the local 
visitors 
bureau. 

 




 

Table 13.2: Default Relationships for Estimating Population Distribution 

 

Distribution of People in Census Tract 

Occupancy 

2:00 a.m. 

2:00 p.m. 

5:00 p.m. 

Indoors 

Residential 

(0.999)0.99(NRES) 

(0.70)0.75(DRES) 

(0.70)0.5(NRES) 

Commercial 

 

(0.999)0.02(COMW) 

(0.99)0.98(COMW) + 
(0.80)0.20(DRES) + 
0.80(HOTEL) + 
0.80(VISIT) 

0.98[0.50(COMW) + 
0.10(NRES)+ 
0.70(HOTEL)] 

Educational 

 

(0.90)0.80(GRADE) + 
0.80(COLLEGE) 

(0.80)0.50(COLLEGE) 

Industrial 

(0.999)0.10(INDW) 

(0.90)0.80(INDW) 

(0.90)0.50(INDW) 

Hotels 

0.999(HOTEL) 

0.19(HOTEL) 

0.299(HOTEL) 

Outdoors 

Residential 

(0.001)0.99(NRES) 

(0.30)0.75(DRES) 

(0.30)0.5(NRES) 

Commercial 

(0.001)0.02(COMW) 

(0.01)0.98(COMW) + 
(0.20)0.20(DRES) + 
(0.20)VISIT + 
0.50(1-PRFIL)0.05(POP) 

0.02[0.50(COMW) + 
0.10(NRES) + 
0.70(HOTEL)] + 
0.50(1-PRFIL) 
[0.05(POP) + 1.0(COMM)] 

Educational 

 

(0.10)0.80(GRADE) + 
0.20(COLLEGE) 

(0.20)0.50(COLLEGE) 

Industrial 

(0.001)0.10(INDW) 

(0.10)0.80(INDW) 

(0.10)0.50(INDW) 

Hotels 

0.001(HOTEL) 

0.01(HOTEL) 

0.001(HOTEL) 

Commuting 

Commuting in 
cars 

0.005(POP) 

(PRFIL)0.05(POP) 

(PRFIL)[0.05(POP) + 
1.0(COMM)] 

Commuting 
using other 
modes 

 

0.50(1-PRFIL)0.05(POP) 

0.50(1-PRFIL) 
[0.05(POP) + 1.0(COMM)] 



 

where: 

POP is the census tract population taken from census data 

DRES is the daytime residential population inferred from census data 

NRES is the nighttime residential population inferred from census data 

COMM is the number of people commuting inferred from census data 

COMW is the number of people employed in the commercial sector 

INDW is the number of people employed in the industrial sector 

GRADE is the number of students in grade schools (K-12) 




COLLEGE is the number of students on college and university campuses in the 
census 
tract 

HOTEL is the number of people staying in hotels in the census tract 

PRFIL is a factor representing the proportion of commuters using automobiles, 
inferred from profile of the community (0.60 for dense urban, 0.80 for less 
dense urban or suburban, and 0.85 for rural). The default is 0.80. 

VISIT is the number of regional residents who do not live in the study area, 
visiting 
the census tract for shopping and entertainment. Default is set to zero. 

 

The commuting population is defined as the number of people expected in 
vehicles, 
public transit, riding bicycles and walking during the commuting time. In this 
methodology, the only roadway casualties estimated are those incurred from 
bridge/overpass damage. This requires the user to estimate the number of people 
located 
on or under bridges during the seismic event. The methodology provides for a 
user-
defined Commuter Distribution Factor, CDF, that corresponds to the percentage of 
the 
commuting population located on or under bridges. The number of people on or 
under 
bridges in a census tract is then computed as follows. 

 

 NBRDG = CDF*Commuter Population (13-1) 

 

where: 

 NBRDG Number of people on or under bridges in the census tract 

 CDF Commuter Distribution Factor: Percent of commuters on or under 
bridges in census tract (Defaults: CDF = 0.01 day, CDF = 0.01 night 
and CDF = 0.02 commute time.) 

 

The methodology defaults the CDF to assumed values of 0.01 during the day and 
night 
time and 0.02 for the commuting time. This value is based on the assumption that 
on a 
typical major urban freeway or highway, an overpass would occur about every two 
miles. 
Local data on the percentage of commuters on or under highway bridges would 
provide 
greater accuracy. 

 

 

General Occupancy to Model Building Type Mapping 

 

The model uses the relationship between the general occupancy classes and the 
model 
building type, which is calculated by combining the following relationships. 

 Specific Occupancy to Model Building Type Relationship (Tables 3A.2 
through 3A.21) 

 General Occupancy to Specific Occupancy Relationship (Table 3.2) 

 

 

 

Damage State Probabilities 

 

The casualty model uses four structural damage states (slight, moderate, 
extensive, and 
complete) computed by the direct physical damage module as well as a subset of 




complete indication building collapse. For each census tract and each model 
building 
type, the probabilities of the structure being in each of the four damage states 
are 
required. In addition, bridge casualties are estimated using the probability of 
the 
complete structural damage state for bridges. 

 

 

Data Specific to The Casualty Module 

 

This module limits itself to the estimation of casualties that would be caused 
by damage 
to buildings and bridges. Excluded are casualties or health effects not 
attributable to 
immediate physical impact, such as heart attacks, psychological effects, toxic 
release, or 
injuries suffered during post-earthquake clean-up or construction activities. 
Exterior 
casualties caused from collapsing masonry parapets, pieces of bearing walls, 
nonstructural wall panels, or from falling signs and other appendages are 
estimated and 
provided as a separate output of the model (outdoor casualties). The casualty 
rates used 
in the methodology are relatively uniform across building types for a given 
damage level, 
with differentiation to account for types of construction that pose higher-than-
average 
hazards at moderate damage levels (e.g., falling of pieces of unreinforced 
masonry) or at 
severe levels (e.g., complete collapse of heavy concrete construction as 
compared to 
complete collapse of wood frame construction). For example, indoor casualty 
rates at 
slight structural damage are the same for all model building types. This is 
because at low 
levels of structural damage casualties most likely would be caused by non-
structural 
components or contents, which do not vary greatly with model building type. 

 

Rates used in the ATC-13 method were evaluated and revised based on comparison 
with 
a limited amount of historical data. General data trends such as, 10 to 20 times 
as many 
non-hospitalized injures as hospitalized injuries occurred in the Northridge 
earthquake 
(Durkin, 1995) and the hospitalization rate (hospitalizations that did not 
result in death) 
for LA county of 1.56 per 100,000 was four times the fatality rate of 0.37 per 
100,000 
(Peek-Asa et al., 1998), were gathered from available data to provide guidance 
as to 
reasonable casualty rates. For several recent events, including the Northridge, 
Loma 
Prieta and Nisqually earthquakes, the casualties estimated by the methodology 
are a 
reasonable representation of the actual numbers observed. 

 

The user should keep in mind the intended use of the casualty estimates: to 
forecast the 
approximate magnitude of injuries and fatalities. For example, an estimate that 
Severity 3 
casualties are in the low hundreds, rather that several thousand, for a future 
event or an 
earthquake that has just occurred, is useful to regional emergency medical 
authorities. Of 
course, for an event that has just occurred, there is no substitute for rapid 
surveys to 
compile actual figures. Note, however, that "actual" casualty counts may still 
contain 
errors. Even for fatalities, data reported for actuals are revised in the weeks 
and months 
following the earthquake. 

 

The following default casualty rates are defined by the methodology. 

 

Indoor Casualty Rates - Structural Damage 




 Casualty rates by model building type for slight, moderate, and extensive 
structural 
damage 

 Casualty rates by model building type for complete structural damage without 
structural collapse 

 Casualty rates by model building type for complete structural damage with 
structural collapse 

 Collapse rates by model building type for complete structural damage state. 

 

Outdoor Casualty Rates - Structural Damage 

 Casualty rates by model building type for slight, moderate, extensive and 
complete 
structural damage 

 

Commuter Casualty Rates - Bridge Damage 

 
Casualty rates by bridge for the complete damage state. 




 

It should be noted that only a portion of the buildings in the complete damage 
state is 
considered to be collapsed. The collapse percentages for each model building 
type are 
given in Chapter 5 and summarized in Table 13.8. The percentages in Table 13.8 
are the 
estimated proportions of building square footage in the complete damage state 
that have 
collapsed for each model building type. Tables 13.3 through 13.11 define the 
values for 
the default casualty module data. 




Table 13.3: Indoor Casualty Rates by Model Building Type for Slight Structural 
Damage 

 

 

 

Casualty Severity Level 

# 

Building Type 

Severity 1 
(%) 

Severity 2 
(%) 

Severity 3 
(%) 

Severity 4 
(%) 

1 

W1 

0.05 

0 

0 

0 

2 

W2 

0.05 

0 

0 

0 

3 

S1L 

0.05 

0 

0 

0 

4 

S1M 

0.05 

0 

0 

0 

5 

S1H 

0.05 

0 

0 

0 

6 

S2L 

0.05 

0 

0 

0 

7 

S2M 

0.05 

0 

0 

0 

8 

S2H 

0.05 

0 

0 

0 

9 

S3 

0.05 

0 

0 

0 

10 

S4L 

0.05 

0 

0 

0 

11 

S4M 

0.05 

0 

0 

0 

12 

S4H 

0.05 

0 

0 

0 

13 

S5L 

0.05 

0 

0 

0 

14 

S5M 

0.05 

0 

0 

0 

15 

S5H 

0.05 

0 

0 

0 

16 

C1L 

0.05 

0 

0 

0 

17 

C1M 

0.05 

0 

0 

0 

18 

C1H 

0.05 

0 

0 

0 

19 

C2L 

0.05 

0 

0 

0 

20 

C2M 

0.05 

0 

0 

0 

21 

C2H 

0.05 

0 

0 

0 

22 

C3L 

0.05 

0 

0 

0 

23 

C3M 

0.05 

0 

0 

0 

24 

C3H 

0.05 

0 

0 

0 

25 

PC1 

0.05 

0 

0 

0 

26 

PC2L 

0.05 

0 

0 

0 

27 

PC2M 

0.05 

0 

0 

0 

28 

PC2H 

0.05 

0 

0 

0 

29 

RM1L 

0.05 

0 

0 

0 

30 

RM1M 

0.05 

0 

0 

0 

31 

RM2L 

0.05 

0 

0 

0 

32 

RM2M 

0.05 

0 

0 

0 

33 

RM2H 

0.05 

0 

0 

0 

34 

URML 

0.05 

0 

0 

0 

35 

URMM 

0.05 

0 

0 

0 

36 

MH 

0.05 

0 

0 

0 

B1 

Major Bridge 

N/A 

N/A 

N/A 

N/A 

B2 

Continuous Bridge 

N/A 

N/A 

N/A 

N/A 

B3 

S.S. Bridge 

N/A 

N/A 

N/A 

N/A 






Table 13.4: Indoor Casualty Rates by Model Building Type for Moderate 
Structural Damage 

 

 

Casualty Severity Level 

# 

Building Type 

Severity 1 
(%) 

Severity 2 
(%) 

Severity 3 
(%) 

Severity 4 
(%) 

1 

W1 

0.25 

0.030 

0 

0 

2 

W2 

0.20 

0.025 

0 

0 

3 

S1L 

0.20 

0.025 

0 

0 

4 

S1M 

0.20 

0.025 

0 

0 

5 

S1H 

0.20 

0.025 

0 

0 

6 

S2L 

0.20 

0.025 

0 

0 

7 

S2M 

0.20 

0.025 

0 

0 

8 

S2H 

0.20 

0.025 

0 

0 

9 

S3 

0.20 

0.025 

0 

0 

10 

S4L 

0.25 

0.030 

0 

0 

11 

S4M 

0.25 

0.030 

0 

0 

12 

S4H 

0.25 

0.030 

0 

0 

13 

S5L 

0.20 

0.025 

0 

0 

14 

S5M 

0.20 

0.025 

0 

0 

15 

S5H 

0.20 

0.025 

0 

0 

16 

C1L 

0.25 

0.030 

0 

0 

17 

C1M 

0.25 

0.030 

0 

0 

18 

C1H 

0.25 

0.030 

0 

0 

19 

C2L 

0.25 

0.030 

0 

0 

20 

C2M 

0.25 

0.030 

0 

0 

21 

C2H 

0.25 

0.030 

0 

0 

22 

C3L 

0.20 

0.025 

0 

0 

23 

C3M 

0.20 

0.025 

0 

0 

24 

C3H 

0.20 

0.025 

0 

0 

25 

PC1 

0.25 

0.030 

0 

0 

26 

PC2L 

0.25 

0.030 

0 

0 

27 

PC2M 

0.25 

0.030 

0 

0 

28 

PC2H 

0.25 

0.030 

0 

0 

29 

RM1L 

0.20 

0.025 

0 

0 

30 

RM1M 

0.20 

0.025 

0 

0 

31 

RM2L 

0.20 

0.025 

0 

0 

32 

RM2M 

0.20 

0.025 

0 

0 

33 

RM2H 

0.20 

0.025 

0 

0 

34 

URML 

0.35 

0.400 

0.001 

0.001 

35 

URMM 

0.35 

0.400 

0.001 

0.001 

36 

MH 

0.25 

0.030 

0 

0 

B1 

Major Bridge 

N/A 

N/A 

N/A 

N/A 

B2 

Continuous 
Bridge 

N/A 

N/A 

N/A 

N/A 

B3 

S.S. Bridge 

N/A 

N/A 

N/A 

N/A 






Table 13.5: Indoor Casualty Rates by Model Building Type for Extensive 
Structural Damage 

 

 

Casualty Severity Level 

# 

Building Type 

Severity 1 
(%) 

Severity 2 
(%) 

Severity 3 
(%) 

Severity 4 
(%) 

1 

W1 

1 

0.1 

0.001 

0.001 

2 

W2 

1 

0.1 

0.001 

0.001 

3 

S1L 

1 

0.1 

0.001 

0.001 

4 

S1M 

1 

0.1 

0.001 

0.001 

5 

S1H 

1 

0.1 

0.001 

0.001 

6 

S2L 

1 

0.1 

0.001 

0.001 

7 

S2M 

1 

0.1 

0.001 

0.001 

8 

S2H 

1 

0.1 

0.001 

0.001 

9 

S3 

1 

0.1 

0.001 

0.001 

10 

S4L 

1 

0.1 

0.001 

0.001 

11 

S4M 

1 

0.1 

0.001 

0.001 

12 

S4H 

1 

0.1 

0.001 

0.001 

13 

S5L 

1 

0.1 

0.001 

0.001 

14 

S5M 

1 

0.1 

0.001 

0.001 

15 

S5H 

1 

0.1 

0.001 

0.001 

16 

C1L 

1 

0.1 

0.001 

0.001 

17 

C1M 

1 

0.1 

0.001 

0.001 

18 

C1H 

1 

0.1 

0.001 

0.001 

19 

C2L 

1 

0.1 

0.001 

0.001 

20 

C2M 

1 

0.1 

0.001 

0.001 

21 

C2H 

1 

0.1 

0.001 

0.001 

22 

C3L 

1 

0.1 

0.001 

0.001 

23 

C3M 

1 

0.1 

0.001 

0.001 

24 

C3H 

1 

0.1 

0.001 

0.001 

25 

PC1 

1 

0.1 

0.001 

0.001 

26 

PC2L 

1 

0.1 

0.001 

0.001 

27 

PC2M 

1 

0.1 

0.001 

0.001 

28 

PC2H 

1 

0.1 

0.001 

0.001 

29 

RM1L 

1 

0.1 

0.001 

0.001 

30 

RM1M 

1 

0.1 

0.001 

0.001 

31 

RM2L 

1 

0.1 

0.001 

0.001 

32 

RM2M 

1 

0.1 

0.001 

0.001 

33 

RM2H 

1 

0.1 

0.001 

0.001 

34 

URML 

2 

0.2 

0.002 

0.002 

35 

URMM 

2 

0.2 

0.002 

0.002 

36 

MH 

1 

0.1 

0.001 

0.001 

B1 

Major Bridge 

N/A 

N/A 

N/A 

N/A 

B2 

Continuous 
Bridge 

N/A 

N/A 

N/A 

N/A 

B3 

S.S. Bridge 

N/A 

N/A 

N/A 

N/A 






Table 13.6: Indoor Casualty Rates by Model Building Type for Complete 

Structural Damage (No Collapse) 

 

 

Casualty Severity Level 

# 

Building Type 

Severity 1 
(%) 

Severity 2 
(%) 

Severity 3 
(%) 

Severity 4 
(%) 

1 

W1 

5 

1 

0.01 

0.01 

2 

W2 

5 

1 

0.01 

0.01 

3 

S1L 

5 

1 

0.01 

0.01 

4 

S1M 

5 

1 

0.01 

0.01 

5 

S1H 

5 

1 

0.01 

0.01 

6 

S2L 

5 

1 

0.01 

0.01 

7 

S2M 

5 

1 

0.01 

0.01 

8 

S2H 

5 

1 

0.01 

0.01 

9 

S3 

5 

1 

0.01 

0.01 

10 

S4L 

5 

1 

0.01 

0.01 

11 

S4M 

5 

1 

0.01 

0.01 

12 

S4H 

5 

1 

0.01 

0.01 

13 

S5L 

5 

1 

0.01 

0.01 

14 

S5M 

5 

1 

0.01 

0.01 

15 

S5H 

5 

1 

0.01 

0.01 

16 

C1L 

5 

1 

0.01 

0.01 

17 

C1M 

5 

1 

0.01 

0.01 

18 

C1H 

5 

1 

0.01 

0.01 

19 

C2L 

5 

1 

0.01 

0.01 

20 

C2M 

5 

1 

0.01 

0.01 

21 

C2H 

5 

1 

0.01 

0.01 

22 

C3L 

5 

1 

0.01 

0.01 

23 

C3M 

5 

1 

0.01 

0.01 

24 

C3H 

5 

1 

0.01 

0.01 

25 

PC1 

5 

1 

0.01 

0.01 

26 

PC2L 

5 

1 

0.01 

0.01 

27 

PC2M 

5 

1 

0.01 

0.01 

28 

PC2H 

5 

1 

0.01 

0.01 

29 

RM1L 

5 

1 

0.01 

0.01 

30 

RM1M 

5 

1 

0.01 

0.01 

31 

RM2L 

5 

1 

0.01 

0.01 

32 

RM2M 

5 

1 

0.01 

0.01 

33 

RM2H 

5 

1 

0.01 

0.01 

34 

URML 

10 

2 

0.02 

0.02 

35 

URMM 

10 

2 

0.02 

0.02 

36 

MH 

5 

1 

0.01 

0.01 

B1 

Major Bridge 

17 

20 

37 

7 

B2 

Continuous 
Bridge 

17 

20 

37 

7 

B3 

S.S. Bridge 

5 

25 

20 

5 






Table 13.7: Indoor Casualty Rates by Model Building Type for Complete 
Structural Damage (With Collapse) 

 

 

Casualty Severity Level 

# 

Building Type 

Severity 1 
(%) 

Severity 2 
(%) 

Severity 3 
(%) 

Severity 4 
(%) 

1 

W1 

40 

20 

3 

5 

2 

W2 

40 

20 

5 

10 

3 

S1L 

40 

20 

5 

10 

4 

S1M 

40 

20 

5 

10 

5 

S1H 

40 

20 

5 

10 

6 

S2L 

40 

20 

5 

10 

7 

S2M 

40 

20 

5 

10 

8 

S2H 

40 

20 

5 

10 

9 

S3 

40 

20 

3 

5 

10 

S4L 

40 

20 

5 

10 

11 

S4M 

40 

20 

5 

10 

12 

S4H 

40 

20 

5 

10 

13 

S5L 

40 

20 

5 

10 

14 

S5M 

40 

20 

5 

10 

15 

S5H 

40 

20 

5 

10 

16 

C1L 

40 

20 

5 

10 

17 

C1M 

40 

20 

5 

10 

18 

C1H 

40 

20 

5 

10 

19 

C2L 

40 

20 

5 

10 

20 

C2M 

40 

20 

5 

10 

21 

C2H 

40 

20 

5 

10 

22 

C3L 

40 

20 

5 

10 

23 

C3M 

40 

20 

5 

10 

24 

C3H 

40 

20 

5 

10 

25 

PC1 

40 

20 

5 

10 

26 

PC2L 

40 

20 

5 

10 

27 

PC2M 

40 

20 

5 

10 

28 

PC2H 

40 

20 

5 

10 

29 

RM1L 

40 

20 

5 

10 

30 

RM1M 

40 

20 

5 

10 

31 

RM2L 

40 

20 

5 

10 

32 

RM2M 

40 

20 

5 

10 

33 

RM2H 

40 

20 

5 

10 

34 

URML 

40 

20 

5 

10 

35 

URMM 

40 

20 

5 

10 

36 

MH 

40 

20 

3 

5 

B1 

Major Bridge 

N/A 

N/A 

N/A 

N/A 

B2 

Continuous 
Bridge 

N/A 

N/A 

N/A 

N/A 

B3 

S.S. Bridge 

N/A 

N/A 

N/A 

N/A 






Table 13.8: Collapse Rates by Model Building Type for Complete Structural 
Damage 

 

 

Model Building 
Type 

Probability of Collapse 
Given a Complete 
Damage State* 

1 

W1 

3.0% 

2 

W2 

3.0% 

3 

S1L 

8.0% 

4 

S1M 

5.0% 

5 

S1H 

3.0% 

6 

S2L 

8.0% 

7 

S2M 

5.0% 

8 

S2H 

3.0% 

9 

S3 

3.0% 

10 

S4L 

8.0% 

11 

S4M 

5.0% 

12 

S4H 

3.0% 

13 

S5L 

8.0% 

14 

S5M 

5.0% 

15 

S5H 

3.0% 

16 

C1L 

13.0% 

17 

C1M 

10.0% 

18 

C1H 

5.0% 

19 

C2L 

13.0% 

20 

C2M 

10.0% 

21 

C2H 

5.0% 

22 

C3L 

15.0% 

23 

C3M 

13.0% 

24 

C3H 

10.0% 

25 

PC1 

15.0% 

26 

PC2L 

15.0% 

27 

PC2M 

13.0% 

28 

PC2H 

10.0% 

29 

RM1L 

13.0% 

30 

RM1M 

10.0% 

31 

RM2L 

13.0% 

32 

RM2M 

10.0% 

33 

RM2H 

5.0% 

34 

URML 

15.0% 

35 

URMM 

15.0% 

36 

MH 

3.0% 



 

* See Chapter 5, Section 5.3 for derivation of these values 

* See Chapter 5 for derivation of these values 




Table 13.9: Outdoor Casualty Rates by Model Building Type for Moderate 
Structural Damage* 

 

 

Casualty Severity Level 

# 

Building Type 

Severity 1 
(%) 

Severity 2 
(%) 

Severity 3 
(%) 

Severity 4 
(%) 

1 

W1 

0.05 

0.005 

0.0001 

0.0001 

2 

W2 

0.05 

0.005 

0 

0 

3 

S1L 

0.05 

0.005 

0 

0 

4 

S1M 

0.05 

0.005 

0 

0 

5 

S1H 

0.05 

0.005 

0 

0 

6 

S2L 

0.05 

0.005 

0 

0 

7 

S2M 

0.05 

0.005 

0 

0 

8 

S2H 

0.05 

0.005 

0 

0 

9 

S3 

0 

0 

0 

0 

10 

S4L 

0.05 

0.005 

0 

0 

11 

S4M 

0.05 

0.005 

0 

0 

12 

S4H 

0.05 

0.005 

0 

0 

13 

S5L 

0.05 

0.005 

0 

0 

14 

S5M 

0.05 

0.005 

0 

0 

15 

S5H 

0.05 

0.005 

0 

0 

16 

C1L 

0.05 

0.005 

0 

0 

17 

C1M 

0.05 

0.005 

0 

0 

18 

C1H 

0.05 

0.005 

0 

0 

19 

C2L 

0.05 

0.005 

0 

0 

20 

C2M 

0.05 

0.005 

0 

0 

21 

C2H 

0.05 

0.005 

0 

0 

22 

C3L 

0.05 

0.005 

0 

0 

23 

C3M 

0.05 

0.005 

0 

0 

24 

C3H 

0.05 

0.005 

0 

0 

25 

PC1 

0.05 

0.005 

0 

0 

26 

PC2L 

0.05 

0.005 

0 

0 

27 

PC2M 

0.05 

0.005 

0 

0 

28 

PC2H 

0.05 

0.005 

0 

0 

29 

RM1L 

0.05 

0.005 

0 

0 

30 

RM1M 

0.05 

0.005 

0 

0 

31 

RM2L 

0.05 

0.005 

0 

0 

32 

RM2M 

0.05 

0.005 

0 

0 

33 

RM2H 

0.05 

0.005 

0 

0 

34 

URML 

0.15 

0.015 

0.0003 

0.0003 

35 

URMM 

0.15 

0.015 

0.0003 

0.0003 

36 

MH 

0 

0 

0 

0 



 

* The model assumes that there are no outdoor casualties for slight structural 
damage. 

 




Table 13.10: Outdoor Casualty Rates by Model Building Type for Extensive 
Structural Damage 

 

 

 

Casualty Severity Level 

# 

Building Type 

Severity 1 
(%) 

Severity 2 
(%) 

Severity 3 
(%) 

Severity 4 
(%) 

1 

W1 

0.3 

0.03 

0.0003 

0.0003 

2 

W2 

0.3 

0.03 

0.0003 

0.0003 

3 

S1L 

0.1 

0.01 

0.0001 

0.0001 

4 

S1M 

0.2 

0.02 

0.0002 

0.0002 

5 

S1H 

0.3 

0.03 

0.0003 

0.0003 

6 

S2L 

0.1 

0.01 

0.0001 

0.0001 

7 

S2M 

0.2 

0.02 

0.0002 

0.0002 

8 

S2H 

0.3 

0.03 

0.0003 

0.0003 

9 

S3 

0 

0 

0 

0 

10 

S4L 

0.1 

0.01 

0.0001 

0.0001 

11 

S4M 

0.2 

0.02 

0.0002 

0.0002 

12 

S4H 

0.3 

0.03 

0.0003 

0.0003 

13 

S5L 

0.2 

0.02 

0.0002 

0.0002 

14 

S5M 

0.4 

0.04 

0.0004 

0.0004 

15 

S5H 

0.6 

0.06 

0.0006 

0.0006 

16 

C1L 

0.1 

0.01 

0.0001 

0.0001 

17 

C1M 

0.2 

0.02 

0.0002 

0.0002 

18 

C1H 

0.3 

0.03 

0.0003 

0.0003 

19 

C2L 

0.1 

0.01 

0.0001 

0.0001 

20 

C2M 

0.2 

0.02 

0.0002 

0.0002 

21 

C2H 

0.3 

0.03 

0.0003 

0.0003 

22 

C3L 

0.2 

0.02 

0.0002 

0.0002 

23 

C3M 

0.4 

0.04 

0.0004 

0.0004 

24 

C3H 

0.6 

0.06 

0.0006 

0.0006 

25 

PC1 

0.2 

0.02 

0.0002 

0.0002 

26 

PC2L 

0.1 

0.01 

0.0001 

0.0001 

27 

PC2M 

0.2 

0.02 

0.0002 

0.0002 

28 

PC2H 

0.3 

0.03 

0.0003 

0.0003 

29 

RM1L 

0.2 

0.02 

0.0002 

0.0002 

30 

RM1M 

0.3 

0.03 

0.0003 

0.0003 

31 

RM2L 

0.2 

0.02 

0.0002 

0.0002 

32 

RM2M 

0.3 

0.03 

0.0003 

0.0003 

33 

RM2H 

0.4 

0.04 

0.0004 

0.0004 

34 

URML 

0.6 

0.06 

0.0006 

0.0006 

35 

URMM 

0.6 

0.06 

0.0006 

0.0006 

36 

MH 

0 

0 

0 

0 






Table 13.11: Outdoor Casualty Rates by Model Building Type for 

Complete Structural Damage 

 

 

 

Casualty Severity Level 

# 

Building Type 

Severity 1 
(%) 

Severity 2 
(%) 

Severity 3 
(%) 

Severity 4 
(%) 

1 

W1 

2 

0.5 

0.1 

0.05 

2 

W2 

2 

0.5 

0.1 

0.05 

3 

S1L 

2 

0.5 

0.1 

0.1 

4 

S1M 

2.2 

0.7 

0.2 

0.2 

5 

S1H 

2.5 

1 

0.3 

0.3 

6 

S2L 

2 

0.5 

0.1 

0.1 

7 

S2M 

2.2 

0.7 

0.2 

0.2 

8 

S2H 

2.5 

1 

0.3 

0.3 

9 

S3 

0.01 

0.001 

0.001 

0.01 

10 

S4L 

2 

0.5 

0.1 

0.1 

11 

S4M 

2.2 

0.7 

0.2 

0.2 

12 

S4H 

2.5 

1 

0.3 

0.3 

13 

S5L 

2.7 

1 

0.2 

0.3 

14 

S5M 

3 

1.2 

0.3 

0.4 

15 

S5H 

3.3 

1.4 

0.4 

0.6 

16 

C1L 

2 

0.5 

0.1 

0.1 

17 

C1M 

2.2 

0.7 

0.2 

0.2 

18 

C1H 

2.5 

1 

0.3 

0.3 

19 

C2L 

2 

0.5 

0.1 

0.1 

20 

C2M 

2.2 

0.7 

0.2 

0.2 

21 

C2H 

2.5 

1 

0.3 

0.3 

22 

C3L 

2.7 

1 

0.2 

0.3 

23 

C3M 

3 

1.2 

0.3 

0.4 

24 

C3H 

3.3 

1.4 

0.4 

0.6 

25 

PC1 

2 

0.5 

0.1 

0.1 

26 

PC2L 

2.7 

1 

0.2 

0.3 

27 

PC2M 

3 

1.2 

0.3 

0.4 

28 

PC2H 

3.3 

1.4 

0.4 

0.6 

29 

RM1L 

2 

0.5 

0.1 

0.1 

30 

RM1M 

2.2 

0.7 

0.2 

0.2 

31 

RM2L 

2 

0.5 

0.1 

0.1 

32 

RM2M 

2.2 

0.7 

0.2 

0.2 

33 

RM2H 

2.5 

1 

0.3 

0.3 

34 

URML 

5 

2 

0.4 

0.6 

35 

URMM 

5 

2 

0.4 

0.6 

36 

MH 

0.01 

0.001 

0.001 

0.01 



 




13.2 Description of Methodology 

 

The casualty model is complementary to the concepts put forward by some other 
models 
(Coburn and Spence, 1992; Murkami, 1992, Shiono, et. al., 1991). The Coburn and 
Spence model uses the same four-level injury severity scale (light injuries, 
hospitalized 
injuries, life threatening injuries and deaths) and underlying concepts 
associated with 
building collapse. However, it is not in event tree format and does not account 
for non-
collapse (damage) related casualties, nor does it account for the population not 
indoors at 
the time of earthquake. The Murkami model is an event tree model that includes 
only 
fatalities caused by collapsed buildings and does not account for lesser 
injuries. Shiono's 
model is similar to the other two models and only estimated fatalities. 

 

The methodology takes into account a wider range of causal relationships in the 
casualty 
modeling. It is an extension of the model proposed by Stojanovski and Dong 
(1994). 

 

 

13.2.1 Earthquake Casualty Model 

 

Casualties caused by a postulated earthquake can be modeled by developing a tree 
of 
events leading to their occurrence. As with any event tree, the earthquake-
related 
casualty event tree begins with an initiating event (earthquake scenario) and 
follows the 
possible course of events leading to loss of life or injuries. The logic of its 
construction 
is forward (inductive). At each node of the tree, the (node branching) question 
is: What 
happens if the preceding event leading to the node occurs? The answers to this 
question 
are represented by the branches of the tree. The number of branches from any 
node is 
equal to the number of answers defined for the node branching question. Each 
branch of 
the tree is assigned a probability of occurrence. As noted earlier, data for 
earthquake 
related casualties are relatively scarce, particularly for U.S. earthquakes. 
Therefore, to 
some extent the casualty rates are inferred from the available data statistics 
and combined 
with expert opinion. 

 

As an example, one particular severity of casualty, the expected number of 
occupants 
killed in a building during a given earthquake, could be simulated with an event 
tree as 
shown in Figure 13.1. For illustrative purposes it contains only "occupants 
killed, as 
events of interest and does not depict lesser severities of casualties. 
Evaluation of the 
branching probabilities constitutes the main effort in the earthquake casualty 
modeling. 
Assuming that all the branching probabilities are known or inferred, the 
probability of an 
occupant being killed (Pkilled) is given as follows. 

 

(Various events are described in Figure 13.1) 

 

Pkilled = PA*PE + PB*PF + PC*PG + PD*( PH*PJ + PI*PK ) (13-2) 

 

By introducing the substitutions 

Pkilled | collapse = PD*PI*PK (13-3) 




and 

Pkilled | no-collapse = PA*PE + PB*PF + PC*PG + PD*PH*PJ (13-4) 

 

Equation (13-2) could be simply re-written as: 

 

Pkilled = Pkilled | collapse + Pkilled | no-collapse (13-5) 

 

 

Occupants 
KilledWithoutCollapseOccupants 
KilledEarthquake 
PAPBCEvent FEvent HEvent IPHPJPFPEEvent CEvent DEvent EPPDEvent AEvent 
BPGScenarioDamage 
State 2Damage 
State 1Damage 
State 3Damage 
State 4Occupants 
KilledEvent GOccupants 
KilledOccupants 
KilledEvent JEvent KWithCollapsePIPK 

 

Figure 13.1 Casualty Event Tree Modeling. 

 

The first term in equation 13-5 represents casualties associated with the 
building 
collapse. The second term represents casualties associated with the level of 
non-collapse 
damage the building sustains during the earthquake. Records from past 
earthquakes 
show that for different regions in the world with different kinds of 
construction there are 
different threshold intensities at which the first term begins to dominate. For 
intensities 
below that shaking level, casualties are primarily damage or non-collapse 
related. For 
intensities above that level, the collapse, often of only a few structures, may 
control the 
casualty pattern. 

 

The expected number of occupants killed (ENoccupants killed) is a product of the 
number of occupants of the building at the time of earthquake (Noccupants) and 
the 
probability of an occupant being killed (Pkilled). 

 

 ENoccupants killed = Noccupants*Pkilled (13-6) 

 

Figure 13.2 presents a more complete earthquake related casualty event tree for 
indoor 
casualties, which is used in the methodology. The branching probabilities are 
not shown 
in the figure in order to make the model presentation simpler. The events are 
represented 
with rectangular boxes, with a short event or state description given in each 
box. The 




symbol "<" attached to an event box means that branching out from that node is 
identical 
to branching from other nodes for the same category event (obviously, the 
appropriate 
probabilities would be different). 

 

The event tree in Figure 13.2 is conceptual. It integrates several different 
event trees into 
one (light injuries, injuries requiring medical care, life threatening injuries 
and deaths) 
for different occupancy types (residential, commercial, industrial, commuting) 
for people 
inside buildings. A similar event tree for outdoor casualties is used in the 
model. 
Casualty rates are different depending on the preceding causal events: model 
building 
type, damage state, collapse, etc. 

 

Residential PopulationCommercial PopulationIndustrial PopulationCommuting 
Population
..
Bldg. Type 1Bldg. Type 36
..
..
Damage State 4Damage State 1Damage State 2Damage State 3Level 1Level 4Level 
2Level 3Level 1Level 4Level 2Level 3Level 1Level 4Level 2Level 3
..
Bridge 1Bridge 4Damage State 4Damage State 4Level 1Level 4Level 2Level 
3POPULATION DISTRIBUTIONTable 13.2INVENTORYVULNERABILITYCASUALTYTable 13.5Table 
13.6Table 13.7CASUALTYCASUALTYCASUALTYTable 13.3Table 13.4With CollapseNo 
Collapse 


Figure 13.2: Indoor Casualty Event Tree Model. 

 

 

13.2.2 Alternative Estimation of Casualty Rates 

 

In the absence of adequate U.S.-specific casualty data (as a consequence of 
structural 
collapse), international data on the casualty rates for specific structural 
types may be 
used. If overseas casualty rates are used, U.S. construction practices, design 
and 
construction quality would have to be reflected in the appropriate region-
specific fragility 
curves. If average worldwide casualty statistics or data from one or a few other 
countries 
are to be used for collapse-related casualty modeling in the United States, 
special 
attention must be given to the relationship between the U.S. structural types 
and the 
structural types represented by these other data sets. Also, appropriate mapping 
between 
injury classification scales must be established. Finally, it is possible that 
differing levels 
of earthquake preparedness, such as the effectiveness of the emergency medical 
system, 




and the training of the public in personal protective measures, such as "duck 
and cover, 
might cause U.S. casualty rates to differ from those overseas, but this is 
unlikely to be a 
significant factor in cases of collapse, and at the present no data is available 
on these 
kinds of issues. 

 

Published data on collapse-related casualty rates is limited. Noji (1990) 
provided this 
type of data for stone masonry and precast concrete buildings based on data from 
the 
1988 Armenia earthquake. Murakami (1992) used these rates in a model that 
simulated 
the fatalities from the same event. Durkin and Murakami (1989) reported casualty 
rates 
for two reinforced concrete buildings collapsed during the 1985 Mexico and 1986 
San 
Salvador earthquakes. Shiono et al. (1991) provided fatality rates after 
collapse for most 
common worldwide structural types. Coburn et al. (1992) have summarized 
approximate 
casualty rates for masonry and reinforced concrete structures based on worldwide 
data. 

 

The casualty patterns for people who evacuate collapsed buildings, either before 
or 
immediately after the collapse, are more difficult to quantify. Statistical data 
on these 
casualty patterns is lacking, since in most post-earthquake reconnaissance 
efforts these 
injuries are not distinguished from other causes of injuries. In some cases, the 
lighter 
injuries may not be reported. An assumption may be applied that those who manage 
to 
evacuate are neither killed nor receive life threatening injuries. Often it is 
assumed that 
50% of the occupants of the first floor manage to evacuate. 

 

 

13.2.3 Casualties Due to Outdoor Falling Hazards 

 

Experience in earthquakes overseas and in the United States has shown that a 
number of 
casualties occur outside buildings due to falling materials. People that are 
outside, but 
close to buildings could be hurt by structural or non-structural elements 
falling from the 
buildings. Examples are damaged parapets, loosened bricks, broken window glass, 
signage, awnings, or non-structural panels. In the 1987 Whittier Narrows 
earthquake a 
student at California State University, Los Angeles was killed when a concrete 
panel fell 
from a parking structure, and in the 1983 Coalinga earthquake one person was 
severely 
injured when the faade of a building collapsed onto the sidewalk and two people 
sitting 
in a parked car were hit by bricks from a collapsing building. Five people in 
San 
Francisco died when a brick wall collapsed onto their cars during the Loma 
Prieta 
earthquake. In the United States, casualties due to outdoor falling hazards have 
been 
caused primarily by falling unreinforced masonry, which may cause damage to an 
adjoining building and result in casualties, or fall directly on people outside 
the building. 

 

People outside of buildings are less likely to be injured or killed than those 
inside 
buildings. For example, in the Loma Prieta earthquake out of 185 people who were 
injured or killed in Santa Cruz County, 20 people were outside and 1 was in a 
car 
(Wagner, 1996). An epidemiological study of casualties in the Loma Prieta 
earthquake 
indicates that injury risk in Santa Cruz County was 2.87 times higher for those 
in a 
building versus outside of a building (Jones et al., 1994). Note that the sample 
of 
residents surveyed was located mostly in suburban and rural surroundings. It is 
quite 




possible for a given earthquake to occur at a time of day and in a densely 
built-up locale 
where relatively more exterior casualties would occur. The HAZUS methodology is 
based on probable outcomes, not the "worst case scenario." 

 

This model attempts to account for casualties due to falling hazards, 
particularly with 
respect to areas where people congregate such as sidewalks. To accomplish this, 
the 
number of people on sidewalks or similar exterior areas is estimated from Table 
13.2. 
The table is designed to prevent double counting of casualties from outdoor 
falling 
hazards with building occupant casualties. 

 

The model for estimating casualties due to outside fall hazards is an event tree 
similar to 
that for indoor casualties. One difference is that the outdoor casualty event 
tree does not 
branch into collapse or no collapse for the complete damage state. Instead, the 
four 
severities of casualties depend only on the damage state of the building. The 
justification 
for this simplification is that people outside of buildings are much less likely 
to be 
trapped by collapsed floors. Another difference is that the model assumes that 
slight 
structural damage does not generate outdoor casualties. This is equivalent to 
eliminating 
Damage State 1 from the event tree in Figure 13.2. The probabilities for the 
event tree 
branches are in Tables 13.9 through 13.11. 

 

 

13.2.4 Casualty Rates Resulting from Bridge Collapse 

 

The model attempts to estimate casualties to people either on or under bridges 
that 
experience complete damage. The number of people on or under bridges is 
calculated 
from Table 13.2 and equation 13-1. The bridge casualty rates are found in Table 
13.6. 

 

Single Span Bridges 

One reference that reports on many aspects of a single span bridge collapse is 
"Loma 
Prieta Earthquake October 17, 1989; I-80 San Francisco - Oakland Bay Bridge, 
Closure 
Span Collapse, published by the California Highway Patrol (Golden Gate MAIT, 
1990). 
This document systematically reports most of the facts related to the collapse 
of one of 
the spans of the bridge. The only fatality was recorded approximately half an 
hour after 
the event, when a car drove into the gap created by the collapse. 

 

Estimates of casualty rates for single span (SS) bridges are provided in Table 
13.6 
(Casualty Rates for Complete Structural damage) only. Lack of data did not allow 
similar inferences for other damage states. 

 

Major and Continuous Bridges 

A report published by the California Highway Patrol "Loma Prieta Earthquake 
October 
17, 1989; I-880 Cypress Street Viaduct Structure Collapse, (Golden Gate MAIT, 
1990) 
summarizes many aspects of a continuous (major) bridge collapse. This reference 
systematically reports most of the facts related to the collapse of the 
structure. Most of 
the injuries and fatalities occurred on the lower northbound deck as a 
consequence of the 




collapse of the upper deck onto the lower deck. A significant portion of 
injuries and 
fatalities also occurred among the people driving on the upper southbound deck. 
A small 
portion of casualties resulted from vehicles on the surface streets adjacent to 
the 
collapsed structure. 

 

For casualty rates for major and continuous bridges, casualty statistics on the 
upper deck 
of the Cypress Viaduct and on the adjacent surface streets have been used. 
Double 
decker highway bridges are unusual and are not specifically modeled in HAZUS. 
Thus 
casualty statistics associated with the vehicles on the lower deck are not 
considered 
representative. 

 

 

13.3 References 

 
Allen and Hoshall, Jack R. Benjamin and Associates, and Systan Inc. 1985. An 
Assessment of Damage and Casualties for Six Cities in the Central United States 
Resulting from Earthquakes in the New Madrid Seismic Zone. Prepared for FEMA. 

Aroni, S., and Durkin, M. E. 1985, "Injuries and Occupant Behavior in 
Earthquakes," 
Proc. Joint U.S. - Romanian Seminar on Earthquake & Energy, Bucharest, Romania, 
Vol. 2, September: 3 - 40. 

ATC-13 (1985), Earthquake Damage Evaluation Data for California, Applied 
Technology Council, Redwood City, CA. 

Brismar, B. (1989), "The Medical Severity Index of Disasters," Proc. 
International 
Workshop on Earthquake Injury Epidemiology, The John Hopkins University, July: 
316 
- 320. 

Charter of the U.S. Ad Hoc Working Group on the Health Effects of Earthquakes 
(1992), 
First International Forum on Earthquake Related Casualties, Madrid, Spain, July: 
22 -
26. 

Cheu, D. H. (1994), Personal Communication - Comments on Casualty Issues, April. 

Coburn, A.W. and Spence, R.J.S., 1992, "Factors Determining Human Casualty 
Levels in 
Earthquakes: Mortality Prediction in Building Collapse," Proceedings of the 10 
WCEE, 
Madrid, Spain: 5989 - 5994. 

Durkin, M.E., 1992, "Improving Earthquake Casualty and Loss Estimation," Proc. 
10 
WCEE, Madrid, Spain: 557 - 562. 

Durkin, M. E., 1995, Fatalities, Nonfatal Injuries, and Medical Aspects of the 
Northridge Earthquake," The Northridge, California Earthquake of 17 January 
1994, 
CDMG, Spec. Pub. 116, pp. 247-254. 

Durkin, M. and Murakami, H., 1989, "Casualties, Survival, and Entrapment in 
Heavily 
Damaged Buildings," Proc. 9 WCEE, Kyoto, Japan: Vol. VII, 977 - 982. 

Durkin, M. E. and Thiel, C. C., 1991, "Integrating Earthquake Casualty and Loss 
Estimation," Proc. of the Workshop on Modeling Earthquake Casualties for 
Planning 
and Response, Sacramento. 




Durkin, M. E. and Thiel, C. C. 1993, "Toward a Comprehensive Regional Earthquake 
Casualty Modeling Process," Proc. National Earthquake Conference, Vol. I, 
Central U.S. 
Earthquake Consortium, May: 557 - 566. 

Fussell, J., 1976, "Fault Tree Analysis - Concepts and Techniques," Generic 
Techniques 
in Reliability Assessment, Henley, E. and Lynn, J. (eds.), Noordhoff Publishing 
Co., 
Leyden, Holland. 

Golden Gate Divisional Multidisciplinary Accident Investigation Team (MAIT) 
(1990), 
"I-80 San Francisco-Oakland Bay Bridge Structure Collapse Report," California 
Highway Patrol, Sacramento, CA. 

Golden Gate Divisional Multidisciplinary Accident Investigation Team (MAIT) 
(1990), 
"I-880 Nimitz Freeway (Cypress viaduct) Structure Collapse Report," California 
Highway Patrol, Sacramento, CA. 

Haney, T., 1990, "Model Definition and User Output Requirements," Proc. Workshop 
on 
Modeling Earthquake Casualties for Planning and Response, Pacific Grove, CA, 
December: A1 - A11. 

Henley, E. J. and Kumamoto, H., 1992, Probabilistic Risk Assessment - 
Reliability 
Engineering, Design, and Analysis, IEEE. 

Jones, N. P., 1990, "Reducing Earthquake Casualties: New Considerations for 
Engineers," Proc. Conference XLIX - A Meeting of the U.S. Ad Hoc Working Group 
on 
Earthquake Related Casualties, U.S.G.S. Open File Report 90244, Reston, 
Virginia: 60 - 
70. 

Jones, N. P., Noji, E.K., Smith, G.S. and Wagner, R.M., 1993, "Casualty in 
Earthquakes," 
Monograph 5 - Socioeconomic Impacts, National Earthquake Conference, Memphis, 
Tennessee, Central U.S. Earthquake Consortium, May: 19 - 68. 

Jones, N. P., Smith, G. S., and R. M. Wagner, 1994, "Morbidity and Mortality in 
the 
Loma Prieta Earthquake: A Review of Recent Findings," Research Accomplishments 
1986-1994, NCEER, pp. 95-106. 

Jones, N. P., Wagner R.M. and Smith, G.S., 1993, "Injuries and Building Data 
Pertinent 
to the Loma Prieta Earthquake: County of Santa Cruz," Proc. National Earthquake 
Conference, Vol. I, Central United States Earthquake Consortium: 531 - 540. 

Krimgold, F., 1992, "Current Application of Casualty Information for Loss 
Estimation 
and Post Earthquake Response," First International Forum on Earthquake Related 
Casualties, Madrid, Spain, May: 21 - 30. 

Murakami H. O., 1992, "A Simulation Model to Estimate Human Loss for Occupants 
of 
Collapsed Buildings in an Earthquake," Proceedings of the 10. WCEE, Madrid, 
Spain: 
5969 - 5974. 

Noji, E.K., 1990, "Epidemic Studies from the 1988 Armenia Earthquake: 
Implications for 
Casualty Modeling," Workshop on Modeling Earthquake Casualties for Planning and 
Response, VSP Associates, Asilomar Conference Center, Pacific Grove, California. 




Peek-Asa, C., Kraus, J.F., Bourque, L. B., Vimalachandra, D., Yu, J. and J. 
Abrams 
1998, "Fatal and Hospitalized Injuries Resulting From the 1994 Northridge 
Earthquake," 
International Journal of Epidemiology, V27, 459-465. 

Shiono, K., Krimgold, F. and Ohta, Y., 1991, "A Method for the Estimation of 
Earthquake Fatalities and its Applicability to the Global Macro-Zonation of 
Human 
Casualty Risk," Proc. Fourth International Conference on Seismic Zonation, 
Stanford, 
CA, Vol. III: 277 - 284. 

Shiono, K., Krimgold, F., Ohta, Y., 1991, "Post-Event Rapid Estimation of 
Earthquake 
Fatalities for the Management of Rescue Activity," Comprehensive Urban Studies, 
No. 
44, pp. 61 - 105. 

Stojanovski, P., Dong, W., 1994, "Simulation Model for Earthquake Casualty 
Estimation," Proc. Fifth U.S. National Conference on Earthquake Engineering, 
Paper 
No. 00592, Chicago, Illinois, July 10-14. 

Tierney, J. T., 1990, "Developing Multivariate Models for Earthquake Casualty 
Estimation," Proc. Workshop on Modeling Earthquake Casualties for Planning and 
Response, Pacific Grove, CA, December: D1 - D24. 

Wagner, R. M., 1996, A Case-Control Study of Risk Factors for Physical Injury 
During 
the Mainshock of the 1989 Loma Prieta earthquake in the County of Santa Cruz, 
California, Ph.D. Dissertation, Johns Hopkins University. 

 

 




Chapter 14 

Direct Social Losses - Displaced Households Due to Loss of 

Housing Habitability and Short Term Shelter Needs 

 

 

14.1 Introduction 

 

Earthquakes can cause loss of function or habitability of buildings that contain 
housing 
units, resulting in approximately predictable numbers of displaced households. 
These 
households may need alternative short-term shelter, provided by family, friends, 
renting 
apartments or houses, or public shelters provided by relief organizations such 
as the Red 
Cross, Salvation Army, and others. For units where repair takes longer than a 
few weeks, 
long-term alternative housing can be accommodated by importing mobile homes, 
occupancy of vacant units, net emigration from the impacted area, and, 
eventually, by the 
repair or reconstruction of new public and private housing. While the number of 
people 
seeking short-term public shelter is of great concern to emergency response 
organizations, the longer-term impacts on the housing stock are of great concern 
to local 
governments, such as cities and counties. The methodology highlighting the 
Shelter 
component is shown in Flowchart 14.1. 

 

14.1.1 Scope 

 

The shelter model provides two estimates: 

 

 The number of displaced households (due to loss of habitability) 

 The number of people requiring only short-term shelter 

 

Loss of habitability is calculated directly from damage to the residential 
occupancy 
inventory, and from loss of water and power. The methodology for calculating 
short-
term shelter requirements recognizes that only a portion of those displaced from 
their 
homes will seek public shelter, and some will seek shelter even though their 
residence 
may have no or insignificant damage. 

 

Households may also be displaced as result of fire following earthquake, 
inundation (or 
the threat of inundation) due to dam failure, and by significant hazardous waste 
releases. 
This module does not specifically deal with these issues, but an approximate 
estimate of 
displacement due to fire or inundation can be obtained by multiplying the 
residential 
inventory in affected census tracts by the areas of fire damage or inundation 
derived from 
those modules. The hazardous materials module is confined to identifying 
locations of 
hazardous materials and no methodology for calculations of damage or loss is 
provided. 
If the particular characteristics of the study region give the user cause for 
concern about 
the possibility of housing loss from fire, dam failure, or hazardous materials, 
it would be 
advisable to initiate specific studies directed towards the problem, as a Level 
3 study. 




 

 

 

8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Flowchart 14.1: Direct Social Losses (Displaced Households) Relationship to 
other 
Components of the Earthquake Loss Estimation Methodology 




14.2 Displaced Households - Form of Loss Estimate 

 

The total number of uninhabitable dwelling units (#UNU) for each census tract of 
the 
study region is the output of this portion of the model. In addition, by 
applying an 
occupancy rate (households vs. dwelling units), the model converts the 
habitability data 
to the number of displaced households. The number of displaced households will 
be used 
in Section 14.3 to estimate the short-term shelter needs. 

 

14.2.1 Input Requirements - Displaced Households 

 

The following inputs are required to compute the number of uninhabitable 
dwelling units 
and the number of displaced households. The total number of units or households 
is 
provided in the default inventory based on census data (Section 3.6.2 of Chapter 
3). The 
user can modify any values based on improved information. 

 

 Total Number of Single-Family Dwelling Units (#SFU) 

 Total Number of Multi-Family Dwelling Units (#MFU) 

 Total Number of Households (#HH) 

 Damage state probability for moderate structural damage in the single-family 
residential occupancy class (%SFM). 

 Damage state probability for extensive structural damage state in the single-
family 
residential occupancy class (%SFE). 

 Damage state probability for complete structural damage state in the single-
family 
residential occupancy class (%SFC). 

 Damage state probability for moderate structural damage state in the multi- 
family 
residential occupancy class (%MFM). 

 Damage state probability for extensive structural damage state in the multi- 
family 
residential occupancy class (%MFE). 

 Damage state probability for complete structural damage state in the multi- 
family 
residential occupancy class (%MFC). 

[Note: The probabilities %SFM, %SFE, %SFC, %MFM, %MFE, and %MFC are 
provided by the Direct Physical Damage Module - Buildings (Chapter 5)]. 

 

14.2.2 Description of Methodology 

The estimated number of uninhabitable dwelling units is calculated by combining 
a) the 
number of uninhabitable dwelling units due to actual structural damage, and b) 
the 
number of damaged units that are perceived to be uninhabitable by their 
occupants. 
Based on comparisons with previous work (Perkins, 1992; Perkins and Harrald, et. 
al., 
unpublished), the methodology considers all dwelling units located in buildings 
that are 
in the complete damage state to be uninhabitable. In addition, dwelling units 
that are in 
moderately and extensively damaged multi-family structures are also considered 
to be 
uninhabitable due to the fact that renters perceive some moderately damaged 
rental 
property as uninhabitable. On the other hand, those living in single-family 
homes are 




much more likely to tolerate damage and continue to live in their home. By 
applying an 
occupancy rate (households vs. dwelling units), the total number of displaced 
households 
(#DH) is calculated by the following relationship. 

 

()..
.
..
.
+
+=
++=
++=
MFU# SFU#
HH#* %MFMFU# %SFSFU# DH#
%MFC w %MFE w %MFMw%MF%SFC w %SFE w %SFMw%SFMFCMFEMFMSFCSFESFM 
(14-1) 

 

The values in Table 14.1 are provided as defaults. Due to the subjective nature 
of 
perceptions, users may want to change these values1. 

 

Table 14.1: Default Values for Damage State Probabilities 

 

Weight Factor 

Default Value 

wSFM 

0.0 

wSFE 

0.0 

wSFC 

1.0 

wMFM 

0.0 

wMFE 

0.9 

wMFC 

1.0 



 

 

 

14.3 Short Term Shelter Needs - Form of Loss Estimate 

 

All households living in uninhabitable dwellings will seek alternative shelter. 
Many will 
stay with friends and relatives or in the family car. Some will stay in public 
shelters 
provided by the Red Cross or others, or rent motel or apartment lodging. This 
methodology estimates the number of displaced persons seeking public shelter. In 
addition, observations from past disasters show that approximately 80% of the 
pre-
disaster homeless will seek public shelter. Finally, data from Northridge 
indicates that 
approximately one-third of those in public shelters came from residences with 
little or no 
structural damage. Depending on the degree to which infrastructure damage is 
incorporated into #DH, that number of displaced persons could be increased by up 
to 
50% to account for "perceived" structural damage as well as lack of water and 
power. 

 

1For guidance, research has shown a much clearer relationship between the red-, 
yellow- and green- 
tagging assigned by building inspectors and perceived habitability than between 
damage state and 
perceived habitability (Perkins and Harrald, et al., unpublished). Red- and 
yellow-tagged multi-family 
dwellings are considered uninhabitable, while only red-tagged single family 
homes are considered 
uninhabitable. 




14.3.1 Input Requirements - Short-Term Shelter Needs 

 

The inputs required to estimate short-term housing needs are obtained from the 
displaced 
household calculations in Section 14.2 and from the default census data. As with 
the 
entire methodology, the census data can be modified with improved user 
information. 
The inputs listed below are the required census data inputs. 

 

 Number of people in census tract (POP) 

l Number of Households (#HH) 

 Percentage of households whose income is under $10,000 (HI1) 

 Percentage of households whose income is $10,001 to $15,000 (HI2) 

 Percentage of households whose income is $15,001 to $25,000 (HI3) 

 Percentage of households whose income is $25,001 to $35,000 (HI4) 

 Percentage of households whose income is over $35,000 (HI5) 

 Percentage of white households (HE1) 

 Percentage of black households (HE2) 

 Percentage of Hispanic households (HE3) 

 Percentage of Native American households (HE4) 

 Percentage of Asian households (HE5) 

 Percentage of households owned by householder (HO1) 

 Percentage of households rented by householder (HO2) 

 Percentage of population under 16 years old (HA1) 

 Percentage of population between 16 and 65 years old (HA2) 

 Percentage of population over 65 years old (HA3) 

 

14.3.2 Description of Methodology 

 

Those seeking public shelter can be estimated from experience in past disasters, 
including both hurricanes and earthquakes. Those seeking shelter typically have 
very 
low incomes, for these families have fewer options. In addition, they tend to 
have young 
children or are over 65. Finally, even given similar incomes, Hispanic 
populations from 
Central America and Mexico tend to be more concerned about reoccupying buildings 
than other groups. This tendency appears to be because of the fear of collapsed 
buildings 
instilled from past disastrous Latin American earthquakes. 

 

The number of people who require short-term housing can be calculated using the 
following relationship. 

#STP = #DH*POP#HH*HI*HE*HOHAijklijkll=13k12j15i=15a**...
...
.
..
.
..
SSSS== 
(14-2) 

 

where #STP - Number of people requiring short term housing 

 aijkl - is a constant defined by Equation 14-5 

 HIi 
- Percentage of population in the ith income class 




 HEj - Percentage of population in the jth ethnic class 

 HOk - Percentage of population in the kth ownership class 

 HAl - Percentage of population in the lth age class 

 POP - Population in census tract 

 

The value of the aijkl constant can be calculated using a combination of shelter 
category 
"weights" (Table 14.2) (which sum to 1.00) and assigning a relative modification 
factor 
(Table 14.3) for each subdivision of each category. In the methodology, default 
values 
for the variables for ownership and age are zero. 

 

()()()()aijklijkl = IW*IM+EW*EMOW*OMAWAM++* (14-3) 

 

Table 14.2: Shelter Category Weights 

 

Class 

Description 

Default 

IW 

Income Weighting Factor 

0.73 

EW 

Ethnic Weighting Factor 

0.27 

OW 

Ownership Weighting Factor 

0.00 

AW 

Age Weighting Factor 

0.00 



 




Table 14.3: Shelter Relative Modification Factors 

 

Class 

Description 

Default 

Income 

 

 

IM1 

Household Income < $10000 

0.62 

IM2 

$10000 < Household Income < $15000 

0.42 

IM3 

$15000 < Household Income < $25000 

0.29 

IM4 

$25000 < Household Income < $35000 

0.22 

IM5 

$35000 < Household Income 

0.13 

Ethnic 

 

 

EM1 

White 

0.24 

EM2 

Black 

0.48 

EM3 

Hispanic 

0.47 

EM4 

Asian 

0.26 

EM5 

Native American 

0.26 

Ownership 

 

 

OM1 

Own Dwelling Unit 

0.40 

OM2 

Rent Dwelling Unit 

0.40 

Age 

 

 

AM1 

Population Under 16 Years Old 

0.40 

AM2 

Population Between 16 and 65 Years Old 

0.40 

AM3 

Population Over 65 Years Old 

0.40 



 

Within each of these categories, the default relative modification factors given 
in Table 
14.3 can be used to calculate aijkl values (i.e., estimate the percentage of 
each category 
that will seek shelter) (with an average value for each category being 0.33 to 
0.45). 
These constants were originally developed by George Washington University under 
contract with the Red Cross and are based on "expert" opinion (Harrald, Fouladi, 
and Al-
Hajj, 1992). Recently collected data from over 200 victims of the Northridge 
earthquake 
disaster were analyzed and used in finalizing these constants (Harrald, et. al., 
1994). The 
modification factors provided in Table 14.3 are the mean of the George 
Washington 
University modification factors described in these two reports. Data for Native 
Americans are extremely scarce. Some information from Alaskan disasters 
indicates that 
the factor for those seeking shelter is similar for whites and Asians. 

 

14.3.3 User-defined Changes to Weight and Modification Factors 

 

In the methodology, weights can be added which account for age and ownership. As 
noted in Section 14.3.1, the required population distribution data are 
available. 
Remember that the weights must sum to 1.0. Young families tended to seek shelter 
in a 
larger proportion than other age groups in Northridge, in part because of lower 
per capita 
income. This result is consistent with data from hurricanes. In hurricanes, and 
Northridge, the elderly populations were also more likely to seek public shelter 
than 
average. Use special care if you want to add ownership to ensure that you are 
not double 
counting because the multi-family versus single-family issue has already been 
taken into 




account when estimating habitability (moderately damaged multi-family units are 
considered uninhabitable while moderately damaged single family units are 
considered 
habitable). 

 

Most recent earthquake disasters and hurricanes have occurred in warm weather 
areas. A 
major non-shelter location was the family car and tents in the family's 
backyard. Should 
an earthquake occur in a colder climate, more people would probably find these 
alternate 
shelters unacceptable. In the methodology, the user is able to adjust the 
factors 
specifying the percentage of those displaced that seek public shelter (i.e. the 
shelter 
relative modification factors in Table 14.3). When making modifications for 
weather, be 
careful not to double count. The adjustment for this module should only take 
into 
account the larger percentage of those displaced that will seek public shelter 
(versus the 
family car or camping in one's backyard.) 

 

14.3.4 Guidance for Estimates Using Advanced Data and Models 

 

The recent Loma Prieta and Northridge earthquakes in California have not been 
catastrophic events. Although many people have been displaced in these recent 
earthquake disasters, the size of the area or the spottiness of the damage have 
left people 
with more than minimal incomes the options of alternate shelters. 

 

As noted above, Hispanic populations from areas of Central America and Mexico 
tended 
to be more concerned about reoccupying buildings with insignificant or minor 
damage 
than other groups because of the fear of collapsed buildings instilled from past 
disastrous 
earthquakes in Latin America. Such tendencies will probably expand to all ethnic 
groups 
should a large number of casualties occur. 

 

 

14.4 Guidance for Estimating Long-Term Housing Recovery 

 

Although not calculated by the methodology, the damage to residential units 
(calculated 
in the general building stock module) can be combined with relationships between 
damage and restoration times (in the functional loss module) to estimate the 
need for 
longer-term replacement housing. Longer-term needs are accommodated by importing 
mobile homes, reductions in the vacancy rates, net emigration from an area, and 
eventual 
repair or reconstruction of the housing units. Because replacement of permanent 
housing 
is subject to normal market and financial forces, low-income housing is the last 
type of 
housing to be replaced. 

 

Based on experience in Loma Prieta (Perkins, 1992) and preliminary Northridge 
analyses 
(Perkins and Harrald, et. al., unpublished) housing recovery times span a wide 
range, and 
are typically far longer than might be estimated from typical planning rules of 
thumb, and 
longer than most commercial, industrial and institutional recovery. Housing 
recovery 
tends to be very dependent on settlement of insurance claims, federal disaster 
relief, the 
effectiveness of the generally smaller contractors who do much residential work, 
and the 
financial viability of the home or apartment owner, together with actions taken 
by state 




and local governments to expedite the process, and public support of 
reconstruction (such 
as the potential desire for historic preservation). The median recovery time 
figures for 
residential occupancies shown in Table 15.11 reflect these issues, but there 
will tend to 
be very wide variation about the mean. In particular, recovery times for non-
wood frame 
multi-family housing, especially low-income single room occupancy buildings, 
ought to 
be measured in years. 

 

 

14.5 References 

 

Applied Technology Council (ATC), 1991, "Procedures for Postearthquake Safety 
Evaluation of Buildings (ATC-20)". State of California Governor's Office of 
Emergency 
Services: Sacramento, CA, 144 pp. 

 

Cuny, F. C., 1975, "Strategies and Approaches for the Provision of Emergency 
Shelter 
and Post-Disaster Housing". Intertect: Dallas, Texas. 

 

Dunne, R. G., and Sonnenfeld, P., 1991, "Estimation of Homeless Caseload for 
Disaster 
Assistance due to an Earthquake". SCEPP publication of a 1980 draft document 
originally prepared for FEMA: Pasadena, CA. 

 

Harrald, J. R., Abchee, M., Alharthi, H., and Boukari, D., 1991, "The 
Development of a 
Methodology for American Red Cross Staffing of a Disaster Under the Federal 
Response 
Plan". George Washington Univ. Research Report. 

 

Harrald, J. R., Abchee, M., Cho, S., and Boukari, D., 1990, "Development of a 
Planning 
Methodology for Red Cross Catastrophic Earthquake Response". Geo. Wash. Univ. 
Research Report. 

 

Harrald, J. R., Abchee, M., Cho, S., and Boukari, D., 1990, "An Analysis of the 
American National Red Cross Staffing for the Hurricane Hugo and the Loma Prieta 
Earthquake Disaster Relief Operations". Geo. Washington Univ. Research Report. 

 

Harrald, J. R., Abchee, M., Cho, S., and Scholarios, T., 1990, "Analysis of 
Narrative 
Reports for Hurricane Hugo and the Loma Prieta Earthquake". Geo. Wash. Univ. 
Research Report. 

 

Harrald, J. R., Al-Hajj, S., Fouladi, B., and Jeong, D., 1994, "Estimating the 
Demand for 
Sheltering in Future Earthquakes". IEEE Transactions in Engineering Management, 
(Publication currently pending). 

 

Harrald, J. R., Fouladi, B., and Al-Hajj, S. F., 1992, "Estimates of Demand for 
Mass Care 
Services in Future Earthquakes Affecting the San Francisco Bay Region". Prepared 
by 
George Washington University for the American Red Cross Northern California 
Earthquake Relief and Preparedness Project (NCERPP), 41 pp. plus appendices. 

 




Perkins, J. B., 1992, "Estimates of Uninhabitable Dwelling Units in Future 
Earthquakes 
Affecting the San Francisco Bay Region". ABAG: Oakland, California, 89 pp. 

 

Perkins, J.B., Harrald, J.R., and others, unpublished, "Preliminary Results of 
an NSF-
Sponsored Project on Modeling Housing Damage in Earthquakes and Resulting Mass-
Care Needs", NSF Grant BCS-9441459. 

 

U.S. Bureau of the Census, May 1991, Standard Tape File 1 (STF-1A). 

 

U.S. Bureau of the Census, May 1992, Standard Tape File 3 (STF-3). 

 




Chapter 15 

Direct Economic Losses 

 

 

15.1 Introduction 

 

This chapter describes the conversion of damage state information, developed in 
previous 
modules, into estimates of dollar loss. In the past, loss estimation studies 
have generally 
limited the consideration of loss to estimates of the repair and replacement 
costs of the 
building stock. 

 

The methodology provides estimates of the structural and nonstructural repair 
costs 
caused by building damage and the associated loss of building contents and 
business 
inventory. Building damage can also cause additional losses by restricting the 
buildings 
ability to function properly. To account for this, business interruption and 
rental income 
losses are estimated. These losses are calculated from the building damage 
estimates by 
use of methods described later. The methodology highlighting the Direct Economic 
Loss 
component is shown in Flowchart 15.1. 

 

This expression of losses provides an estimate of the costs of building repair 
and 
replacement that is a frequently required output of a loss estimation study. The 
additional estimates of consequential losses give an indication of the immediate 
impact of 
such building damage on the community: the financial consequences to the 
community's 
businesses due to businesses interruption, the financial resources that will be 
needed to 
make good the damage, and an indication of job and housing losses. 

 

In strict economic terms, buildings, inventories, and public facilities 
represent capital 
investments that produce income, and the value of the building and inventory 
will be the 
capitalized value of the income produced by the investment that created the 
building or 
inventory. Hence, if we estimate the dollar value of the buildings damaged or 
destroyed, 
and add the income lost from the absence of the functioning facilities we may be 
overestimating the indirect economic loss (Chapter 16). However, for the 
assessment of 
direct economic loss, the losses can be estimated and evaluated independently. 




 

 

 

 

8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Flowchart 15.1: Direct Economic Losses Relationship to other Components of the 
Earthquake Loss Estimation Methodology 




Since a significant use for loss estimation studies is expected to be that of 
providing input 
into future benefit-cost studies used to evaluate mitigation strategies and 
budgets, the list 
of these consequential losses is similar to that developed for the FEMA benefit-
cost 
procedure described in FEMA publications 227 and 228, and 255 and 256. This 
procedure is, however, limited to conventional real-estate parameters similar to 
those 
used in evaluating the feasibility of a development project and does not attempt 
to 
evaluate the full range of socio/economic impacts that might follow specific 
mitigation 
strategies. 

 

Thus, for this loss estimation methodology, even though the derivation of these 
consequential losses represents a considerable expansion of the normal 
consideration of 
building damage/loss, this module is still limited in its consideration of 
economic loss to 
those losses that can be directly derived from building and infrastructure 
damage, and 
that lend themselves to ready conversion from damage to dollars. The real 
socio/economic picture is much more complex: economic impacts may have major 
societal effects on individuals or discrete population groups, and there may be 
social 
impacts that ultimately manifest themselves in economic consequences. In many 
cases 
the linkages are hard to trace with accuracy and the effects, while easy to 
discern, are 
difficult to quantify because definite systematic data is lacking. 

 

For example, the closing of the Oakland/San Francisco Bay Bridge for 30 days 
following 
the Loma Prieta earthquake of 1989 required approximately a quarter of a million 
daily 
users of the bridge to rearrange their travel patterns. Many individual 
commuters were 
forced to take a significantly longer and more costly route to their 
destinations. At the 
same time, other commuters changed to use of the BART rail system or bus 
services, 
which also altered their family expenditure patterns. More lengthy trips for 
business 
service travelers and material suppliers resulted in varying degrees of loss of 
productivity. Businesses directly related to normal operation of the bridge, 
such as gas 
stations and automobile repair shops on the approach routes to the bridge 
suffered losses. 

 

Repairs to the bridge represented a direct cost to the state budget. At the same 
time, the 
revenues from bridge tolls were nonexistent. However, some businesses gained 
from 
closure: some gas stations had improved business, and revenues to other bridges, 
the 
BART system, and bus companies increased. 

 

Increased commuting time resulted in loss of leisure and family time, and shifts 
in the 
customer and sales patterns of many small businesses resulted in an increase in 
normal 
business worries. 

 

If this 30-day loss of function had, instead, been a period of years (as is the 
case for 
elements of the Bay Area Freeway system) the socio/economic impacts would have 
been 
profound and long lasting throughout the Bay region. 

 

This example suggests the range of inter-related consequential impacts stemming 
from 
damage to a single structure: but these impacts were accompanied by a host of 
other 
impacts to individuals, businesses, institutions and communities that serve 
further to 




increase the complexity of post-earthquake effects. As understanding is gained 
of these 
interactions, and data collection becomes richer and more systematic, 
quantification of 
the consequential losses of earthquake damage can become broader and more 
accurate. 

 

Given the complexity of the problem and the present paucity of data, the 
methodology 
focuses on a few key issues that are of critical importance to government and 
the 
community, that can be quantified with reasonable assurance, and that provide a 
picture 
of the cost consequences of building and infrastructure damage that are 
understandable 
and would be of major concern to a municipality or region. In addition, 
application of 
the methodology will provide information that would be useful in a more detailed 
study 
of a particular economic or social sector, such as impact on housing stock or on 
a 
significant local industry. Finally, the structure of the methodology should be 
of 
assistance in future data gathering efforts. 

 

While the links between this module and the previous modules dealing with damage 
are 
very direct and the derivations are very transparent, the links between this 
module and 
that of Chapter 16, Indirect Economic Losses, are less so. While some of the 
estimates 
derived in this module, such as income loss by sector, building repair costs, 
and the loss 
of contents and inventories, may be imported directly into the Indirect Loss 
Module, 
some interpretation of the direct economic loss estimates would be necessary for 
a more 
detailed indirect economic loss study. It would be necessary, for example, to 
translate 
the repair and replacement times and costs derived in this module to monthly 
reconstruction investment estimates for use in a longer-term indirect loss 
estimate. 

 

 

15.1.1 Scope 

 

This chapter provides descriptions of the methodologies, the derivation of 
default data, 
and explanatory tables for a number of direct economic loss items, derived from 
estimates of building and lifeline damage. For building related items, methods 
for 
calculating the following dollar losses are provided: 

 

 Building Repair and Replacement Costs 

 Building Contents Losses 

 Building Inventory Losses 

 

To enable time dependent losses to be calculated, default values are provided 
for: 

 

 Building Recovery Time and Loss of Function (business interruption) time 

 

Procedures for calculating the following time dependent losses are provided: 

 Relocation Expenses 

 Loss of Proprietors' Income 

 Rental Income Losses 

 




For each lifeline, information is provided on replacement values and assumed 
numerical 
damage ratios corresponding to damage states. Chapters 7 and 8 provide 
restoration 
curves corresponding to lifeline damage states. With this information the cost 
of damage 
to lifelines and the elapsed time for their restoration could be calculated; 
however, no 
attempt is made to estimate losses due to interruption of customer service, 
alternative 
supply services, and the like. 

 

The following lifelines are covered: 

 

Transportation Systems 

 Highway Systems 

 Railroads 

 Light Rail Systems 

 Bus Systems 

 Port Systems 

 Ferry Services 

 Airport Systems 

Utility Systems: 

 Potable Water 

 Waste Water 

 Oil 

 Natural Gas 

 Electric Power 

 Communication 

 

Dollar losses due to fire and inundation are not explicitly addressed. However, 
the 
methodology enables the area of inundation to be estimated and related to the 
quantity of 
building stock in the affected census tracts. This, in turn, can be converted 
into a dollar 
value. 

 

In a similar manner, a value for building losses from fire can be estimated by 
relating the 
area of fire spread to the volume of construction and the construction cost. In 
both cases, 
the nature of damage states (which would vary from those of ground shaking 
damage) are 
not developed and estimates of dollar loss from these causes should be regarded 
as very 
broad estimates. In addition, since the concern is for earthquake-induced fire 
or 
inundation, the possibility of double counting of damage is present. More 
specific 
studies should be undertaken if the user believes that either fire or inundation 
might 
represent a serious risk. 

 

Since the methodology goes no further than indicating sources of hazardous 
materials, no 
methodology is provided for estimating losses due to the release of such 
materials. 
Again, if the possibility of serious losses from this cause is a matter of 
concern, specific 
studies should be undertaken. 

 

 

15.1.2 Form of Direct Economic Loss Estimates 




 

Direct economic loss estimates are provided in 1994 dollars. In some instances, 
as in the 
cost of building replacement, a procedure is provided for the conversion of 
default dollar 
values to those prevalent at the time of the loss estimation study. In other 
instances, user 
provided information, such as local rental costs, would be provided in current 
dollar 
values. 

 

15.1.3 Input Requirements 

 

In general, input data for direct economic losses consists of building damage 
estimates 
from the direct physical damage module. The damage estimates are in the form of 
probabilities of being in each damage state, for each structural type or 
occupancy class. 
The building classification system is as discussed in Chapter 3. Damage states 
are 
discussed in detail in Chapter 5. Damage state probabilities are provided from 
the direct 
physical damage module for both structural and non-structural damage. These 
damage 
state probabilities are then converted to monetary losses using inventory 
information and 
economic data. For Default Data Analysis values, the buildings are classified 
into three 
broad occupancy/use-related categories: residential, commercial/institutional, 
and 
industrial. These categories are used to determine the non-structural element 
make-up of 
the buildings and the nature and value of their contents. For User-Supplied Data 
and 
Advanced Data and Models Analyses, a 28-category occupancy classification (See 
Table 
15.1) is defined that provides for a more refined economic loss analysis. 
Building 
replacement cost data is provided for this classification level. 

 

The types of economic data that the user will be expected to supply include 
repair and 
replacement costs, contents value for different occupancies, annual gross sales 
by 
occupancy, relocation expenses and income by occupancy. While default values are 
provided for these data, the user may wish to provide more accurate local values 
or 
update default values to current dollars. 

 

Direct economic losses for transportation and lifeline systems are limited to 
the cost of 
repairing damage to the lifeline system. Default values are provided for 
replacement 
values of lifeline components as a guide. It is expected that in a User-Supplied 
Data 
Analysis, the user will input replacement values based on knowledge of lifeline 
values in 
the region. 

 

 




 

Table 15.1: Building Occupancy Classes 

 

No. 

Label 

Occupancy Class 

Description 

 

 

Residential 

 

1 

RES1 

Single Family Dwelling 

Detached House 

2 

RES2 

Mobile Home 

Mobile Home 

3-8 

RES3a-f 

Multi Family Dwelling 

Apartment/Condominium 

9 

RES4 

Temporary Lodging 

Hotel/Motel 

10 

RES5 

Institutional Dormitory 

Group Housing (military, college), 
Jails 

11 

RES6 

Nursing Home 

 

 

 

Commercial 

 

12 

COM1 

Retail Trade 

Store 

13 

COM2 

Wholesale Trade 

Warehouse 

14 

COM3 

Personal and Repair Services 

Service Station/Shop 

15 

COM4 

Professional/Technical Services 

Offices 

16 

COM5 

Banks/Financial Institutions 

 

17 

COM6 

Hospital 

 

18 

COM7 

Medical Office/Clinic 

Offices 

19 

COM8 

Entertainment & Recreation 

Restaurants/Bars 

20 

COM9 

Theaters 

Theaters 

21 

COM10 

Parking 

Garages 

 

 

Industrial 

 

22 

IND1 

Heavy 

Factory 

23 

IND2 

Light 

Factory 

24 

IND3 

Food/Drugs/Chemicals 

Factory 

25 

IND4 

Metals/Minerals Processing 

Factory 

26 

IND5 

High Technology 

Factory 

27 

IND6 

Construction 

Office 

 

 

Agriculture 

 

28 

AGR1 

Agriculture 

 

 

 

Religion/Non-Profit 

 

29 

REL1 

Church 

 

 

 

Government 

 

30 

GOV1 

General Services 

Office 

31 

GOV2 

Emergency Response 

Police/Fire Station 

 

 

Education 

 

32 

EDU1 

Schools 

 

33 

EDU2 

Colleges/Universities 

Does not include group housing 



 

15.2 Description of Methodology: Buildings 

 

This section describes the estimation of building-related direct economic 
losses. 

 

15.2.1 Building Repair and Replacement Costs 

 

To establish dollar loss estimates, the damage state probabilities must be 
converted to 
dollar loss equivalents. Losses will be due to both structural and non-
structural damage. 
For a given occupancy and damage state, building repair and replacement costs 
are 




estimated as the product of the floor area of each building type within the 
given 
occupancy, the probability of the building type being in the given damage state, 
and 
repair costs of the building type per square foot for the given damage state, 
summed over 
all building types within the occupancy. 

 

It can be argued that the true cost of buildings damaged or destroyed is their 
loss of 
market value, reflecting the age of the building, depreciation, and the like. 
Replacement 
value is a frequently requested output of a loss estimation study, because it 
gives an 
immediately understandable picture of the community building losses, and 
disaster 
assistance is currently granted on the basis of replacement value. In fact, 
market value is 
by no means constant in relation to replacement value. For example, typical 
estimates of 
market value include the value of the lot: in locations of high land cost, 
market value may 
greatly exceed replacement value (which excludes lot value). Moreover, building 
age 
does not necessarily result in a linear loss of market value: after a certain 
age some 
buildings begin to acquire additional value by virtue of architectural style and 
craftsmanship and true replacement cost might greatly exceed market value. 

 

These issues may need to be considered in a detailed evaluation of the direct 
economic 
losses where particular building inventories or economic aspects of the damage 
are being 
evaluated. Full discussion of these and other related issues may be found in 
Howe and 
Cochrane, 1993. 

 

For structural damage, losses are calculated as follows: 

 

 CSds,i = BRCi*PMBTSTRds,i*RCSds,i (15-1) S=
331i

 

 CSi = CSds,i (15-2) 
ds=S25

 

where: 

CSds,i cost of structural damage (repair and replacement costs) for 
damage state ds and occupancy i 

BRCj building replacement cost of occupancy i as described in Chapter 
3 

PMBTSTRds,j probability of occupancy i being in structural damage state ds, 
see Chapter 5 

RCSds,i structural repair and replacement ratio for occupancy i in damage 
state ds, Tables 15.2 

 

The structural repair cost ratio for structural damage for each damage state and 
occupancyare shown in Table 15.2. Note that damage state "none" (ds = 1) does 
not 
contribute to the calculation of the cost of structural damage and thus the 
summation in 
Equation 15-2 is from ds = 2 to ds = 5. 

 




A similar calculation is performed for non-structural damage. Non-structural 
damage is 
broken down into acceleration sensitive damage (damage to ceilings, equipment 
that is an 
integral part of the facility such as mechanical and electrical equipment, 
piping and 
elevators) and drift sensitive damage (partitions, exterior walls, ornamentation 
and glass). 
Non-structural damage does not include the damage to contents such as furniture 
and 
computers that is accounted for in Section 15.2.2. Non-structural damage costs 
are 
calculated as follows: 

 

CNSAds,i = BRCj *PONSAds,i*RCAds,i, (15-3) 

 

CNSAi = CNSAds,i (15-4) 
ds=S25

 

CNSDds,i = BRCi*PONSDds,i*RCDds,i (15-5) 

 

CNSDi = CNSDds,i (15-6) 
ds=S25

 

where: 

CNSAds,i cost of acceleration-sensitive non-structural damage (repair and 
replacement costs) for damage state ds and occupancy i 

CNSAi cost of acceleration-sensitive non-structural damage (repair and 
replacement costs) for occupancy i 

CNSDds,i cost of drift-sensitive non-structural damage (repair and 
replacement costs) for damage state ds and occupancy i 

CNSDi cost of drift-sensitive non-structural damage (repair and 
replacement costs) for occupancy i 

BRCj Building replacement cost of occupancy i as described in Chapter 
3 

PONSAds,i probability of occupancy i being in non-structural acceleration 
sensitive damage state ds, see Chapter 5 

PONSDds,i probability of occupancy i being in non-structural drift sensitive 
damage state ds, see Chapter 5 

RCAds,i acceleration sensitive non-structural repair and replacement ratio 
for occupancy i in damage state ds (Table 15-3) 

RCDds,i drift sensitive non-structural repair and replacement ratio for 
occupancy i in damage state ds (Table 15-4) 

 

The repair cost ratios for non-structural damage for each damage state are shown 
in 
Tables 15.3 and 15.4 for acceleration and drift sensitive non-structural 
components, 
respectively. 

 

To determine the total cost of non-structural damage for occupancy class i 
(CNSi), 
Equations 15-4 and 15-6 must be summed. 

 




 CNSi = CNSAi + CNSDi (15-7) 

 

The total cost of building damage (CBDi) for occupancy class i is the sum of the 
structural and non-structural damage. 

 

 CBDi = CSi + CNSi (15-8) 

 

Finally, to determine the total cost of building damage (CBD), Equation 15-8 
must be 
summed over all occupancy classes. 

 CBD = CBDi (15-9) 
iS

 

15.2.1.1 Default Values for Building Repair Costs 

 

Table 15.2 show the default values for the structural repair cost ratios related 
to the 33 
occupancy classifications. The relative percentage of total building cost 
allocated to 
structural and non-structural components is derived from the Means component 
breakdowns for each occupancy class. 

 

Tables 15.3 and 15.4 show the default values for the repair cost ratios of the 
acceleration 
sensitive and drift sensitive components. Acceleration sensitive non-structural 
components include hung ceilings, mechanical and electrical equipment, and 
elevators. 
Drift sensitive components include partitions, exterior wall panels, and 
glazing. The 
relative percentages of drift and acceleration sensitive components are based on 
the 
component breakdown provided in Means. 

 

The damage ratios expressed as a percentage of the building replacement value. 
These 
values are consistent with and in the range of the damage definitions and 
corresponding 
damage ratios presented in ATC-13 Earthquake Damage Evaluation Data for 
California. 
For specific building inventories, at an Advanced Data and Models Analysis, more 
precise estimates of structural/non-structural quantity and cost relationships 
can be 
obtained by the user. 




Table 15.2: Structural Repair Cost Ratios 

(in % of building replacement cost) 

 

No. 

Label 

Occupancy Class 

Structural Damage State 

 

 

 

Slight 

Moderate 

Extensive 

Complete 

 

 

Residential 

 

 

 

 

1 

RES1 

Single Family Dwelling 

0.5 

2.3 

11.7 

23.4 

2 

RES2 

Mobile Home 

0.4 

2.4 

7.3 

24.4 

3-8 

RES3a-
f 

Multi Family Dwelling 

0.3 

1.4 

6.9 

13.8 

9 

RES4 

Temporary Lodging 

0.2 

1.4 

6.8 

13.6 

10 

RES5 

Institutional Dormitory 

0.4 

1.9 

9.4 

18.8 

11 

RES6 

Nursing Home 

0.4 

1.8 

9.2 

18.4 

 

 

Commercial 

 

 

 

 

12 

COM1 

Retail Trade 

0.6 

2.9 

14.7 

29.4 

13 

COM2 

Wholesale Trade 

0.6 

3.2 

16.2 

32.4 

14 

COM3 

Personal and Repair Services 

0.3 

1.6 

8.1 

16.2 

15 

COM4 

Professional/Technical/ 

Business Services 

0.4 

1.9 

9.6 

19.2 

16 

COM5 

Banks/Financial Institutions 

0.3 

1.4 

6.9 

13.8 

17 

COM6 

Hospital 

0.2 

1.4 

7.0 

14.0 

18 

COM7 

Medical Office/Clinic 

0.3 

1.4 

7.2 

14.4 

19 

COM8 

Entertainment & Recreation 

0.2 

1.0 

5.0 

10.0 

20 

COM9 

Theaters 

0.3 

1.2 

6.1 

12.2 

21 

COM10 

Parking 

1.3 

6.1 

30.4 

60.9 

 

 

Industrial 

 

 

 

 

22 

IND1 

Heavy 

0.4 

1.6 

7.8 

15.7 

23 

IND2 

Light 

0.4 

1.6 

7.8 

15.7 

24 

IND3 

Food/Drugs/Chemicals 

0.4 

1.6 

7.8 

15.7 

25 

IND4 

Metals/Minerals Processing 

0.4 

1.6 

7.8 

15.7 

26 

IND5 

High Technology 

0.4 

1.6 

7.8 

15.7 

27 

IND6 

Construction 

0.4 

1.6 

7.8 

15.7 

 

 

Agriculture 

 

 

 

 

28 

AGR1 

Agriculture 

0.8 

4.6 

23.1 

46.2 

 

 

Religion/Non-Profit 

 

 

 

 

29 

REL1 

 Church/Membership 
Organization 

0.3 

2.0 

9.9 

19.8 

 

 

Government 

 

 

 

 

30 

GOV1 

General Services 

0.3 

1.8 

9.0 

17.9 

31 

GOV2 

Emergency Response 

0.3 

1.5 

7.7 

15.3 

 

 

Education 

 

 

 

 

32 

EDU1 

 Schools/Libraries 

0.4 

1.9 

9.5 

18.9 

33 

EDU2 

Colleges/Universities 

0.2 

1.1 

5.5 

11.0 



 




 

Table 15.3: Acceleration Sensitive Non-structural Repair Cost Ratios 

(in % of building replacement cost) 

 

 

No. 

 

Label 

 

Occupancy Class 

Acceleration Sensitive 

Non-structural Damage State 

 

 

 

Slight 

Moderate 

Extensive 

Complete 

 

 

Residential 

 

 

 

 

1 

RES1 

Single Family Dwelling 

0.5 

2.7 

8.0 

26.6 

2 

RES2 

Mobile Home 

0.8 

3.8 

11.3 

37.8 

3-8 

RES3a-
f 

Multi Family Dwelling 

0.8 

4.3 

13.1 

43.7 

9 

RES4 

Temporary Lodging 

0.9 

4.3 

13.0 

43.2 

10 

RES5 

Institutional Dormitory 

0.8 

4.1 

12.4 

41.2 

11 

RES6 

Nursing Home 

0.8 

4.1 

12.2 

40.8 

 

 

Commercial 

 

 

 

 

12 

COM1 

Retail Trade 

0.8 

4.4 

12.9 

43.1 

13 

COM2 

Wholesale Trade 

0.8 

4.2 

12.4 

41.1 

14 

COM3 

Personal and Repair Services 

1.0 

5 

15 

50.0 

15 

COM4 

Professional/Technical/ 

Business Services 

0.9 

4.8 

14.4 

47.9 

16 

COM5 

Banks/Financial Institutions 

1.0 

5.2 

15.5 

51.7 

17 

COM6 

Hospital 

1.0 

5.1 

15.4 

51.3 

18 

COM7 

Medical Office/Clinic 

1.0 

5.2 

15.3 

51.2 

19 

COM8 

Entertainment & Recreation 

1.1 

5.4 

16.3 

54.4 

20 

COM9 

Theaters 

1.0 

5.3 

15.8 

52.7 

21 

COM10 

Parking 

0.3 

2.2 

6.5 

21.7 

 

 

Industrial 

 

 

 

 

22 

IND1 

Heavy 

1.4 

7.2 

21.8 

72.5 

23 

IND2 

Light 

1.4 

7.2 

21.8 

72.5 

24 

IND3 

Food/Drugs/Chemicals 

1.4 

7.2 

21.8 

72.5 

25 

IND4 

Metals/Minerals Processing 

1.4 

7.2 

21.8 

72.5 

26 

IND5 

High Technology 

1.4 

7.2 

21.8 

72.5 

27 

IND6 

Construction 

1.4 

7.2 

21.8 

72.5 

 

 

Agriculture 

 

 

 

 

28 

AGR1 

Agriculture 

0.8 

4.6 

13.8 

46.1 

 

 

Religion/Non-Profit 

 

 

 

 

29 

REL1 

 Church/Membership 
Organization 

0.9 

4.7 

14.3 

47.6 

 

 

Government 

 

 

 

 

30 

GOV1 

General Services 

1.0 

4.9 

14.8 

49.3 

31 

GOV2 

Emergency Response 

1.0 

5.1 

15.1 

50.5 

 

 

Education 

 

 

 

 

32 

EDU1 

 Schools/Libraries 

0.7 

3.2 

9.7 

32.4 

33 

EDU2 

Colleges/Universities 

0.6 

2.9 

8.7 

29.0 



 




Table 15.4: Drift Sensitive Non-structural Repair Costs 

(in % of building replacement cost) 

 

 

No. 

 

Label 

 

Occupancy Class 

Drift Sensitive Non-structural 

Damage State 

 

 

 

Slight 

Moderate 

Extensive 

Complete 

 

 

Residential 

 

 

 

 

1 

RES1 

Single Family Dwelling 

1.0 

5.0 

25.0 

50.0 

2 

RES2 

Mobile Home 

0.8 

3.8 

18.9 

37.8 

3-8 

RES3a-f 

Multi Family Dwelling 

0.9 

4.3 

21.3 

42.5 

9 

RES4 

Temporary Lodging 

0.9 

4.3 

21.6 

43.2 

10 

RES5 

Institutional Dormitory 

0.8 

4.0 

20.0 

40.0 

11 

RES6 

Nursing Home 

0.8 

4.1 

20.4 

40.8 

 

 

Commercial 

 

 

 

 

12 

COM1 

Retail Trade 

0.6 

2.7 

13.8 

27.5 

13 

COM2 

Wholesale Trade 

0.6 

2.6 

13.2 

26.5 

14 

COM3 

Personal and Repair Services 

0.7 

3.4 

16.9 

33.8 

15 

COM4 

Professional/Technical/ 

Business Services 

0.7 

3.3 

16.4 

32.9 

16 

COM5 

Banks/Financial Institutions 

0.7 

3.4 

17.2 

34.5 

17 

COM6 

Hospital 

0.8 

3.5 

17.4 

34.7 

18 

COM7 

Medical Office/Clinic 

0.7 

3.4 

17.2 

34.4 

19 

COM8 

Entertainment & Recreation 

0.7 

3.6 

17.8 

35.6 

20 

COM9 

Theaters 

0.7 

3.5 

17.6 

35.1 

21 

COM10 

Parking 

0.4 

1.7 

8.7 

17.4 

 

 

Industrial 

 

 

 

 

22 

IND1 

Heavy 

0.2 

1.2 

5.9 

11.8 

23 

IND2 

Light 

0.2 

1.2 

5.9 

11.8 

24 

IND3 

Food/Drugs/Chemicals 

0.2 

1.2 

5.9 

11.8 

25 

IND4 

Metals/Minerals Processing 

0.2 

1.2 

5.9 

11.8 

26 

IND5 

High Technology 

0.2 

1.2 

5.9 

11.8 

27 

IND6 

Construction 

0.2 

1.2 

5.9 

11.8 

 

 

Agriculture 

 

 

 

 

28 

AGR1 

Agriculture 

0.0 

0.8 

3.8 

7.7 

 

 

Religion/Non-Profit 

 

 

 

 

29 

REL1 

 Church/Membership 
Organization 

0.8 

3.3 

16.3 

32.6 

 

 

Government 

 

 

 

 

30 

GOV1 

General Services 

0.7 

3.3 

16.4 

32.8 

31 

GOV2 

Emergency Response 

0.7 

3.4 

17.1 

34.2 

 

 

Education 

 

 

 

 

32 

EDU1 

 Schools/Libraries 

0.9 

4.9 

24.3 

48.7 

33 

EDU2 

Colleges/Universities 

1.2 

6.0 

30.0 

60.0 



 

 

Note that the values in the last column of Tables 15.2, 15.3 and 15.4 must sum 
to 100 
since the complete damage state implies that the structure must be replaced. The 
replacement value of the structure is the sum of the structural and non-
structural 
components. 

 




 

15.2.1.2 Procedure for Updating Building Replacement Cost Estimates 

 

The building replacement cost estimates were developed based on 2003 estimates. 
The 
historical cost indices provided in the Means publication can also be used to 
adjust costs 
(generally upwards) to the year in which the loss estimate is being implemented. 
(It will 
be necessary for the user to obtain access to the Means publication for the year 
of 
implementation.) 

 

 

15.2.2 Building Contents Losses 

 

Building contents are defined as furniture, equipment that is not integral with 
the 
structure, computers and other supplies. Contents do not include inventory or 
non-
structural components (see Section 15.2.1) such as lighting, ceilings, 
mechanical and 
electrical equipment and other fixtures. It is assumed that most contents 
damage, such as 
overturned cabinets and equipment or equipment sliding off tables and counters, 
is a 
function of building accelerations. Therefore, acceleration sensitive non-
structural 
damage is considered to be a good indicator of contents damage. That is, if 
there is no 
acceleration sensitive non-structural damage, it is unlikely that there will be 
contents 
damage. The cost of contents damage is calculated as follows: 

 

 CCDi = CRVi * CDds,i* PMBTNSAds,j (15-10) 
ds=S25

 

 

where: 

CCDi cost of contents damage for occupancy i 

CRVi contents replacement value for occupancy i 

CDds,i contents damage ratio for occupancy i in damage state ds (from 
Table 15.5) 

PMBTNSAds,j the probability of occupancy i being in non-structural acceleration 
sensitive damage state ds, see Chapter 5 

 

 

The contents damage ratios in Table 15.5 assume that at complete damage state 
some 
percentage of contents, set at 50%, can be retrieved. At the present time, 
contents 
damage percentages in Table 15. are the same for all occupancies. 

 




Table 15.5: Contents Damage Ratios 

(in % of contents replacement cost) 

 

No. 

Label 

Occupancy Class 

Acceleration Sensitive 

Non-structural Damage State 

 

 

 

Slight 

Moderate 

Extensive 

Complete* 

 

 

Residential 

 

 

 

 

1 

RES1 

Single Family Dwelling 

1 

5 

25 

50 

2 

RES2 

Mobile Home 

1 

5 

25 

50 

3-8 

RES3a-
f 

Multi Family Dwelling 

1 

5 

25 

50 

9 

RES4 

Temporary Lodging 

1 

5 

25 

50 

10 

RES5 

Institutional Dormitory 

1 

5 

25 

50 

11 

RES6 

Nursing Home 

1 

5 

25 

50 

 

 

Commercial 

 

 

 

 

12 

COM1 

Retail Trade 

1 

5 

25 

50 

13 

COM2 

Wholesale Trade 

1 

5 

25 

50 

14 

COM3 

Personal and Repair Services 

1 

5 

25 

50 

15 

COM4 

Professional/Technical/ 

Business Services 

1 

5 

25 

50 

16 

COM5 

Banks/Financial Institutions 

1 

5 

25 

50 

17 

COM6 

Hospital 

1 

5 

25 

50 

18 

COM7 

Medical Office/Clinic 

1 

5 

25 

50 

19 

COM8 

Entertainment & Recreation 

1 

5 

25 

50 

20 

COM9 

Theaters 

1 

5 

25 

50 

21 

COM10 

Parking 

1 

5 

25 

50 

 

 

Industrial 

 

 

 

 

22 

IND1 

Heavy 

1 

5 

25 

50 

23 

IND2 

Light 

1 

5 

25 

50 

24 

IND3 

Food/Drugs/Chemicals 

1 

5 

25 

50 

25 

IND4 

Metals/Minerals Processing 

1 

5 

25 

50 

26 

IND5 

High Technology 

1 

5 

25 

50 

27 

IND6 

Construction 

1 

5 

25 

50 

 

 

Agriculture 

 

 

 

 

28 

AGR1 

Agriculture 

1 

5 

25 

50 

 

 

Religion/Non-Profit 

 

 

 

 

29 

REL1 

 Church/Membership 
Organization 

1 

5 

25 

50 

 

 

Government 

 

 

 

 

30 

GOV1 

General Services 

1 

5 

25 

50 

31 

GOV2 

Emergency Response 

1 

5 

25 

50 

 

 

Education 

 

 

 

 

32 

EDU1 

 Schools/Libraries 

1 

5 

25 

50 

33 

EDU2 

Colleges/Universities 

1 

5 

25 

50 



*At complete damage state, it is assumed that some salvage of contents will take 
place. 

 




15.2.3 Business Inventory Losses 

 

Business inventories vary considerably with occupancy. For example, the value of 
inventory for a high tech manufacturing facility would be very different from 
that of a 
retail store. Thus, it is assumed for this model that business inventory for 
each 
occupancy class is based on annual sales. Since losses to business inventory 
most likely 
occur from stacks of inventory falling over, objects falling off shelves, or 
from water 
damage when piping breaks, it is assumed, as it was with building contents, that 
acceleration sensitive non-structural damage is a good indicator of losses to 
business 
inventory. Business inventory losses then become the product of the total 
inventory 
value (floor area times the percent of gross sales or production per square 
foot) of 
buildings of a given occupancy in a given acceleration-sensitive damage state, 
the 
percent loss to the inventory and the probability of given damage states. The 
business 
inventory losses are given by the following expressions. 

 

 INVi = FAi*SALESi*BIi*PONSAds,i*INVDds,i (15-11) 
ds=S25

 

 INV = INV7 + INV8 + INVi (15-12) 
i=S1723

where: 

INVi value of inventory losses for occupancy i 

INV total value of inventory losses 

FAi floor area of occupancy group i (in square feet) 

SALESi annual gross sales or production (per square foot) for occupancy i 
(see Table 15.6) 

BIi business inventory as a percentage of annual gross sales for 
occupancy i (i = 7, 8, 17-23, see Table 15.7) 

PONSAds,i probability of occupancy i being in non-structural acceleration 
sensitive damage state ds, see Chapter 5 

INVDds,i percent inventory damage for occupancy i in damage state ds (from 
Table 15.8) 

 

Statistics representing national or state economic sectors may not adequately 
reflect the 
regional situation. Therefore, estimates of annual gross sales or the value of 
production 
for any one of the 28 economic sectors can vary widely depending on the type of 
firms 
that are located in the region. It is important to review and adjust any data to 
insure that 
the regional economy is correctly portrayed. Annual sales or production per 
square foot 
of building can be estimated by dividing the output-employment ratio (sector 
output/sector employment) by the average floor space occupied by employee. 
Current 
data to derive the regional (county or standard metropolitan statistical area), 
sector 
output-employment ratio is usually available from either the state or the U.S. 
Department 
of Commerce's Bureau of Economic Analysis [(202) 482-1986]. The annual sales per 
square foot for the agriculture category are for greenhouses. The average sector 
floor 
space occupied per employee is based on values found in ATC-13, table 4.7 (pages 
94-




97). Judgment was used in estimating of business inventory as a percent of gross 
annual 
sales. 

 

Table 15.6: Annual Gross Sales or Production (Dollars per Square Foot) 

No. 

Label 

Occupancy Class 

1990 Output/ 
Employment*

Sq. ft. floor 
Space/Employee** 

Annual Sales 
($/ft2) 

 

 

Commercial 

 

 

 

7 

COM1 

Retail Trade 

$24,979 

825 

41 

8 

COM2 

Wholesale Trade 

$38,338 

900 

59 

 

 

Industrial 

 

 

 

17 

IND1 

Heavy 

$220,212 

550 

551 

18 

IND2 

Light 

$74,930 

590 

175 

19 

IND3 

Food/Drugs/Chemicals 

$210,943 

540 

538 

20 

IND4 

Metals/Minerals Processing 

$268,385 

730 

507 

21 

IND5 

High Technology 

$73,517 

300 

337 

22 

IND6 

Construction 

$107,739 

250 

593 

 

 

Agriculture 

 

 

 

23 

AGR1 

Agriculture 

$20,771 

250 

114 



* Typical sector values. 

** ATC-13, Table 4.7, pages 94-97 (ATC, 1985). 

 

 

Table 15.7: Business Inventory (% of Gross Annual Sales) 

No. 

Label 

Occupancy Class 

Business Inventory (%) 

 

 

Commercial 

 

7 

COM1 

Retail Trade 

13 

8 

COM2 

Wholesale Trade 

10 

 

 

Industrial 

 

17 

IND1 

Heavy 

5 

18 

IND2 

Light 

4 

19 

IND3 

Food/Drugs/Chemicals 

5 

20 

IND4 

Metals/Minerals Processing 

3 

21 

IND5 

High Technology 

4 

22 

IND6 

Construction 

2 

 

 

Agriculture 

 

23 

AGR1 

Agriculture 

8 



 




Table 15.8: Percent Business Inventory Damage 

 

No. 

 

Label 

 

Occupancy Class 

Acceleration Sensitive 

Non-structural Damage State 

 

 

 

Slight 

Moderate 

Extensive 

Complete* 

 

 

Commercial 

 

 

 

 

7 

COM1 

Retail Trade 

1 

5 

25 

50 

8 

COM2 

Wholesale Trade 

1 

5 

25 

50 

 

 

Industrial 

 

 

 

 

17 

IND1 

Heavy 

1 

5 

25 

50 

18 

IND2 

Light 

1 

5 

25 

50 

19 

IND3 

Food/Drugs/Chemicals 

1 

5 

25 

50 

20 

IND4 

Metals/Minerals Processing 

1 

5 

25 

50 

21 

IND5 

High Technology 

1 

5 

25 

50 

22 

IND6 

Construction 

1 

5 

25 

50 

 

 

Agriculture 

 

 

 

 

23 

AGR1 

Agriculture 

1 

5 

25 

50 



*At complete damage state, it is assumed that some salvage of inventory will 
take place. 

 

15.2.4 Building Repair Time/Loss of Function 

 

The damage state descriptions provide a basis for establishing loss of function 
and repair 
time. A distinction should be made between loss of function and repair time. 
Here loss 
of function is the time that a facility is not capable of conducting business. 
This, in 
general, will be shorter than repair time because business will rent alternative 
space while 
repairs and construction are being completed. The time to repair a damaged 
building can 
be divided into two parts: construction and clean-up time, and time to obtain 
financing, 
permits and complete design. For the lower damage states, the construction time 
will be 
close to the real repair time. At the higher damage levels, a number of 
additional tasks 
must be undertaken that typically will considerably increase the actual repair 
time. These 
tasks, which may vary considerably in scope and time between individual 
projects, 
include: 

 

 Decision-making (related to business of institutional constraints, plans, 
financial 
status, etc.) 

 Negotiation with FEMA (for public and non-profit), SBA etc. 

 Negotiation with insurance company, if insured 

 Obtain financing 

 Contract negotiation with design firms(s) 

 Detailed inspections and recommendations 

 Preparation of contract documents 

 Obtain building and other permits 

 Bid/negotiate construction contract 

 Start-up and occupancy activities after construction completion 

 

Building repair and clean-up times are presented in Table 15.9. These times 
represent 
estimates of the median time for actual cleanup and repair, or construction. 
These 
estimates are extended in Table 15.10 to account for delays in decision-making, 




financing, inspection etc., as outlined above, and represent estimates of the 
median time 
for recovery of building functions. 

 

Table 15.9: Building Cleanup and Repair Time (Construction) 

(Time in Days) 

 

 

 

Construction Time 

No. 

Label 

Occupancy Class 

Structural Damage State 

 

 

 

None

Slight 

Moderate

Extensive 

Complete

 

 

Residential 

 

 

 

 

 

1 

RES1 

Single Family Dwelling 

0 

2 

30 

90 

180 

2 

RES2 

Mobile Home 

0 

2 

10 

30 

60 

3-8 

RES3a-f 

Multi Family Dwelling 

0 

5 

30 

120 

240 

9 

RES4 

Temporary Lodging 

0 

5 

30 

120 

240 

10 

RES5 

Institutional Dormitory 

0 

5 

30 

120 

240 

11 

RES6 

Nursing Home 

0 

5 

30 

120 

240 

 

 

Commercial 

 

 

 

 

 

12 

COM1 

Retail Trade 

0 

5 

30 

90 

180 

13 

COM2 

Wholesale Trade 

0 

5 

30 

90 

180 

14 

COM3 

Personal and Repair Services 

0 

5 

30 

90 

180 

15 

COM4 

Professional/Technical/ 

Business Services 

0 

5 

30 

120 

240 

16 

COM5 

Banks/Financial Institutions 

0 

5 

30 

90 

180 

17 

COM6 

Hospital 

0 

10 

45 

180 

360 

18 

COM7 

Medical Office/Clinic 

0 

10 

45 

180 

240 

19 

COM8 

Entertainment & Recreation 

0 

5 

30 

90 

180 

20 

COM9 

Theaters 

0 

5 

30 

120 

240 

21 

COM10 

Parking 

0 

2 

20 

80 

160 

 

 

Industrial 

 

 

 

 

 

22 

IND1 

Heavy 

0 

10 

30 

120 

240 

23 

IND2 

Light 

0 

10 

30 

120 

240 

24 

IND3 

Food/Drugs/Chemicals 

0 

10 

30 

120 

240 

25 

IND4 

Metals/Minerals Processing 

0 

10 

30 

120 

240 

26 

IND5 

High Technology 

0 

20 

45 

180 

360 

27 

IND6 

Construction 

0 

5 

20 

80 

160 

 

 

Agriculture 

 

 

 

 

 

28 

AGR1 

Agriculture 

0 

2 

10 

30 

60 

 

 

Religion/Non-Profit 

 

 

 

 

 

29 

REL1 

Church/Membership 
Organization 

0 

10 

30 

120 

240 

 

 

Government 

 

 

 

 

 

30 

GOV1 

General Services 

0 

10 

30 

120 

240 

31 

GOV2 

Emergency Response 

0 

5 

20 

90 

180 

 

 

Education 

 

 

 

 

 

32 

EDU1 

 Schools/Libraries 

0 

10 

30 

120 

240 

33 

EDU2 

Colleges/Universities 

0 

10 

45 

180 

360 



 




Table 15.10: Building Recovery Time 

 (Time in Days) 

 

 

 

Recovery Time 

No. 

Label 

Occupancy Class 

Structural Damage State 

 

 

 

None

Slight 

Moderate

Extensive 

Complete

 

 

Residential 

 

 

 

 

 

1 

RES1 

Single Family Dwelling 

0 

5 

120 

360 

720 

2 

RES2 

Mobile Home 

0 

5 

20 

120 

240 

3-8 

RES3a-f 

Multi Family Dwelling 

0 

10 

120 

480 

960 

9 

RES4 

Temporary Lodging 

0 

10 

90 

360 

480 

10 

RES5 

Institutional Dormitory 

0 

10 

90 

360 

480 

11 

RES6 

Nursing Home 

0 

10 

120 

480 

960 

 

 

Commercial 

 

 

 

 

 

12 

COM1 

Retail Trade 

0 

10 

90 

270 

360 

13 

COM2 

Wholesale Trade 

0 

10 

90 

270 

360 

14 

COM3 

Personal and Repair Services 

0 

10 

90 

270 

360 

15 

COM4 

Professional/Technical/ 

Business Services 

0 

20 

90 

360 

480 

16 

COM5 

Banks/Financial Institutions 

0 

20 

90 

180 

360 

17 

COM6 

Hospital 

0 

20 

135 

540 

720 

18 

COM7 

Medical Office/Clinic 

0 

20 

135 

270 

540 

19 

COM8 

Entertainment & Recreation 

0 

20 

90 

180 

360 

20 

COM9 

Theaters 

0 

20 

90 

180 

360 

21 

COM10 

Parking 

0 

5 

60 

180 

360 

 

 

Industrial 

 

 

 

 

 

22 

IND1 

Heavy 

0 

10 

90 

240 

360 

23 

IND2 

Light 

0 

10 

90 

240 

360 

24 

IND3 

Food/Drugs/Chemicals 

0 

10 

90 

240 

360 

25 

IND4 

Metals/Minerals Processing 

0 

10 

90 

240 

360 

26 

IND5 

High Technology 

0 

20 

135 

360 

540 

27 

IND6 

Construction 

0 

10 

60 

160 

320 

 

 

Agriculture 

 

 

 

 

 

28 

AGR1 

Agriculture 

0 

2 

20 

60 

120 

 

 

Religion/Non-Profit 

 

 

 

 

 

29 

REL1 

Church/Membership 
Organization 

0 

5 

120 

480 

960 

 

 

Government 

 

 

 

 

 

30 

GOV1 

General Services 

0 

10 

90 

360 

480 

31 

GOV2 

Emergency Response 

0 

10 

60 

270 

360 

 

 

Education 

 

 

 

 

 

32 

EDU1 

 Schools/Libraries 

0 

10 

90 

360 

480 

33 

EDU2 

Colleges/Universities 

0 

10 

120 

480 

960 



 

Repair times differ for similar damage states depending on building occupancy: 
thus 
simpler and smaller buildings will take less time to repair than more complex, 
heavily 
serviced or larger buildings. It has also been noted that large well-financed 
corporations 
can sometimes accelerate the repair time compared to normal construction 
procedures. 

 

However, establishment of a more realistic repair time does not translate 
directly into 
business or service interruption. For some businesses, building repair time is 
largely 




irrelevant, because these businesses can rent alternative space or use spare 
industrial/commercial capacity elsewhere. These factors are reflected in Table 
15.11, 
which provides multipliers to be applied to the values in Table 15.10 to arrive 
at 
estimates of business interruption for economic purposes. The factors in Tables 
15.9, 
15.10, and 15.11 are judgmentally derived, using ATC-13, Table 9.11 as a 
starting point. 

 

The times resulting from the application of the Table 15.11 multipliers to the 
times 
shown in Table 15.10 represent median values for the probability of business or 
service 
interruption. For none and slight damage the time loss is assumed to be short, 
with 
cleanup by staff, but work can resume while slight repairs are done. For most 
commercial and industrial businesses that suffer moderate or extensive damage, 
the 
business interruption time is shown as short on the assumption that these 
concerns will 
find alternate ways of continuing their activities. The values in Table15.11 
also reflect 
the fact that a proportion of business will suffer longer outages or even fail 
completely. 
Church and Membership Organizations generally quickly find temporary 
accommodation, and government offices also resume operating almost at once. It 
is 
assumed that hospitals and medical offices can continue operating, perhaps with 
some 
temporary rearrangement and departmental relocation if necessary, after moderate 
damage, but with extensive damage their loss of function time is also assumed to 
be 
equal to the total time for repair. 

 

For other businesses and facilities, the interruption time is assumed to be 
equal to, or 
approaching, the total time for repair. This applies to residential, 
entertainment, theaters, 
parking, and religious facilities whose revenue or continued service, is 
dependent on the 
existence and continued operation of the facility. 

 

The modifiers from Table 15.11 are multiplied by extended building construction 
times 
as follows: 

 

 LOFds = BCTds * MODds (15-13) 

where: 

LOFds loss of function for damage state ds 

BCTds building construction and clean up time for damage state ds (See Table 
15.10) 

MODds construction time modifiers for damage state ds (See Table 15.11) 

 

The median value applies to a large inventory of facilities. Thus, at moderate 
damage, 
some marginal businesses may close, while others will open after a day's 
cleanup. Even 
with extensive damage, some businesses will accelerate repair, while a number 
will also 
close or be demolished. For example, one might reasonably assume that a URM 
building 
that suffers moderate damage is more likely to be demolished than a newer 
building that 
suffers moderate, or even, extensive damage. If the URM building is an historic 
structure 
its likelihood of survival and repair will probably increase. There will also be 
a small 
number of extreme cases: the slightly damaged building that becomes derelict, or 
the 
extensively damaged building that continues to function for years, with 
temporary 
shoring, until an expensive repair is financed and executed. 




 

Table 15.11: Building and Service Interruption Time Multipliers 

 

 

 

Construction Time 

No. 

Label 

Occupancy Class 

Structural Damage State 

 

 

 

None

Slight 

Moderate

Extensive 

Complete

 

 

Residential 

 

 

 

 

 

1 

RES1 

Single Family Dwelling 

0 

0 

0.5 

1.0 

1.0 

2 

RES2 

Mobile Home 

0 

0 

0.5 

1.0 

1.0 

3-8 

RES3a-f 

Multi Family Dwelling 

0 

0 

0.5 

1.0 

1.0 

9 

RES4 

Temporary Lodging 

0 

0 

0.5 

1.0 

1.0 

10 

RES5 

Institutional Dormitory 

0 

0 

0.5 

1.0 

1.0 

11 

RES6 

Nursing Home 

0 

0 

0.5 

1.0 

1.0 

 

 

Commercial 

 

 

 

 

 

12 

COM1 

Retail Trade 

0.5 

0.1 

0.1 

0.3 

0.4 

13 

COM2 

Wholesale Trade 

0.5 

0.1 

0.2 

0.3 

0.4 

14 

COM3 

Personal and Repair Services 

0.5 

0.1 

0.2 

0.3 

0.4 

15 

COM4 

Professional/Technical/ 

Business Services 

0.5 

0.1 

0.1 

0.2 

0.3 

16 

COM5 

Banks/Financial Institutions 

0.5 

0.1 

0.05 

0.03 

0.03 

17 

COM6 

Hospital 

0.5 

0.1 

0.5 

0.5 

0.5 

18 

COM7 

Medical Office/Clinic 

0.5 

0.1 

0.5 

0.5 

0.5 

19 

COM8 

Entertainment & Recreation 

0.5 

0.1 

1.0 

1.0 

1.0 

20 

COM9 

Theaters 

0.5 

0.1 

1.0 

1.0 

1.0 

21 

COM10 

Parking 

0.1 

0.1 

1.0 

1.0 

1.0 

 

 

Industrial 

 

 

 

 

 

22 

IND1 

Heavy 

0.5 

0.5 

1.0 

1.0 

1.0 

23 

IND2 

Light 

0.5 

0.1 

0.2 

0.3 

0.4 

24 

IND3 

Food/Drugs/Chemicals 

0.5 

0.2 

0.2 

0.3 

0.4 

25 

IND4 

Metals/Minerals Processing 

0.5 

0.2 

0.2 

0.3 

0.4 

26 

IND5 

High Technology 

0.5 

0.2 

0.2 

0.3 

0.4 

27 

IND6 

Construction 

0.5 

0.1 

0.2 

0.3 

0.4 

 

 

Agriculture 

 

 

 

 

 

28 

AGR1 

Agriculture 

0 

0 

0.05 

0.1 

0.2 

 

 

Religion/Non-Profit 

 

 

 

 

 

29 

REL1 

Church/Membership 
Organization 

1 

0.2 

0.05 

0.03 

0.03 

 

 

Government 

 

 

 

 

 

30 

GOV1 

General Services 

0.5 

0.1 

0.02 

0.03 

0.03 

31 

GOV2 

Emergency Response 

0.5 

0.1 

0.02 

0.03 

0.03 

 

 

Education 

 

 

 

 

 

32 

EDU1 

 Schools/Libraries 

0.5 

0.1 

0.02 

0.05 

0.05 

33 

EDU2 

Colleges/Universities 

0.5 

0.1 

0.02 

0.03 

0.03 



 

 

 

15.2.5 Relocation Expenses 

 

Relocation costs may be incurred when the level of building damage is such that 
the 
building or portions of the building are unusable while repairs are being made. 
While 
relocation costs may include a number of expenses, in this model, only the 
following 




components are considered: disruption costs that include the cost of shifting 
and 
transferring, and the rental of temporary space. It should be noted that the 
burden of 
relocation expenses are not expected to be borne by the renter. Instead it is 
assumed that 
the building owners will incur the expense of moving their tenants to a new 
location. It 
should also be noted that a renter who has been displaced from a property due to 
earthquake damage would cease to pay rent to the owner of the damaged property 
and 
only pay rent to the new landlord. Therefore, the renter has no new rental 
expenses. It is 
assumed that the owner of the damaged property will pay the disruption costs for 
his 
renter. If the damaged property is owner occupied, then the owner will have to 
pay for 
disruption costs in addition to the cost of rent while he is repairing his 
building. 

 

It is assumed in this model that it is unlikely that an occupant will relocate 
if a building is 
in the damage states none or slight. The exceptions are some government or 
emergency 
response services that need to be operational immediately after an earthquake. 
However 
these are considered to contribute very little to the total relocation expenses 
for a region 
and are ignored. Finally, it is assumed that entertainment, theaters, parking 
facilities and 
heavy industry (occupancy classes 14 to 17) will not relocate to new facilities. 
Instead 
they will resume operation when their facilities have been repaired or replaced. 
Relocation expenses are then a function of the floor area, the rental costs per 
day per 
square foot, a disruption cost, the expected days of loss of function for each 
damage state, 
the type of occupancy and the damage state itself. These are given by the 
following 
expression. 

 

RELi = FAi* (15-14) 
(%)*(*)
%*(*(*)*
,
,13535-+
+
.
.
....
.
.
....
=
=
SSOOPOSTRDCOOPOSTRDCRENTRTidsiidsidsiiids

 

where: 

RELi relocation costs for occupancy class i (i = 1-13 and 18-28) 

FAi floor area of occupancy class i (in square feet) 

POSTRds,i probability of occupancy class i being in structural damage state ds 

DCi disruption costs for occupancy i ($/ft2, See Table 15.12) 

RTds recovery time for damage state ds (See Table 15.10) 

%OO percent owner occupied for occupancy i (See Table 15.13) 

RENTi rental cost ($/ft2/day) for occupancy i (See Table 15.12) 

 

The default values for rental costs and disruption costs are 1994 values. 
However, actual 
values will vary from region to region; local numbers should be substituted for 
the 
default values. Regional numbers are commonly available from Chambers of 
Commerce 
or state and/or local regional economic development agencies. 

 




Table 15.12: Rental Costs and Disruption Costs 

 

 

 

Rental Cost 

Disruption Costs 

# 

Label 

Occupancy Class 

($/ft2/month) 

($/ft2/day) 

($/ft2) 

 

 

Residential 

 

 

 

1 

RES1 

Single Family Dwelling 

0.61 

0.02 

0.73 

2 

RES2 

Mobile Home 

0.42 

0.01 

0.73 

3-8 

RES3a-f 

Multi Family Dwelling 

0.55 

0.02 

0.73 

9 

RES4 

Temporary Lodging 

1.82 

0.06 

0.73 

10 

RES5 

Institutional Dormitory 

0.36 

0.01 

0.73 

11 

RES6 

Nursing Home 

0.67 

0.02 

0.73 

 

 

Commercial 

 

 

 

12 

COM1 

Retail Trade 

1.03 

0.03 

0.97 

13 

COM2 

Wholesale Trade 

0.42 

0.01 

0.85 

14 

COM3 

Personal and Repair Services 

1.21 

0.04 

0.85 

15 

COM4 

Professional/Technical/ 

Business Services 

1.21 

0.04 

0.85 

16 

COM5 

Banks 

1.52 

0.05 

0.85 

17 

COM6 

Hospital 

1.21 

0.04 

1.21 

18 

COM7 

Medical Office/Clinic 

1.21 

0.04 

1.21 

19 

COM8 

Entertainment & Recreation 

1.52 

0.05 

N/A 

20 

COM9 

Theaters 

1.52 

0.05 

N/A 

21 

COM10 

Parking 

0.3 

0.01 

N/A 

 

 

Industrial 

 

 

 

22 

IND1 

Heavy 

0.18 

0.01 

N/A 

23 

IND2 

Light 

0.24 

0.01 

0.85 

24 

IND3 

Food/Drugs/Chemicals 

0.24 

0.01 

0.85 

25 

IND4 

Metals/Minerals Processing 

0.18 

0.01 

0.85 

26 

IND5 

High Technology 

0.3 

0.01 

0.85 

27 

IND6 

Construction 

0.12 

0 

0.85 

 

 

Agriculture 

 

 

 

28 

AGR1 

Agriculture 

0.61 

0.02 

0.61 

 

 

Religion/Non/Profit 

 

 

 

29 

REL1 

 Church/Membership 
Organization 

0.91 

0.03 

0.85 

 

 

Government 

 

 

 

30 

GOV1 

General Services 

1.21 

0.04 

0.85 

31 

GOV2 

Emergency Response 

1.21 

0.04 

0.85 

 

 

Education 

 

 

 

32 

EDU1 

 Schools/Libraries 

0.91 

0.03 

0.85 

33 

EDU2 

Colleges/Universities 

1.21 

0.04 

0.85 



 

Table 15.13: Percent Owner Occupied 

 

No. 

 

Label 

 

Occupancy Class 

Percent Owner 
Occupied 

 

 

Residential 

 

1 

RES1 

Single Family Dwelling 

75 

2 

RES2 

Mobile Home 

85 

3-8 

RES3a-
f 

Multi Family Dwelling 

35 






9 

RES4 

Temporary Lodging 

0 

10 

RES5 

Institutional Dormitory 

0 

11 

RES6 

Nursing Home 

0 

 

 

Commercial 

 

12 

COM1 

Retail Trade 

55 

13 

COM2 

Wholesale Trade 

55 

14 

COM3 

Personal and Repair Services 

55 

15 

COM4 

Professional/Technical/ 

Business Services 

55 

16 

COM5 

Banks 

75 

17 

COM6 

Hospital 

95 

18 

COM7 

Medical Office/Clinic 

65 

19 

COM8 

Entertainment & Recreation 

55 

20 

COM9 

Theaters 

45 

21 

COM10 

Parking 

25 

 

 

Industrial 

 

22 

IND1 

Heavy 

75 

23 

IND2 

Light 

75 

24 

IND3 

Food/Drugs/Chemicals 

75 

25 

IND4 

Metals/Minerals Processing 

75 

26 

IND5 

High Technology 

55 

27 

IND6 

Construction 

85 

 

 

Agriculture 

 

28 

AGR1 

Agriculture 

95 

 

 

Religion/Non/Profit 

 

29 

REL1 

Church/Membership 
Organization 

90 

 

 

Government 

 

30 

GOV1 

General Services 

70 

31 

GOV2 

Emergency Response 

95 

 

 

Education 

 

32 

EDU1 

Schools/Libraries 

95 

33 

EDU2 

Colleges/Universities 

90 



 




15.2.6 Loss of Income 

 

Business activity generates several types of income. First is income associated 
with 
capital, or property ownership. Business generates profits, and a portion of 
this is paid 
out to individuals (as well as to pension funds and other businesses) as 
dividends, while 
another portion, retained earnings, is plowed back into the enterprise. 
Businesses also 
make interest payments to banks and bondholders for loans. They pay rental 
income on 
property and make royalty payments for the use of tangible assets. Those in 
business for 
themselves, or in partnerships, generate a category called proprietary income, 
one portion 
of which reflects their profits and the other that reflects an imputed salary 
(e.g., the case 
of lawyers or dentists). Finally, the biggest category of income generated/paid 
is 
associated with labor. In most urban regions of the U.S., wage and salary income 
comprises more than 75% of total personal income payments. 

 

It is possible to link income payments to various physical damage measures 
including 
sales, property values, and square footage. The latter approach is used here. 
Income 
losses occur when building damage disrupts economic activity. Income losses are 
the 
product of floor area, income realized per square foot and the expected days of 
loss of 
function for each damage state. Proprietors income losses are expressed as 
follows: 

 

YLOSi = (1-RFi)*FAi*INCi* (15-15) POSTRLOFdsidsds,*
=S15

 

where: 

YLOSi income losses for occupancy class i 

FAi floor area of occupancy class i (in square feet) 

INCi income per day (per square foot) for occupancy class i (Table 
15.15) 

POSTRds,i probability of occupancy i being in structural damage state ds, 

LOFds loss of function time for damage state ds (see Equation 15-13) 

RFi recapture factor for occupancy i (Table 15.14) 

 

National estimates of sectoral income were obtained from the IMPLAN System, 
which in 
turn is based on U.S. Department of Commerce Bureau of Analysis data. The income 
data used was a three-year average to dampen cyclical variations especially 
prevalent for 
profit-related income. Income per square foot of floor space can then be derived 
by 
dividing income by the floor space occupied by a specific sector. As with losses 
and 
costs discussed above, income will vary considerably depending on regional 
economic 
conditions. Therefore, default values need to be adjusted for local conditions. 
Default 
values for floor space were derived from information in Table 4.7 of ATC-13. 

 

15.2.6.1 Recapture Factors 

 

The business-related losses from earthquakes can be recouped to some extent by 
working 
overtime after the event. For example, a factory that is closed for six weeks 
due to 




directly-caused structural damage or indirectly-caused shortage of supplies may 
work 
extra shifts in the weeks or months following its reopening. It is necessary 
that there be a 
demand for its output (including inventory buildup), but this is likely to be 
the case as 
undamaged firms try to overcome input shortages, other firms that were 
temporarily 
closed try to make-up their lost production as well, and firms outside the 
region press for 
resumption of export sales to them. 

 

Table 15.14: Recapture Factors 

Occupancy 

Wage 
Recapture 
(%) 

Employment 
Recapture 

(%) 

Income 
Recapture 

(%) 

Output 
Recapture 

(%) 

RES1 

0 

0 

0 

0 

RES2 

0 

0 

0 

0 

RES3a-f 

0 

0 

0 

0 

RES4 

0.60 

0.60 

0.60 

0.60 

RES5 

0.60 

0.60 

0.60 

0.60 

RES6 

0.60 

0.60 

0.60 

0.60 

COM1 

0.87 

0.87 

0.87 

0.87 

COM2 

0.87 

0.87 

0.87 

0.87 

COM3 

0.51 

0.51 

0.51 

0.51 

COM4 

0.90 

0.90 

0.90 

0.90 

COM5 

0.90 

0.90 

0.90 

0.90 

COM6 

0.60 

0.60 

0.60 

0.60 

COM7 

0.60 

0.60 

0.60 

0.60 

COM8 

0.60 

0.60 

0.60 

0.60 

COM9 

0.60 

0.60 

0.60 

0.60 

COM10 

0.60 

0.60 

0.60 

0.60 

IND1 

0.98 

0.98 

0.98 

0.98 

IND2 

0.98 

0.98 

0.98 

0.98 

IND3 

0.98 

0.98 

0.98 

0.98 

IND4 

0.98 

0.98 

0.98 

0.98 

IND5 

0.98 

0.98 

0.98 

0.98 

IND6 

0.95 

0.95 

0.95 

0.95 

AGR1 

0.75 

0.75 

0.75 

0.75 

REL1 

0.60 

0.60 

0.60 

0.60 

GOV1 

0.80 

0.80 

0.80 

0.80 

GOV2 

0 

0 

0 

0 

EDU1 

0.60 

0.60 

0.60 

0.60 

EDU2 

0.60 

0.60 

0.60 

0.60 



 

Obviously, this ability to recapture production will differ across industries. 
It will be 
high for those that produce durable output and lower for those that produce 
perishables or 
spot products (examples of the latter being utility sales to residential 
customers, hotel 
services, entertainment). Even some durable manufacturing enterprises would seem 
to 
have severe recapture limits because they already work three shifts per day; 
however, 
work on weekends, excess capacity, and temporary production facilities all can 
be used 
to make up lost sales. 

 




The following table presents a set of recapture factors for the economic sectors 
used in 
the direct loss module. They are deemed appropriate for business disruptions 
lasting up 
to three months. As lost production becomes larger, it is increasingly difficult 
to 
recapture it for both demand-side and supply-side reasons. For more advanced 
studies, 
users may choose to adjust recapture factors downward for longer disruptions. 

 

 

15.2.7 Rental Income Losses 

 

Rental income losses are the product of floor area, rental rates per sq. ft. and 
the expected 
days of loss of function for each damage state. Rental income losses include 
residential, 
commercial and industrial properties. It is assumed that a renter will pay full 
rent if the 
property is in the damage state none or slight. Thus rental income losses are 
calculated 
only for damage states 3, 4 and 5. It should be noted that rental income is 
based upon the 
percentage of floor area in occupancy i that is being rented (1 - %OOi). 

 RYi = (1-%OOi) * FAi * RENTi *POSTRds,i * RTds (15-16) 
ds=S35

where: 

RYi rental income losses for occupancy i 

%OOi percent owner occupied for occupancy i (See Table 15.13) 

FAi floor area of occupancy group i (in square feet) 

RENTi rental cost ($/ft2/day) for occupancy i (See Table 15.12) 

POSTRds,I probability of occupancy i being in structural damage state ds, see 
Chapter 5 

RTds recovery time for damage state ds (See Table 15.10) 

 

Rental rates vary widely with region and depend on local economic conditions 
including 
vacancy rate, the desirability of the neighborhood, and the desirability of the 
buildings. 
Regional and city rental rates are published annually by various real estate 
information 
services. The percentage rates given for owner occupancy are judgmentally based. 
For a 
given study region, census data will provide a more accurate measure for 
residential 
numbers. 

 

15.2.8 Guidance for Estimate Using Advanced Data and Models Analysis 

 

The methodological framework shown for the Default and User-Supplied Data 
Analyses 
will still apply for this type of analysis. However, depending on the type of 
analysis 
required, much more detailed inventory and cost information can be obtained from 
consultants. In the area of cost, professional building cost consultants 
maintain detailed 
records of costs and trends, and have knowledge of local building practices that 
might 
affect a loss estimate. Inventory improvement might include substantial 
"windshield" 
surveys that can greatly augment the accuracy of building type and occupancy 
information. It should be noted that while the windshield survey has limitations 
in 
procuring detailed information on structural types it is effective in procuring 
the kind of 




size and occupancy information necessary for the generic cost estimating 
proposed in this 
methodology. 

 

Table 15.15: Proprietor's Income 

 

 

 

Income 

Wages 

Employees 

Output 

 

# 

 

Label 

 

Occupancy Class 

per 
Square 
Foot per 
Year 

per 
Square 
Foot per 
Day 

per 
Square 
Foot per 
Day 

per Square 
Foot 

per 
Square 
Foot 
per Day

 

 

Residential 

 

 

 

 

 

1 

RES1 

Single Family Dwelling 

0.000 

0.000 

0.000 

0.000 

0.000 

2 

RES2 

Mobile Home 

0.000 

0.000 

0.000 

0.000 

0.000 

3-
8 

RES3a-
f 

Multi Family Dwelling 

0.000 

0.000 

0.000 

0.000 

0.000 

9 

RES4 

Temporary Lodging 

32.065 

0.088 

0.206 

0.003 

0.46 

10 

RES5 

Institutional Dormitory 

0.000 

0.000 

0.000 

0.000 

0.000 

11 

RES6 

Nursing Home 

53.442 

0.146 

0.345 

0.005 

0.767 

 

 

Commercial 

 

 

 

 

 

12 

COM1 

Retail Trade 

19.785 

0.054 

0.189 

0.004 

0.401 

13 

COM2 

Wholesale Trade 

32.449 

0.089 

0.233 

0.002 

0.521 

14 

COM3 

Personal and Repair 
Services 

42.754 

0.117 

0.276 

0.004 

0.614 

15 

COM4 

Professional/Technical/ 

Business Services 

336.882 

0.923 

0.328 

0.004 

0.897 

16 

COM5 

Banks 

384.421 

1.053 

0.534 

0.006 

2.912 

17 

COM6 

Hospital 

53.442 

0.146 

0.345 

0.005 

0.767 

18 

COM7 

Medical Office/Clinic 

106.884 

0.293 

0.689 

0.01 

1.534 

19 

COM8 

Entertainment & 
Recreation 

196.013 

0.537 

0.427 

0.007 

0.967 

20 

COM9 

Theaters 

64.13 

0.176 

0.414 

0.006 

0.921 

21 

COM10 

Parking 

0 

0 

0 

0 

0 

 

 

Industrial 

 

 

 

 

 

22 

IND1 

Heavy 

81.098 

0.222 

0.368 

0.003 

1.555 

23 

IND2 

Light 

81.098 

0.222 

0.368 

0.003 

1.555 

24 

IND3 

Food/Drugs/Chemicals 

108.131 

0.296 

0.492 

0.004 

2.073 

25 

IND4 

Metals/Minerals 
Processing 

245.687 

0.673 

0.38 

0.003 

1.645 

26 

IND5 

High Technology 

162.196 

0.444 

0.737 

0.006 

3.109 

27 

IND6 

Construction 

79.065 

0.217 

0.398 

0.005 

1.54 

 

 

Agriculture 

 

 

 

 

 

28 

AGR1 

Agriculture 

75.031 

0.206 

0.081 

0.004 

0.767 

 

 

Religion/Non/Profit 

 

 

 

 

 

29 

REL1 

Church/Membership 
Organization 

42.754 

0.117 

0.276 

0.004 

1.534 

 

 

Government 

 

 

 

 

 

30 

GOV1 

General Services 

35.112 

0.096 

2.646 

0.025 

0.614 

31 

GOV2 

Emergency Response 

0 

0 

4.023 

0.038 

0.705 

 

 

Education 

 

 

 

 

 

32 

EDU1 

 Schools/Libraries 

53.442 

0.146 

0.345 

0.005 

2.973 

33 

EDU2 

Colleges/Universities 

106.884 

0.293 

0.689 

0.01 

4.518 



 




 

15.3 Description of Methodology: Lifelines 

 

This section describes the methodologies used to estimate lifeline related 
direct economic 
losses. Direct physical damage to transportation and utility lifelines was 
discussed in 
Chapters 7 and 8, respectively. Estimation of direct economic losses for the 
extended 
network lifelines such as highways, railroads, water supply, and power supply, 
depends 
on the inventory data providing the location of all nodes and links, and the 
models 
relating ground motions to damage. 

 

Direct economic losses are computed based on (1) probabilities of being in a 
certain 
damage state (P[Ds = dsi]), (2) the replacement value of the component, and (3) 
damage 
ratios (DRi) for each damage state, dsi. Economic losses are evaluated by 
multiplying 
the compounded damage ratio (DRc) by the replacement value. The compounded 
damage ratio is computed as the probabilistic combination of damage ratios as 
follows. 

 

 DRc = DRi x P[dsi] (15-17) 
i=S25

 

where P[dsi] is the probability of being in damage state i, and 1, 2, 3, 4 and 5 
are 
associated with damage states none, slight, moderate, extensive and complete. No 
losses 
are associated with damage state 1, therefore, the summation is from i = 2 to 5. 

 

The probability of being in or exceeding a certain damage state (P[DS > dsi | 
PGA, PGV 
or PGD]), for each component, were presented in Chapter 7 and Chapter 8. The 
probabilities of being in a particular damage state are as follows: 

 

P[Ds = ds1|PGA or PGD] = 1 - P[Ds = ds2 | PGA or PGD] 

 = P1 (15-18) 

 

P[Ds = ds2|PGA or PGD] = P[Ds = ds2 | PGA or PGD] - P[Ds = ds3 | PGA or PGD] 

 = P2 (15-19) 

 

P[Ds = ds3|PGA or PGD] = P[Ds = ds3 | PGA or PGD] - P[Ds = ds4 | PGA or PGD] 

 = P3 (15-20) 

 

P[Ds = ds4|PGA or PGD] = P[Ds = ds4 | PGA or PGD] - P[Ds = ds5 | PGA or PGD] 

 = P4 (15-21) 

 

P[Ds = ds5|PGA or PGD] = P[Ds = ds5 | PGA or PGD] 

 = P5 (15-22) 

 

The estimates of replacement values of all lifeline system components are given 
in Tables 
15.16 and 15.17. Table 15.16 provides the replacement values for the components 
of the 
transportation system, while Table 15.17 provides the replacement values for the 
utility 




system components. Most of the replacement value data comes from ATC-13 and ATC-
25. These values are rough estimates and should only be used as a guide. It is 
expected 
that that user will input replacement values based on specific knowledge of the 
lifeline 
components in the study area. In cases where a range is given in Tables 15.16 
and 15.17, 
the default value is set equal to the midpoint of the range. 

 

Table 15.16: Default Replacement Values of Transportation System Components 

System 

Replacement 
Value (thous. $) 

Label 

Component Classification 

Highway 

10,000 

HRD1 

Major Roads (value based on one km length, 4 lanes) 

 

5,000 

HRD2 

Urban Streets (value based on one km length, 2 lanes) 

 

20,000 

HWB1/HWB2 

Major Bridges 

 

5,000 

HWB8, 9, 10, 
11, 15, 16, 20, 
21, 22, 23, 26, 
27 

 

Continuous Bridges 

 

1,000 

HWB3, 4, 5, 6, 
7, 12, 13, 14, 
17, 18, 19, 24, 
25, 28 

 

Other Bridges 

 

20,000 

HTU1 

Highway Bored/Drilled Tunnel (value based on liner) 

 

20,000 

HTU2 

Highway Cut and Cover Tunnel (value based on liner) 

Rail 

1,500 

RTR1 

Rail Track (value based on one km length) 

 

5,000 

RBR1 

Rail Bridge - Seismically Designed 

 

5,000 

RBR2 

Rail Bridge - Conventionally Designed 

 

10,000 

RTU1 

Rail Bored/Drilled Tunnel (value based on liner) 

 

10,000 

RTU2 

Rail Cut and Cover Tunnel (value based on liner) 

 

2,000 

RST1 

Rail Urban Station (C2L) 

 

2,000 

RST2 

Rail Urban Station (S2L) 

 

2,000 

RST3 

Rail Urban Station (S1L) 

 

2,000 

RST4 

Rail Urban Station (S5L) 

 

2,000 

RST5 

Rail Urban Station (PC1) 

 

2,000 

RST6 

Rail Urban Station (C3L) 

 

2,000 

RST7 

Rail Urban Station (W1L) 

 

3,000 

3,000 

3,000 

3,000 

3,000 

RFF1 

RFF2 

RFF3 

RFF4 

RFF5 

Rail Fuel Facility w/ Anchored Tanks, w/ BU Power 

Rail Fuel Facility w/ Anchored Tanks, wo/ BU Power 

Rail Fuel Facility w/ Unanchored Tanks, w/ BU Power 

Rail Fuel Facility w/ Unanchored Tanks, wo/ BU Power 

Rail Fuel Facility w/ Buried Tanks 

 

3,000 

3,000 

3,000 

3,000 

RDF1 

RDF2 

RDF3 

RDF4 

Rail Dispatch Facility w/ Anchored Sub-Comp., w/ BU Power 

Rail Dispatch Facility w/ Anchored Sub-Comp., wo/ BU Power 

Rail Dispatch Facility w/ Unanchored Sub-Comp., w/ BU Power 

Rail Dispatch Facility w/ Unanchored. Sub-Comp., w/0 BU Power

 

2,800 

RMF1 

Rail Maintenance Facility (C2L) 

 

2,800 

RMF2 

Rail Maintenance Facility (S2L) 

 

2,800 

RMF3 

Rail Maintenance Facility (S1L) 

 

2,800 

RMF4 

Rail Maintenance Facility (S5L) 

 

2,800 

RMF5 

Rail Maintenance Facility (PC1) 

 

2,800 

RMF6 

Rail Maintenance Facility (C3L) 

 

2,800 

RMF7 

Rail Maintenance Facility (W1) 



 




Table 15.16: Default Replacement Values of Transportation System Components 
(con't) 

System 

Replacement 
Value (thous $) 

Label 

Component Classification 

Light 

1,500 

LTR1 

Light Rail Track 

Rail 

5,000 

LBR1 

Light Rail Bridge - Seismically Designed/Retrofitted 

 

5,000 

LBR2 

Light Rail Bridge - Conventionally Designed 

 

10,000 

LTU1 

Light Rail Bored/Drilled Tunnel (value based on liner) 

 

10,000 

LTU2 

Light Rail Cut and Cover Tunnel (value based on liner) 

 

2,000 

LDC1 

Light Rail DC Substation w/ Anchored Sub-Components 

 

2,000 

LDC2 

Light Rail DC Substation w/ Unanchored Sub-Comp. 

 

3,000 

LDF1 

Lt Rail Dispatch Fac w/ Anchored Sub-Comp., w/ BU Power 

 

3,000 

LDF2 

Lt Rail Dispatch Fac w/ Anchored Sub-Comp., wo/ BU 
Power 

 

3,000 

LDF3 

Lt Rail Dispatch Fac w/ Unanchored Sub-Comp., w/ BU 
Power 

 

3,000 

LDF4 

Lt Rail Dispatch Fac w/ Unanchored Sub-Comp., wo/ BU 
Power 

 

2,600 

LMF1 

Light Rail Maintenance Facility (C2L) 

 

2,600 

LMF2 

Light Rail Maintenance Facility (S2L) 

 

2,600 

LMF3 

Light Rail Maintenance Facility (S1L) 

 

2,600 

LMF4 

Light Rail Maintenance Facility (S5L) 

 

2,600 

LMF5 

Light Rail Maintenance Facility (PC1) 

 

2,600 

LMF6 

Light Rail Maintenance Facility (C3L) 

 

 2,600 

LMF7 

Light Rail Maintenance Facility (W1) 

Bus 

1,000 

BPT1 

Bus Urban Station (C2L) 

 

1,000 

BPT2 

Bus Urban Station (S2L) 

 

1,000 

BPT3 

Bus Urban Station (S1L) 

 

1,000 

BPT4 

Bus Urban Station (S5L) 

 

1,000 

BPT5 

Bus Urban Station (PC1) 

 

1,000 

BPT6 

Bus Urban Station (C3L) 

 

1,000 

BPT7 

Bus Urban Station (W1) 

 

150 

BFF1 

Bus Fuel Facility w/ Anchored Tanks, w/ BU Power 

 

150 

BFF2 

Bus Fuel Facility w/ Anchored Tanks, wo/ BU Power 

 

150 

BFF3 

Bus Fuel Facility w/ Unanchored Tanks, w/ BU Power 

 

150 

BFF4 

Bus Fuel Facility w/ Unanchored Tanks, wo/ BU Power 

 

150 

BFF5 

Bus Fuel Facility w/ Buried Tanks 

 

400 

BDF1 

Bus Dispatch Fac. w/ Anchored. Sub-Comp., w/ BU Power 

 

400 

BDF2 

Bus Dispatch Fac. w/ Anchored. Sub-Comp., wo/ BU Power 

 

400 

BDF3 

Bus Dispatch Fac. w/ Unanchored. Sub-Comp., w/ BU Power 

 

400 

BDF4 

Bus Dispatch Fac. w/ Unanchored. Sub-Comp., wo/ BU 
Power 

 

1,300 

BMF1 

Bus Maintenance Facility (C2L) 

 

1,300 

BMF2 

Bus Maintenance Facility (S2L) 

 

1,300 

BMF3 

Bus Maintenance Facility (S1L) 

 

1,300 

BMF4 

Bus Maintenance Facility (S5L) 

 

1,300 

BMF5 

Bus Maintenance Facility (PC1) 

 

1,300 

BMF6 

Bus Maintenance Facility (C3L) 

 

1,300 

BMF7 

Bus Maintenance Facility (W1) 



 

 




Table 15.16: Default Replacement Values of Transportation System Components 
(con't) 

System 

Replacement 
Value (thous $) 

Label 

Component Classification 

Port 

1,500 

PWS1 

Port Waterfront Structures 

 

2,000 

2,000 

PEQ1 

PEQ2 

Anchored Port Handling Equipment 

Unanchored Port Handling Equipment 

 

1,200 

PWH1 

Port Warehouses (C2L) 

 

1,200 

PWH2 

Port Warehouses (S2L) 

 

1,200 

PWH3 

Port Warehouses (S1L) 

 

1,200 

PWH4 

Port Warehouses (S5L) 

 

1,200 

PWH5 

Port Warehouses (PC1) 

 

1,200 

PWH6 

Port Warehouses (C3L) 

 

1,200 

PWH7 

Port Warehouses (W1) 

 

2,000 

PFF1 

Port Fuel Facility w/ Anchored Tanks, w/ BU Power 

 

2,000 

PFF2 

Port Fuel Facility w/ Anchored Tanks, wo/ BU Power 

 

2,000 

PFF3 

Port Fuel Facility w/ Unanchored Tanks, w/ BU Power 

 

2,000 

PFF4 

Port Fuel Facility w/ Unanchored Tanks, wo/ BU Power 

 

2,000 

PFF5 

Port Fuel Facility w/ Buried Tanks 

Ferry 

1,500 

FWS1 

Ferry Waterfront Structures (Value for 7,500 ft2 facility) 

 

1,000 

FPT1 

Ferry Passenger Terminals (C2L) 

 

1,000 

FPT2 

Ferry Passenger Terminals (S2L) 

 

1,000 

FPT3 

Ferry Passenger Terminals (S1L) 

 

1,000 

FPT4 

Ferry Passenger Terminals (S5L) 

 

1,000 

FPT5 

Ferry Passenger Terminals (PC1) 

 

1,000 

FPT6 

Ferry Passenger Terminals (C3L) 

 

1,000 

FPT7 

Ferry Passenger Terminals (W1) 

 

400 

FFF1 

Ferry Fuel Facility w/ Anchored Tanks, w/ BU Power 

 

400 

FFF2 

Ferry Fuel Facility w/ Anchored Tanks, wo/ BU Power 

 

400 

FFF3 

Ferry Fuel Facility w/ Unanchored Tanks, w/ BU Power 

 

400 

FFF4 

Ferry Fuel Facility w/ Unanchored Tanks, wo/ BU Power 

 

400 

FFF5 

Ferry Fuel Facility w/ Buried Tanks 

 

200 

FDF1 

Ferry Dispatch Fac. w/ Anchored. Sub-Comp., w/ BU 
Power 

 

200 

FDF2 

Ferry Dispatch Fac. w/ Anchored. Sub-Comp., wo/ BU 
Power 

 

200 

FDF3 

Ferry Dispatch Fac. w/ Unanchored. Sub-Comp., w/ BU 
Power 

 

200 

FDF4 

Ferry Dispatch Fac. w/ Unanchored. Sub-Comp., wo/ BU 
Power 

 

520 

FMF1 

Ferry Maintenance Facility (C2L) 

 

520 

FMF2 

Ferry Maintenance Facility (S2L) 

 

520 

FMF3 

Ferry Maintenance Facility (S1L) 

 

520 

FMF4 

Ferry Maintenance Facility (S5L) 

 

520 

FMF5 

Ferry Maintenance Facility (PC1) 

 

520 

FMF6 

Ferry Maintenance Facility (C3L) 

 

520 

FMF7 

Ferry Maintenance Facility (W1) 



 

 




Table 15.16: Default Replacement Values of Transportation System Components 
(con't) 

System 

Replacement 
Value ( thous $) 

Label 

Component Classification 

Airport 

5,000 

ACT1 

Airport Control Towers (C2L) 

 

5,000 

ACT2 

Airport Control Towers (S2L) 

 

5,000 

ACT3 

Airport Control Towers (S1L) 

 

5,000 

ACT4 

Airport Control Towers (S5L) 

 

5,000 

ACT5 

Airport Control Towers (PC1) 

 

5,000 

ACT6 

Airport Control Towers (C3L) 

 

5,000 

ACT7 

Airport Control Towers (W1) 

 

28,000 

ARW1 

Airport Runways 

 

 8,000 

ATB1 

Airport Terminal Buildings (C2L) 

 

 8,000 

ATB2 

Airport Terminal Buildings (S2L) 

 

 8,000 

ATB3 

Airport Terminal Buildings (S1L) 

 

 8,000 

ATB4 

Airport Terminal Buildings (S5L) 

 

 8,000 

ATB5 

Airport Terminal Buildings (PC1) 

 

 8,000 

ATB6 

Airport Terminal Buildings (C3L) 

 

 8,000 

ATB7 

Airport Terminal Buildings (W1) 

 

 1,400 

APS1 

Airport Parking Structures (C2L) 

 

1,400 

APS2 

Airport Parking Structures (S2L) 

 

 1,400 

APS3 

Airport Parking Structures (S1L) 

 

 1,400 

APS4 

Airport Parking Structures (S5L) 

 

 1,400 

APS5 

Airport Parking Structures (PC1) 

 

 1,400 

APS65 

Airport Parking Structures (C3L) 

 

5,000 

AFF1 

Airport Fuel Facility w/ Anchored Tanks, w/ BU Power 

 

5,000 

AFF2 

Airport Fuel Facility w/ Anchored Tanks, wo/ BU Power 

 

5,000 

AFF3 

Airport Fuel Facility w/ Unanchored Tanks, w/ BU Power 

 

5,000 

AFF4 

Airport Fuel Facility w/ Unanchored Tanks, wo/ BU Power 

 

5,000 

AFF5 

Airport Fuel Facility w/ Buried Tanks 

 

3,200 

AMF1 

Airport Maintenance & Hanger Facility 

 

8,000 

ATBU1 

Airport - General 

 

2,000 

AFH1 

Heliport 

 

500 

AFO1 

Seaport / Stolport / Gliderport / Seaplane 



 

 




Table 15.17: Default Replacement Values of Utility System Components 

System 

Replacement 
Value (thous $) 

Label 

Component Classification 

Potable 

1 

PWP1 

Brittle Pipe (per break) 

Water 

1 

PWP2 

Ductile Pipe (per break) 

 

30,000 

PWT1 

Small WTP with Anchored Components < 50 MGD 

 

30,000 

PWT2 

Small WTP with Unanchored Components <50 MGD 

 

100,000 

PWT3 

Medium WTP with Anchored Components 50-200 MGD 

 

100,000 

PWT4 

Medium WTP with Unanchored Components 50-200 MGD 

 

360,000 

PWT5 

Large WTP with Anchored Components >200 MGD 

 

360,000 

PWT6 

Large WTP with Unanchored Components >200 MGD 

 

400 

PWE1 

Wells 

 

1,500 

PST1 

On Ground Anchored Concrete Tank 

 

1,500 

PST2 

On Ground Unanchored Concrete Tank 

 

800 

PST3 

On Ground Anchored Steel Tank 

 

800 

PST4 

On Ground Unanchored Steel Tank 

 

800 

PST5 

Above Ground Anchored Steel Tank 

 

800 

PST6 

Above Ground Unanchored Steel Tank 

 

30 

PST7 

On Ground Wood Tank 

 

150 

PPP1 

Small Pumping Plant with Anchored Equipment <10 MGD 

 

150 

PPP2 

Small Pumping Plant with Unanchored Equipment <10 MGD 

 

525 

PPP3 

Medium/Large Pumping Plant with Anchored Equipment >10 
MGD 

 

525 

PPP4 

Med./Large Pumping Plant with Unanchored Equipment >10 
MGD 

Waste 

1 

WWP1 

Brittle Pipe (per break) 

Water 

1 

WWP2 

Ductile Pipe (per break) 

 

60,000 

WWT1 

Small WWTP with Anchored Components <50 MGD 

 

60,000 

WWT2 

Small WWTP with Unanchored Components <50 MGD 

 

200,000 

WWT3 

Medium WWTP with Anchored Components 50-200 MGD 

 

200,000 

WWT4 

Medium WWTP with Unanchored Components 50-200 MGD 

 

720,000 

WWT5 

Large WWTP with Anchored Components >200 MGD 

 

720,000 

WWT6 

Large WWTP with Unanchored Components >200 MGD 

 

300 

WLS1 

Small Lift Stations with Anchored Components <10 MGD 

 

300 

WLS2 

Small Lift Stations with Unanchored Components <10 MGD 

 

1,050 

WLS3 

Medium/Large Lift Stations with Anchored Components >10 
MGD 

 

1,050 

WLS4 

Med./Large Lift Stations with Unanchored Components >10 
MGD 

Oil 

1 

OIP1 

Welded Steel Pipe with Gas Welded Joints (per break) 

 

1 

OIP2 

Welded Steel Pipe with Arc Welded Joints (per break) 

 

175,000 

ORF1 

Small Refinery with Anchored Equipment <100,000 bl/day 

 

175,000 

ORF2 

Small Refinery with Unanchored Equipment <100,000 bl/day 

 

750,000 

ORF3 

Medium/Large Refinery with Anchored Equipment >100,000 
bl/day 

 

750,000 

ORF4 

Medium/Large Refinery with Unanchored Equipment >100,000 
bl/day 

 

1,000 

OPP1 

Pumping Plant with Anchored Equipment 

 

1,000 

OPP2 

Pumping Plant with Unanchored Equipment 

 

2,000 

OTF1 

Tank Farms with Anchored Tanks 

 

2,000 

OTF2 

Tank Farms with Unanchored Tanks 



 




Table 15.17: Default Replacement Values of Utility System Components (con't) 

System 

Replacement 
Value (thous $) 

Label 

Component Classification 

Natural 

1 

NGP1 

Welded Steel Pipe with Gas Welded Joints (per break) 

Gas 

1 

NGP2 

Welded Steel Pipe with Arc Welded Joints (per break) 

 

1,000 

NGC1 

Compressor Stations with Anchored Components 

 

1,000 

NGC2 

Compressor Stations with Unanchored Components 

Electric 

10,000 

ESS1 

Low voltage (115 KV) substation, anchored comp. 

Power 

10,000 

ESS2 

Low voltage (115 KV) substation, unanchored comp. 

Systems 

20,000 

ESS3 

Medium Voltage (230 KV) substation, anchored comp. 

 

20,000 

ESS4 

Medium Voltage (230 KV) substation, unanchored. comp. 

 

50,000 

ESS5 

High Voltage (500 KV) substation, anchored comp. 

 

50,000 

ESS6 

High Voltage (500 KV) substation, unanchored comp. 

 

3 

EDC1 

Distribution Circuits with seismically designed components 

 

3 

EDC2 

Distribution Circuits with standard components 

 

100,000 

EPP1 

Small Power Plants with Anchored Comp < 100 MW 

 

100,000 

EPP2 

Small Power Plants with Unanchored Comp <100 MW 

 

500,000 

EPP3 

Medium/Large Power Plants with Anchored Comp >100 MW 

 

500,000 

EPP4 

Medium/Large Power Plants with Unanchored Comp >100 MW

Commu- 

5,000 

CCO1 

Central Office with Anchored Components, w/BU Power 

nication 

5,000 

CCO2 

Central Office with Anchored Components, w/o BU Power 

Systems 

5,000 

CCO3 

Central Office with Unanchored Components, w/BU Power 

 

5,000 

CCO4 

Central Office with Unanchored Components, w/o BU Power 

 

2,000 

CBR1 

Radio Broadcasting Station 

 

2,000 

CBT1 

TV Broadcasting Station 

 

2,000 

CBW1 

Weather Broadcasting Station 

 

2,000 

CBO1 

Other Communication Facility 



 

15.3.1 Transportation Systems 

 

This section describes the methodologies used to estimate direct economic losses 
related 
to transportation system damage. Transportation systems include highway, 
railway, light 
rail, bus, port, ferry, and airport systems. Damage models for each of these 
systems was 
discussed in detail in Chapter 7. 

 

15.3.1.1 Highway Systems 

 

In this subsection, damage ratios are presented for the following highway system 
components: roadways; bridges; tunnels. Damage ratios for bridges are expressed 
as a 
fraction of the component (bridge) replacement cost. Damage ratios for roadways 
are 
expressed as a fraction of the roadway replacement cost per unit length. Damage 
ratios 
for highway tunnels are expressed as a fraction of the liner replacement cost 
per unit 
length. The damage ratios for roadways, tunnels, and bridges are presented in 
Table 
15.18. 

 




Table 15.18: Damage Ratios for Highway System Components 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Roadways 

slight 

moderate 

extensive/ 

complete 

0.05 

0.20 

0.70 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 1.0 

Tunnel's Lining 

slight 

moderate 

extensive 

complete 

0.01 

0.30 

0.70 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Bridges 

slight 

moderate 

extensive 

complete 

0.03 

0.08 

0.25 

1.00* 

0.01 to 0.03 

0.02 to 0.15 

0.10 to 0.40 

0.30 to 1.00 



 

* If the number of spans is greater than two, then the best estimate damage 
ratio for 
complete damage is [2/(number of spans)] 

 

 

15.3.1.2 Railway Systems 

 

In this subsection, damage ratios are presented for the following railway system 
components: tracks/roadbeds; bridges; tunnels; facilities. Damage ratios 
associated with 
bridges and facilities are expressed as a fraction of the component replacement 
cost. 
Damage ratios for tracks are expressed as a fraction of the replacement cost per 
length. 
Damage ratios for railway tunnels are expressed as a fraction of the liner 
replacement 
cost per unit length. 

 

The damage ratios for railway bridges, fuel facilities, dispatch facilities, and 
urban 
stations and maintenance facilities, are presented in Table 15.19. The damage 
ratios for 
railway tracks and tunnels are the same as for urban roads and tunnels for the 
highway 
systems presented in Section 15.3.1.1. The damage ratios for bridges are 
computed in the 
same manner as for highway bridges. For a given damage state, the damage ratios 
for 
fuel and dispatch facilities are evaluated as the sum of the damage ratios of 
all the 
subcomponents multiplied by their respective percentages of the total component 
(fuel or 
dispatch facility) value. The subcomponents information is presented in Table 
15D.1 of 
Appendix 15D. 

 

 




Table 15.19: Damage Ratios for Railway System Components 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Bridges 

slight 

moderate 

extensive 

complete 

0.12 

0.19 

0.40 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Fuel Facilities 

slight 

moderate 

extensive 

complete 

0.15 

0.39 

0.80 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Dispatch 
Facilities 

slight 

moderate 

extensive 

complete 

0.04 

0.4 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Urban Stations 
and 
Maintenance 
Facilities 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

15.3.1.3 Light Rail Systems 

 

In this subsection, damage ratios are presented for the following light rail 
system 
components: tracks/roadbeds; bridges; tunnels; facilities. Damage ratios for 
bridges and 
facilities are expressed as a fraction of the component replacement cost. Damage 
ratios 
for tracks are expressed as a fraction of the replacement value per unit length. 
Damage 
ratios for light rail tunnels are expressed as a fraction of the linear 
replacement cost. 

 

The damage ratios for DC substations are presented in Table 15.20. The damage 
ratios 
for light rail tracks and tunnels are the same as for urban roads and tunnels 
for highway 
systems presented in Section 15.3.1.1. The damage ratios for dispatch facilities 
and 
bridges are the same as those for railway systems presented in Section 15.3.1.2. 
The 
damage ratios for the subcomponents of DC substations are estimated as the sum 
of the 
damage ratios of all the subcomponents multiplied by their respective 
percentages of the 
total substation value. The subcomponent information for the DC substations are 
presented in Table 15D.2 of Appendix 15D. 

 




Table 15.20: Damage Ratios for DC Substations 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

DC Substations 

slight 

moderate 

extensive 

complete 

0.04 

0.4 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

15.3.1.4 Bus Systems 

 

In this subsection, damage ratios are presented for the following bus system 
components: 
urban stations; maintenance, fuel, and dispatch facilities. Damage ratios for 
these 
components are expressed as a fraction of the component replacement cost. 

 

The damage ratios for bus system components are presented in Table 15.21. The 
damage 
ratios for urban stations and maintenance facilities are the same as those for 
railway 
systems presented in Section 15.3.1.2. The damage ratios for fuel and dispatch 
facilities 
are evaluated as the sum of the damage ratios of all the subcomponents 
multiplied by 
their respective percentages of the total component (fuel or dispatch facility) 
value. The 
subcomponent information is presented in Table 15D.3 of Appendix 15D. 

 

Table 15.21: Damage Ratios for Bus System Components 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Fuel Facilities 

slight 

moderate 

extensive 

complete 

0.15 

0.39 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Dispatch 
Facilities 

slight 

moderate 

extensive 

complete 

0.06 

0.4 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

15.3.1.5 Port Systems 

 

In this subsection, damage ratios are presented for the following port system 
components: 
waterfront structures (e.g., wharves, piers and sea-walls); cranes and cargo 
handling 
equipment; fuel facilities; warehouses. Damage ratios for these components are 
expressed as a fraction of the component replacement cost. 

 

The damage ratios for port system components are presented in Table 15.22. The 
damage ratios for fuel facilities are evaluated as the sum of the damage ratios 
of all the 
subcomponents multiplied by their respective percentages of the total component 
(fuel 




facility) value. The subcomponent information is presented in Table 15D.4 of 
Appendix 
15D. 

 

Table 15.22: Damage Ratios for Port System Components 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Waterfront 
Structures 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Cranes/Cargo 
Handling 
Equipment 

slight 

moderate 

extensive/ 

complete 

0.05 

0.25 

0.75 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 1.0 

Warehouses 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Fuel Facilities 

slight 

moderate 

extensive 

complete 

0.16 

0.39 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

15.3.1.6 Ferry Systems 

 

In this subsection, damage ratios are presented for the following ferry system 
components: waterfront structures (e.g., wharfs piers and sea-walls); fuel, 
maintenance, 
and dispatch facilities; passenger terminals. Damage ratios for ferry system 
components 
are expressed as a fraction of the component replacement cost. 

 

The damage ratios for ferry system components are presented in Table 15.23. The 
damage ratios for waterfront structures are the same as those for port systems. 
The 
damage ratios for maintenance and dispatch facilities are the same as those for 
railway 
systems. The damage ratios for passenger terminals are the same as those for 
urban 
stations in railway systems. The damage ratios for fuel facilities are evaluated 
as the sum 
of the damage ratios of all the subcomponents multiplied by their respective 
percentages 
of the total component (fuel facility) value. The subcomponent information is 
presented 
in Table 15D.4 of Appendix 15D. 

 




Table 15.23: Damage Ratios for Ferry System Component 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Fuel Facilities 

slight 

moderate 

extensive 

complete 

0.15 

0.37 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

15.3.1.7 Airport Systems 

 

In this subsection, damage ratios are presented for the following airport system 
components: runways; control towers; fuel facilities; terminal buildings; 
maintenance and 
hangar facilities; parking structures. Damage ratios for the airport system 
components 
are expressed as a fraction of the component replacement cost. 

 

The damage ratios for airport system components are presented in Table 15.24. 
The 
damage ratios for fuel facilities are evaluated as the sum of the damage ratios 
of all the 
subcomponents multiplied by their respective percentages of the total component 
(fuel 
facility) value. The subcomponent information is presented in Table 15D.4 of 
Appendix 
15D. 

 

Table 15.24: Damage Ratios for Airport System Components 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Runways 

slight 

moderate 

extensive 

complete 

0.05 

0.05 

0.8 

1.0 

0.01 to 0.4 

0.01 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Control Towers 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Terminal 
Buildings 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

 

 

 




Table 15.24: Damage Ratios for Airport System Components (Continued) 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Parking 
Structures 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Fuel Facilities 

slight 

moderate 

extensive 

complete 

0.14 

0.37 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Maintenance & 
Hangar 
Facilities 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

15.3.2 Utility Systems 

 

This section describes the methodologies used to estimate direct economic losses 
related 
to utility system damage. Utility systems include potable water, waste water, 
oil, natural 
gas, electric power, and communication systems. The estimation of the direct 
economic 
losses associated with each of these systems is presented in the following 
sections. 

 

15.3.2.1 Potable Water Systems 

 

In this subsection, damage ratios are presented for the following potable water 
system 
components: pipelines; water treatment plants; wells; storage tanks; pumping 
plants. 
Damage ratios for these components are expressed as a fraction of the component 
replacement cost. 

 

The damage ratios for potable water system components are presented in Table 
15.25. 
The damage ratios for water treatment plants, wells, and pumping plants are 
evaluated as 
the sum of the damage ratios of all the subcomponents multiplied by their 
respective 
percentages of the total component value. The subcomponent information is 
presented in 
Table 15D.5 of Appendix 15D. 

 

 




Table 15.25: Damage Ratios for Potable Water Systems 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Pipelines 

 

leak 

break 

 

0.10* 

0.75* 

 

 

0.05 to 0.20 

0.5 to 1.0 

 

Water Treatment 
Plants 

slight 

moderate 

extensive 

complete 

0.08 

0.4 

0.77 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

 

Tanks 

slight 

moderate 

extensive 

complete 

0.20 

0.40 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

 

Wells and 
Pumping Plants 

slight 

moderate 

extensive 

complete 

0.05 

0.38 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 * % of the replacement cost for one 20 ft. pipe segment 

 

15.3.2.2 Waste Water Systems 

 

In this subsection, damage ratios are presented for the following waste water 
system 
components: underground sewers and interceptors; waste water treatment plants; 
lift 
stations. Damage ratios for these components are expressed as a fraction of the 
component replacement cost. 

 

The damage ratios for waste water system components are presented in Table 
15.26. The 
damage ratios for lift stations are same as those for pumping plants in potable 
water 
systems presented in Section 15.3.2.2. The damage ratios for waste water 
treatment 
plants are evaluated as the sum of the damage ratios of all the subcomponents 
multiplied 
by their respective percentages of the total component value. The subcomponent 
information is presented in Table 15D.6 of Appendix 15D. 

 

 




Table 15.26: Damage Ratios for Waste Water Systems 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Underground 
Sewers & 
Interceptors 

 

leak 

break 

 

0.10 

0.75 

 

 

0.05 to 0.20 

0.5 to 1.0 

Waste Water 
Treatment Plants 

slight 

moderate 

extensive 

complete 

0.10 

0.37 

0.65 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

15.3.2.3 Oil Systems 

 

In this subsection, damage ratios are presented for the following oil system 
components: 
buried pipes; refineries; pumping plants; tank farms. Damage ratios for these 
components are expressed as a function of the component replacement cost. 

 

The damage ratios for oil system components are presented in Table 15.27. The 
damage 
ratios for refineries, pumping plants, and tank farms are evaluated as the sum 
of the 
damage ratios of all the subcomponents multiplied by their respective 
percentages of the 
total component value. The subcomponent information is presented in Table 15D.7 
of 
Appendix 15D. 

 

Table 15.27: Damage Ratios for Oil Systems 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Buried Pipes 

leak 

break 

0.10 

0.75 

0.05 to 0.20 

0.5 to 1.0 

Refineries 

slight 

moderate 

extensive 

complete 

0.09 

0.23 

0.78 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Pumping Plants 

slight 

moderate 

extensive 

complete 

0.08 

0.4 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Tank Farms 

slight 

moderate 

extensive 

complete 

0.13 

0.4 

0.8 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

 




15.3.2.4 Natural Gas Systems 

 

In this subsection, damage ratios are presented for the following gas system 
components: 
buried pipes; compressor stations. Damage ratios for these components are 
expressed as 
a fraction of the component replacement cost. The damage ratios for buried pipes 
are the 
same as those for oil systems. The damage ratios for compressor stations are the 
same as 
those for pumping plants in the oil system. 

 

15.3.2.5 Electric Power Systems 

 

In this subsection, damage ratios are presented for the following electric power 
system 
components: substations; distribution circuits; generation plants. Damage ratios 
for these 
components are expressed as a fraction of the component replacement cost. 

 

The damage ratios for electric power system components are presented in Table 
15.28. 
The damage ratios for substations and generation plants are evaluated as the sum 
of the 
damage ratios of all the subcomponents multiplied by their respective 
percentages of the 
total component value. The subcomponent information is presented in Table 15D.8 
& 
15D.9 of Appendix 15D. 

 

Table 15.28: Damage Ratios for Electric Power Systems 

Classification 

Damage State 

Best Estimate 
Damage Ratio 

Range of 
Damage 
Ratios 

Substations 

slight 

moderate 

extensive 

complete 

0.05 

0.11 

0.55 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Distribution 
Circuits 

slight 

moderate 

extensive 

complete 

0.05 

0.15 

0.60 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 

Generation 
Plants 

slight 

moderate 

extensive 

complete 

0.08 

0.35 

0.72 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

15.3.2.6 Communication Systems 

 

In this subsection, damage ratios are presented for communication system central 
offices. 
Damage ratios for central offices are expressed as a fraction of the central 
office 
replacement cost. 

 

The damage ratios for central offices are presented in Table 15.29. The damage 
ratios for 
a central office are evaluated as the sum of the damage ratios of all the 
subcomponents 




multiplied by their respective percentages of the total component (central 
office) value. 
The subcomponent information is presented in Table 15D.10 of Appendix 15D. 

 

Table 15.29: Damage Ratios for Communication System Component 

Classification 

Damage State 

Best Estimate 
Damage Ratio

Range of 
Damage 
Ratios 

Central Office 

slight 

moderate 

extensive 

complete 

0.09 

0.35 

0.73 

1.00 

0.01 to 0.15 

0.15 to 0.4 

0.4 to 0.8 

0.8 to 1.0 



 

15.4. References 

 

Applied Technology Council (1985). Earthquake Damage Evaluation Data for 
California, ATC-13, Applied Technology Council, Redwood City, CA. 

 

G&E Engineering Systems, Inc., May 1994, "NIBS Earthquake Loss Estimation 
Methods, Technical Manual, Transportation and Utility Systems". 

 

Howe, C.W., Cochrane, H.C. (1993), Guidelines for the Uniform Identification, 
Definition, and Measurement of Economic Damages from Natural Hazard Events, 
Institute of Behavioral Science, University of Colorado. 

 

Jackson, P.L., Editor, Means Square Foot Costs, (1994), R.S. Means Company, 
Inc., 
Kingston, MA. 

 

Phipps, M., et. al., 1992, Degenkolb Report on Effects of Loma Prieta Earthquake 
on 
Industry. 

 

VSP Associates (1991), A Benefit-Cost Model for the Seismic Rehabilitation of 
Hazardous Buildings, Volume 1, User's Manual, Volume 2, Supporting 
Documentation, 
FEMA 227 and 228, Federal Emergency Management Agency, Washington, D. C. 

 




 

Appendix 15A 

Relationship Between Building Damage and Business Interruption 

 

 

The subject of business and service interruption due to building damage has been 
identified for some time as an important contributor to indirect economic losses 
following earthquakes. 

 

The issue of relating building damage to business interruption, and developing 
some 
statistical measures has been little researched, and available information is 
largely 
anecdotal. ATC-13 provided extensive coverage of the topic of building repair 
and loss 
of function, at the same time noting that: 

 

 " ... it is clear that there is a great variation in repair and demolition 
actions taken in 
connection with buildings that are moderately or severely damaged. There is also 
great 
variation for the loss of function associated with a given degree of damage.... 
The paucity 
of data currently available precludes describing loss of function based on 
statistical data 
from past events." 

 

ATC-13 provided detailed tables with estimates of loss of function times for all 
the ATC-
13 social classes of buildings (and all lifelines). These tables, which were 
developed by 
expert opinion, provided estimates of the time to restore 30%, 60%, and 100% of 
useability, for each of the six ATC-13 damage states. 

 

Since ATC-13 was published, the information that relates building damage to loss 
of 
function continues to be unsystematic and anecdotal. A study of damage and loss 
of 
function for 14 industrial and administrative buildings in the Loma Prieta 
earthquake 
shows a typical wide spread of conditions and consequences (Phipps, et. al, 
1992). Table 
15A-1 summarizes some of the information from this study. It is possible that 
surveys 
of the recovery after the Northridge earthquake may provide some more systematic 
information on this issue. 

 




Table 15A.1: Summary of Building Damage Vs Restoration Time: 

for 14 Industrial/Administrative Low-Rise Buildings, Loma Prieta Earthquake 
(Time in Days) (from Phipps, et. al., 1992) 

 

# 

Structure 
Type 

Damage 
Percentage 

Restoration 
Time (days)

Description of 
Damage 

1 

Tilt-up 

2 

5 

roof-wall 
connections 

2. 

Steel 

20 

180 

window wall 
cracked 

3 

Steel 

2 

1 

piping, clogs 

4 

Steel 

37 

270 

floor cracked 

5 

Steel 

33 

270 

bracing buckled 

6 

Steel 

32 

270 

bracing buckled 

7 

Steel 

33 

270 

bracing buckled 

8 

Steel 

NA 

360 

sprinklers 

9 

Steel 

23 

150 

buckled bracing 

10 

Tilt-up 

89 

540 

cracked walls 

11 

Tilt-up 

60 

90 

failed roof 

12 

Precast 

NA 

90 

wall-floor 
connections 

13 

Steel 

42 

180 

asbestos 

14 

Steel 

NA 

21 

radioactive 
contamination 



 

Surveys of available information and experience suggest that the ATC-13 attempt 
to use 
expert opinion resulted in more apparent precision in estimating than was 
justified by the 
data. In addition, the attempt to provide 30%, 60% and 100% restoration 
estimates may 
be relevant for lifelines, but has little meaning for building function. Typical 
business 
and service facilities either provide something approaching 100% function in a 
fairly 
short time after the earthquake or cease to exist. Considerable improvisation 
and 
ingenuity is usually applied by management and staff to ensure rapid restoration 
. 

 

Thus, this methodology presents a much simplified set of estimates, which it is 
felt match 
the current state of knowledge. In doing this, the distinction between the time 
needed for 
repair and the often much longer time needed for the whole repair project is 
recognized 
by multipliers applied to the extended construction time. In addition, the fact 
that 
business function can be to a large extent divorced from the building that 
housed it is also 
recognized by these multipliers. The latter situation might vary greatly among 
different 
kinds of business and users of the methodology may find it useful to discuss 
with key 
businesses in their area the functional consequences of building damage. It is 
also a 
reasonable supposition that businesses that have not experienced earthquake 
damage tend 
to overestimate its effect on their operation because it is hard for them to 
imagine 
emergency improvisation since they lack the experience. 

 

Table 15A-2 shows a correlation between the the methodologys damage states and 
the 
ATC-13 estimates for functional restoration time: these may be compared with the 




estimates in Tables 15.09, 15.10 and 15.11 The ATC estimates assume that repair 
time is 
equivalent to restoration time. 

 

 

Table 15A.2: ATC-13: Restoration Times Related to Occupancy Classes 

(Time in days) (ATC-13, 1985) 

 

 

 

Damage State 

No. 

Label 

Occupancy Class 

Slight 

Moderate 

Extensive 

 

 

Residential 

 

 

 

1 

RES1 

Single Family Dwelling 

3 

11-72 

72-146 

2 

RES2 

Mobile Home 

3 

11-72 

72-146 

3-8 

RES3a-
f 

Multi Family Dwelling 

3 

11-72 

72-146 

9 

RES4 

Temporary Lodging 

3 

11-72 

72-146 

10 

RES5 

Institutional Dormitory 

3 

11-72 

72-146 

11 

RES6 

Nursing Home 

3 

11-72 

72-146 

 

 

Commercial 

 

 

 

12 

COM1 

Retail Trade 

20 

71-202 

202-347 

13 

COM2 

Wholesale Trade 

20 

71-202 

202-347 

14 

COM3 

Personal and Repair Services 

20 

71-202 

202-347 

15 

COM4 

Professional/Technical Services 

20 

71-202 

202-347 

16 

COM5 

Banks/Financial Institutions 

20 

71-202 

202-347 

17 

COM6 

Hospital 

56 

156-338 

338-613 

18 

COM7 

Medical Office/Clinic 

56 

156-338 

338-613 

19 

COM8 

Entertainment & Recreation 

20 

71-202 

202-343 

20 

COM9 

Theaters 

20 

71-202 

202-343 

21 

COM10 

Parking 

6 

24-76 

76-172 

 

 

Industrial 

 

 

 

22 

IND1 

Heavy 

23 

99-240 

240-405 

23 

IND2 

Light 

23 

99-240 

240-405 

24 

IND3 

Food/Drugs/Chemicals 

16 

72-235 

235-380 

25 

IND4 

Metals/Minerals Processing 

22 

99-248 

248-405 

26 

IND5 

High Technology 

16 

112-258 

258-429 

27 

IND6 

Construction 

28 

68-121 

121-257 

 

 

Agriculture 

 

 

 

28 

AGR1 

Agriculture 

9 

26-77 

77-154 

 

 

Religion/Non-Profit 

 

 

 

29 

REL1 

 Church/Membership 
Organization 

17 

72-215 

215-382 

 

 

Government 

 

 

 

30 

GOV1 

General Services 

28 

91-196 

196-396 

31 

GOV2 

Emergency Response 

18 

60-134 

134-256 

 

 

Education 

 

 

 

32 

EDU1 

 Schools/Libraries 

16 

72-183 

183-362 

33 

EDU2 

Colleges/Universities 

16 

72-183 

183-362 



 

Note: Methodology Damage State 

 Slight = ATC #3: (CDF 5%) 

 Moderate: 30%, = between ATC 4-5 (20 - 45%) 




 Extensive 50%, = between ATC 5-6 (45 - 80%) 




 

APPENDIX 15B. Lifeline Subcomponent Information 

(Damage Ratios & Fraction of Value) 

 

 

 

Table15B.1. Subcomponents for the Railway System(G&E, 1994) 

 

Sub-Component 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 

Fuel Facilities 

Electric Backup Power 

2 % 

slight 

moderate 

0.20 

0.70 

Tanks 

86 % 

slight 

moderate 

extensive 

complete 

0.20 

0.40 

0.85 

1.00 

Pump Building 

2 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Horizontal Pumps 

5 % 

extensive 

0.75 

Electrical Equipment 

5 % 

moderate 

0.50 

Dispatch Facilities 

Electric Backup Power 

30 % 

slight 

moderate 

0.20 

0.70 

Building 

20 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Electrical Equipment 

20 % 

moderate 

0.80 

Railway Bridges 

Column 

 

slight 

extensive 

complete 

0.05 

0.25 

0.8 

Abutment 

 

slight 

moderate 

extensive 

0.02 

0.075 

0.15 

Connection 

 

moderate 

extensive 

0.01 

0.02 

Deck 

 

slight 

0.05 



 




 

Table15B.2. Subcomponents for DC Substations (G&E, 1994) 

Subcomponent 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 

Building 

35 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Equipment 

65 % 

moderate 

0.80 



 

 

Table15B.3. Subcomponents for the Bus System (G&E, 1994) 

Subcomponent 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 

Fuel Facilities 

Electric Backup Power 

2 % 

slight 

moderate 

0.20 

0.70 

Tanks 

79 % 

slight 

moderate 

extensive 

complete 

0.20 

0.40 

0.85 

1.00 

Building 

11 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Pumps 

4 % 

extensive 

0.75 

Electrical Equipment 

4 % 

moderate 

0.50 

Dispatch Facilities 

Electric Backup Power 

15 % 

slight 

moderate 

0.20 

0.70 

Building 

30 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Electrical Equipment 

55 % 

moderate 

0.80 



 




 

Table15B.4. Subcomponents for Port, Ferry and Airport Systems (G&E, 1994) 

 

Sub-Component 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 

Port Fuel Facilities 

Electric Backup Power 

5 % 

slight 

moderate 

0.20 

0.70 

Tanks 

70 % 

slight 

moderate 

extensive 

complete 

0.20 

0.40 

0.85 

1.00 

Pump Building 

5 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Horizontal Pumps 

10 % 

extensive 

0.75 

Electrical Equipment 

10 % 

moderate 

0.50 

Ferry Fuel Facilities 

Electric Backup Power 

3 % 

slight 

moderate 

0.20 

0.70 

Tanks 

72 % 

slight 

moderate 

extensive 

complete 

0.20 

0.40 

0.85 

1.00 

Pump Building 

5 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Horizontal Pumps 

10 % 

extensive 

0.75 

Electrical Equipment 

10 % 

moderate 

0.50 

Airport Fuel Facilities 

Electric Backup Power 

6 % 

slight 

moderate 

0.20 

0.70 

Tanks 

64 % 

slight 

moderate 

extensive 

complete 

0.20 

0.40 

0.85 

1.00 

Pump Building 

6 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Horizontal Pumps 

12 % 

extensive 

0.75 

Electrical Equipment 

12 % 

moderate 

0.50 






 

Table15B.5. Subcomponent for Potable Water System Components (G&E, 1994) 

 

Sub-Component 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 



Water Treatment Plant 

Electric Backup Power 

4 % 

slight 

moderate 

0.20 

0.70 

Chlorination 

Equipment 

4 % 

slight 

moderate 

0.15 

0.50 

Sediment Flocculation 

12 % 

slight 

moderate 

0.20 

0.50 

Chemical 

Tanks 

20 % 

slight 

moderate 

0.20 

0.75 

Electric Equipment 

30 % 

moderate 

0.60 

Elevated Pipe 

10 % 

extensive 

complete 

0.65 

0.90 

Filter Gallery 

20 % 

complete 

1.00 



Wells 

Electric Backup Power 

16 % 

slight 

moderate 

0.20 

0.70 

Well Pump 

34 % 

extensive 

0.75 

Building 

16 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Electric Equipment 

34 % 

moderate 

0.60 



Pumping Plants 

Electric Backup Power 

16 % 

slight 

moderate 

0.20 

0.70 

Pumps 

34 % 

extensive 

0.75 

Building 

16 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Electrical Equipment 

34 % 

moderate 

0.60 



 




 

Table15B.6. Subcomponets for Waste Water Treatment (G&E, 1994) 

 

Subcomponents 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 

Electric Backup Power 

5 % 

slight 

moderate 

0.20 

0.70 

Chlorination Equipment 

3 % 

slight 

moderate 

0.15 

0.50 

Sediment Flocculation 

36 % 

slight 

moderate 

extensive 

0.20 

0.50 

0.80 

Chemical 

Tanks 

7 % 

slight 

moderate 

0.20 

0.75 

Electrical/ Mechanical 
Equipment 

14 % 

moderate 

0.60 

Elevated Pipe 

8 % 

extensive 

complete 

0.65 

0.90 

Buildings 

27 % 

complete 

1.00 



 




 

Table15B.7 Subcomponents for Crude & Refined Oil Systems(G&E, 1994) 

 

Sub-Component 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 

Refineries 

Electric Backup Power 

3 % 

slight 

moderate 

0.20 

0.70 

Electrical/ Mechanical 
Equipment 

6 % 

moderate 

0.60 

Tanks 

42 % 

slight 

moderate 

extensive 

complete 

0.20 

0.40 

0.85 

1.00 

Stacks 

42 % 

extensive 

0.80 

Elevated Pipe 

7 % 

complete 

1.00 

Pumping Plants 

Electric Backup Power 

30 % 

slight 

moderate 

0.20 

0.70 

Pump 

20 % 

extensive 

0.75 

Building 

20 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Electrical/ Mechanical 
Equipment 

30 % 

moderate 

0.60 

Tank Farms 

Electric Backup Power 

6 % 

slight 

moderate 

0.20 

0.70 

Electrical/ Mechanical 
Equipment 

24 % 

moderate 

0.60 

Tanks 

58 % 

slight 

moderate 

extensive 

complete 

0.20 

0.40 

0.85 

1.00 

Elevated Pipes 

12 % 

extensive 

complete 

0.65 

0.90 



 




 

Table15B.8. Subcomponents for Electrical Substations (G&E, 1994) 

 

Classification 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 

Transformers 

68 % 

extensive 

complete 

0.50 

1.00 

Circuit Breakers 

26 % 

slight 

moderate 

extensive 

complete 

0.17 

0.33 

0.67 

1.00 

Disconnect Switches 

3 % 

slight 

moderate 

extensive 

complete 

0.17 

0.42 

0.67 

1.00 

Current Transformers 

3 % 

extensive 

complete 

0.67 

1.00 



 

 

Table15B.9. Subcomponents for Generation Plant (G&E, 1994) 

 

Subcomponents 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 

Electrical Equipment 

17 % 

slight 

moderate 

0.30 

0.60 

Boilers & Pressure 
Vessels 

19 % 

moderate 

0.50 

Vertical vessels 

5 % 

moderate 

extensive 

0.50 

0.80 

Pumps 

9 % 

extensive 

0.75 

Horizontal vessels 

14 % 

complete 

1.00 

Large motor operated 
valves 

5 % 

complete 

1.00 

Boiler Building 

17 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 

Turbine Building 

14 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 



 




 

Table15B.10. Subcomponents for Communication Centers (G&E, 1994) 

 

Subcomponents 

Fraction of Total 
Component Value 

Damage State 

Damage Ratio 

Electric Power (Backup) 

15 % 

slight 

moderate 

0.20 

0.70 

Switching Equipment 

49 % 

slight 

moderate 

extensive 

complete 

0.05 

0.20 

0.60 

1.00 

Building 

36 % 

slight 

moderate 

extensive 

complete 

0.10 

0.40 

0.80 

1.00 



 




Chapter 16 

Indirect Economic Losses 

 

16.1 Introduction 

 

This Chapter is written with several goals in mind. First, it is intended to 
familiarize the 
reader with the concept of indirect loss, including a brief discussion of input-
output 
models, the traditional approach for tracing interindustry ripple effects 
(Sections 16.2 and 
16.3). 

 

 Second, an algorithm for addressing supply shocks (the engine of the Indirect 
Loss 
Module) is developed and explained. Section 16.4 develops a method for computing 
indirect losses, one that addresses the effects of supply and demand 
disruptions. The 
Indirect Loss Module is a computational algorithm which accounts for earthquake 
induced supply shortages (forward linkages) and demand reductions (backward 
linkages). 
The module is a version of a computable general equilibrium model designed to 
rebalance a region's interindustry trade flows based on discrepancies between 
sector 
supplies and demands. The flowchart of the overall methodology, highlighting the 
Indirect Loss Module and its relationship to other modules is shown in Figure 
16.1. 

 

 Third, the chapter discusses data requirements and operational issues related 
to running 
the module for different levels of analysis. Section 16.5 provides an overview 
of input 
data, module operation, and results output in a Default or User-Supplied Data 
Analysis. 
It also includes suggestions for approaches to conducting a Advanced analysis. 

 

 Finally, a number of experiments are reported to assist the user in 
interpreting the 
Modules results. Section 16.6 analyzes how patterns of direct damage, 
preexisting 
economic conditions (unemployment, import-export options, and economic 
structure) 
and external assistance alter indirect loss. Example solutions based on the 
Northridge 
earthquake are provided, along with the results of Monte Carlo simulations. The 
former 
is provided to illustrate how the model can be applied, the latter to suggest 
the wide range 
of possible outcomes. Lastly, a set of helpful observations are presented. 

 

16.2 What are Indirect Losses? 

 

Earthquakes may produce dislocations in economic sectors not sustaining direct 
damage. 
All businesses are forward-linked (rely on regional customers to purchase their 
output) or 
backward-linked (rely on regional suppliers to provide their inputs) and are 
thus 
potentially vulnerable to interruptions in their operation. Such interruptions 
are called 
indirect economic losses. Note that these losses are not confined to immediate 
customers 
or suppliers of damaged enterprises. All of the successive rounds of customers 
of 
customers and suppliers of suppliers are impacted. In this way, even limited 
earthquake 
physical damage causes a chain reaction, or ripple effect, that is transmitted 
throughout 
the regional economy. 

 




8. Lifelines-
UtilitySystems4. Ground Motion4. Ground FailureDirect Physical 
Damage6. Essential and 
High Potential 
Loss Facilities12. Debris10. Fire15. Economic14. Shelter9. Inundation11. 
HazMat16. IndirectEconomicLossesPotential Earth Science HazardsDirect Economic/ 
Social LossesInduced Physical 
Damage7. Lifelines-
TransportationSystems5. GeneralBuildingStock13. Casualities 


 

Figure 16.1 Indirect Loss Estimation Relationship to Other Modules in the 
Earthquake Loss Estimation Methodology 




 

The extent of indirect losses depends upon such factors as the availability of 
alternative 
sources of supply and markets for products, the length of the production 
disturbance, and 
deferability of production. Figure 16.2 provides a highly-simplified depiction 
of how 
direct damages induce indirect losses. In this economy firm A ships its output 
to one of 
the factories that produce B, and that factory ships to C. Firm C supplies 
households 
with a final product (an example of a final demand, FD) and could also be a 
supplier of 
intermediate input demand to A and B. There are two factories producing output 
B, one 
of which is destroyed in the earthquake. The first round of indirect losses 
occurs 
because: 1) direct damage to production facilities and to inventories cause 
shortages of 
inputs for firms needing these supplies (forward-linked indirect loss); 2) 
damaged 
production facilities reduce their demand for inputs from other producers 
(backward-
linked indirect loss); or 3) reduced availability of goods and services stunt 
household, 
government, investment, and export demands (all part of final demand). 

 

BACKWARD -
LINKED LOSSFORWARD -
LINKED LOSSABCBEXPORTSIMPORTSINVENTORIESFDINVENTORIESUNUSED 
CAPACITYRECONSTRUCTION 
DEMANDS 


 

Figure 16.2 Indirect Losses and Adjustments to Lessen Them 

 

16.2.1 Supply Shortages and Forward Linked Losses 

 

The supply shortages caused as a result of reduced availability of input B could 
cripple 
factory C, if C is unable to locate alternative sources. Three options are 
possible: 1) 
secure additional supplies from outside the region (imports); 2) obtain 
additional supplies 
from the undamaged factory (excess capacity); and 3) draw from B's unsold stock 
of 
output (inventories). The net effect of diminished supplies are referred to as 
forward-
linked losses, the term forward (often referred to as downstream) implying that 
the 
impact of direct damages is shifted to the next stage or stages of the 
production process. 

 




16.2.2 Demand Effects and Backward Linked Losses 

 

Disasters can also produce indirect losses if producer and consumer demands for 
goods 
and services are reduced. If, in the example provided in Figure 16.2, firm B has 
a 
reduced demand for inputs from A, then A may be forced to scale back operations. 
As in 
the case of forward-linked losses, the affected firms may be able to circumvent 
a 
weakened market, in this case by either finding alternative outlets such as 
exports or 
building up inventory.1 

 

The higher rate of unemployment caused by direct damages and subsequent indirect 
factory slowdowns or closures would reduce personal income payments and could 
cause 
normal household demands to erode. However, it is more likely that the receipt 
of 
disaster assistance, unemployment compensation, or borrowing, would buoy 
household 
spending throughout the reconstruction period. Evidence from recent events 
(Hurricanes 
Andrew and Hugo, the Loma Prieta Earthquake and the Northridge Earthquake) 
confirms 
that normal household demands are only slightly altered by disaster in the 
short-run. As 
a result of this observation, the Indirect Loss Module discussed below delinks 
household 
incomes and demands. 

 

16.2.3 Regional vs. National Losses 

 

It has sometimes appeared that natural disasters tend to stimulate employment 
and 
revitalize a region. Clearly, the generous federal disaster relief policies in 
place after the 
1964 Alaskan earthquake, the 1971 San Fernando earthquake, and Hurricane Agnes 
in 
1972, served to buoy the affected economies, thereby preventing the measurement 
of 
significant indirect losses. From a regional accounting stance, it appeared that 
the net 
losses were inconsequential. However, this viewpoint fails to take into account 
the cost 
of disasters on both household and federal budgets. 

 

Some, if not most, public and private post-disaster spending is unfunded; that 
is, it is not 
paid for out of current tax revenues and incomes. In the case of households this 
amounts 
to additional indebtedness which shifts the burden or repayment to some future 
time 
period. Federal expenditures are not budget neutral either. As in the case of 
households, 
governments cannot escape the financial implications of increased spending for 
disaster 
relief. Either lower priority programs must be cut, taxes raised, or the federal 
debt 
increased. The first two options simply shift the reduction in demand and 
associated 
indirect damages to other regions. Projects elsewhere may be canceled, services 
curtailed, and/or household spending diminished as after-tax incomes shrink. The 
debt 
option provides no escape either, since it, too, places the burden on others, 
e.g., a future 
generation of taxpayers. 

 

From a national accounting stance, indirect losses can be measured by deriving 
regional 
indirect impacts, adjusted for the liability the Federal government incurs in 
providing 

1Building up inventory is not a permanent solution, since eventually the 
inventories have to be sold. Firms 
may be willing to do so on a temporary basis, hoping that market conditions will 
improve at a later date. 




disaster relief, and for offsetting increases in outputs elsewhere. The positive 
effects 
outside aid produces for the region are to some degree offset by negative 
effects 
produced by the three federal budget options. Since it is impossible to know a 
priori 
which option the federal government will utilize, it is safest to assume that 
the two effects 
cancel, i.e., that the positive outcomes from federal aid are offset by the 
negative national 
consequences caused by the budget shortfall. 

 

Since the primary user of the Loss Estimation Methodology is likely to be the 
local entity 
involved in seismic design and zoning decisions, the Indirect Loss Module is 
designed 
accordingly. That is, it adopts a local accounting stance. One simplistic 
approach to 
obtaining a national measure of net loss would be to exercise the Loss Module 
excluding 
outside federal assistance. 

 

16.3 Interindustry Models 

 

Input-output techniques are widely utilized to assess the total (direct plus 
higher-order) 
economic gains and losses caused by sudden changes in the demand for a region's 
products. Higher demand for rebuilding and a lower demand for tourism, for 
example, 
lend themselves to traditional input-output I-O methods. This technique is 
relatively 
simple to apply and is already in widespread use in state and local agencies, 
though not 
necessarily those associated with emergency management. However, input-output 
models compromise realism, primarily in the area of supply bottlenecks. Although 
the 
Indirect Loss Module addresses both supply and demand shocks in a more 
sophisticated 
manner, it is based on the same foundation as the input-output modela region's 
interindustry input requirements. Because the two approaches share a common 
base, we 
begin by introducing the principles underlying input-output analysis, with an 
emphasis on 
demand disturbances, and then extend the framework to accommodate supply shocks. 

 

Input-output analysis was first formulated by Nobel laureate Wassily Leontief 
and has 
gone through several decades of refinement by Leontief and many other 
economists. At 
its core is a static, linear model of all purchases and sales between sectors of 
an economy, 
based on the technological relationships of production. Input-output (I-O) 
modeling 
traces the flows of goods and services among industries and from industries to 
household, 
governments, investment, and exports. These trade flows indicate how much of 
each 
industry's output is comprised of its regional suppliers' products, as well as 
inputs of 
labor, capital, imported goods, and the services of government. The resultant 
matrix can 
be manipulated in several ways to reveal the economy's interconnectedness, not 
only in 
the obvious manner of direct transactions but also in terms of dependencies 
several steps 
removed (e.g., the construction of a bridge generates not only a direct demand 
for steel 
but also indirect demands via steel used in machines for its fabrication and in 
railroad 
cars for its transportation). 

 

The very nature of this technique lays it open to several criticisms: the models 
are 
insensitive to price changes, technological improvements, and the potential for 
input 
substitution at any given point in time. However, even with these limitations, 
I-O 
techniques are a valuable guide for the measurement of some indirect losses. A 
very 




2 Input-output and interindustry are often used synonymously because of the 
emphasis in I-O on the 
sectoral unit of analysis, mainly comprised of producing industries. Strictly 
speaking, however, 
interindustry refers to a broad set of modeling approaches that focus on 
industry interactions, including 
activity analysis, linear programming, social accounting matrices, and even 
computable general 
equilibrium models. Most of these have an input-output table at their core. The 
reader interested in a more 
complete understanding of I-O analysis is referred to Rose and Miernyk (1989) 
for a brief survey; Miller 
and Blair (1985) for an extensive textbook treatment; and Boisvert (1992) for a 
discussion of its 
application to earthquake impacts. For other types of interindustry models 
applied to earthquake impact 
analysis, the reader is referred to the work of Rose and Benavides (1997) for a 
discussion of mathematical 
programming and to Brookshire and McKee (1992) for a discussion of computable 
general equilibrium 
analysis. 
brief technical review is provided for those readers who may be unfamiliar with 
interindustry modeling.2 

 

16.3.1 A Primer on Input-Output Techniques 

 

The presentation is restricted to a simple three industry economy. The shipments 
depicted as arrows in Figure 16.2 are represented as annual flows in Table 16.1. 
The X's 
represent the dollar value of the good or service shipped from the industry 
listed in the 
left-hand heading to the industry listed in the top heading. The Y's are 
shipments to 
consumers (goods and services), businesses (investment in plant and equipment 
and 
retained inventories), government (goods, services and equipment), to other 
regions 
(exported goods and services). The V's are the values-added in each sector, 
representing 
payments to labor (wages and salaries), capital (dividends, rents, and 
interest), natural 
resources (royalties and farm rents), and government (indirect business taxes). 
The M's 
represent imports to each producing sector from other regions. 

 

A basic accounting balance holds: total output of any good is sold as an 
intermediate 
input to all sectors and as final goods and services: 

 

 XA = XAA + XAB + XAC + YA (16-1) 

 

Rearranging terms, the amount of output available from any industry for final 
demand is 
simply the amount produced less the amount shipped to other industries. 

 

 




Table 16.1 Intersectoral Flows of a Hypothetical Regional Economy (dollars) 

 

To 

From 

 

A 

 

B 

 

C 

Final 

Demand 

Gross 

Output 

A 

XAA 

XAB 

XAC 

YA 

XA 

B 

XBA 

XBB 

XBC 

YB 

XB 

C 

XCA 

XCB 

XCC 

YC 

XC 

V 

VA 

VB 

VC 

 

 

M 

MA 

MB 

MC 

 

 

Gross Outlay 

XA 

XB 

XC 

Y 

X 



 

To transform the I-O accounts into an analytical model, it is then assumed that 
the 
purchases by each of the industries have some regularity and thus represent 
technological 
requirements. Technical coefficients that comprise the structural I-O matrix are 
derived 
by dividing each input value by its corresponding total output. That is: 

 

 aXXAAAAA=; ;
BABABXXa= aXXACACC=; (16-2) 

 

The a's are simply the ratios of inputs to outputs. An of 0.2 means that 20 
percent of 
industry B's total output is comprised of product A. 
ABa

 

Equation (16-1) can then be written as: 

 

 ACACBABAAAAYXaXaXaX+++= (16-3) 

 

In matrix form Equation (16-3) is: 

 

 X = AX + Y (16-4) 

 

To solve for the gross output of each sector, given a set of final demand 
requirements, we 
proceed through the following steps: 

 

 (I - A)X = Y (16-5) 

 

 (I - A)-1Y = X (16-6) 

 

The term (I - A)--1 is known as the Leontief Inverse. It indicates how much each 
sectors 
output must increase as a result of (direct and indirect) demands to deliver an 
additional 
unit of final goods and services of each type. It might seem that a $1 increase 
in the final 
demand for product A would result in the production of just an additional $1 
worth of A. 
However, this ignores the interdependent nature of the industries. The 
production of A 
requires ingredients from a combination of industries, A, B, and/or C. 
Production of B, 
requires output from A, B, and/or C, and so on. Thus, the one dollar increase in 
demand 
for A will stimulate A's production to change by more than one dollar. The 
result is a 




3 Note that the previous discussion pertains to demand-side (backward-linked) 
multipliers. A different set 
of calculations is required to compute supply-side (forward-linked) multipliers. 
(Computationally, the 
structural coefficients of the supply-side model are computed by dividing each 
element in a given row by 
the row sum.) Though mathematically symmetric, the two versions of the model are 
not held in equal 
regard. There is near universal consensus that demand-side multipliers have 
merit because there is no 
question that material input requirements are needed directly and indirectly in 
the production. However, 
the supply-side multipliers have a different connotationthat the availability 
of an input stimulates its very 
use. To many, this implies the fallacy of supply creates its own demand. Thus, 
supply-side multipliers 
must be used with great caution, if at all, and are not explored at length here. 
For further discussion of the 
conceptual and computational weaknesses of the supply-side model, see 
Oosterhaven (1988) and Rose and 
Allison (1988). 

 

Note also that the multipliers discussed thus far pertain to output 
relationships. Multipliers can also be 
calculated for employment, income, and income distribution effects in analogous 
ways. Also note that 
sectoral output multipliers usually have values of between 2.0 and 4.0 at the 
national level and are lower 
for regions, progressively shrinking as these entities become less self-
sufficient and hence the endogenous 
cycle of spending is short-circuited by import leakages. Sectoral output 
multipliers for Suffolk County, the 
core of the Boston Metropolitan Statistical Area, are for the most part in the 
range of 1.5 to 2.0. 

multiple of the original stimulus, hence, the term "multiplier effect (a 
technical synonym 
for ripple effect). 

 

Given the assumed regularity in each industry's production requirements, the 
Leontief 
Inverse need only be computed once for any region (at a given point in time) and 
can 
then be used for various policy simulations reflected in changes in final demand 
(e.g., the 
impact of public sector investment) as follows: 

 

 (I - A)-1.Y = .X (16-7) 

 

More simply, the column sums of the Leontief Inverse are sectoral multipliers, 
M, 
specifying the total gross output of the economy directly and indirectly 
stimulated by a 
one unit change in final demand for each sector. This allows for a 
simplification of 
Equation (16-7) for cases where only one sector is affected (or where one wishes 
to 
isolate the impacts due to changes in one sector) as follows:3 

 

 MA.YA = .X (16-8) 

 

Under normal circumstances final demand changes will alter household incomes and 
subsequently consumer spending. Thus, under some uses of input-output 
techniques, 
households (broadly defined as the recipients of all income payments) are 
"endogenized" 
(included within the A matrix) by treating it as any other sector, i.e., a user 
(consumer) of 
outputs and as a supplier of services. An augmented Leontief inverse is computed 
and 
yields a set of coefficients, or multipliers, that capture both indirect 
(interindustry) and 
subsequent induced (household income) effects. Multipliers are computed from a 
matrix with respect to households. These are referred to as Type II multipliers 
in contrast 
to the Type I multipliers derived from the open I-O table, which excludes 
households. 
Of course, since they incorporate an additional set of spending linkages, Type 
II 
multipliers are larger than Type I, typically by around 25%. 

 




4 Economic activity can be gauged by several indicators. One is Gross Output 
(sales volume). Another is 
Value-Added, or Gross National Product (GNP), which measures the contribution to 
the economy over and 
above the value of intermediate inputs already produced, thereby avoiding 
double-counting (note the 
Gross in GNP simply refers to the inclusion of depreciation and differs from 
double-counting meaning of 
the term in Gross Output.) Specifically, Value-Added refers to returns to 
primary factors of production: 
labor, capital, and natural resources. The concept is identical to the oft used 
term National Income, which 
is numerically equal to GNP. 

16.3.2 An Illustration of Backward Linked Losses 

 

Conventional input-output models provide a starting point for measuring indirect 
damages that are backward-linked, providing that the disaster does not 
significantly alter 
the regions input patterns and trade flows. In the next section, we will 
discuss 
modifications of the methodology for such changes. The calculation of indirect 
damages 
for the more simple case is illustrated in the following example beginning with 
the input-
output transactions matrix presented in Table 16.2. 

 

Table 16.2: Interindustry Transactions 

 

To 

From 

A 

B 

Households 

Other Final 
Demand 

Gross 

Output 

A 

20 

45 

30 

5 

100 

B 

40 

15 

30 

65 

150 

Households 

20 

60 

10 

10 

100 

Imports 

20 

30 

30 

0 

80 

Gross Outlay 

100 

150 

100 

80 

430 



 

This simplified transactions table is read as follows: $20 of industry As 
output is used 
by itself (e.g., a refinery uses fuel to transform crude oil into gasoline and 
heating oil). 
$45 of output A is shipped to industry B. $30 is marketed to the household 
sector and $5 
is sold to government, used in investment, or exported to another region. $20 
worth of 
household services is required to produce $100 of output A, and $60 is needed 
for $150 
of B. According to the table, 30 percent of the consumers gross outlay is 
allocated to the 
purchase of A, 30 percent to B, 10 percent to household services, and 30 percent 
to 
imports. 

 

Assume that the input-output tables shown above represent a tourist-based 
seaside 
economy. Industry A represents construction while B represents tourism. What 
would 
happen to this economy if an earthquake destroyed half the regions beachside 
hotels? 
Direct economic losses are comprised of manmade assets destroyed in the 
earthquake 
plus the reductions in economic activity4 in the tourist sector. Assume that the 
damage to 
hotels influences some tourists to vacation elsewhere the year of the disaster, 
reducing 
the annual $95 million demand for hotel accommodations by $45 million. 

 

For the purposes of this illustration, household spending and demands are 
linked. 
Therefore, a Type II multiplier would be utilized to assess the income and 
output changes 
anticipated. The effect of declining tourism on the regions economy is easily 
derived 
from the initial change in demand and the Type II multipliers presented in 
Figure 16.3. 




Each tourist dollar not spent results in a loss of $1.20 and $2.03 worth of 
production from 
A and B, respectively. 

 

The resultant total (direct plus indirect) decline in regional household income 
is $1.17 per 
tourist dollar lost (row 3 column 2 of the closed Leontief Inverse). If nothing 
else 
changed (including no pick up in construction activity), the regional income 
lost for the 
year is $52.65 million ($45 million times 1.17). Of this total, $18 million (40 
cents of 
lost income for each tourist dollar lost, or .4 times $45 million) is directly 
traceable to the 
disaster, while the other $34.65 million in regional income loss represents 
indirect 
income losses cause by reduced demands for intermediate goods and consumer items 
via 
backward interindustry linkages and normal household spending. 

 

TOTAL COEFFICIENTS 
(TYPE II MULTIPLIER) 

DIRECT COEFFICIENTS 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2.12 

1.20 

1.11

 

 

.2 

.3 

.3 

 

 

(I-A)1- = 

 

1.29 

2.03 

1.11

 

A =

.4 

.1 

.3 

 

 

 

 

1.04 

1.17 

1.85

 

 

.2 

.4 

.1 

 

 

 

 

 
x $45 MILLION

 

 

 
x $45 MILLION 

 

= $52.65 MILLION 

 

 = $18 MILLION 

 

DIRECT, INDIRECT, INDUCED 
INCOME LOSSES 

 

 DIRECT INCOME LOSSES 

SECONDARY 
INCOME 

= $52.65 MILLION 

minus 

$18 MILLION 

LOSS 

 

= $34.65 MILLION 

 

 

 

 



 

Figure 16.3 Illustrative Computation 

 

16.3.3 The Impact of Outside Reconstruction Aid on the Region and the Nation 

 

Negative effects would be countered by the stimulative impact of state and 
federal 
disaster aid and insurance settlements. Whether these positive forces completely 
offset 
the negatives produced by the reduction in tourist trade hinges on the magnitude 
of the 
direct effects and the associated multipliers for these two activities. Assume, 
for 
example, that $50 million of outside reconstruction funds pour into the 
community in the 
first year. The Type II income multiplier for the construction industry is 1.04. 
The net 
regional income loss the year of the disaster is, therefore: ($50 million x 
1.04) - ($45 
million x 1.17), or a net loss of $0.65 million. 

 




Indirect income changes in this case are very significant and can be computed as 
the 
difference of total income impacts and direct income impacts. We know from the 
direct 
coefficients matrix that household income changes directly by 20 and 40 cents, 
respectively, for each dollar change in construction and tourist expenditures. 
The net 
indirect regional impact from the reduction in tourism, and the aid program are 
therefore: 
($50 x 1.04 - $50 x .2) - ($45 x 1.17 - $45 x .4), or a net gain of $7.35 
million. 

 

This is what the region loses; however, national impacts are quite different. 
The $50 
million of federal assistance injected into the region must be paid for either 
by cutting 
federal programs elsewhere, raising taxes, or borrowing. Each option impacts 
demand 
and outputs negatively. Although it is unlikely that they will precisely offset 
the gains 
the region enjoys, it is safe to assume that they will be similar in magnitude. 
If so, 
indirect losses from a national perspective is the net regional loss with the 
positive effects 
from federal aid omitted. The national net income loss will then remain $52.65 
million. 

 

The foregoing analysis was limited to the year of the disaster and presupposed 
that 
unemployed households did not dip into savings or receive outside assistance in 
the form 
of unemployment compensation, both of which are often the case. In terms of the 
summation of impacts over an extended time horizon, results do not significantly 
change 
if alternative possibilities are introduced. For example, if households choose 
to borrow 
or utilize savings while unemployed or to self-finance rebuilding, future 
spending is 
sacrificed. Therefore, even though an unemployed household may be able to 
continue to 
meet expenses throughout the reconstruction period, long-term levels of 
expenditure and 
hence product demand, must decline. 

 

In the preceding analysis, indirect losses were derived from demand changes 
only. This 
approach lends itself to events in which supply disruptions are minimal, or 
where 
sufficient excess capacity exists. A different method is required when direct 
damage 
causes supply shortages. The Indirect Loss Module, to which we now turn, 
modifies the 
basic I-O methodology to accommodate both supply and demand disruptions. 

 

16.4 The Indirect Loss Module 

 

The foregoing example illustrated how demand shocks filter through the economy 
to 
produce indirect losses. As indicated, supply shocks require a different 
treatment. Most 
supply shock models begin with the same trading pattern which produced the A 
matrix 
and subsequent multipliers inherent in the input-output method. However, once 
damage 
to buildings and lifelines constrain the capacity of each economic sector to 
ship its output 
to other sectors, or receive shipments, the trading patterns have to be 
readjusted. There 
are several ways to accomplish this. The simplest (Cochrane and Steenson, 1994) 
is to 
estimate how much each sector's output will decline as a result of direct damage 
and then 
address how the resultant excess demands and/or supplies will be filled and or 
disposed 
of. In the event that the sum of all interindustry demands and final demands 
exceed the 
post-disaster constraint on production, then available imports and inventory 
changes 
could temporarily help to rebalance the economy. In some sectors excess supplies 
might 
exist. If so, inventories may be allowed to accumulate or new markets might be 
found 




5This approach relies on both the existence of regional input-output tables and 
several assumptions 
regarding: inventory management, importability of shortages, exportability of 
surpluses and the amount of 
excess capacity existing in each sector. It does not accommodate the effects of 
relative price changes on 
final demands, nor does it entertain the degree to which labor and capital are 
substitutable in the underlying 
production functions. Treatment of these issues require a more sophisticated 
approach, one which is 
discussed in the literature under the topic heading Computable General 
Equilibrium (CGE) Systems. 

outside the affected region. Surviving production is reallocated according to 
the 
interindustry direct coefficients matrix until all sector excess supplies and 
demands are 
eliminated. At this point, a new level of regional output, value added and 
employment is 
computed and contrasted with the levels observed prior to the disaster. The 
difference 
between these levels approximates indirect loss.5 

 

16.4.1 Damage -- Linkage to the Direct Loss Module 

 

The Indirect Economic Loss module is linked to preceding modules through three 
channels in which damage, the direct shock, is introduced. First, building 
damage causes 
a certain degree of loss of function to each sector, forcing them to cut output. 
A vector of 
loss of function by industry in the first year of the disaster provides a set of 
constraints to 
the Indirect Loss module that is related to the general building stock damage 
levels. Loss 
of function is based upon the time needed to clean up and repair a facility or 
to rent an 
alternative facility to resume business functions (see Section 15.2.4). Loss of 
function is 
calculated for each occupancy class. Table 16.3 links the sectors in the 
Indirect Loss 
Module to the occupancy classes in the Direct Loss Module. Loss of function 
associated 
with lifeline disruption is not evaluated. 

 

Table 16.3 NIBS Occupancy Classes and Indirect Loss Module Economic 
Sectors 

Direct Loss Module 

Indirect Loss Module 

IND3 

Agriculture (Ag) 

NONE 

Mining (Mine) 

IND6 

Construction (Cnst) 

IND 1,2,3,4,5 (AVG.) 

Manufacturing (Mfg) 

COM3 

Transportation (TRANS) 

COM 1,2 (AVG.) 

Trade (Trde) 

COM 5,4 (AVG.) 

Finance, Insurance and Real Estate (FIRE) 

(COM 2,4,6,7,8,9; RES 4,6; REL; ED 1,2) (AVG.) 

Service (Serv) 

GOV1 

Government (Govt) 

NONE 

Miscellaneous (Misc) 



 

Second, post-disaster spending on reconstruction, repair and replacement of 
damaged 
buildings and their contents causes a stimulus effect in the Indirect Loss 
Module. This 
stimulus is based on the total dollar damage to buildings and contents. Third, 
reconstruction inputs for transportation and utility lifeline damage also 
provide a stimulus 
effect to the module. 

 




6The reason for this device is that many items are sold through wholesale and 
retail outlets and transported 
commercially, and, if included as "inputs" to these sectors, the linkage between 
buyers and sellers would be 
lost, i.e., it would appear that most purchases were from Wholesale/Retail Trade 
or Transportation, as if 
these sectors produced most items in the economy. 

 

Total levels of reconstruction expenditures are equivalent to damage estimates, 
but two 
modifications are needed before they can be incorporated into the analysis. One 
modification is the timing of the reconstruction in terms of weeks, months, or 
years after 
the earthquake. The distribution of reconstruction expenditures over time is 
discussed in 
Section 16.5.1.1 in relation to user inputs to the module. 

 

The other modification is the itemization of expenditures by type (plant, 
equipment, etc.) 
so that this spending injection is compatible with the economic model used to 
determine 
indirect effects. The input-output (I-O) model at the core of the module 
disaggregates the 
economy into sectors according to one-digit Standard Industrial Classification 
(SIC) 
codes. The brunt of the reconstruction expenditures will be assigned to 
Manufacturing 
and Construction sectors. 

 

One idiosyncrasy of the I-O model is the role of Wholesale and Retail Trade and 
of 
Transportation. These sectors are based on the concept of a "margin," i.e., the 
cost of 
doing business (labor, insurance, electricity, gasoline, office supplies) plus 
profits, but 
does not include the items sold or shipped (which are merely a pass-through in 
any 
case).6 Those expenditures assigned to Construction require no adjustment, but 
when 
spending on manufactured goods is inserted into the model, portions of the total 
should 
be assigned to the Wholesale/Retail Trade sector and to the Transportation 
sector. For 
very large items bought directly from the factory, there is no Trade sector 
activity, but for 
smaller items (e.g., office equipment, trucks), the adjustment is necessary. 
Generally, the 
Wholesale margin is 80%. Whether purchased from the factory or from the Trade 
sector, 
the Transportation margin is always applicable and is typically equal to 20%. 

 

A similar adjustment is necessary in nearly all cases for consumer spending for 
replacement of contents. In this case, it is more appropriate to use the Retail 
Trade 
margin of 80%. Again, the Transportation margin of 20% would be applicable to 
purchases of larger items. 

 

In cases where the margin adjustment is required, the user simply applies the 
following 
formulas: 

 

 
..LtmYM1+
= (16-9) 

 

 (16-10) ...LYTM-=

 

where: 




7The tolerable limit is the degree to which the solution values vary from one 
iteration to the next. 
.L = Portion of loss estimate (reconstruction/replacement) to which margin 
adjustment applies. 

 .YM = Manufacturing expenditures after margin adjustment. 

 .T = Retail/wholesale, trade or transportation expenditures. 

 tm = Retail/wholesale, trade or transportation margin. 

 

 

16.4.2 Supply-Side Adjustments and Rebalancing the Economy 

 

The Indirect Loss Module is a computational algorithm that utilizes input-output 
coefficients to reallocate surviving production. The algorithm computes post-
event 
excess demands and supplies. It rebalances the economy by drawing from imports, 
inventories, and idle capacity when supplies are constrained. It allows for 
inventory 
accumulation, production for export (to other regions) and sales to meet 
reconstruction 
needs in the event that normal demands are insufficient to absorb excess 
supplies The 
process of reallocation is governed by the amount of imbalance detected in each 
of the 
economy's sectors. Rebalancing is accomplished iteratively by adjusting 
production 
proportionately until the discrepancy between supplies and demands is within a 
tolerable 
limit.7 A simple schematic of the process is provided in Figure 16.4. 

 

User InputsDamageI/O Transactions 
DataSearch for supplies: 
Imports 
Local firms 
InventoriesSearch for 
outlets: 
Exports 
Local firms 
Households 
Final DemandFindOutlets?
FindSupplies?
ReduceOutputReduceOutputRecalculateTransactions 
MatrixTooMuch?
TooLittle?
ENDNoTooLittleTooMuchNoNoYesYes 


Figure 16.4 Indirect Loss Module Schematic 




 

This section illustrates how the model adjusts to supply-side constraints when a 
disaster 
causes disruption in the level and pattern of local production. 

 

Table 16.4 illustrates a simple economy with three industries: construction, 
manufacturing, and trade. There are also two rows for payments to households 
from 
those industries and imports which those industries require, plus two columns 
that 
represent household demands and exports. Households make no purchases from other 
households. All amounts in the table are in dollars. In the economys initial 
state, the 
row and column sums are equal. 

 

Table 16.4 Initial Transactions 

From/To 

Constr 

Mfg 

Trade 

HH 

Export 

Sum 

Constr 

10 

30 

20 

20 

35 

115 

Mfg 

20 

20 

10 

30 

80 

160 

Trade 

15 

20 

5 

40 

5 

85 

HH 

30 

40 

20 

 

 

90 

Import 

40 

50 

30 

 

 

120 

Sum 

115 

160 

85 

90 

120 

 



 

Table 16.5 shows how the economy changes due to the direct impact from a 
disaster. In 
this case, there is a 10% loss of manufacturing output as the result of damage 
to 
manufacturing facilities. Corresponding to this loss, both the purchases and 
sales of the 
manufacturing sector fall by 10%, as reflected in the row and column sums. The 
transactions directly affected are highlighted in bold type in the table. A new 
column, 
named Lost HH, has been added to this table to reflect manufacturing output 
that is 
unavailable to households because of the earthquake. 

 

Table 16.5 10% Direct Loss in Manufacturing 

From/To 

Constr 

Mfg 

Trade 

HH 

Export 

Sum 

Lost HH 

Constr 

10 

27 

20 

20 

35 

112 

 

Mfg 

18 

18 

9 

27 

72 

144 

3 

Trade 

15 

18 

5 

40 

5 

83 

 

HH 

30 

36 

20 

 

 

86 

 

Import 

40 

45 

30 

 

 

115 

 

Sum 

113 

144 

84 

87 

112 

 

 



 

Table 16.6 illustrates the first example of the indirect response to this 
situation. This is a 
fully-constrained economy, characterized by no more than 2% unemployment, 0% 
import replacement, 0% inventory availability or replacement, and 0% additional 
exports. 
This means that there are no ways for manufacturers to replace inputs that were 
disrupted 
by the disaster. 

 

Under these circumstances, construction and trade firms must cut their previous 
manufacturing by 10%. There is full employment in the local economy, meaning 
that 
other firms in manufacturing cannot increase output to meet the desired 
purchases by 




8 Construction only needs to increase its level of imports by 2, 5% of its 
initial imports of 40, and trade 
only requires an increase in imports of 1, or 3.3% of 30. Construction requires 
additional exports of 3, or 
8.6% of original exports. The limiting sector is trade, required to find export 
markets for 2 units, 40% of 
the 5 units it originally exported. 

9 Even if the slack assumptions are set higher, the algorithm limits sectoral 
production to be no higher than 
prior to the earthquake (unless there is a positive counter-stimulus from, say, 
reconstruction activity). 

construction and trade. Further imports are not allowed, and there are no 
inventories of 
manufacturing output to use. Construction and trade firms, faced with an 
irreplaceable 
10% loss in manufactured goods have no choice but to reduce their production by 
10%. 
The net result is that the 10% direct loss in manufacturing translates into a 
10% loss 
throughout the entire economy. Portions of the table affected by indirect loss 
are 
highlighted in italics. The row and column sums are once again in balance. 
Household 
consumption is decreased for all three sectors, and there is no way to make up 
for it. 

 

Table 16.6 Response to Loss with Fully Constrained Economy 

From/To 

Constr 

Mfg 

Trade 

HH 

Export 

Sum 

Lost HH 

Constr 

9 

27 

18 

18 

31.5 

103.5 

2 

Mfg 

18 

18 

9 

27 

72 

144 

3 

Trade 

13.5 

18 

4.5 

36 

4.5 

76.5 

4 

HH 

27 

36 

18 

 

 

81 

 

Import 

36 

45 

27 

 

 

108 

 

Sum 

103.5 

144 

76.5 

81 

108 

 

 



 

The fully constrained economy is an extreme case, and most economies are 
characterized 
by some flexibility, or slack, so that inputs can be replaced and outputs can be 
sold. We 
illustrate this by raising the potential level of additional imports by 10%, and 
the 
potential level of additional exports by 40%. This is insufficient to ensure 
that 
construction and trade can acquire the supplies they need to meet local demands 
and sell 
products that are no longer being bought by manufacturing.8 Sectors not 
suffering direct 
losses return to their pre-event levels of production.9 Manufacturing might 
import 
additional manufactured inputs where needed to replace its own direct losses, 
but labor is 
not available due to the low unemployment rate and the assumption that the 
temporarily 
unemployed labor in manufacturing will not be available to other firms in the 
sector. 
Manufacturing losses will only be replaced as damaged manufacturing facilities 
return to 
production. 

 

In Table 16.7, the underlined values show where the important changes have 
occurred. 
Both construction and trade were allowed to import the manufactured inputs they 
lost as 
a result of the earthquake. Also, construction and trade exported that portion 
of their 
output that manufacturing no longer purchased. Because of these two factors, 
there is no 
indirect loss in the case illustrated in Table 16.7. 

 

The same results may be obtained in other ways. Instead of increasing imports, 
there 
might be some unemployment in the local economy. In this case, other firms in 
the 
manufacturing sector could hire some of the unemployed resources to make up the 
shortfall. Alternatively, there might be inventories of manufactured goods, 
either at the 




manufacturers or in storage at the construction and trade firms that require 
those goods. 
On the output side, firms faced with a reduction in purchases from the 
manufacturing 
sector may decide to continue production and store the resulting product in 
inventory 
until the disrupted facilities are back in production or until they can find new 
export 
markets. 

 

Table 16.7 Response to Loss with Relaxed Import and Export Constraints 

From/To 

Constr 

Mfg 

Trade 

HH 

Export 

Sum 

Lost HH 

Constr 

10 

27 

20 

20 

38

115 

 

Mfg 

18 

18 

9 

27 

72 

144 

3 

Trade 

15 

18 

5 

40 

7

85 

 

HH 

30 

36 

20 

 

 

86 

 

Import 

42

45 

31

 

 

118 

 

Sum 

115 

144 

85 

87 

117 

 

 



 

In Table 16.7, manufacturing remains at its immediate post-disaster level 
because the 
situation being illustrated is immediately after the event, before 
reconstruction can take 
place. If the slack in the system came from unemployment instead of imports, the 
results 
would be different. That portion of the manufacturing sector undamaged by the 
earthquake could hire additional resources and make up the direct losses. 
Overall 
production would regain its pre-disaster levels. Therefore, unlike the example 
illustrated 
which shows no net indirect change, there would be a net indirect increase in 
sales that 
would be equal to the direct loss, making for a net economic change of zero. 

 

Tables 16.6 and 16.7 show an important way in which this algorithm departs from 
traditional I-O analysis. The technical coefficients for both Tables are 
different from 
those of the original economy. This is because imports and exports have been 
allowed to 
replace lost supplies and sales in the system. The usual technical coefficients 
in an I-O 
table assume that the relationships between imports and intermediate inputs are 
fixed, as 
well as assuming that the relationships between exports and intermediate outputs 
are 
fixed. Though these assumptions are convenient for the purposes of I-O analysis, 
they 
are a departure from reality in general, and especially so in emergency 
situations. Also 
note, from Table 16.7, that the household and import/export sectors are no 
longer 
balanced in terms of row and column sums. This is due to the short-run nature of 
the 
problems being solved in the model. In the longer run, households must repay 
their 
borrowing, and exports must rise to repay the short-run imports, unless 
government 
disaster aid or some other form of external financing is used to pay for the 
short-run 
consumption and imports. 

 

Tables 16.6 and 16.7 illustrate the two extremes that the model can reflect in 
responding 
to pure supply-side disruptions. In its fully functional implementation, the 
model adjusts 
simultaneously for multiple shocks of varying amplitude in any number of 
sectors, while 
also accounting for demand-side (final demand) increases that typically 
accompany 
disasters. 

 




16.4.3 The Time Dimension 

 

The model is evaluated at various levels of temporal resolution for the fifteen 
(15) year 
period following the earthquake. For the first two (2) months after the 
earthquake, 
weekly time intervals are used. Between two (2) months and twenty four (24) 
months, 
the economy is evaluated on a monthly basis. From two (2) years to fifteen (15) 
years, 
the economy is evaluated annually. It is made dynamic by considering how 
industry loss 
of function is restored and reconstruction expenditures are made over the time 
windows. 
Thus while the inputs to the Indirect Economic Loss module differ with each time 
interval, the rebalancing algorithm for the economy and adjustment factors 
(e.g., 
availability of supplemental imports to make up for lost production) do not 
change. The 
time patterns of functional restoration and reconstruction are user inputs and 
are 
discussed in Section 16.5. 

 

16.4.4 The Effects of Rebuilding and Borrowing 

 

Borrowing impacts the model in that future demands are reduced in proportion to 
the 
temporal payments for rebuilding. In the case of Northridge this amounted to 
less than 
50 percent. Federal assistance and insurance settlements provided the bulk of 
the 
financial resources for reconstruction. The importance of refinancing lies in 
longer-term 
effects of repayment. If the affected region receives no assistance then the 
stimulative 
effects of rebuilding are only temporary. The region will eventually have to 
repay loans 
and future spending will suffer. This is accounted for in the model as follows. 

 

1. It is assumed that all loans mature 15 years from the time of the earthquake. 
Therefore, the first year's loans are for 15 years. The second year's loans are 
for 
14 years, and so on. 

2. Tax implications are ignored. Interest is not tax deductible. 

3. Borrowing costs are assumed to be 6 percent. This is a real interest rate 
(inflation free). The discount rate is assumed to be 3 percent. It too is 
inflation 
free. 

The loan payments are computed as follows (Table 16.8). 

 

Table 16.8 Annual Borrowing Costs 

Year 

1 

2 through 15 

Annual Payment 

 

 

Explanation 

loan 1 times the annual payment factor 

(r is real interest) 

payment from t-1 plus loan t times the 
annual payment factor 



 

Future demands are reduced by the annual payments times the percentage 
households 
spend on each sectors output. For example, if households are paying back $50 
million in 
year 1 then spending from all categories decline as shown in the following 
table. The 
second column in Table 16.9 is the pre-disaster spending pattern. For example, 
0.2 
percent of household income was spent on agricultural products; 24.6 percent was 
spent 




on services. This percentage times $50 million loan repayment cost yields the 
reduction 
in household spending by sector in year 1. 

 

Table 16.9 The Effect of Loan Repayment on Household Demands 

Sector 

Household Spending 

(% spent on each sector) 

Reduced Demand in $ millions 

(% times loan payment) 

Ag 

0.2% 

0.08 

Mine 

0.0% 

0 

Cnst 

11.2% 

5.59 

Mfg 

7.5% 

3.75 

Trns 

6.2% 

3.08 

Trde 

21.6% 

10.82 

FIRE 

23.2% 

11.59 

Serv 

24.6% 

12.3 

Govt 

5.3% 

2.63 

Misc 

0.3% 

0.15 



 

Exercising the module sequentially using average values over the reconstruction 
period 
derives time dependent indirect losses. 

 

16.4.5 The Issue of Aggregation 

 

Study regions may consist of single counties, higher levels of aggregation such 
as several 
counties comprising a metropolitan area, or lower levels of aggregation such as 
a group 
of contiguous census tracts. In principal, the methodology underlying the 
Indirect 
Economic Loss module is applicable regardless of the level of aggregation. 
However, its 
accuracy is likely to be greater for study regions that represent cohesive 
economic 
regions, often called trading areas (e.g., cities or metropolitan areas) than 
for those at 
lower levels of aggregation because of the ability of the core Input-Output 
model to 
meaningfully represent the regions economic structure. Furthermore, in 
evaluating 
regional employment impacts, the module requires input data on the number of 
jobs 
located within the study region -- that is, data on employment by place of work 
rather 
than by place of residence. While this information can be obtained at the county 
level, its 
availability and reliability at lower levels of aggregation are much more 
problematic. 
Similar problems are associated with other input data such as unemployment 
rates. More 
generally, the user should also be aware that some of the input assumptions to 
the model 
(such as the availability of alternate markets) are related to the study 
regions level of 
aggregation. By adjusting the nature of the economy and the linkage to 
surrounding 
regions, the analyst can get a ball park estimate of what the real indirect 
losses and 
gains might be. Tracing the effects to a specific geographic area (beyond that 
directly 
impacted by the earthquake) is problematic. Section 16.5 below provides some 
discussion of appropriate input data and assumptions to the module. 

 

 

 

 

16.5 Running the Module 

 




This section describes operational issues related to the methodologys Indirect 
Economic 
Loss module, including data inputs, the operation of the software module, and 
the format 
and interpretation of the output. Default Data Analysis utilizes primarily 
default data and 
requires minimal user input. In User-Supplied Data Analysis, while the same 
types of 
data are required, the user provides information specific to the economy of the 
study 
region and the disaster being modeled. Advanced Data and Models analysis assumes 
expert participation and may involve expanding the module framework or applying 
alternative frameworks. 

 

16.5.1 Default Data Analysis Inputs, Operation and Output 

 

16.5.1.1 User Inputs and Default Data 

 

Running the Indirect Economic Loss module requires a number of user inputs. 
While 
default values are provided for all of these inputs, as discussed below, it is 
advisable even 
in a Default Data Analysis to override certain of them with data for the study 
region 
where available. Table 16.10 describes the inputs required and their default 
values. 

 

The methodology provides default values for the current employment based on Dun 
& 
Bradstreet data and income levels for the region based on County Business 
Pattern data. 
Note that in contrast to some other sources of regional employment data, this 
estimate of 
workers represents the number of persons who work within the study region, 
rather than 
the number of employed persons who reside there. Employment by place of work is 
appropriate in this type of analysis because the model will estimate job loss 
within the 
study region due to physical damage there from the disaster. It is recommended 
that the 
Default Data Analysis user review the default values provided and replace them 
if more 
accurate or recent data is available. Note that in User-Supplied Data Analysis, 
where a 
user-provided IMPLAN Input-Output table is used instead of a synthetic table, 
the 
current employment and income levels are read in from the IMPLAN files and 
override 
the default values. 

 

The type or composition of the economy, together with the employment level, is 
used by 
the module to automatically select a synthetic Input-Output transactions table 
to represent 
the study region economy. Default Data Analysis utilizes a synthetic 
transactions table 
aggregated from three basic classes of economies: 1) primarily manufacturing, 2) 
primarily service, secondarily manufacturing, and 3) primarily service, 
secondarily trade. 
These 3 archetypical economies represent approximately 90 percent of the 113 
transactions tables used to construct the three synthetic tables. Each type is 
broken into 
four size classifications: super (greater than 2 million in employment), large 
(greater than 
0.6 million but less than 2 million), mid range (greater than 30 thousand but 
less than .6 
million) and low (less than 30 thousand). Appendix 16A provides examples of 
regions 
in each type and size class. While type 1 (manufacturing) is the default, the 
user should 
revise this as appropriate. Appendix Tables A2, A3, and A4 can be used as a 
guide. 

 

Supplemental imports, inventories (demands), inventories (supplies), and new 
export 
markets represent available channels for excess supply or demand that can help 
reduce 
the bottleneck effects in the post-disaster economy. As mentioned above, 
appropriate 




values depend in part on the level of aggregation of the study region. Default 
values are 
set at 0 for inventories supply and demand for all industries. Default values 
for imports 
and exports are set at values considered appropriate for a distinct or self-
contained 
study region such as a metropolitan area. The default values are presented, 
together with 
discussion of how they can be modified in a User-Supplied Data Analysis, in 
Section 
16.5.2.2. 

 

The supplemental imports variable, due to limitations on available data, needs 
further 
explanation. Data on the amount of imports per sector are available only in the 
aggregate. For any one sector in the economy, the total amount of intermediate 
products 
imported is known, but the amount of these imports that comes from any 
individual 
sector is not known. The amount of new imports that may be allowed must be set 
to a 
very small level. Otherwise, the amount of products that may be imported will 
almost 
always replace any intermediate goods lost from local suppliers, and no indirect 
output 
losses will be observed. The level of supplemental imports also needs to be kept 
low 
because of factor homogeneity problems. There will be cases when there are no 
substitutes for locally obtained intermediate goods. In such cases, allowing 
imports 
would unreasonably eliminate indirect losses. Being conservative in the amount 
of 
imports allowed helps avoid both of these problems. The default values for 
imports have 
been tested in the model, and are felt to yield realistic results. 

 

Table 16.10 User Supplied Inputs for Indirect Economic Module 

Variable 

Definition 

Units(a) 

Default 
Value 

Current Level of 
Employment 

The number of people gainfully employed, by 
place of work (not residence). 

Employed persons 

Region-
specific 

Current Level of 
Income 

Total personal income for the study region. 

Million dollars 

Region-
specific 

Composition of 
the Economy 
(Default Data 
Analysis only) 

1. Primarily manufacturing 

2. Primarily service, secondarily manufacturing. 

3. Primarily service, secondarily trade. 

1, 2, or 3 

1 

Supplemental 
Imports 

In the event of a shortage, the amount of an 
immediate product unavailable from local 
suppliers which may be obtained from new 
imports. 

Percent of current 
total current annual 
imports (by 
industry) 

Defaults 
for 
distinct 
region 

Inventories 
(Supplies) 

In the event of a shortage, the amount of a good 
that was supplied from within a region that can 
be drawn from inventories within the region. 

Percent of annual 
sales (by industry) 

0 (for all 
industries) 

Inventories 
(Demand) 

In the event of a surplus, the amount of a good 
placed in inventory for future sale. 

Percent of current 
annual sales (by 
industry) 

0 (for all 
industries) 

New Export 
Markets 

In the event of a surplus, the amount of a good 
which was once sold within the region that is 
now exported elsewhere. 

Percent of current 
annual exports (by 
industry) 

Defaults 
for 
distinct 
region 

Percent 
Rebuilding 

The percent of damaged structures that are 
repaired or replaced 

Percent 

95% 






Unemployment 
Rate 

The pre-event unemployment rate as reported by 
the U.S. Bureau of Labor Statistics 

Percent 

6% 

Outside 
Aid/Insurance 

The percentage of reconstruction expenditures 
that will be financed by Federal/State aid 
(grants) and insurance payouts. 

Percent 

50% 

Interest Rate 

Current market interest rate for commercial 
loans. 

Percent 

5% 

Restoration of 
function 

The percent of total annual production capacity 
that is lost due to direct physical damage, taking 
into account reconstruction progress. 

Percent (by 
industry, by time 
interval for 5 years) 

Defaults 
for 
moderate-
major event

Rebuilding 
(buildings) 

The percent of total building repair and 
reconstruction that takes place in a specific year. 

Percent (by time 
interval for 5 years) 

56% (yr.1), 

36% (yr.2) 

10% (yr.3) 

Rebuilding 
(lifelines) 

The percent of total transportation and utility 
lifeline repair and reconstruction that takes place 
in a specific year. 

Percent (by time 
interval for 5 years) 

70% (yr.1), 

25% (yr.2) 

5% (yr.3) 

Stimulus 

The amount of reconstruction stimulus 
anticipated in addition to buildings and lifelines 
repair and reconstruction. 

Percent (by 
industry, by Time 
interval for 5 years) 

0% (for all) 



 Notes: (a) Percent data should be entered as percentage points, e.g. 60 for 
60%. 

(b) The methodology provides a default value for the counties in the study 
region. 

(c) See Section 16.5.2.2. 

 

The variables for percent rebuilding, unemployment rate, percent outside aid, 
and interest 
rate all influence how the economy is expected to react to the disaster, in 
particular the 
reconstruction stimulus, the available slack or unused capacity in the economy, 
and the 
associated indebtedness that would be incurred from reconstruction financing. 
The user 
is recommended to revise the unemployment and interest rates as appropriate. 
However, 
all of these variables can be adjusted for purposes of what-if scenario 
modeling. For 
example, how would regional indirect economic losses change if only 20 percent 
of 
reconstruction was financed by sources outside the region such as insurance or 
federal 
disaster aid? 

 

Parameters for functional restoration, as well as rebuilding for both buildings 
and 
lifelines, are associated with the anticipated speed of reconstruction and 
recovery. To 
specify functional restoration, user inputs are required for the percent of each 
industrys 
production capacity that is lost as a result of physical damage in each year for 
the first 5 
years after the disaster. Default parameters are provided that are designed to 
be 
consistent with a moderate-to-major scale of disaster. These parameter values 
and 
suggestions for modifying them in a User-Supplied Data Analysis are provided in 
Section 
16.5.2.2 below. 

 

In terms of rebuilding, the module requires user inputs as to the percent of 
total 
rebuilding expenditures for buildings and lifelines respectively that are 
expected to be 
made in each of the first 5 years following the disaster. Table 16.11 provides 
an example. 
Note that the total dollar amount required to fully rebuild damaged and 
destroyed public 
and private capital is provided by the Direct Economic Loss module. The percent 
of this 




total that is actually rebuilt is specified by the user input on percent 
rebuilding and may 
be less than 100 percent if not all of the damage is repaired or replaced. The 
annual 
percents for rebuilding buildings and lifelines as shown in Table 16.11 provide 
the 
timeline over which the reconstruction expenditures are made and should 
therefore sum 
to 100 percent over the 5-year period. 

 

Table 16.11 Rebuilding Expenditures Example 

Year 

1 

2 

3 

4 

5 

Total 

% of Total Rebuilding Expenditures (Buildings) 

54 

36 

10 

0 

0 

100 

% of Total Rebuilding Expenditures (Lifelines) 

70 

25 

5 

0 

0 

100 



 

Reconstruction speed is also to a large extent related to the scale of the 
disaster. In 
general, lifeline reconstruction is expected to proceed much more quickly than 
building 
reconstruction, as has been the experience in previous disasters. For a Default 
Data 
Analysis, default parameters are provided that are designed to be consistent 
with a 
moderate-to-major scale of disaster. Modifying these parameters would be 
appropriate 
in a User-Supplied Data Analysis, and guidelines are provided in Section 
16.5.2.2 below. 
These parameters can also be adjusted in Default Data Analysis for purposes of 
what-if 
scenario modeling for faster or slower paces of reconstruction. 

 

The additional reconstruction stimulus parameters can also be adjusted for 
what-if 
evaluations. 

 

16.5.1.2 Calculation of Indirect Loss 

 

A direct shock is introduced into the Indirect Loss Module by adjusting the 
outputs and 
purchases in proportion to a sector's loss of function. Restrictions on 
shipments (forward 
linkages) and purchases (backward linkages) are computed and the resultant 
excess 
demands or supplies are derived. See Figure 16.5. The sample transactions table 
provided in Table 16.20 (Section 16.6.2) is used to illustrate. The first two 
rows above 
the table indicate the total direct shock and associated indirect losses, which 
are initially 
zero. The first round effects are simply the direct loss of function times the 
inputs to that 
sector (backward links) and shipments from that sector (forward links). In the 
event of a 
30 percent loss of function in the transportation sector, for example, demand 
for 
manufactured goods would fall by 15.6 (0.3 times 51.9). The remainder of the 
column 
effects is computed similarly. 

 




Direct ShockInitial ShockTotal 
Change0.00%0.00%0.00%0.00%0.00%0.00%0.00%0.00%0.00%0.00%0.00%
Total 
Change0.00%0.00%0.00%0.00%0.00%0.00%0.00%0.00%0.00%0.00%0.00%
AgMineCnstMfgTrnsTrdeFIREServGovtMiscHHAg730.00.124.6503.82.335.1141.134.01.90.0
145.50.00%
Mine1.111.66.112.74.20.80.21.62.10.020.70.00%
Cnst87.56.013.8295.4248.448.1403.8313.4172.60.00.00.00%
Mfg71.68.4384.64791.051.9178.837.3424.17.80.01564.70.00%
Trns218.320.4261.21468.2456.7200.1126.7361.376.20.01623.60.00%
Trde99.84.1461.8994.144.278.727.2214.012.80.08477.10.00%
FIRE195.324.585.4279.491.5228.41131.6702.113.00.010005.00.00%
Serv93.412.7552.5789.5171.3294.6300.61032.119.30.010146.50.00%
Govt28.66.022.8313.536.878.371.3169.729.00.0582.00.00%
Misc0.00.00.00.00.00.00.00.00.00.00.00.00%
HH1878.7195.03704.112729.32266.37305.82108.09724.16567.00.00.00.00%
Sum Direct and Indirect Change0.00%
Direct ShockRestricted purchasesBackward linksExcess supply of inputsIMPACT OF 
THE INITIAL SHOCKRestricted shipmentsForward linksExcess demand for 
inputs and final productsNET EXCESSDEMAND 


 

Figure 16.5 Initial Effects of the Shock 

 

The same 30 percent shock would limit shipments to other sectors; finance, 
insurance, 
and real estate, for example, will initially receive 38.0 less (0.3 times 126.7) 
in services 
from transportation. 

 

These first round effects produce excess demands and supplies that trigger a 
search for 
markets and alternative supply sources. 

 

In building the model, several critical choices had to be made regarding post-
event 
household spending patterns, labor mobility, elasticity of supplies from the 
construction 
industry, and the potential for product substitutions due to relative price 
changes. 
Evidence from previous disasters (summarized in the Users Manual) suggests 
that: 1) 
normal spending patterns are not significantly altered; 2) the workforce is 
highly mobile, 
particularly in the construction sector; and 3) relative prices do not change 
appreciably. 
Therefore, labor and construction sales are not constrained, and normal 
household 
spending is fixed and independent of current income. Given these conditions, the 
model 
assesses the net excess supplies (output less the sum of intermediate and final 
demands). 
A positive net value implies an excess supply; a negative indicates excess 
demand. It 
then attempts to resolve sectoral imbalances through a series of adjustments. If 
excess 
demand is detected, the algorithm checks to see if sufficient capacity exists in 
a sector. 
Excess capacities are a function of user defined level of unemployment and is 
calculated 
within the model using the following equation. 

 

AC = 2.36 x (UR - .02) (16-11) 

 

Where: 

AC is available production capacity and expressed as a percentage (measured 
as a decimal) of the pre-event capacity 

UR is the unemployment rate (e.g., .05). 




 

If idle capacity is insufficient to meet excess demand then the model explores 
the 
potential of importing and/or drawing down inventories. These options are also 
provided 
by the user and are expressed as a percent of pre-event capacities. 

 

Disposal of excess supplies is logically similar. Two options, inventory 
accumulation 
and exports, are explored. As in the case of the previous options, both are 
expressed as a 
percentage and are determined by the user. In most cases excess supplies are not 
critical 
to the model's, operation, particularly when reconstruction spending looms 
large. Much 
of the excesses are drawn into the rebuilding process. 

 

After completing the first iteration of output adjustments, the algorithm 
recalculates the 
intermediate supplies and demands and then reinvestigates the adjustment options 
previously explored. Outputs are revised in proportion to the amount each sector 
is out 
of balance. A moving average of previously attempted outputs is used to 
initialize each 
iteration's search. The search is terminated once the sum of the absolute 
sectoral output 
differences diminishes to a specified level; the default is set at .00001. 

 

Indirect income loss is calculated as using the following formula. 

 

 
tTitititijtdddYr=S=S-
+111()
()
,, (16-12) 

 

where: tdi,t is the total percent reduction in sector i income during period t. 

 Yt is income of sector i. 

 ddi,t is the direct percent reduction in sector i income during period t. 

 r is the real interest rate to discount the indirect losses 

 j is the number of sectors 

 

dd is computed in the model by multiplying the initial sectoral income by the 
respective 
loss of function. The variable td is the total percentage reduction in income 
caused by 
the combination of direct loss and forward and backward linked losses. The 
difference 
between the two is then the percentage reduction in income attributable to 
indirect 
effects. The difference is pure indirect loss. This percentage when multiplied 
by sectoral 
incomes yields indirect income lost. A similar formula to Equation 16-12, 
without 
discounting, is used to evaluate indirect employment loss. 

 

16.5.1.3 The Format of the Output 

 

The module produces two reports on the results. The first provides the percent 
and level 
of indirect economic impact for the study region economy in terms of employment 
and 
income effects for a region that receives outside aid after the disaster. Note 
that impacts 
may be either losses (negative numbers) or gains (positive numbers). Results are 
given 
by time interval for the first 5 years. Average figures are also provided for 
years 6 to 15. 
All incomes are discounted at the rate of 3 percent. In the case of income, Year 
6 to Year 




15 losses or gains are discounted to the present. Employment loss or gains are 
shown as 
numbers of workers. 

 

 Table 16.12 Summary Tables for Indirect Economic Impact 

 

Year 1

Year 2

Year 3

Year 4 

Year 5 

Year 

6 to 15 

% Net Indirect Employment Impact 

 

% Net Indirect Income Impact 

 

Net Indirect Employment Impact 

 

Net Indirect Income Impact in Millions $

 



 

The second report provides the percent and level of indirect economic impact for 
the 
study region economy in terms of employment and income effects for a region that 
does 
not receive outside aid after the disaster. Differences in impacts and recovery 
trends 
typically are very significant between industries, in part because much of the 
gains from 
the reconstruction stimulus accrues to the construction industry (and to some 
extent the 
manufacturing and trade industries). 

 

It is important to note that to get a complete picture of the economic impact of 
the 
disaster, both the direct and indirect economic losses or gains should be 
considered. 

 

16.5.2 User-Supplied Data Analysis 

 

This level of Analysis differs from the Default Data level of analysis in two 
main 
respects: (1) interindustry trade flows, as represented in the Input-Output 
model of the 
economy, and (2) specification of restoration and rebuilding parameters. Rather 
than 
selecting from built-in synthetic Input-Output transactions tables, the user 
should obtain 
specific tables for the study region from a standard source, the Minnesota 
IMPLAN 
Group. In terms of specifying restoration and rebuilding parameters, the user 
can replace 
the built-in data with suggested parameter packages appropriate to the 
disaster being 
modeled. In addition, other parameters such as the availability of supplementary 
imports 
can also be modified. 

 

16.5.2.1 IMPLAN Input-Output Data 

 

The methodology requires five files from the IMPLAN input-output data set: The 
required files are as follows: 

 

 
Household Industry Demand (II031).txt 


 
Industry Output-Outlay Summary (II050).txt 


 
Institution Industry Demand (II030).txt 


 
Output, VA, Employment (SA050).txt 


 
Regional Industry x Industry (Text502).txt 




Details regarding the operation of the IMPLAN program and the construction of 
these 
files can be obtained from the technical documentation for the system. IMPLAN is 




currently sold and supported by the Minnesota IMPLAN Group; the Group can be 
reached at: 

 

 
Minnesota IMPLAN Group, Inc. 


 
1725 Tower Drive West: Suite 140 


 
Stillwater, MN 55082 


 
Phone: 651-439-4421 


 
Fax: 651-439-4813 


 
Web site: http://www.implan.com. 

 




Software and data for any county in the United States can be obtained from the 
IMPLAN 
group. When requesting data, regions can also be defined by specifying a zip 
code 
aggregation. 

 

The user can either request the five data files for the study region from MIG or 
obtain the 
software and database to construct the files. In the former case, the user 
should specify 
that the required industry aggregation scheme is essentially a one-digit 
Standard 
Industrial Classification (SIC) grouping that maps detailed IMPLAN industries 
into the 
ten industry groups used in the methodology. Table 16.13 describes the 
correspondence 
between IMPLAN and the methodologys industry classes. 

 

 

 

 

 

 

 

 

Table 16.13 Industry Classification Bridge Table 

 

IMPLAN 

HAZUS Methodology 

1-27 

AG (Agriculture) 

28-47 

MINE (Mining) 

48-57 

CNST (Construction) 

58-432 

MFG (Manufacturing) 

433-446 

TRNS (Transportation) 

447-455 

TRDE (Trade) 

456-462 

FIRE (Finance, Insurance and Real Estate) 

463-509 

SERV (Service) 

510-523 

GOVT (Government) 

524 

MISC (Miscellaneous) 



 




If the user obtains the IMPLAN software, the three data files can be constructed 
by 
following the instructions and constructing an aggregated Input-Output account 
using an 
existing or built-in template for 1-digit SIC classification. 

 

16.5.2.2 Specifying Indirect Loss Factors 

 

In addition to applying IMPLAN Input-Output data for the study region, a User-
Supplied 
Data Analysis can involve adjusting module parameters to more closely fit the 
study 
region and disaster being modeled. Parameter sets and selection algorithms are 
suggested below for both the four indirect loss factors -- supplemental 
imports, new 
export markets, inventories supply, and inventories demand -- and industry 
restoration 
and rebuilding. 

 

As previously noted in the Default Data Analysis discussion, availability of 
supplemental 
imports and new export markets is related in part to the size or level of 
aggregation of the 
study region and its geographic situation. A single county making up part of a 
large 
metropolitan area would have a much higher new import/export capacity (i.e., to 
neighboring counties) than would a single-county city that was geographically a 
distinct 
urban area and at some distance from other urban areas. Table 16.14 suggests two 
possible sets of factor values for geographically distinct and component 
study 
regions based on expert opinion. 

 

Table 16.14 Suggested Indirect Economic Loss Factors 

 (percentage points) 

 

Distinct Region 

Component Region 

Industry 

Imports 

Inv. 
Supply 

Inv. 
Demand 

Exports 

Imports 

Inv. 
Supply 

Inv. 
Demand 

Exports 

AGR 

5 

0 

0 

20 

6 

0 

0 

35 

MINE 

5 

0 

0 

30 

6 

0 

0 

45 

CON 

999 

0 

0 

10 

999 

0 

0 

25 

MFG 

4 

1 

1 

30 

6 

1 

1 

45 

TRNS 

2 

0 

0 

0 

4 

0 

0 

0 

TRDE 

3 

1 

1 

0 

5 

1 

1 

0 

FIRE 

3 

0 

0 

0 

5 

0 

0 

0 

SVC 

3 

0 

0 

0 

5 

0 

0 

0 

GOVT 

3 

0 

0 

0 

5 

0 

0 

0 

OTHER 

4 

0 

0 

0 

6 

0 

0 

0 



 

Selection of appropriate restoration and rebuilding parameters presents a more 
complex 
problem because of the need to link these values to physical damage levels in 
the 
disaster. Industry functional restoration and rebuilding will generally proceed 
more 
slowly with increasing severity of the disaster and extent of physical damage. 
For this 
reason, it is recommended that to run a User-Supplied Data Analysis for Indirect 
Economic Loss that the user first complete a comprehensive analysis, examine the 
damage results, modify the restoration and rebuilding parameters as appropriate, 
and then 
finally run the Indirect Loss module. Several example restoration and rebuilding 
parameter sets designed based on expert opinion to represent different scales of 
disaster 




are presented below, together with a suggested algorithm for the user to select 
the most 
appropriate one. 

 

The following suggested procedure attempts to provide a rough but simple and 
credible 
link between restoration and rebuilding parameters in the Indirect Loss module 
and 
methodology results on physical damage. Lifeline rebuilding and transportation 
industry 
functional restoration are linked to highway bridge damage. Manufacturing 
industry 
restoration is linked to industrial building damage. Buildings rebuilding and 
restoration 
for all other industries is linked to commercial building damage. The values of 
the 
industry functional restoration parameters are intended to reflect not only 
facility damage 
levels but also each industrys resiliency to damage to its facilities, such as 
for example 
its ability to relocate or utilize alternative facilities. These parameters were 
derived 
judgmentally with consideration of observations from previous disasters. Note 
that 
values for restoration represent the percent loss of industry function 
averaged over the 
specified time window. 

 

STEP 1. Calculate damage indices for highway bridges and commercial and 
industrial buildings, respectively. The damage index consists of the percent of 
structures in the extensive or complete damage states. For example, if 
results 
indicate that 5 percent of bridges will suffer extensive damage and 3 percent 
complete damage, the damage index is 8 percent. Damage results for bridges can 
be 
found in the HAZUS summary report on Transportation Highway Bridge Damage. 
Damage results for commercial and industrial buildings can be found in the HAZUS 
summary report on Building Damage by General Occupancy. 

 

STEP 2. Select transportation industry restoration parameters and rebuilding 
parameters for lifelines. Use the highway bridge damage index from Step 1 to 
read off 
parameters from Table 16.15. 

 

STEP 3. Select manufacturing industry restoration parameters. Use the industrial 
building damage index from Step 1 to read off parameters from Table 16.16. 

 

STEP 4. Select restoration parameters for all other industries and rebuilding 
parameters for buildings. Use the commercial building damage index from Step 1 
to 
read off parameters from Table 16.17. 

 




Table 16.15 Transportation Restoration and Lifeline Rebuilding Parameters 

(percentage points) 

Highway bridge 

Impact 
description 

 

 

 

 

 

 

damage index 

 

Parameter Set 

Year 1

Year 2 

Year 3 

Year 4 

Year 5

 0% 

None/ 

minimal 

Restoration function - TRNS 
Ind. 

0 

0 

0 

0 

0 

 

 

Rebuilding expenditures - 
Lifelines 

100 

0 

0 

0 

0 

 0-1% 

Minor 

Restoration function - TRNS 
Ind. 

2 

0 

0 

0 

0 

 

 

Rebuilding expenditures - 
Lifelines 

100 

0 

0 

0 

0 

 1-5% 

Moderate 

Restoration function - TRNS 
Ind. 

5 

0 

0 

0 

0 

 

 

Rebuilding expenditures - 
Lifelines 

95 

5 

0 

0 

0 

 5-10% 

Mod.-major 

Restoration function - TRNS 
Ind. 

10 

2 

0 

0 

0 

 

 

Rebuilding expenditures - 
Lifelines 

90 

10 

0 

0 

0 

 10-20% 

Major 

Restoration function - TRNS 
Ind. 

15 

3 

0 

0 

0 

 

 

Rebuilding expenditures - 
Lifelines 

85 

15 

0 

0 

0 

 >20% 

Catastrophic 

Restoration function - TRNS 
Ind. 

20 

5 

0 

0 

0 

 

 

Rebuilding expenditures - 
Lifelines 

80 

20 

0 

0 

0 



 

 

Table 16.16 Manufacturing Restoration Parameters 

(percentage points) 

Industrial 
building 
damage index 

 

Impact 
description 

 

 

Parameter Set 

Year 1

Year 2

 

 

Year 3 

 

 

Year 4 

Year 5

 0% 

None/minor 

Restoration function - MFG 
Ind. 

1 

0 

0 

0 

0 

 0-1% 

Moderate 

Restoration function - MFG 
Ind. 

2 

0 

0 

0 

0 

 1-5% 

Mod.-major 

Restoration function - MFG 
Ind. 

4 

0 

0 

0 

0 

 5-10% 

Major 

Restoration function - MFG 
Ind. 

8 

2 

0 

0 

0 

 >10% 

Catastrophic 

Restoration function - MFG 
Ind. 

20 

10 

5 

0 

0 



 




Table 16.17 All Other Industries Restoration and Buildings Rebuilding Parameters 

(percentage points) 

Commercial 
bldg. damage 
index 

Impact 
description 

Parameter Set 

Year 
1 

Year 

2 

Year 
3 

Year 
4 

Year 
5 

0% 

None/minor 

Restoration function - AG Ind. 

0 

0 

0 

0 

0 

 

 

Restoration function - MINE Ind. 

0 

0 

0 

0 

0 

 

 

Restoration function - CNST Ind. 

0 

0 

0 

0 

0 

 

 

Restoration function - TRDE Ind. 

1 

0 

0 

0 

0 

 

 

Restoration function - FIRE Ind. 

0 

0 

0 

0 

0 

 

 

Restoration function - SERV Ind. 

1 

0 

0 

0 

0 

 

 

Restoration function - GOVT Ind. 

1 

0 

0 

0 

0 

 

 

Restoration function - MISC Ind. 

1 

0 

0 

0 

0 

 

 

Rebuilding expenditures - buildings 

100 

0 

0 

0 

0 

0-1% 

Moderate 

Restoration function - AG Ind. 

0 

0 

0 

0 

0 

 

 

Restoration function - MINE Ind. 

0 

0 

0 

0 

0 

 

 

Restoration function - CNST Ind. 

1 

0 

0 

0 

0 

 

 

Restoration function - TRDE Ind. 

2 

0 

0 

0 

0 

 

 

Restoration function - FIRE Ind. 

1 

0 

0 

0 

0 

 

 

Restoration function - SERV Ind. 

2 

0 

0 

0 

0 

 

 

Restoration function - GOVT Ind. 

2 

0 

0 

0 

0 

 

 

Restoration function - MISC Ind. 

2 

0 

0 

0 

0 

 

 

Rebuilding expenditures - buildings 

80 

20 

0 

0 

0 

1-5% 

Mod.-major 

Restoration function - AG Ind. 

0 

0 

0 

0 

0 

 

 

Restoration function - MINE Ind. 

0 

0 

0 

0 

0 

 

 

Restoration function - CNST Ind. 

2 

0 

0 

0 

0 

 

 

Restoration function - TRDE Ind. 

4 

0 

0 

0 

0 

 

 

Restoration function - FIRE Ind. 

2 

0 

0 

0 

0 

 

 

Restoration function - SERV Ind. 

4 

0 

0 

0 

0 

 

 

Restoration function - GOVT Ind. 

4 

0 

0 

0 

0 

 

 

Restoration function - MISC Ind. 

4 

0 

0 

0 

0 

 

 

Rebuilding expenditures - buildings 

70 

30 

0 

0 

0 

5-10% 

Major 

Restoration function - AG Ind. 

1 

0 

0 

0 

0 

 

 

Restoration function - MINE Ind. 

1 

0 

0 

0 

0 

 

 

Restoration function - CNST Ind. 

4 

0 

0 

0 

0 

 

 

Restoration function - TRDE Ind. 

8 

2 

0 

0 

0 

 

 

Restoration function - FIRE Ind. 

4 

0 

0 

0 

0 

 

 

Restoration function - SERV Ind. 

8 

2 

0 

0 

0 

 

 

Restoration function - GOVT Ind. 

8 

2 

0 

0 

0 

 

 

Restoration function - MISC Ind. 

8 

2 

0 

0 

0 

 

 

Rebuilding expenditures - buildings 

60 

30 

10 

0 

0 

>10% 

Catastrophic 

Restoration function - AG Ind. 

2 

0 

0 

0 

0 

 

 

Restoration function - MINE Ind. 

2 

0 

0 

0 

0 

 

 

Restoration function - CNST Ind. 

10 

5 

0 

0 

0 

 

 

Restoration function - TRDE Ind. 

20 

10 

5 

0 

0 

 

 

Restoration function - FIRE Ind. 

10 

5 

0 

0 

0 

 

 

Restoration function - SERV Ind. 

20 

10 

5 

0 

0 

 

 

Restoration function - GOVT Ind. 

20 

10 

5 

0 

0 

 

 

Restoration function - MISC Ind. 

20 

10 

5 

0 

0 

 

 

Rebuilding expenditures - buildings 

50 

30 

15 

5 

0 



 




16.5.3 Advanced Data and Models Analysis 

 

For this level of analysis, it is presumed that an economist with experience in 
the 
economics of natural hazards will be conducting the study. 

 

16.5.3.1 Extending the Indirect Loss Module 

 

The Indirect Loss Module above holds great potential for further development. 
Some of 
the alterations that could be incorporated are: 

 

1. Expand the number of industries to better reflect building classes and 
individual 
lifelines. 

 

2. Investigate the implications of how shortages and surpluses are addressed. 
The 
current Module follows a particular sequence for alleviating bottlenecks; it is 
possible 
that this sequence may influence the final results. As currently programmed, the 
algorithm attempts to resolve shortfalls by looking first to regional excess 
capacities. 
In some instances it may be more realistic to expect local producers to look to 
imports as a source of replacement. There is no obvious a priori way of knowing 
which alternative will be chosen. The particular sequence currently imbedded in 
the 
program will tend to maximize production at the local level and therefore 
minimize 
the indirect losses associated with an earthquake. 

 

A more appealing method would be to randomize the priority in which different 
avenues of ameliorating bottlenecks are chosen. Under this regime, the entire 
modeling process would be imbedded in a larger iterative loop that could explore 
a 
full range of options. By so doing, the robustness of the solution set can be 
assessed. 

 

Alternatively, survey research might be conducted which would ascertain how 
producers might actually respond to an earthquake. The model could then be 
modified to reflect this information. 

 

3. Make parameter values sector specific. Currently, the methodology is designed 
so that the supply and demand options (imports, exports, capacity, and inventory 
adjustments) are identical across sectors. The next logical step would be to 
make 
these adjustments sector dependent. This would allow the analyst to better 
tailor the 
model to the circumstances of a particular location. For instance, if industry A 
required the output of industry B, and no substitutes or imports were permitted, 
a 
matrix of import probabilities would assign 0% at the intersection of these two 
industries. 

 

Additionally, such matrices would allow for consideration of instances where 
different industries have dissimilar responses to changes in the same input. If 
industry A requires a large amount of input C, while industry B requires a 
smaller 
amount, industry B would be more likely to pay a premium to import input C. 

 




Although this notion seems daunting, it might be possible to incorporate the 
parameter matrix idea without making the modeling process totally infeasible. 
For 
example, one might begin by assigning a scalar, say 10%, to the entire matrix of 
import probabilities. Then, entire industries could be modified by inputting 
vectors 
of new values to those industries. Finally, key intersections for the local 
economy 
could be located and specific parameters applied to those intersections. 
Therefore, at 
its simplest level, the parameter matrix concept is no more complex than what is 
currently programmed into the Indirect Loss Module. 

 

4. Approximate price effects. A common complaint leveled against I/O models is 
that they do not incorporate prices. While this is true, a couple of points need 
to be 
made in reference to this particular Loss Module. Significant relative price 
changes 
have not been observed after disaster. This may be due in part to special 
circumstances emerging during the post-disaster period, where price gouging" is 
frowned upon, or made illegal (as in Los Angeles after the Northridge 
earthquake). 

 

However, if concerns about price effects remain, it should be possible to modify 
the 
Module accordingly. As the system is currently configured, there are fixed 
constraints on output, imports, etc. In a supply and demand framework, these 
could 
be thought of as a series of discontinuous supply curves which are horizontal 
until the 
quantity constraint is reached, at which point they turn perfectly vertical. 
Enhancement of this system with a function that reduces output as new input 
sources 
are tapped would mimic a price-sensitive supply function. However, it must be 
pointed out that parameterization of such functions is an extremely difficult 
task. 
This is one of the problems that Computable General Equilibrium models also 
face. 

 

5. Extend the model to asses indirect loss/gain incurred by surrounding regions 
and 
the national economy. As it now stands, the model is best suited to analysis of 
the 
immediately impacted region. However, as pointed out early in the Chapter, 
regional 
consequences may be quite different than that measured at the national level. 
Figure 
16.19 indicates how the module could be extended to account for these broader 
economic linkages. Direct damages and subsequent indirect loss is transmitted to 
other regions via changes in the import-export relationships. The national 
economy 
is impacted in that external aid has to be financed, either at the expense of 
canceled 
federal projects, or increased tax liability. In either case demands elsewhere 
will 
suffer. 

 

 




10 See, for example, Shoven and Whaley (1992) for general discussion of CGE 
systems, and Brookshire 
and McKee (1992) and Boisvert (1995) for applications to earthquakes. 
Linear programming offers a simpler alternative to the CGE approach (Cochrane, 
1975; Rose et al., 1997). 
Again, interindustry trade flows form the basis of the model. As in the previous 
two methods, the A matrix 
guides the reallocation of production; the output of each sector is comprised of 
a fixed proportion of other 
sector outputs. However, unlike the previous methods, an optimizing routine is 
utilized to search for that 
production combination that minimizes the extent to which regional income is 
impacted by the event. 

 

The results derived from I-O, LP and CGE models are likely to vary. Linear 
programming is likely to 
provide the most optimistic projection of loss and the Indirect Loss Module the 
most pessimistic. The 
reason for this conclusion rests on the high degree of flexibility assumed (in 
both the CGE and linear 
programming) in shifting resource use. It is unlikely that production could be 
redirected without concern 
for contractual arrangements, or without considering household preferences. The 
optimization alternative 
typically ignores both, though this problem can be mitigated somewhat by the 
inclusion of explicit 
constraints (see, for example, Rose and Benavides, 1997). 

 

ConsequenceConsequenceConsequence

LocalEconomyShockLoss of 
function by 
sectorExternal Aid+ 
InsuranceNationalEconomyRegionalEconomyTradeEffectsImportsExports 


 

Figure 16.6 Extending the Model to Include 

 Larger Regional and National Losses 

 

 

16.5.3.2 Alternative Modeling Techniques 

 

It is possible for an economist to use other modeling strategies in conjunction 
with this 
loss estimation methodology. For instance, if the region being studied already 
utilizes a 
working Computable General Equilibrium model, it could be used to estimate 
indirect 
economic loss. Linear Programming methods are also potentially useful. Finally, 
though 
not recommended, it is possible to simply feed the direct loss information 
through a 
standard set of I-O multipliers (see the discussions in Sections 16.2 and 16.3 
above).10 

 




16.6 Example Solutions 

 

The following examples are provided to both illustrate how a typical indirect 
loss 
analysis is performed, and to show the wide range of results possible. Indirect 
loss 
patterns (produced from thousands of monte carlo simulations) are then analyzed 
to 
derive several general principles relating direct and indirect losses. The 
resultant 
patterns and assessments are provided to assist the user in interpreting their 
own results. 
First, a simple one-sector supply shock is analyzed to clarify how the model 
works. The 
Colorado State Hazards Assessment Laboratory version of the Indirect Loss Module 
was 
utilized to perform these analyses. This was done in order to isolate and 
analyze 
particular damage patterns. This will create discrepancies between the 
methodologys 
output and what is reported by the CSU model. 

 

16.6.1 Simple One-Sector Supply Shock - No Excess Capacity 

 

Table 16.20 shows the final solution for the example discussed above in Section 
16.5.1.2, 
i.e., a 30 percent decline in the functionality of the transportation sector. In 
this 
experiment no adjustments were permitted (all percentages are zero except for 
the supply 
shock). Table 16.19 shows the initial conditions (output, income and employment) 
and 
the adjusted capacities. The mobility of the construction industry shows up as 
excess 
capacity. Because reconstruction spending in the example is assumed zero, the 
capacity 
goes unutilized. Table 16.20 (right hand side) shows the resultant impact on 
output, 
income and employment. The overall percent reduction in these three categories 
is 
computed from regional outputs, incomes and employments with and without the 
event. 

 

In this example of a highly constrained economy, the 30 percent shock to 
transportation, 
produces 1.07, 1.46, and a 1.06 percent change in direct output, income and 
employment, 
respectively. Because of the constraints assumed, total losses (direct and 
indirect) are 
approximately 30 times the direct loss (nearly 30 percent). 

 

16.6.2 The Northridge Earthquake 

 

The following scenarios illustrate the sensitivity of indirect loss to the 
amounts of outside 
assistance provided and the degree to which the lifelines (particularly 
transportation) are 
disrupted. Four scenarios are presented along with the inputs required to run 
the Indirect 
Loss Module. Scenario A looks at the twin effects of $26 billion of 
reconstruction 
spending, financed internally (i.e., no external aid), and temporary disruption 
to the 
transportation system. Scenario B removes reconstruction spending. Scenario C 
removes 
the transportation constraint, but eliminates rebuilding. Scenario D removes the 
transportation constraint, while the $26 billion of rebuilding expenditures is 
assumed to 
be financed by a combination of insurance moneys and federal aid. 

 

Table 16.21 shows the IMPLAN transactions matrix for Los Angeles county. Tables 
16.23 and 16.24 summarize the inputs used. The results provided in Tables 16.22, 
16.25, 
16.27 and 16.31 point out several important issues. First, Scenario D comes 
closest to 
capturing what did occur. A relatively small proportion of the rebuilding costs 
were 




financed internally. As a result, the negative effects of the disruption to 
transportation 
were masked by the stimulative effect of rebuilding. The 7.83% net increase in 
incomes 
earned in the county are surprisingly close to the observed rise in Los Angeles 
County 
taxable sales (7.35%). 

 

 

 

Table 16.18 Initial Transactions Matrix 

Initial Shock 

0.00 

0.00 

0.00 

0.00 

30.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

Total 

Total Change 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

Change 

 

Ag 

Mine 

Cnst 

Mfg 

Trns 

Trde 

FIRE 

Serv 

Govt 

Misc 

HH 

 

 Ag 

730 

0.1 

24.6 

503.8 

2.3 

35.1 

141.1 

34 

1.9 

0 

145.5 

0.00%

 Mine 

1.1 

11.6 

6.1 

12.7 

4.2 

0.8 

0.2 

1.6 

2.1 

0 

20.7 

0.00%

 Cnst 

87.5 

6 

13.8 

295.4 

248.4 

48.1 

403.8 

313.4 

172.6 

0 

0 

0.00%

 Mfg 

71.6 

8.4 

384.6 

4,791 

51.9 

178.8 

37.3 

424.1 

7.8 

0 

1,565 

0.00%

 Trns 

218.3 

20.4 

261.2 

1,468.2 

456.7 

200.1 

126.7 

361.3 

76.2 

0 

1,624 

0.00%

 Trde 

99.8 

4.1 

461.8 

994.1 

44.2 

78.7 

27.2 

214 

12.8 

0 

8,477 

0.00%

 FIRE 

195.3 

24.5 

85.4 

279.4 

91.5 

228.4 

1,132 

702.1 

13 

0 

10,005 

0.00%

 Serv 

93.4 

12.7 

552.5 

789.5 

171.3 

294.6 

300.6 

1,032.1 

19.3 

0 

10,147 

0.00%

 Govt 

28.6 

6 

22.8 

313.5 

36.8 

78.3 

71.3 

169.7 

29 

0 

582 

0.00%

 Misc 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.00%

 HH 

1,879 

195 

3,704 

12,729 

2,266.3 

7,305 

2,108 

9,724 

6,567 

0 

0 

0.00%

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sum 

0.00%



 




 

Table 16.19 Original Conditions and Adjustments 

 

 

Original Conditions 

 

 

Additional Demands 

 

 

Additional Supplies 

 

Sector 

Output 

HH 
Payments 

Employ. 

Inventory 
Buildup 
Capability

Export 
Capability

Desired 
New Final 
Demand 

Potential 
Output 
Increase 

Potential 
Imports 

Potential 
Inventory 
Drawdown

Ag 

5,964 

1,879 

106,253 

0 

0 

0 

0 

0 

0 

Mine 

1,092 

195 

4,739 

0 

0 

0 

0 

0 

0 

Cnst 

10,984 

3,704 

144,407 

0 

0 

0 

10,040 

0 

0 

Mfg 

52,811 

12,729 

378,400 

0 

0 

0 

0 

0 

0 

Trns 

7,169 

2,266 

72,169 

0 

0 

0 

0 

0 

0 

Trde 

13,484 

7,306 

451,276 

0 

0 

0 

0 

0 

0 

FIRE 

15,791 

2,108 

124,514 

0 

0 

0 

0 

0 

0 

Serv 

19,065 

9,724 

492,969 

0 

0 

0 

0 

0 

0 

Govt 

7,550 

6,567 

266,107 

0 

0 

0 

0 

0 

0 

Misc 

0 

0 

0 

0 

0 

0 

0 

0 

0 

HH 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Totals 

66,312 

46,478 

2,040,834 

 

 

 

 

 

 



 

Table 16.20 Final Conditions 

 

 

Post- Event 

Spending 

 

 

 Final 

Losses 

 

 

 

 

 

Sector 

Net 
Change 
Next 
Round 

Hhld 
Spending 

Exports 

Post-
Event 
Final 
Output 

Final 
Output 
Direct 
Loss Only 

Post-
Event 
Hhld 
Payments 

Hhld 
Payments 
Direct 
Loss Only 

Post-
Event 
Employ. 

Employ. 
Direct 
Loss 
Only 

Ag 

29.98% 

102 

1,284 

4,176 

5,964 

1,316 

1,879 

74,398 

106,253 

Mine 

29.98% 

15 

285 

765 

1,092 

137 

195 

3,318 

4,739 

Cnst 

29.98% 

0 

252 

7,691 

10,984 

2,594 

3,704 

101,113 

144,407 

Mfg 

29.98% 

1,096 

12,565 

36,978 

52,811 

8,914 

12,729 

264,955 

378,400 

Trns 

30.00% 

1,137 

617 

5,018 

5,018 

1,586 

1,586 

50,518 

50,518 

Trde 

29.98% 

5,936 

801 

9,442 

13,484 

5,116 

7,306 

315,982 

451,276 

FIRE 

29.98% 

7,005 

865 

11,057 

15,791 

1,476 

2,108 

87,184 

124,514 

Serv 

29.98% 

7,105 

1,608 

13,349 

19,065 

6,809 

9,724 

345,175 

492,969 

Govt 

29.98% 

408 

97 

5,287 

7,550 

4,599 

6,567 

186,327 

266,107 

Misc 

0.00% 

0 

0 

0 

0 

0 

0 

0 

0 

HH 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Totals 

 

22,802 

18,375 

140,194 

198,072 

32,544 

45,798 

1,428,970 

2,019,183 

Total 
% 
Change 

29.98% 

-29.98% 

-29.98% 

-29.98% 

-1.07% 

-29.98% 

-1.46% 

-29.98% 

-1.06% 



 

Second, the effects of transportation bottlenecks alone can only be observed by 
stripping away 
rebuilding expenditures, Scenario B. Here we can see that income would have 
fallen, not risen. 
The disaster would have caused another $10 billion in indirect losses. Third, 
outside assistance 
is an important element in the recovery process. The effects of internal 
financing are shown in 




Scenario A. Here, an additional $1.5 billion in income losses would have been 
observed had the 
victims been forced to borrow to rebuild. 

 

These scenarios underscore the importance of rebuilding on the impacted regions 
post-disaster 
economic performance. This is particularly true when insurance and federal 
assistance is made 
available. Another important lesson learned from these experiments is that case 
studies of 
indirect loss can produce misleading results. Clearly Northridge and Los Angeles 
County did not 
benefit from disruptions to its transportation network. Yet, an analysis of 
post-disaster spending 
and incomes (taxable sales reported after the earthquake) tends to indicate such 
had occurred. As 
just shown the Indirect Loss Module is capable of separating the stimulative 
effects of rebuilding 
from the true indirect losses produced as a result of forward and backward 
linked damages. 

 

 

Table 16.21 Los Angeles County Transactions Matrix 

 

 

Ag 

Mine 

Cnst 

Mfg 

Trns 

Trde 

FIRE 

Serv 

Govt 

Misc 

HH 

Ag 

26 

0 

28 

173 

2 

13 

213 

46 

5 

0 

49 

Mine 

2 

1 

13 

66 

44 

16 

2 

22 

53 

0 

119 

Cnst 

14 

10 

24 

353 

482 

167 

1162 

694 

603 

0 

0 

Mfg 

121 

25 

1942 

13201 

1363 

1707 

378 

3415 

285 

0 

12219 

Trns 

50 

38 

929 

4069 

2381 

1724 

920 

2741 

1078 

0 

6677 

Trde 

43 

6 

1609 

2662 

207 

511 

140 

904 

103 

0 

21900 

FIRE 

60 

189 

301 

1080 

653 

1519 

7279 

4210 

134 

0 

28696 

Serv 

122 

37 

2839 

4933 

1916 

4636 

3177 

14326 

275 

0 

31357 

Govt 

17 

25 

96 

1195 

200 

651 

389 

1213 

255 

0 

2514 

Misc 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

HH 

660 

424 

8846 

30473 

8601 

25129 

10985 

51410 

17318 

0 

0 

TypeII sum 

1115 

754 

16627 

58204 

15850 

36072 

24645 

78981 

20111 

0 

103530 

TypeII FP 

431 

4936 

7708 

62601 

10039 

13605 

32460 

13019 

1838 

0 

57838 

Imports 

403 

1201 

6920 

42925 

3400 

3284 

1744 

6543 

669 

0 

0 

Ind Out 

1546 

5690 

24335 

120805 

25888 

49677 

57105 

92000 

21948 

0 

161368 



 

 




 

Table 16.22 Results  Scenario A 

 Constrained Transportation Sector 

Reconstruction 

 

Direct Output Loss 

($15,508) 

 -2.77% 

Indirect Output Loss 

 $8,286 

 1.48% 

Total Loss (Direct+Indirect) 

 ($7,222) 

 -1.29% 

 

 

 

Direct Income Loss 

 ($3,710) 

 -2.41% 

Indirect Income Loss 

 $1,552 

 1.01% 

Total Loss Income (Direct+Indirect) 

 ($2,158) 

-1.40% 

 

 

 

Direct Employment Loss 

(122,015) 

 -2.39% 

Indirect Employment Loss 

 24,013 

 0.47% 

Total Employment Loss (Direct+Indirect) 

 (98,002) 

 -1.92% 



 

 

Table 16.23 Scenario A; Damage and User Inputs 

 

Economic Sector 

 Percent Damage 

Agriculture 

 0.00% 

Mining 

0.00% 

Construction 

0.00% 

Manufacturing 

3.80% 

Transportation 

10.00% 

Trade 

3.50% 

Finance, Insurance and Real Estate 

2.00% 

Service 

0.86% 

Government 

0.87% 

Misc. 

0.00% 



 

 

 

Assumptions 

 Value 

Rate of Unemployment 

 8.00% 

Excess Capacity in Transportation 

0.00% 

Earthquake Construction Spending 

$26 billion 



 




 

Table 16.24 Restoration and Reconstruction Spending after Northridge 

 

SECTOR 

Months after the Northridge Earthquake 

 

1 

2 

3 

6 

9 

12 

24 

36 

48 

60 

120 

Agriculture 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

Mining 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

Construction 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

Manufacturing 

3.80 

3.19 

2.58 

1.98 

1.37 

0.76 

0.15 

0.00 

0.00 

0.00 

0.00 

Transportation 

10.00 

8.40 

6.80 

5.20 

3.60 

2.00 

0.40 

0.00 

0.00 

0.00 

0.00 

Trade 

3.50 

2.94 

2.38 

1.82 

1.26 

0.70 

0.14 

0.00 

0.00 

0.00 

0.00 

FIRE 

2.00 

1.68 

1.36 

1.04 

0.72 

0.40 

0.08 

0.00 

0.00 

0.00 

0.00 

Service 

0.86 

0.72 

0.58 

0.45 

0.31 

0.17 

0.03 

0.00 

0.00 

0.00 

0.00 

Government 

0.87 

0.73 

0.59 

0.45 

0.31 

0.17 

0.03 

0.00 

0.00 

0.00 

0.00 

Misc. 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 



 

Spending/Mn 

Months after the Northridge Earthquake 

 

1 

2 

3 

6 

9 

12 

24 

36 

48 

60 

120 

$ Billons 

 0.10 

 0.30 

 0.60 

 0.70 

0.70 

 0.60 

 0.30 

 0.12 

0.00 

 0.00 

 0.00 



 

 




 

 

Table 16.25 Results  Scenario B 

 Constrained Transportation Sector 

No Reconstruction 

 

Direct Output Loss 

 ($15,508) 

 -2.77% 

Indirect Output Loss 

 ($33,685) 

 -6.01% 

Total Loss (Direct+Indirect) 

 ($49,193) 

-8.78% 

 

 

 

Direct Income Loss 

 ($3,710) 

 -2.41% 

Indirect Income Loss 

 ($9,692) 

 -6.30% 

Total Loss Income (Direct+Indirect) 

 ($13,403) 

 -8.71% 

 

 

 

Direct Employment Loss 

 (122,015) 

 -2.39% 

Indirect Employment Loss 

 (318,930) 

 -6.24% 

Total Employment Loss (Direct+Indirect) 

 (440,945) 

 -8.63% 



 

Table 16.26 Scenario B, User Inputs 

 

Assumptions 

 Value 

Rate of Unemployment 

8.0% 

Excess Capacity in Transportation 

0.00% 

Earthquake Construction Spending 

$0 billion 



 

 

Table 16.27 Results  Scenario C 

 Unconstrained Transportation Sector 

No Reconstruction 

 

Direct Output Loss 

 ($15,508) 

 -2.77% 

Indirect Output Loss 

 $2,648 

 0.47% 

Total Loss (Direct+Indirect) 

 ($12,860) 

 -2.29% 

 

 

 

Direct Income Loss 

 ($3,710) 

 -2.41% 

Indirect Income Loss 

 $640 

0.42% 

Total Loss Income (Direct+Indirect) 

 ($3,070) 

 -2.00% 

 

 

 

Direct Employment Loss 

 (122,015) 

 -2.39% 

Indirect Employment Loss 

 21,250 

 0.42% 

Total Employment Loss (Direct+Indirect) 

 (100,765) 

 -1.97% 



 

 




Table 16.28 Scenario C, User Inputs 

 

Assumptions 

 Value 

Rate of Unemployment 

 8.00% 

Excess Capacity in Transportation 

 no constraint 

Earthquake Construction Spending 

$0 billion 



 

 

Table 16.29 Results  Scenario D 

 Unconstrained Transportation Sector 

Reconstruction, No Indebtedness 

 

Direct Output Loss 

 ($9,754) 

 -2.12% 

Indirect Output Loss 

 $37,061 

 8.05% 

Total Loss (Direct+Indirect) 

 $27,307 

 5.93% 

 

 

 

Direct Income Loss 

 ($2,850) 

 -1.85% 

Indirect Income Loss 

 $12,046 

 7.83% 

Total Loss Income (Direct+Indirect) 

 $9,196 

5.98% 

 

 

 

Direct Employment Loss 

 (99,044) 

 -1.94% 

Indirect Employment Loss 

 370,072 

 7.24% 

Total Employment Loss (Direct+Indirect) 

 271,028 

 5.31% 



 

 

Table 16.30 Scenario D, User Inputs 

 

Assumptions 

 Value 

Rate of Unemployment 

 8.00% 

Excess Capacity in Transportation 

 no constraint 

Earthquake Construction Spending 

$26 billion 



 

 

 

 

16.6.3 The Sensitivity of Indirect Loss to Capacity, Damage and Reconstruction 

 

Our analysis to date suggests that there may not be a simple relationship 
between direct 
and indirect losses. Much depends upon the pattern of damage, which sectors 
sustain the 
greatest disruption, and their relative importance in the economy. In addition, 
the 
demand stimulus inherent in the rebuilding process would lessen indirect loss, 
possibly 
producing gains in instances where large amounts of excess capacity exist. The 
sensitivity of indirect loss to random patterns of damage and rebuilding was 
determined 
through a series of experiments that are presented in summary form below. Four 
major 
classes of experiments were conducted; they are identified and explained in 
Table 16.31. 

 

 




6Damage to each of 10 economic sectors was determined by generating a random 
number between zero 
and one for the uniform distribution and cubing the random number to arrive at a 
skewed distribution. 
Table 16.31 Monte Carlo Experiments 

 

Experiment 

Explanation 

Damage Pattern 

1. Random damage pattern drawn from a uniform 
probability distribution (all sectors). 

2. Random damage pattern drawn from a skewed 
probability distribution (all sectors). 

3. Random pattern of damage to the lifelines 
sector, no damage to all other sectors. 

Outside Assistance 

4. Random amounts of rebuilding. 

5. Rebuilding in proportion to direct losses 

Economic Structure 

Different transactions matrices were utilized to 
evaluate the extent to which economic structure 
impacted indirect loss when the economy was fully 
constrained 

Internal and External Capacity 

The effects of eliminating supplemental imports and 
exports and varying internal capacity. 



 

 

Indirect and direct losses were recorded for twenty thousand experiments11. The 
joint 
density function of direct and indirect loss, along with the probability density 
function of 
indirect loss were then plotted to derive relationships capable of being 
generalized. See 
Figure 16.7. The joint density function is displayed on the higher of the two 
horizontal 
plains. Regions of indirect gain and loss are identified. The lower of the two 
planes is a 
contour map (projection) of the joint probability of indirect and direct loss. 
The back 
projection is the indirect loss probability density function. 

 

The results of the experiments are plotted in Figures 16.8 through 16.17. As 
shown, 
either regional indirect loss or gain can be observed. Which occurs depends upon 
the 
combination of the damage pattern, preexisting economic conditions and the 
amount of 
outside assistance received. Several of the maps have ready explanations. The 
map 
shown in Figure 16.8 is based on two assumptions: 1) the existence of sufficient 
(to avoid 
shortages) excess capacity and 2) rebuilding expenditures are proportionate to 
direct loss. 
The first assumption eliminates all constraints and, therefore, indirect losses 
are 
eliminated as well. By linking reconstruction spending to direct loss, indirect 
gain (the 
effect of the construction multiplier) is made proportionate to direct loss. It 
will be 
shown below that the slope implied by the contour is a function of the 
construction 
multiplier. 

 

It appears from these experiments that reconstruction spending exerts a powerful 
influence on indirect loss. Figure 16.9 shows the results of an experiment where 
internal 
capacity was varied randomly from zero to 30 percent, the shocks were drawn 
randomly 
from a uniform probability distribution, and reconstruction spending was random. 
As 
shown, indirect losses were recorded for fewer than 10 percent of the cases. 
Figure 16.10 




shows the effect of eliminating reconstruction expenditures. As expected, the 
gains 
shown in Figure 16.8 disappear. 

 

Risk MapRegions of Gain and Loss 


Figure 16.7 Risk Map - Direct vs. Indirect 




Even Damage and Reconstructionin Proportion to Direct Losses 


Figure 16.8 Risk Map - No Constraints 




Skewed Damage Distribution30% Capacity and Reconstruction 


Figure 16.9 Risk Map - Random Capacity 




12Uniform means that each sector suffers an equal ratio of damage. 
Even Damage Distribution30% Capacity No Reconstruction 


Figure 16.10 Risk Map - No Rebuilding 

In contrast, Figure 16.11 shows that when the economy is constrained (internally 
and 
externally) indirect losses can be quite high and indirect gains are impossible. 
The shape 
of this result map can be explained. The outline of the contour map provided in 
Figure 
16.11 and several regions of the solution set are identified in Figure 16.12. 
The 
triangular shape of the map follows directly from the way in which the economy 
responds to damages. Point B, the uppermost level of indirect loss, results from 
a 
maximum shock to the smallest sector. Even though B proved to be improbable, 
other 
combinations of low direct loss and relatively high indirect loss were observed. 
The Line 
segment D-C shows the effect of a uniform12 damage patterns. An even pattern of 
damages produce no indirect loss since the economy remains balanced. Only an 
uneven 
pattern of damage produces bottleneck effects and indirect losses. The line 
segment A-C 
can be interpreted as the indirect loss frontier. At the extreme, when direct 
loss is total, 
indirect loss must be zero. Similarly, when direct loss is total for the 
smallest sector, 
indirect loss is maximum. Hence, point A would be observed if the size of the 
smallest 




sector approached zero. Line segment D-B shows the influence of increased 
variance in 
the pattern of loss. The variance is zero at D and maximum at B. 

 

Even Damage DistributionFully Constrained 


Figure 16.11 Risk Map Fully Constrained 




00.20.40.60.80DIRECT DAMAGE RATIOINDIRECT DAMAGE RATIOABCDEVEN DAMAGE 
DISTRIBUTION 
INCREASED VARIATION IN DIRECT DAMAGE 
MAXIMUM INDIRECT LOSSAT SMALLEST SECTOR TOTAL LOSS 
MAXIMUM INDIRECT LOSS 
RELATIONHIP BETWEEN DIRECTAND INDIRECT DAMAGES1.5 


 

Figure 16.12 Relationship Between Direct and Indirect Damages 

 

Figures 16.13 and 16.14 show the effect of a shock to lifelines (transportation) 
alone. 
The only difference between the two experiments is the amount of excess capacity 
assumed, 30 percent in the former and none in the latter. It is not surprising 
that this 
latter scenario produces the potential for sizable indirect 
losses.




 Even Damage Distribution (Transportation Only) 
30% Capacity, Reconstruction 


Figure 16.13 Risk Map - Transportation Disruption and Excess Capacity 




 Even Damage Distribution (Transportation Only)
Fully Constrained Reconstruction 


Figure 16.14 Risk Map - Transportation Disruption and No Excess Capacity 

 

Figures 16.15, 16.16 and 16.17 provide a comparison of how economies respond to 
differing damage patterns, capacities and economic structure. Figure 16.15 
summarizes 
the experiments that varied capacity. Figure 16.16 contrasts the degree of 
skewness in 
sectoral damage. As shown, the greater the concentration of damage, the greater 
the 
indirect loss as a proportion of total loss. The greater the capacity the 
greater the chances 
of indirect gain. Rebuilding expenditures enhances such gains. It is somewhat 
surprising in Figure 16.17 that economic structure appears to play an 
insignificant role in 
determining indirect losses when the economy is fully constrained. All three 
economies 
shown appear to produce very similar joint density functions. Clearly, the same 
conclusion will not apply in the event that internal excess capacity exists. In 
that case, 
economic gains are sensitive to economic structure, through a construction 
multiplier. 

 

It was asserted above that, if unconstrained, this model produces a solution 
that is 
equivalent to what conventional input-output techniques yield. This is easily 
demonstrated by making reconstruction expenditures proportionate to direct loss. 
A 
simple linear regression of spending and indirect gain should produce a slope 
(zero 
intercept) equal to the construction multiplier. Figure 16.18 shows the result 
of this 
experiment. The slopes of the indirect gain functions for Los Angeles and Santa 
Cruz are 




1.397 and 1.145 respectively. The respective IMPLAN construction multipliers for 
these 
two counties are 1.431 and 1.141. 

 

The Effect of Capacity on Indirect LossInternal Capacity 
No External CapacityNo ReconstructionInternal Capacity 
External CapacityNo ReconstructionInternal Capacity 
No External CapacityReconstructionInternal Capacity 
External CapacityNo ReconstructionABCDDirect LossDirect LossDirect LossDirect 
LossIndirect LossIndirect LossIndirect LossIndirecct LossMaximum Indirect Loss 


Figure 16.15 Risk MapsThe Effects of Capacity 

 




 The Effect of DamageDistribution on Indirect LossUniform DistributionSkewed 
DistributionOne Sector-TransportationDirect LossIndirect LossGainDirect 
LossDirect LossIndirect LossGainIndirect LossGain 


Figure 16.16 Risk Maps  The Effects of Damage Distributions 

 

The Effect of the 
Transactions Matrix on 
Indirect LossLos AngelesSanta CruzTennesseeDirecct LossDirecct LossDirecct 
LossIndirecct LossIndirecct LossIndirecct LossMaximum Indirect Loss 


Figure 16.17 Risk Map -- The Effect of the Transactions Matrix When Fully 
Constrained 

 




-1.0-0.9-0.8-0.7-0.6-0.5-0.4-0.3-0.2-0.10.0Direct 
Loss0.00.20.40.60.81.01.21.4Indirect GainLos AngelesIndirect vs. Direct Income-
1.0-0.9-0.8-0.7-0.6-0.5-0.4-0.3-0.2-0.10.0Direct 
Loss0.00.20.40.60.81.01.21.41.6Indirect GainSanta CruzIndirect vs. Direct 
IncomeReconstruction SpendingChange in Output1.397*SpendingLos AngelesR squared 
= .999n=4261Indirect Output vs. SpendingReconstruction SpendingChange in 
Output1.145*SpendingSanta CruzR squared = .989n=416Indirect Output vs. Spending 


Figure 16.18 Indirect Gains and the Construction Multiplier 

 

16.6.4 Observations About Indirect Loss 

 

The following generalizations can be drawn from the foregoing experiments: 

 

1. 
Holding capacity and rebuilding fixed, indirect losses are inversely 
proportional to 
the size of the sector shocked. For example, in the extreme case of an economy 
with 
a dominant sector, the rest of the economy in which indirect effects take place 
is 
relatively small. 




 

2. 
Imports can either reduce or promote indirect loss, dampening losses if used to 
supply 
industry with raw and semi-finished ingredients so that production can be 
resumed, 
and accentuating losses if imports are used to satisfy unmet household demand, 
thus 
displacing local production. 




 

3. 
Shocks to a fully constrained economy produce indirect losses, but not indirect 
gains 
because there is no leeway for the latter (e.g., multiplier effects from 
construction). 
In such an economy, the probability of indirect losses exceeding direct damage 
is 
approximately 50 percent. 




 

4. 
The greater the variance in the pattern of damage, the greater the indirect loss 
due to factors such as bottleneck effects. 




 




5. 
A uniform pattern of loss produces no indirect loss because internal 
rearrangements of buyers and sellers can be perfectly matched (barring 
transportation problems and contractual constraints). 




 

6. 
If the economy is fully constrained, indirect losses are maximum when the 
economy's smallest sector is totally destroyed (this is the inverse of 
generalization No. 1). 




 

7. 
When unconstrained, the economy expands from the construction stimulus as 
conventional I-O techniques (multipliers) would predict. 




 

8. 
A dynamic analysis of indirect loss reflects both the forward and backward 
linked losses and future demand changes resulting from disaster caused 
indebtedness, both of which are generally long-run dampening effects. 




 

9. 
When economies are fully constrained, indirect loss appears to be insensitive 
to economic structure. Different transactions matrices yield marginally 
different indirect losses, most likely because of similarities of multiplier 
values or stochastic offsets of multipliers of differing values. 




 

10. 
From a regional accounting stance reconstruction gains tend to dominate 
indirect losses when excess capacity exists. 




 

16.7 References 

 

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Boisvert, R. 1992. Indirect Economic Consequences of a Catastrophic Earthquake, 
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Brookshire, D., and M. McKee, 1992. Indirect Economic Consequences of a 
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Cochrane, H.; J. Eugene Haas; and R. W. Kates. "Social Science Perspectives on 
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Cochrane, H. A Preliminary Analysis of Damages and Economic Dislocation, in the 
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Doherty, N., A. E. Kleffner, and H. Kunreuther, 1991. The Impact of a 
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Monroe, Tapan, 1989. "The Economic Impact of the Loma Prieta Earthquake of 1989: 
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Preliminary Look", unpublished paper, Pacific Gas & Electric Company. 

 

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Appendix 16A 

 

Default Data Analysis 

Synthetic Economies 

 

113 state and county IMPLAN tables were analyzed to derive synthetic 
transactions 
matrices for the Default Data Analysis model. A frequency histogram of 
employment 
(See Tables 16A.2 through 16A.4) revealed that 90 percent of the tables could be 
classified as Manufacturing/Service, Service/Manufacturing, or Service/Trade. 
Since 
nearly two thirds of employment in these tables can be traced to these three 
sectors, it 
was decided that this means of classifying economies could be used as a basis 
for 
deriving Default Data Analysis interindustry trade flows. Further adjustments 
were made 
to reflect the size of the economy. Four size classes were created resulting in 
the 12 way 
classification shown below. 

 

Table 16A.1 Classification of Synthetic Economies 

 

 Employment 

Type 



 

 Upper 
Bound 

Lower Bound

Manufacturing/ 
Service 

Service/ 
Manufacturing 

Service/ 

Trade 

unlimited 

2 million 

SUP1 

SUP2 

SUP3 

2 million 

.6 million 

LAR1 

LAR2 

LAR3 

.6 million 

30,000 

MID1 

MID2 

MID3 

30,000 

0 

LOW1 

LOW2 

LOW3 



 

 

The particular states and counties which were utilized to create the 12 
synthetic tables are 
shown in Tables 16A.5 through 16A.6. 

 

 

Table 16A.2 Manufacturing/Service 

Sector 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

AVG 

Manufacturing 

0 

0 

0 

9 

25 

10 

4 

1 

0 

0 

0 

37.5%

Government 

0 

0 

14 

35 

0 

0 

0 

0 

0 

0 

0 

21.5%

FIRE 

0 

3 

44 

2 

0 

0 

0 

0 

0 

0 

0 

13.6%

Trade 

0 

42 

7 

0 

0 

0 

0 

0 

0 

0 

0 

7.5%

Service 

0 

46 

3 

0 

0 

0 

0 

0 

0 

0 

0 

6.3%

Construction 

0 

46 

3 

0 

0 

0 

0 

0 

0 

0 

0 

6.3%

Transportation 

0 

48 

1 

0 

0 

0 

0 

0 

0 

0 

0 

6.1%

Agriculture 

0 

49 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.6%

Mining 

0 

49 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.6%



 




Table 16A.3 Service/Manufacturing 

Sector 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

AVG 

Government 

0 

0 

1 

20 

11 

1 

0 

0 

0 

0 

0 

28.6% 

Manufacturing 

0 

0 

12 

18 

2 

0 

1 

0 

0 

0 

0 

23.4% 

FIRE 

0 

2 

29 

2 

0 

0 

0 

0 

0 

0 

0 

13.9% 

Trade 

0 

27 

6 

0 

0 

0 

0 

0 

0 

0 

0 

8.4% 

Transportation 

0 

25 

8 

0 

0 

0 

0 

0 

0 

0 

0 

8.3% 

Service 

0 

28 

5 

0 

0 

0 

0 

0 

0 

0 

0 

7.8% 

Construction 

0 

28 

5 

0 

0 

0 

0 

0 

0 

0 

0 

7.1% 

Mining 

0 

32 

1 

0 

0 

0 

0 

0 

0 

0 

0 

2.2% 

Agriculture 

0 

33 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.4% 



 

Table 16A.4 Service/Trade 

Sector 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

AVG 

Government 

0 

0 

0 

2 

7 

6 

0 

1 

0 

0 

0 

37.4%

Service 

0 

1 

8 

7 

0 

0 

0 

0 

0 

0 

0 

18.2%

Transportation 

0 

10 

6 

0 

0 

0 

0 

0 

0 

0 

0 

9.3%

Manufacturing 

0 

9 

7 

0 

0 

0 

0 

0 

0 

0 

0 

9.2%

Construction 

0 

13 

3 

0 

0 

0 

0 

0 

0 

0 

0 

7.8%

FIRE 

0 

13 

3 

0 

0 

0 

0 

0 

0 

0 

0 

7.4%

Trade 

0 

14 

2 

0 

0 

0 

0 

0 

0 

0 

0 

6.0%

Mining 

0 

13 

2 

1 

0 

0 

0 

0 

0 

0 

0 

4.1%

Agriculture 

0 

16 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0.5%



 

 




Table 16A.5 Manufacturing/Service Economy 

 

Super 

 

 

Large 

 

FIPS 

STATE/CNTY. 

EMPLOY. 

FIPS 

STATE/CNTY. 

EMPLOY. 

39,000 

Ohio 

5,831,755

53,033

King, WA 

1,112,072

26,000 

Michigan 

4,714,837

9,000

Connecticut 

1,989,824

13,000 

Georgia 

3,673,183

19,000

Iowa 

1,635,164

37,000 

North Carolina 

3,858,712

5,000

Arkansas 

1,194,095

18,000 

Indiana 

3,064,277

28,000

Mississippi 

1,186,175

29,000 

Missouri 

2,986,395

33,000

New Hampshire 

655,638

53,000 

Washington 

2,777,829

6,059

Orange, CA 

1,514,438

27,000 

Minnesota 

2,642,082

41,000

Oregon 

1,621,333

47,000 

Tennessee 

2,733,161

23,000

Maine 

709,529

55,000 

Wisconsin 

2,796,572

1,000 

Alabama 

2,028,495



 

 

 

Mid 

 

 

Low 

 

FIPS 

STATE/CNTY. 

EMPLOY. 

FIPS 

STATE/CNTY. 

EMPLOY. 

8,059 

Jefferson, CO 

224,465

48,257

Kaufman, TX 

19,758

53,061 

Snohomish, WA 

212,107

6,069

San Benito, CA 

16,274

41,067 

Washington, OR 

179,331

55,029

Door, WI 

15,682

55,009 

Brown, WI 

123,090

55,093

Pierce, WI 

13,707

41,005 

Clackamas, OR 

129,712

55,099

Price, WI 

8,637

55,087 

Outagamie, WI 

89,502

8,087

Morgan, CO 

12,408

48,121 

Denton, TX 

88,726

41,015

Curry, OR 

8,996

49,057 

Weber, UT 

77,041

48,285

Lavaca, TX 

9,272

55,089 

Ozaukee, WI 

36,021

55,129

Washburn, WI 

6,590

48,139 

Ellis, TX 

31,798

41,035

Klamath, OR 

28,783

41,071 

Yamhill, OR 

30,416

55,109

St.Croix, WI 

23,213

16,000 

Idaho 

547,056

50,000 

Vermont 

345,166

44,000 

Rhode Island 

554,121

10,000 

Delaware 

414,343



 




Table 16A.6 Service/Manufacturing Economy 

 

Super 

 

 

Large 

 

FIPS 

STATE/CNTY. 

EMPLOY. 

FIPS 

STATE/CNTY. 

EMPLOY. 

36,000 

New York 

9,747,535

19,000

Iowa 

1,635,164

6,037 

Los Angeles, CA 

5,108,213

40,000

Oklahoma 

1,614,109

48,000 

Texas 

8,900,073

4,013

Maricopa, AZ 

1,212,392

34,000 

New Jersey 

4,327,815

22,000

Louisiana 

1,969,967

25,000 

Massachusetts 

3,644,604

5,000

Arkansas 

1,194,095

6,000 

California 

16,532,145

31,000

Nebraska 

987,260

13,000 

Georgia 

3,673,183

54,000

West Virginia 

769,662

51,000 

Virginia 

3,695,334

4,000

Arizona 

1,870,344

24,000 

Maryland 

2,697,448

20,000

Kansas 

1,485,215

8,000 

Colorado 

2,017,818

49,000

Utah 

895,454



 

 

Mid 

 

 

Low 

 

FIPS 

STATE/CNTY. 

EMPLOY. 

FIPS 

STATE/CNTY. 

EMPLOY. 

35,001 

Bernalillo, NM 

306,176

35,041

Roosevelt, NM 

7,593

53,053 

Pierce, WA 

263,512

 

41,051 

Multnomah, OR 

441,788

 

53,063 

Spokane, WA 

192,662

 

48,085 

Collin, TX 

103,086

 

6,089 

Shasta, CA 

71,398

 

48,485 

Wichita, TX 

74,491

 

49,011 

Davis, UT 

78,170

 

6,071 

San Bernardino, CA 

529,198

 

49,035 

Salt Lake, UT 

436,832

 

6,065 

Riverside, CA 

434,846

 

6,111 

Ventura, CA 

313,911

 



 

Table 16A.7 Service/Trade Economy 

 

Super 

 

 

Large 

 

FIPS 

STATE/CNTY. 

EMPLOY. 

FIPS 

STATE/CNTY. 

EMPLOY. 

 

NONE 

 

11,000

District of Columbia 

761,680

 

 

 

32,000

Nevada 

741,574

 

 

 

15,000

Hawaii 

696,759

 

 

 

35,000

New Mexico 

745,539



 

 

Mid 

 

 

Low 

 

FIPS 

STATE/CNTY. 

EMPLOY. 

FIPS 

STATE/CNTY. 

EMPLOY. 

30,000 

Montana 

433,623

48,397

Rockwall, TX 

9,140

8,005 

Arapahoe, CO 

217,208

8,067

La Plata, CO 

19,079

4,003 

Cochise, AZ 

39,611

56,001

Albany, WY 

16,959

38,000 

North Dakota 

377,987

56,041

Uinta, WY 

9,948

6,029 

Kern, CA 

262,422

55,125

Vilas, WI 

8,364

56,021 

Laramie, WY 

44,438

35,061

Valencia, NM 

11,787



 

 




Design level is a combination of Seismic Design Level and Building Quality and 
is 
explicitly considered in the earthquake model. The following table illustrates 
how 
Seismic Design Level and Building Quality are combined to obtain the seven 
Design 
Levels that are used by the methodology. 

 

New Code 

Description 

Old Code Combination 

BldgQuality 

DesigLevel 

HC 

High  Code 

Code 

High 

MC 

Moderate - Code 

Code 

Moderate 

LC 

Low  Code 

Code 

Low 

PC 

Pre  Code 

Inferior 

Low 

HS 

Special High  Code 

Superior 

High 

MS 

Special Moderate  Code 

Superior 

Moderate 

MS 

Special Moderate  Code 

Inferior 

High 

LS 

Special Low  Code 

Superior 

Low 

LS 

Special Low  Code 

Inferior 

Moderate