Multi-hazard Loss Estimation Methodology 

Hurricane Model 

HAZUS.MH MR3 

Technical Manual 

Developed by: 

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

Under a contract with: 

National Institute of Building Sciences 
Washington, D.C. 

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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 nongovernmental, 
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. 

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ACKNOWLEDGMENTS 

HAZUS-MH, HAZUS-MH MR1, HAZUS-MH MR2 and HAZUS-MH MR3 

Hurricane Model Methodology Development 

Wind Committee 

Chairman, Joseph Minor, Consultant, Rockport, Texas 
Arthur Chiu, University of Hawaii, Honolulu, Hawaii 
Mark Levitan, Louisiana State University, Baton Rouge, Louisiana 
Richard Marshall, Consultant, Poolesville, Maryland 
Robert McComb, Texas Tech University, Lubbock, Texas 
Kishor Mehta, Texas Tech University, Lubbock, Texas 
Dale Perry, Texas A&M University, College Station, Texas 
Mark Powell, National Oceanic and Atmospheric Administration, Miami, Florida 
Douglas Smits, City of Charleston, Charleston, South Carolina 
Masoud Zadeh, Consultant, San Jose, California 

Applied Research Associates, Inc., Raleigh, North Carolina 
Peter Vickery, Project Manager; Lawrence Twisdale, Jr., Project Manager; 
Jason Lin, 
Peter Skerlj, Michael Young, Francis Lavelle, Reddy Kadasani, Chris Driscoll, 
Kevin 
Huang, Peter Montpellier, Andrew Steckley, Jeffrey Sciaudone, Yingzhao Chen, 
Dhiraj 
Wadhera 

Consultants 
Impact Forecasting, Chicago; Illinois, Sethu Raman, North Carolina State 
University, 
Raleigh, North Carolina; Thomas Smith, TLSmith Consulting, Rockton, Illinois; 
Timothy 
Reinhold, Clemson University, Clemson, South Carolina 

Hurricane 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. 

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Applied Research Associates, Inc., Raleigh, North Carolina 
Francis Lavelle, Project Manager; Chris Driscoll, Reddy Kadasani, Richard 
Pearson, 
Jr., Peter Vickery, Steve Brooks, Andrew Steckley 

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, Federal Emergency Management 
Agency, 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 

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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; Pushpendra Johari, Sandeep Mehndiratta 

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

Project Sponsorhsip and Oversight 

Department of Homeland Security, FEMA, 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; Mark Crowell, Physical Scientist; John 
Ingargiola, 
Douglas Bellemo, Allyson Lichtenfels, Divisional Coordination 

Special thanks to the National Aeronautics and Space Administration for 
providing financial 
assistance for developing tree debris estimation capability in the Hurricane 
Model 

Program Management 

National Institute of Building Sciences, Washington, D.C. 
Philip Schneider, Director, Multihazard Loss Estimation Methodology Program; 
Barbara Schauer, Senior Project Manager 

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MESSAGE TO USERS

The HAZUS Hurricane Model is designed to produce loss estimates for use by 
federal, state, 
regional and local governments in planning for hurricane risk mitigation, 
emergency 
preparedness, response and recovery. The methodology deals with important 
aspects of the 
built environment, and a wide range of different types of losses. Extensive 
national databases 
are embedded within HAZUS, 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 
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 hurricanes 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. Where 
inventories, 
demographics and economic parameters track closely those assumed and built 
into the basic 
methodology, estimates of loss should be within a factor of two. Where one or 
more of these 
parameters are incomplete or inaccurate the range of uncertainty may exceed a 
factor of two 
or more. 

The hurricane loss estimation methodology is based on sound scientific and 
engineering 
principals and experimental and experience data. The methodology has been 
tested against 
the judgment of experts and, to the extent possible, against records from 
several past 
hurricanes. However, limited and incomplete data about actual hurricane 
damage precludes 
complete calibration of the methodology. Nevertheless, when used with 
embedded 
inventories and parameters, the HAZUS Hurricane Model has provided a credible 
estimate of 
such aggregated losses as the total cost of damage. 

Users should be aware of the following specific limitations: 

  While the HAZUS Hurricane 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 a hurricane. 
  The Hurricane model contains definitions and assumptions regarding building 
strengths that represent a norm for construction in hurricane zones. These 
norms are 
defined in the technical manual. Where construction quality is known to be 
different 
from the defined norms, larger uncertainties in loss projections may be 
realized. 
HAZUS should still be regarded as a work in progress. Additional damage and 
loss data from 
actual hurricanes and further experience in using the software will 
contribute to 

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improvements in future releases. To assist us in further improving HAZUS, 
users are invited 
to submit comments on methodological and software issues by letter, fax or e-
mail to: 

Philip Schneider 
National Institute of Building Sciences 
1090 Vermont Ave., N.W. 
Washington, DC 20005 
Tel: 202-289-7800 ext. 127 
Fax: 202-289-1092 
E-Mail: HAZUSGenHelp@nibs.org

Eric Berman 
Department of Homeland Security 
Federal Emergency Management Agency 
Mitigation Division 
500 C Street, S.W. 
Washington, DC 20472 
Tel: 202-646-3427 
Fax: 202-646-2787 
E-Mail: Eric.Berman@dhs.gov 

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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, but SP2 is preferable. ArcGIS 9.2 SP2 is available from the ESRI 
website. 
  HAZUS-MH MR3 is certified to run on MS Windows 2000 SP2, SP3 and SP 4 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. This 
system has a size limit of 4 GB per database, which limits the size of the 
regions to 
about 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 2005 Workgroup Standard Edition must be used (see 
Appendix F in the Hurricane User Manual). 
  Multihazard loss analysis capability is limited to the 23 states that 
experience 
hurricane, flood and earthquake hazards and requires that the user first run 
annualized 
losses for each of the three hazards. 
  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 to 
a 
maximum of 2048 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. Replace the initial and maximum values 
6. Click Apply 
  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 
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wizard. If, at the time of installation, the user specifies the folder where 
the data will 
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 doesn't 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., NC1), 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 
the 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, 
HAZUS99SR1, 
HAZUS99-SR2, HAZUS-MH and HAZUS-MH MR1 to HAZUS-MH MR3 
will require the assistance of technical support. 
  Inventory data and subsequently the Level 1 analysis functionality are 
unavailable for 
the US held territories. 
  Components of 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. 
  The Hurricane Model can be run for 22 states on the Gulf and Atlantic 
coasts and for 
the state of Hawaii. Default tree inventory data is not available for Hawaii. 
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  Loss estimates for large study regions of 2,000 census tracts or blocks 
might require 4 
hours analysis time. 
Technical Support 

  Technical support is available via telephone, e-mail, or FAX. The numbers 
and 
addresses 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/. 
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WHAT S NEW IN HAZUS-MH MR1   HURRICANE MODEL 

Data 

  Updated valuations for the general building stock. 
Methodology 

  Capability to assess hurricane mitigation options for multi-unit buildings 
and 
manufactured housing. 
  Capability to define hurricanes with NWS forecasts/advisories downloaded 
directly from the Internet. 
Other Features 

  Operation on the new ArcGIS 9.0 SP1 platform. 
  Capability to utilize third-party tools. 
  Optimized software for rapid loss assessment. 
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WHAT S NEW IN HAZUS-MH MR 2   HURRICANE 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 

  Availability of mitigation analysis options for all building classes and 
capability 
to specify the percentages of each building class that have one or more 
mitigation 
features. 
  Capability to estimate tree debris weight or volume that is likely to be 
collected 
and discarded at public expense. 
  Capability through an  Automatic Outputs  tool in the analysis options 
window to 
specify a standard set of summary reports and map layers that will be 
generated 
after each analysis and automatically exported to the study region folder. 
  Capability to generate a range, representing the 5th and 95th percentiles, 
of 
potential damage and loss estimates for a given hurricane forecast, based on 
track 
and intensity errors in forecast advisories issued during the past 12 years, 
and to 
include the results in a new rapid loss summary report. 
  Minor adjustments to the Hurricane Model Summary Reports to improve 
consistency among the three models. 
Other Features 

  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. 
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WHAT S NEW IN HAZUS-MH MR 3   HURRICANE MODEL 

Data 

  Commercial data updated to Dun & Bradstreet 2006. 
  Building valuations updated to R.S. Means 2006. 
  Building counts based on census housing unit counts for RES1 (single-family 
dwellings) and RES2 (manufactured housing) instead of calculated building 
counts. 
Methodology 

  Storms for 2004 and 2005 added to the historic storms database and updates 
made 
for several existing historic storms. 
  Use of NOAA hurricane advisory data to model storms restored with an 
adjustment feature for calculating building damage and loss. 
  New probabilistic storm set reflecting updates to the Holland pressure 
profile 
model and filling model. 
  Updated wind field model for user-defined storms. 
  New vulnerability functions to permit calculation of additional losses to 
manufactured housing due to tree blowdown. 
Other Features 

  Operation on the ArcView 9.2 SP2 platform. 
  Operation on Microsoft SQL 2005 Express. 
  Operation on the MDAC 2.8 SP1 data access engine from Microsoft. 
  Operation on FarPoint Spread 7   Spreadsheet 
  Operation on Crystal Reports 11. 
  Enhancements to the existing aggregation routines to reflect improved 
procedures 
for processing study region information. 
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TABLE OF CONTENTS 

VOLUME I 

CHAPTER 1. 
INTRODUCTION.................................................................
.................................1-1 

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

1.2 
Scope........................................................................
...................................................1-1 

1.3 Technical Approach 
.............................................................................
........................ 1-3

1.4 Technical Manual Organization 
.............................................................................
....... 1-5 

CHAPTER 2. HURRICANE RISK MODEL 
.............................................................................
...2-1 

2.1 Review of Hurricane Wind Models 
.............................................................................
..2-1 

2.2 Hurricane Wind Field Model 
.............................................................................
............2-2 

2.2.1 Mean Wind Field 
Model........................................................................
...........2-3 

2.2.2 Surface Level 
Winds........................................................................
................ 2-3 

2.2.3 Summary 
.............................................................................
..........................2-25 

2.3 Hurricane Simulation 
Methodology..................................................................
...........2-25 

2.3.1 
Introduction.................................................................
...................................2-25 

2.3.2 Storm Track Modeling 
.............................................................................
......2-26 

2.3.3 Modeling of Radius to Maximum 
Winds........................................................2-27 

2.3.4 Pressure Profile Parameter (B) 
.....................................................................2-28 

2.3.5 Filling Models 
.............................................................................
...................2-32 

2.3.6 Track and Central Pressure Model 
Evaluation..............................................2-34 

2.3.7 Landfalling Hurricanes 
.............................................................................
.....2-34 

2.3.8 Predicted Wind Speeds vs. Return 
Period....................................................2-43 

2.3.9 Summary 
.............................................................................
..........................2-45 

2.4 Hurricane Rainfall Rate 
Model........................................................................
............2-47 

2.4.1 
Introduction.................................................................
...................................2-47 

2.4.2 Data and Prior Studies 
.............................................................................
....2-48 

2.4.3 Model 
Development..................................................................
.....................2-49 

2.4.4 Hurricane Rainfall Rate Model Implementation and Calibration 
...................2-53 

2.4.5 Summary 
.............................................................................
..........................2-58 

CHAPTER 3. SURFACE ROUGHNESS MODELING3-1 

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

3.2 Land Use Databases 
.............................................................................
....................... 3-3 

3.2.1 Florida Water Management District Data 
........................................................ 3-4 

3.2.2 MRLC-NLCD 
Database.....................................................................
..............3-7 

3.3 Surface Roughness Lengths in the Southeast Florida Area 
......................................3-12 

3.4 Surface Roughness Lengths in the Florida Panhandle Area 
.....................................3-16 

3.5 Roughness Length Database Development for All Coastal 
States............................3-21 

3.5.1 The National Land Cover 
Data......................................................................3-
21 

3.5.2 Mapping NLCD Land Cover Codes to Roughness 
Lengths..........................3-27 

3.5.3 Census Tract-Averaged Roughness 
Length.................................................3-34 

3.5.4 Comparisons of zo Values Computed from NLCD Data on Rectangular 
Grids with Empirically Assigned zo Values 
....................................................3-36 

3.6 Example of Roughness Length (zo) Calculation Using Lettau's 
Formula...................3-40 

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3.7 Effect of Surface Roughness on Near Ground Gust Wind 
Speeds............................3-49 

CHAPTER 4. WIND LOADS 
.............................................................................
......................... 4-1 

4.1 
Introduction.................................................................
..................................................4-1 

4.2 Wall Pressures-Low-Rise 
Buildings....................................................................
.......... 4-2 

4.3 Roof Pressures - Low-Rise 
Buildings....................................................................
.......4-4 

4.4 Wind Loads on Low-Rise Buildings- Effect of Nearby 
Buildings...............................4-16 

4.5 Integrated (Overall) Wind Loads on Low-Rise 
Buildings............................................4-17 

4.6 Wind Loads on High-Rise 
Buildings....................................................................
.......4-28 

CHAPTER 5. WINDBORNE DEBRIS 
.............................................................................
...........5-1 

5.1 Windborne Debris - Residential Missile 
Model.............................................................5-1 

5.2 Windborne Debris - Commercial Missile Model 
...........................................................5-7 

5.2.1 Windborne Gravel Debris Simulation and Case 
Studies.................................5-8 

5.2.2 Probability of Damage to Building Envelopes by Windborne Gravel 
Debris 
.............................................................................
...............................5-25 

CHAPTER 6. PHYSICAL DAMAGE 
MODELING.....................................................................
.6-1 

6.1 General 
Approach.....................................................................
....................................6-1 

6.2 Resistance Models for Residential Buildings 
..............................................................6-3 

6.2.1 Roof Sheathing in Wood Frame 
Construction.................................................6-3 

6.2.2 Air Permeable Roof Cover 
Systems................................................................ 6-4 

6.2.3 Windows and Sliding Glass Doors 
.................................................................. 6-5 

6.2.4 Roof-Wall 
Connections..................................................................
..................6-6 

6.2.5 Masonry Walls 
.............................................................................
.................... 6-7 

6.2.6 Wood Frame 
Walls........................................................................
................6-16 

6.3 Damage Model Validation for Residential 
Buildings.......................................................6-32 

6.3.1 Roof Cover and Roof Deck 
Damage.............................................................6-32 

6.3.1.1 Hurricane Andrew 
(1992)................................................................6-32 

6.3.1.2 Hurricane Erin 
(1995)......................................................................6
-42 

6.3.1.3 Hurricane Fran 
(1996).....................................................................6-
43 

6.3.2 Window Damage 
.............................................................................
..............6-44 

6.3.3 Whole Roof 
Failures.....................................................................
.................6-44 

6.4 Damage Model Results for Residential Buildings 
......................................................6-46 

6.4.1 Effect of Mitigation on Residential Building 
Damage.....................................6-59 

6.5 Manufactured Homes 
.............................................................................
....................6-62 

6.5.1 Resistance Models for Manufactured Homes 
...............................................6-64 

6.5.2 Damage Model Validation for Manufactured 
Homes.....................................6-67 

6.5.3 Damage Model Results for Manufactured Homes 
........................................6-72 

6.6 Roof Covers on Flat 
Roofs........................................................................
.................6-73 

6.6.1 Resistance Model for Roof Covers on Flat 
Roofs.........................................6-74 

6.6.2 Damage Model Validation for Roof Covers on Flat 
Roofs.............................6-76 

6.7 Open-Web Steel Joist Roof 
System.......................................................................
....6-78 

6.7.1 Resistance Model for Open-Web Steel 
Joists...............................................6-80 

6.7.2 Resistance Model for Metal Deck on Open-Web Steel Joists 
......................6-83 

6.8 Pre-Engineered Metal 
Buildings....................................................................
.............6-95 

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6.8.1 Resistance Model for Metal Building Wall System 
.......................................6-96 

6.8.2 Resistance Model for Metal Building Roof System 
.....................................6-101 

6.9 Damage Model Results for Marginally- or Non-Engineered Hotel/Motel and 
Multi-
Family Residential 
Buildings....................................................................
.................6-102 

6.10 Damage Model Results for Low-Rise Masonry Strip Mall 
Buildings........................6-105 

6.11 Damage Model Results for Pre-Engineered Metal Buildings 
..................................6-125 

6.12 Damage Model Results for Engineered Residential and Commercial Buildings 
.....6-129 

6.13 Damage Model Results for Industrial 
Buildings........................................................6-134 

CHAPTER 7. DIRECT ECONOMIC 
LOSSES.......................................................................
..... 7-1 

7.1 
Introduction.................................................................
..................................................7-1 

7.2 Residential Input 
Parameters...................................................................
....................7-1 

7.3 Residential Subassembly 
Costs........................................................................
...........7-3 

7.3.1 Default Residence 
.............................................................................
.............. 7-3 

7.3.2 Subassembly Cost Ratios 
.............................................................................
.7-4 

7.4 Explicit Costing of Residental Losses 
..........................................................................7-5 

7.5 Modeling of Residential Interior and Content Losses and Loss of 
Use.............................7-7 

7.5.1 Economic Damage to the Building 
Interior......................................................7-7 

7.5.2 Economic Damage to 
Contents.....................................................................
..7-9 

7.5.3 Loss of Use for Residential 
Buildings............................................................7-11 

7.6 Residential Loss Model Validation 
Examples.............................................................7-11 

7.7 Loss Model Results for Residential Buildings 
...........................................................7-14 

7.8 Manufactured Home Loss 
Model........................................................................
........7-19 

7.8.1 Manufactured Home Loss Model Costing 
.....................................................7-19 

7.8.2 Fast Running Manufactured Home Loss 
Functions......................................7-36 

7.9 Economic Loss Model for Commercial Buildings 
......................................................7-36 

7.9.1 Subassembly Cost Ratios 
.............................................................................
...7-37 

7.9.2 Building Loss Model 
.............................................................................
.............7-39 

7.9.3 Content Loss 
Model........................................................................
...................7-43 

7.9.4 Business Interruption 
Model........................................................................
......7-44 

7.10 Loss Model Results for Marginally-Engineered or Non-Engineered 
Hotel/Motel 
and Multi-Family Residential Buildings 
......................................................................7-46 

7.11 Loss Model Results for Low-Rise Masonry Strip Mall Buildings 
................................7-46 

7.12 Loss Model Results for Pre-Engineered Metal Buildings 
..........................................7-79 

7.13 Loss Model Results for Engineered Residential and Commercial Buildings 
.............7-79 

7.14 Loss Model Results for Industrial 
Buildings................................................................7-95 

CHAPTER 8. BUILDING STOCK CLASSIFICATION METHODS 
............................................ 8-1 

8.1 
Introduction.................................................................
..................................................8-1 

8.2 Aerial Photography 
Samples......................................................................
..................8-1 

8.3 Contractor 
Survey.......................................................................
.................................. 8-4 

CHAPTER 9. LOSS MODEL VALIDATION STUDIES FOR HURRICANES ANDREW, 

HAZUS-MH MR3 Technical Manual 

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ERIN, OPAL AND HUGO 
.............................................................................
....... 9-1 

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

9.2 Example Results - Hurricane 
Andrew.......................................................................
....9-1 

9.3 Example Results - Hurricanes Erin and 
Opal.............................................................9-10 

9.4 Example Results - Hurricane 
Hugo.........................................................................
...9-15 

9.5 Summary 
.............................................................................
.......................................9-23 

CHAPTER 10. DEBRIS GENERATED FROM DAMAGED 
BUILDINGS..................................10-1 

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

10.2 Description of 
Methodology..................................................................
......................10-2 

10.3 Validation 
Studies......................................................................
.................................10-8 

10.4 Final 
Remarks......................................................................
.....................................10-15

CHAPTER 11. SHORT TERM SHELTER 
REQUIREMENTS....................................................11-1 

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

11.2 Description of 
Methodology..................................................................
......................11-1 

11.3 Simplified 
Methodology..................................................................
.............................11-4 

CHAPTER 12. TREE 
BLOWDOWN.....................................................................
......................12-1 

12.1 Introduction and Background 
.............................................................................
........12-1 

12.2 Related Research 
.............................................................................
.........................12-2 

12.3 Wind Throw 
Model........................................................................
..............................12-2

12.3.1 Wind Load Response and Breakage 
Model..................................................12-2 

12.3.2 Wind Modeling for Simple Terrains 
...............................................................12-7 

12.3.3 Example Tree Response - Ponderosa 
Pine................................................12-10 

12.3.4 Wind Modeling in Forested Areas 
...............................................................12-13 

12.3.4.1 Canopy 
Modeling.....................................................................
.....12-14 

12.3.4.2 Effective Windspeeds in Forested 
Areas......................................12-16 

12.3.5 Simulation 
Methodology..................................................................
............12-19 

12.3 Simulation 
Methodology..................................................................
.........................12-20 

12.4 Blowdown Results 
.............................................................................
.......................12-20 

12.4.1 Treee Blowdown Curves 
.............................................................................
12-20 

12.4.2 Tree Blowdown 
Validation...................................................................
........12-20 

12.5 Tree Inventory Data by 
County.......................................................................
..........12-24 

12.5.1 Forest Inventory Analysis (FIA) Program and 
Database.............................12-24 

12.5.2 Average Tree Density and Tree Height Distribution at County 
Level..........12-25 

12.6 HAZUS Tree Coverage 
Database.....................................................................
.......12-27 

12.6.1 MRLC National Land Cover 
Data................................................................12-27 

12.6.2 Tree Density, Tree Height, and Predominant Tree Type by Census 
Tract.12-28 

12.7 Debris Generated from Tree 
Blowdown...................................................................12
-29 

12.7.1 Total Weight and Volume of Downed 
Trees................................................12-30 

12.7.2 Tree Debris Collection Models 
....................................................................12-32 

12.7.2.1 Tree Debris Collection Model Description 
....................................12-32 

12.7.2.2 Implementing into HAZUS-
MH......................................................12-35 

12.7.2.3 Other Considerations for HAZUS-
MH...........................................12-37 

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12.7.3 Comparison of Modeled and Reported Tree 
Debris....................................12-38 

12.7.3.1 Example Tree Collection Data   Hurricane Isabel, North 
Carolina.....................................................................
....................12-38 

12.7.3.2 Example Tree Collection Data   Hurricane Isabel, 
Virginia..........12-39 

12.7.3.3 Comparison to Other Collection 
Data...........................................12-43 

12.8 Tree Blowdown Damage to Buildings 
......................................................................12-44 

12.8.1 
Overview.....................................................................
.................................12-44 

12.8.2 Tree Drop 
Tests........................................................................
...................12-44 

12.8.3 Relationship between Damage Severity and Impact 
Energy......................12-45 

12.9 Estimation of Direct Economic Loss from Physical Damage 
States........................12-48 

12.10 Mean Loss as a Function of Wind 
Speed.................................................................12-54 

12.10.1 Losses Due to Tree Blow-
Down..................................................................12-54 

12.10.2 Loss Function for a Specific Building Type in Given Census 
Tract.............12-55 

12.10.3 Combining Tree Blowdown Normalized Loss Functions with the Basic 
Fast-Running Building and Contents Loss Functions 
.................................12-55 

CHAPTER 13. 
REFERENCES...................................................................
................................13-1 
VOLUME II 
APPENDIX A. Damage State Functions for Residential 
Buildings....................................... A-1 
APPENDIX B. Damage State Functions for Manufactured 
Homes....................................... B-1 
APPENDIX C. Damage State Functions for Marginally- or Non-Engineered 
Hotel/Motel 

and Multi-Family Residential Buildings 
.......................................................... C-1 
APPENDIX D. Damage State Functions for Low Rise Masonry Strip Mall Buildings 
......... D-1 
APPENDIX E. Damage State Functions for Pre-Engineered Metal Buildings 
......................E-1 
APPENDIX F. Damage State Functions for Engineered Residential and Commercial 

Buildings 
.............................................................................
................................F-1 
APPENDIX G. Damage State Functions for Industrial 
Buildings..........................................G-1 
APPENDIX H. Loss Functions for Residential Buildings 
...................................................... H-1 
APPENDIX I. Loss Functions for Manufactured Homes 
........................................................ I-1 
APPENDIX J. Loss Functions for Marginally- or Non-Engineered Hotel/Motel and 
Multi-

Family Residential Buildings 
............................................................................J
-1 
APPENDIX K. Loss Functions for Pre-Engineered Metal 
Buildings......................................L-1 
APPENDIX M. Loss Functions for Engineered Residential and Commercial 
Buildings ....M-1 
APPENDIX N. Loss Functions for Industrial Buildings 
......................................................... N-1 

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xxvi 

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Chapter 1. Introduction 

1.1 Background 
In 1997, the National Institute of Building Sciences (NIBS), under a 
cooperative 
agreement with the Federal Emergency Management Agency (FEMA), released the 
first 
HAZUS Earthquake Model, a national, standardized GIS-based tool for 
estimating 
potential losses from earthquake. Earthquake loss estimates calculated with 
HAZUS are 
used by local, state, and regional officials for planning and stimulating 
mitigation efforts 
to reduce losses from earthquakes, and preparing for emergency response and 
recovery 
following earthquakes. HAZUS has also been used to perform a nationwide 
assessment 
of earthquake risk (FEMA, 2001). 

HAZUS has now been expanded to perform similar loss evaluations for wind and 
flood. 
FEMA and NIBS initiated development of the wind and flood models in 1997 with 
the 
creation of two committees to oversee technical development of the models. 
The 
resulting HAZUS software is an integrated, multi-hazard loss estimation 
program, 
packaged to run within a full-featured, commercial GIS platform. 

The HAZUS Hurricane Model allows practitioners to estimate the economic and 
social 
losses from hurricane winds. The information provided by the model will 
assist state and 
local officials in evaluating, planning for, and mitigating the effects of 
hurricane winds. 
The Hurricane Model provides practitioners and policy makers with a tool to 
help reduce 
wind damage, reduce disaster payments, and make wise use of the nation s 
emergency 
management resources. 

The Hurricane Model is the first component of a planned HAZUS Wind Model. 
When 
fully implemented, the Wind Model will address the wind hazard and effects 
associated 
with hurricanes, tornadoes, thunderstorms, extratropical storms and hail. The 
need for 
HAZUS to treat the different meteorological phenomena is indicated in Figure 
1.1, where 
it is seen that different regions of the United States are affected by 
different types of wind 
storms. In many regions of the country, damaging winds are produced by more 
than one 
meteorological phenomenon. 

1.2 Scope 
The geographic scope of the HAZUS Hurricane Model is limited to the Atlantic 
and Gulf 
coasts of the United States. The capabilities of the model are summarized in 
Table 1.1. 
The Hurricane Model includes a default terrain roughness based on digitized 
Land Use 
and Land Cover (LULC) data, but it does not model topographic speedups. 
Damage, 
direct economic losses, and building debris are modeled for the General 
Building Stock 
(i.e., residential, commercial, industrial, agricultural, educational, and 
government 
building occupancies). Short-term shelter requirements for displaced 
households and loss 
of function for essential facilities are also included in the model. 

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1-2 
Chapter 1. Introduction 
Tornadoes 
  Produce strongest winds 
  Localized, severe damage 
  Windfield model and missiles 
  Point vs area risk important 
  Building size important 
Extratropical Cyclones 
  Dominate West Coast & New 
England 
  Produce winter storms 
  Produce typical Noreasters in 
New England 
Thunderstorms and Hail 
  Dominate much of Central & 
Eastern U.S. 
  Produces hail damage 
  Frequent producer of losses 
Hurricanes 
  Dominate Gulf and East Coast 
  Storm duration and wind 
direction 
  Topographic and terrain 
effects important 
  Distance from coast important 
  Hurricanes 
  Tornadoes 
  Thunderstorms 
  Extratropical 
Cyclones 
Figure 1.1. Meteorological Events Contributing to the Wind Hazard in 
Different 
Regions of the Continental United States. 
Table 1.1. Summary of Hurricane Model Capabilities 
Parameter/Data 
Level 1 
(Default Data) 
Level 2 
(User-Supplied Data) 
Level 3 
(Advanced Data) 
Wind Model Default Probabilistic User-Defined Scenario 
Building Inventory Default User-Supplied 
Facilities and Building Classes 
Residential Commercial 
Industrial Essential 
Facilities 
Utility, Transportation, and High 
Potential Loss Facilities Display/Edit Locations Only   No Damage or Loss 
Estimates 
Terrain Default Expert-Supplied 
Loss Functions Default 
Damage Functions Default 
Shelter Requirements Default User-Supplied Parameters 
Debris Default 
?
1.3 Technical Approach 
The approach and framework of the HAZUS Wind Model are outlined in Figure 
1.2, with 
the elements of the present Hurricane Model indicated in bold type. The 
approach is 
based on a hazard-load-resistance-damage-loss methodology developed from an 
individual risk framework. The basic model components (hazard model, load 
model, 
resistance models, etc.) are developed separately. Each model component is, 
wherever 
possible, separately validated using full scale data, model scale data, or 
experimental 
data. A major factor driving the use of a first principles based hazard-load-
resistance-loss 
model, rather than the simple wind speed dependent loss models traditionally 
used, is the 
ability for the approach to be extended to model the effects of code changes 
and 
mitigation strategies on reduction in damage and loss. Furthermore, since 
economic 
damage (loss) is modeled separately from physical damage to a building, 
estimates of 
both building damage and loss are separately modeled and predicted. 

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
    
   
 
  
  
    
 
 
 
    
   
 
  
    
 
 
 
Inventory 
Terrain 
Databases 
Physical Damage 
Buildings/Facilities 
 Industrial 
 Commercial 
 Residential 
 Essential Facilities 
Building 
Debris 
Direct Economic Losses 
 Structure 
 Contents 
 Loss of Use 
Losses 
Hazard 
 Hurricane Modes of Analysis 
 Probabilistic 
 Scenario 
Historical Storms 
Wind Pressure 
Missiles 
Duration 
Rain 
Effects 
. . . 
Tree 
Blowdown Utilities 
Transportation Agriculture 
Topography 
Bathymetry 
Indirect 
Economic Losses CasualtiesShelter 
Hazardous 
Materials 
 Thunderstorm 
 Tornado 
 Hail 
Storm Surge 
 Waves 
Atmosphere Pressure Change 
Extratropical Storm 
 
    
 
 
 
Figure 1.2. HAZUS Wind Model Framework   Elements Shown in Bold are 
Implemented in the Current Version of the Hurricane Model. 

The performance of a building class under wind loading events is formulated 
probabilistically using simple concepts of structural reliability. For a 
single failure mode, 
the failure or damage probability is the probability that the wind load 
effect (e.g., 
aerodynamic pressure or impact energy) is greater than the resistance of the 
element. 
Because of uncertainties in the prediction of loads and structural response, 
the load effect 
S (e.g., the uplift pressure on a piece of roof sheathing) and structural 
resistance R (e.g., 

HAZUS-MH MR3 Technical Manual 

?
the pull-out resistance of the roof sheathing fasteners per unit area) are 
uncertain. These 
uncertainties are represented by probability density functions fS(s) and 
fR(r), respectively. 
The probability of damage, Pd, is simply 

Pd = P(R   S < 0) (1.1) 

which can be expressed as 

Pd = .-
8
8FR(x) fS(x)dx (1.2) 

where FR(x) is the cumulative distribution function (e.g., the probability of 
sheathing 
failure given an uplift pressure = x). By performing simulations on 
representative 
buildings within a building class, the damage probabilities for building 
components are 
estimated and the relationships between physical damage and wind hazard are 
developed. 
Similarly, losses are estimated using repair and restoration models for 
physical damage 
states. 

This approach logically leads to a simple concept of probabilistic analysis 
for estimating 
building/facility damage and loss. From Equation 1.1, the basic performance 
equation for 
reliability-based analysis is used to estimate damage probabilities for each 
individual 
building component. The probability of the building class damage states are 
conceptually 
estimated using the multivariate extension of Equation 1.1, generally 
expressed as a limit 
or damage state function, g, in terms of the basic variables, X, such that 

M = g (X) = g (X1, X2, .Xm). (1.3) 

Damage or failure is defined as M < 0; hence, M = 0 is the failure surface. 
The basic 
variables, X1, X2, .Xm, are the load effect and resistance variables required 
to model all 
of the failure modes of interest, such as: roof sheathing uplift failures, 
window impact 
failures, wall failures, whole roof failures, etc. There are many methods to 
estimate Pd, 
including advanced first order second moment methods and Monte Carlo 
simulation. 
Simulation methods are used herein in detailed analyses to develop the damage 
state and 
loss estimation probability distributions. These results are used with simple 
probabilistic 
(numerical and analytical) methods in the HAZUS software tool to compute the 
damage 
and loss estimates. 

For purposes of calibration and validation of the damage and loss predictions 
using 
simplified load and resistance models, it is conceptually useful to 
generalize Equation 1.1 
to include a factor f on the nominal resistance. With this extension, damage 
occurs when 

fR < S . (1.4) 

In Equation 1.4, f is a factor that is developed as part of the research to 
calibrate 
predicted damage to observed damage in actual wind events. While in Load and 
Resistance Factor Design (LRFD) f is partial safety factor on resistance 
(generally less 
than one), f takes on a more general calibration interpretation for the 
purpose of accurate 
damage and loss estimation. The introduction of f does not imply that all the 
building 

Chapter 1. Introduction 

?
class damage states and loss estimates will require a calibration adjustment 
with non-
unity f. It is introduced to recognize that given the use of code-based loads 
and nominal 
resistances, along with other simplifications and approximations, a simple 
factored 
approach to calibration may be useful for certain damage or loss predictions. 
No factor is 
applied to the load side of the equation since the objective of the HAZUS 
wind model is 
to predict damage and loss using standardized methods and not to attempt to 
develop 
load-factor adjustments to code-based loads. 

These concepts are implemented in the model development phase to produce the 
fast-
running damage and loss functions required in HAZUS software tool. The non-
technical 
user of HAZUS is not required to understand the technical issues of the 
probabilistic 
analysis and model development. Rather, the important issues to the users 
center on the 
accuracy of the model predictions, as applied to damage and loss estimates 
across broad 
classes of buildings. 

1.4 Technical Manual Organization 
This manual contains 13 chapters and 14 appendices. Chapters 2-5 describe the 
hurricane 
hazard and effects models. Chapter 6 presents the methodology and results of 
the 
physical damage model for the general building stock, and Appendices A-G 
provide 
damage state functions for each of the analyzed model buildings. Chapter 7 
parallels 
Chapter 6 by presenting the methodology and results for the direct economic 
loss model, 
and Appendices H-N provide loss functions for each of the analyzed model 
buildings. In 
Chapter 8, methods are discussed for characterizing the frequencies of 
various wind-
resistive features in the general building stock. End-to-end validations of 
the Hurricane 
Model are presented in Chapter 9 for Hurricanes Andrew, Erin, Opal, and Hugo. 
Chapter 
10 and 11 present the methodologies for the building debris and short-term 
shelter 
requirements models, respectively. Chapter 12 describes the tree blowdown 
model, which 
is used to estimate tree debris as well as additional building and content 
losses in 
residential areas due to tree impacts. The references are listed in Chapter 
13. 

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1-6 

Chapter 1. Introduction 

?
Chapter 2. Hurricane Risk Model 

2.1 Review of Hurricane Wind Models 
The use of mathematical simulation methods to estimate hurricane wind speeds 
was first 
implemented by Russell (1968, 1971) for the Texas coast. Others have used 
this approach 
for portions of the United States coastline (Russell and Schueller, 1974; 
Tryggvason, et 
al., 1976; Batts, et al., 1980; Georgiou, et al., 1983; Twisdale and Dunn, 
1983; Georgiou, 
1985; and Vickery and Twisdale, 1995a, 1995b). The study of Batts, et al. 
(1980) was a 
milestone, being the first study to examine the entire United States 
Coastline, and it 
provided a rational means to determine design wind speeds associated with the 
Gulf and 
Atlantic coasts of the United States. At the time the Batts study was being 
carried out, 
there was relatively little good quality, full-scale data available with 
which to evaluate the 
physical models used in the simulation. 

The basic approach in all these studies is the same in that site specific 
statistics of key 
hurricane parameters including central pressure difference (.p), radius to 
maximum 
winds (Rmax), heading (.), translation speed (c), and the coast crossing 
position or 
distance of closest approach (dmin) are first obtained. Given that the 
statistical 
distributions of these key hurricane parameters are known, a Monte Carlo 
approach is 
used to sample from each distribution, and a mathematical representation of a 
hurricane is 
passed along the straight line path satisfying the sampled data, while the 
simulated wind 
speeds are recorded. The intensity of the hurricane is held constant until 
landfall is 
achieved, after which time the hurricane is decayed using filling rate 
models. As 
indicated in Vickery and Twisdale (1995b), the approaches used in the 
previously noted 
studies are similar, with the major differences being associated with the 
physical models 
used, including the filling rate models and wind field models. Other 
differences include 
the size of the region over which the hurricane climatology can be considered 
uniform 
(i.e., the extent of the area surrounding the site of interest for which the 
statistical 
distributions are derived), and the use of a coast segment crossing approach 
(e.g., Russell, 
1971; and Batts, et al., 1980), or a circular subregion approach (e.g., 
Georgiou, et al., 
1983; Georgiou, 1985; Neumann, 1991; Vickery and Twisdale, 1995b). 

For insurance loss prediction applications, the book by Friedman (1987) 
discusses 
hurricane and loss modeling issues, and how model results can be used by the 
insurance 
industry. Other hurricane models that have been described in the literature 
for the 
purposes of loss estimation include Clarke (1986), Boissonnade and Dong 
(1993), and 
Daneshvaran and Morden (1998). The modeling approach used by Clarke considers 
only 
landfalling storms, and the wind field model is not described. In none of 
these papers are 
predictions of wind speed vs. return period given along the Gulf and Atlantic 
coastlines 
which would allow the reader to assess how the models perform against peer 
review and 
published engineering models (such as Georgiou, 1983; Batts, et al., 1980; 
etc.). 

Under funding by the NSF, with additional support provided by the National 
Association 
of Homebuilders, as well as Applied Research Associates, Inc., an advanced 
hurricane 

HAZUS-MH MR3 Technical Manual 

?
model was developed by Applied Research Associates during the period 1995-
1997. This 
model has been reviewed by the ASCE-7 Committee in 1998 and published as two 
journal papers (Vickery, et al., 2000a, 2000b). Predicted hurricane wind 
speeds resulting 
from this new model are the basis of the national standard for wind load 
design 
provisions as defined in ASCE 7-98, ASCE 7-02 and ASCE 7-05. 

In this model, a new simulation approach is developed where the full track of 
a hurricane 
or tropical storm is modeled, beginning with its initiation over the ocean 
and ending with 
its final dissipation. Using this approach, the central pressure is modeled 
as a function of 
sea surface temperature, and the storm heading, speed, etc., are updated at 
each six-hour 
point in the storm history. Linear interpolation is used between the six-hour 
points. The 
approach is validated by comparing the site-specific statistics of the key 
hurricane 
parameters of the simulated hurricane tracks with the statistics derived from 
the historical 
data. This model includes a numerical wind field model (Vickery, et al., 
2000b) which 
incorporates a full nonlinear solution to the equations of motion. For use in 
HAZUS-MH, 
the model described in Vickery, et al, (2000a, 2000b) has been updated to 
include all 
historical storms occurring between 1886 through 2005.The new model included 
improved modeling of storm weakening after landfall, an improved statistical 
representation of the Holland B parameter, and an updated windfield model. 

2.2 Hurricane Wind Field Model 
A critical component in the simulation of hurricanes is a good representation 
of the 
hurricane wind field given information regarding the storm intensity, size 
and translation 
speed. The hurricane wind field model contains two components. The first 
component is 
the overall mean flow field describing the upper level winds, and the second 
is the 
boundary layer model used to estimate wind speeds at the surface of the 
earth, given the 
upper level wind speeds. 

The mean flow field model (Vickery, et al., 2000b) solves the full nonlinear 
equations of 
motion of a translating hurricane and then parameterizes these for use in 
fast running 
simulations. The use of a full numerical solution to the equations of motion 
for a 
hurricane allows the modeling of asymmetries in the storm that arise from the 
complex 
interaction of the frictional forces and the winds which vary throughout the 
storm. They 
can produce very high wind speeds wrapping around the eyewall in some small 
and 
intense storms. The use of simple empirical models to define the hurricane 
will not 
reproduce these effects. 

The new hurricane boundary layer model (Vickery et al., 2008) is developed 
using a 
combination of velocity profiles computed using dropsonde data and a linear 
theoretical 
hurricane boundary layer model developed by Kepert (2001). The final 
hurricane 
boundary layer model incorporates a combined logarithmic-quadratic variation 
of the 
mean wind speed with height used to replicate the height of the low level jet 
observed in 
the hurricane boundary layer. This allows for a more realistic representation 
of the wind 
speeds near the surface, and for better estimates of the effect of the sea-
land interface in 
reducing wind speeds near the coast. 

Chapter 2. Hurricane Risk Model 

?
Numerous comparisons between modeled and observed hurricane wind speed 
records 
have been performed. These include both comparisons of the mean wind speeds 
and the 
peak gust wind speeds. The resulting wind field model is the most physically 
based and 
validated model currently in use for estimating hurricane wind speed 
exceedance 
probabilities. 

2.2.1 Mean Wind Field Model 
The wind field model is based on a dynamic numerical model of the planetary 
boundary 
layer (PBL). The model considers the equation of horizontal motion, 
vertically averaged 
over the height of the PBL. A finite difference scheme is used to solve for 
the steady-
state wind field over a set of nested rectangular grids. These wind fields 
are then fit using 
a Fourier fitting approach so that each wind field can be described using a 
relatively 
small number of parameters. The equations are solved for 1560 combinations of 
central 
pressure, translation speed and radius to maximum winds for hurricanes both 
over land 
and over water. Parameterizing the solved wind field models retains the more 
accurate 
modeling associated with the numerical modeling of the hurricane while still 
enabling 
rapid computational storm. 

A similar approach for modeling hurricane wind fields resulting from a 
numerical 
solution to the equations of motion for a translating hurricane was first 
used by Georgiou 
(1985) and then by Vickery and Twisdale (1995a). In both of these studies the 
numerical 
model results were obtained from Shapiro s (1983) model, where the solutions 
to the 
equations of motion were themselves solved using a spectral approach 
employing the first 
two terms of the expansion. The approach used here has an advantage over the 
use of the 
Shapiro model, in that the full non-linear equations are solved, and then the 
results are fit 
to a Fourier series using more than two terms, hence maintaining a more 
precise solution 
to the equations of motion. 

2.2.2 Surface Level Winds 
In all hurricane simulation procedures published to date, the hurricane 
boundary layer has 
been defined using empirical relationships between the upper level winds and 
the surface 
(10 m) level winds. The ratio of the surface level winds to the upper level 
winds within 
these empirical models is very high (0.8-0.9) compared to typical values in 
extra-tropical 
storms (ratio of about 0.6 in open country terrain). The new hurricane wind 
field model 
(Vickery, et al., 2008) described here uses a more theoretically based model 
of the 
hurricane boundary layer and is developed using a combination of velocity 
profiles 
computed using dropsonde data and a linear theoretical hurricane boundary 
layer model 
developed by Kepert (2001). The final hurricane boundary layer model 
incorporates a 
combined logarithmic-quadratic variation of the mean wind speed with height 
used to 
replicate the height of the low level jet observed in the hurricane boundary 
layer. The 
empirical hurricane boundary layer model reproduced the shape of the 
hurricane 
boundary layer over the lower 1000 m. The analysis of the profiles from 
dropsonde data 
reproduce the observations noted in Powell, et al. (2003) that the sea drag 
coefficient 
reaches a maximum value. The results also suggest that the magnitude of this 
maximum 
value decreases with decreasing storm RMW. 

HAZUS-MH MR3 Technical Manual 

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2.2.2.1 Hurricane Boundary Layer Model 
Dropsondes are dropped from reconnaissance aircraft generally from heights 
between 
1.5-3 km. Dropsondes fall vertically downwards at a speed of about 10 m/sec 
measuring 
wind speed, temperature, humidity etc at every 0.5 sec. Dropsondes hit the 
sea surface 
after several minutes from the time of drop, drifting 10-15 km tangentially 
and hundreds 
of meters radially (Powell, et. al.. 2003). Further dropsonde details are 
given in Hock and 
Franklin (1999), and Franklin, et al. (2003). The dropsonde dataset used here 
was 
obtained from the Atlantic Oceanographic and Meteorological Laboratories -
Hurricane 
Research Division (AOML-HRD) and consists of all profiles collected during 
the 19972003 
hurricane seasons. Most of these profiles are from Atlantic and Gulf of 
Mexico 
hurricanes with a few coming from Pacific Ocean hurricanes. The dropsonde 
data has 
been previously subjected to a quality control criterion by AOML-HRD to 
remove any 
identifiable errors. All measurements have been smoothed by AOML-HRD using a 
5-sec 
low pass filter to remove noise due to switching of the satellites. 

In the analysis of the dropsonde data, the drops were separated into those 
associated with 
profiles measured at or near the radius to maximum winds (RMW) and those 
associated 
with drops away from the RMW. The assignment of a particular profile to 
either the 
RMW region or the outer vortex region was determined using comments of the 
flight 
meteorologist, an examination of concurrent flight level radial wind profiles 
(if 
available), airborne radar reflectivity imagery, and H*Wind (Powell, et al. 
1996,1998) 
objective wind field analyses. Following the approach of Powell, et al. 
(2003), the wind 
profiles were analyzed in a composite sense, as a function of the mean 
boundary layer 
(MBL) wind speed, defined as the mean wind speed averaged over a height range 
of 10 
m to 500 m. The six different MBL groups correspond to mean MBL wind speeds 
of 20   
29 m/sec, 30   39 m/sec, 40   49 m/sec, 50   59 m/sec, 60   69 m/sec and 70-
85 m/sec. 
Each group was divided vertically into height bins chosen to provide maximum 
resolution close to the sea surface. The mean wind speeds vs. height were 
computed by 
taking the average of all wind speed measurements within height bins of 10 m 
for heights 
less than 300 m, 20 m bins for heights ranging between 300 and 500 m, 50 m 
bins for 
heights between 500 m and 1000 m, and 100 m bins for heights greater than 
1000 m. The 
single height value assigned to each bin is the mean value computed within 
height bin. 

Figure 2.1 shows the mean velocity profiles near the RMW for each of the six 
MBL wind 
speed groups. Qualitatively, Figure 2.1 indicates that in the lower few 
hundred meters of 
the boundary layer, the mean velocity profile is approximately logarithmic, 
and the height 
at which the maximum wind speed occurs decreases with increasing wind speed, 
ranging 
from a maximum height of about 700 m for the 20   29 m/sec MBL case, to a 
minimum 
of about 400 m for the 60   69 m/sec MBL case. The lowering of the height at 
which the 
maximum wind speed occurs is consistent with the analysis described in Kepert 
(2001) 
and Kepert and Wang (2001), where the existence of a lower level  jet  is 
discussed. The 
jet strength is defined as the ratio of the maximum wind speed divided by the 
gradient 
balance wind speed. Kepert considers the jet height to be equivalent to the 
boundary layer 
height, and that concept is carried through here. Kepert (2001) demonstrates 
that both the 
magnitude and height of the jet are a function of the inertial stability, I, 
defined as: 

Chapter 2. Hurricane Risk Model 

?
10 
100 
1000 
10000 
Height (m) 
20 3040 5060 7080 
Wind Speed (m/sec) 

Figure 2.1. Mean Wind Profiles (20-29 m/sec, 30-39 m/sec, 40-49 m/sec, 50-59 
m/sec, 
60-69 m/sec, 70-85 m/sec). 

))(2( 
r 
V 
r 
Vf 
r 
VfI 
.
.
+++= (2.1) 
where, V is the azimuthally averaged tangential gradient wind speed (as 
computed from 
the surface pressure field), f is the Coriolis parameter and r is the radial 
distance from the 
center of the storm. As indicated in Equation 2.1 the inertial stability 
factor is a function 
of both wind speed and radius, and thus to take into account the effect of 
radius, the 
dropsonde data were further divided into bins having similar values of r. Two 
sets of radii 
groups were developed. The first group corresponding to drops performed at or 
near the 
radius to maximum winds, and the second group corresponding to drops 
performed 
outside of the RMW. 

For a neutrally stable boundary layer, the increase in the mean wind speed 
with height 
near the surface is given by: 

U (z) =u* ln( z ) (2.2)

kz

o 

where k is the von-Karman coefficient having a value of 0.4, u* is the 
friction velocity, 
and, z0 is the surface roughness. 

The friction velocity is related to the surface shear stress (t) as: 

t=.u*
2 =.CdU10 
2 (2.3) 

HAZUS-MH MR3 Technical Manual 

?
where Cd is the surface drag coefficient and U10 is the mean wind speed 
(representative of 
the wind speed averaged over a period of 30 minutes to an hour) at a height 
of 10 m. 

For each combination of r and MBL, an estimate of the surface roughness and 
sea surface 
drag coefficient was obtained using a least squares fit (in linear-
logarithmic space) of the 
measured wind speeds over height ranges of 20 to 200 m, 20 to 150 m and 20 
to100 m 
(Figure 2.2). The velocity profiles given in Figure 2.2 show a trend for the 
height of the 
mean wind speed maxima to decrease as both the RMW decreases and the wind 
speed 
increases, consistent with the analysis of Kepert (2001). Using the 
intercepts from the 
least squares fits, estimates of the effective surface roughness, z0, and the 
associated 
uncertainty, were obtained for each profile. Given a value of z0, the surface 
drag 
coefficient is computed using Equations 2.2 and 2.3. 

Figure 2.3 presents the resulting Cd vs. U10 data for the three height 
ranges. Results are 
given for the MBL data separated by RMW bins, and for the case with no 
separation by 
RMW (as in Powell, et al., 2003). The 95% confidence bounds shown in Figure 
2.3 
represent a lower bound because the error in the intercept does not include 
any errors 
associated with the estimates of the dropsonde height, z, nor the errors in 
the estimates of 
the measured wind speed at each height. As seen in Figure 2.3, on average, 
the drag 
coefficient increases with increasing wind speed up to wind speeds of about 
24-28 m/sec 
and then starts to level off or perhaps even decrease for higher wind speeds, 
consistent 
with the results of Powell, et al. (2003). The data suggest that the 
magnitude of the 
maximum value of Cd decreases with decreasing RMW. Also shown in Figure 2.3 
is the 
Large and Pond (1981) drag coefficient model, modified to have a maximum 
value that 
varies with the RMW. The maximum (cap) values range from 0.0019 for the 
smallest 
storms up to 0.0024 for the largest storms. 

The approach taken for examining the characteristics of the hurricane 
boundary layer 
near the RMW was repeated for dropsonde profiles measured outside the RMW. 
The 
radius groups chosen were determined based on the number of profiles 
associated within 
each MBL group. Figure 2.4 shows the resulting mean velocity profiles along 
with the 
logarithmic fits to the wind data, fit using the wind speed data for a 
surface layer of 20 - 
200 m range. As indicated in Figure 2.4, there is a trend for the height of 
the wind 
maxima to increase as the radius increases and decrease with increase in wind 
speed, 
again, consistent with the analysis described by Kepert (2001). Figure 2.5 
shows a 
comparison of the estimated values of Cd and the error bounds corresponding 
to 95% 
confidence interval plotted vs. U10. As seen in Figure 2.5, on average, the 
drag coefficient 
increases with increasing wind speed up to about 30 m/sec and then levels 
off, but the 
apparent decrease in Cd seen in the case of the near RMW observations is not 
evident 
here. To incorporate the effect of radius on the limiting value of Cd, the 
limiting value of 
Cd is modeled as a function of radius, r in the form: 

Cdmax 
= (0.0881r +17.66)10-3 ; 0.0019 = Cdmax 
= 0.0025 (2.4) 

where r is the radial distance from the storm center (km), but is constrained 
to have a 
minimum value equal to the RMW. 

Chapter 2. Hurricane Risk Model 

?
2-7 
HAZUS-MH MR3 Technical Manual 
RMW 10 - 30 km 
10 
100 
1000 
10000 
20 30 40 50 60 70 80 
Wind Speed (m/sec) 
Height (m) 
RMW 30 - 60 km 
20 30 40 50 60 70 80 
Wind Speed (m/sec) 
RMW 60 - 100 km 
10 
100 
1000 
10000 
20 30 40 50 60 70 80 
Wind Speed (m/sec) 
Height (m) 
ALL RMW 
20 30 40 50 60 70 80 
Wind Speed (m/sec) 
Figure 2.2. Mean and Fitted Logarithmic Profiles for Drops Near the RMW for 
all 
MBL Cases. Horizontal error bars represent the 95th percentile error on the 
estimate of the mean wind speed. LSF fits are for the 20   200 m case. (MBL 
cases 
correspond to 20-29 m/sec, 30-39 m/sec, 40-49 m/sec, 50-59 m/sec, 60-69 
m/sec, and 
70-85 m/sec). 
0 
0.001 
0.002 
0.003 
0.004 
0.005 
0.006 
0.007
0 10 20 30 40 50 60 
Mean Wind Speed at 10m (m/sec) 
Drag Coefficient 
RMW 10 - 30 km, LSF over 20 m - 100 m 
RMW 10 - 30 km, LSF over 20 m - 150 m 
RMW 10 - 30 km, LSF over 20 m - 200 m 
Large and Pond (1981) with 0.0019 maximum 
0.000 
0.001 
0.002 
0.003 
0.004 
0.005 
0.006 
0.007
0 10 20 30 40 50 60 
Mean Wind Speed at 10m (m/sec) 
Drag Coefficient 
RMW 30 - 60 km, LSF over 20 m - 100 m 
RMW 30 - 60 km, LSF over 20 m - 150 m 
RMW 30 - 60 km, LSF over 20 m - 200 m 
Large and Pond (1981) with 0.0022 maximum 
0 
0.001 
0.002 
0.003 
0.004 
0.005 
0.006 
0.007
0 10 20 30 40 50 60 
Mean Wind Speed at 10m (m/sec) 
Drag Coefficient 
RMW 60 - 100 km, LSF over 20 m - 100 m 
RMW 60 - 100 km, LSF over 20 m - 150 m 
RMW 60 - 100 km, LSF over 20 m - 200 m 
Large and Pond (1981) with 0.0025 maximum 
0 
0.001 
0.002 
0.003 
0.004 
0.005 
0.006 
0.007
0 10 20 30 40 50 60 
Mean Wind Speed at 10m (m/sec) 
Drag Coefficient 
All RMW - LSF over 20 m - 200 m 
All RMW - LSF over 20 m - 150 m 
All RMW - LSF over 20 m - 100 m 
Large and Pond (1981) with blended maximum 
Figure 2.3. Variation of the Sea Surface Drag Coefficient with Mean Wind 
Speed at 
10 m, Near the RMW. 
?
10 
100 
1000 
10000 
Height(m) 
40-80km 
30-65km 
30-90km 
30-50km 
50-75km 80-105km 
65-105km 50-150km 
90-150km 105-150km 
105-150km 
1525 3545 5565 751525 3545 556515253545 

Wind Speed(m/sec) Wind Speed(m/sec) Wind Speed(m/sec) 

Figure 2.4. Mean Wind Profiles and Fitted Logarithmic Profiles for Outside 
RMW 
Case. The radius range used to compute the mean velocity profile is noted in 
the 
figure. MBL cases correspond to 20-29 m/sec, 30-39 m/sec, 40-49 m/sec, 50-59 
m/sec, and 
60-69 m/sec). 

A possible explanation for this reduction in Cd as a function of radius is 
given in Makin 
(2005) where it is suggested that a limiting value of Cd is caused by the 
production of sea 
spray inhibiting the transfer of momentum from the wind to the sea surface. 
Makin 
suggests that most of the sea spray is produced by the mechanical tearing by 
the wind 
from steep short waves, and thus for storms with small RMW (and hence small 
wind 
fetches) more of the waves will be short as compared to the large RMW case. 

2.2.2.1.1 Empirical Model for the Marine Hurricane Boundary Layer 
In the lower few hundred meters, the atmospheric boundary layer of the 
hurricane is 
adequately modeled using the logarithmic law, however; the profiles shown in 
Figures 

2.2 and 2.4 clearly indicate that in some cases such a model can be used well 
beyond 
heights of about 300 m, in most of the high wind speed cases, the logarithmic 
law breaks 
down, and the wind speeds begin to decrease with increasing height. The 
height at which 
the logarithmic law fails to describe the variation of the mean wind speed 
with height is 
strongly correlated with the height at which the mean wind speed reaches a 
maximum 
value (i.e., jet or boundary layer height). With this observation noted, the 
hurricane 
boundary layer was modeled in the form: 
Chapter 2. Hurricane Risk Model 

?
0.007 

0.006 

0.005 

0.003 

0.002 
0.003 
0.004 
Drag coefficient 
0 10 20 30 40 50 60 
Wind Speed at 10 m (m/sec) 
Large& P ond (1981) with 0.0025 maximum 
20-30m/s,40-80km 
20-30m/s,80-105km 
20-30m/s,105-150km 
30-40m/s,30-65km 
30-40m/s,65-105km 
30-40m/s,105-150km 
40-50m/s,30-90km 
40-50m/s,90-150km 
50-60m/s,30-50km 
50-60m/s,90-150km 
60-70m/s,50-75km 
(a) 
0.002 

0.002 

0.001 
0 
0.001 

0 

0.007 

0.003 
0.004 
Drag Coefficient 
0 10 20 30 40 50 60 
Wind Speed at 10 m (m/sec) 
(c) 
0.007 

0 10 20 30 40 50 60 
Wind Speed at 10 m (m/sec) 
(b) 
Larege&P ond(1981) with 0.0025 maximum 
LSF over 20-200m 
LSF over 20-150m 
LSF over 20-100m 
(d) 
0 10 20 30 40 50 60 
Wind Speed at 10 m (m/sec) 
Drag Coefficient Drag Coefficient 

0.006 

0.005 

0.005 

0.004 

0.003 

0.002 

0.001 

0.001 

0 

0 

Figure 2.5. Variation of the Sea Surface Drag Coefficient with Mean Wind 
Speed at 
10 m, Outside RMW case.(a) Least square fit for a height range of 20 m   200 
m 
(b) Least square fit for a height range of 20 m   150 m (c) Least square fit 
for a 
height range of 20 m - 100 m (d) No separation by radius. 

.

)

-

a

(

z 

H

* 

)

n

.

.
.

(2.5)
u

* 

z

U (z)

=

ln(

.
.

k 

z

o 

Each of the parameters, a and n were treated as free parameters but were 
required to be 
constant for all values of r, and H* is a boundary layer height parameter. 
The boundary 
layer or jet height was allowed to vary with each profile. To determine the 
appropriate 
parameters for use in Equation 2.5, two approaches were taken, namely: 

Method 1: The values of u* and z0 were computed from the regression analysis 
and the values of u*, a, n and H* were selected to minimize the error 
over the height range of 20 m to 1000 m 

Method 2: The values of z0 were computed using the capped Large and Pond drag 
coefficient model and the values of u*, a, n and H* were selected to 
minimize the error over the height range of 20 m to 1000 m. 

HAZUS-MH MR3 Technical Manual 

?
Figure 2.6 presents the observed and modeled mean velocity profiles near the 
RMW for 
the 10 km to 30 km and 30 to 60 km RMW cases. In all cases, the best values a 
and n 
were 0.4 and 2.0 respectively. By setting the derivative of Equation 2.5 with 
respect to z 
equal to zero it is seen that the boundary layer or jet height, H is equal to 
1.12H* . 

10 km -30kmRMW 30km-60 km RMW 

10 
100 
1000 
10000 
Height (m) 
20 406080 20 40 60 80 

Mean Wind Speed (m/sec) Mean Wind Speed (m/sec) 

Figure 2.6. Observed and Modeled Velocity Profiles for the 10 km to 30 km and 
30 
km   60 km RMW Cases. Solid lines represent model data derived using least 
squares fit values of surface roughness and friction velocity using LSF over 
20 - 200 m range. Dashed lines represent model results using Cd derived from 
truncated Large and Pond (1981) drag coefficient model. 

2.2.2.1.2 Hurricane Boundary Layer Height 
According to Kepert (2001), the jet height or boundary layer height is 
inversely 
proportional to the square root of the inertial stability parameter as given 
by: 

. 

.(2.6a)
. 

.= CdV 2 (2.6b)

KI 

where K is the turbulent diffusivity of momentum, I is inertial stability, 
and Cd is the 
surface drag coefficient. 

Here, the boundary layer height is also modeled as a function of I, where in 
the 
calculation of I, an average value of the radius within a radius bin is used 
to define r, and 
the maximum wind speed from mean velocity profile is used as a surrogate for 
the 
gradient velocity, V. Using the estimates of the boundary layer height, H* 
for r both near 
and outside the RMW, a regression models for relating H* and I are given by: 

...
.
--=-
.21tan2 1 
I 
KH 
Chapter 2. Hurricane Risk Model 

?
H * = 343.7 + 0.260 / Ir 2 = 0.75, s e = 99m (2.7a) 

H * = 186.6 +12.66 / Ir 2 = 0.70, s H = 106m (2.7b) 
The regression model for H*, modeled as a function of 1/I explains 75% of the 
variance 
associated with the underlying data, whereas when H* is modeled as a function 
of 1/ I 
the model explains 70% of the variance. The boundary layer height model is in 
general 
agreement with Kepert s (2001) analysis, where the boundary layer height 
scaling 
parameter was shown to be inversely proportional to the square root of the 
inertial 
stability. Based on the observed values of H*, the model is capped using a 
lower bound of 
300 m and an upper bound of 1200 m. Figure 2.7 shows a comparison of the H* 
derived 
from the regression models, H derived using Equation 2.6 with K=75 m2/sec 
(Kepert, 
2001) and the observed values of H* . 

2.2.2.1.3 Marine Boundary Layer Model Verification 
Using Equations 2.3 and 2.5 to define the variation of the mean wind speed 
with height, 
coupled with Equation 2.7a to define the boundary layer height, the 
characteristics of the 
boundary layer were estimated given only Umax and r. In the verification 
process, the 
value of Umax is set equal to the maximum mean wind speed obtained by the 
dropsondes 
over the range of 20 to 1000 m, and r is equal to the mean value of r used to 
determine 
the RMW or r bin. Figure 2.8 presents examples of the modeled and measured 
boundary 
layers for the 10 to 30 km and 30 to 60 km RMW cases. Figure 2.9 shows the 
mean error 
plotted vs. height (with the errors computed over ranges of heights) where it 
is seen that 
in most cases the error is less than 5%, however; there is a weak trend 
evident where the 
underestimate of wind speeds near the surface increases as RMW decreases. The 
modeled 
and observed wind speeds were grouped into height bins of 10 - 50 m, 50 - 100 
m, 
100   200 m, 200 - 300 m, 300 - 400 m, 400 - 500 m, 500 - 700 m and 700 - 
1000 m. 

Figure 2.7. Comparison of Regression Model, Kepert (2001) Model and Observed 
Boundary Layer Heights. 

HAZUS-MH MR3 Technical Manual 

?
10km - 30 km RMW 30 km -60 km RMW 

10 
100 
1000 
10000 
Height (m) 
204060 8020 40 60 80 
Mean Wind Speed (m/sec) Mean Wind Speed (m/sec) 

Figure 2.8 Observed and Modeled Velocity Profiles Near the RMW for the 10 km 
to 
30 km and 30 km to 60 km RMW Cases. Solid lines represent model wind speeds 
computed given Umax, RMW and f. 

10 - 30 km RMW 30 - 60 km RMW 60 - 100 km RMW 

900 

900 

900 

800

800 

800 

700 

700 

700 

Height Bin Center 

300

300 

300 

200

200 

200 

100

100 

100 

0 
-10% -5% 0% 5% 10% -10% -5% 0% 5% 10% -10% -5% 0% 5% 10% 
Wind Speed Error Wind Speed Error Wind Speed Error 

0 

0 

Figure 2.9. Mean Error in Modeled Wind Speeds vs. Height for Dropsonde Data 
Taken Near RMW. 

20-29 m/s 
30-39 m/s 
40-49 m/s 
50-59 m/s 
60-69 m/s 
70-85 m/s 

600

600 

600 

Height Bin Center 

Height Bin Center 

500

500 

500 

400

400 

400 

Chapter 2. Hurricane Risk Model 

?
Figure 2.10 presents a comparison of the modeled and observed ratios of 
U10/Umax plotted 
vs. Umax for dropsonde data collected around the RMW. Observed values of 
U10/Umax are 
obtained by extending the log-law velocity profiles to the 10 m level using 
the LSF 
results over the three different height ranges discussed earlier. The mean 
value of 
U10/Umax obtained from the model is 0.716. The mean values of U10/Umax 
computed from 
the data using a LSF over height ranges of 20-200 m, 20-150 m and 20-100 m 
are 0.703, 

0.713 and 0.719, respectively. The corresponding r2 values are 0.32, 0.57 and 
0.65, 
respectively. The mean modeled value of U10 /U 500 , where U 500 is the wind 
speed 
averaged over the lower 500 m of the boundary layer is 0.755, which is 
slightly lower 
than the value of 0.78 reported in Powell, et al., (2003). 

Figure 2.11 presents the variation of the mean error with height for the 
outside RMW 
cases. It is seen that in all the cases the mean error is less than 5%. 
Outside of the RMW, 
the mean ratios of U10/Umax for the observed and the modeled wind speeds are 
0.698 and 

0.686 respectively, implying a mean underestimate of the surface level wind 
speeds of 
about 2%. The empirical hurricane boundary layer model described here is 
shown to be 
able to reasonably well reproduce the shape of the marine hurricane boundary 
layer given 
only a wind speed at gradient or jet height, and a distance, r, from the 
center of the storm. 
The height of the jet is adequately described using a simple model where the 
jet height is 
inversely proportional to either, I or I . Due to the limitation of the 
number of available 
mean velocity profiles, there was insufficient data to further separate the 
profiles by 
azimuth in addition to MBL and r bins, and as a result, any variation in the 
jet height as a 
function of azimuth (as is indicated by Kepert (2001) does in fact exist) is 
lost in the 
hurricane boundary layer model presented here. However, the magnitude of the 
jet 
strength and its variation with azimuth is modeled using the slab model 
representation of 
the hurricane wind field, as described later. 

2.2.2.1.4 Sea Land Transition 
The characteristics of the hurricane boundary layer described previously are 
representative of open water (marine) conditions and not for the over land 
case. Over 
land there is virtually no dropsonde data to determine the characteristics of 
the over land 
hurricane boundary layer. The approach taken here to model the sea-land 
transition 
follows the classical approach (e.g., Deaves, 1981; Kao, et al., 1974) where 
the wind 
speed at the top of the boundary layer is assumed to remain unchanged as the 
flow moves 
over a new roughness regime. The shape of the mean boundary layer over land 
is 
assumed to be adequately represented by Equation 2.5, and the methodology 
outlined in 
Kepert (2001) is used to estimate the increase in the boundary layer height 
associated 
with a change in surface roughness. The increase in the boundary layer 
height, H is 
modeled using Equation 2.6, which requires an estimate of the increase in K 
as a function 
of the increase in Cd. For estimating the increase in the value of K as the 
wind moves 
from sea to land (open terrain), K is taken as: 

K   CdU 2/ 
.U = u*
2 / 
.U   kzu* (2.8)

.z .z 

HAZUS-MH MR3 Technical Manual 

?
2-14 

Observed Ratios Computed Using 20 m - 200 m LSF Observed Ratios ComputedUsing 
20 m - 150 m LSF 
Model Model 

1 

1 

0.9 

0.9 

0.6 

Model 
RMW 10 -30 km 
RMW 30 -60 km 
RMW 60 -100 km 
0.6 

0.5 

0.5 

20 30 40 5060 70 80 90
Mean Wind Speed at Top of Boundary Layer (m/sec)

Observed Ratios Computed Using 20 m - 100 m LSF
Model

U(10)/Uma 

20 30 40 50 60 70 80 90 
Mean Wind Speed at Top of Boundary Layer (m/sec) 
Model 
RMW 10 30 
km 
RMW 30 60 
km 
RMW 60 100 
km 
U(10)/Uma 

0.8 

0.8 

0.7 

0.7 

0.5 
0.6 
0.7 
0.8 
0.9 
1 
U(10)/Uma 
Model 
RMW 10 - 30 km 
RMW 30 - 60 km 
RMW 60 - 100 km 
20 30 40 50 60 70 80 90
Mean Wind Speed at Top of Boundary Layer (m/sec)

Figure 2.10. Comparison of Modeled and Observed Ratios of Mean Wind Speed at 
10 m to Mean Wind Speeds at the Top of the Boundary Layer Near the RMW. 

20-30m/ s,4080km 
30-40m/ s,3065km 
40-50m/ s,3090km 
50-60m/ s,3050km 
60-70m/ s,5075km 

20-30m/ s,80105km 
30-40m/ s,65105km 
40-50m/ s,90150km 
50-60m/ s,50150km 

2 0-30m/ s,105150km 
3 0-40m/ s,105150km 
Height Bin Center 

800 
700 
600 
500 
400 
300 
200 
100 
0 

-10% -5% 0% 5% 10% -10% -5% 0% 5% 10% -10% -5% 0% 5% 10% 
Wind Speed Error Wind Speed Error Wind Speed Error 

Figure 2.11. Mean Error in Modeled Wind Speeds vs. Height for Dropsonde Data 
Taken Outside RMW Region. 

Chapter 2. Hurricane Risk Model 

?
Equations 2.6 and 2.8 are used to estimate the increase in H, yielding 
typical estimates of 
the increase in H ranging between 20% and 30% for land defined with z0=0.03 
m, with 
the magnitude of the increase varying with both wind speed and radius. The 
value of H 
computed using Equations 2.6 and 2.8 is dependent on the value of z used in 
Equation 
2.8, but the ratio of the two boundary layer heights is, for practical 
purposes, independent 
of the value of z used in Equation 2.8. 

Figure 2.12 presents the ratio of the mean wind speed over land (zo=0.03 m) 
to the mean 
wind speed over water (zo=0.0013 m) as a function of the marine boundary 
layer height. 
Also shown in Figure 2.12 is the wind speed ratio computed using the ESDU 
(1982) 
transition model. Figure 2.12 shows that the reduction of the mean wind speed 
as the 
wind moves from the sea to the land associated with a hurricane is less than 
that 
estimated using the ESDU model, and that the wind speed reduction factor is 
dependent 
on H, and hence storm intensity and size. The wind speed reduction factors 
given in 
Figure 2.12 are larger than to those predicted by ESDU for large H, but match 
the model 
given in Simiu and Scanlan (1996) for large H. In model implementation, the 
increase in 
the boundary layer height predicted using Equations 2.6 and 2.8 is further 
increased so 
that the resulting reduction in the wind speed associated with the sea land 
transition 
matches that given in ESDU (1982) for large H. Using this approach, for 
typical values of 
H (~600 m) near the RMW, the predicted reduction in the mean wind speed 
matched that 
given by the model given in Simiu and Scanlan (1996). The wind speed 
reduction factors 
shown in Figure 2.12 are representative of a fully transitioned boundary 
layer. 

As the wind moves from the sea to the land, the value of the maximum wind 
speed at a 
given height in the new rougher terrain approaches the fully transitioned 
value 
asymptotically over some fetch distance, F. Published estimates of the fetch 
length vary 
markedly, ranging from a few kilometers (e.g. Melbourne, 1992) to in excess 
of 100 km 
(ESDU, 1982, Deaves, 1981). Powell, et al., (1996) suggest that wind 
measurements at a 
height of 10 m taken as far as 20 km to 30 km inland are still influenced by 
the upstream 
marine roughness. For modeling the transition from sea to land, the ESDU 
model is used, 
but the limiting fetch distance is reduced to 20 km rather than the ~100 km 
used in ESDU 
(1982). The use of the smaller fetch distance is consistent with the lower 
boundary layer 
heights associated with tropical cyclones (~600 m) compared to much larger 
values 

(~3000 m) used in ESDU where H scales as u*/f rather than 

IK2. 
The ESDU transition model was chosen since it provides a means to transition 
the wind 
speeds associated with an arbitrary averaging time (i.e. hourly, 10 minute 
mean, peak 
gust, etc.). Figure 2.13 shows a comparison of the original and modified ESDU 
transition 
functions for the gust and hourly mean wind speeds. In either model it is 
evident that at a 
distance of about 1 km approximately 60% of the transition (or wind speed 
reduction) has 
already occurred. An exact value of F is considered to be difficult, if not 
impossible to 
verify, and an inspection of Figure 2.13 indicates that the error in the 
predicted wind 
speed is not particularly sensitive to the value of F (for F>10 km). For 
example, the 
difference between the model estimates of the degree to which the wind speed 
has 
reached equilibrium at 10 km, (approximately where the difference between the 
ESDU, 

HAZUS-MH MR3 Technical Manual 

?
0.75 
0.80 
0.85 
0.90 
0.95 
1.00 
U10(land)/U10(water)
C yclonic Flow 
ESD U 
Simiu and Scanlan (1986) 
C yclonic Flow - Scaled to M atch ESDU at Large H 
0 1000 2000 3000 4000 5000 

Boundary Layer Height (m) 

Figure 2.12. Ratio of the Fully Transitioned Mean Wind Speed Over Land 
(zo=0.03m) to the Mean Wind Speed Over Water (zo=0.0013m) as a Function of 
Boundary Layer Height. 

3 Second Gust Hourly Average 

0% 
20% 
40% 
60% 
80% 
100% 
Degree TransitionedModified ESDU 
ESDU 
0% 
20% 
40% 
60% 
80% 
100% 
Degree TransitionedModified ESDU 
ESDU 
1 10 100 1000 10000 100000 1 10 100 1000 10000 100000 
Fetch (m) Fetch (m) 

Figure 2.13. ESDU and Modified ESDU Wind Speed Transition Functions. 

(F~100 km) and the modified ESDU, (F=20 km) function reaches a maximum) is 
about 
10%. Referring to Figure 2.12 it is seen that the maximum reduction in the 
mean wind 
speed using the ESDU model is about 17% thus, the magnitude of the wind speed 
error 
associated with the assumed fetch length is about 10% of 17%, or ~1.7%. The 
error in the 
estimate of the peak gust wind speed associated with the assumed fully 
transitioned fetch 
length is even less. 

2.2.2.2 Model Verification 
Verification of the boundary layer model as a whole, with emphasis on the 
sea-land 
transition, is made difficult because of the paucity of measurements of both 
marine and 
near-by over land wind speeds in landfalling hurricanes. Furthermore, 
experimental 
verification of the results suggesting that the reduction in wind speed 
associated with the 
change in roughness from sea to land is less than what is expected in non-
cyclonic winds, 

Chapter 2. Hurricane Risk Model 

?
is complicated by the fact that the reduction in wind speed as a function of 
distance 
inland includes the combined effect of the transition model and the wind 
speed reduction 
model. The approach taken here to verify the combined boundary layer-
transition model 
uses comparisons of time series of measured mean and gust wind speeds 
combined with 
model estimates of wind speeds made using a slab representation of a 
hurricane coupled 
with the hurricane boundary layer model and terrain transition models 
described herein. 
The mean wind speeds obtained from the HBL model are converted to gust or 
sustained 
(one minute) values using the ESDU gust factor models. The ESDU models have 
been 
shown by Vickery and Skerlj (2005) to be valid for hurricanes. Details of the 
slab model 
approach are given in Thompson and Cardone (1996) and Vickery, et al. (2000). 
The slab 
model used here is a modification of the model described in Vickery, et al. 
(2000), where 
the drag coefficient used in Vickery, et al (2000) has been replaced by the 
truncated 
Large and Pond model, and the number of nested grids has been increased from 
4 to 6. In 
the use of the slab model, the resulting integrated wind speed (mean value 
throughout the 
boundary layer) is adjusted to be representative of the maximum wind speed in 
the 
boundary layer. The difference between the maximum wind speed and the depth 
averaged wind speed is only a few percent, and varies dependent upon when the 
averaging is performed over the assumed 1000 m boundary layer height used in 
the slab 
model, or if the averaging height taken is the modeled boundary layer, or jet 
height. In 
the comparisons of modeled and observed marine and land wind speeds, an 
additional 2% 
increase in the modeled winds was required to eliminate a low overall bias in 
the 
comparisons. This small increase is consistent with the underestimate of the 
surface 
winds discussed earlier in the case of small RMW storms, the underestimate 
for winds 

outside the eyewall, and the difference between U10 /U 500 given in Powell, 
et al. (2003) 
and that computed herein. The slab model approach to modeling the hurricane 
wind field 
brings out features of the wind field that are not reproduced in simple 
gradient balance 
vortex models or empirical models such as those described by in NWS-23 
(Schwerdt, et 
al., 1979). For example, Figure 2.14 presents the distribution and magnitude 
of the jet 
determined (here defined as the depth average wind speed divided by the 
gradient 
balance wind speed) using the slab model. A comparison of the jet strength 
and its 
variation with azimuth resulting from the 2-D slab model with the results of 
a full 3-D 
model of a translating hurricane as presented in Kepert and Wang (2001) 
indicate that the 
2-D numerical slab model is able to reproduce the horizontal variation and 
magnitude of 
the jet characteristics corresponding to a height of ~ 500 m produced by a 
full 3-D 
numerical model. The variation of the jet height with azimuth is not 
maintained using the 
slab model. 

In the verification of the model with an emphasis on the sea-land transition, 
only 
hurricanes having continuous measurements of wind speeds both over land and 
over 
water near the land fall location are useful. The only reasonably well 
documented storms 
that meet this criterion include Hurricanes Bertha and Fran (1996), Hurricane 
Bonnie 
(1999), near the North Carolina coast, and Hurricane Ivan (2004) along the 
Gulf Coast. 
The validation approach is indirect, with the process involving a comparison 
of modeled 
and observed maximum peak gust wind speeds produced by the hurricanes over 
both 
marine and land terrains. Conclusions as to the validity of the model are 
inferred by 

HAZUS-MH MR3 Technical Manual 

?
2-18 
Chapter 2. Hurricane Risk Model 
x/Rmax 
y/Rmax 
1.15 
1.2 
1.15 
1.1
1.05 
1
0.95 
0.95
0.9 
0.9 
1 
1 
1 
1.05 
1.05 
1.1 
1.1 
1.05 
1.2 
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 
-2 
-1.5 
-1 
-0.5
0 
0.5
1 
1.5
2 
Figure 2.14. Jet Strength Computed Using the Slab Model for a Hurricane 
Moving 
Towards the Top of the Page. 
determining if there is a bias in the estimates of the modeled marine winds 
vs. the 
modeled over land winds. All measured gust wind speeds have been adjusted to 
be 
representative of 3 second gust speeds at a height of 10 m, in either open 
terrain (zo = 
0.03 m) or marine conditions. In the case of marine wind speed measurements 
obtained 
from 3-m discus buoys, the measured wind speeds in high wind cases have been 
increased by 10% to account for the underestimates in the measured wind 
speeds as 
described in Gilhousen (2006). Each hurricane is modeled using information on 
the 
hurricane track (position and central pressure) obtained from the National 
Hurricane 
center, coupled with estimates of the RMW and the Holland B parameter. The 
modeled 
pressure field is axi-symmetric, but varies with time. The initial estimate 
of the RMW is 
usually obtained from H*Wind snapshots of the hurricane wind field at or near 
the time 
of landfall. The final estimates of B and the RMW and their variation with 
time after 
landfall are obtained through an iterative approach by reproducing the 
overall shapes of 
the wind speed and direction traces and surface pressure traces obtained from 
as many 
ground stations as possible. Figure 2.15 presents examples of comparison of 
wind speed 
and pressure data obtained from stations located near the point of land fall 
for different 
hurricanes. 
An extensive set of validation studies has been performed using the hurricane 
windfield/boundary layer model described herein through comparisons of 
modeled and 
observed wind speed and pressure data obtained for 19 landfalling hurricanes 
occurring 
since 1985. In each validation study, estimates of RMW and B, and their 
variation in time 
were obtained using the iterative approach described above. The number of 
anemometer 
stations with either complete continuous records of wind speeds, or records 
where the 
maximum wind speed during the storm was measured, is given in Table 2.1. Note 
that in 
?
Hurricane Ivan -DPIA1 Hurricane Ivan - DPIA1 

75 

1020 
1000 

50 

25 

(mbar) 

980 

960 
940 

Mean Wind SpeedPeak Gust Wind Mean Wind SpeedPeak Gust WindMean Wind Speed 
Peak Gust Wind(m s -1) Speed (m s -1) (m s-1) Speed (m s -1) (m s-1) Speed (m 
s -1) 

Wind Direction Central PressureWind Direction Central Pressure(mbar) Wind 
Direction Central Pressure 

0 

9/15/2004 9/15/2004 9/16/2004 9/16/2004 9/15/2004 9/15/2004 9/16/2004 
9/16/2004 

12:00 21:00 6:00 15:00 12:00 21:00 6:00 15:00 
Time Time 
Hurricane Ivan - DPIA1 Hurricane Ivan - DPIA1
50

360 

270 
180 
90 

25 

0 

0 
9/15/2004 9/15/2004 9/16/2004 9/16/2004 9/15/2004 9/15/2004 9/16/2004 
9/16/2004 

12:00 21:00 6:00 15:00 12:00 21:00 6:00 15:00 
Time Time
Hurricane Ivan - KMOB Hurricane Ivan - KMOB
75

1020 

1000 

980 

960 

940 

50 

25 

0 

9/15/2004 9/15/2004 9/16/2004 9/16/2004 9/15/2004 9/15/2004 9/16/2004 
9/16/2004 

12:00 21:00 6:00 15:00 12:00 21:00 6:00 15:00 
Time Time 
Hurricane Ivan -KMOB Hurricane Ivan - KMOB 

50 

360 

270 
180 
90 

25 

0 

0 
9/15/2004 9/15/2004 9/16/2004 9/16/2004 9/15/2004 9/15/2004 9/16/2004 
9/16/2004 

12:00 21:00 6:00 15:00 12:00 21:00 6:00 15:00 
(mbar) 

Time Time 
Hurricane Charley - KMLB Hurricane Charley - KMLB 

75 

1020 
1000 

50 

25 

980 

0 

960 

8/13/2004 8/13/2004 8/14/2004 8/14/2004 8/13/2004 8/13/2004 8/14/2004 
8/14/2004 

18:00 21:36 1:12 4:48 18:00 21:36 1:12 4:48 
Time Time (UTC) 
Hurricane Charley - KMLB Hurricane Charley - KMLB
50

360 

0 

0 
8/13/2004 8/13/2004 8/14/2004 8/14/2004 8/13/2004 8/13/2004 8/14/2004 
8/14/2004 

18:00 21:36 1:12 4:48 18:00 21:36 1:12 4:48 
Time Time 
270 
180 
90 

25 

Figure 2.15. Example Plots Showing Modeled and Observed Wind Speeds, Surface 
Pressures and Wind Directions. Model results represented by solid line, 
observed 
values represented by the open squares. 

HAZUS-MH MR3 Technical Manual 

?
2-20 

Hurricane Andrew - FWFY1 Hurricane Andrew - FWFY1 

100 

1020 

1000 

980 

75 

50 

Mean WindPeak Gust Wind

Wind Direction Central Pressure 

Speed (m s -1) 

25 

(mb) 

960 
940 

920 

0 

900 
8/24/1992 0:00 8/24/1992 6:00 8/24/1992 12:00 8/24/1992 0:00 8/24/1992 6:00 
8/24/1992 12:00 
Time Time 
Hurricane Andrew - FWFY1 

Hurricane Andrew -FWFY1 360 

100 

270 

Speed (m s -1) 

75 

180

50 

90

25 

0

0 
8/24/1992 0:00 8/24/1992 6:00 8/24/1992 12:00 8/24/1992 0:00 8/24/1992 6:00 
8/24/1992 12:00 
Time Time 

Hurricane Bertha - FPSN7 Hurricane Bertha - FPSN7 

0 
25 
50 
75 
7/12/1996 12:00 7/12/1996 21:00 7/13/1996 6:00 
Time (UTC) 
Speed (m s -1 ) 
940 
960 
980 
1000 
1020 
7/12/1996 12:00 7/12/1996 21:00 7/13/1996 6:0 
Time (UTC) 
(mb) 
Mean WindPeak Gust WIndMean WindPeak Gust Wind

Wind Direction Central PressureWind Direction Central Pressure

Hurricane Bertha - FPSN7 Hurricane Bertha - FPSN7 

75 360 

Speed (m s -1) 

270 

180 

90 

50 

25 

0 

0 
7/12/1996 12:00 7/12/1996 21:00 7/13/1996 6:00 7/12/1996 12:00 7/12/1996 
21:00 7/13/1996 6:0 
Time (UTC) Time (UTC) 
Hurricane Fran - FPSN7 Hurricane Fran - FPSN7 

0 
25 
50 
75 
9/5/1996 12:00 9/5/1996 21:00 9/6/1996 6:00 
Time 
Speed (m s -1 ) 
940 
960 
980 
1000 
1020 
9/5/1996 12:00 9/5/1996 21:00 9/6/1996 6:0 
Time 
(mb) 
Hurricane Fran - FPSN7 Hurricane Fran -FPSN7 

0 
25 
50 
75 
9/5/1996 12:00 9/5/1996 21:00 9/6/1996 6:00 
Time Speed (m s -1 ) 
0 
90 
180 
270 
360 
9/5/1996 12:00 9/5/1996 21:00 9/6/1996 6:0 
Time 
Figure 2.15. Example Plots Showing Modeled and Observed Wind Speeds, Surface 
Pressures and Wind Directions. Model results represented by solid line, 
observed 
values represented by the open squares. (Continued) 

Chapter 2. Hurricane Risk Model 

?
Hurricane Bob - DSLN7 Hurricane Bob - DSLN7 
1020 

1000 

50 

(mb) 

980 

25 

0 

960 

940 

Mean Wind SpeedPeak Gust WindMean Wind SpeedPeak Gust Wind

Wind Direction Central Pressure 
Wind Direction Central Pressure 

Speed(m s -1) 

8/18/1991 8/19/1991 8/19/1991 8/19/1991 8/18/1991 8/19/1991 8/19/1991 
8/19/1991 

18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 
Time Time 
Hurricane Bob - DSLN7 

75 

Hurricane Bob - DSLN7 

360 

50 

25 

270 

180 

90 

(m s-1) 

0 

0 
8/18/1991 8/19/1991 8/19/1991 8/19/1991 8/18/1991 8/19/1991 8/19/1991 
8/19/1991 

18:00 0:00 
Time 
6:00 12:00 18:00 0:00 Time 6:00 12:00 
Hurricane Emily - DSLN7 Hurricane Emily - DSLN7
75

1020 

Speed (m s-1) 

1000 

50 

25 

(mb) 

980 

960 

940 

0 

8/31/1993 8/31/1993 9/1/1993 9/1/1993 8/31/1993 8/31/1993 9/1/1993 9/1/1993 

12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 
Time Time 
Hurricane Emily - DSLN7 Hurricane Emily - DSLN7 

75 

360 

270 

180 

90 

50 

25

(m -1) 

0 

0 
8/31/1993 8/31/1993 9/1/1993 9/1/1993 8/31/1993 8/31/1993 9/1/1993 9/1/1993 

12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00
Time Time
Hurricane Fran - CLKN7 Hurricane Fran - CLKN7
0 
25 
50 
75 
Peak Gust WIndSpeed (m s -1) 
940 
960 
980 
1000 
1020 
Central Pressure(mb) 
9/5/1996 
12:00 
9/5/1996 
21:00 
9/6/1996 
6:00 
Time 
9/6/1996 
15:00 
9/5/1996 
12:00 
9/5/1996 
21:00 
9/6/1996 
6:00 
Time 
9/6/1996 
15:00 
Hurricane Fran - CLKN7 Hurricane Fran - CLKN7 

0 
25 
50 
75Mean Wind Speed(m s-1 ) 
0 
90 
180 
270 
360 
Wind Direction 
9/5/1996 9/5/1996 9/6/1996 9/6/1996 9/5/1996 9/5/1996 9/6/1996 9/6/1996 

12:00 21:00 6:00 15:00 12:00 21:00 6:00 15:00 
Time Time 
Figure 2.15. Example Plots Showing Modeled and Observed Wind Speeds, Surface 
Pressures and Wind Directions. Model results represented by solid line, 
observed values represented by the open squares. (Concluded) 

HAZUS-MH MR3 Technical Manual 

?
Table 2.1. Number of Wind Speed Records for Each Hurricane 

Year 

1979 
1985 
1989 
1991 
1992 
1993 
1995 
1996 
1996 
1996 
1998 
1998 
1999 
1999 
2003 
2004 
2004 
2004 
2004 
2005 
2005 
2005 
2005 
2005 

Hurricane 
Name 

Frederic 
Elena 
Hugo
Bob 
Andrew 
Emily
Erin 
Bertha
Fran 
Opal 
Bonnie 
Georges 
Floyd 
Irene 
Isabel 
Charley
Frances 
Ivan 
Jeanne 
Dennis 
Katrina 
Ophelia 
Rita 
Wilma 

Total 

Number of complete wind 
speed records 

Land 

4 
3 
6 
2 
1 

5 
5 
9 
5 
2 
3 
2 
4 
10 
7 
13 
13 
14 
9 
8 
5 
14 
21 

165 

Marine 

2 
1 
5 
2 
9 
2 
3 
3 
2 
4 
10 
4 
5 
7 

6 
1 
3 
2 
7 

2 

80 

Total 

4 
5 
7 
7 
3 
9 
7 
8 
12 
7 
6 
13 
6 
9 
17 
7 
13 
19 
15 
12 
10 
12 
14 
23 

245 

many cases, additional incomplete records of wind speeds and pressures were 
used to 
assist in estimating the variation in both RMW and B. Figure 2.16 presents 
scatter plots 
summarizing the comparisons of modeled and observed maximum peak gust wind 
speeds 
produced by the storms. In each plot, the slope and r2 values are given 
resulting from a 
linear regression analysis where the regression line is forced to pass 
through the origin. In 
all cases, the regression slopes are within 3% of unity. Figure 2.17 presents 
a summary 
comparison of the maximum peak gust wind speeds computed using the wind field 
model 
described in Vickery, et al., (2008) to observations for both marine and land 
based 
anemometers. There are a total of 245 comparisons summarized in data 
presented in 
Figure 2.17 (165 land based measurements and 80 marine based measurements). 
The 
agreement between the model and observed wind speeds is good, however there 
are 
relatively few measured gust wind speeds greater than 100 mph (45 m/sec). 

Chapter 2. Hurricane Risk Model 

?
2005 - Rita 2005 -Katrina 2005 - Dennis 
75 

75 

60 

60 

60 

y = 1.00x 
R2 = 0.93 
0 15 30 45 60 75 
Observed Gust Wind Speed (m s-1) 
y = 0.99x 
R2 = 0.95 
0 15 30 45 60 75 
Observed Gust Wind Speed (m s-1) 
45 

30 
15 
0 

y = 0.93x 
R2 = 0.78 
0 15 30 45 60 75 
Observed Gust Wind Speed (m s-1) 
Modeled Gust Wind Modeled Gust WindModeled Gust WindModeled Gust WindModeled 
Gust Wind 
speed (m s -1) speed (m s -1) speed (m s -1) speed (m s -1) speed (m s -1) 

Modeled Gust WindModeled Gust WindModeled Gust WindModeled Gust Wind Modeled 
Gust Windspeed (m s -1) speed (m s -1) speed (m s -1) speed (m s -1) speed (m 
s -1) 

Modeled Gust WindModeled Gust WindModeled Gust WindModeled Gust WindModeled 
Gust Windspeed (m s -1) speed (m s -1) speed (m s -1) speed (m s -1) speed (m 
s -1) 

45 

30 

15 

45 

30 

15 

0 

0 

2004 -Ivan 2004 - Jeanne 2004 - Frances 

75 

60 

45 

30 

15 

75

y = 0.99x 
R2 = 0.91 
y = 0.98x 
R2 = 0.90 
75 

60 

45 

30 

15 

y = 1.00x 
R2 = 0.50 
60 

45 

30 

15 

0 

0 

0 
0 15 30 45 60 75 0 15 30 45 60 75 0 15 30 45 60 75 
Observed Gust Wind Speed (m s-1) Observed Gust Wind Speed (m s-1) Observed 
Gust Wind Speed (m s-1) 

2004 - Charley 1999- Irene 1999 - Floyd 

75 

75 

75 

y = 0.99x 
R2 = 0.96 
y = 0.99x 
R2 = 0.50 
y = 1.03x 
R2 = 0.80 
60 

45 

30 

15 

60 

45 

30 

15 

60 

45 

30 

15 

0 

0 

0 
0 15 30 45 60 75 0 15 30 45 60 75 0 15 30 45 60 75 
Observed Gust Wind Speed (m s-1) Observed Gust Wind Speed (m s-1) Observed 
Gust Wind Speed (m s-1) 

1999 - Bonnie 1998- Georges 1996- Fran 

75 

y = 1.00x 
R2 = 0.90 
0 
15 
30 
45 
60 
75
y = 0.96x 
R2 = 0.80 
y = 1.02x 
R2 = 0.93 
0 
15 
30 
45 
60 
75 
60 

45 

30 

15 

0 

0 15 30 45 60 75 0 15 30 45 60 75 

0 15 30 45 60 75 
Observed Gust Wind Speed (m s-1) Observed Gust Wind Speed (m s-1) Observed 
Gust Wind Speed (m s-1) 

1996 -Bertha 1995- Opal 1995 -Erin 

75 

75 

75

y = 0.97x 
R2 = 0.70 
y = 1.00x 
R2 = 0.18 
0 
15 
30 
45 
60 
y = 0.99x 
R2 = 0.64 
0 
15 
30 
45 
60
60 

45 

30 

15 

0 
0 15 30 45 60 75 0 15 30 45 60 75 0 15 30 45 60 75 
Observed Gust Wind Speed (m s-1) Observed Gust Wind Speed (m s-1) Observed 
Gust Wind Speed (m s-1) 

Figure 2.16. Comparison of Modeled and Observed Maximum Peak Gust Wind 
Speeds for 15 Land Falling Hurricanes. Open squares represent land based 
measurements; solid squares represent marine based measurements. All wind 
speeds are at a height of 10m in either open terrain or for marine 
conditions. 

HAZUS-MH MR3 Technical Manual 

?
All Hurricanes - Land All Hurricanes - Marine 

y = 0.994x 
0 
10 
20 
30 
40 
50 
60 
Modeled Peak Gust Wind Speed(m s-1 ) 
y = 0.991x 
0 
10 
20 
30 
40 
50 
60 
Modeled Peak Gust Wind Speed 
(m s-1 ) 
010 20 30 40 50 60 0 10 20 30 40 50 
Observed Peak Gust Wind Speed (m/s) Observed Peak Gust Wind Speed (m/s) 

All Hurricanes -Land and Marine 

60 

Modeled Peak Gust Wind Speed 

50 
40 

(m s-1) 

30 
20 
10 
0 

y=0.993x 
0 10 20 30 40 50 60 
Observed Peak Gust Wind Speed (m/s) 
Figure 2.17. Example Comparisons of Modeled and Predicted Maximum Surface 
Level 
Peak Gust Wind Speeds in Open Terrain from US Landfalling Hurricanes. Wind 
speeds 
measured on land are given for open terrain and wind speeds measured over 
water are 
given for marine terrain. 

The largest observed gust wind speed is only 128 mph (57 m/s). The 
differences between 
the modeled and observed wind speeds is caused by a combination of the 
inability of 
wind field model to be adequately described by a single value of B and RMW, 
errors in 
the modeled boundary layer, errors in height, terrain and averaging time 
adjustments 
applied to measured wind speeds (if required) as well as storm track position 
errors and 
errors in the estimated values of .p, RMW and B. 

Chapter 2. Hurricane Risk Model 

?
2.2.3 Summary 
The boundary layer modeling has been improved over the models used in prior 
hurricane 
risk studies, by taking into account the dropsonde data, the theoretical 
model of Kepert 
(2003), and by using a physically based gust factor model that properly 
models the 
variation in the gust factor with surface roughness. 

The modeling of the hurricane wind field has also been improved in comparison 
to 
models used in previous studies. It employs the full non-linear solution to 
the equations 
of motion of a hurricane (rather than the spectral model used in Georgiou 
(1985) or 
Vickery and Twisdale (1995a), or the empirical models used in all other 
studies). 
Evaluation of the hurricane model through comparisons with real hurricane 
wind speed 
data shows that the model provides a good representation of the hurricane 
wind field. The 
hurricane wind field model relies, wherever possible, on physical models 
rather than 
empirical models to describe the wind speeds within the storm, and is the 
most advanced 
hurricane model currently in use for estimating hurricane wind speed risk. 

2.3 Hurricane Simulation Methodology 
2.3.1 Introduction 
Mathematical simulation of hurricanes is the most accepted approach for 
estimating wind 
speeds for the design of structures and assessment of hurricane risk. The 
basic approach 
in all previously published hurricane simulation studies is the same in that 
site specific 
statistics of key hurricane parameters (including central pressure difference 
(.p), Holland 
pressure profile parameter (B), radius to maximum winds (Rmax), heading (.), 
translation 
speed (c), and the coast crossing position or distance of closest approach 
(dmin) are first 
obtained. Given that the statistical distributions of these key hurricane 
parameters are 
known, a Monte Carlo approach is used to sample values from each of the 
aforementioned distributions and a mathematical representation of a hurricane 
is passed 
along the straight line path, satisfying the sampled data, while the 
simulated wind speeds 
are recorded. The intensity of the hurricane is held constant until landfall 
is achieved, 
after which time the hurricane is decayed using filling rate models. 

A hurricane simulation approach has been developed where the full track of a 
hurricane 
or tropical storm is modeled, beginning with its initiation over the ocean 
and ending with 
its final dissipation (Vickery, et al., 2000a). Using this approach, the 
central pressure is 
modeled as a function of sea surface temperature and the storm heading, speed 
etc. are 
updated at each six-hour point in the storm history. Linear interpolation is 
used between 
the six-hour points. The approach is validated by comparing the site specific 
statistics of 
the key hurricane parameters of the simulated hurricane tracks with 
statistics derived 
from the historical data. When coupled with the wind field model, hurricane 
wind speeds 
can be computed at any point along or near the hurricane coastline. The model 
is able to 
reproduce the continually varying hurricane statistics along the US 
coastline, and can 
treat multiple landfalling storms, by-passing storms, storm curvature and re-
intensification in cases where the storm re-enters the water before making a 
second or 
third landfall. 

HAZUS-MH MR3 Technical Manual 

?
2.3.2 Storm Track Modeling 
The storm track simulation model is initiated by randomly sampling a starting 
position, 
date, time, heading and translation speed from one of the tropical storms 
given in the 
HURDAT database. The number of storms to be simulated in any one year is 
obtained by 
sampling from a negative binomial distribution having a mean value of 8.4 
storms/yr and 
a standard deviation of 3.56 storms/yr. Given the initial storm heading, 
speed, and 
intensity, the simulation model estimates the new position and speed of the 
storm based 
on the changes in the translation speed, c, and direction (or heading) of the 
storm, ., over 
the current six-hour period. The changes in the speed and direction between 
times i and 
i+1 are obtained from: 

.lnc = a1 + a2.+ a3.+ a4 lnci + a5.i +e (2.9) 

.. = b1 + b2.+ b3.+ b4ci + b5.i + b6.i-1 +e (2.10) 

where a1, a2, etc., are constants, . and . are the storm latitude and 
longitude, ci is the 
storm translation speed at time step i, .i is the storm heading at time step 
i, .i-1 is the 
heading of the storm at time step i-1 and e is a random error term. The 
coefficients a1, a2 
etc. have been developed using 50 by 50 grids over the entire Atlantic basin. 
A different 
set of coefficients for easterly and westerly headed storms is used. As the 
storm moves 
into a different 50 by 50 square, the coefficients used to define the changes 
in heading and 
speed change accordingly. The maximum change in the direction of a simulated 
storm 
was truncated to ensure realistic limits on the maximum change in storm 
direction as a 
function of wind speed. The bounding value of the upper limit in the change 
of storm 
direction over a six hour period is a function of storm translation speed. 

The central pressure modeling method used in the approach eliminates the need 
to model 
the central pressure of the hurricane explicitly, by using a relative 
intensity parameter 
which is coupled to the sea surface temperature. This relative intensity 
approach is an 
improvement over traditional simulation techniques in that the central 
pressures derived 
are bounded by physical constraints, thus eliminating the need to 
artificially truncate the 
central pressure distribution. The method was first used in single point 
simulations by 
Darling (1991). The approach is validated by comparing the statistics of the 
observed and 
modeled hurricane central pressure statistics along the US coastline. 

During the hurricane simulation process, the values of the relative 
intensity, I, at each 
time step are obtained from, 

ln( Ii+1) = c0 + c1 ln(Ii ) + c2 ln( Ii-1 ) + c3 ln( Ii-2 ) + c4Ts + c5(Tsi+1 
- Tsi ) +e (2.11) 

where Ts is the sea surface temperature and the coefficients c0, c1, etc. 
vary with storm 
latitude, basin (Gulf of Mexico or Atlantic Ocean), and heading (i.e., 
Easterly or Westerly 
direction). Near the US coastline, where more continuous pressure data are 
available, 
finer, regionally specific values of these coefficients are developed. These 
regionally 
specific coefficients take into account changes in the relationships between 
sea surface 
temperature and storm intensity that are influenced by subsurface water 
temperatures as 

Chapter 2. Hurricane Risk Model 

?
2-27 

described in Cooper (1988). The maximum increase in storm intensity over a 
six hour 
period was set equal to 40 mbar, (6.7 mbar/hr) which is greater than the 
largest average 
six hour change of about 25 mbar/6hrs (4.2 mbar/hr) observed in Super Typhoon 
Forrest 
in the western Pacific in 1983, but less than the maximum rate of 7.14 
mbar/hr also 
observed in Super Typhoon Forrest, but for a period of less than 3 hrs. Once 
a simulated 
storm makes landfall, the change in central pressure with time is modeled 
using the 
filling models described in Vickery (2005). If a storm moves back over water, 
Equation 

2.11 is again used to model the variation in central pressure with time. 
Using this 
modeling approach, the central pressure of the storm continuously changes 
with time, 
unlike traditional hurricane simulations, where a constant value of central 
pressure is 
assumed until landfall is achieved. 
2.3.3 Modeling of Radius to Maximum Winds 
The statistical model describing the relationship between the radius of 
maximum winds 
and the storm central pressure and latitude is an update of the model given 
in Vickery, et. 
al (2000b). The model described in Vickery, et. al (2000b) is primarily based 
on 
information on landfalling storms provided in NWS38. The revised model has 
been 
updated to include information of the radius to maximum winds from some of 
the more 
recent storms occurring in the Atlantic Basin including hurricane Mitch 
(1998), 
Hurricane Brett (1999), Hurricane Floyd (1999), in addition to the inclusion 
of Hurricane 
Gilbert (1988). 

The relationship between the radius to maximum winds, Rmax, central pressure 
difference, 
.p, and latitude, ., is given as: 

ln Rmax = 2.556 - 0.000050255.p2 + 0.042243032.; r2=0.3249 (2.12) 

Comparisons of the observed values of Rmax and those predicted from the 
statistical model 
are shown in Figure 2.18. The statistical model reproduces the observations 
that the more 
intense storms have smaller values of Rmax and that the storms located in 
more northern 
latitudes tend to have larger values of Rmax. In the simulation of 
hurricanes, Rmax is 
constrained to have a maximum value of 150 km and a minimum value of 8 km. 

160 

5.5 
R2 = 0.3385 
2 2.5 3 3.5 4 4.5 
Ln(Rmax) Model 
Radius to Maximum Winds (km)
0 20 40 60 80 100 120 140 
Central Pressure Difference (mbar) 
Rmax Observed 
Rmax Modeled 
140 

120 

100 

80 

60 

40 
20 
0 

Ln(Rmax) Observed 

4.5 

Figure 2.18. Modeled and Observed Values of Radius of Maximum Winds. 

HAZUS-MH MR3 Technical Manual 

?
2.3.4 Pressure Profile Parameter (B) 
Upper level aircraft data available at NOAA site were used to estimate 
Holland s 
pressure profile parameter (B) (Vickery and Wadhera, 2008). The upper level 
aircraft 
dataset used here contains a total of 4546 radial profiles from 62 Atlantic 
storms. This 
data is the same as that used by Willoughby, et al (2006), in their analysis 
of B, with the 
main difference in the analysis methodology being that pressures are used 
here and 
Willoughby, et al (2006) performed their analysis using wind speeds. For 
every storm, 
data has been organized based on the different flights that passed through 
the storm. For 
each flight, the airplane traversed through the hurricane a number of times 
in different 
directions. For every pass the data was collected from the center of the 
storm to a certain 
radius (usually 150 km). Available data is then organized according to their 
radial 
distance from the center of the storm. For each bin (based on the radius from 
the center of 
the storm) flight level pressure, flight altitude, dew point temperature, 
wind speed and air 
temperature are available. Each profile from every flight and every storm is 
treated as an 
independent observation. Holland, (1980) describes the radial distribution of 
surface 
pressure in a hurricane in the form: 

p(r) = p0+.p 7 exp-. RMW .B 
(2.13)

.. r .. 

where p(r) is the surface pressure at a distance r from the storm center, p0 
is the central 
pressure, .p is the central pressure difference, A is the location parameter 
and B is the 
Holland s pressure profile parameter. 

The surface pressure and radial distance are transformed to the form of 
Equation 2.13. 
The missing quantities in Equation 2.13 are RMW and B. First estimate of RMW 
is made 
from the recorded wind speed profile i.e. RMW is the radius to the measured 
maximum 
wind speed. From here on, the radius corresponding to the maximum wind speed 
in a 
profile is referred to as RMW. To estimate the optimum values of B and RMW, 
RMW and 
B are varied over the range [0.5RMW, 1.5RMW] and [0.5, 2.5] respectively. The 
algorithm calculates an optimum B value by minimizing the mean of the square 
differences between the measured and the modeled surface pressure in a range 
of 
0.5RMW to 1.5RMW for different B and RMW values. Mathematically, the mean 
square 
error between the measured and the modeled surface pressure can be written 
as: 

1.5RMW 2 
S(Pobsi 
- Ptheoi ) 
e2 = i=0.5RMW (2.14)

n 

where Pobsi is the measured pressure, Ptheoi is the theoretical pressure 
calculated using 
Equation 2.13 and n is the number of data points in the range [0.5RMW, 
1.5RMW]. The 
values of B and RMW chosen correspond to those yielding the minimum mean 
square 
error, e2. The corresponding r2 value for the fit is given by: 

Chapter 2. Hurricane Risk Model 

?
2 e 2 
r = 1- 2 (2.15)

s 

where s is the standard deviation of the measured pressure data in the range 
of 
[0.5RMW, 1.5RMW]. 

A quality control criterion was used to filter out profiles. Each of the 
filtered profiles has 
at least one of the following characteristics associated with it, (a) Flight 
level pressure is 
less than 700 mbar i.e. height greater than 3000 m, (b) Central pressure 
difference is less 
than 25 mbar, (c) Radius to maximum winds is greater than two-third of the 
sampling 
domain, (d) the distance of aircrafts closest approach to the center is 
greater than half of 
the radius to maximum winds, (e) Data is available for less than one third of 
the sampling 
range i.e. less than 50 km, (f) visual inspection which involved eliminating 
profiles with 
a considerable amount of data missing in the range of interest [0.5RMW, 
1.5RMW ]. The 
rationale for using criteria (a) is that higher the measurement height, less 
representative 
measurements are of the surface observations. Criteria (b) results in the 
data associated 
with Category 1 or higher hurricanes only. The rationale for using criteria 
(c), (d), (e) and 

(f) is to ensure that there is a sufficient number of measurements on both 
sides of the 
radius to maximum winds to have a clear representation of the shape of the 
profile 
(Willoughby et al. 2006). The use of the quality control criteria eliminated 
a total of 2291 
profiles from a set of 4556 profiles. Figure 2.19 presents a few examples of 
pressure 
profiles that were eliminated from the analysis. Both the measured pressure 
data and the 
corresponding fit to Holland s equation are shown. It is observed that each 
of the subplots 
in Figure 2.19 is compromised by at least one of the above mentioned quality 
control 
criteria. 
Figure 2.20 presents examples of pressure profiles that were retained for 
analysis. Each 
row in Figure 2.20 corresponds to a complete airplane traverse in one 
direction. The 
shaded regions in Figure 2.20 represent the error minimizing range of 0.5RMW 
to 
1.5RMW. The fit parameters i.e. the B value, the central pressure difference 
and the RMW 
are also provided in the title of every profile. For a given traverse through 
a hurricane, 
differences in the B values for two different profiles is due to the change 
in the radius to 
the maximum winds and the central pressure difference. The geographical 
distribution of 
the filtered profiles, based on the storm center, is shown in Figure 2.21. 
The filtered 
profiles have a wide geographical distribution and provides with a wide 
domain of 
hurricane climatic characteristics. The filtered dataset has an average RMW 
of 46 km 
(standard deviation of 22 km), an average central pressure difference of 51 
mbar (18 
mbar) and an average location of 25.840N (5.740N) and 74.780W (12.820W). 71% 
of the 
fits yield r2 values greater than 0.95 and 80% of the fits have a mean square 
error less 
than 2.5 mbar. The maximum mean square error was 24.6 mbar which occurred for 
one 
of Hurricane Opal s profiles where Holland s equation overestimated the 
pressures at all 
points. The approach for analyzing the B data involved the estimation of RMW 
and B 
from each single pass of a flight through the storm. 

The B values computed as discussed above were found to be correlated to the 
radius to 
maximum winds, central pressure difference, latitude and sea surface 
temperature. 

HAZUS-MH MR3 Technical Manual 

?
2-30 
Chapter 2. Hurricane Risk Model 
982 
988 
995 
Bertha 12/07/1996-08:30 
Pressure (mb) 
983 
989 
995 
Josephine 07/10/1996-11:35 
981 
987 
994 
Michelle 03/11/2001-17:24 
Pressure (mb) 
953 
978 
1004 
Floyd 13/10/1987-02:15 
958 
983 
1007 
Floyd 16/09/1999-03:42 
Pressure (mb) 
964 
988 
1012 
Claudette 07/09/1991-17:28 
987 
998 
1008 
Bertha 12/07/1996-01:12 
Pressure (mb) 
990 
997 
1003 
Earl 03/09/1998-00:45 
0 50 100 150 
943 
972 
1002 
Allen 05/08/1980-16:15 
Radius (km) 
Pressure (mb) 
0 50 100 150 
952 
980 
1008 
Andrew 26/08/1992-02:10 
Radius (km) 
Figure 2.19. Examples of the Eliminated Profiles. 
?
2-31 
HAZUS-MH MR3 Technical Manual 
150 100 50 0 
940 
960 
980 
1000 
1020
Andrew -1992/08/23-0542, B=1.6,.p=75mb,Rmax=11km 
Radius(km) 
Pressure(mb) 
0 50 100 150 
940 
960 
980 
1000 
1020
Andrew -1992/08/23-0550, B=1.7,.p=79mb,Rmax=13km 
Radius(km) 
Pressure(mb) 
150 100 50 0 
970 
980 
990 
1000 
1010 
1020 
1030
Bertha-1996/07/09-0647, B=1.45, .p=52mb, Rmax=35km 
Radius(km) 
Pressure(mb) 
0 50 100 150 
970 
980 
990 
1000 
1010 
1020 
1030
Bertha-1996/07/09-0658, B=1.25, .p=53mb, Rmax=32km 
Radius(km) 
Pressure(mb) 
150 100 50 0 
970 
980 
990 
1000 
1010 
1020 
1030
Bret-1999/08/21-2110, B=1.7, .p=52mb, Rmax=22km 
Radius(km) 
Pressure(mb) 
0 50 100 150 
970 
980 
990 
1000 
1010 
1020 
1030
Bret-1999/08/21-2118, B=1.9, .p=53mb, Rmax=20km 
Radius(km) 
Pressure(mb) 
150 100 50 0 
940 
960 
980 
1000 
1020 
Edouard-1996/08/26-1855, B=1.35, .p=74mb, Rmax=22.5km 
Radius(km) 
Pressure(mb) 
0 50 100 150 
960 
980 
1000 
1020
Edouard-1996/08/26-1904, B=1.65, .p=70mb, Rmax=30km 
Radius(km) 
Pressure(mb) 
Figure 2.20. Examples of Surface Pressure Profiles for a Traverse Across a 
Given 
Hurricane. 
?
2-32 
Chapter 2. Hurricane Risk Model 
10500'0"W 
10500'0"W 
10000'0"W 
10000'0"W 
9500'0"W 
9500'0"W 
9000'0"W 
9000'0"W 
8500'0"W 
8500'0"W 
8000'0"W 
8000'0"W 
7500'0"W 
7500'0"W 
7000'0"W 
7000'0"W 
6500'0"W 
6500'0"W 
6000'0"W 
6000'0"W 
5500'0"W 
5500'0"W 
5000'0"W 
5000'0"W 
1000'0"N 1000'0"N 
1500'0"N 1500'0"N 
2000'0"N 2000'0"N 
2500'0"N 2500'0"N 
3000'0"N 3000'0"N 
3500'0"N 3500'0"N 
4000'0"N 4000'0"N 
4500'0"N 4500'0"N 
Figure 2.21. Geographical Distribution of all the Filtered Profiles. 
Only points associated with central pressures less than 980 mbar are included 
in the 
analysis. The resulting model of B is 
0.145, 0.302 
0.36467 0.15182 ( ) 0.081639 ( ) 0.11663 ( ) 0.67354 ( ) 
2
max 
= s = 
= - - 7 + 7 . - 7 . + 7 
r e 
B LN R LN p LN LN T (2.16) 
2.3.5 Filling Models 
The filling models used in HAZUS-MH are described in Vickery (2005). The form 
of the 
filling models is an exponential decay function in the form 
p(t) p exp( at) o . = . - 
where .p(t), expressed in mb, is the difference between the central pressure 
of the storm 
and the far field pressure (normally taken as the as the pressure associated 
with the 
outermost closed isobar) t hours after landfall, .po (mb) is the difference 
between the 
central pressure of the storm and the far field pressure at the time the 
storm makes 
landfall and a is the filling constant. 
The magnitude of the filling constant used in the exponential decay function 
is modeled 
as a function of readily defined characteristics of a tropical cyclone at the 
time of landfall. 
For storms making landfall along the Gulf Coast of the United States and the 
Florida 
peninsula, the reduction in the central pressure difference following 
landfall is modeled 
as a function of .poc/RMW. Along the Mid-Atlantic coast, the reduction in the 
central 
pressure difference following landfall is adequately modeled either as a 
function of 
.poc/RMW or a function of .po alone. Along the New England coast, the 
reduction in 
central pressure difference following landfall is modeled with decay constant 
modeled as 
?
a function of .po. Figure 2.22 shows the values of decay constant versus 
.poc/RMW for 
Gulf, Florida and Mid Atlantic coast and versus .po for New England coast. 
The models 
implemented in HAZUS-MH are as follows: 

Gulf Coast a =0.0413 + 0.0018(.p0c / RMW ) ; se=0.0169 
Florida Peninsula Coast a =0.0225 + 0.0017(.p0c / RMW ) ; se=0.0158 
Mid-Atlantic Coast a =0.0364 + 0.0016(.p0c / RMW ) ; se=0.0161 
New England Coast a = 0.0034 + 0.0010.p0; se=0.0114 
When implemented, the minimum allowable value of a sampled filling 
coefficient is set 

at 0.015, with the sampled error constrained to lie within 13se. 
. 

Gulf Coast Florida Peninsula 

0.16 

0.16 

0.14 

0 10 20 30 40 50 60 70 80 
.Po7c/RMW
0.14 

0 10 20 30 40 50 60 70 80 
.Po7c/RMW 
Exponential DecayExponential Decay 
Constant-Constant-

Exponential DecayExponential Decay 
Constant- Constant- 

0.12 

0.12 

0.10 

0.10 

0.08 

0.08 

0.06 

0.06 

0.04 

0.04 

0.02 

0.02 

0.00 

0.00 

Mid Atlantic New England Coast 

0.16 

0.16 

0.14 

0.14 

0 20 40 60 80 100 120 
Central Pressure Difference (mbar) 
0.12 

0.12 

0.10 

0.10 

0.08 

0.08 

0.06 

0.06 

0.04 

0.04 

0.02 

0.02 

0.00 
0 10 20 30 40 50 60 70 80
.Po7c/RMW

0.00 

Figure 2.22. Filling Constant, a, vs. .poc/RMW for Gulf, Florida and Atlantic 
Coast 
and vs. .po for New England Coast. Solid line represents linear regression 
line. Thin 
dashed lines represent the mean error 12se 

HAZUS-MH MR3 Technical Manual 

?
2.3.6 Track and Central Pressure Model Evaluation 
The hurricane modeling methodology described above is evaluated through 
comparisons 
of observed and simulated hurricane statistics along the US coastline. The 
simulated 
statistics are derived from a 100,000 year simulation of storms occurring in 
the Atlantic 
Basin. Comparisons are given for the statistics of simulated and real storms 
approaching 
within 250 kilometers of the coastal mileposts shown in Figure 2.23. The 
central pressure 
difference statistics are computed using the minimum values observed within 
the 250 km 
radius sub-region. All other parameters are those computed or observed at the 
point of 
closest approach to the milepost. 

Figure 2.24 shows comparisons of the mean and, in some cases, standard 
deviation of the 
simulated and observed values of heading, translation speed, minimum approach 
distance 
(dmin), occurrence rate and central pressure along the Mexican and U.S. 
coastlines at 50 
nautical mile increments (~185 km). The HURDAT statistics given in Figure 
2.24 are 
derived for storms occurring during the period 1886-2001. The comparison of 
simulated 
and observed hurricane statistics shown in Figure 2.24 indicates that the 
modeling 
approach provides good estimates of the five key hurricane parameters at 
nearly all 
locations along the coastline. The empirical storm track modeling approach 
has been 
shown to successfully reproduce the statistics of the key hurricane 
parameters along the 
US coastline. The approach is able to properly model the probabilities of a 
single storm 
passing near multiple sites (results not presented here). Using this 
simulation procedure, 
the storm intensities change with time, and the storms change both direction 
and speed as 
they pass by a site. The approach more realistically models storm paths when 
compared 
to traditional hurricane simulation methods. 

2.3.7 Landfalling Hurricanes 
The historical information on landfalling hurricanes along the coastline 
provides another 
means to evaluate the hurricane simulation model. During the simulation 
process when a 
simulated storm makes landfall, the central pressure, maximum sustained wind 
speed 
(one minute average, over water), and landfall location are retained. Using 
the magnitude 
of the central pressure and wind speed at the time of landfall, the simulated 
storm is 
assigned a category as defined by Table 2.2. Historical information on the 
central 
pressure at the time of landfall is available for all intense hurricanes, IH, 
as defined by 
Saffir-Simpson Category 3 and higher storms, and for all other landfalling 
hurricanes 
occurring since 1964. Between 1900 and 2005 there have been a total of 189 
hurricane 
landfalls, 172 of which have central pressure data associated with the storm 
at the time of 
landfall. Note that in the counting of the 189 landfalls, each time a storm 
crosses land 
from the water, a landfall is considered to have occurred. 

Chapter 2. Hurricane Risk Model 

?
Figure 2.23. Locations of Milepost Stations Along the U.S. Coastline. 

For example, Hurricane Andrew made two landfalls, once as a Category 4 storm 
in South 
Florida, and once as a Category 3 storm in Louisiana1. Thus, the definition 
of a 
landfalling storm used herein differs from that usually used by the NHC that 
scores only 
one landfall per storm when counting the total number of landfalls in the US. 
All of the 
189 landfalls have a Saffir-Simpson Category, based on the estimated maximum 
sustained wind speed at the time of landfall. The comparisons of landfalling 
storms that 
follow concentrate on comparing the modeled and observed landfall intensities 
based on 
central pressure rather than wind speed. Figure 2.25 shows comparisons of the 
modeled 
and observed rates of intense hurricane landfalls, by region, for the Saffir-
Simpson storm 
category defined by central pressure at the time of landfall. Table 2.3 
presents the 

1 The upgrade of Hurricane Andrew from a Category 4 to a Category 5 in South 
Florida occurred after the analysis 
presented in this section was completed. 

HAZUS-MH MR3 Technical Manual 

?
2-36 

1.5 

HURDAT (MEAN) 
SIMULATED (MEAN) 
60 

1.25 

40 

1 

20 

HURDAT (MEAN) 
HURDAT (STD DEV) 
SIMULATED (MEAN) 
SIMULATED (STD DEV) 
Central Pressure Difference Translation Speed (m/sec) Annual Occurrence Rate 

Heading

0

0.75 

-20 

0.5 

-40 

0.25 

-60 

0 

-80 

0 500 1000 1500 2000 2500 3000 

0 500 1000 1500 2000 2500 3000 
Milepost Milepost 

0 500 1000 1500 2000 2500 3000 
Milepost 
HURDAT (MEAN) 
HURDAT (STD DEV) 
SIMULATED (MEAN) 
SIMULATED (STD DEV) 
-70 
-60 
-50 
-40 
-30 
-20 
-10 
0 
10 
20 
0 500 1000 1500 2000 2500 3000 
Milepost 
Minimum Approach Distance (km)
HURDAT (MEAN) 
SIMULATED (MEAN) 
HURDAT (MEAN) 
HURDAT (STD DEV) 
SIMULATED (MEAN) 
SIMULATED (STD DEV) 
16 
14 
12 
10 
8 
6 
4 
2 
0 

40 
35 
30 
25 
20 
15 
10 
5 
0 

0 500 1000 1500 2000 2500 3000
Milepost

Figure 2.24. Comparison of Simulated and Observed Key Hurricane Parameters 
vs. 
Milepost. 

Table 2.2. Saffir-Simpson Storm Categories 

Saffir-
Simpson 
Category 
Minimum 
Central 
Pressure 
Maximum Sustained Wind 
Speed (Over Water) 
Maximum Gust Speed 
(Over Water) 
Maximum Gust Speed 
(Over Land, z0 = 0.03m) 
(mb) (m/s) (mph) (m/s) (mph) (m/s) (mph) 
1 =980 33.1-42.0 74-94 40.6-51.9 91-116 36.8-48.1 82-108 
2 979-965 42.0-49.6 94-110 51.9-61.7 116-140 48.1-58.1 108-130 
3 964-945 49.6-58.1 110-130 61.7-72.7 140-165 58.1-69.7 130-156 
4 944-920 58.1-69.3 130-155 72.7-87.3 165-195 69.7-85.5 156-191 
5 <920 >69.3 >155 87.3 >195 >85.5 >191 

Chapter 2. Hurricane Risk Model 

?
2-37 
0.000 
0.050 
0.100 
0.150 
0.200 
0.250 
Mean Number of Landfalling IH/Year 
Mean (Simulated) 
Observed 
Figure 2.25. Comparison of Rate of Landfalling Intense Hurricanes by Region 
Along the US Coastline. (IH defined by pressure). 

information used to categorize each landfalling storm, along with the 
category of the 
storm as defined by central pressure and the category of the storm as given 
in HURDAT. 
The source of the central pressure data are given for each instance where 
central pressure 
data is given, with the majority of the central pressure data obtained from 
the NOAA 
publication NWS38 (Ho, et al., 1984). 

The simulated landfall rates given in Figure 2.25 have been derived from a 
100,000-year 
simulation of storms in the Atlantic basin. The observed data are given as 
the mean value 
11.96s (i.e., the 95th percentile confidence range) for observed IH storms 
categorized by 
central pressure. Modeled data are given as mean values only. Based on 
central pressure, 
the agreement between the observed and simulated rates of landfalling intense 
hurricanes 
is seen to be very good, with the variation of intense hurricane landfall 
rate with location 
along the coastline reproduced very well. The simulation yields a landfall 
rate of 0.71 
intense hurricanes per year vs. and observed rate of 0.72 intense hurricanes 
per year. 

Figure 2.26 presents similar data to that presented in Figure 2.25, except 
that the 
distribution of landfall intensity by storm category is presented without 
regard to the 
landfall region. In the case of Category 4 and higher storms, the simulation 
produces an 
average annual landfall rate, derived from central pressure, of about one 
Category 4 or 5 
storm every 5 years. This landfall rate is slightly less than that given in 
Vickery, et. al 
(2000b), where a rate of one Category 4 or 5 storm every 4 years was 
simulated. 

The comparison of modeled and observed intense hurricanes, where the observed 
hurricane intensity is based on wind speed, suggests that the model 
underestimates the 
occurrence of intense hurricanes. The apparent underestimation of the 
landfall rate of 
intense hurricanes when defined by wind speed is discussed in Vickery, et al. 
(2000b), 
where it is shown that many of the Category 3 storms (as defined in HURDAT) 
should 
have been Category 2 storms. Table 2.4 shows a comparison of the Saffir-
Simpson rating 

HAZUS-MH MR3 Technical Manual 

?
Table 2.3. Storm Category and Central Pressure at Landfall 

Year Name Land Fall State Category Central Pressure Source HURDAT Category 
1900 UN-NAMED TX 4 936 NWS38 4 
1901 UN-NAMED LA 2 2 
1901 UN-NAMED MS 2 973 HURDAT 2 
1901 UN-NAMED NC 1 973 NWS38 1 
1903 UN-NAMED SE FL 2 977 NWS38 2 
1903 UN-NAMED NW FL 1 1 
1903 UN-NAMED NJ 1 1 
1904 UN-NAMED SC 1 1 
1906 UN-NAMED SC 3 947 NWS38 3 
1906 UN-NAMED MS 2 965 NWS38 3 
1906 UN-NAMED SW FL 2 967 NWS38 3 
1906 UN-NAMED SE FL 1 979 FCHLM 1 
1908 UN-NAMED NC 1 1 
1909 UN-NAMED LA 4 931 Hebert, et. al 4 
1909 UN-NAMED TX 3 959 NWS38 3 
1910 UN-NAMED SW FL 3 955 Hebert, et. al 3 
1910 UN-NAMED TX 2 2 
1911 UN-NAMED SC 2 2 
1911 UN-NAMED NW FL 1 990 FCHLM 1 
1912 UN-NAMED TX 1 1 
1912 UN-NAMED AL 1 1 
1913 UN-NAMED NC 2 976 NWS38 1 
1913 UN-NAMED TX 1 1 
1915 UN-NAMED LA 4 932 NWS38 4 
1915 UN-NAMED TX 3 949 NWS38 4 
1915 UN-NAMED NW FL 1 1 
1916 UN-NAMED TX 3 948 NWS38 3 
1916 UN-NAMED AL 3 950 NWS38 3 
1916 UN-NAMED NW FL 2 974 NWS38 2 
1916 UN-NAMED SE FL 1 1 
1916 UN-NAMED SC 1 982 HURDAT 1 
1916 UN-NAMED MA 1 1 
1917 UN-NAMED NW FL 3 964 NWS38 3 
1918 UN-NAMED LA 3 955 Hebert, et. al 3 
1919 UN-NAMED TX 3 948 NWS38 4 
1920 UN-NAMED LA 1 980 NWS38 2 
1920 UN-NAMED NC 1 1 
1921 UN-NAMED TX 3 954 NWS38 2 
1921 UN-NAMED SW FL 3 960 NWS38 3 
1923 UN-NAMED LA 1 1 
1924 UN-NAMED SW FL 2 978 NWS38 1 
1924 UN-NAMED NW FL 1 1 
1925 UN-NAMED SW FL 1 1 
1926 UN-NAMED SE FL 4 932 NWS38 4 
1926 UN-NAMED LA 3 955 NWS38 3 
1926 UN-NAMED MS 3 3 
1926 UN-NAMED SE FL 3 960 NWS38 2 
1928 UN-NAMED SE FL 4 935 NWS38 4 
1928 UN-NAMED SE FL 2 977 HURDAT 2 
1929 UN-NAMED SE FL 3 948 NWS38 3 
1929 UN-NAMED TX 2 970 NWS38 1 
1929 UN-NAMED NW FL 2 975 NWS38 2 
1932 UN-NAMED TX 4 942 NWS38 4 
1932 UN-NAMED AL 1 1 
1933 UN-NAMED TX 3 949 NWS38 3 
1933 UN-NAMED SE FL 3 948 NWS38 3 
1933 UN-NAMED NC 3 948 NWS38 3 
1933 UN-NAMED NC 3 957 NWS38 3 

Chapter 2. Hurricane Risk Model 

?
Table 2.3. Storm Category and Central Pressure at Landfall (Continued) 

Year Name Land Fall State Category Central Pressure Source HURDAT Category 
1933 UN-NAMED SE FL 1 1 
1934 UN-NAMED TX 2 2 
1934 UN-NAMED LA 2 966 NWS38 3 
1935 UN-NAMED SW FL 5 892 NWS38 5 
1935 UN-NAMED NW FL 1 985 MWR 1935 2 
1935 UN-NAMED SE FL 2 973 NWS38 2 
1936 UN-NAMED NW FL 3 963.8 NWS38 3 
1936 UN-NAMED TX 1 1 
1938 UN-NAMED NY 3 946 NWS38 3 
1938 UN-NAMED LA 1 1 
1939 UN-NAMED NW FL 1 1 
1939 UN-NAMED SE FL 1 1 
1940 UN-NAMED LA 2 972 HURDAT 2 
1940 UN-NAMED SC 2 975 NWS38 2 
1941 UN-NAMED TX 3 959 NWS38 3 
1941 UN-NAMED SE FL 2 2 
1941 UN-NAMED NW FL 1 981 NWS38 2 
1942 UN-NAMED TX 3 951 NWS38 3 
1942 UN-NAMED TX 1 1 
1943 UN-NAMED TX 2 975 NWS38 2 
1944 UN-NAMED SW FL 3 962 NWS38 3 
1944 UN-NAMED NY 3 955 NWS38 3 
1944 UN-NAMED NC 1 1 
1945 UN-NAMED SE FL 3 951 NWS38 3 
1945 UN-NAMED TX 2 967 NWS38 2 
1945 UN-NAMED NW FL 1 1 
1946 UN-NAMED SW FL 1 1 
1947 UN-NAMED SE FL 4 940 NWS38 4 
1947 UN-NAMED LA 2 966 NWS38 2 
1947 UN-NAMED SC 2 967 NWS38 2 
1947 UN-NAMED TX 1 1 
1948 UN-NAMED SW FL 3 963 HURDAT 3 
1948 UN-NAMED SE FL 2 965 NWS38 2 
1948 UN-NAMED LA 1 989 HURDAT 1 
1949 UN-NAMED TX 3 963 NWS38 2 
1949 UN-NAMED SE FL 3 954 HURDAT 3 
1950 EASY NW FL 3 958 NWS38 3 
1950 KING SE FL 3 955 NWS38 3 
1950 BAKER AL 1 979 NWS38 1 
1952 ABLE SC 1 1 
1953 FLORENCE NW FL 1 985 HURDAT 1 
1953 BARBARA NC 1 987 HURDAT 1 
1954 HAZEL NC 4 938 NWS38 4 
1954 CAROL NY 3 962 NWS38 3 
1954 EDNA MA 3 954 Hebert, et. al 3 
1954 EDNA ME 1 1 
1955 CONNIE NC 3 963 NWS38 3 
1955 IONE NC 3 960 NWS38 3 
1955 DIANE NC 1 982 NWS38 1 
1956 FLOSSY NW FL 2 974 NWS38 2 
1956 FLOSSY LA 1 983 HURDAT 1 
1957 AUDREY LA 3 947 NWS38 4 
1959 GRACIE SC 3 951 NWS38 3 
1959 DEBRA TX 1 984 HURDAT 1 
1959 CINDY SC 1 1 
1960 DONNA SW FL 4 930 NWS38 4 
1960 DONNA NC 3 958 NWS38 3 
1960 DONNA NY 3 959 NWS38 3 

HAZUS-MH MR3 Technical Manual 

?
Table 2.3. Storm Category and Central Pressure at Landfall (Continued) 

Year Name Land Fall State Category Central Pressure Source HURDAT Category 
1960 ETHEL MS 1 1 
1961 CARLA TX 4 931 HURDAT 4 
1963 CINDY TX 1 1 
1964 HILDA LA 3 959 HURDAT 3 
1964 DORA NE FL 3 961 NWS38 3 
1964 ISABELL SW FL 2 969 HURDAT 2 
1964 CLEO SE FL 2 968 NWS38 2 
1965 BETSY LA 3 945 HURDAT 3 
1965 BETSY SE FL 3 952 HURDAT 3 
1966 ALMA NW FL 2 977 HURDAT 2 
1966 INEZ SW FL 1 984 HURDAT 1 
1967 BEULAH TX 3 950 Hebert, et. al 3 
1968 GLADYS NW FL 2 977 HURDAT 2 
1969 CAMILLE MS 5 909 HURDAT 5 
1969 GERDA ME 1 980 HURDAT 1 
1970 CELIA TX 3 945 HURDAT 3 
1971 FERN TX 2 978 HURDAT 2 
1971 EDITH LA 2 978 HURDAT 2 
1971 GINGER NC 1 990 HURDAT 1 
1972 AGNES NW FL 1 984 HURDAT 1 
1974 CARMAN LA 3 952 HURDAT 3 
1975 ELOISE NW FL 3 955 MWR 4/76 3 
1976 BELLE NY 1 980 MWR 4/77 1 
1977 BABE LA 1 1000 MWR 4/78 1 
1979 FREDERIC AL 3 949 HURDAT 3 
1979 DAVID SE FL 2 972 MWR 7/80 2 
1979 DAVID GA 2 970 MWR 7/80 2 
1979 BOB LA 1 986 MWR 7/80 1 
1980 ALLEN TX 3 945 MWR 7/81 3 
1983 ALICIA TX 3 962 MWR 5/84 3 
1984 DIANA NC 2 970 MWR 7/85 2 
1985 ELENA MS 3 960 HURDAT 3 
1985 GLORIA NY 3 960 HURDAT 3 
1985 JUAN LA 2 974 HURDAT 2 
1985 KATE NW FL 2 967 MWR 7/86 2 
1985 DANNY LA 1 987 MWR 7/86 1 
1985 BOB SC 1 1002 MWR 7/86 1 
1986 BONNIE TX 1 991 HURDAT 1 
1986 CHARLEY NC 1 992 HURDAT 1 
1987 FLOYD SW FL 1 993 MWR 4/88 1 
1988 FLORENCE LA 1 983 HURDAT 1 
1989 HUGO SC 4 934 MWR 5/90 4 
1989 CHANTEL TX 1 986 MWR 5/90 1 
1989 JERRY TX 1 983 MWR 5/90 1 
1991 BOB RI 2 965 MWR 11/92 2 
1992 ANDREW SE FL 4 922 MWR 3/94 4 
1992 ANDREW LA 3 956 MWR 3/94 3 
1995 OPAL NW FL 4 942 Hebert, et. al 3 
1995 ERIN NW FL 2 973 Prelim Report 2 
1995 ERIN SE FL 1 984 Prelim Report 1 
1996 FRAN NC 3 954 Prelim Report 3 
1996 BERTHA NC 2 974 Prelim Report 2 
1997 DANNY AL 1 984 Prelim Report 1 
1998 GEORGES MS 3 964 Prelim Report 2 
1998 BONNIE NC 3 964 Prelim Report 2 
1998 EARL NW FL 1 987 Prelim Report 1 
1999 BRET TX 3 951 Prelim Report 3 
1999 FLOYD NC 3 956 Prelim Report 2 
1999 IRENE SW FL 1 987 Prelim Report 1 

Chapter 2. Hurricane Risk Model 

?
Table 2.3. Storm Category and Central Pressure at Landfall (Concluded) 

Year Name Land Fall State Category Central Pressure Source HURDAT Category 
2003 CLAUDETTE TX 2 979 Prelim Report 1 
2003 ISABEL NC 3 957 Prelim Report 2 
2004 IVAN MS 3 946 Prelim Report 4 
2004 CHARLEY SW FL 4 941 Prelim Report 1 
2004 JEANNE SE FL 3 950 Prelim Report 2 
2004 FRANCES SE FL 3 960 Prelim Report 1 
2004 GASTON SC 1 985 Prelim Report 3 
2004 CHARLEY SC 1 992 Prelim Report 3 
2005 KATRINA LA 4 920 Prelim Report 1 
2005 RITA LA 4 937 Prelim Report 3 
2005 CINDY LA 1 991 Prelim Report 4 
2005 DENNIS NW FL 3 946 Prelim Report 1 
2005 WILMA SW FL 3 950 Prelim Report 3 
2005 KATRINA SE FL 1 984 Prelim Report 3 

0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
Mean Number of Landfalling IH/Year 
Observed 
Mean (Simulated) 
All IH Storms Category 3 Category 4 Category 5 
Storms Storms Storms 

Model Storm Intensity Based on Central Pressure 

Figure 2.26. Comparison of Number of Annual Landfalling Intense Hurricanes by 
Storm Category (IH defined by pressure). 

of recent landfalling storms that have been followed by detailed studies of 
the wind field 
at the time of landfall. All of the reconstructed wind fields were produced 
by 
NOAA/HRD. The results of these studies are given in Powell (1987), Powell, 
Dodge and 
Black (1991), Powell and Houston (1996), Powell and Houston (1998) and 
Houston, 
Powell and Dodge (1997). 

As indicated in Table 2.4, the official rating (defined by wind speed) given 
to many of the 
hurricanes is high. In Table 2.4 the shaded rows indicate storms where the 
official 
landfall category is higher than that indicated by the wind speed analysis 
performed and 
published by NOAA/HRD. Most of these over-ratings occur when Category 2 
storms are 
scored as Category 3 storms. At least 50% of the Category 3 storms given in 
Table 2.4 

HAZUS-MH MR3 Technical Manual 

?
Table 2.4. Comparison of HURDAT Hurricane Classification to Classifications 
Resulting from Detailed Studies Performed by NOAA/HRD 

Hurricane Year 
HURDAT Category at 
Landfall 
Category by Central 
Pressure at Landfall 
Category by Maximum Wind 
Speeds at Landfall (NOAA/HRD) 
Frederic 
Alicia 
Hugo 
Andrew 
EmilyErin 
Opal 
Bertha 
Fran 
1979 
1983 
1989 
1991 
1993 
1995 
1995 
1996 
1996 
3 
3 
4 
4 
3 
2 
3 
2 
3 
3 
3 
4 
4 
3 
2 
4 
2 
3 
3 
1-2 
4 
4 
3 
1 
2 
2 
2 

are over-rated by one category. In the case of the Hurricane Alicia surface 
wind field 
analysis given in Powell (1987), the averaging time is not explicitly stated, 
although a 
sustained (or one-minute average) is implied. The maximum surface level wind 
speed 
given in Powell (1987) for Alicia at landfall is 39 m/sec. If this wind speed 
is taken as a 
one-minute average then the storm would be a Category 1 hurricane. If this 39 
m/s wind 
speed is taken to correspond to a mean hourly value, then the storm would be 
rated as a 
Category 2 storm; hence, a range of storm categories is given in Table 2.4. 

If the hurricanes given in Table 2.4 had been categorized by central pressure 
instead of 
the estimated maximum wind speed at the time of landfall, there would almost 
be a one-
to-one correspondence between the scored category and the actual category, 
with only 
Opal having a different category. Considering that the hurricane categories 
assigned to 
the storms by the National Hurricane Center (NHC) (given in Table 2.2) are 
based on 
upper level wind speeds measured by numerous aircraft, coupled with surface 
level wind 
speeds measured at data buoy and C-MAN stations, it is not unreasonable to 
assume that 
the errors in hurricane classification (as defined by the peak wind speed) in 
earlier years, 
when the quantity and quality of full scale data was not as high as today s, 
were even 
more frequent. In general, it is much simpler to determine the minimum 
central pressure 
in a hurricane than it is to reconstruct the wind field, and only over the 
past two decades 
has a significant effort gone into post-storm hurricane wind field 
reconstruction. Errors 
associated with the measurements of wind speed are much greater than those 
associated 
with the measurements of central pressure. Figure 2.27 shows comparisons of 
landfall 
rates of all hurricanes as a function of location along the coastline and 
storm intensity 
(defined by central pressures). Note that for the landfalling storms that 
have no value of 
central pressure assigned to them, it has been assumed that the landfall 
category of the 
storm as given in HURDAT (as given in Table 2.3) is consistent with the 
landfall 
category associated with the storm central pressure at the time of landfall. 
A simulated 
storm is categorized as a hurricane only if the modeled sustained wind speed 
at the time 
of landfall (one minute average over water) exceeds 74 mph, irrespective of 
the value of 
the central pressure. The model yields an average of 1.79 hurricane landfalls 
per year vs. 
a historical average of 1.72 storms per year. The histograms of landfall 
rates shown in 

Chapter 2. Hurricane Risk Model 

?
Figure 2.27 are statistically equivalent as indicated by a .2 test performed 
at a 5% 
significance level. Figure 2.28 shows a comparisons of the modeled and 
observed central 
pressures at landfall plotted vs. return period for storms making landfall 
along the Gulf 
coast (west of the FL-AL border), the Florida coastline, the Atlantic 
coastline north of 
Florida and for the entire Gulf and Atlantic U.S. coastline. Note that in the 
development 
of Figure 2.28, there is some uncertainty in the ranking of some of the 
storms having 
central pressures higher than Category 3 values since central pressure data 
is not 
available for all Category 1 and 2 storms. The probability distributions of 
the historical 
data used to derive the central pressure vs. return period plots given in 
Figure 2.28 have 
been developed assuming that all the storms having no central pressure data 
are weaker 
(i.e., have higher central pressures) than the strongest Category 2 storm 
which has central 
pressure data. Figure 2.28 shows excellent agreement between the modeled and 
observed 
central pressures as a function of return period for the coastal US as a 
whole and for the 
three individual regions. 

The agreement is particularly good for storms having intensities of Category 
3 or higher, 
where no assumptions have had to be made with respect to missing central 
pressure data. 
Again, the landfall statistics for the modeled data have been derived from a 
100,000-year 
simulation of storms occurring in the Atlantic Basin. Figure 2.29 presents 
plots similar to 
those shown in Figure 2.28 except for smaller geographic areas (or coastal 
segments). 
The agreement between the modeled and observed data, while still very good, 
is not as 
good as that seen in the case of the large coastal segments primarily because 
the number 
of storms making landfall along these smaller segments is relatively small. 

2.3.8 Predicted Wind Speeds vs. Return Period 
Using the storm track simulation methodology combined with the wind field and 
gust 
factor models described in Vickery, et al. (2000a), 100,000 years of storms 
were 
simulated. When a simulated storm is within 250 km of a milepost, the peak 
gust wind 
speeds are recorded at 15-minute intervals, with the largest wind speed in 
each of 16 
directions and the largest overall being retained. Upon completion of a 
100,000-year 
simulation, the wind speed data are rank ordered and then used to define the 
wind speed 
probability distribution conditional on a storm being within 250 km of the 
site. The 
probability that the tropical cyclone wind speed (independent of direction) 
is exceeded 
during time period t is, 

8 
Pt (v > V ) = 1-. P(v < V | x) pt (x) (2.17) 
x =0 

where P(v < V | x) is the probability that velocity v is less than V given 
that x storms 
occur, and pt(x) is the probability of x storms occurring during time period 
t. From 2. 20, 
with pt(x) defined as Poisson and setting t to one year, the annual 
probability of 
exceeding a given wind speed is, 

Pa (v >V ) = 1- exp[-.P(v > V )] (2.18) 

HAZUS-MH MR3 Technical Manual 

?
2-44 

Texas Louisiana 

14.0 

14.0 

12.0 

12.0 

10.0 

8.0 

Modeled 
Observed 
10.0 

Modeled 

8.0 

Observed 

Number of Storms Number of Storms Number of Storms Number of Storms Number of 
Storms Number of Storms

Number of Storms Number of Storms 

Number of Storms 
Number of Storms Number of StormsNumber of Storms

6.0 

4.0 

6.0 

4.0 

2.0 

0.0 
1 2345 12345 
Category -Defined by Central Pressure Category Defined by Central Pressure 

0.0 

MS/AL Northwest FL 

14.0 

14.0 

2.0 

Modeled 
Observed 
2.0 

2.0 

0.0 
Modeled 
Observed 
12345 12345 
Category - Defined by CentralPressure Category Defined by Central Pressure 

Southwest FL Southeast FL 

0.0 

14.0 

14.0 

12.0 

12.0 

10.0 

10.0 

8.0 

8.0 

6.0 

6.0 

4.0 

4.0 

Modeled 
Observed 
2.0 

2.0 

0.0 
1 2345 12345 
Category - Defined by Central Pressure Category - Defined by Central Pressure 

0.0 

Northeast FL GA/SC 

14.0 

14.0 

Modeled 

12.0 

12.0 

Observed 

10.0 

10.0 

8.0 

8.0 

6.0 

6.0 

4.0 

4.0 

Modeled 
Observed 
Modeled 

12.0 

12.0 

Observed 

10.0 

10.0 

8.0 

6.0 

4.0 

8.0 

6.0 

4.0 

2.0 

2.0 

0.0 
12345 12345 
Category - Defined by Central Pressure Category - Definedby CentralPressure 

NC VA/MD/DE/NJ 

0.0 

14.0 

14.0 

Modeled 

Modeled 
Observed 

12.0 

Observed 

10.0 

8.0 

6.0 

4.0 

2.0 

12.0 

10.0 

8.0 

6.0 

4.0 

2.0 

0.0 

0.0 
12345 12345 
Category- Defined by CentralPressure Category - Defined by Central Pressure 

NY/NE All US 

14.0 

70.0 

Modeled 

Modeled 

12.0 

60.0 

Observed 

Observed 

10.0 

8.0 

50.0 

40.0 

2.0 

1 2 3 4 5 
Category - Defined by Central Pressure 
10.0 

0.0 

0.0 
1 2 345 
Category - Defined by Central Pressure 

Figure 2.27. Comparisons of Modeled and Observed Hurricane Landfall Rates as 
a 
Function of Storm Category. 

6.0 

30.0 

4.0 

20.0 

Chapter 2. Hurricane Risk Model 

?
2-45 

Entire US Coastline Gulf 

980 

Observed 
Modeled 
980 

970 

970 

Observed 
Modeled 
CAT 3 

CAT 3 

960 

Central Pressure at Landfall
(millibar)Central Pressure at Landfall (millibar)

Central Pressure at LandfallCentral Pressure at Landfall(millibar) (millibar) 

950 

940 

CAT 4 

930 

920 

CAT 5 

910 

900 

CAT 4 

CAT 5 

890 

1 10 100 1000 880 

1 10 100 1000 

Return Period (Years) Return Period (Years) 
Florida 

1 10 100 1000 
Return Period (Years) 
Observed 
Modeled 
Atlantic 

980 

980 

970 

Observed 
Modeled 
970

CAT 3 

CAT 3 

960 

950

CAT 4 

940 

CAT 4 

940

930 

930

920 

CAT 5 

920 

CAT 5 

910 

900 

890

880 

880 
1 10 100 1000 
Return Period (Years) 

Figure 2.28. Comparison of Modeled and Observed Storm Central Pressure at 
Landfall vs. Return Period for Various Geographic Regions. 

where . represents the average annual number of storms approaching with 250 
km of the 
site (i.e., the annual occurrence rate). 

Figure 2.30 presents a plot of 100 year return period peak gust wind speeds 
along the 
Gulf and Atlantic coastlines derived from the updated hurricane simulation 
model used in 
HAZUS. The wind speeds given in Figure 2.30 are peak gust wind speeds at a 
height of 
10 m above ground over open terrain. 

2.3.9 Summary 
The hurricane simulation methodology models the entire track of a tropical 
storm in the 
Atlantic basin and has been validated through comparisons to historical data 
along the US 
coastline. The modeling approach allow the storms to curve and change speed 
and 
intensify as they move, and is able to reproduce the continually varying 
statistics 
associated with central pressure, heading, etc., along the US coastline. The 
model is an 
improvement over earlier hurricane simulation techniques since it eliminates 
the 
problems associated with the selection of a sub-region from which to derive 
the statistical 
distributions needed in traditional simulation models. In traditional models, 
the use of 
large areas to derive the hurricane statistics will smear any local 
climatological features 
that are inherently reproduced in this method. The prime advantage of this 
approach 
compared to the traditional approaches is the ability to properly model 
hurricane wind 
risk over large regions, within which the hurricane climatology can vary 
significantly. 
This improved prediction capability provides significant technical advantages 
for 

HAZUS-MH MR3 Technical Manual 

?
2-46 

Texas LA 

990 

990 

980 

980 

970 

970 

960 

960 

Observed 
Modeled 
Observed 
Modeled 
Central Pressure at LandfallCentral Pressure at Landfall 
Central Pressure at Landfall Central Pressure at Landfall 
(millibar)(millibar) 

Central Pressure at Landfall Central Pressure at LandfallCentral Pressure at 
LandfallCentral Pressure at Landfall(millibar)(millibar) (millibar)(millibar) 

950 

940 

930 

920 

910 

950 

940 

930 

920 

910 

900 

900 
1 10 100 1000 1 10 100 1000 
Return Period (Years) 

Return Period (Years) 

MS/AL Northwest Florida
990

990
980

980 

Observed 
Modeled 
Observed 
Modeled 
970 

970 

960 

960 

950 

950 

940 

940 

930 

930 

920 

920 

910 

910 

900 

900 
1 10 100 1000 1 10 100 1000Return Period (Years) 

Return Period (Years) 

Southwest Florida 

Southeast Florida 

990 

990
980

Observed 
Modeled 
Observed 
Modeled 
980 

970 

960 

970 

960 

950 

940 

930 

920 

910 

900 

(millibar)(millibar)

950 

940 

930 

920 

910 

900 

890 

890 
1 10 100 1000 
Return Period (Years) 1 10 100 1000 

Return Period (Years) 
GA/SC North Carolina 

990 

990 
980 

Observed 
Modeled 
Observed 
Modeled 
980 

970 

960 

950 

940 

970 

960 

950 

940 

930 

920 

910 

930 

920 

910 

900 

900 

1 10 100 1000 

1 10 100 1000 

Return Period (Years) 
Return Period (Years) 
NY/NE 

900 
910 
920 
930 
940 
950 
960 
970 
980 
990 
Central Pressure at Landfall(millibar)
Observed 
Modeled 
1 10 100 1000
Return Period (Years)

Figure 2.29. Comparison of Modeled and Observed Storm Central Pressure at 
Landfall vs. Return Period for Various Local Geographic Regions. 

Chapter 2. Hurricane Risk Model 

?
Figure 2.30. Predicted 100 Year Return Period Wind Speed for US Atlantic and 
Gulf Coast. 

portfolio analyses which encompass large geographic areas than do the 
previous 
published and accepted models such as those described by Batts, et al. 
(1980), Georgiou 
(1985), Neumann (1991), and the NOAA publications NWS 23 and NWS 38. 

2.4 Hurricane Rainfall Rate Model 
2.4.1 Introduction 
Hurricane flow is a complex atmospheric system comprising of multiscale 
systems 
interacting in a nonlinear and varying degree of intensity. Some of the known 
environmental features affecting the hurricane dynamics include: vertical 
wind shear, 
trough interactions, warm eddy core interactions, outflow patterns, eddy 
angular flow 
convergence, upper level cooling, dry air intrusions, eye wall cycles, low-
level 
temperature advection, rain band downdrafts, and ocean currents (cf., Hong, 
et al., 1998). 
For near-land cyclonic flows, additional interactions such as fronts, 
coastline shape, 
topography and soil moisture availability play a significant role in the 
storm dynamics 
particularly during the land falling stages (Reddy and Raman, 1997a, 1997b; 
Schneider, 
1998). 

HAZUS-MH MR3 Technical Manual 

?
Traditionally, one of the most difficult variables to model within an 
atmospheric system 
is the rainfall. The variation of the rainfall amount within the hurricane 
system is a result 
of numerous nonlinear processes interacting simultaneously. Even the most 
comprehensive modeling systems, with very detailed physics, and numerical 
algorithms 
assimilating observational data, have limited success estimating the rainfall 
intensity and 
location associated with the hurricane (Elsberry, 1998). 

However, rainfall rates and their locations are important variables to be 
considered for a 
risk management system. Even approximate estimates can provide vital 
information 
necessary for resource planning, flooding, erosion, as well as structural 
risk assessment. 
Hence, there is an ongoing requirement to adopt even heuristic approaches for 
estimating 
rainfall rates and distribution within the hurricane flow. To satisfy this 
need, an empirical 
approach (referred to as  HuRRDE : Hurricane Rainfall Rate and Distribution 
Estimator), relating the sectorial rainfall rate with annular distance from 
the hurricane 
center, has been developed and implemented in the HAZUS hurricane model. 
Section 

2.4.2 reviews the available data and prior studies. Section 2.4.3 discusses 
the model 
development and the corrections for intensity, translation speed, and 
frictional 
convergence during landfall. Section 2.4.4 presents the model implementation 
and 
calibration using a number of recent storms. Some comments about the rainfall 
model are 
included in Section 2.4.5. 
2.4.2 Data and Prior Studies 
There is a paucity of high resolution, continuous observations of rainfall 
rates within the 
hurricane flow. However, several efforts have been made to assimilate 
satellite 
information with surface and aircraft data (e.g., Shi, et al., 1997). The 
present model 
focuses on published analyses of Special Sensor Microwave/Imagery (SSM/I) 
(see Alliss, 
et al., 1992; Rodgers, et al., 1994; Ferraro, et al., 1996). The SSM/I 
analyses provide 
detailed radial rainfall rate information. Over fifty studies were evaluated 
for the purpose 
of obtaining relevant data. 

The starting point for developing the model was the observational study by 
Rodgers, et 
al., in which 103 SSM/I observations for 18 western North Atlantic tropical 
cyclones 
from 1987 to 1989 were used for documenting the precipitation 
characteristics. The 
tropical cyclones used in their study were: 

1987 storms: Cindy, Dennis, Emily, and Floyd; 

1988 storms: Chris, Debby, Florence, Gilbert, Helene, Isacc, Joan, and Keith; 

1989 storms: Dean, Felix, Gabrielle, Hugo, Iris, and Jerry. 

Figure 2.31 adopted from Rogers, et al. (1994), summarizes the SSM/I derived 
azimuthally averaged rain rates for the eight 55 km annuli (rings) around the 
center. The 
distribution is considered robust as it is averaged over 47 depressions (Pmin 
> 1000 mb), 
29 tropical storms (Pmin = 1000-977 mb), and 27 hurricanes (Pmin < 977 mb). 
The radial 
distribution for the hurricane case is considered the first step in the model 
development. 
However, it should be noted that the Rogers, et al. (1994) data has a 55 km 
resolution. 

Chapter 2. Hurricane Risk Model 

?
Figure 2.31. Average Rain Rate. 

Evidence from both observational studies (Alliss, et al., 1994) and numerical 
studies 
(Drury and Evans, 1998) suggests there is even more intense precipitation at 
the inner-
core than at the first 55 km annular ring. Hence, in the next step of the 
model 
development, a sharp increase from the first ring to the inner core is 
assumed. The inner 
core is assumed to be at a distance of Rmax from the  eye . Though, typically 
Rmax will 
vary for every hurricane at different stages of development, for the purpose 
of developing 
the simple rainfall rate model, Rmax was initially assumed to be constant 
close to landfall. 
Accordingly, Rmax was considered to be 30 km. Note that considering the 
overall 
uncertainty associated with entire system, this first-order assumption is not 
expected to 
affect the results significantly. Accordingly, Figure 2.32 shows the 
 observed  or 
 analyzed  radial rainfall distribution used for model development. The sharp 
increase in 
the rainfall rate from 1.83 Rmax (55 km) to Rmax is apparent. It should be 
noted that the 
center ( eye ) generally does not experience any significant precipitation 
and hence the 
R = 0 case is assigned to be rainfall free (see also Alliss, et al., 1994, 
for Hurricane 
Florence case). 

2.4.3 Model Development 
For developing the relation between rainfall rate (RR) and radial distance, 
the first 
approach was to relate RR through a fifth-order polynomial to R/Rmax. However 
this 
approach has obvious serious stability considerations. The higher order 
(cubic and 
beyond) terms are linked with increasingly larger distances (R/Rmax increases 
as R 
increases). Thus, a small error would amplify in the system several folds 
making the 
outcome extremely sensitive and unrealistic. To overcome this, a relationship 
between 
RR and Rmax/R was developed. Accordingly, for the data shown in Figure 2.32, 
the 
relation is of the form: 

RR =- 5.5+110(Rmax / R)-390(Rmax / R)2 +550(Rmax / R)3 - 250(Rmax / R)4 
(2.19) 

HAZUS-MH MR3 Technical Manual 

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0 
5 
10 
15 
051015Rainfall Rate (mm/hour) 
Hurricane Rainfall Rate 

1.0 1.833 3.667 5.5 7.333 9.167 11 12.83 14.67 
Normalized Radial Distance ( R/Rmax) 

Figure 2.32. Rainfall Rate as Function of Normalized Radial Distance from the 
 Eye  (Rmax is assumed to be 30 km). 

Figure 2.33 shows the rainfall rates from Figure 2.32 and the rainfall rates 
represented by 
Equation 2.19. Overall there is a good agreement amongst the two (r2 = 0.92). 
Some 
differences are also seen between the  analyzed  data and the prediction 
using Equation 
2.19, particularly in terms of the decreased precipitation from Rmax to the 
 eye.  This 
 error  exists due to the approximate estimation of Rmax in this particular 
case. Also, 
caution was exercised so as not to  over fit  the equation to the data set, 
which could 
cause loss in predictive skills. Within these constraints, Equation 2.19 
provides a 
reasonable means of estimating rainfall rate in the hurricane as a function 
of annular 
distance from the eye. 

In order to make the empirical relation applicable to various hurricane 
intensities, 
corrections need to be made to Equation 2.19. Three corrections, as described 
in the 
following paragraphs, have been implemented. 

The first correction involves the changes in the central pressure with time 
(dP/dt) (and, 
hence, the intensity of the hurricane). Note that, Equation 2.19, due to the 
averaging 
involved, may best represent Category 1-2 type-hurricanes. Typically, as the 
central 
pressure drops, a storm becomes more intense, yielding larger rainfall rates. 
For Equation 
2.19, this correction is inherent, if Rmax is calculated as function of .P. 

By adopting relations between Rmax, .P, and latitude, ., similar to Equation 
2.19, 
changes in the rainfall rate will occur with changes in .P. Figure 2.34 shows 
a summary 
of a review from Rogers, et. al (1994) comparing hurricanes (Pmin < 977 mb), 
storms (977 
mb < Pmin < 1000 mb), and depressions (Pmin > 1000 mb). As seen in the 
figure, the 
hurricane rainfall rate is about 30 to 40% more than the storm rainfall rate, 
and about 

Chapter 2. Hurricane Risk Model 

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Comparing Original Dataset with HuRRDE 

0 
2 
4 
6 
8 
10 
12 
14 
16 
HuRRDE 
RR(mm/h) 
Rainfall Rate (mm/hour) 
0 0.2 0.4 0.6 0.8 1 1.2 
Rmax/R 

Figure 2.33. Plot Showing the Original Rainfall Rate with the Estimation from 
Equation 2.19. 

Ratio of Rainfall Rates for Differing Intensity 

1 
1.2 
1.4 
1.6 
1.8 
2 
H/S 
H/D 
Ratio (Hurricane/Non-Hurricane) 
1 0.8 0.6 0.4 0.2 
Rmax/R 

Figure 2.34. Ratio of Rainfall Rates for Hurricane Against Non-hurricane 
Tropical 
Cyclones as a Function of Normalized Radial Distance, Following Rogers, et 
al. 
(1994). (H/S refers to the ratio of hurricane (Pmin < 977 mb) to storm 
(977 mb < Pmin < 1000 mb) rainfall rates. H/D refers the ratio of hurricane 
to 
depression (Pmin > 1000 mb) rainfall rates). 

HAZUS-MH MR3 Technical Manual 

?
75% more than the depression rainfall rate. Extrapolating this, it is assumed 
that for every 
20 mb drop a 35% higher effective rainfall rate for the tropical cyclone. 
Hence, RR can be 
corrected for the hurricane category as follows: 

RRe = k7RR (2.20) 

The mid-values of k for Category 1 through 4 hurricanes are assumed to be 1, 
1.35, 1.85, 
and 2.5. A Category 5 hurricane making landfall, will have rainfall rates 
that would be 
governed by very special forcing and cannot be estimated in any simple 
manner. 

Additionally, for a more dynamic change in the rainfall rate, due to the rate 
of change of 
central pressure (without change in the category), it is assumed that: 

RRp = RRe [1-(dP/ dt)/ 100] (2.21) 

Thus, for example, a 10 mb drop would lead to a change in the rainfall rate 
by a factor of 
1.1, while a rise in the central pressure (positive dP/dt) by 5 mb would 
effectively 
decrease the rainfall rate to 0.95RRe. Thus, following Equations 2.19 and 
2.21, an 
increase in the central pressure over time will lead to increased Rmax, hence 
an indirect 
reduction in RR, and a direct decrease in the central pressure intensity 
corrected rainfall 
rate (RRp). 

The second correction is dependent on how fast the storm is moving. Studies 
suggest (see 
Jones, 1987; Baik, 1989, for instance) that there is an asymmetric rainfall 
distribution as a 
function of the storm motion. Once again, using the data from Rogers, et al. 
(1994), 
sectorial rainfall rate corrections can be made for slow (<8 knots) and fast 
(>15 knots) 
moving tropical systems as follows: 

RRsp = s7RRp (2.22) 

The value of the coefficient  s' is shown in Table 2.5. 

Table 2.5. Sector Values for Rainfall Rate Parameter, s 

Sector s in slow storms s in fast storms 
0-45 1.45 1.15 
46-90 1.05 1.15 
91-135 0.55 1.35 
16-180 0.65 1.15 
181-225 0.85 0.85 
226-270 0.95 0.65 
271-335 1.15 0.8 
336-359 1.35 0.95 

A third potential correction is for land falling component. Studies, 
particularly numerical 
simulations, suggest that in the first sector with the onshore winds (0-45 
with zero angle 
corresponding to the coastline) rainfall rate is increased by 25%. However, 
further 

Chapter 2. Hurricane Risk Model 

?
research is required to determine whether this correction should be included 
in a future 
update to the rainfall model. 

Figure 2.35 shows an independent verification for data obtained for Hurricane 
Florence 
and Hurricane Hugo (in the developing as well as developed stage). The 
observations are 
obtained from Alliss (1992). Note that, there is lack of rainfall rate 
observations at higher 
Rmax. In this  off-line  comparison, there has been no correction for the 
storm speed or for 
the sector. Only the category change has been accounted for. Thus, for 
Florence, a 
Category 2 was assumed. For Hugo, we assumed initially a storm correction 
(reducing 
the RRe), and for the developed hurricane, a Category 4 correction was 
assumed. As can 
be observed, there is an encouraging agreement in the  observed  and the 
estimated 
rainfall rates. 

2.4.4 Hurricane Rainfall Rate Model Implementation and Calibration 
In the final hurricane simulation model, Equation 2.19 is used to determine 
the base 
rainfall rate (RR) for R/Rmax greater than or equal to one. For values of 
R/Rmax between 
zero and one, a linear model is used where the RR is zero at R/Rmax equal to 
zero and then 
increases linearly up to the value of RR determined by Equation 2.19 at 
R/Rmax equal to 
one. The base rainfall rate is adjusted for storm category. Instead of using 
distinct k 
factors for a given storm category (see Equation 2.19), k is determined using 
the 
following linear function of central pressure deficit (mb): 

k = 0.0319.p   0.0395, k = 1k (2.23) 

1 
10 
Flo 
Pred-Flo 
Hugo1 
Pred-Hu1 
Hugo2 
Pred-Hu2 
Rainfall Rate (mm/hour) 
0.5 0.4 0.3 0.2 0.1 
Rmax/R 

Figure 2.35. Observed and Estimated Rainfall Rates for Hurricane Florence 
(Flo, 
Pred-Flo), Hurricane Hugo in Developing Stages (Hugo1, Pred-Hu1), and in 
Developed Stages (Hugo2, Pred-Hu2). 

HAZUS-MH MR3 Technical Manual 

?
This line was developed by plotting the factors given following Equation 2.19 
at the 
middle of the range in central pressure deficit for the particular storm 
category and then 
fitting a line (r2 = 0.9933). 

The base rainfall rate is also adjusted for storm translation speed using the 
sector 
dependant factors given in Table 2.5. Not yet implemented into the rainfall 
rate model is 
the correction for the increase in rainfall rate within the onshore flow 
regime as the 
hurricane makes landfall. This factor, when implemented, will further 
increase the 
rainfall rate for locations on or near the coast receiving onshore winds by 
25%. 

Several hurricanes, including Hugo (1989), Bertha (1996), Fran (1996) and 
Bonnie 
(1998), for which relatively high quality rainfall measurements at a number 
of 
meteorological stations are available, were used to calibrate the rainfall 
rate model. By 
comparing the model predictions and the actual measurements, the rainfall 
rate model 
was found to provide reasonably accurate estimations of the peak rainfall 
rates for most 
of the stations investigated. However, the off-peak rainfall rates were 
significantly 
overestimated. A number of factors may contribute to the overestimation: (1) 
the drop of 
rainfall rate with the increase in R/Rmax may be much faster than that 
predicted using 
Equation 2.19; (2) rain gauges located on the ground often under predict the 
rainfall 
during high winds due to the effects of the flow aerodynamics around the rain 
gauges; (3) 
other features, such as the hurricane s interaction with other weather 
systems, outflow 
patterns and sea-to-land transitions may also contribute to the inconsistency 
between the 
measured rainfall rates and the predicted ones. To overcome this problem, a 
modification 
factor (MF), defined as the ratio of the actual measurement to the model 
prediction, was 
developed so that the proposed model will provide the best estimation of the 
rainfall rate 
in a hurricane. Figure 2.36 shows the relationship between the modification 
factor and 
R/Rmax for the hurricanes investigated. The modification factor is seen to 
decrease with 
the increase of R/Rmax. To be used in the rainfall rate model, a relationship 
between the 
modification factor and R/Rmax was determined using least square regression, 
which is of 
the form: 

MF =-0.7* ln(R / Rmax )+1.0 . (2.24) 

Note that when MF is greater than 3.0, it is taken as 3.0 and when MF is less 
than 0.2, it 
is taken as 0.2. Note also that when R/Rmax is less than 1.0 (i.e., within 
the hurricane eye), 
the modification factor is greater than 1.0, which indicates the rainfall 
rate around the is 
eyewall relatively uniform (i.e., stable rainband). Accordingly, the linear 
model assumed 
previously may underestimate the rainfall rate for this region. Considering 
the 
modification factor, the final rainfall rate output from the simulation model 
is: 

RRMFsp =MF 7 RRsp (2.25) 

Figure 2.37 shows the comparison between the model predictions and the actual 
measurements for Hurricanes Hugo (1989), Bertha (1996), Fran (1996), and 
Bonnie 
(1998). Most of the sites investigated for these four storms are ordinary 
commercial 

Chapter 2. Hurricane Risk Model 

?
0.0 
0.5 
1.0 
1.5 
2.0 
2.5 
3.0 
3.5 
4.0 
4.5 
5.0 
0 2 4 6 810 12 

R/Rmax 

Figure 2.36. Relationship Between the Modification Factor and R/Rmax. 

airports with open terrain except Kure Beach, which is a private 
meteorological station 
located about 300 m inland. All the data collected are continuous hourly 
rainfall rates. 
The model is seen to provide reasonably accurate predictions for most of the 
sites 
investigated. However, abnormalities still exist for several sites and will 
be addressed 
individually in the following paragraphs. 

The eye of Hurricane Hugo passed over the coast of South Carolina near 
Charleston at 
approximately 04:00 UTC on September 22, 1989. The model is seen to 
underestimate 
the rainfall rate when Hugo was approaching Charleston Airport. One possible 
explanation is that the rainfall might be intensified significantly within 
the onshore flow 
regime when Hugo made landfall and this rainfall enhancement effect was not 
considered 
in the proposed model. 

Considering rainfall intensification within the onshore flow regime for a 
landfalling 
hurricane can be an area of future development. Besides the underestimation 
of the 
rainfall rate at the approaching stage of Hurricane Hugo, the peak rainfall 
rate at 
Charleston Airport was significantly overestimated by the model. The reason 
of the 
overestimation is unknown. The rainfall rate model predictions agree well 
with the actual 
measurements at Columbia Airport and Greenville/Spartanburg Airport, two 
inland sites 
located about 180 km and 330 km to the coast, respectively. 

The rainfall model catches the general trend and the first peak of the 
rainfall rate at 
Wilmington Airport for Hurricane Bertha (1996). The model indicates a second 
peak 
when the backside of the storm moved across Wilmington Airport. However, this 
peak 
was not apparent on the actual records. Similar trend was also observed at 
Kure Beach. 
Moreover, at Kure beach, one extremely high peak rainfall rate (compared to 
the value at 
Wilmington Airport, which is fairly close to Kure Beach) was recorded by the 
rain gauge, 

HAZUS-MH MR3 Technical Manual 

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40
35
30
25
20
15
10

5
0
9/21/89 16:00 9/22/89 0:00 9/22/89 8:00
Time (UTC)

Charleston Airport, Hugo (1989) 

10 

8 

6 

4 

2 

0 
9/22/89 0:00 9/22/89 8:00 9/22/89 16:00 
Time (UTC) 

Greenville/Spartanburg Airport, Hugo (1989) 

40
35
30
25

Observed 
Modeled 
Observed 
Modeled 
Observed 
Modeled 
20
15
10
5
0

7/11/96 21:00 

7/12/96 13:00 7/13/96 5:00 
Time (UTC) 

Kure Beach, Bertha (1996) 

25 

20 

15 

10 

5 

0 
9/21/89 20:00 9/22/89 4:00 9/22/89 12:00 
Time (UTC) 

Observed 
Modeled 
Columbia Airport, Hugo (1989) 

20 
Observed 
15 

Modeled 

10 

5 

0 
7/11/96 21:00 7/12/96 13:00 7/13/96 5:00 
Time (UTC) 

Wilmington Airport, Bertha (1996) 

35
30
25
20
15
10

5 
0 
9/5/96 18:00 9/6/96 0:00 9/6/96 6:00 
Time (UTC) 

Observed 
Model 
Wilmington Airport, Fran (1996) 

Figure 2.37. Comparison of Observed and Modeled Hurricane Rainfall Rates. 

Chapter 2. Hurricane Risk Model 

?
35 
30 
25 
20 
15 
10 
5 
0 

9/5/96 22:00 9/6/96 6:00 9/6/96 14:00 
Time (UTC) 
Observed 
Modeled 
Greensboro Airport, Fran (1996) 

30 
25 
20 
15 
10 
5 
0 

9/5/96 21:00 9/6/96 5:00 9/6/96 13:00 
Time (UTC) 
Observed 
Modeled 
Raleigh/Durham Airport, Fran (1996) 

0 
5 
10 
15 
20 
25 
30 
Observed 
Modeled 
0 
5 
10 
15 
20 
25 
30 
35 
8/26/98 0:00 8/26/98 12:00 8/27/98 0:00 8/27/98 12:00 
Time (UTC) 
Observed 
Modeled 
8/26/98 0:00 8/26/98 12:00 8/27/98 0:00 8/27/98 12:00 

Time (UTC) 

Kure Beach, Bonnie (1998) Wilmington Airport, Bonnie (1998) 

Figure 2.37. Comparison of Observed and Modeled Hurricane Rainfall Rates 
(concluded). 

which is believed to be caused by rainband downdrafts. Note that the rainfall 
data 
obtained at Kure Beach in Hurricane Bertha was not used in the calibration. 
Therefore, 
the fairly good agreement between the model predictions and the actual 
measurements at 
this site validated the rainfall rate model. 

Fran was a Cape Verde hurricane that moved across the Atlantic during the 
peak of the 
hurricane season. It made landfall around 00:30 UTC on September 6, 1996, on 
the North 
Carolina coast. The model is seen to provide very good estimation of the 
rainfall rate 
during the passage of Hurricane Fran at Wilmington Airport. However, the 
model 
overestimated the rainfall rate in the eye when virtually no rain was 
recorded by the rain 
gauge. The peak rainfall rate at Greensboro Airport was underestimated by the 
model. 
The rainband shifting to the north-west quadrant may cause unusually high 
precipitation 
at Greensboro Airport. The model predictions at Raleigh/Durham Airport agree 
relatively 
well with the actual measurements. 

Bonnie (1998) made landfall at almost the same location as Fran, but instead 
of moving 
straight inland, drifted along the North Carolina coast, causing difficulties 
in predicting 

HAZUS-MH MR3 Technical Manual 

?
its intensity and rainfall rate. From the last two charts in Figure 2.37, the 
model is seen to 
follow the general trend of the actual measurements at Kure Beach and 
Wilmington 
Airport. However, the correlation between the model predictions and the 
actual 
measurements is poor (compared with those in Hurricanes Hugo, Bertha and 
Fran). When 
a hurricane is drifting along the coast, complex internal boundary layers and 
significant 
transverse eddies may be observed in the storm, resulting in unusually large 
fluctuations 
in the measured wind speeds and rainfall rates. Further efforts are required 
to model this 
complex situation. 

Another feature to the interest of risk management is the total rainfall at a 
particular 
location during the whole event. Therefore, total rainfall data collected 
from eight 
hurricanes, i.e., Frederic (1979), Elena (1985), Hugo (1989), Erin (1995), 
Opal (1995), 
Bertha (1996), Fran (1996), and Bonnie (1998), were used to validate the 
rainfall model. 
The comparisons between the model predictions and the actual measurements are 
shown 
in Table 2.6. The total rainfalls listed in the column, Full Scale 1, were 
calculated from 
continuous hourly rainfall data shown in Figure 2.37. The values in the 
column, Full 
Scale 2, were obtained from annual Atlantic hurricane season summary for the 
years of 
1979, 1985, 1989, and 1996 (Hebert, 1980; Case, 1986; Case and Mayfield, 
1990; Pasch 
and Avila, 1999). Preliminary reports by the National Hurricane Center for 
Hurricanes 
Erin and Opal were used to determine the total rainfall for these two storms 
(Rappaport, 
1995; Mayfield, 1995). Since no information about the time span for the total 
rainfall was 
given in the annual Atlantic hurricane season summaries or the preliminary 
reports, a 
period of plus and minus 12 hours from the time when peak rainfall rate 
occurred was 
assumed in the model to calculate the total rainfall, which means a 24-hour 
cumulative 
rainfall given by the model. Accordingly, the model predictions are expected 
to be 
slightly lower than the values shown in the column denoted Full Scale 2. The 
mean and 
standard deviation of the ratios of the model predictions to the full scale 
measurements 
are 0.96 and 0.50, respectively. Note that if the extremely high value (2.53) 
at 
Wilmington in Hurricane Fran is taken out from the data set, the mean and 
standard 
deviation drop to 0.91 and 0.41, respectively. Note also that when both 
continuous station 
records and total rainfall data for an event are available, the model 
predictions always lie 
in between these two values, i.e., full scale set 1 and full scale set 2. 

2.4.5 Summary 
The proposed rainfall rate model is capable of providing reasonable rainfall 
rate 
predictions in a hurricane. However, due to the complex nature of a 
hurricane, a 
significant degree of variability should be expected. Further development is 
needed to 
account for all possible aspects affecting rainfall rate in a hurricane. 
Uncertainties 
associated with input parameters and physical models should also be addressed 
to provide 
more reliable predictions. 

Chapter 2. Hurricane Risk Model 

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Table 2.6. 24-Hour Total Rainfall Comparison 

Hurricane Location Full Scale 1* 
(mm) 
Full Scale 2* 
(mm) 
Model 
(mm) 
Model/Full 
Scale ** 
Pensacola FSS 83 59 0.71 
Frederic (1979) Dauphin Island 215 176 0.82 
Mobile 217 167 0.77 
Pensacola NAS 63 37 0.58 
Pensacola Reg. Airport 65 34 0.52 
Elena (1985) Tallahassee 84 24 0.28 
Mobile 60 31 0.52 
Dauphin Island 76 85 1.12 
Charleston Airport 135 101 0.75 
Hugo (1989) Columbia 75 67 0.90 
GSP Airport 27 26 0.98 
Myrtle Beach 58 41 0.71 
Panama City Beach 137 50 0.36 
Erin (1995) Pensacola NAS 56 96 1.72 
Mobile 65 95 1.46 
Pensacola NAS 185 93 0.50 
Opal (1995) Pensacola NPA 176 93 0.53 
Mobile 190 48 0.25 
Wilmington 59 144 116 0.80 
Bertha (1996) Kure Beach 107 123 1.14 
Greenville 104 117 1.12 
New Bern 116 145 1.26 
Wilmington 79 200 2.53 
Raleigh/Durham 128 224 161 0.72 
Greensboro 89 99 94 0.95 
Fran (1996) Fort Bragg 119 139 1.16 
New River 179 190 1.06 
Pope AFB 171 136 0.80 
Rocky Mount 94 142 1.51 
Bonnie (1998) Kure Beach 207 351 1.70 
Wilmington 230 377 1.64 

* Full scale 1 represents total rainfall calculated from continuous hourly 
rainfall data reported by individual stations. Full 
scale 2 represents total rainfall reported in annual Atlantic hurricane 
season summary included in Monthly Weather 
Review except for Hurricanes Erin and Opal, for which preliminary reports 
from the National Hurricane Center were used. 
** In the case when both full scale data are available, the total full scale 
rainfall is taken as the value given in the column of 
full scale 2. 

HAZUS-MH MR3 Technical Manual 

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2-60 

Chapter 2. Hurricane Risk Model 

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Chapter 3. Surface Roughness Modeling 

3.1 Introduction 
A critical component in the modeling of wind effects, damage, and loss to 
buildings and 
facilities is the assessment of the ground roughness. As the ground surface 
becomes 
rougher, the wind speeds near the ground decrease although the upper level 
wind speed 
remains the same. The wind loads experienced by structures located in a 
typical 
suburban, treed, or urban environment are much lower than those experienced 
by 
buildings located in relatively unobstructed regions such as waterfront and 
open field 
locations. The wind loads experienced by one- and two-story structures 
located in 
forested areas may be as low as one half of those experienced by similar 
structures 
located in an open environment. 

The effect of surface roughness is treated in a simple fashion in building 
codes and 
standards using exposure categories. For example, open terrain and suburban 
terrain are 
designated as Exposures C and B, respectively, in ASCE-7. The approach taken 
in that 
standard, and most national and international standards, is to define a basic 
wind speed, 
which represents the wind speed at a height of 10 m in open terrain. The 
effect of the 
actual local terrain is then taken into account by modifying that wind speed 
by a factor, 
which is dictated by the exposure category for the local terrain. 

The ground surface roughness is defined using a characteristic roughness 
length, denoted 
as z0. This roughness length is a function of the height and spacing of the 
buildings, trees 
and other obstructions on the ground surface. Given the open terrain wind 
speed, the local 
terrain roughness length, and the fetch over which the wind has blown over 
the surface, it 
is possible to estimate the local terrain wind speeds at any height. As 
described in 
Chapter 2, the hurricane model yields estimates of wind speed at any location 
for open 
terrain conditions. Hence, given information on the upstream fetch and the 
associated 
surface roughness length, the wind speed at any location and any height can 
be estimated. 

Numerous studies have been performed over the last several decades attempting 
to 
categorize z0 using surface exposure description. However, up to date, no 
consistent 
agreement has been reached among researchers. Wieringa (1992,1993) summarized 
most 
of the traceable studies on roughness lengths for various terrains performed 
in the last 
thirty years (including field projects, numerical modeling studies, and wind 
tunnel 
investigations) and gave a table showing his best estimations. This table, 
included herein 
as Table 3.1, serves as a reasonable basis for determining appropriate 
roughness lengths 
for areas on the mainland. For comparison, roughness lengths given by one set 
of 
researchers, Simiu and Scanlan (1996), are shown in Table 3.2. Surface 
roughness 
lengths proposed by a number of researchers, emphasizing built-up areas, are 
presented in 
Table 3.3. 

HAZUS-MH MR3 Technical Manual 

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Table 3.1. Roughness Lengths of Homogeneous Surface Types 
(from Wieringa, 1993) 

Surface Type 

Roughness Length (m) 

Sea, loose sand and snow 

  0.0002 (wind speed dependent) 

Concrete, flat desert, tidal flat 

0.0002   0.0005 

Flat snow field 

0.0001   0.0007 

Rough ice field 

0.001   0.012 
Fallow ground 

0.001   0.004 
Short grass and moss 

0.008   0.03 
Long grass and heather 

0.02   0.06 
Low mature agricultural crops 

0.04   0.09 
High mature crops ( grain ) 

0.12   0.18 
Continuous bushland 

0.35   0.45 
Mature pine forest 

0.8   1.6 
Tropical forest 

1.7   2.3 
Dense low buildings ( suburb ) 

0.4   0.7 
Regularly-built large town 

0.7   1.5 
Table 3.2. Roughness Lengths For Various Surface Types 
(from Simiu and Scanlan, 1996) 

Type of Surface z0 (m) 
Sand 
Snow Surface 
Mown Grass (~0.01m) 
Low Grass, Steppe 
Fallow Field 
High Grass 
Palmetto 
Pine Forest (mean height of trees: 15 m; one tree per 10 m2Sparsely Built up 
suburbs 
Densely Built up suburbs and towns 
Centers of large Cities 
0.0001-0.001 
0.001-0.006 
0.001-0.01 
0.01-0.04 
0.02-0.03 
0.04-0.10 
0.10-0.30 
0.90-1.00 
0.20-0.40 
0.80-1.20 
2.00-3.00 

Table 3.3. Roughness Lengths of Rather Homogeneously Built-up Areas 
(from Wieringa, 1993) 

Surface Type z0 (m) Reference 
Scattered low buildings 
Low buildings and trees 
Regular dense low houses 
 Regular  city buildings 
 Regular  city buildings 
 Regular  city buildings 
 Regular  city buildings 
0.510.2 
 0.7 
0.510.1 
 1.3 
1.110.4 
1.010.6 
0.910.3 
Shiotani (1962) 
Duchjne-Marullaz (1979) 
Steyn (1982) 
Jensen (1958) 
Brook (1972) 
Karlsson (1986) 
Yersel and Goble (1986) 

Chapter 3. Surface Roughness Modeling 

?
Note that the data in these tables are for homogeneous terrain only, which 
means the 
wind must pass several kilometers of fetch of the same terrain before it 
reaches the area 
investigated. For areas on the mainland, due to relatively homogeneous land 
exposure, 
the transition layer in the vertical wind profile can be taken as fully 
adapted up to the 
standard height of 10 m (even though local integration over an area of 
several square 
kilometers may be used to determine the effective value of heterogeneous 
roughness). 
However, this is not the case for barrier islands. Typically the overland 
fetch is fairly 
short in this case. When a hurricane moves from the open water onto the 
barrier island, 
no obvious over-land boundary layer can be developed (i.e., not enough fetch 
distance). 
Therefore, the effective roughness length will be much smaller than that for 
areas on the 
mainland for the same land use category. 

As an alternative to the information given in Tables 3.1 through 3.3, the 
effective 
roughness length over a particular local area can be estimated using a simple 
equation 
proposed by Lettau (1969): 

z0  0.5HS/ A (3.1) 

where H = average height within the area, S = total projected frontal area of 
the obstacles, 
and A = surface area. 

Because the ground surface roughness has a major impact on the magnitude of 
the loads 
experienced by a structure, it is important to be able to estimate the local 
surface 
roughness. Currently, no direct databases exist describing the distribution 
of the surface 
roughness over regions within the US. Consequently, indirect methods are used 
to 
estimate the surface roughness distribution. The approach for estimating the 
surface 
roughness is to obtain information on land use and land cover (LULC), for 
which 
databases do exist, and then estimate the surface roughness for each LULC 
class. By 
assigning a value of the surface roughness associated with a given land use, 
a surface 
roughness map can be developed directly from a LULC map and the mean wind 
speeds 
and gustiness can be reasonably estimated at any surface location within a 
storm. 

In the following sections, some of the LULC databases that are available and 
potentially 
of use in HAZUS are discussed. 

3.2 Land Use Databases 
Although land cover and land use data has become increasingly available in 
digital 
format from a variety of data sources and agencies, a single source of up-to-
date, 
nationally consistent land cover mapping at a moderate to high spatial detail 
had not been 
readily available. 

The U.S. Environmental Protection Agency s Office of Water Management has 
researched the availability of single state and multi-state land cover data 
sets across the 
country and has compiled a summary of their findings. In all, 75 different 
data sets were 
identified, most covering a single state or parts of a state. The national 
data sets that have 
been available to date, except for the MRLC-NLCD database discussed later, 
are either 

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?
very coarse or are substantially out of date and thus have limited use for 
the purpose of 
HAZUS. 

The USGS LULC data cover most of the contiguous United States and Hawaii, and 
have 
a resolution of about 200m for most urban areas. This resolution is minimally 
suitable, 
and the data are available in both raster and vector formats; however, they 
are based 
mostly on old 1970 s aerial photography. The EPA also has a vector database 
based on 
essentially the same data as the USGS LULC data. 

The conterminous U.S. Land Cover Characteristics database was developed 
jointly by the 
USGS EROS Data Center and the Center for Advanced Land Management Information 
Technologies (CALMIT) at the University of Nebraska-Lincoln. Although it is 
based on 
more recent 1990 satellite imagery from NOAA, the resolution is rather coarse 
at 1 km. 

The United States GAP Analysis Program of the USGS Biological Resources 
Division 
provides 30m resolution raster data based on more recent Landsat Thematic 
Mapper 
(TM) imagery augmented by several other data sources. The classification 
system uses 
the Federal Geographic Data Committee (FGDC) vegetation classification system 
which 
is very detailed for non-developed vegetated areas, but is unfortunately much 
less 
detailed for agricultural and built up areas which are of most interest for 
use in HAZUS. 

3.2.1 Florida Water Management District Data 
Florida s five Water Management Districts maintain LULC databases that 
collectively 
cover the entire state. The five districts are listed below and their regions 
are shown in 
Figure 3.1: 

1. Northwest Florida Water Management District 
2. Suwanee River Water Management District 
3. St. Johns River Water Management District 
4. Southwest Water Management District 
5. South Florida Water Management District. 
The LULC databases from these agencies are considered a superior source for 
Florida 
terrain information because they are more recent and the classification 
system is more 
refined than, for example, the USGS LULC data. 

Each water management district independently prepared their LULC databases. 
Although 
all districts worked from the same source media, and all used the same coding 
system, 
each used a different contractor to perform the work. Consequently, there are 
slight 
differences in the preparation of each database. 

The source data for the versions of the databases used in HAZUS was the 
National Aerial 
Photography Program s 1:40,000 scale infrared imagery of 1994/1995. The 
districts 
intend to update their databases every five years to keep up with the changes 
in land use 
associated with urbanization. 

Chapter 3. Surface Roughness Modeling 

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3-5 
HAZUS-MH MR3 Technical Manual 
SFWMD 
NWFWMD 
SRWMD 
SJRWMD 
SWFWMD 
Figure 3.1. The Five Florida Water Management Districts. 
All of the districts used the Florida Land Use and Land Cover Classification 
System 
(FLULCCS) developed by the Florida Department of Transportation. The system 
is 
arranged in four levels with each higher level providing more specific land 
information. 
For example, the Level I classification  Urban and Built-Up  is broken down 
into nine 
Level II classifications as shown below: 
1. Residential Low Density, 
2. Residential Medium Density, 
3. Residential High Density, 
4. Commercial and Services, 
5. Industrial, 
6. Extractive, 
7. Institutional, 
8. Recreational, and 
9. Open Land. 
A variable number of Level III classifications are available for each of the 
Level II 
classifications listed above, and some of these have Level IV 
classifications. For 
?
example, the Level II classification  Residential Low Density  is divided 
into the 
following four Level III categories: 

1. Manufactured Home Units Any Density, 
2. Fixed Single Family Units, 
3. Mixed Units (Fixed and Manufactured Home Units), and 
4. Low Density Under Construction. 
There are a total of nine Level I land use classifications that can be 
expanded in more 
detailed categories, as illustrated above, through the use of the Levels II 
and III 
classification systems. The nine Level I classifications are: 

1. Urban And Built-Up, 
2. Agriculture, 
3. Rangeland, 
4. Upland Forest, 
5. Water, 
6. Wetlands, 
7. Barren Land, 
8. Transportation, Communication and Utilities, and 
9. Special Classifications. 
All of the districts make use of the FLULCCS to Level II, and some use it to 
Level III. 

In general the identification of polygon regions was done manually by 
personnel trained 
to recognize and categorize the different land-use regions. This process 
aimed at a 
minimum polygon area-resolution of = acre to 5 acres depending on the land-
use 
category and the particular process followed by the contractor preparing the 
database. 
Each district carried out a different degree of quality assurance and field 
testing on their 
databases. The resulting classifications have been quoted as being from 85% 
to 95% 
accurate. In general, the classification of upland areas (which are of 
greatest concern for 
the wind-hazard methodology) are considered to be of higher overall quality 
because they 
tend to be easier to identify, categorize, and delineate. 

Land use databases similar to those of the Florida Water Management Districts 
are not 
available in other states along the Atlantic and Gulf Coast. There are 
numerous smaller 
databases of various qualities prepared by agencies in other states; however, 
these have 
been prepared using disparate data sources, time periods, and classification 
systems, and, 
in most cases, they are complete only for localized regions that were of 
concern to the 
agency for a particular reason. The final decision on LULC database 
implementation 
reflects the desire for a universal LULC database source for the hurricane 
model vs. the 
use of multiple databases that may have higher quality/resolution data for 
certain states. 
This decision can also be supported by an analysis of how sensitive the 
damage and loss 
predictions are to different LULC databases. 

Chapter 3. Surface Roughness Modeling 

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3.2.2 MRLC-NLCD Database 
The most nationally consistent and up-to-date source of land-use data is the 
National 
Land Cover Data (NLCD) compiled by the Multi-Resolution Land Characteristics 
(MRLC) Consortium. This is a partnership of six federal environmental 
monitoring 
programs along with the EROS Data Center of the USGS. Their goal was to 
combine 
their resources in purchasing Landsat satellite imagery and to use the 
experience, 
expertise, and resources of the respective programs to generate LULC data and 
functional 
land characteristics databases for the conterminous United States. 

The participating programs are: 

1. Environmental Monitoring and Assessment Program (EMAP) of the US EPA 
2. GAP Analysis Program of the USGS Biological Resources Division 
3. National Water Quality Assessment Program (NAWQA) of the USGS 
4. Coastal Change Analysis Program (C-CAP) of NOAA 
5. North American Landscape Characterization (NALC) Project of the US EPA 
and the USGS 
6. Remote Sensing Applications Center (RSAC) of the USFS 
Although some of the participants have used the Landsat data for their own 
particular 
purposes, the EROS Data Center processed the data to produce a national 
database at 
30m resolution for the entire conterminous United States and stored it in 
generic raster 
data structures. It uses a nationally consistent hierarchical land cover 
classification 
system. The sources for this system are the C-CAP Level 2 legend (Dobson, et 
al., 1995) 
reorganized to easily collapse into the level 1 classes of the Anderson 
System (Anderson, 
et al., 1976). The classification system is summarized in Table 3.4. 

The primary data source is the nominally 1991 (15 year) Landsat TM coverage 
purchased 
by the consortium members. This is augmented with ancillary data sets to help 
resolve 
uncertainties and otherwise enhance the data set. All data sets have been 
validated prior 
to release. A three-tiered validation procedure was used and includes general 
data 
integrity screening, comparisons to existing data sets, and formal accuracy 
assessments. 

The land use categories given in the MRLC database are similar to those 
provided by the 
dated USGS database. The release notes of MRLC-NLCD data provide detailed 
descriptions on the land characteristics for each land category, which is 
helpful in 
defining a z0 value for a category. However, as indicated by Table 3.4, the 
categorization 
of the developed areas is much more coarse than the categorization given in 
the South 
Florida Water District database. For example, there is a total of only 3 
categories for 
developed areas in the MLRC whereas there are nine Level II classes in the 
SFWMD 
database with additional Level III classes for each Level II. The advantage 
of the MRLC 
database over the FWMD database is that it is a consistent data set for the 
entire United 
States. 

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?
Table 3.4. MRLC Database Classification System 

Water 11 
12 
Open Water 
Perennial Ice/Snow 
Developed 
21 
22 
23 
Low Intensity Residential 
High Intensity Residential 
Commercial/Industrial/ Transportation 
Barren 
31 
32 
33 
Bare Rock/Sand 
Quarries/Strip Mines/Gravel Pits 
Transitional 
Natural Forested Upland (non-wet) 
41 
42 
43 
Deciduous Forest 
Evergreen Forest 
Mixed Forest 
Natural Shrubland 51 Deciduous Shrubland 
Non-Natural Woody 61 Planted/Cultivated (orchards, vineyards, groves) 
Herbaceous Upland Natural/ Semi-
Natural Vegetation 
71 Grassland/Herbaceous 
Herbaceous Planted/Cultivated 
81 
82 
83 
84 
85 
Pasture/Hay 
Row Crops 
Small Grains 
Bare Soil 
Other Grasses (Urban/recreational; e.g. parks, lawns, golf courses) 
Wetlands 91 
92 
Woody Wetlands 
Emergent Herbaceous Wetlands 

Calibration of NLCD Land Cover Classifications against Aerial Photos. The 
MRLCNLCD 
database is still under refinement, updating, and the resolution of confused 
land 
cover classes. There are accuracy problems recognized for the current version 
as of the 
March 2000 release. Some of these problems are acknowledged in the NLCD 
release 
notes. These include difficulties in, for example, new development since 1992 
not 
captured in the Landsat TM images, data extraction for wetland classes, and 
separation of 
grass, shrub and woody areas, as well as location match, etc. 

To ensure the overall correctness of regional roughness lengths derived from 
the MRLCNLCD 
database, the land cover classifications have been verified and calibrated 
for 
representative roughness lengths against aerial photos. The z0 values to be 
used for a land 
cover class have been determined by the calibration results, with adjustments 
based on 
the detailed descriptions on the land cover characteristics provided by the 
NLCD release 
notes for each land cover class. Aerial photos of twelve regions in Southeast 
Florida 
ranging from 0.2 to 0.5 square miles in area were used, with eleven of them 
in Dade 
County and one in Broward County. The photos were taken as of early 1998. 
Most of 
them are for suburban locations. The calibration process is described below. 
Since the 
land cover characteristics are expected to be different across regions, the 
z0 values are 
valid for Southeast Florida. 

Examples of the MRLC-NLCD data maps for areas corresponding to the aerial 
photos 
were displayed on a GIS system using color codes, as shown in Figures 3.2 to 
3.4. Each 
small square block (pixel) represents a 30 mW30 m area. The data maps are 
overlaid with 
street layout data from another source to help match locations to the aerial 
photos. The 

Chapter 3. Surface Roughness Modeling 

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Figure 3.2. Example A of Overlaid NLCD Data Map and Street Layout 
(COMM 01: Dade County). 

Figure 3.3. Example B of Overlaid NLCD Data Map and Street Layout 
(INDU01: Dade County). 

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Figure 3.4. Example C of Overlaid NLCD Data Map and Street Layout 
(INDU06: Dade County). 

boundaries of the aerial photos are identified and indicated by the polygon 
shown in the 
center of the data map. Within the polygon, each small sub-region of roughly 
equal area 
and of similar land cover characteristics as shown by the aerial photos is 
assigned a 
representative roughness length (z0 value) empirically, consistent with the 
criteria for z0 
assignment used throughout this document. (Normally, the small sub-region 
coincides 
with an area represented by a cluster of 30 mW30 m NLCD pixels with the same 
classification code.) The statistics of the z0 values assigned to the small 
sub-regions 
having the same NLCD classification are listed in Table 3.5, along with the 
number of 
sub-region z0 values used for computing these statistics for each NLCD class. 
These 
statistics are derived based on all twelve aerial photos. 

It was found that the general pattern of NLCD data is consistent with the 
land cover/land 
use as shown by the aerial photos. However, two types of errors were observed 
during the 
verification and calibration process, which have also been acknowledged in 
the NLCD 
release notes. The first is a location mismatch, which is estimated to be up 
to about 120 m 
from the comparison with the aerial photos and the street layout. The aerial 
photos and 
the street layout data generally correspond very well. The second type is 
misclassification 
of land cover. For example, areas shown as barren land in the NLCD map were 
actually 
residential areas in the aerial photos. This could be due to the fact that 
the aerial photos 
are more recent and showing new development not captured by NLCD data. There 
are 
also misclassifications across the three sub-classes (Low Intensity 
Residential, High 

Chapter 3. Surface Roughness Modeling 

?
Table 3.5. NLCD Land Cover Classification vs Surface Roughness Length 

MRLC-NLCD Land Cover Classification and 
Numerical Coding 
Land Surface Roughness Length, z0 (m) Number of 
Sub-Regions 
UsedUsed Avg. COV Max. Min. 
Water 
11 Open Water 0.010 0.013 0.612 0.030 0.010 6 
12 Perennial Ice/Snow 0.012 0 
Developed 
21 Low Intensity Residential 0.350 0.307 0.496 0.600 0.010 51 
22 High Intensity Residential 0.600 0.401 0.498 0.800 0.080 39 
23 Commercial/Industrial/Transportation 0.350 0.265 0.605 0.600 0.030 28 
Barren 
31 Bare Rock/Sand/Clay 0.200 0.300 1 
32 Quarries/Strip Mines/Gravel Pits 0.400 0 
33 Transitional 0.400 0.400 0.661 0.600 0.100 3 
Forested Upland 
41 Deciduous Forest 0.600 0 
42 Evergreen Forest 0.600 0.500 1 
43 Mixed Forest 0.600 0 
Shrubland 
51 Shrubland 0.060 0.040 1 
Non-natural Woody 
61 Orchards/Vineyards/Other 0.210 0.213 0.777 0.400 0.050 4 
Herbaceous Upland 
71 Grasslands/Herbaceous 0.150 0.193 0.662 0.300 0.050 4 
Herbaceous Planted/Cultivated 
81 Pasture/Hay 0.150 0.160 1.301 0.400 0.030 3 
82 Row Crops 0.100 0.100 0.913 0.300 0.030 8 
83 Small Grains 0.030 0 
84 Fallow 0.030 0 
85 Urban/Recreational Grasses 0.150 0.155 0.514 0.250 0.030 6 
Wetlands 
91 Woody Wetlands 0.300 0.545 0.594 1.100 0.300 10 
92 Emergent Herbaceous Wetlands 0.030 0.188 0.959 0.600 0.050 10 

Intensity Residential, and Commercial/Industrial/Transportation) under the 
Developed 
class. Less frequently, some areas that are shown to be undeveloped in the 
aerial photos 
are classified as Developed in the NLCD database. The latter two cases of 
errors are 
indications of inaccuracy in the NLCD data acquisition and processing instead 
of the 
results of new development. 

Based on the calibration results, and considering other factors such as 
possible bias 
towards suburban regions with the sample aerial photos used, a set of z0 
values 
corresponding to specific NLCD classifications was adopted for use in 
estimating 
regional representative roughness lengths for Southeast Florida, as included 
in Table 3.5. 
In principle, the adopted z0 values are based on the average values from the 
calibration 
with mostly minor adjustments based on the detailed descriptions on the land 
cover 

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?
characteristics provided by the NLCD release notes for each land cover class. 
However, 
two major adjustments from the calibrated averages, which are associated with 
the 
Developed and Wetlands classes, are worth mentioning. For the Developed 
class, since 
the aerial photos used contain only suburban developments, the z0 values for 
all three 
sub-classes of the Developed class were adjusted upwards to accommodate those 
areas 
closer to city centers that are rougher but have the same NLCD 
classifications, for 
example, areas with many multi-story residential and commercial buildings. 
For the 
wetlands, the aerial photos do not include any everglade areas that have much 
lower z0 
values than those from the photos but are also classified as either Woody 
Wetlands or 
Emergent Herbaceous Wetlands. Thus, the z0 values for Wetlands were adjusted 
downwards to accommodate such areas as the everglades that were not 
represented by the 
aerial photos. The adopted set of z0 values is used for computing the zip 
code average z0 
values as presented in Section 3.3 for Southeast Florida. Note that zip codes 
are used here 
instead of census tracts since, as is discussed in Chapter 9 of this manual, 
the loss 
validation studies (using insurance data) were performed using zip codes. 

3.3 Surface Roughness Lengths in the Southeast Florida Area 
In order to estimate the surface roughness associated with a given land use 
category of 
the FWMD database, sample aerial photographs corresponding to known land use 
categories were obtained and calibrations similar to those for the MRLC-NLCD 
data 
were performed. Figure 3.5 shows an example of a part of one of the aerial 
photographs 
used in the roughness length determination. In the example given in Figure 
3.5, most of 
the visible area consists of a medium density residential area. Using aerial 
photographs 
that cover the key areas of the land use database, aerodynamic roughness 
lengths were 
assigned to each of the various land use categories as given in Table 3.6. 
The estimates of 
the surface roughness were based on a combination of judgment, Equation 3.1, 
and the 
data presented later in Tables 3.7 through 3.8. The assignment of roughness 
lengths was 
performed through exact matching of the LULC database with the aerial 
photographs. 
Within each land use category a best estimate of the surface roughness has 
been assigned 
as well as a likely range. Figure 3.6 shows an example of the variation in 
roughness 
lengths for an area containing Homestead AFB in South Dade County. 

Again, for many of the terrain categories, the assigned values of roughness 
length given 
in Table 3.6 for the FWMD database are applicable for the South Florida area 
only and 
cannot be used elsewhere. The major reason that the z0 mapping developed for 
South 
Florida should not be used elsewhere is related to the height and density of 
trees. In the 
South Florida area, tree height and density was shown (from the aerial 
photographs) to be 
much less than is usually observed elsewhere (North Florida, Atlantic Coast, 
etc.). Trees 
play an important role in reducing wind speeds over the ground, and in most 
regions of 
the U.S., typical suburban areas are more heavily treed (with taller trees) 
and thus the 
wind speeds experienced by buildings (for the same hurricane) will be lower. 
These 
different terrain characteristics are discussed further in Section 3.4. 

Chapter 3. Surface Roughness Modeling 

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Figure 3.5. Example Aerial Photograph Used to Determine Aerodynamic 
Roughness Lengths in the South Florida Area. Photograph Primarily Shows a 
Medium Density Residential Area.

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Table 3.6. Land Use Categories and Assigned Values of z0 for Use in the 
Southeast 
Florida Region with the FWMD Data 

Level II 
Classification Code Description 
Mid-Point Value of z0 
(m) Range of z0 (m) 
110 
120 
130 
140 
150 
160 
170 
180 
190 
Low Density Residential 
Medium Density Residential 
High Density Residential   Low-Rise (131) 
  High-Rise (132) 
Commercial and Services 
Industrial 
Extraction 
Institutional 
Recreational 
Open Land 
0.15 
0.35 
0.30 
0.50 
0.50 
0.50 
0.03 
0.30 
0.10 
0.03 
0.10-0.30 
0.10-0.70 
0.10-0.50 
0.30-0.80 
0.20-1.00 
0.20-0.70 
0.20-0.40 
0.05-0.30 
0.01-0.07 
210 
220 
230 
240 
250 
260 
Cropland and Pasture 
Tree Crops 
Feeding Operations 
Nurseries and Vineyards 
Specialty Farms 
Other Open Lands 
0.03 
0.50 
0.03 
0.10 
0.05 
0.05 
0.02-0.07 
0.30-0.80 
0.02-0.07 
0.05-0.30 
0.03-0.08 
0.03-0.08 
310 
320 
330 
Herbaceous Rangeland 
Shrub and Bushland 
Mixed Rangeland 
0.10 
0.10 
0.10 
0.05-0.20 
0.05-0.20 
0.05-0.20 
410 
420 
430 
440 
Coniferous Forests 
Hardwood Forests 
Other Hardwood Forests 
Tree Plantations 
1.0 
1.0 
1.0 
1.0 
0.50-1.50 
0.50-1.50 
0.50-1.50 
0.50-1.50 
500 Water Computed or 0.01 
610 
620 
630 
640 
650 
Hardwood Forests   Wetlands 
Coniferous Forests   Wetlands 
Mixed Forests   Wetlands 
Vegetated Non-Forest Wetlands 
Non-Vegetated Wetlands 
0.50 
0.50 
0.50 
0.03 
0.01 
0.40-0.60 
0.40-0.60 
0.40-0.60 
0.01-0.07 
0.005-0.02 
710 
720 
730 
740 
Beaches other than Swimming 
Sand   Other than Beaches 
Exposed Rock 
Disturbed 
0.02 
0.02 
0.05 
0.05 
0.01-0.03 
0.01-0.03 
0.01-0.07 
0.01-0.07 
810 
820 
830 
Transportation 
Commercial 
Utility 
0.05 
0.05 
0.05 
0.03-0.10 
0.03-0.10 
0.03-0.10 

Comparison Between Zip Code Average z0 Values Derived Using FWMD and MRLC 
Land Cover Databases. For Southeast Florida as an example, the average z0 
values for 
the zip codes using the two databases are shown in Figures 3.7 and 3.8, 
respectively. The 
agreement between the two sets of results is encouraging and suggests that 
given the 
uncertainty in the estimation of z0, either land use database can be used to 
define the 
terrain. This preliminary comparison indicates that the more refined land use 
categories 

Chapter 3. Surface Roughness Modeling 

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Roughness Length in Meters 
0.01 -0.03 0.03 -0.3 0.3 -1.0 
Zip Code 33031 Zip Code 33032 
Zip Code 33033 
Zip Code 33039 
Miami-Dade County 
Figure 3.6. Example Roughness Length Map. 

Figure 3.7. Zip Code Average Roughness Length Derived from the FWMD 
Database. 

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Figure 3.8. Zip Code Average Roughness Length Derived from the MRLC 
Database. 

given in the SFWMD data may have only a moderate impact on predicted damages 
and 
losses for a regional loss study. Examples of the effect of the different 
LULC databases 
on loss estimation are provided in Chapter 9. 

3.4 Surface Roughness Lengths in the Florida Panhandle Area 
The aerial photography available for the Florida Panhandle area is in the 
format of Digital 
Orthophoto Quarter Quadrangles (DOQQ). The DOQQs are rectified aerial photos 
with 
1m resolution (which is the highest resolution currently available) obtained 
from Florida 
Resources & Environmental Analysis Center at Florida State University. The 
mapping 
function for this area was developed by overlaying the DOQQs on the land use 
coverage 
obtained from the Northwest Florida Water Management District (NWFWMD). A 
number of aerial photo samples were then identified for each land use 
category, which 
formed the basis for the development of mapping functions (using engineering 
judgment 
and referring to the published literatures on roughness length determination, 
as well). The 
following section gives more detailed discussion about the methods and 
reasoning used to 
determine the roughness lengths. Figures 3.9 to 3.16 show some examples of 
the 
identified aerial photos for the categories of low density residential (LDR), 
medium 
density residential (MDR), high density residential (HDR), and commercial and 
service 
(C&S), for barriers islands and mainland, respectively. The land exposure 
differences for 
each land use category between the barrier island and the mainland are 
obvious. To 
reflect these differences, two distinct sets of mapping functions were 
developed for these 
two different land features. 

Chapter 3. Surface Roughness Modeling 

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LDR 
Figure 3.9. Example DOQQ for Low Density Residential Area on Barrier Islands
Located along the Florida Panhandle. 

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MDR 
Figure 3.10. Example DOQQ for Medium Density Residential Area on Barrier 
Islands Located along the Florida Panhandle. 

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Figure 3.11. Example DOQQ for High Density Residential Area on Barrier 
Islands 
along the Florida Panhandle. 

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C&S 
Figure 3.12. Example DOQQ for Commercial and Service Area on Barrier Islands 
along the Florida Panhandle. 

Chapter 3. Surface Roughness Modeling 

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LDR 
Figure 3.13. Example DOQQ for a Low Density Residential Area on the Mainland 
Portion of the Florida Panhandle. 

MDR 
Figure 3.14. Example DOQQ for a Medium Density Residential Area on the 
Mainland Portion of the Florida Panhandle. 

HAZUS-MH MR3 Technical Manual 

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#Y 
Rosemont 
r 
h 
Stat 
HDR03 
Heath 
Figure 3.15. Example DOQQ for a High Density Residential Area on the Mainland 
Portion of the Florida Panhandle. 

# 
# # # 
##
#Y 
9th 
Brent 
College 
Airport 
rport 
12th 
CS01 
32504 
Figure 3.16. Example DOQQ for a Commercial and Services Area on the Mainland 
Portion of the Florida Panhandle. 

Chapter 3. Surface Roughness Modeling 

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Even with the help of aerial photos, it is still not trivial to define the 
mapping function for 
each land use category due to numerous factors affecting the roughness 
length, including 
primarily, the size, shape, density, and distribution of the surface 
obstacles and the 
upwind fetch distances. 

Since few trees exist on the barrier islands, the total frontal area can be 
taken as the total 
area of the buildings. From the identified aerial photos, the lengths of the 
building can be 
estimated. The range and the mid-range values of z0 were determined using 
engineering 
judgment. Other land use categories, such as cropland, forests, and wetlands, 
are not 
applicable to the areas on the barrier islands investigated. Therefore, the 
roughness 
lengths for these categories are noted as N/A. The determined roughness 
lengths for each 
land use category for barrier islands and mainland are listed in Tables 3.7 
and 3.8, 
respectively. The mid-range values in Tables 3.7 and 3.8 serve as the mapping 
functions 
for determining appropriate roughness lengths from the land use categories 
for Florida 
Panhandle area. 

Note also that for some coastal areas (such as those in Bay County), 
typically within one 
km of the coast, the land exposure is rather similar to that on a barrier 
island, i.e., very 
open and without any significant amount of trees. The roughness lengths for 
these areas 
have been assigned according to the table for barrier islands, i.e., Table 
3.7. 

3.5 Roughness Length Database Development for All Coastal States 
3.5.1 The National Land Cover Data 
Following the study to develop surface roughness length data for Southeast 
and 
Northwest Florida described in previous sections, a study for other coastal 
states was 
carried out using MRLC-NLCD land cover database, which is the most nationally 
consistent database available. Using the same procedure described in Section 
3.2.2, a 
surface roughness length database has been developed for all coastal states. 

To verify the accuracy of the NLCD land cover classification and map land 
cover codes 
to roughness lengths for different regions of the US, aerial photographs 
downloaded from 
Microsoft TerraServer were used to empirically determine the roughness 
lengths of 
selected areas with constant NLCD land cover codes. Some examples are 
presented 
below for North Carolina and Texas. 

North Carolina. Figure 3.17 shows the land cover for the entire state of 
North Carolina, 
where the land cover codes (or pixel values) are represented with the color 
scheme shown 
in Figure 3.18, which approximately matches natural color and will be used 
throughout 
this section. It is seen that the NLCD data used reflects the characteristics 
of the state s 
general landscape. Figure 3.19 shows the aerial photograph and the NLCD land 
cover 
data for the Raleigh area of North Carolina, which demonstrates that the 
patterns match 
reasonably well. By zooming into a smaller area, Figure 3.20 shows that the 
accuracy of 
the NLCD data seems to be satisfactorily representing the true land cover 
types shown by 
the 1993 aerial photograph. (The NLCD data were derived from satellite images 
taken 
between November 1990 and June 1993). Further review was conducted in areas 
where 

HAZUS-MH MR3 Technical Manual 

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Table 3.7. Land Use Categories and Assigned Values of z0 for Barrier Islands 
along 
the Florida Panhandle 

Level II 
Classification 
Code Description 
Mid-Point Value of z0 
(m) Range of z0 (m) 
110 
120 
130 
140 
150 
160 
170 
180 
190 
Low Density Residential 
Medium Density Residential 
High Density Residential 
Commercial and Services 
Industrial 
Extraction 
Institutional 
Recreational 
Open Land 
0.07 
0.10 
0.15 
0.15 
0.10 
0.03 
0.10 
0.05 
0.03 
0.03-0.15 
0.05-0.20 
0.05-0.30 
0.05-0.35 
0.05-0.25 
0.05-0.25 
0.02-0.15 
0.01-0.07 
210 
220 
230 
240 
250 
260 
Cropland and Pasture 
Tree Crops 
Feeding Operations 
Nurseries and Vineyard 
Specialty Farms 
Other Open Lands 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
310 
320 
330 
Herbaceous Rangeland 
Shrub and Bushland 
Mixed Rangeland 
0.03 
0.03 
0.03 
0.01-0.1 
0.01-0.1 
0.01-0.1 
410 
420 
430 
440 
Coniferous Forests 
Hardwood Forests 
Other Hardwood Forests 
Tree Plantations 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
N/A 
500 Water Computed or 0.01 
610 
620 
630 
640 
650 
Hardwood Forests - Wetlands 
Coniferous Forests - Wetlands 
Mixed Forests - Wetland 
Vegetated Non-Forest Wetlands 
Non-Vegetated Wetlands 
N/A 
N/A 
N/A 
N/A 
0.01 
N/A 
N/A 
N/A 
N/A 
0.005-0.02 
710 
720 
730 
740 
Beaches Other than Swimming 
Sand - Other than Beaches 
Exposed Rock 
Disturbed 
0.01 
0.01 
0.02 
0.02 
0.001-0.02 
0.001-0.03 
0.005-0.05 
0.005-0.05 
810 
820 
830 
Transportation 
Commercial 
Utility 
0.05 
0.05 
0.05 
0.03-0.10 
0.03-0.11 
0.03-0.12 

Chapter 3. Surface Roughness Modeling 

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Table 3.8. Land Use Categories and Assigned Values of z0 for Mainland Florida 
Panhandle 

Level II 
Classification 
Code Description 
Mid-Point Value of z0 
(m) Range of z0 (m) 
110 
120 
130 
140 
150 
160 
170 
180 
190 
Low Density Residential 
Medium Density Residential 
High Density Residential 
Commercial and Services 
Industrial 
Extraction 
Institutional 
Recreational 
Open Land 
0.30 
0.50 
1.00 
0.50 
0.30 
0.03 
0.30 
0.10 
0.03 
0.10-0.60 
0.20-1.20 
0.30-1.50 
0.20-1.00 
0.10-0.60 
0.20-0.40 
0.05-0.30 
0.01-0.07 
210 
220 
230 
240 
250 
260 
Cropland and Pasture 
Tree Crops 
Feeding Operations 
Nurseries and Vineyard 
Specialty Farms 
Other Open Lands 
0.03 
0.50 
0.03 
0.10 
0.05 
0.05 
0.02-0.07 
0.30-0.80 
0.02-0.07 
0.05-0.30 
0.03-0.08 
0.03-0.08 
310 
320 
330 
Herbaceous Rangeland 
Shrub and Bushland 
Mixed Rangeland 
0.10 
0.10 
0.10 
0.05-0.20 
0.05-0.21 
0.05-0.22 
410 
420 
430 
440 
Coniferous Forests 
Hardwood Forests 
Other Hardwood Forests 
Tree Plantations 
1.00 
1.00 
1.00 
1.00 
0.50-1.50 
0.50-1.51 
0.50-1.52 
0.50-1.53 
500 Water Computed or 0.01 
610 
620 
630 
640 
650 
Hardwood Forests - Wetlands 
Coniferous Forests - Wetlands 
Mixed Forests - Wetland 
Vegetated Non-Forest Wetlands 
Non-Vegetated Wetlands 
0.50 
0.50 
0.50 
0.03 
0.01 
0.40-0.60 
0.40-0.60 
0.40-0.60 
0.01-0.07 
0.005-0.02 
710 
720 
730 
740 
Beaches Other than Swimming 
Sand - Other than Beaches 
Exposed Rock 
Disturbed 
0.02 
0.02 
0.05 
0.05 
0.01-0.03 
0.01-0.04 
0.01-0.07 
0.01-0.07 
810 
820 
830 
Transportation 
Commercial 
Utility 
0.05 
0.05 
0.05 
0.03-0.10 
0.03-0.11 
0.03-0.12 

HAZUS-MH MR3 Technical Manual 

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Figure 3.17. Color-Coded Land Cover Characteristics of the State of North 
Carolina as Derived from NLCD Database. 

Figure 3.18. Color Scheme Used throughout Section 3.5. 

Chapter 3. Surface Roughness Modeling 

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Figure 3.19. Metropolitan Raleigh, NC 
(upper: Aerial photograph; lower: NLCD). 

HAZUS-MH MR3 Technical Manual 

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Figure 3.20. West Suburb of Raleigh, NC 
(upper: Aerial photograph; lower: NLCD). 

Chapter 3. Surface Roughness Modeling 

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doubts existed. For example, areas A and B initially appeared to be developed 
areas in 
the aerial photograph (Figure 3.5-4), but they are classified by the NLCD 
data as 
herbaceous land (Pasture/Hay and Row Crop). However, zooming in on the aerial 
photograph confirms that areas A and B are herbaceous land, as shown in 
Figure 3.21. 

As examples of the empirical assignment of roughness length, a roughness 
length of 0.05 
was assigned to these two specific areas. For the developed areas next to 
them, roughness 
lengths of 0.25 to 0.3 were assigned, while roughnesses of 0.55 to 0.7 were 
estimated for 
the surrounding forest and other woody areas. 

Figure 3.22 shows downtown Wilmington, North Carolina, and the predominantly 
herbaceous wetland across from the river. Consistent patterns are observed 
between the 
NLCD data and the aerial photograph. Close-ups near the USS North Carolina 
battleship 
museum are presented in Figure 3.23, where a roughness length of 0.65 was 
assigned to 
the downtown portion on the right, 0.01 for the water, 0.10 for the 
herbaceous wetland 
(reed marsh) and 0.7 for the woody wetland (treed). For the highways, which 
are 
classified as comm./industrial/trans. (Coded 23), a value of 0.1 was used. 

Texas. The Galveston-Texas City-Santa Fe area of Texas is presented in Figure 
3.24. 
Again, the general patterns and detailed land cover characteristics are 
consistent. By 
zooming into sampled areas on the aerial photograph, roughness lengths were 
assigned. 
As an example, a close-up of the region corresponding to area #1 of the NLCD 
data map 
reveals that it is predominantly a cluster of residential buildings, 
consistent with the 
NLCD data, as shown in Figure 3.25. The area next to it and between the two 
major 
highways is shown as being covered by a mixture of trees, farming and other 
features, 
also consistent with the NLCD data. 

Another example for Texas is shown in Figure 3.26, which is a suburban area 
about 
40km northeast of downtown Houston. The land cover characteristics 
represented by 
NLCD data and shown by the aerial photograph are remarkably consistent for 
this area of 
complicated land covers, as interlaced with residential, 
commercial/industrial, highways, 
recreational, waters, forests, wetlands and farms. Roughness lengths were 
assigned to 
various featured areas, respectively, by zooming into corresponding regions. 

3.5.2 Mapping NLCD Land Cover Codes to Roughness Lengths 
With the help of aerial photographs, in addition to that of the NLCD 
classification 
descriptions, roughness lengths were empirically estimated for sampled 
individual areas, 
as illustrated in the above examples. There is variability in the assigned 
values for areas 
with the same land cover code. Area-weighted averages of the assigned values 
yield the 
representative roughness length (z0) for a specific land cover class. The 
averaged value 
and standard deviation of z0 are shown in Table 3.9 for all Gulf and Atlantic 
coastal 
states/regions. There are differences in the averaged value and standard 
deviation of z0 
among the states for a specific land cover class. For example, the values for 
forested 
upland are generally higher for North Carolina than Texas, which can be 
attributed to the 
larger tree heights commonly seen in North Carolina. 

HAZUS-MH MR3 Technical Manual 

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Area A 

Area B 

Figure 3.21. Close-Up Views of Two Areas in West Suburb of Raleigh, NC. 

Chapter 3. Surface Roughness Modeling 

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Figure 3.22. Downtown Wilmington, NC 
(upper: Aerial photograph; lower: NLCD). 

HAZUS-MH MR3 Technical Manual 

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Figure 3.23. Close-Up of Downtown Wilmington, NC 
(upper: Aerial photograph; lower: NLCD). 

Chapter 3. Surface Roughness Modeling 

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Figure 3.24. Galveston-Texas City-Santa Fe Area, TX 
(upper: Aerial photo; lower: NLCD). 

HAZUS-MH MR3 Technical Manual 

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Figure 3.25. Close-up of Santa Fe Area, TX 
(upper: Aerial photograph; lower: NLCD). 

Chapter 3. Surface Roughness Modeling 

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Figure 3.26. Northeast Suburb of Houston, TX 
(upper: Aerial photograph; lower: NLCD). 

HAZUS-MH MR3 Technical Manual 

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Table 3.9. Roughness Length for NLCD Land Cover Classes 

 State/RegionTX LA MS AL FL Pan FL W FL SE FL NE GA SC NC VA 
Land Cover Class Ave Stdev Ave Stdev Ave Stdev Ave Stdev Ave Stdev Ave Stdev 
Ave Stdev Ave Stdev Ave Stdev Ave Stdev Ave Stdev Ave Stdev 
Water 
11 Open Water 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 
0.010 0.010 
12 Perennial Ice/Snow 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 
0.012 0.012 0.012 
Developed 
21 Low Intensity Residential 0.35 0.10 0.33 0.13 0.33 0.11 0.33 0.11 0.33 
0.11 0.34 0.12 0.34 0.11 0.33 0.12 0.35 0.11 0.36 0.11 0.35 0.10 0.35 0.15 
22 High Intensity Residential 0.55 0.10 0.50 0.14 0.53 0.14 0.53 0.14 0.53 
0.14 0.54 0.15 0.57 0.18 0.54 0.14 0.57 0.11 0.57 0.16 0.55 0.11 0.53 0.19 
23 Commercial/Industrial/Transportation 0.44 0.38 0.39 0.35 0.35 0.21 0.35 
0.21 0.35 0.21 0.39 0.27 0.38 0.26 0.34 0.26 0.35 0.20 0.34 0.20 0.33 0.21 
0.38 0.20 
Barren 
31 Bare Rock/Sand/Clay 0.10 0.04 0.09 0.05 0.09 0.05 0.09 0.05 0.09 0.05 0.10 
0.06 0.09 0.04 0.09 0.05 0.10 0.05 0.10 0.06 0.10 0.05 0.10 0.04 
32 Quarries/Strip Mines/Gravel Pits 0.14 0.08 0.18 0.09 0.18 0.09 0.18 0.09 
0.18 0.09 0.17 0.09 0.20 0.09 0.18 0.08 0.17 0.06 0.17 0.07 0.17 0.09 0.12 
0.05 
33 Transitional 0.15 0.09 0.18 0.10 0.20 0.07 0.20 0.07 0.20 0.07 0.17 0.09 
0.19 0.08 0.16 0.09 0.13 0.06 0.17 0.10 0.15 0.09 0.20 0.09 
Forested Upland 
41 Deciduous Forest 0.55 0.16 0.65 0.19 0.68 0.17 0.68 0.17 0.68 0.17 0.65 
0.18 0.69 0.13 0.72 0.13 0.76 0.15 0.79 0.19 0.78 0.15 0.79 0.16 
42 Evergreen Forest 0.56 0.14 0.72 0.20 0.73 0.12 0.73 0.12 0.73 0.12 0.76 
0.13 0.71 0.10 0.74 0.14 0.75 0.15 0.82 0.14 0.81 0.15 0.82 0.11 
43 Mixed Forest 0.55 0.14 0.71 0.21 0.71 0.12 0.71 0.12 0.71 0.12 0.74 0.13 
0.71 0.10 0.73 0.15 0.76 0.14 0.80 0.16 0.80 0.14 0.81 0.14 
Shrubland 
51 Shrubland 0.10 0.05 0.12 0.03 0.12 0.03 0.12 0.03 0.12 0.03 0.12 0.04 0.12 
0.03 0.12 0.03 0.11 0.03 0.13 0.04 0.10 0.03 0.12 0.03 
Non-Natural Woody 
61 Orchards/Vineyards/Other 0.25 0.11 0.27 0.13 0.25 0.13 0.25 0.13 0.25 0.13 
0.24 0.12 0.27 0.14 0.24 0.12 0.26 0.14 0.29 0.16 0.25 0.10 0.24 0.15 
Herbaceous Upland 
71 Grasslands/Herbaceous 0.04 0.02 0.04 0.01 0.04 0.01 0.04 0.01 0.04 0.01 
0.04 0.02 0.04 0.02 0.04 0.02 0.05 0.02 0.04 0.02 0.04 0.02 0.04 0.02 
Herbaceous Planted / Cultivated 
81 Pasture/Hay 0.04 0.02 0.06 0.02 0.05 0.02 0.05 0.02 0.05 0.02 0.05 0.02 
0.05 0.02 0.06 0.01 0.05 0.02 0.06 0.02 0.06 0.02 0.05 0.02 
82 Row Crops 0.06 0.02 0.06 0.02 0.05 0.02 0.05 0.02 0.05 0.02 0.06 0.03 0.06 
0.02 0.06 0.02 0.06 0.03 0.06 0.02 0.06 0.02 0.05 0.02 
83 Small Grains 0.04 0.02 0.05 0.02 0.06 0.02 0.06 0.02 0.06 0.02 0.05 0.03 
0.06 0.02 0.07 0.02 0.06 0.03 0.07 0.03 0.06 0.02 0.06 0.02 
84 Fallow 0.03 0.02 0.04 0.02 0.04 0.02 0.04 0.02 0.04 0.02 0.03 0.01 0.03 
0.02 0.04 0.02 0.04 0.02 0.03 0.01 0.03 0.02 0.04 0.01 
85 Urban/Recreational Grasses 0.07 0.04 0.05 0.03 0.03 0.04 0.03 0.04 0.03 
0.04 0.04 0.04 0.06 0.03 0.06 0.04 0.06 0.04 0.06 0.03 0.07 0.04 0.05 0.04 
Wetland 
91 Woody Wetlands 0.50 0.20 0.55 0.21 0.58 0.21 0.58 0.21 0.58 0.21 0.59 0.22 
0.54 0.20 0.57 0.26 0.60 0.27 0.61 0.22 0.60 0.21 0.59 0.24 
92 Emergent Herbaceous Wetlands 0.10 0.05 0.11 0.05 0.09 0.04 0.09 0.04 0.09 
0.04 0.08 0.04 0.04 0.01 0.09 0.03 0.10 0.04 0.10 0.04 0.10 0.05 0.10 0.05

 State/RegionMD DE PA NJ NY (Man&LI) Manhattan Long Is. CT RI MA NH ME 
Land Cover Class Ave Stdev Ave Stdev Ave Stdev Ave Stdev Ave Stdev Ave Stdev 
Ave Stdev Ave Stdev Ave Stdev Ave Stdev Ave Stdev Ave Stdev 
Water 
11 Open Water 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 0.010 
0.010 0.010 
12 Perennial Ice/Snow 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 0.012 
0.012 0.012 0.012 
Developed 
21 Low Intensity Residential 0.35 0.15 0.35 0.15 0.36 0.14 0.35 0.14 0.43 
0.17 0.50 0.10 0.42 0.17 0.34 0.10 0.34 0.10 0.36 0.10 0.29 0.10 0.29 0.10 
22 High Intensity Residential 0.53 0.19 0.53 0.19 0.62 0.17 0.58 0.21 0.73 
0.34 0.84 0.42 0.62 0.18 0.48 0.16 0.48 0.16 0.59 0.18 0.53 0.08 0.53 0.08 
23 Commercial/Industrial/Transportation 0.38 0.20 0.38 0.20 0.44 0.33 0.42 
0.22 0.92 0.82 1.55 0.93 0.44 0.12 0.35 0.15 0.35 0.15 0.51 0.38 0.31 0.15 
0.31 0.15 
Barren 
31 Bare Rock/Sand/Clay 0.10 0.04 0.10 0.04 0.14 0.04 0.08 0.03 0.09 0.04 0.09 
0.04 0.09 0.04 0.10 0.04 0.10 0.04 0.12 0.05 0.12 0.05 0.12 0.05 
32 Quarries/Strip Mines/Gravel Pits 0.12 0.05 0.12 0.05 0.12 0.06 0.15 0.08 
0.17 0.08 0.17 0.08 0.17 0.08 0.15 0.08 0.15 0.08 0.16 0.10 0.15 0.06 0.15 
0.06 
33 Transitional 0.20 0.09 0.20 0.09 0.14 0.09 0.22 0.08 0.21 0.10 0.21 0.10 
0.21 0.10 0.17 0.09 0.17 0.09 0.19 0.07 0.13 0.09 0.13 0.09 
Forested Upland 
41 Deciduous Forest 0.79 0.16 0.79 0.16 0.77 0.11 0.78 0.15 0.78 0.19 0.78 
0.19 0.78 0.19 0.79 0.09 0.79 0.09 0.77 0.14 0.78 0.13 0.78 0.13 
42 Evergreen Forest 0.82 0.11 0.82 0.11 0.77 0.16 0.82 0.10 0.78 0.12 0.78 
0.12 0.78 0.12 0.80 0.15 0.80 0.15 0.78 0.14 0.78 0.13 0.78 0.13 
43 Mixed Forest 0.81 0.14 0.81 0.14 0.78 0.14 0.80 0.12 0.79 0.12 0.79 0.12 
0.79 0.12 0.80 0.16 0.80 0.16 0.79 0.12 0.79 0.13 0.79 0.13 
Shrubland 
51 Shrubland 0.12 0.03 0.12 0.03 0.14 0.04 0.13 0.04 0.13 0.04 0.13 0.04 0.13 
0.04 0.11 0.02 0.11 0.02 0.13 0.03 0.11 0.04 0.11 0.04 
Non-Natural Woody 
61 Orchards/Vineyards/Other 0.24 0.15 0.24 0.15 0.22 0.15 0.24 0.16 0.26 0.11 
0.26 0.11 0.26 0.11 0.26 0.12 0.26 0.12 0.26 0.14 0.25 0.13 0.25 0.13 
Herbaceous Upland 
71 Grasslands/Herbaceous 0.04 0.02 0.04 0.02 0.04 0.02 0.04 0.02 0.05 0.02 
0.05 0.02 0.05 0.02 0.04 0.02 0.04 0.02 0.04 0.02 0.04 0.02 0.04 0.02 
Herbaceous Planted / Cultivated 
81 Pasture/Hay 0.05 0.02 0.05 0.02 0.05 0.02 0.06 0.02 0.05 0.02 0.05 0.02 
0.05 0.02 0.05 0.02 0.05 0.02 0.05 0.02 0.06 0.02 0.06 0.02 
82 Row Crops 0.05 0.02 0.05 0.02 0.05 0.03 0.05 0.01 0.06 0.03 0.06 0.03 0.06 
0.03 0.06 0.03 0.06 0.03 0.06 0.03 0.06 0.02 0.06 0.02 
83 Small Grains 0.06 0.02 0.06 0.02 0.06 0.03 0.06 0.02 0.06 0.03 0.06 0.03 
0.06 0.03 0.06 0.02 0.06 0.02 0.06 0.02 0.07 0.03 0.07 0.03 
84 Fallow 0.04 0.01 0.04 0.01 0.04 0.02 0.03 0.01 0.04 0.02 0.04 0.02 0.04 
0.02 0.03 0.01 0.03 0.01 0.04 0.02 0.04 0.02 0.04 0.02 
85 Urban/Recreational Grasses 0.05 0.04 0.05 0.04 0.07 0.05 0.05 0.04 0.08 
0.03 0.08 0.03 0.08 0.03 0.05 0.04 0.05 0.04 0.06 0.04 0.03 0.04 0.03 0.04 
Wetland 
91 Woody Wetlands 0.59 0.24 0.59 0.24 0.60 0.24 0.60 0.24 0.60 0.22 0.60 0.22 
0.60 0.22 0.58 0.22 0.58 0.22 0.58 0.21 0.61 0.20 0.61 0.20 
92 Emergent Herbaceous Wetlands 0.10 0.05 0.10 0.05 0.10 0.04 0.08 0.04 0.10 
0.04 0.10 0.04 0.10 0.04 0.09 0.05 0.09 0.05 0.09 0.04 0.09 0.04 0.09 0.04 

3.5.3 Census Tract-Averaged Roughness Length 
Using the roughness length-mapped NLCD data, census tract-averaged roughness 
lengths 
have been computed for the Year 2000 census tracts. Examples developed using 
the 1990 
census tract shapes are presented in Figures 3.27 and 3.28 for North Carolina 
and Texas, 
respectively, where two counties are shown for each state, one coastal county 
and another 
relatively inland. For North Carolina, New Hanover County is the coastal 
county and 
Wake County is the inland county. For Texas, two adjacent counties were 
arbitrarily 
selected. The results are reasonable. 

Chapter 3. Surface Roughness Modeling 

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Figure 3.27. Census Tract-Averaged Roughness Length Derived 
from NLCD Data, NC. 

HAZUS-MH MR3 Technical Manual 

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Figure 3.28. Census Tract-Averaged Roughness Length Derived 
from NLCD Data, Texas. 

3.5.4 Comparisons of z0 Values Computed from NLCD Data on Rectangular Grids 
with Empirically Assigned z0 Values 
Averaged z0 values are also computed from NLCD data on 1.8km x 1.2km 
rectangular 
grids and compared to empirically assigned z0 values based on observations 
from aerial 
photographs. Examples of the comparison are presented in Figures 3.29 through 
3.32 for 
selected locations in Texas, Florida, North Carolina and Rhode Island, 
respectively. The 
correlation between the two sets of z0 values is shown in Figures 3.33 
through 3.36 for 
these four locations, respectively. The correlation coefficients are, 
respectively, 0.88, 
0.93, 0.63 and 0.77 for the four locations. The results from all four 
locations are 
combined in Figure 3.37, for which the correlation coefficient is 0.926, 
which is higher 
than the average of the four individual correlation coefficients. This 
reflects that 
estimation of z0 values using NLCD data provides better relative accuracy 
when 
involving a larger variety of land cover characteristics than for a smaller, 
localized land 
area. These comparisons serve as a check on the reasonableness of averaged z0 
values 
computed from NLCD data. It indicates that the degree of agreement is 
acceptable in 
general, except for a few grid cells that, in particular, involve commercial 
high-rise 
buildings in downtown areas, such as for the studied cases of Raleigh, NC, 
and Miami, 
FL. 

Chapter 3. Surface Roughness Modeling 

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1 23 

45 67 

1 
2 
3 
4 
5 
6 
7 
Assigned Zo 1 2 3 4 5 6 7 
1 0.45 0.45 0.45 0.40 0.40 0.40 0.40 
2 0.40 0.45 0.32 0.30 0.40 0.40 0.40 
3 0.45 0.48 0.40 0.32 0.33 0.30 0.35 
4 0.50 0.45 0.42 0.40 0.45 0.30 0.28 
5 0.42 0.30 0.40 0.35 0.32 0.32 0.35 
6 0.42 0.30 0.42 0.40 0.35 0.40 0.38 
7 0.40 0.45 0.48 0.32 0.32 0.40 0.42 
Computed Zo 1 2 3 4 5 6 7 
1 0.447 0.441 0.438 0.367 0.372 0.384 0.408 
2 0.418 0.428 0.372 0.320 0.414 0.341 0.414 
3 0.471 0.483 0.399 0.332 0.330 0.341 0.373 
4 0.491 0.468 0.429 0.403 0.421 0.339 0.305 
5 0.416 0.304 0.385 0.358 0.361 0.327 0.389 
6 0.448 0.319 0.409 0.410 0.397 0.429 0.418 
7 0.394 0.439 0.431 0.391 0.389 0.403 0.423 

Figure 3.29. z0 Values Computed from NLCD Data and Assigned Empirically for a 
Location in East Suburban of Houston, TX. 

HAZUS-MH MR3 Technical Manual 

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23 45

1 
1 
2 
3 
4 
5 
6 
Assigned Zo 1 2 3 4 5 6 
1 0.45 0.42 0.20 0.01 0.10 0.30 
2 0.42 0.45 0.10 0.02 0.15 0.25 
3 0.45 0.50 0.15 0.01 0.20 0.30 
4 0.52 0.60 0.35 0.10 0.10 0.30 
5 0.55 0.50 0.40 0.15 0.10 0.25 
6 0.55 0.50 0.50 0.02 0.10 0.10 

Computed Zo 1 2 3 4 5 6 
1 0.472 0.470 0.192 0.014 0.113 0.300 
2 0.447 0.464 0.140 0.036 0.129 0.260 
3 0.494 0.463 0.178 0.017 0.160 0.290 
4 0.463 0.447 0.225 0.127 0.137 0.306 
5 0.423 0.426 0.255 0.196 0.093 0.266 
6 0.485 0.468 0.224 0.040 0.107 0.114 

Figure 3.30. z0 Values Computed from NLCD Data and Assigned Empirically for 
Miami, FL, Including Downtown (Cell # 6-3). 

Chapter 3. Surface Roughness Modeling 

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1 2 3 
45 

6 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
Figure 3.31. z0 Values Computed from NLCD Data and Assigned Empirically for 
Raleigh, NC, Including Downtown (Cell # 10-4) 

HAZUS-MH MR3 Technical Manual 

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Assigned Zo 1 2 3 4 5 6 
1 0.60 0.60 0.55 0.45 0.50 0.55 
2 0.60 0.65 0.60 0.50 0.55 0.50 
3 0.60 0.55 0.52 0.55 0.55 0.48 
4 0.60 0.60 0.60 0.60 0.58 0.50 
5 0.50 0.58 0.50 0.55 0.55 0.55 
6 0.50 0.55 0.58 0.55 0.50 0.50 
7 0.55 0.55 0.45 0.50 0.45 0.50 
8 0.50 0.60 0.60 0.55 0.48 0.55 
9 0.35 0.55 0.50 0.48 0.50 0.55 
10 0.42 0.42 0.45 0.62 0.45 0.40 
11 0.45 0.40 0.35 0.62 0.45 0.45 
12 0.50 0.50 0.35 0.45 0.55 0.55 

Computed Zo 1 2 3 4 5 6 
1 0.557 0.546 0.518 0.482 0.619 0.666 
2 0.567 0.562 0.604 0.498 0.521 0.491 
3 0.622 0.580 0.514 0.535 0.534 0.479 
4 0.607 0.597 0.542 0.533 0.569 0.495 
5 0.519 0.587 0.524 0.522 0.525 0.520 
6 0.537 0.528 0.542 0.490 0.487 0.476 
7 0.573 0.572 0.499 0.529 0.448 0.500 
8 0.491 0.552 0.535 0.487 0.449 0.536 
9 0.365 0.521 0.489 0.465 0.513 0.524 
10 0.418 0.430 0.451 0.399 0.495 0.462 
11 0.527 0.466 0.428 0.411 0.508 0.539 
12 0.554 0.522 0.375 0.430 0.532 0.524 

Figure 3.31. z0 Values Computed from NLCD Data and Assigned Empirically for 
Raleigh, 
NC, Including Downtown (Cell#10-4) (concluded). 

3.6 Example of Roughness Length (z0) Calculation Using Lettau s Formula 
The empirical relationship between z0 and the ground roughness physical 
dimensions 
proposed by Lettau (1969) forms the basis of the methodology given in ASCE-7-
02 for 
the computation of the roughness length (z0) for the purpose of determining 
if a building 
is located in Exposure B (defined as Suburban Terrain) or Exposure C (defined 
as Open 
Terrain). In ASCE-7-02, a building is considered to be located in a suburban 
terrain 
(Exposure B) if the value of z0 computed using Lettau s method is greater 
than or equal to 

0.15 m and less than 0.7 m. The representative value of Exposure B in ASCE-7-
02 is 
defined with a surface roughness of 0.3 m. 
Recall that Lettau s formula for estimating the surface roughness length, z0, 
is: 

z0   0.5HS/A (3.2) 

Chapter 3. Surface Roughness Modeling 

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1 
2 
3 
4 
5 
6 
1 
2 
3 
4 
5 
Assigned Zo 1 2 3 4 5 
1 0.38 0.35 0.32 0.34 0.32 
2 0.40 0.32 0.35 0.40 0.30 
3 0.45 0.35 0.28 0.20 0.40 
4 0.40 0.40 0.32 0.35 0.40 
5 0.40 0.30 0.38 0.42 0.38 
6 0.45 0.40 0.38 0.45 0.32 

Computed Zo 1 2 3 4 5 
1 0.409 0.368 0.367 0.397 0.318 
2 0.382 0.346 0.357 0.388 0.324 
3 0.406 0.354 0.312 0.250 0.419 
4 0.394 0.364 0.305 0.349 0.389 
5 0.427 0.381 0.330 0.380 0.367 
6 0.507 0.447 0.328 0.405 0.351 

Figure 3.32. z0 Values Computed from NLCD Data and Assigned Empirically for a 
Location in South Suburban of Providence, RI. 

HAZUS-MH MR3 Technical Manual 

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Computed Zo vs Assigned Zo 
0.2 
0.3 
0.4 
0.5 
0.6 
0.2 0.3 0.4 0.5 0.6 
Assigned 
Computed 
Correl. Coef. = 0.883 
Figure 3.33. Comparison Between z0 Values Computed from NLCD Data and 
Assigned Empirically for a Location in East Suburban of Houston, TX. 

Computed Zo vs Assigned Zo 
0.0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 
Assigned Computed 
Correl. Coef. = 0.931 
Figure 3.34. Comparison Between z0 Values Computed from NLCD Data and 
Assigned Empirically for Miami, FL, Including Downtown Area. 

Chapter 3. Surface Roughness Modeling 

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Computed Zo vs Assigned Zo 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.2 0.3 0.4 0.5 0.6 0.7 
Assigned 
Computed 
Correl. Coef. = 0.627 
Figure 3.35. Comparison Between z0 Values Computed from NLCD Data and 
Assigned Empirically for Raleigh, NC, Including Downtown Area. 

Computed Zo vs Assigned Zo 

0.6 

Computed 

0.5 

0.4 

0.3 

0.2 

0.1 

0.1 0.2 0.3 0.4 0.5 0.6 
Assigned 
Correl. Coef. = 0.773 
Figure 3.36. Comparison Between z0 Values Computed from NLCD Data and 
Assigned Empirically for a Location in Southern Providence, RI. 

HAZUS-MH MR3 Technical Manual 

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Computed Zo vs Assigned Zo 
0.0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 
Assigned 
Computed 
Correl. Coef. = 0.926 
Figure 3.37. Comparison Between z0 Values Computed from NLCD Data and 
Assigned Empirically for the Four Locations Studied. 

where H = average height of the obstacle in the upwind terrain, S = the 
average projected 
frontal area per obstruction presented to the wind, and A = the average area 
of ground 
occupied by each obstruction (including the open area surrounding it). When 
calculating 
an average value of z0 over a region, S and A can be substituted by the total 
projected 
frontal area of all the obstacles upwind of a site and the total ground area 
these obstacles 
collectively occupy. 

When trees or bushes are present, their contribution to the frontal area must 
also be 
considered. ASCE-7-02 suggests that for conifers and other evergreens no more 
than 
50% of their gross frontal area can be taken to be effective in obstructing 
the wind. For 
deciduous trees and bushes ASCE-7-02 states that no more than 15% of the 
their gross 
frontal area can be taken to be effective in obstructing the wind, however in 
hurricane 
prone regions, trees are generally still in full leaf during the time period 
hurricanes are 
likely to impact a region, thus an effective area of 50% is probably more 
appropriate. 
Recall, that the objective in estimating the surface roughness is to enable a 
realistic 
estimate of losses, and thus there should not be any tendency to choose a low 
value of 
frontal area in an attempt to obtain a conservative (low) value of z0 as may 
be done in a 
building design situation. 

Following is a step-by-step demonstration to show how to use the DOQQs to 
obtain the 
information required for Lettau s formula and the logic in defining the input 
parameters. 
For demonstration purpose, the DOQQs from one medium density residential area 
in 

Chapter 3. Surface Roughness Modeling 

?
each of five counties in Florida (Escambia, Lee, Dade, Palm Beach, and Duval, 
see 
Figures 3.38 through 3.42) are used. 

Medium density residential areas are selected because: 

1. Building density is in such a range that the possibility of a building 
being 
shadowed by trees is small and ambiguity does not exist in distinguishing two 
separate buildings 
2. Building heights for a residential area are relatively uniform and 
relatively 
easy to estimate, thus providing more reliable results. 
It may be difficult to accurately determine an obstacle s height by looking 
at the DOQQs 
or aerial photography. Sometimes, the length of the sun shadow projected by 
an obstacle 
can help if any known reference object exists. Familiarity with a region will 
aid 
considerably in reducing errors associated with estimates of both building 
and tree 
heights. 

Here, using Figure 3.38 as an example, the steps used to calculate the 
roughness length 
with Lettau s method are demonstrated. This is a medium density residential 
area in the 
Panhandle of Florida (Escambia County). The wind was assumed coming from the 
direction shown by the arrow in Figure 3.38. The study area defined by the 
polygon 
shown in Figure 3.38 has a total ground area of about 60800 m2. By counting 
all the 

MDR 
Wind Direction 
50 0 50 100 150 Meters 

Figure 3.38. Medium Density Residential Area in Escambia County. 

HAZUS-MH MR3 Technical Manual 

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MDR 
Wind Direction 
100 0 100 200 Meters 

Figure 3.39. Medium Density Residential Area in Lee County. 

MDR 
Wind Direction 
100 0 100 200M 

Figure 3.40. Medium Density Residential Area in Dade County. 

Chapter 3. Surface Roughness Modeling 

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Wind Direction 
100 0 100 200 Meters 

Figure 3.41. Medium Density Residential Area in Palm Beach County. 

MDR 
Wind Direction 
100 0 100 200 Mete 

Figure 3.42. Medium Density Residential Area in Duval County. 

HAZUS-MH MR3 Technical Manual 

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3-48 

buildings in the study area, a total frontal (perpendicular to the wind 
direction) length of 
about 630 m was estimated. In this example, the homes are assumed to exist in 
a 
subdivision having a mixture of one and two story homes, with an assumed 
average roof 
height of about 6m. Therefore, the total frontal area from all the buildings 
is: 6 m W 630 
m = 3780 m2. 

The trees were assumed to be about 15m tall. The gross tree frontal area is 
estimated to 
be roughly 1.5 times the building frontal area by looking at Figure 3.38. 
Therefore, the 
total effective tree frontal area is about (1.5 W 3780) W 50% = 2835 m2, 
given that the 
ratio of effective area to gross frontal area is assumed to be 50% for both 
deciduous and 
coniferous trees during hurricane seasons. 

Then by using Latteus equation, the roughness length for this area is 
calculated from: 

z0 = 0.5 HS = 0.5 
( HS )bldg + ( HS )tree = 0.56 W 3780 +15 W 2835 = 0.5 22680 + 42525 = 0.56m 

AA 60800 60800 

Note that when the trees are the major obstacles in the areas being 
investigated (which is 
common), significant uncertainties may exist in the estimated tree heights, 
frontal areas, 
and relative densities but as seen in the example given above, trees account 
for about 
two-thirds of the surface roughness length computed using Lettau s approach. 

Similar procedures have been applied to the other DOQQs included in this 
section. The 
parameter values determined at each step for the five examples are listed in 
Table 3.10. 
The assumed wind directions are shown in Figures 3.38 to 3.42, respectively. 
The 
computed roughness lengths all fall into the definition of Exposure B (as 
defined in 
ASCE 7), and all roughness lengths fall within the roughness length ranges 
given in 
Tables 3.6 and 3.8 for Medium Density Residential. 

Table 3.10. Examples for Roughness Length Calculation Using Lettau s Formula 

Parameter 
Fig. 
1(Escambia 
County) 
Fig. 2(Lee 
County) 
Fig. 3(Dade 
County) 
Fig. 4(Palm 
Beach 
County) 
Fig. 
5(Duval 
County) 
Ground Area (m2) 
Estimated Mean Roof Height (m) 
Building Frontal Length (m) 
Building Frontal Area (m2) 
Mean Tree Height (m) 
Tree/Building Area Ratio 
Tree Frontal Area (m2) 
SH (building) 
Effective SH (trees) 
60800 
6 
630 
3780 
15 
1.50 
5670 
22680 
42525 
225000 
5 
2800 
14000 
6 
0.15 
2100 
70000 
6300 
77000 
4.5 
1000 
4500 
-
0.00 
0 
20250 
0 
150000 
4.5 
2900 
13050 
6 
0.10 
1305 
58725 
3915 
35000 
6 
240 
1440 
15 
3.00 
4320 
8640 
32400 
Roughness Length z0 (m) 0.56 0.17 0.13 0.21 0.59 

Note that the calculated roughness length is wind-direction dependent. For 
uniform 
building orientations within a region, the difference in the computed 
roughness lengths 
for different wind directions can be significant. For instance, in the Palm 
Beach County 

Chapter 3. Surface Roughness Modeling 

?
example, if the wind direction changes 1900, the projected width of the 
buildings is in the 
range of 50% to 70% of that seen in the worked example, and thus the computed 
roughness length reduces to 50% to 70% of the original value. To remove the 
effect of 
directionality associated with building orientation, the frontal width of the 
building can 
be substituted with an effective width defined as the square root of the 
estimated plan 
area. 

Although Lettau s method provides a convenient and quantitative means to 
estimate 
roughness length from DOQQs, aerial photography, etc., it should be used with 
caution. 
Engineering judgment needs to be applied to the results through comparisons 
with 
estimates of z0 given in the literature. Inevitably, some variations and 
uncertainties are 
associated with the estimation of the surface roughness length in any 
terrain, but as will 
be shown later, this parameter plays a very important role in the estimation 
of wind 
induced damage and loss. 

3.7 Effect of Surface Roughness on Near Ground Gust Wind Speeds 
In this section, the effects of z0 on gust wind speeds (defined as a 3 second 
average) near 
the ground surface and on the hourly mean wind speed near the ground surface 
are 
shown. It is important to note that changes in wind speeds in areas of 
transitioning 
surface roughness are not treated in the default HAZUS surface roughness 
model. 
However, a discussion of transition effects is provided in this section to 
assist the user in 
understanding their possible impact on damage and loss estimates. Transition 
effects are 
illustrated using the methodology described in ESDU (1983). The ESDU 
methodology 
forms the basis of the fetch length requirements given in ASCE-7-02 to enable 
the user to 
determine what exposure category a building is in given information on the 
upstream 
fetch lengths. 

Figure 3.43 shows the ratio of the wind speed, as normalized by the reference 
open 
terrain value, as a function of surface roughness and height above ground. 
Wind speed 
ratios are given for both the peak gust wind speed and the one hour mean wind 
speed. 
The data in Figure 3.43 clearly show that the effect of surface roughness is 
greatest near 
the ground (z = 3 m and z = 5 m), around the eave height of most single story 
buildings. 
Figure 3.43, also shows that the change in wind speed (with respect to the 
reference 
terrain value) is less for the peak gust wind speed than for the mean wind 
speed. For 
example, at a height of 10 m, a change in z0 from 0.03 m (open terrain) to 
0.35 m (typical 
suburban terrain), the peak gust wind speed reduces by about 18%, whereas the 
hourly 
average wind speed reduces by about 32%. The reduction in the wind speed 
associated 
with an averaging time of one minute is about 25%, falling almost exactly 
half way 
between the reduction in gust wind speed and the reduction in the one hour 
mean wind 
speed. In HAZUS, all damage and loss functions are given as a function of 
peak gust 
wind speed, not the one minute wind speed. 

The wind speed ratio data given in Figure 3.43 are for the case of a fully 
transitioned 
boundary layer (i.e., the wind has blown over a long enough fetch of new 
terrain that the 
flow characteristics are influenced only by the local terrain, and not the 
previous terrain). 
In reality, the wind at a given height does not change immediately to reflect 
the new 

HAZUS-MH MR3 Technical Manual 

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1.3 
1.2 
1.1 
1 
0.9 z=3 m 
0.8 
z=5 m 
z=10 m 
0.7 
Gust Wind Speed Ratioz=20 m 
z=50 m 
0.6 z=100 m 
0.5 
0.001 0.01 0.1 1 
Surface Roughness Length (m) 
1.5 
1.3 
1.1 
0.9 z=3 m 
z=5 m 
0.7 z=10 m 
Mean Wind Speed Ratioz=20 m 
0.5 z=50 m 
z=100 m 
0.3 
0.001 0.01 0.1 1 
Surface Roughness Length (m) 

Figure 3.43. Wind Speed Ratios as a Function of Surface Roughness for Various 
Heights. 

terrain, but rather the flow gradually makes a transition, changing to 
reflect the 
characteristics associated with the new terrain. The distance over which this 
transition 
takes place varies with height, with the wind at lower heights changing more 
rapidly than 
wind at higher levels. Figures 3.44, 3.45 and 3.46 show examples of the rates 
of change 
of the mean and gust wind speeds as the terrain changes from an open terrain 
(z0 = 0.03m) to an example suburban terrain (z0 = 0.3 m) for heights of 3 m, 
10 m and 50 
m, respectively. In each of Figures 3.44 through 3.46, the upper plot shows 
the wind 
speed ratio with respect to open terrain as a function of the distance into 
the new terrain, 
and the lower plot shows the percentage adjustment of the wind to the new 
terrain as a 

Chapter 3. Surface Roughness Modeling 

?
Wind Speed Ratio

Z0(1)=0.03m, Z0(2)=0.3m, z=3m 

1 

0.8 

0.6 

0.4 

0.2 
0 
10 100 1000 10000 
Fetch (m) 
Mean Ratio 
Gust Ratio 
Mean Ratio (Infinite Fetch) 
Gust Ratio (Infinite Fetch) 
0 
10 
20 
30 
40 
50 
60 
70 
80 
90 
100% Adjustment of Wind Speed toLocal RoughnessMean 
Gust 
10 100 1000 10000 

Fetch (m) 

Figure 3.44. Reduction in Wind Speed at a Height of 3 m 
(Open Terrain to Suburban Terrain). 

function of the distance. The lower plots suggest that the wind speed in the 
new terrain 
asymptotically approaches (but never reaches) the fully transitioned value. 
In ASCE-702, 
this problem was handled by defining the fetch required to assume a fully 
transitioned 
case as the distance over which the wind speed adjustment reaches about 80% 
of the fully 
transitioned value. Notice in the 3 m height example, about 50% of the 
reduction occurs 
within the first 10m of the terrain change. Thus, homes located on the front 
row of a 
barrier island, or the front row in a subdivision facing open farmland, are 
actually situated 
within a transition zone. The effective wind speeds that these front row 
structures 
experience will be notably higher than those experienced by homes located 
even as close 
as one row back from these front line homes. 

In cases where loss studies are being performed in relatively small regions 
that 
encompass significant changes in terrain, it would be advisable to properly 
estimate the 
fraction of buildings that are likely to experience winds associated with 
terrains other 
than the default suburban values that would be appropriate for most of the 
building 
population. 

HAZUS-MH MR3 Technical Manual 

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z0(1)=0.03m, z0(2)=0.3m, z=10m 

0 
0.2 
0.4 
0.6 
0.8 
1 
Wind Speed RatioMean Ratio 
Gust Ratio 
Mean Ratio (Infinite Fetch) 
Gust Ratio (Infinite Fetch) 
10 100 1000 10000 
Fetch (m) 

Mean
Gust

0 
10 
20 
30 
40 
50 
60 
70 
80 
90 
100 
% Adjustment of Wind Speed toLocal Roughness 
10 100 1000 10000 

Fetch (m) 

Figure 3.45. Reduction in Wind Speed at a Height of 10 m 
(Open Terrain to Suburban Terrain). 

In the case of the 50 m height example (Figure 3.46), it is readily seen that 
even for a 
fetch distance of 1 km, the wind speed has only undergone 30% of the full 
adjustment. 
This example indicates that for taller buildings relatively isolated or in 
front row, the 
appropriate terrain selection is governed by the terrain at distances of 1km 
or more away 
from the building rather than at the location of the building itself. It 
should also be 
recognized, that the taller the building, the less the effect of terrain on 
the wind speeds at 
roof height. 

Figure 3.47 shows the rapid and significant reduction in wind speed at a 
height of 3m 
associated with a change in terrain from an open terrain to a heavily treed 
terrain. As 
shown in the upper graph, a 30% reduction in the peak gust wind speed (~70% 
reduction 
in wind load) occurs within the first 100 m of the transition. This example 
clearly shows 
why significant reductions in observed damage are seen when comparing damage 
on 
barrier islands to that seen within forested regions of the mainland, even 
right at the 
coast. 

Chapter 3. Surface Roughness Modeling 

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Z0(1)=0.03m, Z0(2)=0.3m, z=50m 

0 
0.2 
0.4 
0.6 
0.8 
1 
Wind Speed RatioMean Ratio 
Gust Ratio 
Mean Ratio (Infinite Fetch) 
Gust Ratio (Infinite Fetch) 
10 100 1000 10000 

Fetch (m) 

0 
10 
20 
30 
40 
50 
60 
70 
80% Adjustment of Wind Speed to Local 
Roughness 
Mean 
Gust 
10 100 1000 10000 

Fetch (m) 

Figure 3.46. Reduction in Wind Speed at a Height of 50 m 
(Open Terrain to Suburban Terrain). 

Large changes in roughness associated with trees located very near the intra-
coastal 
waterways are seen in many regions along the Gulf and Atlantic coasts. 

Examples of fully transitioned peak gust profiles are shown in Figure 3.48 
for a range of 
roughness lengths. These examples are given to show the impact of z0 on the 
magnitude 
of the peak gust wind speed as a function of height. All gust profiles given 
in Figure3.7-6 
are referenced to the peak gust wind speed at a height of 10 m in open 
terrain. 

HAZUS-MH MR3 Technical Manual 

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Z0(1)=0.03m, Z0(2)=1.0m,z=3m 

0 
0.2 
0.4 
0.6 
0.8 
1 
Wind Speed Ratio 
Mean Ratio 
Gust Ratio 
Mean Ratio (Infinite Fetch)
Gust Ratio (Infinite Fetch) 
10 100 1000 10000 
Fetch (m) 

0 
10 
20 
30 
40 
50 
60 
70 
80 
90 
100% Adjustment of Wind Speed toLocal RoughnessMean 
Gust 
10 100 1000 10000 
Fetch (m) 

Figure 3.47. Reduction in Wind Speed at a Height of 10m 
(Open Terrain to Heavily Treed Terrain). 

Peak Gust Velocity Profiles 

50 

45 
40 
35 
30 
25 
20 
15 
10 
5 
0 

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 
V(z)/V(z=10m,Z0=0.03m) 
z0=0.01m 
z0=0.03m 
Z0=0.15m 
Z0=0.35m 
Z0=0.70m 
Z0=1.0m 
Height (m) 

Figure 3.48. Examples of Peak Gust Velocity Profiles as a Function of z0. 

Chapter 3. Surface Roughness Modeling 

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Chapter 4. Wind Loads 

4.1 Introduction 
Background. Wind loads on buildings are usually estimated using either 
boundary layer 
wind tunnel tests performed for a specific building or using code-specified 
loads that 
have been developed by committees from boundary layer wind tunnel test data. 
If wind 
tunnel loads are used in the design of a building or its components, the wind 
loading 
coefficients are typically measured for 36 different wind directions, with 
the results 
combined with a statistical model of the wind climate for the location where 
the building 
is to be built. Using this approach, the design loads obtained for the 
building take into 
account the effect of the variation of the wind loads with the direction of 
the approaching 
wind, and how these variations in load with direction align with the 
directional 
characteristics of the wind. 

Essentially all low-rise buildings are designed using wind loads obtained 
from building 
codes. While the loading coefficients given in the codes have been developed 
using wind 
tunnel test data, the directional effects are not explicitly reproduced in 
North American 
building codes and standards. For simplicity, and ease of use, the loads 
given in building 
codes represent an estimate of the maximum load acting on a portion of the 
building. 
Different values of the loading coefficients are given in the codes for 
various zones on 
the roofs and walls. The selection of the number and size of these zones is a 
compromise 
between the true spatial variability of the maximum wind loads, and the use 
of as few 
zones as possible to simplify the design procedure. The greater the number of 
zones 
specified in a building code, the more accurate will be the final estimate of 
the 
distribution of the maximum loads acting on the building. The maximum values 
of the 
pressure coefficients given in the codes for each zone do not necessarily 
occur for winds 
approaching from the same wind direction, and therefore code-specified loads 
alone 
cannot be used to model wind loads on a building for wind approaching from a 
given 
direction. 

Designers are able to take advantage of the variation of the wind loads with 
wind 
direction in both the UK and Australia, if they chose to use the detailed 
design procedures 
given in their respective building codes. 

Approach. For predicting wind loads on buildings in the development of fast 
running 
damage and loss functions, it is necessary to model the variation in the wind 
loads acting 
on the building as a function of the location on the building and as a 
function of the 
direction and magnitude of the wind speed. Clearly, the best approach is to 
use the results 
from wind tunnel tests directly and combine these results with the simulated 
hurricane 
winds and directions to estimate the loads. Unfortunately, this approach is 
not viable 
since the detailed wind tunnel test data (if available) are limited by the 
number and 
locations of the pressure taps used in the models, the number of terrains in 
which the 
model buildings were tested, and the number of different geometries tested. 
Without the 
use of wind tunnel data, the next best method for developing wind loads as a 
function of 

HAZUS-MH MR3 Technical Manual 

?
direction is the development of an analytic or empirical model which is able 
to 
reasonably reproduce the variation of wind loads with direction, as well as 
the effects of 
aspect ration, terrain, etc. The Australian and UK wind loading provisions 
represent 
simple examples of such an approach. 

The methodology selected by the HAZUS Wind Committee for developing damage 
and 
loss functions is to use code-specified loads as a the basis for the model. 
To treat wind 
directionality for roof loads, tabulated values of the pressure coefficients 
as a function of 
direction are estimated using wind tunnel data and the UK Building Code. In 
the case of 
wall loads, the pressure coefficients are modeled with cosine functions. 
Using this 
approach, the peak magnitudes of the loads correspond to the values specified 
in building 
code adopted  standards . The pressure coefficients developed using this 
approach are 
discussed in the following sections, and the results of the empirical 
direction model are 
compared to measurements of loads acting on buildings determined from wind 
tunnel 
tests. The wind loads derived using the hybrid code/directional model are 
applied to a 
number of simply shaped buildings. The shapes of the buildings considered are 
all either 
square or rectangular in plan and have either flat, hip or gable shape roofs. 

4.2 Wall Pressures   Low-Rise Buildings 
The magnitudes of the wall pressures used for modeling wind loads for the 
prediction of 
wind induced failures of components and cladding were derived considering the 
pressure 
coefficients given in North American wind loading standards and/or codes. The 
standards/codes considered in the development of the wind loads are ASCE-7-
95, SBCCI 
(1998 Edition) and the 1995 edition of the National Building Code of Canada. 
In order to 
compare the magnitudes of the loading coefficients given in each 
standard/code, all 
coefficients were adjusted to be referenced to the mean hourly wind speed at 
roof height. 
In the case of both the NBCC and SBCCI pressure coefficients, the 
coefficients were 
divided by a value of 0.8 to remove the directionality factor that is 
explicitly included in 
the pressure coefficients given in the codes (Mehta, 1984). Additionally, in 
the case of 
the SBCCI coefficients, an internal pressure coefficient having a value of 
0.2 was 
removed from the coefficients as given in the code before any comparisons 
were made. 
To convert the coefficients given in ASCE-7-95 from values which are 
normalized by the 
peak gust wind speed at roof height to the value which is normalized by the 
mean hourly 
wind speed, a gust factor of 1.57 was used. The gust factor (1.57) used for 
converting the 
mean hourly wind speed to a 3 second gust was derived from the ESDU (1982) 
gust 
factor models for open terrain conditions (z0 = 0.03 m) and a mean roof 
height of about 4 

m. In the case of the SBCCI coefficients, which are referenced to the fastest 
mile wind 
speed at roof height, a gust factor of 1.27 was used in the change of the 
reference 
dynamic pressure to a mean hourly value. The 1.27 gust factor was also 
computed using 
the ESDU (1982) gust factor model assuming open terrain conditions at a 
height of 4 m 
and an averaging time of 32 seconds (i.e., 3600/110). Tables 4.1 through 4.4 
show the 
comparison of the peak coefficients derived from the three sources for the 
edge and 
central regions of the walls (as shown in Figure 4.1) normalized by the mean 
hourly wind 
speed at the average roof height. 
Chapter 4. Wind Loads 

?
Table 4.1. Comparison of Positive Wall Pressure Coefficients   Buildings with 
Roof 
Slopes Less than 100

Pressure Zone Pressure Coefficient 
NBCC SBCCI ASCE-7-95 NBCC SBCCI ASCE-7-95 
e 
w 
e 
w 
5 
4 
2.3 
2.3 
2.1 
2.1 
2.2 
2.2 

Table 4.2. Comparison of Positive Wall Pressure Coefficients   Buildings with 
Roof 
Slopes Greater than 100

Pressure Zone Pressure Coefficient 
NBCC SBCCI ASCE-7-95 NBCC SBCCI ASCE-7-95 
e 
w 
e 
w 
5 
4 
2.3 
2.3 
2.3 
2.3 
2.5 
2.5 

Table 4.3. Comparison of Negative Wall Pressure Coefficients   Buildings with 
Roof Slopes Less than 100

Pressure Zone Pressure Coefficient 
NBCC SBCCI ASCE-7-95 NBCC SBCCI ASCE-7-95 
e 
w 
e 
w 
5 
4 
-2.6 
-2.3 
-2.5 
-2.1 
-3.1 
-2.5 

Table 4.4. Comparison of Negative Wall Pressure Coefficients   Buildings with 
Roof Slopes Greater than 100

Pressure Zone Pressure Coefficient 
NBCC SBCCI ASCE-7-95 NBCC SBCCI ASCE-7-95 
e 
w 
e 
w 
5 
4 
-2.6 
-2.3 
-2.7 
-2.5 
-3.5 
-2.7 

Figure 4.1. Wall Pressure Zones as Defined in ASCE-7 (Left figure) and SBCCI 
and NBCC (Right figure). 

HAZUS-MH MR3 Technical Manual 

?
The coefficients given in the above tables represent the maximum (or minimum) 
values 
for design purposes and do not reflect the fact that the actual pressure 
coefficients vary as 
a function of wind direction. 

Effect of Wind Direction. In order to take into account the effect of wind 
direction, wind 
tunnel data obtained from various sources including Ho (1993), Stathopoulos 
(1978) and 
Lin and Surry (1997) were used to determine the variation of the pressure 
coefficients 
with wind direction. Tables 4.5 and 4.6 shows example positive and negative 
pressures 
acting on the walls of a rectangular building for wind directions in 300 
increments. The 
locations where the pressure coefficients are computed are given in Figure 
4.2. In the 
example given in Tables 4.5 and 4.6 the magnitudes of the maximum (or 
minimum) 
pressures are defined using the ASCE-7-95 values for the sloped roof case. 

Comparison of ASCE-7 Wall Pressures to Wind Tunnel Tests. Figures 4.3 and 4.4 
show comparisons of wall pressures estimated using the ASCE-7 based model for 
roofs 
with slopes less than 100 to those obtained from boundary layer wind tunnel 
tests (Lin 
and Surry, 1997) in nominally open and suburban terrains. These plots compare 
pressure 
tap-by-pressure tap for a wind tunnel tested rectangular building, and show 
measured vs. 
ASCE-7 based-model pressure coefficients. The plot includes comparisons over 
the full 
azimuth range in 100 increments. The model building, tested at scales of 
1:100 and 1:200 
has an eave height of 30' and plan dimensions of 100'W200'. The model was 
instrumented 
so that the full azimuthal range of data was available for 145 wall taps and 
112 roof taps. 
In the comparisons given in Figures 4.3 and 4.4, the pressure coefficients 
are normalized 
by the mean hourly wind speed at roof height. Note that in the pressure 
modeling process, 
all pressure coefficients are based on the peak gust wind speed at roof 
height. The 
magnitudes of the coefficients, normalized by the mean wind speed at roof 
height, 
therefore increase with increasing surface roughness. 

The banding evident in Figures 4.3 and 4.4 arises primarily from the cosine 
functions 
used in the modeling of the code based pressures and suctions that limit the 
value a 
modeled pressure can have for any given wind direction, whereas the measured 
peak 
pressure data, while following a mean trend with azimuth, are scattered about 
the mean 
azimuthal trend curve. 

The agreement between the ASCE-7 based positive pressures and the measured 
positive 
pressures is reasonable. In the case of the negative pressures, there is 
significantly more 
scatter evident in the comparisons since the ASCE-7 loads for the negative 
pressures in 
Zone 4 (interior zone) have a minimum value of -2.1, whereas in reality, for 
long 
buildings, the minimum negative pressure will have a magnitude which is less 
than 2.1. 

4.3 Roof Pressures   Low-Rise Buildings 
As in the case of the wall pressures, wind loads on the roofs of low-rise 
buildings used 
for the prediction of the failure of components and cladding were derived 
using North 
American based building codes and standards. As in the case of the wall 
pressures, 
comparisons of the roof pressures specified by ASCE-7, SBCCI and NBCC were 

Chapter 4. Wind Loads 

?
Table 4.5. Positive Wall Pressure Coefficients Estimated for Building Shown 
in 
Figure 4.2 Given as a Function of Wind Direction 

Location 
Wind Direction (Clockwise from North) 
0 30 60 90 120 150 180 210 240 270 300 330 
1 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
2 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
3 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
4 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
5 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
6 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
7 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
8 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
9 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
10 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
11 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
12 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
13 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
14 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
15 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 
16 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 
17 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 
18 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 
19 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 
20 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 
21 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 
22 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 
23 0.1 1.2 2.2 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 
24 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
25 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
26 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
27 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
28 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
29 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
30 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
31 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
32 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
33 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
34 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
35 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
36 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
37 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
38 2.5 2.2 1.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 
39 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 
40 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 
41 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 
42 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 
43 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 
44 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 
45 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 
46 0.1 0.1 0.1 0.1 0.1 0.1 0.1 1.2 2.2 2.5 2.2 1.2 

HAZUS-MH MR3 Technical Manual 

?
Table 4.6. Negative Wall Pressure Coefficients Estimated for Building Shown 
in 
Figure 4.2 Given as a Function of Wind Direction 

Location Wind Direction (Clockwise from North) 
0 30 60 90 120 150 180 210 240 270 300 330 
1 -.9 -.8 -.6 -1.1 -.8 -.3 -.4 -.2 -2.0 -3.5 -2.1 -2.1 
2 -.7 -.6 -.5 -.9 -.6 -.2 -.3 -.2 -1.6 -2.7 -1.6 -1.3 
3 -.7 -.6 -.5 -.9 -.6 -.2 -.3 -.2 -1.6 -2.7 -1.6 -1.0 
4 -.7 -.6 -.5 -.9 -.6 -.2 -.3 -.3 -1.7 -2.7 -1.7 -.8 
5 -.7 -.6 -.5 -.9 -.6 -.2 -.3 -.3 -1.7 -2.7 -1.7 -.7 
6 -.7 -.6 -.5 -.9 -.6 -.2 -.3 -.4 -1.8 -2.7 -1.8 -.6 
7 -.7 -.6 -.5 -.9 -.6 -.2 -.3 -.4 -1.8 -2.7 -1.8 -.6 
8 -.7 -.6 -.5 -.9 -.6 -.2 -.3 -.5 -1.9 -2.7 -1.9 -.6 
9 -.7 -.6 -1.8 -2.7 -1.8 -.4 -.3 -.2 -.6 -.9 -.5 -.6 
10 -.7 -.6 -1.8 -2.7 -1.8 -.4 -.3 -.2 -.6 -.9 -.5 -.6 
11 -.7 -.7 -1.7 -2.7 -1.7 -.3 -.3 -.2 -.6 -.9 -.5 -.6 
12 -.7 -.8 -1.7 -2.7 -1.7 -.3 -.3 -.2 -.6 -.9 -.5 -.6 
13 -.7 -1.0 -1.6 -2.7 -1.6 -.2 -.3 -.2 -.6 -.9 -.5 -.6 
14 -.7 -1.3 -1.6 -2.7 -1.6 -.2 -.3 -.2 -.6 -.9 -.5 -.6 
15 -.9 -2.1 -2.1 -3.5 -2.0 -.2 -.4 -.3 -.8 -1.1 -.6 -.8 
16 -3.5 -2.0 -.2 -.4 -.3 -.8 -1.1 -.5 -.7 -.7 -.7 -2.0 
17 -2.7 -1.6 -.2 -.3 -.2 -.6 -.9 -.4 -.5 -.6 -.5 -1.6 
18 -2.7 -1.6 -.2 -.3 -.2 -.6 -.9 -.4 -.5 -.6 -.5 -1.6 
19 -2.7 -1.7 -.3 -.3 -.2 -.6 -.9 -.4 -.5 -.6 -.5 -1.7 
20 -.9 -.6 -.2 -.3 -.3 -1.6 -2.7 -1.6 -.5 -.6 -.5 -.4 
21 -.9 -.6 -.2 -.3 -.2 -1.6 -2.7 -1.6 -.5 -.6 -.5 -.4 
22 -.9 -.6 -.2 -.3 -.2 -1.5 -2.7 -1.5 -.5 -.6 -.5 -.4 
23 -1.1 -.8 -.3 -.4 -.2 -2.0 -3.5 -2.0 -.7 -.7 -.7 -.5 
24 -.4 -.3 -.8 -1.1 -.6 -.8 -.9 -2.1 -2.1 -3.5 -2.0 -.2 
25 -.3 -.2 -.6 -.9 -.5 -.6 -.7 -1.3 -1.6 -2.7 -1.6 -.2 
26 -.3 -.2 -.6 -.9 -.5 -.6 -.7 -1.0 -1.6 -2.7 -1.6 -.2 
27 -.3 -.2 -.6 -.9 -.5 -.6 -.7 -.8 -1.7 -2.7 -1.7 -.3 
28 -.3 -.2 -.6 -.9 -.5 -.6 -.7 -.7 -1.7 -2.7 -1.7 -.3 
29 -.3 -.2 -.6 -.9 -.5 -.6 -.7 -.6 -1.8 -2.7 -1.8 -.4 
30 -.3 -.2 -.6 -.9 -.5 -.6 -.7 -.6 -1.8 -2.7 -1.8 -.4 
31 -.3 -.2 -.6 -.9 -.5 -.6 -.7 -.6 -1.9 -2.7 -1.9 -.5 
32 -.3 -.4 -1.8 -2.7 -1.8 -.6 -.7 -.6 -.5 -.9 -.6 -.2 
33 -.3 -.4 -1.8 -2.7 -1.8 -.6 -.7 -.6 -.5 -.9 -.6 -.2 
34 -.3 -.3 -1.7 -2.7 -1.7 -.7 -.7 -.6 -.5 -.9 -.6 -.2 
35 -.3 -.3 -1.7 -2.7 -1.7 -.8 -.7 -.6 -.5 -.9 -.6 -.2 
36 -.3 -.2 -1.6 -2.7 -1.6 -1.0 -.7 -.6 -.5 -.9 -.6 -.2 
37 -.3 -.2 -1.6 -2.7 -1.6 -1.3 -.7 -.6 -.5 -.9 -.6 -.2 
38 -.4 -.2 -2.0 -3.5 -2.1 -2.1 -.9 -.8 -.6 -1.1 -.8 -.3 
39 -3.5 -2.0 -.7 -.7 -.7 -.5 -1.1 -.8 -.3 -.4 -.2 -2.0 
40 -2.7 -1.6 -.5 -.6 -.5 -.4 -.9 -.6 -.2 -.3 -.2 -1.6 
41 -2.7 -1.6 -.5 -.6 -.5 -.4 -.9 -.6 -.2 -.3 -.2 -1.6 
42 -2.7 -1.7 -.5 -.6 -.5 -.4 -.9 -.6 -.2 -.3 -.3 -1.7 
43 -.9 -.4 -.5 -.6 -.5 -1.6 -2.7 -1.6 -.3 -.3 -.2 -.6 
44 -.9 -.4 -.5 -.6 -.5 -1.6 -2.7 -1.6 -.2 -.3 -.2 -.6 
45 -.9 -.4 -.5 -.6 -.5 -1.5 -2.7 -1.5 -.2 -.3 -.2 -.6 
46 -1.1 -.5 -.7 -.7 -.7 -2.0 -3.5 -2.0 -.2 -.4 -.3 -.8 

Chapter 4. Wind Loads 

?
Figure 4.2. Locations of Pressure Estimates on Rectangular Building as 
Presented 
in Tables 4.5 and 4.6 (Plan View). 

performed with the coefficients referenced to the mean hourly wind speed at 
the mean 
roof height. Tables 4.7 through 4.9 present comparisons of the various 
coefficients. 
Again, as in the case of wall pressures, the effect of the directionality 
factor has been 
removed from the coefficients given in the SBCCI Code and the NBCC. The 
pressure 
zones associated with each of the codes and standards are given in Figure 
4.5. 

Comparisons of the peak coefficients prescribed in each of the above noted 
standards to 
those produced by Meecham (1988), shown in Figure 4.3, suggest that the 
coefficients 
prescribed in ASCE-7 are generally too high along the roof ridge and eaves, 
whereas 
those prescribed by the NBCC and the SBCCI tend to be low along the roof edge 
and 
eaves. All of the above noted codes/standards appear to underestimate the 
wind-induced 
loads at the ridge/gable end corner, and this underestimation of the loads in 
this region is 
also supported in the pressure coefficient data given in Case (1996). A 
comparison of the 
SBCCI loads and the ASCE loads for hip and gable roofs suggests that the 
average of the 
two sets of pressure coefficients would yield results that most closely 
reproduce those 
obtained from the wind tunnel (except at the gable ridge). For the estimation 
of wind 
loads and resulting damage, both the SBCCI loads and the ASCE loads are 
investigated 
in the damage/loss studies. 

Effect of Wind Direction. The effect of wind direction on the estimated 
pressure 
coefficients on the roofs of low-rise buildings was determined independently 
of the 
building code/standard information using the results of wind tunnel test data 
given in 
Stathopolous (1978); Meecham (1988); Ho (1993); Vickery (1984); Surry and 
Davenport 
and Mikituk (1993). This wind tunnel information was supplemented by the 
directional 
pressure coefficient data given in the United Kingdom Building Code, CP3. 

HAZUS-MH MR3 Technical Manual 

?
4-8 
Chapter 4. Wind Loads 
Rectangular Building - Open Terrain -ASCE-7 Based Negative 
Pressures 
y = 0.9204x 
R2 = 0.4883 
-5 
-4.5 
-4 
-3.5 
-3 
-2.5 
-2 
-1.5 
-1 
-0.5
0 
-5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 
Measured Peak Pressures 
Modeled Peak Pressures 
Rectangular Building - Open Terrain - ASCE-7 Based Positive 
Pressures 
y = 0.9906x 
R2 = 0.8999 
0 
0.5
1 
1.5
2 
2.5
3 
3.5
4 
4.5
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 
Measured Peak Pressures 
Modeled Peak Pressures 
Figure 4.3. Comparison of Measured and Modeled Wall Pressure Coefficients on 
a 
Flat Roof Building in Open Terrain (z0 = 0.1 m). 
?
4-9 
HAZUS-MH MR3 Technical Manual 
Rectangular Building - Suburban Terrain - ASCE-7 Based 
Positive Pressures 
y = 0.9812x 
R2 = 0.896 
0 
0.5
1 
1.5
2 
2.5
3 
3.5
4 
4.5 
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 
Measured Peak Pressures 
Modeled Peak Pressures 
Rectangular Building - Suburban Terrain -ASCE-7 Based 
Negative Pressures 
y = 0.9645x 
R2 = 0.4472 
-5 
-4.5 
-4 
-3.5 
-3 
-2.5 
-2 
-1.5 
-1 
-0.5
0 
-5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 
Measured Peak Pressures 
Modeled Peak Pressures 
Figure 4.4. Comparison of Measured and Modeled Wall Pressure Coefficients on 
a 
Flat Roof Building in Suburban Terrain (z0 = 0.3 m). 
?
Table 4.7. Comparison of Negative Pressure Coefficients on Flat Roofs 

Pressure Zone Pressure Coefficient 
NBCC SBCCI ASCE-7-95 NBCC SBCCI ASCE-7-95 
c c 3 6.8 5.6 6.9 
s si 2 3.1 3.1 4.5 
r re 1 2.3 2.3 2.5 
Table 4.8. Comparison of Negative Pressure Coefficients on Gable Roofs 

Pressure Zone Pressure Coefficient 
NBCC SBCCI ASCE-7-95 NBCC SBCCI ASCE-7-95 
c c 3 5.1 5.2 5.2 
s si 2 2.5 2.5 5.2 
s  se 2 3.9 4.0 5.2 
r re 1 2.0 2.1 2.1 
Table 4.9. Comparison of Negative Pressure Coefficients on Hip Roofs 

Pressure Zone Pressure Coefficient 
NBCC SBCCI ASCE-7-95 NBCC SBCCI ASCE-7-95 
c c 3 5.1 5.2 5.2 
s si 2 2.5 2.5 5.2 
s  se 2 2.5 4.0 5.2 
r re 1 2.0 2.1 2.1 
In the development of the wind loads as a function of direction, a zone-based 
approach 
was used for the hip and gable roofs using the data noted above. In the case 
of the gable 
roof, the zones are based on those given in the SBCCI where, because more 
zones exist 
here than in either the ASCE-7 provisions or the NBCCI provisions, the effect 
of wind 
directionality can be better modeled. For example, the largest loads on eave 
zone occur 
when the wind is blowing perpendicular to the eave or ridge), whereas the 
largest loads in 
the corner zones tend to occur when the wind is approaching from an oblique 
angle, and 
the largest loads in the center of the gable end occur when the wind is 
approaching in a 
direction which is approximately parallel to the ridge line. 

In the case of the hip roof buildings, the zones (or sub-zones) used to 
incorporate the 
effect of directionality are based primarily on the UK wind loading code, CP3 
as shown 
in Figure 4.7. 

Effect of Terrain. A key assumption in the use of pressure coefficients for 
estimating 
wind loads, is that the pressure coefficients, normalized with respect to the 
peak gust 
wind speed at roof height, do not change with changes in the flow 
characteristics (e.g., 
turbulence intensity). This assumption is important because most pressure 
coefficient 
data derived from wind tunnel tests given in the literature are presented as 
coefficients 
normalized by the mean wind speed at roof height. To adjust these 
coefficients referenced 

Chapter 4. Wind Loads 

?
Figure 4.5. Zones used for Defining Pressure Coefficients for Gable and Hip 
Roofs 
for the SBCCI (Top Drawings), the NBCC (Middle Drawings) and ASCE-7-95 
(Bottom Drawings). 

HAZUS-MH MR3 Technical Manual 

?
Figure 4.6. Peak Pressure Coefficients on Hip and Gable on Hip and Gable 
Roofs in 
Open Terrain (taken from Meecham, 1988). 

to the mean wind speed to coefficients referenced to the peak gust speed, 
information on 
the flow characteristics used in the wind tunnel tests must be known. If the 
key flow 
parameters needed to convert the reference wind speed from mean hourly to 
peak gust are 
known (i.e., mean velocity and longitudinal turbulence intensity profiles), 
then the wind 
tunnel pressure coefficient data referenced to the mean dynamic pressure can 
be readily 
converted to a coefficient normalized to the peak gust velocity pressure. In 
instances 
when wind tunnel tests have been performed on the same building in different 
terrain 
conditions (e.g., typical open terrain and typical suburban terrain), one 
would expect the 
pressure coefficients normalized by the peak gust velocity pressure at roof 
height to 
collapse to the same value. 

Chapter 4. Wind Loads 

?
Figure 4.7. Pressure Coefficient Zones Used in the UK Wind Loading Code for 
Hip 
Roof Buildings. 

Using wind tunnel data given in Monroe (1996), Ho (1992), Meecham(1988), Case 
(1996), Stathopolous (1978), and Lin and Surry (1997), all of which present 
roof pressure 
coefficients normalized by the mean dynamic pressure at roof height for more 
than one 
terrain condition, the coefficients were adjusted to be to be normalized with 
respect to the 
peak gust velocity pressure at roof height. The degree to which the pressure 
coefficients 
normalized by the local gust velocity pressure (at mean roof height) 
collapsed to yield the 
same negative pressure coefficient varied from study to study and building to 
building. In 
general, it was found that the peak gust pressure coefficients in the rougher 
terrain 
(normalized to the local gust velocity pressure) were higher than those at 
the same 
location in the smoother terrain. 

For example, Monroe (1996) measured roof suctions on a model building with a 
1:12 
roof slope having an eave height of 4.9 m (full scale) in flow conditions 
having 
turbulence intensities at roof height of 12.5% and 19%. On average, the peak 
roof suction 
coefficients (normalized with respect to the mean velocity pressure at roof 
height) 
obtained in the rougher flow conditions were 75% higher than those obtained 
in the 
smoother flow conditions. By normalizing the pressures by the peak gust 
velocity 
pressure at roof height (defined as the mean wind speed plus three standard 
deviations of 
the fluctuating wind speed), the difference reduces so that the pressure 
coefficients in the 
rougher terrain are about 35% higher than those obtained in the smooth 
terrain. These 
higher coefficients suggest that using the peak gust velocity pressure to 
normalize the 
pressure coefficients is not sufficient to explain the differences in the 
measured pressure 
coefficients associated with changes in the flow characteristics. 

HAZUS-MH MR3 Technical Manual 

?
In the case of Ho (1992), the negative pressures in the suburban terrain case 
do not 
collapse to a constant value, with the peak suction coefficients referenced 
to the peak gust 
velocity pressure being higher in suburban terrain than in open terrain. For 
example, 
converting a roof corner pressure in suburban terrain to be referenced to the 
peak gust 
velocity pressure yields a coefficient which is 15% higher than the open 
country value 
referenced to the local peak gust velocity pressure. 

Similar observations were made using the Lin and Surry pressure data, the 
Case (1996) 
pressure data and a large portion of the Stathopolous (1978) data. The 
observation that 
the pressure coefficients in the rougher terrains, even when normalized with 
respect to the 
local peak gust wind, are larger (in magnitude) than those in open terrain is 
apparently 
recognized in ASCE-7-95 where the wind loads on buildings in suburban terrain 
(Exposure B) are limited to be no less than 85% of the value that would be 
computed if 
the building were in open terrain. 

To take into account this apparent increase in the peak pressure 
coefficients, the basic 
pressure coefficient referenced to the peak gust wind speed is increased by a 
factor equal 
to the square root of the ratio of the turbulence intensity at roof height in 
the local terrain 
to the turbulence at roof height in the reference open terrain. This 
empirical adjustment in 
the pressure coefficients helps collapse the pressure coefficient data noted 
above, but is 
clearly a subject requiring more research. The use of this factor does yield 
estimates of 
pressures in low buildings in a standard suburban terrain (z0 = 0.3 m) which 
are much 
closer to those required by ASCE-7 than if the adjustment were not made. As 
an 
illustration of the effect of the turbulence intensity, consider the 
following example of a 
building located in suburban terrain having a mean roof height of 5 m. 

Using the ESDU representation of the gust velocity profile, the ratio of the 
5 m gust wind 
speed in suburban terrain to the 5 m gust wind speed in open terrain is 
0.787, implying a 
wind load equal to 62% of the open terrain wind load. The roof height 
turbulence 
intensities in the open and suburban terrains (from ESDU, 1992) are 18% and 
29%, 
respectively, resulting in a 27% increase in the pressure coefficient (i.e., 

). The net effect of the reduction in the peak gust velocity combined with 
the increase in the pressure coefficient associated with the turbulence 
intensity 
adjustment is a wind load equal to 79% of the open terrain wind load, which 
is 
comparable to the 85% factor given in ASCE-7-98. 

Comparison of ASCE-7 Roof Pressure Loads to Wind Tunnel Tests. Figure 4.8 
shows 
a comparison of the ASCE-7 based roof pressure loads to those obtained from 
wind 
tunnel tests for a relatively open terrain case and a suburban terrain case. 
Note that the 
surface roughness associated with the nominal open terrain case is described 
by a z0 value 
of 0.1 m, which is larger than the value of about 0.03 m which is typically 
associated with 
open terrain conditions. The suburban terrain case is characterized by a 
roughness length 
of about 0.30 m. The modeled pressures (based on the ASCE-7 loads) presented 
in Figure 

4.8 were produced using the actual values of z0 as derived from the wind 
tunnel tests. 
100 18.0/29.0
Chapter 4. Wind Loads 

?
4-15 
HAZUS-MH MR3 Technical Manual 
Rectangular Building - Suburban Terrain - ASCE-7 Based 
Pressures 
y = 0.9877x + 0.3772 
R2 = 0.7365 
-12 
-10 
-8 
-6 
-4 
-2
0 
-12 -10 -8 -6 -4 -2 0 
Measured Peak Pressures 
Modeled Peak Pressures 
Rectangular Building - Open Terrain ASCE-7 Based Pressures 
y = 1.0536x + 0.1374 
R2 = 0.7025 
-10 
-9 
-8 
-7 
-6 
-5 
-4 
-3 
-2 
-1
0 
-10 -8 -6 -4 -2 0 
Measured Peak Pressure Coefficient 
Modeled Pressure Coefficient 
Figure 4.8. Comparison of Modeled and Measured Peak Roof Pressure 
Coefficients 
on a Flat Roof Low-Rise Building. 
?
The comparisons of the modeled and measured wind loads suggests that the 
ASCE-7 
based loads with the directional models and the empirical turbulence 
intensity adjustment 
factors reproduce the wind tunnel results reasonably well. 

Summary. A directional pressure coefficient model has been developed using 
code based 
loads to define the maximum pressure coefficients. The roof slopes considered 
are in the 
range of 3:12 to 5:12. The effect of directionality is taken into account 
using available 
wind tunnel data, scaled (or truncated) to ensure the maximum values of the 
pressures are 
equivalent to the code specified values. The reduction in the roof loads 
associated with 
decreased wind speeds caused by increasing surface roughness is lessened 
through the 
use of a turbulence intensity adjustment factor, which yields a final 
reduction in wind 
loads comparable to that specified in ASCE-7-95. 

4.4 Wind Loads on Low-Rise Buildings   Effect of Nearby Buildings 
The preceding discussion of wind loads on the roofs and walls of low-rise 
buildings was 
applicable to isolated structures located within a homogeneous open or 
suburban terrain. 
In the case of real buildings situated in real environments surrounded by 
buildings of like 
size, on average there is a reduction in the loads experienced by these 
buildings. The 
reduction in load is clearly a function of both the spacing and size of the 
near by 
buildings as indicated in Holmes (1994). Comprehensive wind tunnel test data 
showing 
the effects of nearby buildings on the wind loads experienced by the test 
building is 
limited. The wind tunnel test results given in Ho (1992) are probably the 
most widely 
quoted results. In Ho (1992), low-rise, flat roofed buildings were tested in 
both open and 
suburban terrain conditions with and without the existence of nearby 
buildings. For the 
cases where nearby buildings were in place, a total of 16 different 
representations of 
surrounding buildings were modeled. The results of the Ho (1992) study 
indicate that the 
average reduction in the wind loads on the roofs is about 25% compared to the 
isolated 
building case, although the amount of the reduction varied with the location 
on the roof. 
The coefficient of variation of the reduction in wind loads was about 20%, 
but this value 
also varied with location on the roof. The reduction in the wind loads on the 
walls of the 
test building was shown to be somewhat lower, ranging between about a minimum 
of a 
10% reduction up to a maximum of a 25% reduction in load. The coefficient of 
variation 
was about 20%. Ho made no attempt so separately examine the effects of nearby 
buildings on the positive and negative pressures. 

In Case (1996), models of a gable roof building with a 4:12 roof slope were 
tested as 
isolated buildings and then tested for three different representations of the 
building 
surrounded by other buildings. Case found that the reduction in the negative 
roof and 
wall loads was similar to that found by Ho (1999) (i.e., a 25% reduction), 
but found no 
mean reduction in the peak positive wall pressures. Case found that the 
positive roof 
pressures actually increased in the presence of nearby buildings. 

In the simulation of wind loads on low-rise buildings for the prediction of 
damage and 
loss, the effects of nearby buildings are taken into account by reducing the 
peak negative 
pressures by a mean value of 25%. No decrease in the positive wall pressures 
is taken. 

Chapter 4. Wind Loads 

?
4.5 Integrated (Overall) Wind Loads on Low-Rise Buildings 
The prime thrust of this effort with respect to wind-induced damage and loss 
estimation is 
directed towards the prediction of damage to the relatively small building 
envelope 
components. Overall (large area) loads are important for the prediction of 
overturning 
and uplifting of manufactured homes, whole roof failures on residential and 
small 
commercial buildings, and failures of structural systems such as those that 
exist in metal 
buildings, roof systems, etc. The estimates of the overall loads must take 
into account the 
fact that the peak pressures which act on the exterior of buildings and other 
structures are 
not fully correlated, and as the area over which the pressures are averaged 
increases, the 
effective loading coefficient decreases. This relationship of decreasing 
loading coefficient 
with increasing area is reflected in the pressure coefficients given in wind 
loading codes 
and standards such as the SBCCI and ASCE-7, but not in the main wind force 
resisting 
calculations as defined in ASCE-7-95. The reduction in the effective 
pressures given in 
these codes/standards are based on limited wind tunnel test data. In the 
development of 
the overall loading model, a model for the prediction of the mean exterior 
pressures was 
developed using the code based wind loading model developed for components 
and 
cladding as described earlier. This mean pressure model did not exist prior 
to the 
development of the overall load model as the loading and damage models were 
directed 
towards envelope component loads and failures only. 

To estimate overall loads for the prediction of overturning moments, uplift 
forces, etc., 
the modeled local pressures described earlier for the hip and gable roof 
buildings were 
integrated over the area of interest with the lack of correlation taken into 
account using a 
correlation coefficient approach similar to that originally developed by 
Davenport (1961). 
The major differences between the approach developed by Davenport and the 
approach 
used herein are: (1) Davenport properly uses the mean and standard deviations 
of the 
fluctuating pressures, whereas the present approach uses the mean and peak 
values (since 
these are estimated using the empirical pressure model) and (2) Davenport s 
approach 
was developed for line-like structures, whereas the present methodology is 
applied to 
three dimensional structures. 

To estimate the peak integrated loads acting on a structure the pressures are 
integrated 
using: 

1/ 2 

R  = 1 .V 2 ..
.. IiC  piI jC  pj exp( -.r/ . )dAidAj 
.
. (4.1)
2 .A . 

where R  is a peak force, moment or structural action, C  pi is the peak 
pressure coefficient 
(minus the mean value) at location i, Ii is an influence coefficient 
converting the pressure 
at location i to a global force, .r is the distance between locations i and 
j, and . is a 
length scale which can be considered a measure of the extent over which the 
fluctuating 
pressures are correlated. In the modeling approach used for negative 
pressures here, the 
length scale decreases with increasing magnitude of the negative pressures 
(i.e., the very 
high local negative pressures are correlated over relatively small areas), 
and the basic 
value of . is a function of the building height. 

HAZUS-MH MR3 Technical Manual 

?
Roof Uplift Loads. Using the integration methodology described above, the 
code based 
component and cladding loads were integrated over the roof surface of a hip 
roof and 
gable roof residence. The roof slopes in both cases are 4:12, the plan 
dimensions of the 
buildings are 30'W60' and the eave height is 9'. 

As indicated in Figure 4.9, predictions of integrated loads for uplift have 
been compared 
to the results of Meecham (1988) for the uplift loads on hip and gable roofs 
with 4:12 
roof slopes. Comparisons the uplift load estimates to those obtained from 
Figure 6-3 of 
ASCE-7-95 are also given in Figure 4.9. 

Hip Roof 

Uplift Coefficient 

1.8 

1.6 

1.4 

1.2 
1 
0.8 

0.6 

0.4 

0.2 
0 
0 10 20 30 40 50 60 70 80 90 
Wind Direction 
Model 
Meecham 
ASCE-7-95 
Gable Roof 

0 
0.2 
0.4 
0.6 
0.8 
1 
1.2 
1.4 
1.6 
1.8 
Uplift Coefficient 
ASCE-7-95 
Model 
Meecham 
0 10 20 30 40 50 60 70 80 90 

Wind Direction 

Figure 4.9. Comparisons of Modeled (Integrated) Uplift Loads on Hip and Gable 
Roofs to those Obtained from Wind Tunnel Experiments. Uplift Coefficients are 
Defined with Respect to the Mean Dynamic Pressure at Roof Height. 

The comparisons of the total uplift forces given in Figure 4.9 indicate that 
the modeled 
uplift loads are in general agreement with the results given in Meecham 
(1988), although 
the variation of the uplift coefficients with wind direction is less evident 
in the model 
results. 

Chapter 4. Wind Loads 

?
ASCE-7 allows for the computation of overall uplift for gable and flat roofs 
using Figure 
6-3 of the standard. The methodology allows for computation of the uplift 
loads for wind 
approaching normal to the roof ridge and parallel to the roof ridge only. The 
uplift loads 
computed for these two directions are shown in Figure 4.9, where it is seen 
that for winds 
approaching normal to the roof ridge, the ASCE-7 uplift loads obtained from 
Figure 6-3 
of ASCE-7 agree well with the uplift loads computed by integrating the 
component and 
cladding loads discussed earlier. In the case of the hip roof, the ASCE-7 
uplift loads 
presented in Figure 4.9 are the same as the gable values since ASCE-7 does 
not provide a 
means to compute uplift loads on roof shapes that are hip shaped. For winds 
approaching 
parallel to the roof ridge (900 as shown in Figure 4.9) the ASCE-7 uplift 
values are lower 
than either the Meecham data or the integrated uplift data. 

For the prediction of roof uplift failure, the integrated loading approach is 
used since the 
methodology is based on code type loads, it produces values of uplift (for 
the 00 case) 
that are very similar to those predicted using ASCE-7. The approach allows 
the effect of 
directionality (however small in this case) to be incorporated in the damage 
model, 
consistent with the approach used throughout model development. 

Integrated Roof and Wall Loads on Low-Rise Buildings with Flat Roofs. In 
order to 
further validate the pressure integration loading model, comparisons were 
made with 
integrated loads obtained directly from wind tunnel tests. The integrated 
wind tunnel 
loads were obtained by integrating the time series of wind induced pressures 
obtained 
from the wind tunnel tests of the 100' by 200' buildings described in Section 
4.3. The 
individual measured pressures acting on the roof were integrated over a 
number of 
different areas as indicated in Figure 4.10. Note that in the case of the 
integrated wall 
loads, panels 1, 2 and 3 are located on the back side of the building, but 
are shown in 
Figure 4.10 as being on the front side for clarity. Comparisons of the 
modeled and 
measured force coefficients for the wall and roof sections are given in 
Figures 4.11 
through 4.13. All force coefficients are expressed as the total wind induced 
force acting 
on the element divided by the mean dynamic pressure at roof height times the 
area of the 
element. 

The model results are seen to agree reasonably well with the measured 
results, 
reproducing both the reduction in the force coefficient with increasing area, 
and the 
directional characteristics of the loads. 

Overall Loads on Manufactured Homes. Using Equation 4.1, the code based roof 
and 
wall loads were integrated over the surface of a manufactured home in order 
to evaluate 
the effectiveness of the approach in estimating the lift, drag and 
overturning moments on 
manufactured homes. The results of the integration are compared to the full 
scale 
measurements described in Marshall (1977) as well as Roy (1983) and the 
estimates of 
lift, drag and overturning obtained from ASCE-7. Figure 4.14 presents the 
comparisons 
of lift, drag and overturning coefficients. 

HAZUS-MH MR3 Technical Manual 

?
Figure 4.10. Integration Areas Used for Comparison of Overall Roof and Wall 
Loads. 

1/2 Wall (Panels 1, 2 and 3) 1/3 Wall (Panels 1 and 2) 

-3 
-2 
-1 
0 
1 
2 
3 
Wall Force Coefficient 
Measured 
Modeled 
-3 
-2 
-1 
0 
1 
2 
3 
Wall Force Coefficient 
Measured 
Modeled 
0 90 180 270 360 0 90 180 270 360 
Wind Direction Wind Direction 
1/6 Wall (Panel A) 

-3 
-2 
-1 
0 
1 
2 
3 
4 
Wall Force Coefficient 
Measured 
Modeled 
0 90 180 270 360
Wind Direction

Figure 4.11. Comparison of Modeled and Measured Wall Forces on Rectangular 
Building in Suburban Terrain. 

Chapter 4. Wind Loads 

?
4-21 

1/4 Roof Area (Panels A, B and C) 1/6 Roof Area (Panels A and B) 

3

3 

2.5 

2 

0 90 180 270 360 
Wind Direction 
Measured 
Modeled 
0.5 

Measured 
Modeled 
0.5 

0 

0 

0 90 180 270 360
Wind Direction

1/12 Roof Area (Panel A) 

Uplift Coefficient 

Uplift Coefficient 

1.5 

1 

0 
0.5 
1 
1.5 
2 
2.5 
3 
Uplift Coefficient 
Measured 
Modeled 
0 90 180 270 360 

Wind Direction 

Figure 4.12. Comparison of Modeled and Observed Measured Uplift Coefficients 
on a Rectangular Building in Open Terrain. Roof Areas Indicated on the Graphs 
are Shown in Figure 4.10. 

1/2 Roof Area (Panels A, B, C, D, E and F) 1/3 Roof Area (Panels A, B, D and 
E) 
33 

0 90 180 270 360 
Wind Direction 
Measured 
Modeled 
0.5 

0 90 180 270 360 
Wind Direction 
Measured 
Modeled 
0.5 

0 

0 

1/6 Roof Area (Panels A and D) 

2.5 

Uplift Coefficient 

Uplift Coefficient 

2

2 

1.5 
1 
1.5 

1 

0 
0.5 
1 
1.5 
2 
2.5 
3 
Uplift Coefficient 
Measured 
Modeled 
0 90 180 270 360 

Wind Direction 

Figure 4.13. Comparison of Modeled and Observed Measured Uplift Coefficients 
on a Rectangular Building in Suburban Terrain. Roof Areas Indicated on the 
Graphs are Shown in Figure 4.11. 

HAZUS-MH MR3 Technical Manual 

?
Lift Coefficient Drag Coefficient

3.5 
3 
2.5 
2 
1.5 
1 
0.5 
0 
Model -DragFull Scale Drag (No Skirt)
Wind Tunnel DragFull Scale Drag - With SkirtASCE_7 

0 10 20 30 40 50 60 70 80 90 
Wind Direction 

3.5 
3 
0 10 20 30 40 50 60 70 80 90 
Wind Direction 
Model - Lift 
Lift - Wind Tunnel -No Skirt 
Lift -Full Scale -No Skirt 
Lift - Full Scale AverageASCE-7 
2.5 
2 
1.5 
1 
0.5 
0 
0 
2 
4 
6 
8 
10 
12 
Moment CoefficientModel -Moment 
Wind Tunnel - Moment 
ASCE-7 
0 10 20 30 40 50 60 70 80 90 

Wind Direction 

Figure 4.14. Comparison of Modeled, Wind Tunnel Measured, Full Scale Measured 
and ASCE-7 Estimated Drag, Lift and Moment Coefficients on a Manufactured 
Home. 

The results given in Figure 4.14 show the modeled load estimates to be in 
general 
agreement with the limited full scale lift and drag data, as well as the wind 
tunnel 
measured lift and moment data. The drag and moment coefficients for the zero 
wind 
direction case (winds approaching the long side of a manufactured home) 
obtained from 
the model are about 20% to 30% lower than the values estimated using ASCE-7. 
For use 
in damage prediction, the modeled lift and drag forces are increased by 10% 
for all wind 
directions examined, yielding loading estimates that have maxima closer to 
the values 

Chapter 4. Wind Loads 

?
produced by ASCE-7 than indicated in Figure 4.14, but limiting the 
overestimate of the 
loads as compared to the bulk of the full scale and model scale data. This 
approach 
retains a reasonable representation of the effect of wind direction on the 
loads and strikes 
a balance between the ASCE-7-98 loads obtained using Figure 6-3 in the 
standard and the 
loads obtained from full scale and wind tunnel experiments. 

Wind Loads on Long Span Roof Elements. To compute the wind induced uplift 
loads 
and bending moments acting on long span roof elements (such as trusses, open 
web steel 
joists, pre-cast concrete Tees, etc.) an influence line approach is used. 
Using this 
methodology, influence functions describing a specific structural action 
associated with 
the application of a load at a given point on the member, are used in 
conjunction with 
Equation 4.1 to produce estimates of the wind loads acting on the structural 
element. The 
influence function methodology was evaluated through comparisons of modeled 
and 
measured uplift coefficients for simply supported joists used as the primary 
roof system 
in a low-rise school building. The uplift loads at the joist supports were 
determined from 
wind tunnel tests performed at the University of Western Ontario using a 
1:100 scale 
model of the building. Details of the wind tunnel tests and results are given 
in Young and 
Vickery (1994). Figures 4.15 and 4.16 show photographs of the model in the 
wind tunnel. 

Figure 4.15. Close-up View of Model of School Building Used in Wind Tunnel 
Tests. 

HAZUS-MH MR3 Technical Manual 

?
Figure 4.16. View of Model in Wind Tunnel Showing Upstream Terrain. 

The roof system of the structure is comprised of open web steel joists (OWSJ) 
spanning 
the school in the North-South direction. Three trusses are used in the 
complete span, each 
supported by a masonry wall. The two longest OWSJs, spanning the classrooms 
(span of 
25') are supported by the outer walls and the inner hall way walls, while one 
shorter 
OWSJ truss spans the hallway. The joist layout is shown in Figure 4.17. 

Figure 4.17. Layout of OWSJs as Modeled in Wind Tunnel Tests. 

The location of the supports as modeled in the wind tunnel are shown in 
Figure 4.18, and 
Figure 4.19 shows the locations of all pressure taps used in the modeling. 
Comparing the 
layout of pressure taps to the locations of the computed up-lift points, it 
is evident that a 
total of four pressure taps are positioned along each main joist. The 
pressures measured at 
the locations of the four individual pressure taps located along the line of 
each main joist 
were combined instantaneously with pre-computed influence coefficients to 
obtain 
estimates of the uplift loads acting at the ends of each OWSJ. The uplift 
loads were 
computed for winds approaching the building for the full 3600 azimuth range 
at intervals 
of 100. 

Chapter 4. Wind Loads 

?
Figure 4.18. Plan View of School Showing Location of Truss Uplift Loads 
(North is towards top of page). 

Figure 4.19. Plan View of School Showing Location of all Pressure Taps Used 
in the 
Wind Tunnel Tests (North is towards top of page). 

To validate the model used to estimate OWSJ loads, comparisons of modeled and 
measured uplift loads were made for the uplift reactions at the points 
designated by the 
numbers 2007 through 2016, and 2101 through 2112. The measured uplift loads 
are 
compared to the modeled uplift (or reaction) loads in coefficient form, where 
both the 
wind tunnel and modeled coefficients are presented in the form: 

R

CR = (4.5-2)1 .UH 
2 L 

2 

where L is the length (or span) of the joist, R is the uplift load per unit 
width, . is the 
density of air and UH is the mean wind speed at roof height. Figure 4.20 
shows 
comparisons of the simulated and measured uplift coefficients as a function 
of wind 
direction for a total of 11 joists (22 reactions). 

HAZUS-MH MR3 Technical Manual 

?
4-26 

2007 2008 

2 

2 

1.8 

1.8 

1.6 

1.4 

Uplift Coefficient Uplift Coefficient Uplift Coefficient 

Uplift Coefficient Uplift Coefficient Uplift Coefficient 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0.2
0
0 
0 90 180 270 360 0 90 180 270 360 
Wind Direction Wind Direction 

2009 2010
2

2 

1.8 

1.8 

1.6 

1.6 

1.4 

1.4 

1.2 

1.2 

1

1 

0.8 

0.8 

0.6 

0.6 

0.4 

0.4 

0.2
0
0.2 

0 

0 90 180 270 360 0 90 180 270 360 
Wind Direction Wind Direction 

2011 2012
2

0 90 180 270 360 
Wind Direction 
2 

1.8 

1.8 

0 90 180 270 360 
Wind Direction 
1.6 

1.6 

1.4 

1.4 

1.2 

1.2 

1 

1 

0.8 

0.8 

0.6 

0.6 

0.4 

0.4 

0.2 

0.2 

0 

0 

Figure 4.20. Comparison of Wind Tunnel Measured (open squares) and Simulated 
(solid squares) Joist Uplift Coefficients as a Function of Wind Direction. 

As indicated in the comparisons between modeled and measured uplift 
coefficients, the 
agreement between the two data sets is generally quite good, particularly for 
OWSJs 
located on the north side of the building, away from the corners. The model 
tends to 
overestimate the maximum uplift loads acting on the OWSJs located on the 
south side of 
the building (denoted as 2012, 2016, 2104, 2108 and 2112) by as much as 30% 
(see 
location 2012) but this overestimate varies from joist to joist, and 
considering the 
geometry of the building, it is expected that the peak loads experienced by 
these joists 
would be nominally the same suggesting that some of the differences can be 
attributed to 
experimental variability. In the case of the OWSJs located on the north side 
of the 
building, the agreement between the measured and modeled uplift coefficients 
is better 
than for those located on the south side of the building, with the model both 
slightly 
overestimating and underestimating the magnitudes of the peak uplift 
coefficients at the 
locations of the individual joists. 

Chapter 4. Wind Loads 

?
2013 2014 

2 

2 

1.8 

1.8 

1.6 

1.6 

1.4 

1.4 

1.2 

Uplift Coefficient Uplift Coefficient Uplift Coefficient Uplift Coefficient 

Uplift Coefficient Uplift Coefficient Uplift Coefficient Uplift Coefficient 

1.2 

1 

0.8 

0.6 

0.4 

1 

0.8 

0.6 

0.4 

0.2 

0.2
0
0 

0 90 180 270 360 0 90 180 270 360 
Wind Direction Wind Direction 

2015 2016
2

1.8 

1.8 

1.6 

1.6 

1.4 

1.4 

1.2 

1.2 

1

1 

0.8 

0.8 

0.6 

0.6 

0.4 

0.4 

0.2
0
0.2 
0 
0 90 180 270 360 0 90 180 270 360 
Wind Direction Wind Direction 

2101 2102
2

2 

1.8 

1.8 

1.6 

1.6 

1.4 

1.4 

1.2 

1.2 
1
1 

0.8 

0.8 

0.6 

0.6 

0.4 

0.4 

0.2 

0.2
0
0 

0 90 180 270 360 0 90 180 270
Wind Direction Wind Direction

2103 2104
2

2 

1.8 

1.8 

1.6 

1.6 

1.4 

1.4 

1.2 

1.2 

1 

1 

0.8 

0.8 

0.6 

0.6 

0.4 

0.4 

0.2 0.2 
0 0 
0 90 180 270 360 0 90 180 270 360 
Wind Direction Wind Direction 

Figure 4.20. Comparison of Wind Tunnel Measured (open squares) and Simulated 
(solid squares) Joist Uplift Coefficients as a Function of Wind Direction 
(continued). 

The overestimate of the wind uplift loads for joists located on the south 
side of the 
building is thought to be a result of the low buildings located to the south 
of the building 
as indicated in Figure 4.12 interfering with the flow. The net reduction in 
wind loads 
produced by these upstream buildings is consistent with the load reduction 
factor applied 
to buildings located in  real  environments as described earlier in Section 
4.3. 

HAZUS-MH MR3 Technical Manual 

?
2105 2106 

2 

0 90 180 270 360 
Wind Direction 
2 

1.8 

1.8 

1.6 

1.6 

1.4 

1.4 

1.2 

1.2 

1 

0.8 

0.6 

0.4 

0.2 
0 
Uplift Coefficient Uplift Coefficient Uplift Coefficient 

Uplift Coefficient Uplift Coefficient Uplift Coefficient Uplift Coefficient 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 90 180 270 360 
Wind Direction 

2107 2108
2

2 

1.8 

1.8 

1.6 

1.6 

1.4 

1.4 

1.2 

1.2 

1 

1 

0.8 

0.8 

0.6 

0.6 

0.4 

0.4 

0.2 

0.2 

0 

0 

0 90 180 270 360 0 90 180 270 360 
Wind Direction Wind Direction 

2109 2110
2

2 

1.8 

1.8 

1.6 

1.6 

1.4 

1.4 

1.2 

1.2 

1 

1 

0.8 

0.8 

0.6 

0.6 

0.4 

0.4 

0.2 

0.2 

0 

0 

0 90 180 270 360 0 90 180 270 360 
Wind Direction Wind Direction 

2111 2112
2

1.8 

1.8 

1.6 

0.2
0
0.2 

0 

0 90 180 270 360 0 90 180 270 360 
Wind Direction Wind Direction 

Figure 4.20. Comparison of Wind Tunnel Measured (open squares) and Simulated 
(solid squares) Joist Uplift Coefficients as a Function of Wind Direction 
(concluded). 

4.6 Wind Loads on High-Rise Buildings 
In the case of high-rise buildings, overall structural loads are not modeled. 
Wind induced 
damage to high-rise buildings is modeled as being associated with wind 
induced failure 
of components (i.e., windows) and damage to windows caused by windborne 
debris. The 
maximum magnitudes of the directionally dependent exterior cladding pressure 
load 

1.6 

Uplift Coefficient 

1.4 

1.4 

1.2 

1.2 

1

1 

0.8 

0.8 

0.6 

0.6 

0.4 

0.4 

Chapter 4. Wind Loads 

?
model are set equal to the peak values given in ASCE-7-02, and information on 
directionality was derived using data given in Djakovich (1985) and the 1995 
Version of 
the British Wind Loading Standard, CP3. 

Example directional plots of modeled wind induced pressures and suctions have 
been 
developed for a rectangular building and a square building. The rectangular 
building has 
a length of 100' and a width of 40'. The square building has a width of 40'. 
The exterior 
pressures are given along a ring around the building spaced a 5' apart, with 
the first 
location positioned 2.5' from the edge. Figures 4.21 and 4.22 indicate the 
locations on the 
model buildings for which the pressure coefficients apply. 

Figure 4.21. Location of Pressures Points for 2.5:1 High-Rise Building. 

Figure 4.22. Location of Pressures Points for 1:1 High-Rise Building. 

HAZUS-MH MR3 Technical Manual 

?
Plots of the pressure coefficients as a function of wind direction are given 
in Figures 4.23 
and 4.24 for the buildings having length to width ratios of 2.5:1 
(rectangular) and 1:1 
(square), respectively. Note that in the case of the building having an 
aspect ratio of 
2.5:1, locations on the short face experience large peak negative pressures 
for winds 
approaching from both 00 and 1800, reflecting the fact that the flow does not 
reattach to 
the short wall. In the case of winds approaching perpendicular to the short 
wall, the peak 
negative pressures on the long wall occur for one direction only, owing to 
the fact that the 
flow reattaches to the long wall. 

Chapter 4. Wind Loads 

?
Wall Location = 1 Wall Location = 2 
1 

0.5 

0.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 3 Wall Location =4
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location = 5 Wall Location = 6 
1 

1 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 7 Wall Location =8
1

1 

0.5 

0.5 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

Figure 4.23. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Rectangular High-Rise Building Having a Length to Width 
Ratio of 2.5. 

HAZUS-MH MR3 Technical Manual 

?
4-32 

Wall Location =9 Wall Location = 10 

1

1 

0.5 

0.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 11 Wall Location =12
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location =13 Wall Location = 14 
1 

1 

0.5

0.5 
0 
-0.5 

0 

-0.5 

-1 

-1 

-1.5 

-1.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 15 Wall Location =16
1

1 

0.5 

0.5 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 -2 
0 90 180 270 360 0 90 180 270 360 
Wind Direction (Degrees) Wind Direction (Degrees) 

Figure 4.23. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Rectangular High-Rise Building Having a Length to Width 
Ratio of 2.5 (continued). 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

Chapter 4. Wind Loads 

?
Wall Location =17 Wall Location = 18 
1 

0.5 

0.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 19 Wall Location =20
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location =21 Wall Location = 22 
1 

1 

0.5 

0.5 
0
0 
-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 23 Wall Location =24
1

1 

0.5 

0.5 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 -2 
0 90 180 270 360 0 90 180 270 360 
Wind Direction (Degrees) Wind Direction (Degrees) 

Figure 4.23. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Rectangular High-Rise Building Having a Length to Width 
Ratio of 2.5 (continued). 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

HAZUS-MH MR3 Technical Manual 

?
4-34 

Wall Location =25 Wall Location = 26 

1

1 

0.5 

0.5 

-2

-2 

0 90 180 270 360

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 27 Wall Location =28
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location =29 Wall Location = 30 
1 

1 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

-2

-2 

0 90 180 270 360

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 31 Wall Location =32
1

1 

0.5 

0.5 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

Figure 4.23. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Rectangular High-Rise Building Having a Length to width 
Ratio of 2.5 (continued). 

Chapter 4. Wind Loads 

?
Wall Location =33 Wall Location = 34 
1 

0.5 

0.5 

-2

-2 

0 90 180 270 360

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 35 Wall Location =36
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location =37 Wall Location = 38 
1 

1 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

-2

-2 

0 90 180 270 360

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 39 Wall Location =40
1

1 

0.5

0.5 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

0.5 

0.5 
0
0 
-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

Figure 4.23. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Rectangular High-Rise Building Having a Length to Width 
Ratio of 2.5 (continued). 

HAZUS-MH MR3 Technical Manual 

?
4-36 

Wall Location =41 Wall Location = 42 

1

1 

0.5 

0.5 

-2

-2 

0 90 180 270 360

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 43 Wall Location = 44
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location =45 Wall Location = 46 
1 

1 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

-2

-2 

0 90 180 270 360

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location =47 Wall Location =48 
1 

1 

0.5 

0.5 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 -2 
0 90 180 270 360 0 90 180 270 360 
Wind Direction (Degrees) Wind Direction (Degrees) 

Figure 4.23. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Rectangular High-Rise Building Having a Length to Width 
Ratio of 2.5 (continued). 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

Chapter 4. Wind Loads 

?
Wall Location =49 Wall Location = 50 
1 

0.5 

0.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 51 Wall Location = 52
1

1 

Pressure Coefficient Pressure Coefficient 
Pressure Coefficient Pressure Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location =53 Wall Location = 54
1

1 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 55 Wall Location =56
1

1 

0.5 

0.5 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

Figure 4.23. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Rectangular High-Rise Building Having a Length to Width 
Ratio of 2.5 (concluded). 

HAZUS-MH MR3 Technical Manual 

?
4-38 

Wall Location = 1 Wall Location = 2 

1

1 

0.5 

0.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 3 Wall Location =4
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location = 5 Wall Location = 6 
1 

1 

0.5 

0.5 
0
0 
-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 7 Wall Location =8
1

1 

0.5 

0.5 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

Figure 4.24. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Square High-Rise Building. 

Chapter 4. Wind Loads 

?
4-39 

Wall Location =9 Wall Location = 10 

1

1 

0.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 11 Wall Location =12
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0 

-0.5 

-1 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location =13 Wall Location = 14 
1 

1 

0.5 

0 

-0.5 

-1 

-1.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 15 Wall Location =16
1

1 

0.5 

0 

-0.5 

-1 

-1.5 

-2 -2 
0 90 180 270 360 0 90 180 270 360 
Wind Direction (Degrees) Wind Direction (Degrees) 

Figure 4.24. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Square High-Rise Building (continued). 

0.5 

0 

-0.5 

-1 

-1.5 

HAZUS-MH MR3 Technical Manual 

?
4-40 

Wall Location =17 Wall Location = 18 

1

1 

0.5 

0.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 19 Wall Location =20
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location =21 Wall Location = 22 
1 

1 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 23 Wall Location =24
1

1 

0.5 

0.5 

0

0 

-0.5 

-0.5 

-1

-1 

-1.5 

-1.5 

-2 -2 
0 90 180 270 360 0 90 180 270 360 
Wind Direction (Degrees) Wind Direction (Degrees) 

Figure 4.24. Maximum and Minimum Pressure Coefficients vs. Wind Direction Use 
d in Modeling of a Square High-Rise Building (continued). 

0.5 

0.5 

0 

0 

-0.5 

-0.5 

-1 

-1 

-1.5 

-1.5 

Chapter 4. Wind Loads 

?
4-41 

Wall Location =25 Wall Location = 26 

1

1 

0.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 27 Wall Location =28
1

1 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

Pressure Coefficient Pressure Coefficient Pressure Coefficient Pressure 
Coefficient 

0 

-0.5 

-1 

-1.5 

-2 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

Wall Location =29 Wall Location = 30 
1 

1 

0.5 

0 

-0.5 

-1 

-1.5 

-2

-2 

0 90 180 270 360 

0 90 180 270 360
Wind Direction (Degrees) Wind Direction (Degrees)

Wall Location = 31 Wall Location =32
1

1 

0.5 

0 

-0.5 

-1 

-1.5 

-2 
0 90 180 270 360 0 90 180 270 360 

Wind Direction (Degrees) Wind Direction (Degrees) 

0.5 

0 

-0.5 

-1 

-1.5 

Figure 4.24. Maximum and Minimum Pressure Coefficients vs. Wind Direction 
Used in Modeling of a Square High-Rise Building (concluded). 

HAZUS-MH MR3 Technical Manual 

?
4-42 

Chapter 4. Wind Loads 

?
Chapter 5. Windborne Debris 

A significant amount of the damage to structures associated with hurricane 
winds is 
produced by windborne debris impacting the buildings and damaging the 
building 
exterior, including roof covering, windows, doors, and other openings. Two 
windborne 
debris models are used in the model. The first applies to residential 
environments and the 
second is a commercial building model for predicting the damage produced by 
windborne 
gravel. The windborne debris model used to estimate impact risk in 
residential 
environments is based on the model described in Twisdale, et al. (2000a, 
2000b). The 
residential windborne debris model uses an explicit damage and missile 
transport 
approach. A description of the residential windborne debris model and its 
simplified 
implementation in damage simulations is provided in Section 5.1. The gravel 
missile 
model, developed to predict the damage to buildings from roof gravel 
missiles, is 
described in Section 5.2. 

5.1 Windborne Debris   Residential Missile Model 
Windborne Debris Simulation and Analysis. The windborne debris model 
developed by 
Twisdale, et al. (2000a, 2000b), is used to quantify the windborne debris 
risk in typical 
residential environments. The residential missile model focuses on debris 
produced from 
the roofs of residential structures since, as observed in the field after 
severe wind events, 
most of the impact damage is caused by debris that is generated from the 
roofs of nearby 
buildings. The debris types modeled include roof tiles, shingles, sheathing 
panels, planks 
(structural members) and whole roofs. The model represents the first to 
attempt to 
quantify, using physically-based models, the risk of impact damage to 
buildings in a 
residential environment. 

The approach used in the residential model consists of modeling typical 
subdivisions, as 
shown for example in Figure 5.1, and impacting the buildings with debris 
generated from 
within the model subdivision. Through simulations of hurricane winds striking 
the 
subdivision, the roof components fail when the modeled wind loads exceed the 
component capacity. Once a component fails, it is released into the turbulent 
hurricane 
wind field, where the trajectory is computed until the component strikes 
either the ground 
or another structure. If a missile strikes the wall of another building, the 
impact velocity, 
energy and momentum are recorded. In the trajectory model, the turbulence 
within the 
hurricane wind field is modeled using an approach in which both the 
longitudinal and 
vertical components of the turbulent wind field are simulated. The missile 
risk 
simulations were performed for the subdivisions located in three different 
surrounding 
terrain environments. 

For each terrain case examined, a total of 36 hurricanes were simulated for 
four 
maximum peak gust speeds of 110 mph, 130 mph, 150 mph and 170 mph, in open 
terrain 
conditions (i.e., z0 = 0.03 m). A total of 9 different representative 
hurricane tracks relative 
to the subdivision site were simulated for each wind speed case. Typical 
terrain 

HAZUS-MH MR3 Technical Manual 

?
5-2 
Chapter 5. Windborne Debris 
Figure 5.1. Example Model Subdivision Used in the Missile Risk Study. 
conditions representative of open, suburban and treed terrain, were modeled 
for the wind 
simulation. The open terrain case was representative of the wind conditions 
that would be 
expected to be experienced on barrier islands. The suburban terrain (i.e., z0 
= 0.3 m) is 
representative of a residential area with relatively few tall trees to reduce 
the wind speeds 
(such as South Florida), and the treed terrain (i.e., z0 = 1.0 m) is 
representative of a terrain 
with trees having heights greater than the heights of the buildings. This 
last terrain type is 
representative of many suburban locations along the US hurricane coastline, 
away from 
barrier islands. 
Examples of the turbulent wind traces generated for open country conditions 
at a height 
of 10 m above ground are given in Figure 5.2. Also shown, in Figure 5.2, is 
the  gust 
speed envelope  represented by the solid line. This envelope is defined as 
the mean wind 
speed multiplied by the three second gust factor. A comparison of the 
turbulent wind 
trace and the theoretical envelope shows that the peak gusts produced by the 
turbulent 
model equal or slightly exceed the envelope value about three to four times 
per hour. 
Upon completing the wind speed simulations (i.e., nine hurricanes), 
information on the 
total number of hits and associated energy and momentum levels are available. 
Given the 
information on the expected number of missile impacts on the walls of a 
structure, the 
risk of damage to an opening, PV(D) for a given wind speed, V can be obtained 
from: 
PV (D) = 1- exp[- .q(1- P(. < . d ))] (5.1) 
?
5-3 
HAZUS-MH MR3 Technical Manual 
Wind Speed at H=10m in Open Terrain, 150mph-Hurricane #1 
0 
20 
40 
60 
80 
100 
120 
140 
160 
0 3600 7200 10800 14400 18000 21600 
Time (sec) 
Wind Speed (mph) 
Wind Speed at H=10m in Open Terrain, 150mph-Hurricane #2 
0 
20 
40 
60 
80 
100 
120 
140 
160 
0 3600 7200 10800 14400 18000 21600 
Time (sec) 
Wind Speed (mph) 
Figure 5.2. Example Hurricane Wind Speed Traces Used for the 150 mph Case. 
?
Wind Speed at H =10m in Open Terrain, 150mph-Hurricane #3 

0 
20 
40 
60 
80 
100 
120 
140 
160 
Wind Speed (mph) 
0 3600 7200 10800 14400 18000 

Time (sec) 

Figure 5.2 . Example Hurricane Wind Speed Traces Used for the 150 mph Case 
(concluded). 

where .d is the energy or momentum level assumed to produce damage, q is the 
fraction 
of the building surface occupied by windows and glass doors, and . represents 
the mean 
number of missile impacts per building. P(.< .d) is the probability for the 
impact energy 
or momentum (.) to be less than the damage threshold value (.d) given an 
impact. The 
probability of no damage, R is expressed in Equation 5.2: 

R =1-PV ()D =exp[-.q (1-P(.<.d ))]. (5.2) 

Using Equation 5.2, probability curves for R vs. wind speed were developed, 
which 
indicate the energy or momentum level a window/door must be able to withstand 
in order 
to achieve a given probability level of no damage. Figure 5.3 shows an 
example of the 
reliability curves generated for one of the cases examined in the windborne 
debris risk 
study. The example in Figure 5.3 presents reliability curves for several 
impact energies 
and a q value of 0.2. 

Implementation of the Windborne Debris Model. In the development of the 
damage and 
loss functions described herein, it is not possible to explicitly model the 
windborne debris 
on a storm-by-storm basis using the detailed first principles based 
trajectory and impact 
model described above because of computer run-time limitations. The 
simplified 
windborne debris model makes use of the results of the explicit study using 
an analytical 
model which yields results similar to those obtained from the explicit model. 

Chapter 5. Windborne Debris 

?
0 
0.05 
0.1 
0.15 
0.2 
0.25 
0.3 
0.35 
0.4 
0.45 
0.5 
0.55 
0.6 
0.65 
0.7 
0.75 
0.8 
0.85 
0.9 
0.95 
1 
R, Reliability 
Eg (lb-ft) 
(b) Reliability vs Gust Speed for Various Design Equivalent Impact Energy, Eg 
20000 
10000 
5000 
2000 
1000 
500 
200 
100 
50 
20 
0 

110 120 130 140 150 160 170 

W, Peak Gust (mph) 

Figure 5.3. Example Energy Reliability Curves Derived for a Subdivision 
Comprised of Single Story Homes with Asphalt Shingle Roofs. 

Using the simplified debris modeling approach, at each time step in a 
hurricane 
simulation, the number of missiles impacting the wall is given as 

Nw =() ()2 v (5.3)

a.P1 vN 

where a is a constant, . is a constant representing the number, density and 
size of the 
missile source buildings in a 450sector, P1(.) is the fraction of missiles 
that hit the wall 
for the wind speed v, and N2(.) represents the number of missiles generated 
at each time 
step. Using a value of .equal to unity for the eight 450 sectors represents a 
missile 
environment similar to that used in the explicit study. Increasing or 
decreasing .has the 
effect of changing the density of the surrounding homes. The function P1(.) 
is obtained 
directly from the explicit missile simulation, whereas the function N2(.) is 
obtained by 
performing roof damage simulations using the same damage model used in the 
missile 
study, but for a larger number of wind speeds. The fraction of missiles 
generated that 
impact another building is a linear function of wind speed, whereas the 
number of 
missiles generated at each time step increases more rapidly. 

Given that the building is impacted by a missile at a given time step, the 
impact energy is 
determined by sampling from Weibull distribution in the form: 

k

P (e <E)=1-exp(-(E/C)) (5.4)

e 

HAZUS-MH MR3 Technical Manual 

?
where the Weibull parameters C and k were determined by fitting the energy 
exceedance 
data derived from the physically-based debris model. Both of the Weibull 
parameters are 
functions of the peak gust wind speed. 

Note that the building can only be impacted by missiles generated from within 
a 900 
sector, centered on a vector normal to the wall surface, and the wind must be 
approaching 
from within this sector. These criteria ensure that walls can be impacted by 
missiles only 
when the wind is blowing towards the wall, and only when there are missile 
sources 
upwind of the structure. 

Figure 5.4 shows a comparison of the reliability curves generated from the 
physically-
based model to those derived using the simplified model. In this figure, the 
points shown 
without lines connecting the data points represent those derived from the 
simplified 
model, whereas those shown with lines connecting the data points represent 
the original 
data as produced by the explicit missile simulation. 

110 120 130 140 150 160 170 

W, Peak Gust (mph) 

Figure 5.4. Comparison of Reliability Curves Derived from the Explicit 
Missile 
(lines) Simulation to Those Obtained from the Simplified Model (points only). 

The comparisons of the two sets of reliability curves show reasonable 
agreement, for all 
impact energy levels for wind speeds less than 130 mph. In the 150 mph case, 
the 
simplified model tends to slightly underestimate the reliability (i.e., 
overestimate the 
probability of missile impact), and at a wind speed of 170 mph, the reverse 
is true. In 
general, for missile impact energies less than 500 ft-lbs, the simplified 
model 
satisfactorily reproduces the results obtained from the explicit model. For 
very high 

0 
0.05 
0.1 
0.15 
0.2 
0.25 
0.3 
0.35 
0.4 
0.45 
0.5 
0.55 
0.6 
0.65 
0.7 
0.75 
0.8 
0.85 
0.9 
0.95 
1 
R, Reliability 
20000 
10000 
5000 
2000 
1000 
500 
200 
100 
50 
20 
0 
0 
20 
50 
100 
200 
500 
1000 
Eg (lb-ft) 
Chapter 5. Windborne Debris 

?
impact energies (~1000 ft-lbs), the simplified model underestimates the 
frequency of 
impact, as indicated by the overestimate in the computed reliability levels. 
This 
underestimate of the frequency of very high energy impacts is not of great 
concern, since 
damage to window protection (discussed later) occurs at energies much lower 
than 1000 
ft-lbs. 

In summary, a simplified, fast running missile impact model is used which 
approximates 
the results derived with an explicit modeling approach. The simplified model, 
which 
yields reasonable estimates of the windborne debris risk, is used in the 
building damage 
model to develop the fast-running HAZUS damage and loss functions. 

5.2 Windborne Debris   Commercial Missile Model 
The study on the risk of damage to commercial buildings by windborne gravel 
debris 
consists of two components. The first component is a mathematical simulation 
of the 
debris generation, transport and impact on building envelopes during a 
hurricane passage. 
This windborne gravel debris model simulates the characteristic layout of the 
gravel 
layers, the turbulent hurricane wind trace, local wind fields on the roof, 
wind action on 
gravel stones, gravel scour and transport, and the eventual interception of 
its trajectory 
with a building s envelope (or with the ground). Throughout a hurricane, the 
simulation 
provides detailed records of individual gravel missiles generated, their 
origin location, 
transport distance, impact location and impact momentum, as well as the 
characteristic 
layout of the gravel remaining on the roof. This model is a stand-alone 
program, and can 
be used for specific case studies or used to derive information for gravel 
debris risk 
assessment for generic settings. The development of this model, along with 
its validation 
against three field-observed cases, is described in detail in Section 5.2.1. 
It is 
demonstrated that the model agrees with field observations reasonably well. 

The second component of the study aims at constructing a fast-running risk 
model to 
estimate the probability of damage to building envelope by windborne gravel 
debris 
given the impact resistance capacity of the target surfaces. The fast-running 
debris risk 
model estimates the expected number and location of fenestration elements 
breached by 
debris within each short time interval in order to interactively account for 
the rainwater 
penetration and internal pressure change in a building. The model utilizes 
the previous 
analytical formulation on debris risk (Twisdale, et. al, 2000a, 2000b) and 
the above 
physical modeling on generic configurations to provide inputs (for example, 
the expected 
number of impacts and impact momentum) required by the analytical risk 
formulation. 
From the simulation records, the number of impacts per unit area within a 
time interval 
and the impact momentum distribution for a target surface element, such as a 
window, 
with specified location and orientation relative to the source are derived. 
The derived 
number of impacts and momentum distribution are functions of the reference 
wind speed, 
wind direction, terrain, height and area of gravel source roof, depth of the 
gravel ballast 
layer and gravel size. Based on this information, a set of simplified 
expressions 
describing the relationships between the variables yields a fast-running 
debris module 
suitable for incorporation into the damage model. This second part of the 
model is 
described in Section 5.2.2. 

HAZUS-MH MR3 Technical Manual 

?
5.2.1 Windborne Gravel Debris Simulation and Case Studies 
Flat, built-up roofs and ballasted membrane roofs are commonly seen on high-
rise and 
low-rise commercial buildings in urban and suburban areas. Gravel used on 
these roofs 
often becomes windborne missiles during high winds. This problem has been 
observed 
and reported by many field investigators (e.g., Minor, et al., 1978; Minor, 
1994; Minor 
and Behr, 1993a, 1993b; and Behr and Minor, 1994). This section describes the 
computer 
modeling of gravel missile generation (scour and blow-off), transport 
trajectory and 
physical impact on surfaces. Three case studies are presented along with 
comparisons 
against field observations. 

Scope of the Simulation. The problem of debris generation, transport and 
impact 
involves many variables. The present model includes the effects of the mean 
hurricane 
wind trace (speed and direction variation with time), correlated three-
dimensional 
turbulence components, building geometry and street layout, roof gravel 
configuration 
(diameter distribution and thickness), as well as upstream terrain 
influences. Local wind 
velocity fields over the roof and in the wake behind the buildings are also 
approximated. 
The gravel scour pattern, trajectories and impact statistics are used to 
compute the 
information required for model calibration and for risk prediction. 

Missile Generation from Roof Gravel. Within an assembly of gravel stones of 
nearly 
spherical shape and variable diameters loosely lying on a flat bed, an 
individual stone on 
the top layer will be subject to a drag, an uplift, and an overturning moment 
caused by the 
wind blowing over the surface. Gravity and the constraint of other stones 
balance these 
wind forces; however, when the wind speed increases and exceeds some 
threshold value, 
the wind forces overcome these constraints. At this point, the stone starts 
to intermittently 
rotate and shift horizontally. At another slightly higher threshold wind 
speed, the stone 
will be lifted and blown away. Both the first and second threshold wind 
speeds have been 
shown to be proportional to the square root of the stone diameter (Kind and 
Wardlaw, 
1984). The model developed here considers the second threshold wind speed, 
since only 
at this higher wind speed is the stone released into the wind field. 

In determining the local wind speeds on the roof surface, the flow separation 
and vortex 
induced velocity field is included in the model as a function of building 
geometry and the 
free stream wind speed and direction. An example of the horizontal component 
of the 
mean wind velocity on the roof surface of a cube-shaped building is shown in 
Figure 5.5 
for an oblique on-coming wind. Figure 5.5 also shows a photograph of stream 
lines 
obtained from a flow visualization test given for comparison. The streamline 
patterns are 
in a good agreement. 

The resulting scour pattern is illustrated in Figure 5.6 and compared with 
qualitative wind 
tunnel and field observations (Kind and Wardlaw, 1984). It is seen that the 
computer 
model reproduces the typical two-lobed scour pattern under oblique winds. 
Note that the 
wind tunnel model of Kind and Wardlaw has a low parapet so that the scour 
pattern shifts 
slightly downstream. Moreover, their model represents a low-rise building 
such that the 
high turbulence in the lower part of the simulated boundary layer flow 
reduces the 
sharpness of the two-lobed pattern compared to the field observation and the 
computer 
model results which are both for multi-story buildings (low turbulence 
intensity). 

Chapter 5. Windborne Debris 

?
Surface Flow Pattern Comparison 

Wind Tunnel 
(Courtesy of Mr. Bengt G. Wiren, 
Swedish Royal Institute of Technology) 
0 
10 
20 
30 
40 
50 
60 
70 
80 
90 
100 
0 10 20 30 40 50 60 70 80 90 100 
X 
Y 
Computer 

Figure 5.5. Example of Computer-Modeled Surface Flow Pattern Compared with 
Wind Tunnel Observation. 

Missile Transport in the Wind Field. When gravel is released into the three-
dimensional 
wind field, it is transported by the turbulent wind. This is modeled by 
numerically solving 
the equation of motion for a particle of mass, with the wind force acting on 
the gravel 
updated as a function of location and time. The influence of the vortex flow 
over the 
rooftop and the wake flow downstream of the buildings are incorporated with 
the oncoming turbulent wind to obtain a resultant wind field. 

As an example, the computed trajectory for a sample gravel missile generated 
from the 
roof of a cube-shaped building is illustrated in Figure 5.7 for a 450 oblique 
wind. The 
gravel missile is first moved sideways toward one of the upstream roof edges 
by the 
spiral vortex flow near the roof surface. Kind and Wardlaw s (1984) wind 
tunnel 
experiments on gravel scour and blow-off also indicate that gravel missiles 
generated 
from the front portion of the roof would leave the roof over the upstream 
edges. After it 
leaves the roof, the trajectory starts to gradually bend into the on-coming 
wind. The 
gravel missiles generated from the downstream portion of the roof would have 
less 
curved trajectories since the spiral vortex flow will be weakened downstream. 

Missile Impact on Building Envelopes. The trajectory of the gravel missile 
will 
eventually intersect with a surface, either the ground or a building 
envelope, which are 
geometrically defined on a global coordinate frame. The impact location and 
velocity are 
recorded. The impact momentum and energy are then calculated for each gravel 
missile 
of a given mass. This information can be used to derive statistical 
distributions of impact 

HAZUS-MH MR3 Technical Manual 

?
5-10 
Chapter 5. Windborne Debris 
Gravel Scour Pattern 
Figure 5.6. Example of Gravel Scour Pattern Compared with Field and 
Experimental Observations. 
location, momentum or energy, and number of impacts on a specific area of the 
building 
envelope, which are subsequently used to predict the risk of building 
envelope breach 
during a storm. The recorded impact results for specific building complexes 
can also be 
used to perform model calibration and validation studies by comparing them 
with the 
field observed data for the same buildings and environments. 
Case Study 1 - Kendall, Florida, Hurricane Andrew. During Hurricane Andrew, 
the 
downstream buildings in a high-rise complex in Kendall, Florida (Figure 5.8), 
suffered 
severe windborne debris impact damage to the windows, which has been 
investigated and 
documented by several researchers (e.g., Smith, 1999; Behr and Minor, 1994; 
and Minor, 
1994). This case provides a basis for examining the results obtained with the 
windborne 
debris model. 
?
0 
5 
10 
15 
20 
25 
30 
35 
-3 5 -30 -2 5 -20 -15 -10 -5 0 5 10 15 20 25 
Y 
Z 
-15 
-10 
-5 
0 
5 
10 
15 
20 
25 
30 
-35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 
Y 
X 
Elevation 
View

Plan 
View 

WIND 

Figure 5.7. Example of Gravel Missile Trajectory. 

The plan of the complex is shown in Figure 5.9. The missile source is the 
Marriott Hotel 
roof gravel ballast. The gravel missiles were generated from the two 
penthouse roofs 
only, since the other parts of the roof have tall parapets that prevented 
gravel from 
leaving the roof. For the case study, a Hurricane Andrew mean wind trace was 
re-created 
for the site using the hurricane model described in Chapter 2. The mean wind 
direction 
changed from northerly to southeasterly in a time period of about 2.5 hours 
during which 

HAZUS-MH MR3 Technical Manual 

?
Figure 5.8. The Marriott-Datran Complex in Kendall, Florida. 

open terrain 10 m-height peak gust winds are above 90 mph. The largest peak 
gusts 
reached 140 mph when the mean wind direction was approximately normal to the 
northeast walls of the buildings. Typical 3-dimensional turbulence components 
for a 
terrain roughness length of 0.5 m (slightly rougher than standard suburban) 
were 
simulated. The mean diameter of the roof gravel was determined to be 12 mm 
from 
survey records reported in Behr and Minor (1994) and Smith (1999) with a 
distribution 
given in ASTM Standards, Designation D1863-93 (Re-approved 1996) for Size # 
67A. 
The depth of the gravel ballast layer on the roof was 76 mm (Behr and Minor, 
1994). The 
window glass on the downstream buildings is 6 mm thick fully tempered 
monolithic glass 
(Behr and Minor, 1994) that provides an estimated threshold average breakage 
momentum of 0.1 kg-m/s (Minor, 1994). 

The impact results obtained with the computer model are shown in Figure 5.10 
for the 
northeast walls of the two downstream buildings, where each dot represents a 
damaging 
hit. A damaging hit is an impact by a gravel missile with its momentum s 
component 
normal to the wall being larger than the 0.1 kg-m/s threshold. The coordinate 
grid 
approximately corresponds to the window grid, one cell containing one window. 
One or 
more damaging hits within a cell yield one count of damaged window. Note 
that, for the 
case where windows occupy only a percentage of the wall area such as these 
buildings, 
only the corresponding percentage of generated gravel missiles that are 
randomly 
sampled is used for damage counts. The results shown in Figure 5.10 are these 
sampled 
hits. The photographs (Minor, 1994; Smith, 1999) documenting the window 
breaches are 
shown in Figure 5.11. For the northeast wall of Datran Tower I, the 
simulation yields a 
window failure rate of 96.7% for the upper 10 floors, in good agreement with 
the 
observed value of 97.4% as counted from Figure 5.10(a) for the same part of 
the wall. 

Chapter 5. Windborne Debris 

?
5-13 
HAZUS-MH MR3 Technical Manual 
Figure 5.9. Approximate Plan of the Marriott-Datran Complex. 
Other photos were used to aid the counting for the upper left corner of the 
wall and to 
confirm the counts on other parts, while the lower few floors were omitted 
from the 
counting because the damage state cannot be determined for this small area 
using any of 
the available photos or reports. For the northeast wall of Datran Tower II, 
the simulation 
shows a failure rate of 61.9% for the upper 10 stories. This is also 
consistent with the 
observed damage-state of 63.2% for the same part as determined from Figure 
5.11(b). 
Again, window breakage on the lower right corner of Datran Tower II cannot be 
clearly 
identified from the photos presented here, nor with other available photos 
and reports. 
It is seen that the agreement between the modeled and observed damage states 
are 
remarkably good for this case. 
?
5-14 
Chapter 5. Windborne Debris 
Datran Tower 1, Northeast Wall 
0
3
6
9 
12 
15 
18 
21 
24 
27 
30 
33 
36 
39 
42 
45 
48 
51 
54 
57 
0 6 12 18 24 30 36 42 48 
Local X (m) 
Local Y (m) 
Datran Tower 2, Northeast Wall 
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6
9 
12 
15 
18 
21 
24 
27 
30 
33 
36 
39 
42 
45 
48 
51 
54 
57 
0 6 12 18 24 30 36 42 48 
Local X (m) 
Local Y (m) 
(a) (b) 
Figure 5.10. Impacts on Walls with Momentum Exceeding the Threshold for 
Damage. 
Case Study 2 - Downtown Houston, Texas, Hurricane Alicia. For this case, only 
partial 
information is available on the details of the missile sources, target 
glazing properties and 
damage states. Assumptions on some of the required information were thus made 
to 
facilitate the simulation. A qualitative comparison of simulated and observed 
glass 
damage is presented. 
Figures 5.12 through 5.14 show the area in downtown Houston for which the 
simulations 
were carried out and compared with available observations. The seven 
buildings bounded 
by McKinney and Polk Streets in the NE to SW direction and by Milam and Smith 
Streets in the SE to NW direction were simulated for missile source and/or 
impact 
damage. The plan geometry of these buildings was modeled as shown in Figure 
5.15. The 
available information and the assumed values of required inputs are listed in 
Table 5. 1. 
The time-varying hurricane wind speed and direction trace at the site was re-
created 
using the hurricane model described in Chapter 2. The site was modeled as an 
urban 
terrain with a roughness length being about 1 m for all upstream directions. 
The strong 
winds were from a quadrant centered on the east. The peak gust wind speed was 
estimated to be about 56 m/s (125 mph) at the roof height (110 m) of the 
gravel missile 
source buildings. 
The results of the simulation are presented in Table 5.2, in comparison with 
the available 
observations. The overall agreement between the simulated results and the 
observations is 
reasonable. 
?
(a) Datran Tower I, Plywood Boarding of Broken Windows Underway (Picture 
from Minor, 1994). Arrows indicate windows that were not damaged. 
Tower II 
(b) Datran Towers I and II, Plywood Boarding of Broken Windows Complete 
Except for Lower Floors (Courtesy of TLSmith Consulting). 
Figure 5.11. Damage Scenarios for Datran Towers I and II Downstream of the 
Marriott Hotel. 

HAZUS-MH MR3 Technical Manual 

?
Figure 5.12. General View of the Houston Central Business District 
(Kareem and Stevens, 1985). 

Case Study 3 - Williston School, Wilmington, NC, Hurricane Bonnie. The 
Williston 
School two-building complex represents a case of low-rise buildings. 
Information on the 
details of the missile sources, target glazing properties and damage states, 
was collected 
shortly after Hurricane Bonnie s landfall at the North Carolina coast. 

Figure 5.16 shows the complex (looking north). The taller portion of the 
building on the 
right (east) was the major source of gravel missiles that hit the windward 
face of the 
2-story building on the left (west). The 3-story building located at the 
north end of the 
complex (the far end in the picture) was not a missile source, nor could it 
act as a 
shielding building, so it was not included in the simulation. Flow 
interference from this 
building was expected to be small and was neglected in the simulation. Figure 
5.17 shows 
the windward face of the main missile source building in the complex (looking 
southwest), which was the upstream building during the high wind period of 
Hurricane 
Bonnie when it was hovered over the Wilmington area. Figure 5.18 indicates 
the sizes of 
the roof gravel on this building. The same type of roof gravel was also used 
on other 
buildings of the complex; however, the majority of the glass damage is 
believed to have 
been caused by gravel originating from the roof of the upstream building, as 
indicated by 
both the field observations and the simulation. Figure 19 shows the glass 
damage on the 
downstream building, in which part of the roof of the upstream building is 
also seen. The 
geometry of the modeled complex is shown in Figure 5.20. The input data for 
the 
simulation are listed in Table 5.3. 

Chapter 5. Windborne Debris 

?
Figure 5.13. Aerial Perspective of Damaged High-Rise Buildings 
(Beason, et al., 1984). 

HAZUS-MH MR3 Technical Manual 

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5-18 
Chapter 5. Windborne Debris 
Figure 5.14. Plan View of Damaged High-Rise Building Towers 
(Beason, et al., 1984). 
A 
Allied Bank 
H=215m 
C 
1st International 
H=165m 
B 
Tenneco 
H=110m 
D 
1100 Milam 
H=140m 
E 
Hyatt Regency 
H=65m 
F 
Entex 
H=108m 
Sheraton H=75m 
G 
North 
0 50 100 (m) 
370 
Figure 5.15. Plan Geometry of the Modeled Buildings in the Houston Downtown 
Central Business District. 
?
Table 5.1. Basic Inputs of Missile Sources (Roof Gravel) and Target Glazing 
Properties 

Roof Gravel Glazing 
Bldg. 
Height(m 
) 
Mean 
Depth 
(cm) 
Mean 
Diameter 
(cm) 
Glaze/ 
Wall 
(%) 
Ave. 
Size (m x 
m) 
Thkns x 
Panes (cm 
x #) 
Glass 
Treatment 
Impact 
Resistance 
(kg-m/s) 
Allied Bank Plaza 100 1 x 1 0.6 x 2 T(2) 0.08 215 
Tenneco Building 2(1) 1 110 
First International Plaza 40 2W1.5 0.6W2 A 0.05 165 
1100 Milam 60 1.8W1 0.6W2 A 0.05 140 
Hyatt Regency Hotel 45 2W2 0.6W2 A 0.05 65 
Entex Building 1 1 100 1.5W1 0.6W2 A 0.05 108 
Sheraton Houston Hotel 40 1.2W1 0.6W2 A 0.05 75 

NOTE: (1) Shaded cells indicate assumed values. 

 (2) T   Tempered, A   Annealed and H   Heat Strengthened. 
Table 5.2. Number of Damaged Windows: Simulation vs. Available Observations 

Building 
Wall 
No. 
Wall 
Height 
(m) 
Wall 
Width 
(m) 
Resistance 
(kg-m/s) 
Total No. 
Window 
Panes 
Simulated 
No. Panes 
Damaged 
Simulated 
% 
Damaged 
Total No. 
Simulated 
Damaged 
Panes per 
Building 
Observed 
No. Panes 
Damaged 
1 215 35 0.08 7500 539 7.2 400~1000Allied Bank 
Plaza 2 215 21 0.08 4500 258 5.7 707 (Kareem) 
3 165 30 0.05 1300 143 11.0 650 
4 165 25 0.05 1100 200 18.2 (Kareem) 
First 
International 
Plaza 
5 165 32 0.05 1400 0 0.0 491 
6 65 55 0.05 1200 36 3.0 NoHyatt-
Regency 7 65 50 0.05 1100 29 2.6 65 Data 
Sheraton 
Houston Hotel 8 75 55 0.05 1400 34 2.4 34 No Data 
Entex 
Building 9 108 55 0.05 4000 98 2.5 98 143 (Minor) 
10 140 33 0.05 1550 223 14.4 
256 (Beason, 
Minor, 
Kareem) 
11 140 33 0.05 1550 0 0.0 No Data 
1100 Milam 
Street 
Building 
12 140 33 0.05 1550 24 1.5 
247 
32 (Beason, 
Minor, 
Kareem) 
HAZUS-MH MR3 Technical Manual 

?
Figure 5.16. A General View of the School Complex. 

Figure 5.17. The Windward Face of the Main Missile Source Building 
(looking southwest). 

Chapter 5. Windborne Debris 

?
Figure 5.18. Roof Gravel on the Upstream Building. 

The time-varying hurricane wind trace at the site was re-created and used as 
the input 
wind speed and direction data for the missile simulation. The site was 
modeled as open 
terrain with a roughness length of 0.03 m for the strong wind directions, 
which was 
within the NE quadrant. The maximum peak gust speed was estimated to be about 
40 m/s 
(90 mph) at roof height (11 m). 

The results of the simulation are presented in Table 5.4 and compared with 
the 
observations. The overall agreement between the simulated results and the 
observations is 
reasonable. One exception is that the number of damaged window glass panes is 
significantly overestimated for the lower-right glazed area on the front wall 
of the 
downstream building. This may be attributed to the shielding effects provided 
by a row of 
trees in front of this glazed area, as shown in Figure 5.21, which was not 
modeled in the 
simulation. 

Concluding Remarks. A first principles based model of windborne gravel debris 
generation and trajectory has been developed for commercial buildings in 
urban or 
suburban areas. Some qualitative comparisons with experimental and field 
observed data 
have been made for rooftop wind fields and roof gravel scour patterns. The 
three case 
studies carried out show, in general, good agreement with the field observed 
data for 
window glass breaches. 

HAZUS-MH MR3 Technical Manual 

?
(a) Looking Southwest 
(b) Looking Northwest 
Figure 5.19. Glass Damage on the Windward Face of the Downstream Building. 

Chapter 5. Windborne Debris 

?
Downstream Building 
Windward Elevation 
upper glaze area, 150 panes 
lower right glaze area, 66 panes lower left glaze, 39 panes 
Downstream Building 
Roof Plan 

H =9 m 

0 50 (m) 25 
Strong Winds 
North 
70 
Upstream Building 
Roof Plan 
H =11m H =6 m 
H =5 m 
H =4 m 
Upstream Building 
Windward Elevation 
(partial) 
upper glaze area, 63 panes 
lower glaze area, 63 panes 
Figure 5.20. Geometry of the Modeled Complex. 

Table 5.3. Basic Inputs of Missile Sources (Roof Gravel) and Target Glazing 
Properties 

Roof Gravel Glazing 
Bldg. 
Height 
(m) 
Mean 
Depth 
(cm) 
Mean 
Diameter 
(cm) 
Glaze/ 
Wall (%) 
Ave. Size 
(mWm) 
Thickness 
W Panes 
(cmW#) 
Glass 
Treatment 
Impact 
Resistance 
(kg-m/s) 
Upstream Bldg 3 1.2 18 1.2W0.6 0.3W2 T* 0.03 11 
Downstream Bldg 3 1.2 45 1.2W0.8 0.3W2 T 0.03 9 

* T   Tempered. 
Table 5.4. Number of Damaged Windows: Simulation vs. Available Observations 

Building 
Glaze Area 
No. on 
Front Wall 
Resistance 
(kg-m/s) 
Total 
No. 
Window 
Panes 
Modeled 
No. 
Damage 
d Panes 
Modeled 
% 
Damaged 
Panes 
Modeled 
No. 
Damaged 
Panes per 
Building 
Observed 
No. 
Damaged 
Panes 
Observed 
% 
Damaged 
Panes 
Observed 
No. 
Damaged 
Panes per 
Building 
Down-Upper 0.03 150 94 63.0 89 60.0 
Stream 
Building 
Lower left 0.03 39 8 20.5 151 13 33.3 122 
Lower right 0.03 66 49 74.2 20 30.0 
Upstream Upper 0.03 63 0 0.0 0 0.0 
Building Lower 0.03 63 0 0.0 0 0 0.0 0 

HAZUS-MH MR3 Technical Manual 

?
(a) The Trees in Front of the Lower-Right Glazed Area 
(b) Significantly Less Windows Damaged on the Lower-Right Glazed Area than on 
the Upper Glazed 
Area 
Figure 5.21. Trees in Front of the Lower-Right Glazed Area of the Downstream 
Building. 

Chapter 5. Windborne Debris 

?
5.2.2 Probability of Damage to Building Envelopes by Windborne Gravel Debris 
This section describes the development of a fast-running model for estimating 
damage to 
building envelopes caused by windborne gravel debris. As a first step, the 
model has been 
developed for low-rise buildings in suburban commercial areas. The model 
utilizes the 
analytical formulation for debris impact damage risk developed previously and 
the 
physical modeling methodology described above to obtain the information 
required by 
the analytical formulation (i.e., expected number of impacts and impact 
momentum 
distribution). The general scheme and the results are presented below. 

Analytical Formulation for Probability of Impact Damage. Several analytical 
formulations have been derived to estimate the probability of impact damage 
as a 
function of the number of impacts and the impact momentum distribution 
(Twisdale, et. 
al, 2000a and 2000b). It was found that the following simple model, which 
assumes 
mutually independent (i.e., noncontingent) impacts and uses a Poisson 
distribution for the 
number of impacts, provides a sufficiently good approximation to the results 
generated 
by the detailed model presented in Section 5.2.1. This simple model is 
adopted as the 
basic formulation for the impact damage risk and is modified to express the 
probability of 
impact damage during a hurricane for a given area of the target, A, and in a 
given time 
interval, T, as follows, 

PD = 1 - e- A7T 7N 7(1-Pm) (5.5) 

where N is the expected number of impacts on the surface per unit area and 
per unit time 
interval and Pm is the probability of an impact having its momentum (m) less 
than the 
impact momentum resistance capacity of the surface (md). Many variables 
influence the 
expected number of impacts and the momentum distribution, and thus the damage 
probability. These variables and their effects are discussed below. 

Physical Modeling to Obtain the Number of Impacts and Momentum Distribution. 
As 
mentioned above, the number of impacts and the momentum distribution are 
functions of 
many variables. The functional relationships are implicit and physical 
modeling is 
required to provide  data  for quantifying these relationships. Table 5.5 
lists the set of 
variables that the present model considers. Also shown in the table are the 
values that 
were used as inputs for the physical modeling to develop the fast-running 
module for 
estimating the risk of windborne debris damage in suburban commercial areas. 

Within the overall damage model, into which the fast-running debris model is 
incorporated, the values of some of the variables, such as the peak gust 
speeds and the 
location, orientation and size of the target fenestration elements, are 
explicitly known for 
each time increment. However, some variables such as the detailed 
configuration of the 
gravel sources are not specifically known and can only be accounted for 
generically or 
statistically. On the other hand, within the detailed physical modeling, 
millions of gravel 
stones are modeled and some critical records are stored for off-line 
analysis. Each 
simulation case will normally take several hours to a couple of days to 
complete. Hence, 
the number of values for each variable needs to be limited to avoid creating 
an 
unrealistically large case matrix. 

HAZUS-MH MR3 Technical Manual 

?
Table 5.5. Variables that Influence the Number of Impacts and Momentum 
Distribution 

Variable 
Wind Parameters Source Parameters (Roof Gravel) Target Parameters (Wall 
Fenestration) 
Shielding 
Effects 
Peak 
Gust 
Mean 
Direction 
Relative to 
Source 
Building 
Time 
Length for 
Sustained 
Mean 
Wind Terrain 
Source 
Roof 
Height 
Source Roof 
Area 
Gravel 
Mean 
Dia. 
Depth 
of 
Gravel 
Distance 
to Source 
Lateral 
Location 
Elevation 
above 
Ground 
Orientation 
Area 
(Size) 
Notation V t T z0 Hs S Dg Dp d l h _ A 
Units m/s 
(mph) deg hr m m (ft) mWm (ftWft) cm cm m m m deg mWm 
Accounted 
for with 
Source 
Reduction 
Factor in 
General 
Model 
Values 
Used for 
Simulation 
40 (90) 0 to 45 in 
increments 
of 5 
0.25 0.3 
6 (20) 15W15 
(50W50) 
1.1 4 Detailed in Figure 5.22 
in 
Equation 
5.560 (134) 
18 (60) 60W60 
(197W197)75 (168) 

For each case, the detailed physical model was run for 15 minutes using a 
mean wind 
speed that would produce the specified open 10-meter peak gust speed when the 
turbulence trace is superimposed. This process was repeated for each mean 
wind 
direction relative to the upstream edge of the source building. The mean wind 
directions 
from 0 to 450 are considered the representative wind angles for a nearly 
square roof, due 
to symmetry. A roughness length for suburban terrain was modeled which is 
typical for 
areas where low-rise commercial buildings are normally located. Two source 
roof heights 
and two source roof areas were modeled, which yield four roof height-area 
combinations. 
The modeled heights and roof areas were chosen to represent lower- and upper-
end 
values for low-rise commercial buildings. Only one value of the mean gravel 
diameter 
was used; however, the model automatically assumes a dual-linear diameter 
distribution 
bounded by zero and twice the mean diameter. The assumed depth of gravel on 
the roof 
is 4 cm. This parameter mainly affects the supply of missiles within local 
regions on the 
roof, which were modeled as 1 m by 1 m cells. 

To model the targets, 12 vertical, semi-infinite planes were placed 
downstream of the 
source building as illustrated in Figure 5.22. Five of the surfaces are 
spaced 30 m apart to 
the right of the source building. The other seven surfaces are spaced 21 m 
apart, at 450 to 
the source building. The normal vectors of these planes are all within 1450 
to any of the 
wind directions modeled. Outside this relative wind quadrant, the general 
model assumes 
no risk of debris damage. This simplification approximates the actual case 
since both the 
number of impacts and the impact momentum (the normal component of the 
debris  
terminal momentum) decrease with increasing wind angle relative to the 
surface normal 
on both sides, forming a bell shape. The number of impacts and the impact 
momentum, 
along with the lateral impact location and its height above ground, were 
recorded for each 
modeled surface for off-line analysis. 

Analysis of Modeled Results. To be suitable for use by the damage model, the 
modeled 
results have been simplified into functional relationships as described 
below. 

Within the damage model, the values of some variables may not be specifically 
known. 
Some others may only be generically specified. For example, the exact number 
of 
buildings having gravel roofs, their detailed layout, respective heights and 
plan areas, will 

Chapter 5. Windborne Debris 

?
Target 
Walls 
Source 
Roof 
Wind 
Directions 
Figure 5.22. The Layout of Modeled Gravel Source Roof and Target Walls 
Relative 
to the Wind Directions Modeled. 

not be specifically known, but may be reflected by reference to the 
geographical region, 
built environment or terrain type. For some of these variables, such as 
source building 
height, plan area and orientation, their effects are accounted for by 
averaging the results 
over all cases modeled. A source reduction factor is also already in place in 
the damage 
model to roughly account for the percentage of buildings having gravel roofs. 
Its value is 
empirically defined to vary between 1 and 0, where the value of 1 is typical 
for areas 
having many gravel roofs and the value of 0 is for areas having no gravel 
roof (e.g., open 
water upstream of the target building). For the areas having some gravel 
roofs and some 
non-gravel roofs, the non-gravel roofed buildings will not produce gravel 
missiles but are 
present as shielding for other buildings. In this case, the source reduction 
factor will be 
some value between 0 and 1, approximately equal to the percentage of gravel 
roofs in the 
area. 

The stream-wise and lateral layout of the upstream source buildings is 
assumed to be 
homogeneous in the general model. Thus, the typical building spacing for the 
area 
becomes representative of the expected distance from the target building to 
the first 
building or the first row of buildings directly upstream, and the lateral 
distribution of 
impact locations on the target wall is treated as homogeneous. To obtain the 
number of 
impacts per unit area on the target surface as a function of wind speed, 
typical building 
spacing and the vertical location above ground, etc., the results were 
averaged laterally 
across the target wall over a defined width, which is the typical building 
spacing in this 

HAZUS-MH MR3 Technical Manual 

?
case. Results for different target wall orientations relative to the wind 
direction were also 
averaged within the 1450 quadrant in consistence with the treatment by the 
general model 
as described in the previous section. 

As a result of the simplifications, the expected number of impacts per unit 
area and the 
impact momentum become a function of the open-10 m peak gust speed (V), 
typical 
center-to-center spacing between buildings (d) and the vertical location 
above ground (h). 
The following expressions fit to the data represent the expected number of 
impacts on a 
target surface per square meter and per hour, 

N = n(V ,d )7 f (d ,h) , (5.6) 

with n(V, d) being the average over the height from ground up to 30 m, 
expressed by, 

. 1.11 .
.1+ tanhV - 80 .

n(V ,d )= 53 .
. 18 .
. 
,x = d -15 , (5.7)

(ex + e-x ) 0.33V 

and f(d, h) as the height factor, 

f (d ,h)=60 cos2 
..
. p h 
..
. 
,h= h,
ho . 2 ho . 
o 
(5.8)
d 

=0 ,h >ho ;ho =30 - . 

10 

where V is the peak gust speed (m/s) in a short time interval T; d represents 
the estimated 
typical center-to-center distance (m) of buildings for the area to be 
modeled; and h the 
vertical location above ground (m) of the target surface element. 

Equation 5.7 is illustrated in Figure 5.23 for peak gust speeds of 40, 60 and 
75 m/s, where 
the average number of impacts per square meter per hour on the wall from 0 to 
30 m is 
shown as the function of typical building spacing. The data derived from the 
physical 
model to which Equation 5.7 is fitted are also shown in these figures. The 
variation of the 
number of impacts with peak gust speed is illustrated in Figure 5.24, for 
selected center-
to-center distances between buildings. 

The height factors calculated from the simulated results are presented in 
Figure 5.25, 
while their empirical approximations as expressed by Equation 5.8 are shown 
in Figure 

5.26. 
The impact momentum, defined as the component of the terminal momentum 
perpendicular to the surface impacted, generally varies with many variables 
such as 
individual gravel diameter, terminal speed and incident angle of impact, etc. 
Some of 
these variables are in turn dependent on many other parameters, for example, 
wind speed, 

Chapter 5. Windborne Debris 

?
Peak Gust Speed V =40 m/s 

0 
1 
2 
3 
4 
5 
6 
Data from Simulation 
n=10.5/(exp(x)+exp(-x)), x=(d-15)/13 
0 20 40 60 80 100 120 140 160 

Typical Center-to-Center Distance between Buildings, d (Meter) 

(a) For Peak Gust Speed of 40m/s (90mph) 
Peak Gust Speed V =60 m/s 

0 20 40 60 80 100 120 140 

160 

Typical Center-to-Center Distance between Buildings, d (Meter) 

 (b) For Peak Gust Speed of 60m/s (134mph) 
Figure 5.23. Number of Impacts per m2 hr on Walls Averaged Over the Lowest 30 
m, as Function of Building Spacing. 

0 
10 
20 
30 
40 
50 
Data from Simulation 
n=88/(exp(x)+exp(-x)), x=(d-15)/20
HAZUS-MH MR3 Technical Manual 

?
Peak Gust Speed V =75 m/s 

0 
10 
20 
30 
40 
50 
60 
Data from Simulation 
n=105/(exp(x)+exp(-x)), x=(d-15)/25 
0 20 40 60 80 100 120 140 160 

Typical Center-to-Center Distance between Buildings, d (Meter) 

(c) For Peak Gust Speed of 75m/s (168mph). 
Figure 5.23. Number of Impacts per m2 hr on Walls Averaged Over the Lowest 30 
m, as Function of Building Spacing (concluded). 

0 
5 
10 
15 
20 
25 
30 
35 
40 
45 
50 
d=30m 
Fitted, d=30m 
d=60m 
Fitted, d=60m 
30 35 40 45 50 55 60 65 70 75 

80 

Peak Gust Speed, V (m/s) 

Figure 5.24. Examples of Fitted Number of Impacts Varying with Peak Gust 
Speed 
for Selected Building Spacing Cases in Comparison with Simulation Results. 

Chapter 5. Windborne Debris 

?
4.00 

3.50 

3.00 

2.50 

2.00 

1.50 

1.00 

0.50 

0.00 
0 5 101520 2530 
d=20m 
d=30m 
d=45m 
d=65m 
d=60m 
d=85m 
d=90m 
d=105m 
d=120m 
d=130m 
d=150m 
d=150m 

Height, h (m) 

Figure 5.25. Height Factor for Number of Impacts for Various Cases with 
Building 
Spacing from 20 m to 150 m. 

0 5 1015 2025 

30 

Height, h (m) 

Figure 5.26. Fitted Height Factor for Number of Impacts for Building Spacing 
from 
20 m to 150 m. 

0.00 
0.50 
1.00 
1.50 
2.00 
2.50 
3.00 
3.50 
4.00 
4.50 
20 
30 
45 
60 
80 
100 
125 
150 
d (m) 
HAZUS-MH MR3 Technical Manual 

?
building spacing, vertical location, configuration of the source and target 
buildings, etc. 
The detailed relationships of its dependence on these parameters are rather 
complicated; 
however, as indicated by the simulation results, the statistical 
distributions of the impact 
momentum for various cases have similar shapes. When normalized by a given 
percentile, the distributions of the impact momentum for various cases tend 
to collapse. 
Figure 5.27 shows the averaged CDF of the impact momentum and the one 
standard 
deviation bounds over all cases, where the impact momentum is normalized by 
its 95th 
percentile. 

Prior to the normalization, the distributions have been truncated at a 
momentum of 0.025 
(kg-m/s), below which gravel missiles are considered having no damage effects 
on 
window glass products. The above results for the numbers of impacts have 
included only 
those that exceed the 0.025 kg-m/s threshold. 

The 95th percentile is empirically chosen as the normalizing factor mainly 
because it is a 
convenient number and for the many cases modeled it represents a moderate 
value of 
momentum that is able to produce damage to window glass. As indicated by the 
results, 
the 95th percentile varies for different cases, dependent on various 
parameters mentioned 
above; however, there appears to be a trend of increase with wind speed, with 
certain 
variability at each wind speed. Figure 5.28 illustrates this trend and 
variability of the data, 
along with the empirical fit to their mean. Each data point plotted is 
derived from an 
individual impact momentum distribution obtained on every 25 m2 wall area 
that received 
at least 25 hits with impact momentum larger than 0.025 kg-m/s. There are 
about 12,500 
such data points for all cases modeled, which are all included in Figure 
5.28. 

Omitting the variability at each wind speed, the impact momentum 
distribution, 
simplified to be conditional on peak gust speed only, is expressed as 

. M - 0.025 .

Pm = Pm(M ) = CDF .. 
95th percentile .. 

.. (5.9)
. M - 0.025 . 

= CDF .. 

. 2.417 W10-5V 2 - 1.379 W10-3V + 6.200 W10-2 - 0.025 . 

where V (m/s) is the peak gust speed in the time interval (T), and M (kg-m/s) 
represents 
the estimated resistance capacity for impact momentum. 

Equations 5.5 through 5.9, along with the mean CDF function presented in 
Figure 5.27, 
constitute the fast-running windborne gravel debris module for low-rise 
commercial 
buildings in suburban areas. It is specifically designed for the general 
simulation model 
for hurricane damages and estimates the probability of impact damage (PD) 
caused by 
windborne gravel missiles to a vulnerable surface element of area A (m2) 
during a short 
time interval T (hour). 

Chapter 5. Windborne Debris 

?
5-33 
HAZUS-MH MR3 Technical Manual 
0 
0.2 
0.4 
0.6 
0.8
1 
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 
Normalized Momentum -- (M-0.025) / (95th percentile) 
Mean 
Mean-RMS 
Mean+RMS 
Figure 5.27. Distribution of the Normalized Impact Momentum. 
0 
0.05 
0.1 
0.15 
0.2 
0.25 
0.3 
30 35 40 45 50 55 60 65 70 75 80 
Peak Gust Speed, V (m/s) 
M = (2.417E-05)V2 - (1.379E-03)V + 6.200E-02 
Figure 5.28. The Mean Trend of the 95th Percentile Impact Momentum with the 
Peak-Gust Speed, and the Variability for which the One Standard Deviation 
Bounds 
are Shown by the Short Bars. 
?
Concluding Remark. A fast-running model has been developed to estimate the 
risk of 
damage by windborne gravel debris to low-rise buildings in suburban 
commercial areas. 
The model utilizes a previously developed analytical formulation for debris 
damage risk 
(Equation 5.5). A detailed physical modeling methodology is used to obtain 
the 
information required by the analytical formulation, including the expected 
number of 
impacts and impact momentum distribution. The detailed physical model 
simulates the 
effects of turbulent boundary layer winds, wind directionality, and the 
general layout of 
the gravel source buildings and target buildings. The output data from the 
detailed 
physical model were analyzed and simplified in accordance with the approach 
used in the 
general hurricane damage simulation model. The resulting fast-running model 
estimates 
the probability of impact damage (PD) caused by windborne gravel missiles to 
a 
vulnerable surface element of area A during a short time interval T. Thus, it 
is readily 
integrated into the general model, which simulates the processes of a 
hurricane strike on a 
built area progressively in time and is required to estimate the number and 
location of 
fenestration elements breached by debris within each short time interval, in 
order to 
interactively account for the rainwater penetration and internal pressure 
change in a 
building. 

In the development of the fast-running model, the effects of some variables, 
such as the 
detailed configurations and layout of upstream buildings, on the damage 
probability were 
averaged over an anticipated range of values since these variables are not 
specifically 
modeled in the general model. This approach provides averaged, approximate 
estimates 
of the damage probability as far as these variables are concerned. There 
exists propagated 
variability about the average resulting from such simplifications, which is 
undetermined 
at this time. The analysis of this uncertainty remains to be a future task. 

Chapter 5. Windborne Debris 

?
Chapter 6. Physical Damage Modeling 

6.1 General Approach 
The physical damage model predicts wind-induced pressure damage to windows, 
doors, 
wall cladding, roof cladding and roof cover. The model also predicts glazing 
failure due 
to impacts from windborne debris. Wall failures due to inward and outward 
pressure 
loads are also modeled for masonry and wood frame walls. Failure of the 
connections 
between the roof frame and the perimeter walls are also modeled for both wood 
and steel 
roof framing systems. In addition to these failure mechanisms, the physical 
damage 
model predicts foundation failures (i.e., sliding, overturning and uplift) 
for manufactured 
homes. 

The physical damage model predicts hurricane-induced building damage by 
comparing 
loads to resistances. For a given building, directionally dependent pressure 
coefficients 
are estimated (see Chapter 4) for all building components and cladding. In 
addition, loads 
acting on walls surfaces (for predicting wall failures), on roof surfaces 
(for predicting 
failure at roof/wall connections), and on the entire building (for predicting 
foundation 
failure) are computed by spatially integrating the pressure coefficients 
estimated at each 
point on a uniform grid covering the appropriate building surface. The 
resistance 
associated with each failure mechanism is defined by a probability 
distribution from 
which resistances are sampled for a given storm simulation. 

Given the estimates of the loads and resistances associated with the modeled 
failure 
mechanisms, the approach used to predict building damage consists of 
monitoring the 
wind speed and direction at fifteen minute intervals over the entire duration 
of the storm. 
At each time step, wind loads are compared to resistances to predict direct 
wind damage. 
At the same time, the number of missiles impacting the building walls is 
computed (using 
the fast running missile impact model described earlier) to predict glazing 
damage as well 
as damage to the wall finish. 

Internal pressurization of the building is also considered in the physical 
damage model. 
That is, the total pressure acting on a window, for example, is computed as 
the sum the 
external suction acting on the outside of the window and the internal 
pressure acting on 
the inside of the window. The internal pressure is estimated based on the 
number and size 
of wall breaches due to failed windows, doors and wall cladding. In a given 
time step, if 
additional envelope breaches occur, the internal pressure is re-computed as 
are the net 
loads associated with each of the failure mechanisms, which again are 
compared to the 
resistance in the same time step to assess additional building damage. This 
modeling 
approach is shown schematically in Figure 6.1. Note that all resistances and 
modeling 
error statistics associated with the wind loads are sampled before the storm 
is passed by 
the building and held constant for the duration of the storm. To obtain 
statistics on the 
possible damage outcomes for each storm, the component resistances and 
loading error 
statistics are re-sampled and a new damage simulation is performed using the 
same 
storm. 

HAZUS-MH MR3 Technical Manual 

?
Compute Wind Speed and Direction 
in Open Terrain at Time t 
Sample: 
(1) Component resistances 
(2) Pressure coefficient error 
(3) Shielding load reduction factor 
(4) Building orientation (for generic orientation) 
  Compute component and 
cladding loads on roof cover, 
roof sheathing, windows, 
doors, etc. 
  Add internal pressures to get 
total loads 
  Compare computed loads for 
resistances 
  Check for debris impact 
  Fail all components where 
load exceeds resistance 
  Check for structural failure 
of roofs, walls, etc., as 
required 
Sample wind speed 
dependent missile 
impact(s) 
Check 
to see 
if any 
windows/doors 
have 
failed 
Have enough 
building 
simulations 
been 
performed? 
Recompute 
internal pressure 
as average of all 
external pressures 
at roof and 
windows 
Compute Damage/Loss 
Statistics for Given Storm 
No 
No 
Step Storm Forward through Time 
Increment .t 
Yes 
0 
180 
8.25 8.75 9.25 9.75 10.25 10.75 
Time (UTC) 
Peak Gust Wind Speed (mph) 
0 
360 
8.25 8.75 9.25 9.75 10.25 10.75 
Wind Direction 
Yes 
Figure 6.1. Approach Used to Simulate Damage to Buildings. 

This section describes and quantifies the resistances of the various 
components used to 
model buildings and, where possible, presents examples of predicted and 
observed 
building damage states to demonstrate the suitability of the load and 
resistance model for 
estimating wind induced damage. 

Chapter 6. Physical Damage Modeling 

?
The following sections describe the resistances of components commonly found 
in 
residential buildings, and a validation of the load-resistance model for 
residential 
buildings. Next, the development of the manufactured home resistance and 
damage 
model is given, again followed by a validation study. Finally, descriptions 
of the models 
developed for modeling the failures of single ply membrane and built-up 
roofs, metal 
buildings, steel joists and metal roof decks are given. The suitability of 
this model is 
tested through comparisons to the limited available full scale damage data. 

6.2 Resistance Models for Residential Buildings 
The direct wind-induced damage (wind pressures and windborne debris) to 
buildings 
modeled herein is directed towards the performance of components and 
cladding, 
including roof covering (shingles, tiles, membrane), roof sheathing (wood 
frame 
construction only), windows and doors. Structural wall failures for masonry 
and wood 
frame walls and uplift of whole roof systems due to failure at the roof/wall 
connections 
are also modeled. Foundation failures (i.e., sliding, overturning and uplift) 
are considered 
for manufactured homes (Section 6.5). 

The wind and/or missile resistance of the various components noted above have 
been 
derived from laboratory test data, engineering analyses given in the 
literature, and 
judgment based on the performance of components observed during post-
hurricane 
damage investigations. The empirical models based on judgment and experience 
are 
required in some cases since information on the loads and failure mechanisms 
for some 
systems are simply not known. Examples of such systems are discussed later. 
The 
resistance of the cladding components used in the damage modeling are 
discussed in the 
following sections. 

6.2.1 Roof Sheathing in Wood Frame Construction 
The roof sheathing failure model is based primarily on the experimental 
uplift failure 
tests performed on 8'W4' sheets of plywood and Oriented Strand Board (OSB) 
reported by 
the American Plywood Association (Cunningham, 1993), and Clemson University 
(Mizzel, 1994; Shane, 1996; Rosowsky, et al., 1997; Rosowsky and Schiff, 
1997). Table 

6.1 summarizes the relevant uplift resistance data from the various tests. 
As indicated in Table 6.1, the variability of uplift capacities for a given 
nail size and 
spacing is significant. This variation is clouded by the fact that different 
species of wood 
having different specific gravities (SG) yield significantly different nail 
pullout 
resistances. The resistance of a nail to pullout varies with the specific 
gravity of the wood 
raised to the power of 2.5 (NDS-97), and the pullout capacity also varies 
with the 
moisture content of the wood. 

The SG of the Douglas Fir (DF) used in the APA experiments had a reported 
mean value 
of 0.51 with a COV of 0.11. In the Clemson tests, the specific gravity of the 
wood is not 
reported, but HUD 1999 suggests a value of 0.37 would be representative of 
the SG for 
the Spruce-Pine-Fir (SPF) wood used in the Clemson tests. HUD (1999) also 
notes that 
the Southern Yellow Pine (SYP) reported in Rosowsky and Schiff (1997) was in 
fact 

HAZUS-MH MR3 Technical Manual 

?
Table 6.1. Uplift Resistances of 8-by-4 Roof Sheathing Panels From Laboratory 
Testing 

Source Nail Size Nail Pattern Species1 Mean (psf) COV 
Cunningham 6d 6/12 DF 60 0.12 
Shane 6d 6/12 Unknown 71 0.12 
Mizzel 6d 6/12 SPF 25 N/S 
Cunningham 8d 6/12 DF 118 0.14 
Shane 8d 6/12 Unknown 90 0.20 
Rosowsky, et al.. (a) 8d 6/12 SPF 72 0.19 
Rosowsky, et al. (b) 8d 6/12 SPF 87 0.28 
Mizzel 8d 6/12 SPF 61 N/S 
Cunningham 8d 6/6 DF 218 N/A 
Rosowsky and Schiff 8d 6/6 SYP 131 0.14 
Mizzel (c) 8d 6/6 SPF 107 N/S 
Mizzel (d) 8d 6/6 SPF 172 N/S 
Mizzel (e) 8d 6/6 SPF 112 N/S 

1

 DF = Douglas Fir; SPF = Spruce-Pine-Fir; SYP = Southern Yellow Pine 

SPF. Thus to compare the uplift capacities of the roof sheathing panels 
obtained from the 
Clemson tests to those obtained from the APA tests, the Clemson results 
should be 
increased by a factor of about 2.2. 

In the South Florida area, most homes are constructed with high density SYP 
lumber 
(HUD, 1999) (SG = 0.55) and thus the uplift resistance obtained from the APA 
tests are 
considered to be most representative of the uplift resistance of the 8'W4' 
panels. Thus, for 
modeling the resistance of well-applied roof sheathing, a mean of 61 psf and 
a COV of 
11% are used for the 6d nail case and a mean of 114 psf and a COV of 11% are 
used for 
the 8d case. In the implementation of the damage model, the mean uplift 
capacities are 
reduced by 10% to account for quality of installation (f = 0.9). 

6.2.2 Air Permeable Roof Cover Systems 
Air permeable roof cover systems include such systems as shingles (asphalt, 
metal, etc), 
tiles, wood shakes and slate. The systems are referred to as air permeable 
since both the 
upper and lower faces of the system experience wind induced pressures. The 
net pressure 
load on the system is equal to difference between the pressures acting on the 
top and 
bottom sides of the cover. The ability of the system (or components of the 
system) to 
resist wind loads is clearly a function of the hold capacity of the adhesive 
or mechanical 
attachment used to connect the roof cover to the deck. Prior to the early 
1990 s, no 
research had been performed to quantify the loads acting on an air permeable 
system in a 
wind storm or the requirements for attaching the system to the roof deck to 
resist these 
loads. The first research was performed by the Redland Roof Company in the 
United 
Kingdom (Cherry, 1991), where effective uplift and overturning coefficients 
on roof tiles 
were obtained. The effective uplift coefficients obtained in the Redland 
study have been 
incorporated in both the Standard Building Code (SBC) and the South Florida 
Building 
Code (SFBC) for use in the design of roof tile connections for both 
residential and 
commercial applications. The research program also addressed the resistance 
of roof tiles 

Chapter 6. Physical Damage Modeling 

?
against blowing off the roof by measuring the moment resistance associated 
with 
different mechanical attachment techniques. The attachment techniques 
examined in the 
Redland Study included connecting the tiles to the roof deck with one 10d 
nail, two 10d 
nails, one 8d nail, two 8d nails, one 2.5. screw and two 2.5. screws. 

For the prediction of the performance of tile roofs, the loading and damage 
model 
incorporates the Redland roofing company results for a typical tile. The 
nominal 
resistance chosen corresponds to that of the one nail case. While it is 
recognized that in 
most of South Florida roof tiles are attached to the roof using mortar rather 
than 
mechanical connections, no data exists for a mortared connection. Thus, while 
the model 
does not properly reproduce the actual resistance of a mortared tile to be 
blown off in a 
storm, it does provide a method to enable reasonable predictions of the 
effective loads 
acting on the tiles; thus, a reasonable representation of the variation of 
these loads with 
both wind speed and location on the roof of a structure. The usefulness of 
the model in 
predicting the failure rates of tiles attached with mortar is evaluated 
through a calibration 
process. 

The only available data on the effective loads acting on asphalt shingles are 
presented in 
Peterka, et al. (1998). The study by Peterka, et al., showed that, as in the 
case of roof 
tiles, the effective loads acting on shingles are reduced because negative 
pressures act on 
both the top and bottom sides of the shingle. The resistance of a shingle to 
the uplift 
pressures acting on the shingle tabs is provided by the adhesive strip on the 
underside of 
the shingle tab towards its leading edge. If the shingle tabs do lift, 
complete failure of the 
shingle occurs when the nails or staples tear from the roof or alternately, 
the shingle tears 
around the nails. To properly assess the uplift capacity of a shingle, the 
uplift capacity of 
the adhesive strip must be known, and at this point in time no public domain 
data are 
available to properly assess the uplift capacity of shingles. Thus, for the 
prediction of the 
performance of shingle roofs, the same load-resistance model are used as for 
the tile roof 
case except that the nominal resistance of the shingles have been reduced by 
10% over 
the basic tile case. While this modeling approach is empirical, the mechanism 
in which 
either the tiles or shingle are loaded is the same; thus, as in the case of 
roof tiles, the 
approach enables a reasonable means to model the variation of the loads with 
both wind 
speed and location on the roof. Again, the usefulness of the model is 
evaluated through 
comparisons of modeled and observed roofing performance. 

6.2.3 Windows and Sliding Glass Doors 
The wind pressure induced failure of both windows and sliding glass doors is 
produced 
by either a failure in the glass, a failure in the frame or a failure in the 
frame-wall 
connection. The wind resistance capabilities of the above noted components 
can vary 
enormously between manufacturers and the required design pressure. For this 
study, the 
assumed failure pressure is based on the minimum loads as specified in the 
SBC. 

Prior to 1974, the design wind pressure specified in the SBC for buildings 
located within 
125 miles of the coast, having a mean roof height of 30' or less, was 25 psf. 
The 
American Architectural Manufacturers Association (AAMA) requirements for 
windows 
and doors states that the product must be able to resist loads of 1.5 times 
the design 

HAZUS-MH MR3 Technical Manual 

?
pressure, thus most products installed in this time period should have a 
minimal wind 
load resistance of about 38 psf. When the product is tested to meet the R25 
rating, a static 
pressure having a magnitude of 37.5 psf is applied to the product and if it 
does not fail it 
meets the R25 specification and can be marketed as such. The ultimate 
capacity of the 
product is unknown and may be as low as 38 psf, but it could be higher. 

For damage simulation purposes, it is assumed that the nominal pressure 
resistance of 
window and sliding glass door components installed in this period can be 
modeled using 
a mean resistance of 40 psf with a coefficient of variation of 0.2. The 
assumed 
distribution of the product capacity is taken as being normal. This 
assumption implies 
that about 80% of the products installed in low-rise buildings during that 
time period 
meet or exceed the minimum requirements as specified by the SBC. It is also 
assumed 
that during this time period, the same products used on low-rise buildings in 
Palm Beach 
County were installed on buildings in Dade and Broward Counties, even though 
the 
Building Code provisions were not the same. 

From 1974 through to 1982, the basic design pressures in the South Florida 
area 
increased to 34 psf for buildings less than 30' in height. For the design of 
windows and 
doors, wind loading coefficients of +1.1 and -0.55 were used to compute the 
design 
pressures, implying the products should be able to resist inward acting loads 
of up to 56 
psf (34W1.1W1.5) and negative pressures of 28 psf. 

The revision of the SBC wind load provisions in 1982 provided more realistic 
estimates 
of the pressure coefficients than the preceding versions; however, the design 
wind 
pressures for typical fenestration products on typical low-rise buildings 
were in the range 
of 30 to 40 psf depending upon the height of the building, the location of 
the fenestration 
and the size of the fenestration. The implied resistances for fenestration 
products meeting 
these load criteria range between 45 psf and 60 psf. 

Although the wind load criteria for the design of windows and doors increased 
somewhat 
between the 1950 s and the early 1990s, the mean resistance of 40 psf with a 
COV of 
20% is used for all single family residential homes constructed prior to 
1994. 

6.2.4 Roof-Wall Connections 
Since the inception of the SFBC in the late 1950 s, tie down straps have been 
required for 
the roof to wall connections in both Dade and Broward Counties. In Palm Beach 
County, 
roof-wall tie downs have been required on every truss-wall connection since 
the late 
1970 s but, prior to this time, strapped connections were required only on 
every other 
truss. The uplift capacity of hurricane straps or clips varies significantly 
with the type of 
wood used to manufacture the truss, the size (thickness and width) of the 
strap, number of 
nails, etc. 

Conner, et al. (1987) reported on the uplift capacity of various wood to wood 
connections 
obtained from laboratory tests using Douglas Fir wood members. For 
conventional toenail 
connections (three 8d nails), they reported a mean uplift capacity of 676 lbs 
with a 
COV of 8%. The strapped connection yielded a mean uplift capacity of 867 lbs 
with a 

Chapter 6. Physical Damage Modeling 

?
COV of 18%. The straps used in the experiments were 0.5. wide (no thickness 
is given) 
with two 6d nails used for both the rafter and top plate connections. The 
clipped 
connection yielded a mean uplift capacity of 908 lbs with COV of 10%. 

Reed, et al. (1997) reported test results using SPF lumber. For a toe nail 
connection using 
three 8d nails they report a mean uplift capacity of 430 lbs and for a small 
strap (five 8d 
nails in each of the rafter and top plate) they report a mean uplift capacity 
of 1900 lbs. 

Canfield, et al. (1991) reported on the uplift capacity of a number of straps 
tested with 
SPF lumber. They reported a mean uplift capacity of 208 lbs for the three 8d 
toe nail 
case, with strapped connections yielding mean uplift capacities ranging 
between about 
500 lbs to 1200 lbs, depending on the strap thickness and number of nails 
used in the 
connection. In the Canfield, et al. (1991) tests, the strap most closely 
representing that 
typically used in the South Florida area yielded a mean uplift resistance of 
1000 lbs with 
a COV of 15%. 

Clearly, the true uplift capacity for a field installed strap connection 
depends on the 
quality of installation, number and size of nails and the size of the strap. 
The South 
Florida Building Code requires a strap having a thickness of 0.125. with 
three 8d nails be 
used to attach the strap to the truss or rafter. Based on the above noted 
experimental 
results, the uplift capacity of a strapped or properly clipped roof-wall 
connection is 
modeled with a mean resistance of 1200 lbs and a COV of 30%. 

6.2.5 Masonry Walls 
Wall failure models were developed as a step towards modeling catastrophic 
structural 
failures. The wall failure model will also be part of the casualty modeling 
in a future 
version of the hurricane model. Wall failure modeling includes failures due 
to both 
inward and outward uniform pressure loading. Two different circumstances have 
been 
considered: walls without roof support and walls with roof support. The 
former is for the 
case where the wind uplift force has already blown off the roof. The latter 
is for walls that 
are within an integrated structure. A wall within an integrated structure can 
become a 
weak link, particularly when mitigation measures have been taken to protect 
windows 
and doors, to improve roof-sheathing connections, and to strengthen roof to 
wall 
connections by using tie downs. Variability of wall resistance capacity, due 
to variable 
material and workmanship etc., is incorporated in the wall failure modeling. 

For edge-supported (by roof, floor, and adjacent walls, etc.), unreinforced 
masonry walls 
under uniform lateral pressure loading, the major failure mechanism is 
associated with 
cracking due to flexural tensile stress. The cracking normally starts at, and 
develops 
along, the mortar joints (both horizontal bed joints and vertical head 
joints) between the 
concrete masonry units (CMU) owing to the failure of mortar-CMU bond as the 
weakest 
link in the wall system. Occasionally, in the vertical direction, the 
developing crack may 
proceed across a CMU from a mortar head joint to another mortar head joint. 
Examples 
of masonry wall or brick veneer damage are shown in Figure 6.2. 

HAZUS-MH MR3 Technical Manual 

?
(a) Brick veneer (Hogan and Karwoski, 1990) (b) Collapse of masonry walls 
(Zollo, 1993): A 
wall of 40' span (top); Walls on a two-story 
building (bottom) 
(c) Collapse of masonry wall and steel frame roof 
system (NRC, 1991) 
(d) Collapse of masonry wall (HUD, 1993) 
Figure 6.2. Wind-Induced Masonry Wall and Brick Veneer Damage. 

Structurally, a masonry wall can be considered as an orthotropic slab with 
different 
bending moment resistance capacities in horizontal and vertical directions. 
Yield line 
theory is used to derive a wall failure model applicable for various wall 
spans, wall 
heights, cross-sectional configurations and material properties. This method 
has been 
adopted for masonry by a number of researchers (e.g., Drysdale and Essawy, 
1988), as 
well as by the British code (BSI, 1978). While true yield-line behavior 
cannot be fully 
justified, a substantial body of data (Haseltine, 1975; Hendry and Kheir, 
1976; West, et 
al., 1973a, 1973b) and some recent tests tend to support use of this 
analytical tool. 

Chapter 6. Physical Damage Modeling 

?
Given the edge support conditions, the accuracy of a yield-line analysis 
depends on the 
correctness of the assumed crack pattern. The resulting capacity will be the 
expected 
capacity if the assumed pattern is correct, or an upper bound value if the 
assumed pattern 
deviates from the correct one. The next section describes in detail the crack 
patterns 
observed by a number of researchers for a series of walls with varying 
dimensions and 
edge support conditions, followed by the yield-line analysis and results for 
the masonry 
wall model. 

Failure Modes and Crack Patterns. Drysdale and Essawy (1988) carried out 
experimental investigations on the failure modes and crack patterns for a 
series of 9' tall, 
8. thick, full-scale masonry walls of varying spans under uniform pressure 
loads. The 
walls were built by an experienced mason using type-S mortar and standard 
8.W8.W16. 
two-cell concrete blocks that are most commonly used in masonry construction. 
The 
running bond pattern, curing time and other parameters were all consistent 
with code 
requirements. The walls were investigated for different span to height ratios 
(i.e., aspect 
ratios) and various support conditions. Three repetitions for each 
combination of 
parameters were performed to provide a better statistical sample, primarily 
for a better 
measurement of the mean. The first-crack pressures and ultimate failure 
pressures were 
recorded. 

The typical crack patterns observed by Drysdale and Essawy are presented in 
Figure 6.3 
for various wall aspect ratios and support conditions. Figure 6.3(a) shows a 
cracking 
pattern for a small span wall (width W = 11.2', aspect ratio = 1.21) simply 
supported 
along all four edges. It was observed that a vertical crack started near the 
middle of the 
wall, where maximum flexural moment is expected. The crack split diagonally 
and 
extended to the four corners of the wall under higher pressures leading to 
its collapse. 
Figure 6.3(b) shows a wider wall (W = 16.4', aspect ratio = 1.79) where the 
initial crack 
was a horizontal one that split at its two ends under higher pressures and 
extended at 
roughly 450 to the four wall corners shortly before collapse. Wider walls 
supported on all 
four sides also generally followed the same failure pattern described in 
Figure 6.3(b). The 
wider the wall, the longer the initial horizontal crack would be. Thus, for a 
wall of 
infinitely large span, hypothetically one could only see the horizontal 
crack. This result 
agrees with Drysdale and Essawy s observations on walls with only top and 
bottom 
supports (equivalent to two-end supported beams), which resemble the middle 
section of 
a wall with an infinite span where little shear and moment load transfer 
would occur 
horizontally under uniform lateral loading. Drysdale and Essawy clearly 
described the 
horizontal crack for the top and bottom supported walls in their text 
although they did not 
reproduce the picture. Dawe and Aridru s work (1993) on prestressed masonry 
confirmed 
this cracking pattern (see Figure 6.4). It can be assumed that the side 
supports become no 
longer significant to the crack pattern and failure pressure for long span 
masonry walls. 
These observations and measured failure pressures provide an important data 
point for 
verifying the wall failure model in terms of long span masonry walls. 

Figure 6.3(c) exhibits a crack pattern of a wall without top support, for a 
specific 
horizontal wall span of 16.4' (aspect ratio = 1.79), where a vertical initial 
crack was 
joined by two diagonal cracks extending to the lower corners. It can be 
expected that, as 

HAZUS-MH MR3 Technical Manual 

?
(a) (c) 
(b) (d) 
Figure 6.3. Typical Cracking Patterns for Various Wall Spans and Different 
Edge 
Support Conditions (Drysdale and Essawy, 1988). 

Figure 6.4. Typical Cracking Pattern for Top and Bottom Supported Prestressed 
Masonry Wall (Dawe and Aridru, 1993). 

Chapter 6. Physical Damage Modeling 

?
6-11 
HAZUS-MH MR3 Technical Manual 
the aspect ratio increases, the vertical crack will shorten and the junction 
of the two 
oblique cracks will move upwards; eventually the pattern will become one with 
only the 
two oblique cracks that extend to the top edge of the wall and disjoint from 
each other. 
The cracking pattern for walls with only two sides supported was also 
investigated by 
Drysdale and Essawy, as reproduced in Figure 6.3(d), where a vertical crack 
ran through 
the height of the wall near the mid-span, alternately passing through head 
joints and 
blocks in a nearly straight line. This would also depict the crack pattern 
for a wall of 
infinitely large height or of infinitely small aspect ratio. 
The cracking patterns are summarized in Figure 6.5 for various edge support 
conditions 
and aspect ratios, based on the above observations and theoretical inferences 
or 
assumptions. The yield line analysis will either confirm or reject an assumed 
pattern. The 
correct pattern should provide the minimum capacity, while the rest will all 
give higher 
values. 
Figure 6.5. Typical Crack Patterns for Masonry Walls of Small and Large 
Aspect 
Ratios, with and without Top Supports. 
Ultimate Failure Pressures. The basic fundamentals and main assumptions for 
yield line 
theory are as follows (Ghali and Neyille, 1989): 
1. At fracture, the bending moment per unit length along all the fracture 
lines is 
constant and equal to the  yield  value. 
2. The fractured slab parts rotate about axes along the supported edges. 
?
3. Fractured slab parts are plane and therefore they intersect in straight 
lines; in 
other words, the  yield lines  are straight. 
4. The lines of fracture on the sides of two adjacent slab parts pass through 
the 
point of intersection of their axes of rotation. 
Based on these assumptions and using the energy method, the solutions for the 
crack 
pattern parameters (x or z) and corresponding ultimate pressure loads (pr) 
are obtained 
through a series of lengthy mathematical manipulations. The results are 
presented in 
Table 6.2, in normalized form, for the walls illustrated in Figure 6.5 and 
with ultimate 
flexural moment capacities of m and fm for vertical and horizontal bending, 
respectively. 

Drysdale and Essawy (1988) argued that, for walls with four simply-supported 
edges and 
moderate spans for which an initial horizontal crack occurs at mid-height and 
near the 
middle of the span at pressures significantly lower than the ultimate values, 
the initial 
horizontal crack should be treated as a free edge when estimating the 
ultimate failure 
load. This assumption results in the modified estimates of ultimate pressures 
shown in 
Table 6.3; however, the estimated failure pressures are not dramatically 
different than the 
unmodified results in Table 6.2. 

For comparison, the ultimate failure pressures measured by Drysdale and 
Essawy are 
reproduced in Figure 6.6 for varying horizontal span, along with the 
estimated values 
obtained by applying the above theoretical results to their tested full-scale 
walls. The 
measured data shown are the average of three repetitions. The variation of 
measured 
failure pressures among the three repetitions is relatively small, with the 
coefficient of 
variation being in the order of 6%. The first set of data (see legend) is for 
walls with all 
four sides supported and without prestress. The three data points for spans 
between 11' 
and 19' are adopted directly from Drysdale and Essawy (1988). Good agreement 
is 
observed between the theoretical estimates and the measured data. The fourth 
point of 
this group, to the far end of the abscissa, is their result for walls with 
only top and bottom 
supports, hypothetically representing the case for all-side simply supported 
walls of 
infinite horizontal span. They re-tested this  extreme span wall case using 
available extra 
specimens. The resulting average value of 47.2 psf nearly coincides with the 
theoretical 
extreme span asymptote or lower bound value. For these wall cases, the 
theoretical 
failure pressures do not significantly change for spans beyond 40' since they 
are 
sufficiently large spans such that the side supports no longer significantly 
influence the 
expected failure pressure. 

The average failure pressure measured by Drysdale and Essawy for top 
unsupported 
walls with a horizontal span of 16.4' is also shown in Figure 6.6 in 
comparison with the 
theoretical estimate. Again, there is good agreement. This was, 
unfortunately, the only 
data they obtained for top unsupported walls. 

Effects of Variable Material and Workmanship. It is recognized that 
relatively large 
variability of wall resistance capacity exists due to variable material and 
workmanship, 
versus laboratory controlled conditions (variation of laboratory measured 
failure pressure 

Chapter 6. Physical Damage Modeling 

?
Table 6.2. Failure Pressures (pr) and Crack Pattern Parameters (x or z) 
Derived 
from Yield-Line Analysis 

With 
Top 
Support 
Modified Aspect Ratio 
H 
L 
r 
1 
r 
H 
L 
f 
== =1 =1 .+8 
Four Simple Edge 
Supports 
2 
H 
2 
H 
2 
H 
r 
2r 
113r 
H 
z 
H 
z 
r 
6 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
f . = 
- 
2 
L 
2 
L 
2 
L 
r 
2r 
113r 
L 
x 
L 
x 
r 
6 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
f . = 
- 
2r H 
8m p . 
Modified Aspect Ratio 
H 
L 
r 
1 r 
H 
L f 
== =1 =1 .+8 
Four Fixed Edge 
Supports 
2 
H 
2 
H 
2 
H 
r 
2r 
113r 
H 
z 
H 
z 
r 
12 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
f . = 
- 
2 
L 
2 
L 
2 
L 
r 
2r 
113r 
L 
x 
L 
x 
r 
12 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
f . = 
- 
2r H 
16m p . 
Modified Aspect Ratio 
H 
L 
r 
1 r 
H 
L f 
== =1.466 =1.466 .+8 
Without 
Top 
Support 
Three Simple Edge 
Supports 
2 
H 
2 
H 
2 
H 
r 
4r 
1112r 
H 
z 
H 
z 
r 
6 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
f . = 
- 
2 
L 
2 
L 
1 
L 
r 
3r 
249r 
L 
x 
L 
x 
r 
8 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
f . = 
- 
( 0pr . ) 
Modified Aspect Ratio 
H 
L 
r 
1 r 
H 
L f 
== =1.657 =1.657 .+8 
Three Fixed Edge 
Supports 
2 
H 
2 
H 
2 
H 
r 
4r 
1112r 
H 
z 
H 
z 
r 
12 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
f . = 
- 
2 
L 
2 
L 
2 
L 
1 
L 
r 
2r 
246r 
L 
x 
8r 
L 
x 
L 
x 
r 
2 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
. +  
. 
. 
.
. 
. 
f . = 
- 
2 
2 
H 
m pr . 

were shown to be relatively small, as mentioned above). The effect of this 
variability is 
accounted for based on the data of Gross, et al. (1969) as quoted by Grimm 
(1999), which 
is reproduced in Figure 6.7. The data was for clay brick masonry; however, it 
is assumed 
also to be typical for the strength variability of concrete block masonry 
since the bond 
strength of the mortar, which is used for both types of masonry walls, 
controls in large 
part the ultimate resistance capacity. The data are presented as the 
distribution of the 

HAZUS-MH MR3 Technical Manual 

?
Table 6.3. Failure Pressures (pr) and Crack Pattern Parameters (x or z) 
Derived 
from Yield-Line Analysis, with the Assumption of Discontinuity at Initial 
Horizontal 
Center Crack 

With 
Top 
Support 
Modified Aspect Ratio 
H 
L 
r 
1 r 
H 
L f 
== =0.733 =0.733 .+8 
FourSimple Edge 
Supports 
2 
H 
2 
H 
2 
H 
r 
2r 
113r 
H 
z 
H 
z 
r 
6 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
f . = 
- 
2 
L 
2 
L 
1 
L 
r 
6r 
119r 
L 
x 
L 
x 
r 
8 
m 
p HL 
-+ 
= 
. 
. 
.
. 
. 
f . = 
- Lower-
Bounded 
by 
2r H 
8m p = 

Wall Height = 9 ft, Thickness = 8 in. 

8 16 24 32 40 

Wall Span, L (ft) 

Figure 6.6. Comparison of Failure Pressure Variation with Wall Span. 

flexural strength ratio between un-inspected workmanship and material and 
inspected or 
laboratory workmanship and material. Assuming a normal distribution for the 
resistance 
capacity, the mean and standard deviation of the flexural strength ratio are 
calculated 
based on the data to be about 0.8 and 0.2, respectively. 

Fattal and Cattaneo (1977) discussed the effect of workmanship on masonry 
wall strength 
and recommended the use of reduction factors in estimating the strength of 
un-inspected 
masonry walls to account for variability in workmanship. They recommended a 
reduction 
factor of 0.67 to be multiplied to the mean strength obtained from laboratory 
or sources 

0 
50 
100 
150 
200 
250 
300 
1) All Sides Supported 
2) Repeated Tests 
3) Yield Line Theory 
4) Top Un-Supported 
5) Yield Line, Top Un-Supported 
Chapter 6. Physical Damage Modeling 

?
Ratio of Brick Masonry Flexural Strength with Un-Inspected Workmanship to 
Nominal Value, R 

Figure 6.7. Effects of Workmanship on Actual Flexural Strength vs. Nominal 
Value. 

other than directly measured from the wall under investigation. They also 
quoted code 
recommended workmanship reduction factors of 0.67 by BIA to 0.5 by UBC and 
NCMA. 
All these reduction factors are used as a simple method to account for the 
overall effect of 
variable workmanship on wall strength, including the effects on both the mean 
and the 
dispersion. A value of 0.67 corresponds approximately to the 25th percentile 
of a normal 
distribution of N(0.8, 0.2), and 0.5 roughly to the 5th percentile. These 
values provide 
further support to the use of a mean strength ratio of 0.8 and a standard 
deviation of 0.2, 
as derived from the data reproduced in Figure 6.7. 

Estimation of Wall Failure Probability. Based on the above discussions, the 
probability 
of failure, Pf, for a masonry wall of span L under uniform lateral pressure 
loading p is 
estimated as 

0.8pr ().

Pf =F...
p 
0- 
.2 pr ()LL .. 
(6.1)
.. 

where F(*) denotes the standard normal cumulative distribution function. 
pr(L) is the 
laboratory measured or nominal failure pressure as a function of wall span 
and heights, 
edge supports, and ultimate flexural moments (dependent on material and 
concrete 
masonry configuration), as represented by the theoretical results in Table 
6.2 for walls 
with and without roof support. Examples of estimated probabilities of failure 
are shown 
in Figures 6.8 and 6.9 for top supported and unsupported walls, respectively. 
Data are 
presented for simply supported, 9'high walls of various spans. 

The present model focuses on the most common failure mechanism of masonry 
walls that 
is associated with cracking of mortar joints and concrete blocks, assuming 
that the wall 
edge supports are perfect when they are present. Thus, it treats the failure 
probability for 

HAZUS-MH MR3 Technical Manual 

?
walls under outward resultant pressure load the same as for walls under 
inward resultant 
pressure load, ignoring potential differences in the resistance capacities of 
wall anchorage 
along the edges between inward and outward loads. Outward loading may cause 
pullout 
of the wall anchorage while inward loading is exerted against walls 
presumably with 
edge supports closer to  perfect . It is possible that the failure 
probability for outward 
loading will be somewhat higher than for inward loading. In addition, it is 
assumed that 
there are no openings on the wall, or the openings and their perimeter 
framing do not 
decrease nor increase the wall capacity in resisting pressure loads. 

Concluding Remarks. Yield-line theory is used to assess the effects of wall 
dimensions, 
support conditions and orthotropic ultimate moment capacities on the failure 
of masonry 
walls. The theoretical results agree well with experimental data. The 
theoretical results of 
expected or nominal failure pressure, along with the data on field 
variability of 
workmanship and material, provide a useful model for estimating failure 
probabilities of 
masonry walls. 

6.2.6 Wood Frame Walls 
For wood frame walls under lateral loads, post-storm surveys show that 
failures are 
nearly always associated with connection failures. Various examples are shown 
in 
Figures 6.10 through 6.14. This situation is similar to the observation that 
failures of 
light-frame roof systems are nearly always a result of inadequate connections 
rather than 
the capacities of the elements themselves, as shown by studies and confirmed 
by 
anecdotal evidence from post-hurricane investigation (Sparks, et al., 1994). 
Hence, at 
present, wall failure modeling for wood framed structures is focused on 
modeling the 
failure mechanism of the nail being bent and/or pulled out on wall edges that 
connect the 
wall to other parts of the structure and connect various components within 
the wall 
system. 

Simplified Structural Model. The main structural components of the exterior 
walls 
include the bottom plate, studs and the double top plates, as well as bracing 
or sheathing. 
Figure 6.15 illustrates a typical wall construction. The present model deals 
with 
connections using nails only, which are typical for residential 
constructions. Figure 6.16 
shows the most common nailing pattern as stipulated by the Wood Frame 
Construction 
Manual (1995 SBC High Wind Edition, AFPA/AWC, 1996), which is also required 
by 
many building codes. Wood frame walls are built by stories. For each story, 
the bottom 
plate of the wood frame wall is installed to the floor with two 16d face 
nails at each of the 
16. spaced stations. The wood studs, typically also spaced at 16. on center, 
are either toe-
nailed to the bottom plate with two 8d nails on each side of a stud, or end-
nailed to the 
bottom plate using two 16d nails before it is installed onto the floor. The 
double top 
plates are end-nailed to the studs with two 16d nails on each stud. The upper 
and lower 
top plates are typically connected together with two 10d face nails at 
stations of 16. 
spacing. Ceiling joists or roof rafters are normally toe-nailed to the double 
top plates of 
the wood frame wall, with three 8d common nails at each of the 16. spaced 
stations along 
the top plate. At the exterior corners of the structure, and sometimes also 
at the 
intersections of exterior and interior (partition) walls, multiple studs are 
typically face-

Chapter 6. Physical Damage Modeling 

?
9'H Block Masonry Wall with Roof Support 

10 20 30 40 50 60 70 80 90 100 

Pressure Load, p (psf) 

Figure 6.8. Estimated Failure Probability as a Function of Pressure Load for 
Various Wall Spans with Roof Support. 

9'H Block Masonry Wall without Roof Support 

0 10 20 30 40 50 60 70 80 90 

Pressure Load, p (psf) 

Figure 6.9. Estimated Failure Probability as a Function of Pressure Load for 
Various Wall Spans without Roof Support. 

0.0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.0 
L=40' 
L=36' 
L=32' 
L=28' 
L=24' 
L=20' 
L=16' 
L=12' 
L=8' 
Wall Span 
0.0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.0 
L=40' 
L=36' 
L=32' 
L=28' 
L=24' 
L=20' 
L=16' 
L=12' 
L=8' 
Wall Span 
HAZUS-MH MR3 Technical Manual 

?
(a) Exterior wall fails in sections at connections to other parts of the 
structure 
(b) Wall failure at the other side of the same building shown in (a) 
Figure 6.10. Examples of Wind-Induced Damage to Residential Wood-Framed Wall 
System (Oklahoma Tornado Out-Break, May 1999). 

Chapter 6. Physical Damage Modeling 

?
(a) Wall failure while roof frame still in place 
(b) This photo reveals that the wall fails at nail connections to the roof 
and foundation 
Figure 6.11. Examples of Wind-Induced Damage to Residential Wood-Framed Wall 
System (Oklahoma Tornado Out-Break, May 1999). 

HAZUS-MH MR3 Technical Manual 

?
(a) A wall section collapses 
(b) Garage wall fails at the connections to the foundation 
Figure 6.12. Examples of Wind-Induced Damage to Residential Wood-Framed Wall 
System (Oklahoma Tornado Out-Break, May 1999). 

Chapter 6. Physical Damage Modeling 

?
(a) A let-in braced wall section collapses, while other sections also fail at 
connections to gable end truss 
(b) Several sections of a let-in braced wall collapse, failed at connections 
to gable end truss 
Figure 6.13. Examples of Wind-Induced Damage to Residential Wood-Framed Wall 
System (FEMA, 1992, Hurricane Andrew, Florida). 

HAZUS-MH MR3 Technical Manual 

?
(c) End wall separated from end unit of a modular home 
(d) Another example of gable end wall connection problem 
Figure 6.13. Examples of Wind-Induced Damage to Residential Wood-Framed Wall 
System (FEMA, 1992, Hurricane Andrew, Florida) (concluded). 

Chapter 6. Physical Damage Modeling 

?
(a) Improper connection in a blown-out wall section 
(b) Complete failure of a wood framing system 
Figure 6.14. Examples of Wind-Induced Damage to Residential Wood-Framed Wall 
System (HUD, 1993, Hurricane Andrew, Florida). 

HAZUS-MH MR3 Technical Manual 

?
Figure 6.15. Typical Wood Frame Walls for Residential Construction 
(CMHC, 1989). 

nailed together with 16d nails spaced at 24.. The above nailing schedule is 
mainly based 
on the Standard Building Code. There are some variations, depending on the 
codes/manuals used; however, the resulting variation in combined resistance 
capacity is 
expected to be relatively insignificant and can be accounted for when needed. 

Wood frame walls are required to be let-in braced with 1.W4. wood strips at 
about 45 0 to 
plumb or be sheathed with 4'W8' plywood or other structural boards, at least 
at the corners 
of the structure. They are nailed to the lower top plate, studs and bottom 
plate of a braced 
wall section. Although the bracing or sheathing is intended mainly to 
strengthen the 
wall s resistance capacity for in-plane loads, it also tends to bond the wall 
components 
within its span together to react as a unit and share the lateral loads. 

The present simple model treats each wall section, which is braced or 
sheathed together, 
as a structural unit, and each unit is supported only by the floor(s) and/or 
roof to resist 
uniform lateral loading, independent of other similar units. This implies 
that there would 
be perfect load sharing horizontally within the unit and no load is 
transferred beyond a 
unit. This assumption is a close approximation of the behavior of the wall 
sections near 
the mid-span of the wall under uniform pressure loading. Corner units gain 
additional 
support from the adjacent wall; however, there normally exist higher suction 
loads near 
corners that are not accounted for by the model. These effects on the corner 
units tend to 
counteract each other. Thus, corner units are treated the same as interior 
units in the 
model. Additional support by interior partition walls is ignored for walls 
with top 
support, but it is considered when dealing with walls without top support. In 
addition, it is 
assumed that wall openings and their perimeter framing do not decrease nor 
increase the 
wall capacity in resisting pressure loads. 

Chapter 6. Physical Damage Modeling 

?
Figure 6.16. Typical Nailing Patterns for Wood Frame Wall Structure of 
Residential Construction (AFPA/AWC, 1996). 

Such a simplified model depicts a span-wise semi-flexible wall structure, 
versus a 
completely rigid slab wall model or a completely flexible model (each 
individual stud 
reacts independently). It is deemed to be a reasonable representation of the 
actual system, 
but it could be improved by properly incorporating the effects of adjacent 
and 
partitioning wall supports. The span width of a unit somehow represents the 
span-wise 
influence width, by which a gradually varying influence function is 
approximated with a 
step function. Thus, the unit width is a critical parameter in the model and 
its effective 
value needs to be calibrated when data become available. At present, it is 
taken to be the 
bracing width for let-in braced walls, which equals the wall height (normally 
8') when 
450 bracing is used. For sheathed walls, the 4'W8' sheathing boards are used 
vertically and 
also horizontally in some occasions, which provide 4' and 8' bracing widths. 
Considering 
the fact that sheathing boards possess higher span-wise load transfer 
capability and 
adjacent boards are nailed to the same stud through the entire length of 
their edge, its 
effective unit width is also set to 8' in the present model. 

Post-storm surveys have indicated that wood frame walls collapse typically in 
sections. 
This observation lends some confidence in the concept for the above sectional 
wall 
model. 

HAZUS-MH MR3 Technical Manual 

?
Lateral Failure Load for Nail Connections. A relatively simple way to 
estimate the 
resistance capacity of nail connections for various wood materials and 
products is to use 
the specified allowable design load data already compiled in the National 
Design 
Specification (AFPA/AWC, 1997) and adjust them to expected ultimate failure 
load with 
proper  safety factors.  This approach is a practical alternative to 
collecting and 
analyzing a large volume of laboratory data, which would be an expensive 
undertaking 
given the large number of wood materials and products as well as their widely 
varying 
properties for different humidity, temperature and nailing application 
methods. 

Several types of timber are more commonly used for residential constructions 
along the 
coast. These include Southern Pine (typical specific gravity, SG = 0.55), 
Douglas Fir-
Larch (SG = 0.50), Northern Douglas Fir-Larch (SG = 0.49), Southern Douglas 
Fir (SG = 
0.46), Spruce-Pine-Fir (SG = 0.42) and Southern Spruce-Pine-Fir (SG = 0.36). 
The basic 
lateral design loads for these timbers as compiled from NDS are listed in 
Table 6. 4 along 
with the values of the applicable adjustment factors for the nailing schedule 
and 
applications described in the last section. Since the exact proportions of 
these timbers  
usage by the industry for residential constructions are not available, their 
average design 
value is used to estimate the anticipated ultimate load with proper safety 
factors. This is 
equivalent to assuming that the usage is equally distributed among these 
timbers. The 
associated root-mean-squared variations and the coefficients of variation due 
to varied 
timber usage are indicated in the table. The coefficients of variation range 
from 0.13 to 

0.20. 
To some extent, the design loads reflect the expected failure loads with some 
safety 
factors. The safety factors may vary from one situation to another since 
normally a 
percentile value, often the 5th percentile, derived from the available 
distribution data of 
failure loads, is used as the base for determining an allowable design load. 
The ratio of 
the mean failure load to this percentile value varies with the coefficient of 
variation in the 
data. After being combined with other factors, the final basic allowable 
design load varies 
greatly relative to the actual mean failure load. For simplicity, a 
representative safety 
factor is desired in estimating the mean or expected failure load from the 
published 
allowable design load. Rosowsky and Reinhold (1999) investigated the rate-of-
load 
effects on nail connections for wood. They presented some test results that 
indicate the 
ratio of tested lateral failure loads to the NDS allowable design value is 
about 4, as shown 
in Figure 6.17. From 16 specimens, Reed, et al. (1996) obtained an average 
withdrawal 
capacity of 350 lbs for a toe-nail connection using three 8d nails versus a 
NDS value of 
96 lbs, indicating a factor of 3.6. Zaitz (1994) loosely suggested, by 
referring to the 
Wood Handbook ( Wood as an Engineering Material,  Forest Products Laboratory, 
DOA, 1987), that safety factors up to a value of 6 have been used to obtain 
the allowable 
loads listed in NDS. The relatively large values of safety factors used may 
stem from the 
inherently large variability with nail connections for wood, which is a 
biological material 
having highly variable properties and strength itself. 

For this present study, a basic safety factor of 4.0 is used for estimating 
the ultimate loads 
from the NDS design loads. Some other values in a range from 4.0 to 5.0 are 
also used 
depending on specific situations. For example, the safety factors used to 
estimate the 

Chapter 6. Physical Damage Modeling 

?
Figure 6.17. Measured Ultimate Capacity (Mean 1 1 Standard Deviation) of 8d 
Nail 
Connections for Lateral Loading in Comparison to the NDS Value, for Various 
Rates of Loading. 

ultimate capacity of the nail connections between the studs and the top plate 
and those 
between the studs and the bottom plate are increased to 4.5 and 5.0 for let-
in braced and 
sheathed walls, respectively. This is because the bracing and sheathing are 
expected to 
provide additional strength to the connection between these components. The 
resulting 
ultimate lateral failure loads are shown in Table 6.4 for the typical nail 
connections on 
wood frame walls. 

Variability and Effects of Workmanship. There exists relatively high 
variability in the 
failure load of nail connections for wood. The overall variability in the 
ultimate capacity 
of wood structures is associated with such factors as variable properties in 
similar species 
of wood, use of different timbers, deviation of installed component s 
strength in a full-
scale system from component test value, and variable workmanship, etc. 

The variability in the strength of the same species of wood is reflected in 
the laboratory 
test result variations. For example, Rosowsky and Reinhold (1999) showed a 
COV of 

0.13 for lateral failure load from ten 8d box nail test specimens, and 0.30 
for withdrawal 
load from over thirty 8d common nail test specimens. Reed, et al. (1996) 
scored a COV 
of 0.23 for withdrawal load from sixteen 8d toenail specimens. The 
variability of strength 
among a number of commonly used timbers is represented by COVs ranging from 
0.13 to 
0.20 (with an average of 0.167) for the typical nailing applications shown in 
Table 6.4, 
assuming an equal utilization probability distribution for these timbers. 
Variation of the 
installed component s strength in a full-scale system from the component s 
test value will 
also be a significant contributing factor to the overall variability; 
unfortunately, it is not 
quantitatively known and it is difficult to estimate. Variable workmanship 
always plays a 
role in the reduction of the expected ultimate capacity and in the 
variability of the 
HAZUS-MH MR3 Technical Manual 

?
Table 6.4. Estimated Resistance Capacity of Nail Connections within One Stud 
Spacing (Typically 16.) 

Connections 
Penny 
Weight 
of Nail 
Thickness 
of Side 
Member 
(inches) 
Resistance of Nail Connections (lbs) 
Average 
(lbs) 
STD 
(lbs) COV 
Southern 
Pine 
(G=0.55) 
Douglas 
Fir-Larch 
(G=0.50) 
Northern 
Douglas 
Fir-Larch 
(G=0.49) 
Southern 
Douglas 
Fir 
(G=0.46) 
Spruce 
Pine Fir 
(G=0.42) 
Southern 
Spruce 
Pine Fir 
(G=0.36) 
Joist to Top 
Plate 8d 0.75 104 90 87 80 70 58 81.5 16.1 0.198 
Double Top 
Plates 10d 1.5 128 118 115 109 96 77 107.2 18.2 0.170 
Top Plate to 
Stud 16d 1.5 154 141 138 131 120 104 131.3 17.5 0.133 
Stud to 
Bottom Plate 8d 0.75 104 90 87 80 70 58 81.5 16.1 0.198 
Bottom Plate 
to Floor 16d 1.5 154 141 138 131 120 104 131.3 17.5 0.133 
Adjustment Factors Required by NDS 
Assumed 
Safety 
Factor 
Reduction 
Factor for 
Workmanship 
Estimated 
Ultimate 
Failure 
Load (lbs) Connections 
Number of 
Nails per 
Connection 
Duration 
of Load 
CD 
Wet 
Service 
CM 
Temp. 
Ct 
Penetration 
Depth 
Cd 
End 
Grain 
Ceg 
Diaphragm 
Cdi 
Toe-
Nail 
Ctn 
Joist to Top 
Plate 3 1 0.85 1 1 1 1 0.83 4 0.75 517 
Double Top 
Plates 2 1 0.85 1 0.84 1 1 1 4 0.8 490 
Top Plate to 
Stud 2 1 0.85 1 1 0.67 1 1 4.5 * 0.8 539 
Stud to 
Bottom Plate 4 1 0.85 1 1 1 1 0.83 4.5 * 0.75 776 
Bottom Plate 
to Floor 2 1 0.85 1 1 1 1 1 4 0.8 714 

* 4.5 for Walls with Let-In Braces, and 5.0 for Walls with Sheathing Boards 
ultimate capacity of a structure. For example, missing nails are frequent in 
residential 
wood construction. There are no data available for workmanship variability 
for wood 
structures, but it may be comparable to that for masonry walls, represented 
by a COV of 

0.25. 
The combined strength variability for wood structural systems is difficult to 
estimate. The 
combined COV due to the above-identified factors could approximately be a 
root-sumsquare 
value of the individual COV s. This would have resulted in a combined COV 
somewhat larger than any individual COV, the largest of which would probably 
be that 
for variable workmanship. On the other hand, when components form a unit in 
resisting 
loads, the components of similar function share the load. For example, within 
a wall 
structural unit as described in the sectional wall model noted above, all 
nails connecting 
the bottom plate to the floor tend to share the lateral load simultaneously. 
Thus, their total 
capacity will be the sum of the individual capacities and the variability of 
the sum will be 
reduced by a factor equal to the square root of the number of nails 
performing the same 
function. 

Given the above factors, a combined COV value of 0.20 is thought to be a 
reasonable 
compromise and is applied in the current version of the model. 

Chapter 6. Physical Damage Modeling 

?
Probability of Wall Failure. Based on the section, all wall model described 
above, 
several assumptions are made in order to derive the probability of wall 
failure: 

1. Within a span-wise section, all nail connections for a link between any 
two 
components, such as all those between the bottom plate and the floor, 
simultaneously resist the load passing through this link, in such a way that 
the 
total resistance is the sum of the individual capacities; 
2. All the links, such as those from studs to bottom plate, from bottom plate 
to floor, 
and from top plate to ceiling joists or roof rafters etc., form a series 
system of the 
section so that the failure of any one of these links results in failure of 
the section; 
3. All sections are identical and form a series system of the wall so that 
the failure of 
any one of the section results in failure of the wall. 
With the concept of a sectional wall and the above assumptions, the 
probability of wall 
failure, Pf, under uniform pressure load, p, can be formulated as 

L/S

(6.2)
R2

0.2
R5

1

1

R1

0.2
0.2
where F(*) denotes the standard normal cumulative distribution function, R1, 
R2,   R5 
are the estimated total ultimate capacity of all nail connections for links 1 
to 5, 
respectively, within a section. Estimated ultimate capacities for the 
individual 
connections are given in Table 6.4, and the number of connections equals to 
the effective 
section span (S) divided by stud spacing. A/2 represents half of the area of 
the section 
since the uniform pressure load is equally divided by the upper and lower 
supports. L is 
the wall span, while S represents the effective section span. 

Examples of Pf versus p for various wall spans, calculated assuming an 
effective section 
span, S, of 8', are shown in Figures 6.18 and 6.19 for let-in braced and 
sheathed walls, 
respectively. 

These results are estimated ignoring the additional supports by adjacent and 
partition 
walls. This approximates the case of outward suction loads more appropriately 
than the 
case of inward pressure loads. Estimated failure probability for inward 
loading would be 
slightly lower than for outward loading of the same magnitude. 

For top-unsupported walls, which models the case where the roof has been 
destroyed and 
the wall is still standing, the required supports will be transferred to the 
adjacent or 
partition walls, and the floor. Walls are normally joined through multiple 
studs, which are 
connected, typically, with 16d face nails spaced at 2' (SBC 1977). The 
probability of wall 
failure is controlled by the failure of the upper nail connections along the 
joining edge of 
the adjacent walls. Assuming linear reaction along the height of the 
supporting walls, the 
most vulnerable top three connections (i.e., 6 nails) together will 
approximately share 

P

f 

1

1

.

.
. 
. 
.
.

. 
. 
. 
..

.

.
- 
.
. 
. 
..

. 
. 
...

.
-

AA A 

pR1 pR2 pR5 

.

22 2 

. 
. 
...

. 
. 
...

.
-.
. 
. 
...

.
. 
. 
.
.

.
. 
. 
.
.

F

-

.
.

.

LL

.
. 
. 
.
.

.
. 
. 
.
.

F

-

.

.

.
. 
. 
.
.

F

-

.
. 
. 
...

. 
. 
. 
..

.

-

=

HAZUS-MH MR3 Technical Manual 

?
0.9 

6-30 

8'H Walls, Let-In Braced 

1.0 

Wall Span 

Estimated Failure Probability, 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

L=64' 
L=56' 
L=48' 
L=40' 
L=32' 
L=24' 
L=16' 
L=8' 
0.0 

10 20 30 40 5060 70 80 90100 

Pressure Load, p (psf) 

Figure 6.18. Estimated Failure Probability as a Function of Pressure Load for 
Various Wall Spans, Top Supported and Let-In Braced Wood Frame Walls. 

8'H Walls, Sheathed 

0.0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.0 
Estimated Failure Probability, PWall Span 
L=64' 
L=56' 
L=48' 
L=40' 
L=32' 
L=24' 
L=16' 
L=8' 

10 20 30 40 5060 70 80 90100 

Pressure Load, p (psf) 

+ 

....

Figure 6.19. Estimated Failure Probability as a Function of Pressure Load for 
Various Wall Spans, Top Supported and Sheathed Wood Frame Walls. 

9/16 of the total pressure load on the wall section spanned between the 
supporting walls. 
The failure probability is then estimated by 

) 1 

1 

(

L/W

9 

pHW

.

.
. 
. 
.
.

-

R

.
. 
. 
..
.
.
.

. 
. 
. 
..

16

Pf 

1

-

-F

(6.3)
=

0.25R
.

Chapter 6. Physical Damage Modeling 

?
where H is the wall height (floor to eave), W is the typical distance between 
supporting 
walls and L is the total wall span. The resistance capacity, R, is provided 
by six 16d face 
nails and is estimated to be 2142 lbs. The coefficient of variation of 0.25 
used here is 
empirically increased from the value of 0.20 for the case of walls with a top 
support, due 
to the uncertainty anticipated with the damage states of roofs and supporting 
walls. 

Examples of Pf versus p for various wall spans, calculated assuming a typical 
distance, 
W, of 12', are shown in Figure 6.20. 

8'H Walls, without Roof Support 

0.0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.0 
Estimated Failure Probability, PL=64' 
L=56' 
L=48' 
L=40' 
L=32' 
L=24' 
L=16' 
L=8' 
Wall Span 
1020 30 4050 

Pressure Load, p (psf) 

Figure 6.20. Estimated Failure Probability as a Function of Pressure Load for 
Various Wall Spans, Top Unsupported Wood Frame Walls, Let-In Braced or 
Sheathed. 

Concluding Remarks. Based on post-storm observations and suggestions from 
published 
literature, wood frame wall failure probability is estimated based on the 
failure of nail 
connections between the wall s double top plate and ceiling joists or roof 
rafters, and 
between the bottom plate and the floor, as well as the nail connections of 
the wood studs 
to these plates. A sectional wall model, along with the concept of an 
effective section 
span, is introduced. A probabilistic failure model has been derived, relating 
the wall 
failure probability for given pressure on the wall to the mean capacity and 
coefficient of 
variation of individual connections. The mean lateral capacity and 
coefficient of variation 
of the nail connections for wood are determined from the analysis of the data 
available in 
technical papers and design manuals. For data from design manuals, 
conversions from the 
specified allowable design value to the expected ultimate capacity are made 
using 
estimated safety factors. Variability of capacity due to various effects is 
also modeled. 

Neither roof weight nor roof uplift is included in the present wall models. 
It is also 
recognized that these models ignore some failure modes. These include, for 
example, the 
interaction of a wall-roof structural failure in which the wall and roof act 
as an interlinked 
structural system and support each other in resisting different load effects 
caused 
by wind, such as the uplift and the lateral forces on the roof, or the 
lateral forces on walls. 

HAZUS-MH MR3 Technical Manual 

?
The failure of one element may in some cases increase the likelihood of the 
failure of 
others. 

6.3 Damage Model Validation for Residential Buildings 
In order to validate the model s ability to predict damage to buildings, 
comparisons of 
simulated and observed damage states are performed. The damage states 
examined here 
include roof cover damage, roof sheathing damage and damage to windows. Roof 
cover 
and roof sheathing damage states are simulated and compared to roof damages 
experienced during Hurricanes Andrew (1992), Erin (1995) and Fran (1996). In 
the case 
of window damage to residential buildings, the information collected by HUD, 
as 
described in Crandell, et al. (1993), provides the only source of relatively 
unbiased 
statistics on window damage associated with Hurricane Andrew. 

In all of the comparisons, the observed damage states are compared to those 
obtained by 
modeling the wind loads experienced by the homes using the wind loading and 
damage 
models described earlier. To obtain estimates of the wind speeds at the sites 
of the 
observed damage, a full reproduction of the wind speed and direction time 
history at the 
site are obtained using the hurricane wind field model described in Section 
2.2. 

6.3.1 Roof Cover and Roof Deck Damage 
6.3.1.1 Hurricane Andrew (1992) 
The most comprehensive existing report on the performance of roof systems for 
residential buildings during Hurricane Andrew is given in HUD (1993). In the 
HUD 
(1993) study, the survey team randomly selected 466 homes located in the high 
damage 
areas and then proceeded to quantify the damage states for each structure. 
The homes 
were located in nine separate clusters (A through I) as shown in Figure 6.21. 

Analysis of Aerial Photographs used to Supplement the HUD Data. To expand on 
the 
HUD roof damage data, aerial photographs were obtained that correspond to 
four of the 
locations used in the HUD damage survey. The aerial photographs were produced 
at a 
scale of 1. = 100'. This scale provides enough resolution so that the damage 
to the roof 
cover and roof sheathing can be directly estimated. The exact location of the 
HUD study 
areas is determined by geo-coding the addresses of the homes surveyed by the 
HUD 
team. 

The wind speed traces at the locations of the four photographs determined 
from the 
simulation of Hurricane Andrew are given in Figure 6.22. As seen from Figure 
6.22, the 
basic wind speeds experienced at each location are very similar. These 
simulated wind 
speeds are discussed further in Chapter 9. 

The HUD building damage states were categorized into 4 distinct damage states 
defined 
as: 

Damage State 0 No Damage
Damage State 1 0 < Damage = 1/3 

Chapter 6. Physical Damage Modeling 

?
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
Miami-Dade County,
State of Florida A 
B 
C 
D 
E 
F 
G 
H 
I 
Cutler 
Goulds 
Naranja 
Perrine 
Homestead 
Princeton 
Cutler Ridge 
Florida City 
Leisure City Homestead AFB 
Lakes by the Bay 
Palmetto Estates 
Richmond Heights 
South Miami Heights 
NAHB Damage Survey Sites 
Figure 6.21. NAHB Site Locations. 

Damage State 2 1/3 < Damage = 2/3 
Damage State 3 Damage > 2/3 

The damage states recorded included roof cover damage, roof sheathing damage, 
roof-
wall connection damage, gable end damage and window damage. 

Based on conversations with members of the HUD damage team, it has been 
assessed 
that the high damage states indicated in the HUD report overstate the total 
damage. Thus, 
the roof damage states used in the analysis of the damage evident in the 
aerial 
photography are as follow: 

Damage State 0: No Damage
Damage State 1: 0.0 < Damage = 0.05 
Damage State 2: 0.05 < Damage = 0.1 
Damage State 3: 0.1 < Damage =0.2 
Damage State 4: 0.2 < Damage = 0.5 
Damage State 5: 0.5 < Damage = 1.0 
Damage State 6: Damage = 1.0 

When examining the photographs, if uncertainty exists as to the extent of 
damage, 
because the roof was covered with a tarp or plastic, the damage state is 
entered as 
unknown. Prior to performing comparisons of simulated and observed damage 
states, 
comparisons of the damage estimates obtained from the photographs to those 
reported by 

HAZUS-MH MR3 Technical Manual 

?
Photograph 1 Photograph 2 
0 
90 
180 
270 
360 
Wind Direction 
0 
90 
180 
270 
360 
Wind Direction 
00.5 11.5 22.5 33.5 4 00.5 11.5 22.5 33.5 
180 

180 

Peak Gust Wind Speed (mph) 

Peak Gust Wind Speed (mph) 

160 

140 

120 

100 

80 

60 

40 

20 

Peak Gust Wind Speed (mph) 

Peak Gust Wind Speed (mph) 

160 

140 

120 

100 

80 

60 

40 

20 

0 

0 

0 0.5 11.5 22.5 33.5 4 00.5 11.5 22.5 33.5
Elapsed Time (Hours) Elapsed Time (Hours)
Photograph 3 Photograph 4 

0 
90 
180 
270 
360 
Wind Direction 
0 
90 
180 
270 
360 
Wind Direction 
00.5 11.5 22.5 33.5 4 0 0.5 1 1.5 2 2.5 3 3.5 
180 

180 

160 

140 

120 

100 

80 

60 

40 

20 

160 

140 

120 

100 

80 

60 

40 

20 

0 0 
0 0.5 1 1.5 2 2.5 3 3.5 4 0 0.5 1 1.5 2 2.5 3 3.5 
Elapsed Time (Hours) Elapsed Time (Hours) 

Figure 6.22. Wind Speed and Direction Traces (10m, Open Terrain) at Locations 
of 
Photographs 1, 2 3, and 4 (HUD Locations, F, E, D, and H). 

HUD were made. To perform these comparisons, damage states 1, 2 and 3 
observed from 
the aerial photographs were combined into one category, corresponding to HUD 
damage 
state 1. The four distinct damage states represent no damage, minor damage, 
moderate 
damage, and major damage, as described in Table 6.5. 

Photograph 1 (HUD Location F). This subdivision was comprised of closely-
spaced two 
story homes. The estimated value of the surface roughness within the 
subdivision is about 

0.6 m. The surrounding terrain to the south and east of the homes is open. In 
the damage 
simulation, the subdivision was modeled with a z0 of 0.3 m, representing a 
compromise 
between the low z0 value around the buildings and the high z0 value within 
the 
subdivision. Upon reviewing the aerial photography, it was obvious that the 
roofs of 
some of the homes had been repaired. All homes with repaired roofs were 
removed from 
the damage analysis. A total of 330 two-story gable homes are shown in the 
photograph. 
Of these 330 homes, 98 had obviously been repaired, leaving a total of 232 
non-repaired 
homes. Of these 232 homes, ARA could identify the roof cover damage for 223 
cases, 
and the roof sheathing damage in 213 cases. The HUD team surveyed 63 homes in 
this 
subdivision, and were able to identify damage for 57 of the 63 cases. 
Chapter 6. Physical Damage Modeling 

?
Table 6.5. Mapping Used to Match Aerial Photograph Estimated Damage States 
and HUD Damage States 

Damage State Aerial Photograph Estimated Damage State HUD Damage State 
0 No Damage No Damage 
1 0-20% 0-33% 
2 20%-50% 33%-66% 
3 >50% >66% 

The comparison of the observed damage states, shown in Figure 6.23, indicates 
that in 
the case of roof sheathing, the larger sample taken from the photograph 
suggests about 
50% of the homes experienced the loss of a least one piece of roof sheathing 
whereas the 
HUD data suggests that about 60% of the homes experienced the loss of at 
least one piece 
of roof sheathing. The only other notable difference between the two damage 
state 
estimates is that the HUD data places a higher percentage of the homes 
experiencing the 
highest roof cover damage state than does the data obtained from the 
photograph, even 
though the highest HUD damage state begins at a higher level of damage than 
does the 
highest damage state used here. 

Photograph 2 (HUD Location E). In the case of Photograph 2, 438 homes appear 
in the 
photograph. The population of these homes was made up of 308 single-story 
gable roof 
units, 97 single-story hip roof units and 30 two-story gable roof buildings. 
The roof shape 
of 3 units could not be identified. Of the 438 homes, 45 were re-roofed 
before the 
photograph was taken. Of the 393 homes that had not been re-roofed, damage 
states 
could be discerned for 239 single-story gable buildings (77% of those in the 
photograph), 
70 single-story hip buildings (from 97 units) and 30 two-story gable 
buildings (from 30 
units). The damage states associated with the remaining 54 homes could not be 
determined. 

The houses within the subdivision were widely spaced. The estimated value of 
the 
surface roughness, based on the approach given by Latteu (1969), within the 
subdivision 
is about 0.15 m. 

The HUD team surveyed a total of 63 homes within the area encompassed by this 
photograph. These 63 homes were comprised of 59 single-story gable buildings, 
one two-
story gable, and three single-story hip roof buildings. Of the 59 single-
story gable roof 
buildings, sheathing damage could be estimated on 42 of these (71%) and 
roofing 
damage for 45 of the homes. 

Figure 6.24 compares the damage states arising from the analysis of the 
aerial photograph 
to the HUD data. A comparison of the roof sheathing damage states clearly 
indicate that 
the HUD data overestimated the true level of sheathing damage. The HUD data 
suggests 
that 89% of the single-story gable buildings experienced the loss of at least 
one piece of 
sheathing, whereas examination of the aerial photography indicates the loss 
of at least 
one piece of roof sheathing for 62% of the homes. 

HAZUS-MH MR3 Technical Manual 

?
6-36 

Roof Cover Roof Cover 

1 

1 

0.8 

0 

0 1 2 3 
Aerial Photograph Estimated Damage State 
0 

0 1 2 3 
HUD Damge State
Roof Sheathing Roof Sheathing 

0.6 

0.6 

Relative Frequency 

Relative Frequency Relative Frequency 

0.6 

0.4 

0.2 

0 0 
0 1 2 3 0 1 2 3 
Aerial Photograph Estimated Damage State HUD Damage State 

Figure 6.23. Comparison of Observed Damage States for Single Story Gable 
Homes 
Obtained from Aerial Photography and HUD Damage Survey   Location of 
Photograph 1 (HUD Location F). 

Roof Cover Roof Cover 

0.6 

Relative Frequency 

0.5 

0.5 

0.4 

0.4 

0.3 

0.3 

0.2 

0.2 

0.1 

0.1 

0 1 2 3 
Aerial Photograph Estimated Damage State 
0

0 

0 1 2 3 
HUD Damage State 
Roof Sheathing Roof Sheathing 

0.6 

0.6 

Relative Frequency Relative Frequency 

Relative Frequency 

0.5 

0.5 

0.4 

0.4 

0.3 

0.3 

0.2 

0.2 

0.1 

0.1 

0 1 2 3 
Aerial Photograp Estimated Damage State 
0 1 2 3 
HUD Damage State 
0 0 

Figure 6.24. Comparison of Observed Damage States for Single Story Gable 
Homes 
Obtained from Aerial Photography and HUD Damage Survey   Location of 
Photograph 2 (HUD Location E). 

Relative Frequency 

0.5 

0.5 

0.4 

0.4 

0.3 

0.3 

0.2 

0.2 

0.1 

0.1 

Chapter 6. Physical Damage Modeling 

?
6-37 

Photograph 3 (HUD Location D). In Photograph 3, the single family home 
population 
was comprised of 193 single-story gable homes and 42 single-story hip roof 
homes. Of 
the 193 gable roof homes, 145 units (75%) had not been re-roofed and the 
damage could 
be estimated. In the case of the hip roof homes, 28 units (67%) had not been 
re-roofed 
and the damage states could be estimated. 

Like the homes examined in Photograph 2, the houses within the subdivision 
were widely 
spaced. Again using the approach given in Latteu (1969), the estimated value 
of the 
surface roughness within the subdivision is 0.11 m. 

Figure 6.25 presents a comparison of the HUD damage states and the damage 
states 
determined from the aerial photography. The agreement between the results 
obtained 
from the two damage surveys is reasonable. In the case of roof sheathing, 
both the 
analysis of the photography and the ground survey performed by HUD indicates 
that 
about 70% of the single-story gable homes had at least one piece of roof 
sheathing fail. 
This 70% value agrees with the results from the analysis of the homes 
analyzed from 
Photograph 2, which was subjected to nearly identical hurricane winds during 
the storm. 

Roof Cover Roof Cover 

0.6 

0.6 

Relative Frequency Relative Frequency 

0 

0 1 2 3 
Aerial Photogrpah Estimated Damage State 
0 

0 1 2 3 
HUD Damage State 
Roof Sheathing Roof Sheathing 

0.6 

0.6 

0.5 

Relative Frequency Relative Frequency 

0.5 

0.4 

0.4 

0.3 

0.3 

0.2 

0.2 

0.1 

0.1 

0.5 

0.4 

0.3 

0.2 

0.1 

0.5 

0 1 2 3 
Aerial Photograph Estimated Damage State 
0.4 

0.3 

0.2 

0.1 

0 

0123 

HUD Damage State 

Figure 6.25. Comparison of Observed Damage States for Single Story Gable 
Homes 
Obtained from Aerial Photography and HUD Damage Survey   Location of 
Photograph 3 (HUD Location D). 

Photograph 4 (HUD Location H). In the case of Photograph 4, too many of the 
homes 
had been repaired and thus estimates of roof damage could not be properly 
ascertained. 

Comparison of Simulated and Observed Damage States. In the comparison of the 
simulated and observed roof damage states, a difficulty arises (particularly 
in the case of 
the roof sheathing) because the nail sizes used to attach the roof sheathing 
to the roof 

HAZUS-MH MR3 Technical Manual 

?
trusses were not known. Prior to 1992, the South Florida Building Code 
required a 
minimum of 6d nails spaced at 6. on center at the edges and 12. on center in 
the field for 
sheathing less than 1/2. thick. For a sheathing thickness greater than 1/2., 
8d nails were 
required using the same 6./12. spacing. As previously discussed, the uplift 
resistance of a 
sheathing panel attached to the rafters using 8d nails is about twice the 
value that is 
obtained using 6d nails. 

In the HUD damage survey, the nail size was recorded on only two of the 466 
homes 
examined (both had 8d nails). Data collected as part of the Residential 
Construction 
Mitigation Program under the direction of the Florida Department of Community 
Affairs, 
suggests that approximately 60% of the homes are constructed using 8d nails 
to attach the 
roof sheathing to the roof trusses, with 6d nails used in the remaining 40% 
of the homes. 
The default for characterizing the building stock is taken as 40% of the 
homes 
constructed using the code minimum requirements with a sheathing thickness of 
less than 
1/2. and the remaining 60% of the homes constructed using the code minimum 
requirements with sheathing roof panels having a thickness greater the 1/2.. 

In the comparisons of simulated and observed roof damage states that follow, 
the 
comparisons are given for all buildings modeled using 6d sheathing nails, all 
buildings 
modeled using 8d sheathing nails, and the default building stock (i.e., 40% 
of the 
buildings modeled with 6d nails and 60% with 8d nails). The simulated damage 
are 
derived using the wind loads specified in ASCE-7 and assuming that the 
buildings are 
randomly oriented. 

Two-Story Gable Houses. Figures 6.26 and 6.27 show comparisons of the 
simulated and 
observed damage states for the two-story gable roof homes. Figure 6.26 
presents the 
comparisons with the data obtained from Photograph 1, whereas Figure 6.27 
shows the 
comparisons for the damage states taken from Photograph 2. Using the default 
building 
stock (i.e., 40% of the buildings have roof decks attached with 6d nails and 
60% with 8d 
nails) as being representative of the building stock associated with the 
observed damage, 
the comparisons given in Figures 6.26 and 6.27 suggest that modeling the 
loads with the 
ASCE-7 based pressure coefficients overestimates the damage to the roof 
sheathing and 
roof covering for two-story given in Figure 6.26 and 6.27 suggest that 
modeling the loads 
with the ASCE-7 based pressure gable houses. 

Single-Story Gable Houses. Figures 6.28 and 6.29 show comparisons of the 
simulated 
and observed roof damage states for the single-story gable roof homes. For 
the default 
building stock case, the modeled roof sheathing and roof covering damage 
states obtained 
using the ASCE-7 based loads agree reasonably well with the observed damage. 

Single-Story Hip Houses. Figures 6.30 and 6.31 show the roof damage state 
comparisons 
for the single-story hip roof homes. The comparison of the roof damage states 
derived 
from the observations taken from Photograph 2 (Figure 6.30) show good 
agreement 
between the observed damage states and the predicted damage states using the 
ASCE-7 
based loads for the default building stock. On the other hand, the 
comparisons of 
predicted and observed roof damage states for Photograph 3 (Figure 6.31) show 
the 
damage model to overestimate the roof damage to the single-story hip roof 
homes. 

Chapter 6. Physical Damage Modeling 

?
6-39 

100 

10 0 

Data Simulated, 6d Sheathing Nails 
80 

Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

80 
60 
40 
20 

60 
40 

0 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Sheathing Damage 
Relative Frequency (%)
0 

100 

10 0 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Sheathing Damage 
20 

Data Simulated, 8d Sheathing Nails 
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

Relative Frequency (%)

80 

80 

60 

40 

60 

40 

0 

0
0% >0-5% 5-10% 10-20% 20-50% 50-100% 0% >0-5% 5-10% 10-20% 20-50% 50-100%

Sheathing Damage Covering Damage 

Figure 6.26. Comparison of Modeled and Observed Roof Damage States to Two-
Story Gable Houses (Photograph 1). 

100 

10 0 

20 

20 

Data Simulated, 6d Sheathing Nails 
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

80 

80 
60 
40 

60 
40 
20 

0 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Sheathing Damage 
Relative Frequency (%)
0 

100 

10 0 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Sheathing Damage 
20 

0
0% >0-5% 5-10% 10-20% 20-50% 50-100%

Sheathing Damage 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Covering Damage 
Figure 6.27. Comparison of Modeled and Observed Roof Damage States to Two-
Story Gable Houses (Photograph 2). 

Data Simulated, 8d Sheathing Nails 
Relative Frequency (%)
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

80 

80 

60 

60 

40 

20 

HAZUS-MH MR3 Technical Manual 

?
10 0 

100 

Data Simulated, 6d Sheathing Nails 
80 

Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

Relative Frequency (%)

60 

40 

0
0% >0-5% 5-10% 10-20% 20-50% 50-100% 0% >0-5% 5-10% 10-20% 20-50% 50-100%

Sheathing Damage Sheathing Damage 

10 0 

100 

20 

0
0% >0-5% 5-10% 10-20% 20-50% 50-100%

Sheathing Damage 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Covering Damage 
Figure 6.28. Comparison of Modeled and Observed Roof Damage States to Single-
Story Gable Houses (Photograph 2). 

100 

Data Simulated, 8d Sheathing Nails 
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

Relative Frequency (%)

80 

80 

60 

60 

40 

40 

20 

20 

0 
20 
40 
60 
80 
100 
Relative Frequency (%)
Data Simulated, 6d Sheathing Nails 
Relative Frequency (%)
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 

80 

60 

40 

20 

0 
0% >0-5% 5-10% 10-20% 20-50% 50-100% 0% >0-5% 5-10% 10-20% 20-50% 50-100% 

Sheathing Damage Sheathing Damage 

100 

100 

0 

0
0% >0-5% 5-10% 10-20% 20-50% 50-100%

Sheathing Damage 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Covering Damage 
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Figure 6.29. Comparison of Modeled and Observed Roof Damage States to Single-
Story Gable Houses (Photograph 3). 

Data Simulated, 8d Sheathing Nails 

Relative Frequency (%)

Relative Frequency (%)

80 

80 

60 

60 

40 

40 

20 

20 

Chapter 6. Physical Damage Modeling 

?
10 0 

100 

80 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Sheathing Damage 
Data Simulated, 6d Sheathing Nails 
Relative Frequency (%)
0 

10 0 

100 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Sheathing Damage 
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

Data Simulated, 8d Sheathing Nails 
60 

40 

20 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Sheathing Damage 
Relative Frequency (%)
0
0% >0-5% 5-10% 10-20% 20-50% 50-100%

Covering Damage 

Figure 6.30. Comparison of Modeled and Observed Roof Damage States to Single-
Story Hip Houses (Photograph 2). 

10 0 

100 

Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

80 

80 

60 

60 

40 

40 

20 

20 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Sheathing Damage 
Data Simulated, 6d Sheathing Nails 
Relative Frequency (%)
0 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 

Sheathing Damage 

10 0 

100 

Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

80 

80 

Data Simulated, 8d Sheathing Nails 
60 

60 

40 

40 

20 

20 

0
0% >0-5% 5-10% 10-20% 20-50% 50-100%

Sheathing Damage 

0% >0-5% 5-10% 10-20% 20-50% 50-100% 
Covering Damage 
Figure 6.31. Comparison of Modeled and Observed Roof Damage States to Single-
Story Hip Houses (Photograph 3). 

Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
Relative Frequency (%)

Relative Frequency (%)

80 

80 

60 

60 

40 

20 

HAZUS-MH MR3 Technical Manual 

?
Summary. In summary, given the assumption of the building stock with respect 
to the 
fraction of homes roofed with 6d and 8d sheathing nails, the predicted roof 
damage states 
obtained using the ASCE-7 based loads compare well with the observed roof 
damage 
states. In all cases examined, the simulated roof sheathing damage using 6d 
nails 
overestimated the observed damage and the simulated roof sheathing damage 
using 8d 
nails underestimated the observed damage   i.e., the observed data was 
contained within 
the two limiting simulations for all cases examined. 

6.3.1.2 Hurricane Erin (1995) 
The roof sheathing failure model is further validated through comparisons to 
damage 
sustained at Navarre Beach, Florida during Hurricane Erin. This area 
experienced peak 
gust wind speeds (open terrain at 10 m above ground) in excess of 100 mph. 
The damage 
survey includes 161 wood frame residential structures. Table 6.6 shows the 
surface 
roughness values used in the simulation. Note that the shoreline at Navarre 
Beach runs 
east-west. The roof sheathing damage simulation is performed with 30% of the 
homes 
located along the first row adjacent to the shoreline and 70% of the homes 
located on 
inside rows. The building stock with respect to sheathing attachment is also 
taken here as 
40% of the buildings roofed with 6d sheathing nails and 60% with 8d sheathing 
nails. 

Table 6.6. Surface Roughness Values used in Hurricane Erin Sheathing Damage 
Validation Study at Navarre Beach, Florida 

Wind Direction 
Surface Roughness (m) 
First Row of Homes Along Shoreline Inside Rows 
Onshore 0.03 0.1 
Offshore 0.2 0.2 
Along Shoreline (1150) 0.1 0.15 

Of the 161 structures surveyed, 91 have gable roofs and 43 have hip roofs. 
The gable roof 
homes are further divided into categories taking into account there 
orientation (i.e., gable 
ridge parallel to the shoreline and perpendicular to the shoreline) and their 
height. 
Comparisons of the observed roof sheathing damage and the simulated roof 
sheathing 
damage are shown in Table 6.7. Note that the modeled number of homes with 
sheathing 
damage is computed as the modeled percentage multiplied by the total number 
of homes 
in the survey rounded to the nearest integer. 

As seen in Table 6.7, the comparison of the observed sheathing damage and 
modeled 
sheathing damage indicates that the damage model performs reasonably well, 
with a bias 
towards overestimating the damage levels for the two-story gable roof homes 
oriented 
perpendicular to the coast and underestimating the damage levels for the two-
story gable 
roof homes oriented parallel to the shoreline. The sheathing damage for the 
hip roof 
homes is simulated considering an equal mix of single-story and two-story 
homes and an 
equal mix of homes with roof ridges parallel and perpendicular to the 
shoreline. 

Chapter 6. Physical Damage Modeling 

?
Table 6.7. Comparison of Observed and Modeled Roof Sheathing Damage at 
Navarre Beach, Florida 

Case 
Total 
Number of 
Structures 
in the 
Survey 
Observed Modeled 
Number of 
Structures 
with 
Sheathing 
Damage 
Percentage of 
Structures 
with 
Sheathing 
Damage 
Number of 
Structures 
with 
Sheathing 
Damage 
Percentage of 
Structures with 
Sheathing 
Damage 
Single Story Gable Roof Homes Oriented 
Parallel to Beach 10 0 0 0 0.60 
Single Story Gable Roof Homes Oriented 
Perpendicular to Beach 5 0 0 0 0.51 
Two Story Gable Roof Homes Oriented 
Parallel to Beach 56 12 21.4 3 4.7 
Two Story Gable Roof Homes Oriented 
Perpendicular to Beach 20 0 0 1 4.2 
Hip Roof Homes 43 0 0 0 0.07 

6.3.1.3 Hurricane Fran (1996) 
Using aerial photography obtained from the American Association of Wind 
Engineers 
and an aerial video obtained from the North Carolina Department of 
Transportation, the 
performance of roof covering during Hurricane Fran on hip and gable roof 
residential 
structures was reviewed. A total of 49 gable roof homes and 112 hip roof 
homes were 
identified for the study. The structures are located in North Carolina at the 
north end of 
Wrightsville Beach and at the south end of Figure Eight Island. Note that 
this area was 
chosen since it received little or no damage during Hurricane Bertha, which 
preceded 
Hurricane Fran by about two months. The study area experienced peak gust 
speeds (open 
terrain at 10 m above ground) between 105 and 110 mph during Hurricane Fran. 

The shoreline at Wrightsville Beach and Figure Eight Island is oriented 
roughly 
northeast-southwest. The surface roughness values used in the simulation are 
the same as 
those used in the Hurricane Erin roof sheathing damage validation study (see 
Table 6.6). 
Consistent with that observed from the aerial photography and video, the 
building stock 
used in the roof cover damage simulation for both the hip and gable roof 
homes 
comprised an equal mix of homes with roof ridges parallel and perpendicular 
to the 
shoreline, a three to one ratio of two-story homes versus single-story homes, 
and a seven 
to three ratio of homes located immediately adjacent to the ocean versus 
homes located at 
least one row away from the ocean. Also, a 40%/60% mix of structures with 6d 
and 8d 
sheathing nails were used in the damage simulation. The results of the 
analysis are shown 
in Figure 6.32. 

As seen in Figure 6.32, the agreement between the observed and modeled damage 
states 
is quite good and the small difference in the performance of hip and gable 
roofs is seen in 
both the modeled and observed data. 

HAZUS-MH MR3 Technical Manual 

?
6-44 

Gable Roof Homes Hip Roof Homes 

100 

100 

80 

80 

60 

60 

0-5% 5-20% 20-50% 50-100% 
Roof Cover Damage 
Observed 
Modeled 
Frequency (%)
0 

0-5% 5-20% 20-50% 50-100% 
Roof Cover Damage 
Observed 
Modeled 
Figure 6.32. Comparison of Simulated and Observed Roof Covering Damage for 
Gable and Hip Roof Houses. 

6.3.2 Window Damage 
Figure 6.33 shows a comparison of the window damage states resulting from the 
HUD 
damage survey following Hurricane Andrew and the damage simulation using 
ASCE-7 
loads. The data given in Figure 6.33, both observed and modeled, are for the 
four 
locations used in the roof damage validation study. The comparison of the 
simulated and 
observed damage states are reasonable, but the model tends to overestimate 
the number 
of single-story homes that experience no window damage. In the case of two-
story 
homes, both the simulation and the observations suggest that nearly all homes 
experienced the loss of at least one window. The damage simulation clearly 
reproduces 
the observation that two-story homes are likely to experience more window 
damage than 
one-story homes for the same wind event. 

Single Story Houses Two Story Houses 

Frequency (%)

40 

40 

20 

20 

0 
20 
40 
60 
80 
100 
Frequency (%) 
HUD 
Modeled 
0 
20 
40 
60 
80 
100 
Frequency (%) 
HUD 
Modeled 
0123 0123 

HUD Damage State HUD Damage State 

Figure 6.33. Comparison of Simulated and Observed Window Damage States for 
One- and Two-Story Houses, Hurricane Andrew (1992). 

6.3.3 Whole Roof Failures 
The whole roof failure model is a relatively simple model, where the roof is 
considered to 
fail as a complete unit if the wind induced uplift loads (computed as a 
combination of the 
internal pressure effect and the external loads) exceed the total uplift 
capacity of the roof 
provided by the roof-wall connections and the self weight. In the case of 
gable roofs, roof 
trusses (or rafters) are assumed to be spaced at 24. on center along two 
walls of the 
building. In the case of hip roofs, a roof-wall connection is assumed to 
exist at 24. 

Chapter 6. Physical Damage Modeling 

?
intervals along the entire perimeter of the building (i.e., for a square 
building, the hip roof 
has twice as many roof-wall connections as the gable roof). 

Using the data collected from the four aerial photographs of damage sustained 
during 
Hurricane Andrew discussed earlier, comparisons between the observed and 
modeled 
whole roof failure rates were performed. Observed whole roof failures from 
the aerial 
photography are assumed to be gable roofs and not hip roofs. Figure 6.34 
shows a section 
from Photograph 3 depicting eight homes, three of which are seen to have 
experienced a 
complete (or near complete) roof failure. 

Figure 6.34. Examples of Whole Roof Failures Depicted in Photograph Number 3. 

All roof-wall connections were modeled as a strapped connection having a mean 
resistance of 1200 lbs and a coefficient of variation of 30%. Roof trusses 
are spaced at 
24. on center. The statistics of the modeled failure rates are computed from 
1000 
simulations of a randomly oriented house located at the position of the 
photograph. Note 
that these comparisons include a calibration factor of 0.8 applied to the 
uplift resistances. 
Shingle and tile roof coverings were both considered in the analysis. The 
self weight of 
the roof with shingles is estimated to be 10.4 psf and with tiles it is 
estimated to be 18.4 
psf. The default building stock with respect to roof covers is taken as 70% 
with shingles 
and 30% with tiles after HUD (1993). Comparisons are given in Table 6.8. In 
the case of 
the observed failure rates, the fraction given in parentheses represent the 
number of 
failures over the number of buildings observed in the photograph. The 
comparisons given 
in Table 6.8 show good agreement between the modeled and observed failure 
rates for 
the default building stock for Photographs 3 and 4. The model overestimates 
the whole 
roof failure rate for Photograph 2. 

HAZUS-MH MR3 Technical Manual 

?
Table 6.8. Comparison of Observed and Modeled Whole Roof Failures at 
Locations 
of Aerial Photographs, Hurricane Andrew (1992) 

Photograph 
Frequency of Whole Roof Failures - Single Story Gable Roof Homes 
Observed 
Modeled 
Shingles Tiles 70% Shingles/ 30% Tiles 
1 
2 
3 
4 
-
0.65% (3/308) 
10.8% (21/193) 
0.32% (1/311) 
-
8.0% 
14.3% 
0.50% 
-
0.60% 
1.4% 
0.00% 
-
5.8% 
10.4% 
0.35% 
Average 3.1% (25/812) 6.6% 0.6% 4.8% 

Figures 6.35 and 6.36 show modeled failure rates for strapped and toe-nailed 
single-story 
hip and gable roof homes, for three different generic terrains (open terrain, 
defined with 
z0 = 0.03 m, standard suburban terrain, defined with z0 = 0.35 m and treed 
terrain, defined 
with z0 = 1.0 m) as a function of the peak gust wind speed at a height of 10 
m above 
ground in open terrain. The mean uplift capacity and coefficient of variation 
of an 
individual toe-nailed connection are 415 lbs and 25%, respectively. Note that 
the 
experimental values of the toe-nail connections discussed in Section 6.2.4 
present results 
with mean capacities ranging between 208 lbs up to 676 lbs, and thus the 415 
lbs value 
used herein is simply a representative value. 

The failure rates were computed using a simulation of hurricanes in the South 
Florida 
area, where for each simulation, the number of homes experiencing a whole 
roof failure 
(using 30 simulations/storm) was saved along with the peak gust wind speed 
(10 m in 
open terrain) experienced at the site. The results given in Figures 6.35 and 
6.36 clearly 
indicate the hip roofs are less susceptible to failure than the gable roofs 
(as has been well 
documented in the literature), and that the results are very strongly 
dependent on the 
assumed terrain. 

The potential for detailed validation of the non-strapped results presented 
in Figures 6.34 
and 6.35 is limited, since there are no known systematic, statistically 
unbiased studies 
examining the performance of roofs in regions where the application of straps 
(or clips) at 
the roof-wall connections are not used. Thus the results given in Figures 
6.35 and 6.36 
are given primarily to assess the reasonableness of the simple uplift failure 
model. 

6.4 Damage Model Results for Residential Buildings 
Following an approach similar to that used by Vann and McDonald (1978) for 
defining 
damage states to manufactured homes, the damage state descriptions used for 
residential 
buildings are given in Table 6.9. The damage states are primarily governed by 
the 
performance of the building envelope, and are divided into five states, 
varying between 0 
(no damage) and 4 (destruction). In Table 6.9, a building is considered to be 
in the higher 
damage state if any of the shaded damage indicators in the corresponding row 
occurs. For 
example, for a building to be considered to have sustained minor damage, the 
building 
must not have sustained structural failure or sheathing failure, and either 
one fenestration 
(window, door, garage door) failed or more than 2% but less than 15% of the 
roof cover 

Chapter 6. Physical Damage Modeling 

?
Single Story Gable with Straps 

% of homes with Whole RoofFailures 

100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
0 

80 100 120 140 160 180 200 
Peak Gust Wind Speed in Open Terrain (mph) 
Open 
Suburban 
Treed 
Single Story Gable with Toe Nails 

0 
10 
20 
30 
40 
50 
60 
70 
80 
90 
100 
% of homes with Whole RoofFailures 
Open 
Suburban 
Treed 
80 100 120 140 160 180 200 

Peak Gust Wind Speed in Open Terrain (mph) 

Figure 6.35. Modeled Failure Rates for Single Story Gable Roof Homes with 
Strapped Roof-Wall Connections and Toe Nailed Roof-Wall Connections. 

failed. Photographs of buildings in each of damage states 0 through 4 are 
given in Figures 

6.37 through 6.41. 
Eight different geometric representations (4 basic shapes with both a gable 
roof and hip 
roof) of  typical  residential buildings were developed for the damage 
assessment. The 
model buildings are shown pictorially in Figure 6.42, and represent the basic 
building 
geometries used to develop the residential building classification scheme and 
the 
development of estimated damage states for each building as a function of 
wind speed. 
All buildings have been modeled as having asphalt shingle roofs. All windows 
are treated 
as being comprised of single pane annealed glass, and all sliding glass doors 
are 
considered to be comprised of tempered glass. In the case of a gable roof 
home, the 
connections between the roof and the wall are assumed to exist at the base of 
every truss 
with no connections along the gable end. In the case of the hip roof, 
connections between 
the roof and the wall are assumed to exist along the entire perimeter of the 
wall. During 
the simulation, the walls are modeled as being connected to the foundation 
and the roof, 

HAZUS-MH MR3 Technical Manual 

?
Single Story Hip with Straps 

% of homes with Whole RoofFailures 

100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
0 

80 100 120 140 160 180 200 
Peak Gust Wind Speed in Open Terrain (mph) 
Open 
Suburban 
Treed 
Single Story Hip with Toe Nails 

0 
10 
20 
30 
40 
50 
60 
70 
80 
90 
100 
% of homes with Whole RoofFailures 
Open 
Suburban 
Treed 
80 100 120 140 160 180 200 
Peak Gust Wind Speed in Open Terrain (mph) 

Figure 6.36. Modeled Failure Rates for Single Story Hip Roof Homes with 
Strapped 
Roof-Wall Connections and Toe Nailed Roof-Wall Connections. 

until the roof fails, after which time the wall connection at the roof level 
is released. The 
model homes reflect the fact that homes with attached garages are typically 
bigger than 
those without garages. The parameters varied in the building description are 
as follows: 

Building Size: 4 Samples as in Figure 6.42 (gable roof only shown) 
Roof Shape: Hip or Gable 
Wall Construction: Wood, Unreinforced Masonry, Reinforced Masonry 
Roof Sheathing Attachment: 6d or 8d 
Roof-Wall Connections: Strapped or Toe-Nailed 
Garage: None, Strong, Weak 
Number of Stories: One or Two 

Damage simulations for each of the 144 different building types have been 
performed for 
the buildings located in terrains described with z0 = 0.03 m (open terrain), 
z0 = 0.35 m 
(typical suburban terrain), and z0 = 0.7 m (suburban terrain with some trees 
or densely 
spaced homes), z0 = 1.0 m (treed suburban terrain). 

Chapter 6. Physical Damage Modeling 

?
Table 6.9. Damage States for Residential Construction Classes 

Damage 
State Qualitative Damage Description 
Roof 
Cover 
Failure 
Window 
Door 
Failures 
Roof 
Deck 
Missile 
Impacts on 
Walls 
Roof 
Structure 
Failure 
Wall 
Structure 
Failure 
0 
No Damage or Very Minor Damage 
Little or no visible damage from the outside. 
No broken windows, or failed roof deck. 
Minimal loss of roof over, with no or very 
limited water penetration. 
=2% No No No No No 
1 
Minor Damage 
Maximum of one broken window, door or 
garage door. Moderate roof cover loss that can 
be covered to prevent additional water 
entering the building. Marks or dents on walls 
requiring painting or patching for repair. 
>2% and 
=15% 
One 
window, 
door, or 
garage door 
failure 
No <5 impacts No No 
2 
Moderate Damage 
Major roof cover damage, moderate window 
breakage. Minor roof sheathing failure. Some 
resulting damage to interior of building from 
water 
>15% and 
=50% 
> one and =
the larger of 
20% & 3 
1 to 3 
panels 
Typically 
5 to 10 
impacts 
No No 
3 
Severe Damage 
Major window damage or roof sheathing loss. 
Major roof cover loss. Extensive damage to 
interior from water. 
>50% 
> the larger 
of 20% & 3 
and =50% 
>3 and 
=25% 
Typically 
10 to 20 
impacts 
No No 
4 
Destruction 
Complete roof failure and/or, failure of wall 
frame. Loss of more than 50% of roof 
sheathing. 
Typically 
>50% >50% >25% 
Typically 
>20 
impacts 
Yes Yes 
The assumed component resistances used in the simulations are given in Table 
6.10. In 
the case of the roof-wall connections a resistance factor of 0.8 was applied. 
A resistance 
factor of 0.9 was applied to the roof sheathing uplift capacity. The 
resistance parameters 
given in Table 6.10 are the unfactored values. 

Statistics on damage states have been developed through the use of a 20,000-
year 
simulation of hurricanes (derived using the South Florida Hurricane climate) 
by 
performing 30 damage simulations for each storm. Prior to the start of each 
of the 30 
simulations, the resistances of the individual building components are re-
sampled, and 
the building orientation is also re-sampled. The building damage indicators 
including roof 
cover loss (as a percentage), number of failed roof sheathing panels, number 
of failed 
windows, doors, sliding glass doors, and garage doors, number of failed wall 
sections, 
and the failure of the entire roof are recorded. This individual component 
failure 
information is then used to define the final damage state of the building as 
defined in 
Table 6.9. 

Appendix A shows the probabilities of achieving each building damage state 
and each 
individual component damage state as a function of wind speed. The component 
damage 
states are the thresholds that move the building damage state from one 
definition to 
another in Table 6.9. For example, in the case of roof cover, damage state 1 
corresponds 
to roof cover loss of more than 2% but less than 15%. Only three damage 
states are given 
for roof cover, since only three roof cover damage states are used to define 
the total 
building damage state. The component damage states are presented to assist in 
determining which component drives the overall building damage state and to 
check how 
reasonably the damage model performs. 

HAZUS-MH MR3 Technical Manual 

?
(a) <2% Roof Cover 
(b) <2% Roof Cover 
Figure 6.37. Damage State 0. 

Chapter 6. Physical Damage Modeling 

?
(a) 2% to 15% Roof Cover Loss, One Broken Window 
(b) 2% to 15% Roof Cover Loss 
Figure 6.38. Damage State 1. 

HAZUS-MH MR3 Technical Manual 

?
(c) 2% to 15% Roof Cover Loss 
(d) One Broken Window 
Figure 6.38. Damage State 1 (concluded). 

Chapter 6. Physical Damage Modeling 

?
(a) More than One Window but less than the Greater of 3 or 20% of the Windows 
(b) At least One Failed Sheathing but Less than Three Failed Pieces of Roof 
Sheathing 
Figure 6.39. Damage State 2. 

HAZUS-MH MR3 Technical Manual 

?
More than Three Pieces of Failed Roof Sheathing but Less than 15% of the 
Panels Missing 

Figure 6.40. Damage State 3. 

The relative performance of the different building types and the impact of 
terrain are 
shown in Table 6.11 through the use of the average per storm damage state. 
This average 
per storm damage state effectively collapses all the information obtained 
from the 
simulations into a single number, allowing a rapid evaluation of the 
importance of 
individual building parameters in assessing the damage potential of a 
particular building 
type. Note that this per storm damage state includes the effect of the 
hurricane climate 
since the modeled hurricanes are drawn directly from the 20,000-year 
simulation, which, 
of course, produces many more storms having low intensity winds than high 
intensity 
winds. In other words, the average building damage states given in Table 6.11 
would be 
much lower if the storm simulation had been performed for a location in New 
England 
rather than South Florida. 

The results shown in Table 6.11 can be used to ascertain the importance that 
the various 
building parameters and terrains have on the performance of residential 
buildings. The 
maximum and minimum changes in the per storm average damage state caused by 
changing a single parameter are summarized in Table 6.12. Note that the 
effect of any 
one parameter on the average damage state varies with other building 
parameters and 
with terrain. 

The results clearly indicate that terrain is very important, where the 
average damage 
states are seen to increase by factors of between about 2 to 3 when moving 
from treed to 
open terrain. The impact of the number of stories on the average damage is a 
35% to 75% 
increase in damage when the number of stories is increased from one to two. 

The effect of roof shape (i.e., gable versus hip) on the damage state varies 
between about 
11% and 33%, with the gable roof buildings experiencing more damage. The 
impact of 
changing from hip to gable is larger when the roof/wall is connected with 
toe-nails versus 
straps and is also larger when the roof sheathing is fastened with 6d nails 
versus 8d nails. 

Chapter 6. Physical Damage Modeling 

?
(a) Complete Roof Failure 
(b) Complete Roof Failure, Greater than 25% Sheathing 
Figure 6.41. Damage State 4. 

HAZUS-MH MR3 Technical Manual 

?
(c) Wall Failure 
(d) Whole Roof Failure, Wall Failure 
Figure 6.41. Damage State 4 (concluded). 

Chapter 6. Physical Damage Modeling 

?
No Garage   50' by 24' Plan, 9' Eave Height With Garage   60' by 30' Plan, 9  
Eave Height 

One-Story Houses 

No Garage   40' by 30' Plan, 17' Eave Height With Garage   60' by 30' Plan, 
17' Eave Height 

Two-Story Houses 
Figure 6.42. Model House Geometries Used in Damage Simulation Study. 
Table 6.10. Component Resistance Values Used to Model Residential Buildings 

Component Distribution Distribution Parameters 
Sheathing Panel (6d) LogNormal Mean = 54.6, COV = 0.11 
Sheathing Panel (8d) LogNormal Mean = 103, COV = 0.11 
Annealed Glass Impact Deterministic 50 lb-ft 
Tempered Glass Impact Deterministic 100 lb-ft 
Window/Sliding Glass Door Pressure Normal Mean = 40 psf, COV = 0.2 
Window Glass (All Windows on 1 Story) Weibull C = 54.9 psf, k = 4.7 
Window Glass (2 Large Windows on 2 Story) Weibull C = 38.7 psf, k = 4.8 
Sliding Glass Door Glass Weibull C = 101.5 psf, k = 4.5 
Interior Garage Door Pressure Normal Mean = 30 psf, COV = 0.2 
Entry Door Pressure Normal Mean = 50 psf, COV = 0.2 
Double Garage Door Pressure (weak) Normal Mean = 10 psf, COV = 0.2 
Double Garage Door Pressure (strong) Normal Mean = 20 psf, COV = 0.2 
Strap Up-Lift Resistance Normal Mean = 1200 lb, COV = 0.3 
Toe-Nail Uplift Resistance Normal Mean = 415 lb, COV = 0.25 

HAZUS-MH MR3 Technical Manual 

?
Table 6.11. Average Building Damage States 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const 
No. of 
Stories 
Roof/Wal 
l Conn. 
Sheath 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d 
None 
Strong 
Weak 
0.5100.5130.557 
0.306 
0.312 
0.322 
0.230 
0.232 
0.240 
0.198 
0.198 
0.204 
0.617 
0.605 
0.661 
0.3780.3670.382 
0.295 
0.283 
0.292 
0.256 
0.246 
0.253 
8d 
None 
Strong 
Weak 
0.4890.4940.526 
0.291 
0.303 
0.311 
0.219 
0.226 
0.229 
0.189 
0.192 
0.196 
0.563 
0.565 
0.592 
0.3360.3400.347 
0.258 
0.258 
0.263 
0.226 
0.223 
0.227 
Toe-nails 
6d 
None 
Strong 
Weak 
0.5360.5440.619 
0.331 
0.327 
0.349 
0.246 
0.244 
0.258 
0.210 
0.206 
0.218 
0.646 
0.648 
0.778 
0.4220.4000.433 
0.318 
0.302 
0.323 
0.275 
0.263 
0.275 
8d 
None 
Strong 
Weak 
0.5310.5400.617 
0.327 
0.324 
0.346 
0.243 
0.241 
0.255 
0.206 
0.203 
0.213 
0.619 
0.623 
0.766 
0.4030.3870.421 
0.304 
0.291 
0.313 
0.262 
0.249 
0.265 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.7250.7170.777 
0.470 
0.465 
0.486 
0.380 
0.374 
0.391 
0.337 
0.332 
0.347 
0.862 
0.837 
0.903 
0.5760.5540.580 
0.475 
0.455 
0.472 
0.429 
0.408 
0.422 
8d 
None 
Strong 
Weak 
0.7060.6930.749 
0.456 
0.449 
0.466 
0.369 
0.361 
0.372 
0.327 
0.319 
0.329 
0.810 
0.793 
0.830 
0.5330.5220.531 
0.438 
0.425 
0.432 
0.389 
0.381 
0.385 
Toe-nails 
6d 
None 
Strong 
Weak 
0.7680.7610.848 
0.506 
0.492 
0.519 
0.404 
0.393 
0.413 
0.357 
0.346 
0.364 
0.901 
0.885 
1.058 
0.6260.5830.651 
0.510 
0.476 
0.524 
0.456 
0.423 
0.459 
8d 
None 
Strong 
Weak 
0.7640.7570.841 
0.503 
0.486 
0.514 
0.402 
0.388 
0.408 
0.356 
0.341 
0.360 
0.870 
0.864 
1.035 
0.6000.5650.633 
0.487 
0.459 
0.507 
0.432 
0.408 
0.448 
Unreinforced Masonry 
One 
Straps 
6d 
None 
Strong 
Weak 
0.5070.5130.561 
0.306 
0.312 
0.324 
0.232 
0.233 
0.239 
0.198 
0.198 
0.204 
0.618 
0.607 
0.659 
0.3770.3670.384 
0.293 
0.282 
0.292 
0.257 
0.245 
0.254 
8d 
None 
Strong 
Weak 
0.4940.4980.528 
0.292 
0.304 
0.309 
0.220 
0.225 
0.230 
0.189 
0.191 
0.196 
0.566 
0.563 
0.592 
0.3350.3410.347 
0.258 
0.257 
0.263 
0.225 
0.224 
0.225 
Toe-nails 
6d 
None 
Strong 
Weak 
0.5400.5440.623 
0.332 
0.328 
0.348 
0.246 
0.242 
0.257 
0.207 
0.207 
0.218 
0.648 
0.647 
0.780 
0.4210.3980.433 
0.321 
0.304 
0.325 
0.275 
0.264 
0.279 
8d 
None 
Strong 
Weak 
0.5310.5400.616 
0.329 
0.324 
0.347 
0.245 
0.241 
0.254 
0.206 
0.203 
0.212 
0.618 
0.624 
0.759 
0.4040.3860.420 
0.307 
0.292 
0.314 
0.261 
0.249 
0.267 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.7240.7190.775 
0.470 
0.466 
0.485 
0.378 
0.373 
0.389 
0.337 
0.331 
0.345 
0.861 
0.837 
0.902 
0.5770.5540.577 
0.476 
0.456 
0.474 
0.429 
0.408 
0.422 
8d 
None 
Strong 
Weak 
0.7080.6940.749 
0.459 
0.451 
0.467 
0.370 
0.360 
0.372 
0.329 
0.318 
0.328 
0.812 
0.795 
0.832 
0.5310.5210.530 
0.439 
0.428 
0.431 
0.391 
0.382 
0.385 
Toe-nails 
6d 
None 
Strong 
Weak 
0.7650.7610.847 
0.506 
0.491 
0.522 
0.404 
0.391 
0.412 
0.357 
0.346 
0.362 
0.904 
0.885 
1.054 
0.6240.5840.650 
0.509 
0.477 
0.520 
0.454 
0.427 
0.460 
8d 
None 
Strong 
Weak 
0.7650.7590.843 
0.504 
0.487 
0.515 
0.403 
0.386 
0.407 
0.356 
0.340 
0.361 
0.871 
0.862 
1.038 
0.6020.5670.634 
0.488 
0.458 
0.505 
0.430 
0.409 
0.444 
Reinforced Masonry 
One 
Straps 
6d 
None 
Strong 
Weak 
0.5100.5140.560 
0.306 
0.311 
0.323 
0.232 
0.233 
0.239 
0.198 
0.197 
0.204 
0.616 
0.608 
0.662 
0.3770.3660.382 
0.292 
0.283 
0.293 
0.257 
0.245 
0.253 
8d 
None 
Strong 
Weak 
0.4930.4970.527 
0.292 
0.304 
0.309 
0.220 
0.225 
0.231 
0.189 
0.191 
0.195 
0.566 
0.565 
0.592 
0.3350.3400.346 
0.257 
0.258 
0.264 
0.224 
0.222 
0.225 
Toe-nails 
6d 
None 
Strong 
Weak 
0.5360.5460.623 
0.330 
0.327 
0.350 
0.246 
0.243 
0.257 
0.208 
0.206 
0.219 
0.644 
0.646 
0.779 
0.4210.4000.432 
0.319 
0.303 
0.323 
0.275 
0.260 
0.277 
8d 
None 
Strong 
Weak 
0.5300.5380.616 
0.328 
0.325 
0.344 
0.244 
0.238 
0.255 
0.206 
0.203 
0.215 
0.618 
0.625 
0.766 
0.4040.3850.421 
0.305 
0.292 
0.313 
0.262 
0.249 
0.265 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.7240.7200.775 
0.470 
0.465 
0.486 
0.378 
0.376 
0.389 
0.336 
0.331 
0.345 
0.860 
0.839 
0.903 
0.5750.5560.579 
0.476 
0.457 
0.472 
0.429 
0.410 
0.423 
8d 
None 
Strong 
Weak 
0.7100.6920.748 
0.459 
0.451 
0.468 
0.369 
0.361 
0.374 
0.327 
0.317 
0.328 
0.811 
0.794 
0.832 
0.5380.5220.536 
0.438 
0.426 
0.431 
0.392 
0.382 
0.385 
Toe-nails 
6d 
None 
Strong 
Weak 
0.7650.7570.849 
0.507 
0.488 
0.520 
0.405 
0.393 
0.413 
0.355 
0.345 
0.364 
0.904 
0.885 
1.053 
0.6240.5830.652 
0.509 
0.478 
0.524 
0.455 
0.423 
0.461 
8d 
None 
Strong 
Weak 
0.7650.7560.848 
0.503 
0.485 
0.513 
0.403 
0.387 
0.411 
0.356 
0.341 
0.359 
0.871 
0.861 
1.036 
0.6030.5670.639 
0.485 
0.460 
0.507 
0.430 
0.409 
0.446 

Chapter 6. Physical Damage Modeling 

?
Table 6.12. Percent Increases in the Per Storm Average Building Damage State 
Due 
to Changes in Building Parameters (Minimum/Average/Maximum) - Residential 
Buildings 

Parameter Increase in Per Storm Average Damage State 
Treed to Open Terrain 98% / 138% / 190% 
One Story to Two Stories 35% / 55% / 75% 
8d to 6d Roof Sheathing Nails 0% / 4% / 15% 
Strapped to Toe-nailed Roof/Wall Connections 3% / 10% / 29% 
Hip to Gable Roof 11% / 21% / 33% 
No Garage to Strong Garage Door -7% / -2% / 4% 
No Garage to Weak Garage Door -2% / 5% / 24% 
Reinforced Masonry to Unreinforced Masonry Walls -1% / 0% / 1% 
Unreinforced Masonry to Wood Frame Walls -1% / 0% / 1% 

In the case of gable roof homes, the use of 6d sheathing nails versus 8d 
sheathing nails 
increases the per storm average damage state by up to 15%, with the larger 
increases 
taking place on the homes with strapped roof-wall connections. In the case of 
hip roof 
homes, the use of 6d sheathing nails increases the mean damage state by up to 
6%, also 
with the larger increases taking place on the homes with strapped roof-wall 
connections. 

The effect of straps on the average damage state also depends on roof shape, 
with the 
reduction in damage associated with the addition of straps being larger in 
the case of 
gable roofs. The results also indicate that the effect of roof straps in 
reducing damage is 
greater for the case where the roof sheathing is attached with 8d nails 
versus 6d nails. 
Overall, the increase in the average damage state associated with the use of 
toe-nail 
connections versus straps is in the range of 3% to 29%. 

The effect of adding a garage with a strong garage door was found to be 
negligible in 
comparison to the effects of other variables. Note that the homes modeled 
with garages 
are larger than those without garages, and as a result, the average damage 
state values 
also reflect the effects of changes in building size. The effect of adding a 
garage with a 
weak garage door is seen to produce an average increase in damage ranging 
from 2% to 
24%. The increase in damage was most pronounced for the open terrain case and 
for 
buildings with toe-nailed roof/wall connections versus strapped roof/wall 
connections. 

The difference between wood frame walls and unreinforced masonry walls is 
negligible, 
with the differences in the average damage states being no more than 1%. The 
effect of 
reinforced masonry on resulting damage state versus unreinforced masonry is 
also found 
to be negligible. The effect of walls on the average damage state is small 
since other type 
of damage (roof sheathing, whole roof failures, etc.) are already significant 
by the time 
the walls begin to fail. Note that none of the modeled reinforced masonry 
walls failed in 
the simulations. 

6.4.1 Effect of Mitigation on Residential Building Damage 
Damage simulations were performed with various mitigation strategies for each 
of the 
residential buildings described in the previous section. The three mitigation 
strategies 
considered are as follows: 

HAZUS-MH MR3 Technical Manual 

?
1. Install Dade County approved shutters 
2. Upgrade roof to new Dade County (SFBC) roof sheathing nail pattern, 
install 
more wind-resistant shingles and apply secondary water resistance 
3. Install Dade County approved shutters, upgrade roof to new Dade County 
(SFBC) roof sheathing nail pattern, install more wind-resistant shingles and 
apply secondary water resistance 
The upgraded roof cover (shingles) are modeled as having a capacity of 120% 
of the base 
case shingles. In the case of the shutters, a shutter is considered to have 
failed if it is 
impacted by debris having an energy equal to or greater than 350 lb-ft. 
(i.e., a 9 lb. wood 
2 by 4 impacting the shutter at a speed of 50 fps as required by the SFBC). 
The wind 
loads acting on the window behind the shutter are assumed to be reduced by 
50%; 
however, the true amount of the reduction in the wind load is clearly 
dependent on the 
amount of leakage around the perimeter of the shutter. It is also assumed 
that the shutters 
do not fail due to pressure loading alone. In cases where shutters are added 
to a house and 
the house has a garage, the garage capacity is upgraded to meet the impact 
and pressure 
requirements of the Dade County building code. The effect of secondary water 
proofing 
is treated in the economic loss portion of the model and not in the damage 
portion of the 
model. 

In the case of upgrading the roof nails, it is assumed that during re-
roofing, the nail 
pattern on the existing roof sheathing panels is increased by nailing 8d 
nails in the field of 
the panel to achieve a 6. on center nailing over the entire panel. In the 
case of a roof 
having 8d nails prior to re-nailing, the resulting uplift capacity is equal 
to that which 
would be provided through the use of the Dade County SFBC on a new building 
(i.e., 8d 
nails at 6. on center in the field and along the edges). 

Appendix A presents example plots showing the probabilities of achieving each 
building 
damage state and each individual component damage state as a function of wind 
speed 
for the three sets of mitigated homes. The average per storm building damage 
states for 
the three sets of buildings are given in Tables 6.13 through 6.15. Note in 
Tables 6.13 
through 6.15, only one garage door case is given, since, when either a weak 
or a strong 
garage door is upgraded, they are upgraded to the same pressure resistance. 
By 
comparing the average damage states in Tables 6.13 through 6.15 with the 
average 
damage states given in Tables 6.11, one can see the effect of the various 
mitigation 
techniques in reducing damage. The statistics of the reductions in the 
average per storm 
damage states as a result of mitigation are summarized in Table 6.16. 

The effect of adding shutters (and upgrading the garage door, if applicable) 
is a reduction 
in the average damage ranging between 6% and 23% with a mean reduction in the 
average damage of 15%. The effect of upgrading the roof (sheathing 
attachments and 
roof cover) yields a reduction in the average damage ranging between 18% and 
44%, 
with an average of 27%. 

Chapter 6. Physical Damage Modeling 

?
Table 6.13. Average Building Damage States   Install Shutters 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/Wall 
Conn. 
Sheath 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.439 
0.424 
0.2580.252 
0.203 
0.197 
0.178 
0.173 
0.5540.524 
0.335 
0.314 
0.268 
0.255 
0.239 
0.224 
8d None 
Strong 
0.414 
0.403 
0.2420.235 
0.190 
0.184 
0.170 
0.165 
0.4800.460 
0.288 
0.274 
0.230 
0.220 
0.205 
0.194 
Toe-nails 
6d None 
Strong 
0.446 
0.436 
0.2660.259 
0.209 
0.202 
0.182 
0.179 
0.5610.533 
0.344 
0.327 
0.273 
0.262 
0.241 
0.231 
8d None 
Strong 
0.434 
0.424 
0.2610.255 
0.203 
0.196 
0.176 
0.173 
0.5140.491 
0.316 
0.304 
0.249 
0.238 
0.221 
0.213 
Two 
Straps 
6d None 
Strong 
0.605 
0.595 
0.4100.402 
0.343 
0.334 
0.310 
0.302 
0.7470.704 
0.518 
0.489 
0.437 
0.413 
0.398 
0.375 
8d None 
Strong 
0.586 
0.567 
0.3910.378 
0.328 
0.316 
0.299 
0.286 
0.6500.629 
0.448 
0.430 
0.376 
0.361 
0.345 
0.328 
Toe-nails 
6d None 
Strong 
0.610 
0.597 
0.4200.408 
0.349 
0.338 
0.313 
0.309 
0.7570.721 
0.529 
0.497 
0.445 
0.422 
0.404 
0.384 
8d None 
Strong 
0.603 
0.588 
0.4140.399 
0.344 
0.333 
0.307 
0.300 
0.6950.671 
0.483 
0.462 
0.404 
0.391 
0.369 
0.354 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.440 
0.424 
0.2580.250 
0.203 
0.198 
0.176 
0.173 
0.5560.522 
0.334 
0.318 
0.269 
0.253 
0.240 
0.226 
8d None 
Strong 
0.415 
0.400 
0.2390.234 
0.190 
0.184 
0.168 
0.165 
0.4790.456 
0.289 
0.272 
0.229 
0.221 
0.206 
0.196 
Toe-nails 
6d None 
Strong 
0.444 
0.437 
0.2670.259 
0.208 
0.202 
0.181 
0.178 
0.5610.529 
0.345 
0.329 
0.271 
0.261 
0.241 
0.231 
8d None 
Strong 
0.433 
0.422 
0.2620.251 
0.202 
0.198 
0.176 
0.173 
0.5140.490 
0.318 
0.302 
0.248 
0.240 
0.220 
0.213 
Two 
Straps 
6d None 
Strong 
0.605 
0.593 
0.4110.401 
0.343 
0.334 
0.311 
0.303 
0.7430.706 
0.516 
0.487 
0.438 
0.412 
0.398 
0.375 
8d None 
Strong 
0.583 
0.568 
0.3890.377 
0.328 
0.317 
0.296 
0.285 
0.6460.626 
0.448 
0.428 
0.376 
0.361 
0.342 
0.328 
Toe-nails 
6d None 
Strong 
0.616 
0.600 
0.4210.409 
0.348 
0.340 
0.314 
0.308 
0.7550.721 
0.527 
0.498 
0.448 
0.422 
0.404 
0.382 
8d None 
Strong 
0.604 
0.585 
0.4140.399 
0.343 
0.331 
0.307 
0.301 
0.6910.667 
0.482 
0.461 
0.405 
0.391 
0.368 
0.355 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.439 
0.426 
0.2580.251 
0.202 
0.197 
0.176 
0.173 
0.5540.523 
0.335 
0.316 
0.268 
0.253 
0.238 
0.227 
8d None 
Strong 
0.415 
0.399 
0.2390.234 
0.189 
0.184 
0.168 
0.165 
0.4800.457 
0.289 
0.273 
0.230 
0.218 
0.207 
0.196 
Toe-nails 
6d None 
Strong 
0.444 
0.434 
0.2660.258 
0.207 
0.202 
0.182 
0.177 
0.5600.531 
0.344 
0.329 
0.273 
0.260 
0.241 
0.231 
8d None 
Strong 
0.432 
0.421 
0.2620.252 
0.202 
0.195 
0.176 
0.171 
0.5120.491 
0.317 
0.304 
0.250 
0.240 
0.220 
0.212 
Two 
Straps 
6d None 
Strong 
0.606 
0.596 
0.4120.402 
0.343 
0.337 
0.310 
0.304 
0.7440.708 
0.518 
0.488 
0.438 
0.403 
0.399 
0.367 
8d None 
Strong 
0.582 
0.567 
0.3900.378 
0.327 
0.317 
0.298 
0.285 
0.6490.627 
0.450 
0.429 
0.378 
0.362 
0.343 
0.327 
Toe-nails 
6d None 
Strong 
0.612 
0.599 
0.4210.409 
0.349 
0.340 
0.314 
0.306 
0.7530.708 
0.526 
0.499 
0.445 
0.421 
0.402 
0.377 
8d None 
Strong 
0.603 
0.585 
0.4130.399 
0.345 
0.333 
0.309 
0.299 
0.6930.669 
0.481 
0.460 
0.407 
0.390 
0.370 
0.351 

The combined effect of upgrading the roof and adding window protection is a 
reduction 
in the average building damage state ranging between 45% and 59% with a mean 
reduction of 52%. Note that the reduction in losses associated with the 
application of both 
window protection and roof strength enhancements results in a reduction in 
damage 
(52%) which is greater than the sum of the reduction in losses of the two 
mitigation 
methods applied separately (15% + 27% = 42%). This synergistic effect is 
expected since 
the full benefits of the individual techniques cannot be realized unless 
other failure modes 
of roughly equal probability are also addressed. Simply put, the building 
performance is 
governed by the performance of the weakest link in the chain. If there are 
two links of 
roughly equal weakness, both must be strengthened. 

HAZUS-MH MR3 Technical Manual 

?
Table 6.14. Average Building Damage States   Upgrade Roof 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/Wall 
Conn. 
Sheath 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.357 
0.380 
0.2170.238 
0.151 
0.164 
0.123 
0.132 
0.4150.429 
0.246 
0.258 
0.174 
0.181 
0.143 
0.150 
8d None 
Strong 
0.358 
0.377 
0.2180.238 
0.151 
0.165 
0.122 
0.131 
0.4100.427 
0.243 
0.258 
0.173 
0.181 
0.143 
0.149 
Toe-nails 
6d None 
Strong 
0.401 
0.425 
0.2580.263 
0.179 
0.182 
0.142 
0.146 
0.4690.495 
0.320 
0.310 
0.227 
0.220 
0.186 
0.178 
8d None 
Strong 
0.402 
0.425 
0.2580.263 
0.179 
0.182 
0.142 
0.146 
0.4710.494 
0.319 
0.310 
0.225 
0.220 
0.186 
0.180 
Two 
Straps 
6d None 
Strong 
0.550 
0.544 
0.3380.335 
0.256 
0.251 
0.219 
0.213 
0.6480.636 
0.405 
0.402 
0.316 
0.311 
0.275 
0.271 
8d None 
Strong 
0.549 
0.546 
0.3380.337 
0.257 
0.252 
0.218 
0.213 
0.6490.638 
0.406 
0.401 
0.316 
0.311 
0.274 
0.269 
Toe-nails 
6d None 
Strong 
0.613 
0.604 
0.3910.375 
0.292 
0.279 
0.247 
0.238 
0.7070.708 
0.482 
0.447 
0.371 
0.346 
0.318 
0.302 
8d None 
Strong 
0.613 
0.604 
0.3910.374 
0.292 
0.279 
0.248 
0.240 
0.7080.708 
0.479 
0.447 
0.373 
0.346 
0.318 
0.301 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.360 
0.379 
0.2180.238 
0.150 
0.165 
0.121 
0.132 
0.4120.429 
0.243 
0.258 
0.174 
0.181 
0.143 
0.148 
8d None 
Strong 
0.359 
0.380 
0.2170.239 
0.150 
0.163 
0.123 
0.131 
0.4140.429 
0.245 
0.259 
0.174 
0.183 
0.144 
0.149 
Toe-nails 
6d None 
Strong 
0.401 
0.424 
0.2580.263 
0.178 
0.181 
0.141 
0.147 
0.4700.495 
0.320 
0.308 
0.227 
0.219 
0.186 
0.181 
8d None 
Strong 
0.401 
0.424 
0.2580.263 
0.177 
0.183 
0.140 
0.147 
0.4710.495 
0.317 
0.309 
0.226 
0.217 
0.186 
0.180 
Two 
Straps 
6d None 
Strong 
0.550 
0.547 
0.3400.338 
0.258 
0.251 
0.219 
0.214 
0.6500.635 
0.407 
0.399 
0.317 
0.314 
0.275 
0.270 
8d None 
Strong 
0.549 
0.545 
0.3370.338 
0.256 
0.253 
0.219 
0.213 
0.6460.635 
0.408 
0.401 
0.317 
0.310 
0.275 
0.270 
Toe-nails 
6d None 
Strong 
0.608 
0.610 
0.3890.375 
0.292 
0.282 
0.249 
0.237 
0.7090.709 
0.479 
0.448 
0.373 
0.346 
0.320 
0.300 
8d None 
Strong 
0.608 
0.610 
0.3890.374 
0.291 
0.282 
0.249 
0.240 
0.7050.708 
0.480 
0.446 
0.369 
0.346 
0.320 
0.299 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.361 
0.380 
0.2180.236 
0.152 
0.165 
0.122 
0.134 
0.4140.430 
0.245 
0.258 
0.174 
0.181 
0.143 
0.149 
8d None 
Strong 
0.362 
0.378 
0.2170.237 
0.150 
0.163 
0.123 
0.132 
0.4130.428 
0.244 
0.257 
0.173 
0.182 
0.144 
0.150 
Toe-nails 
6d None 
Strong 
0.402 
0.428 
0.2580.262 
0.178 
0.180 
0.141 
0.146 
0.4710.494 
0.317 
0.309 
0.226 
0.220 
0.185 
0.180 
8d None 
Strong 
0.402 
0.427 
0.2580.263 
0.178 
0.181 
0.141 
0.145 
0.4730.496 
0.319 
0.309 
0.227 
0.220 
0.185 
0.180 
Two 
Straps 
6d None 
Strong 
0.553 
0.543 
0.3390.337 
0.256 
0.251 
0.220 
0.214 
0.6490.637 
0.406 
0.400 
0.316 
0.310 
0.275 
0.268 
8d None 
Strong 
0.548 
0.545 
0.3380.334 
0.257 
0.253 
0.218 
0.214 
0.6460.635 
0.407 
0.400 
0.317 
0.312 
0.274 
0.267 
Toe-nails 
6d None 
Strong 
0.609 
0.611 
0.3880.374 
0.293 
0.281 
0.248 
0.238 
0.7070.707 
0.480 
0.445 
0.371 
0.346 
0.319 
0.301 
8d None 
Strong 
0.609 
0.611 
0.3890.377 
0.294 
0.281 
0.248 
0.239 
0.7070.708 
0.478 
0.444 
0.368 
0.344 
0.319 
0.300 

Finally, it should be noted that the reduction in the average per storm 
building damage 
states presented here are valid for buildings located in the Miami area and 
will be 
different in locations having different hurricane wind climates. 

6.5 Manufactured Homes 
Manufactured homes are treated separately from single family homes and 
engineered 
structures. The damage model developed for manufactured homes includes a 
stability 
failure mechanism (overturning and sliding off its foundations) that is not 
treated for 
other buildings. The model also allows for frame failure, which is currently 
not treated in 
the modeling of other building types (except roof uplift failures in the case 
of residential 
buildings). This section of the report briefly reviews the history of the 
manufactured 

Chapter 6. Physical Damage Modeling 

?
Table 6.15. Average Building Damage States   Install Shutters and Upgrade 
Roof 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/Wall 
Conn. 
Sheath 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.243 
0.237 
0.1320.133 
0.098 
0.096 
0.085 
0.083 
0.2880.275 
0.162 
0.156 
0.123 
0.119 
0.107 
0.102 
8d None 
Strong 
0.243 
0.239 
0.1310.132 
0.097 
0.096 
0.084 
0.082 
0.2890.276 
0.163 
0.157 
0.123 
0.119 
0.107 
0.101 
Toe-nails 
6d None 
Strong 
0.266 
0.266 
0.1580.155 
0.114 
0.112 
0.095 
0.094 
0.3330.322 
0.200 
0.194 
0.148 
0.145 
0.125 
0.124 
8d None 
Strong 
0.266 
0.265 
0.1580.156 
0.112 
0.111 
0.096 
0.094 
0.3320.321 
0.199 
0.193 
0.147 
0.144 
0.127 
0.125 
Two 
Straps 
6d None 
Strong 
0.364 
0.351 
0.2290.221 
0.183 
0.176 
0.161 
0.156 
0.4140.400 
0.274 
0.260 
0.221 
0.209 
0.195 
0.187 
8d None 
Strong 
0.363 
0.353 
0.2280.223 
0.184 
0.177 
0.164 
0.155 
0.4120.399 
0.272 
0.260 
0.218 
0.210 
0.196 
0.186 
Toe-nails 
6d None 
Strong 
0.387 
0.378 
0.2580.247 
0.204 
0.196 
0.178 
0.171 
0.4720.459 
0.318 
0.305 
0.259 
0.249 
0.231 
0.220 
8d None 
Strong 
0.385 
0.377 
0.2560.247 
0.204 
0.195 
0.179 
0.171 
0.4720.457 
0.318 
0.306 
0.258 
0.247 
0.230 
0.221 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.241 
0.235 
0.1330.131 
0.097 
0.095 
0.085 
0.082 
0.2880.274 
0.163 
0.156 
0.123 
0.119 
0.107 
0.102 
8d None 
Strong 
0.239 
0.235 
0.1320.132 
0.097 
0.096 
0.084 
0.082 
0.2860.273 
0.163 
0.156 
0.121 
0.118 
0.107 
0.102 
Toe-nails 
6d None 
Strong 
0.263 
0.265 
0.1600.157 
0.113 
0.112 
0.096 
0.095 
0.3290.322 
0.202 
0.193 
0.148 
0.146 
0.127 
0.124 
8d None 
Strong 
0.263 
0.266 
0.1590.156 
0.113 
0.112 
0.095 
0.096 
0.3300.322 
0.197 
0.195 
0.147 
0.145 
0.126 
0.125 
Two 
Straps 
6d None 
Strong 
0.359 
0.350 
0.2290.221 
0.184 
0.176 
0.162 
0.156 
0.4130.397 
0.274 
0.261 
0.222 
0.210 
0.195 
0.187 
8d None 
Strong 
0.359 
0.350 
0.2320.222 
0.183 
0.176 
0.161 
0.156 
0.4090.397 
0.273 
0.261 
0.220 
0.208 
0.198 
0.184 
Toe-nails 
6d None 
Strong 
0.385 
0.377 
0.2580.248 
0.202 
0.194 
0.178 
0.173 
0.4710.454 
0.319 
0.303 
0.259 
0.247 
0.231 
0.223 
8d None 
Strong 
0.384 
0.377 
0.2570.249 
0.203 
0.196 
0.179 
0.173 
0.4680.456 
0.319 
0.305 
0.257 
0.250 
0.233 
0.222 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.241 
0.236 
0.1330.131 
0.098 
0.097 
0.085 
0.082 
0.2880.274 
0.163 
0.157 
0.123 
0.118 
0.107 
0.102 
8d None 
Strong 
0.240 
0.236 
0.1310.131 
0.098 
0.096 
0.084 
0.082 
0.2860.275 
0.163 
0.156 
0.122 
0.119 
0.108 
0.101 
Toe-nails 
6d None 
Strong 
0.263 
0.266 
0.1590.156 
0.113 
0.112 
0.096 
0.095 
0.3310.320 
0.200 
0.193 
0.148 
0.146 
0.127 
0.124 
8d None 
Strong 
0.263 
0.265 
0.1570.156 
0.113 
0.112 
0.096 
0.095 
0.3280.321 
0.198 
0.194 
0.148 
0.146 
0.126 
0.124 
Two 
Straps 
6d None 
Strong 
0.360 
0.351 
0.2280.221 
0.182 
0.176 
0.161 
0.156 
0.4120.396 
0.274 
0.259 
0.222 
0.209 
0.197 
0.187 
8d None 
Strong 
0.359 
0.351 
0.2290.222 
0.184 
0.177 
0.163 
0.154 
0.4120.394 
0.274 
0.261 
0.221 
0.210 
0.195 
0.185 
Toe-nails 
6d None 
Strong 
0.384 
0.376 
0.2550.247 
0.201 
0.198 
0.178 
0.172 
0.4680.454 
0.318 
0.304 
0.258 
0.246 
0.231 
0.221 
8d None 
Strong 
0.385 
0.376 
0.2560.248 
0.203 
0.198 
0.178 
0.173 
0.4720.458 
0.318 
0.307 
0.258 
0.248 
0.231 
0.221 

Table 6.16. Percent Decreases in the Per Storm Average Building Damage State 
Due to Mitigation (Minimum/Average/Maximum) - Residential Buildings 

Mitigation Strategy 

Decrease in Per Storm Average Damage State 

Install Shutters 

6% / 15% / 23% 

Upgrade to Dade Roof 

18% / 27% / 44% 

Install Shutters and Upgrade to Dade Roof 

45% / 52% / 59% 

home regulatory environment, which describes how the design criteria changed 
with 
time, as well as describing the failure model and presenting examples of 
predicted and 
observed manufactured home failures. 

A manufactured home is defined as:  ...a structure, transportable in one or 
more sections, 
which in the traveling mode, is eight body feet or more in width and forty 
body feet or 

HAZUS-MH MR3 Technical Manual 

?
more in length, or, when erected on site, is three hundred twenty or more 
square feet, and 
which is built on a permanent chassis and designed to be used as a dwelling 
with or 
without a permanent foundation when connected to the required utilities, ...  
(MHCSS, 
1992). A brief history of the development of manufactured housing in this 
country is 
provided in testimonies of industry leaders before congressional subcommittee 
hearings 
on raising standards (US Congress, 1981). 

The design and fabrication processes are governed by U.S. Department of 
Housing and 
Urban Development regulations known as the  Manufactured Home Construction 
and 
Safety Standards (MHCSS)  24 CFR, Part 3280. These standards went into effect 
in 
1976. There were no national statutes covering manufactured homes. There was, 
however, a national standard entitled American National Standards Institute 
ANSI 
A119.1,  Standard for Manufactured Homes.  Some states adopted this standard, 
but 
even in those states, enforcement was often lax. Homes built in 1976 or later 
are 
sometimes referred to as  HUD-code homes.  

The original MHCSS wind load provisions were similar to those in ANSI A119.1-
1973 
(NFPA, 1973), defining two different wind zones (although the zone boundaries 
differed). Zone I was the standard zone, and Zone II was the Hurricane Zone, 
extending 
along the Gulf, Atlantic, and Alaskan coastlines. For structures located in 
Zone II, the 
wind loads given in the ANSI provisions required the unit to resist wind 
loads of 15 psf 
acting upwards on the roof surface and a wind load of 25 psf acting 
horizontally on one 
side wall. As noted above, the wind loads prescribed in the 1976 HUD code 
were the 
same as those given in ANSI A119.1-1973, the only difference between the pre- 
and 
post-1976 homes being the degree to which the provisions were enforced. 

The wind loading requirements were not changed appreciably until 1994, when 
they were 
increased in response to years of excessive damage, and particularly in the 
wake of 
Hurricane Andrew (HUD, 1994). The 1994 requirements boosted wind loads to 
levels 
much closer to ASCE-7 loads, and redefined the wind zones, adding a more 
stringent 
Zone III located in southern Louisiana, southern Florida, coastal Alaska, and 
Hawaii. 

Although home construction is regulated by HUD, installation is not. It is 
left to states 
and/or local governments. Several states have no tie-down requirements 
(Louisiana, for 
example). The manufacturer's responsibility is to provide a homeowner's 
manual with 
installation details for the specific model. The American National Standards 
Institute's 
Standard A225.1,  Manufactured Home Installations  (most recently A225.1-
1994), is a 
consensus standard for the installation of manufactured homes and minimum 
construction 
requirements for manufactured home communities. Model building codes also 
address 
the issue of tie-down of manufactured homes (SBCCI, 1991). 

6.5.1 Resistance Models for Manufactured Homes 
The manufactured home damage and loss model is based primarily on the work of 
Vasquez (1994). The failure modes considered in the model include damage to 
components and cladding (roof cover, roof sheathing, windows, doors and 
siding), 
stability failures (overturning and sliding) and failure of the main wind 
force resisting 

Chapter 6. Physical Damage Modeling 

?
system (roof uplift and failure of the wall-to-floor connections). For 
modeling windborne 
debris, the same model developed for residential buildings is used. 

The roof membrane systems on manufactured homes vary considerably, and may 
consist 
of metal skins attached directly to the roof trusses, or roof sheathing 
attached to the roof 
trusses where shingles provide the water proof layer. The siding of the older 
manufactured homes typically was comprised of either wood, vinyl, or metal 
panels. The 
model manufactured home selected for the development of a first principles 
based load-
resistance damage model employs metal panel siding and either plywood or OSB 
roof 
sheathing. 

Main Wind Force Resistance Modeling. The nominal resistance of the 
superstructure of 
the manufactured home is computed using the wind load criteria for 
manufactured homes 
located in Zone II, noted above. The factor of safety assumed in the 
development of the 
superstructure resistance functions is assigned a mean value of 1.5 and a 
coefficient of 
variation of 25%. If the load acting on the roof of the structure exceeds 
this nominal 
capacity, the roof-wall connection is considered to be damaged, but the roof 
does not fail. 
A complete roof failure is assumed to occur if the uplift forces on the roof 
exceed a value 
equal to 1.8 times the nominal resistance (i.e., 20% higher than the load 
used to define 
initial damage to the roof-wall connection). Using this approach, the mean 
capacity of the 
roof to resist uplift failure is defined as 15 psf multiplied by the safety 
factor. 

In the estimation of the capacity of the floor-to-wall connections, a simple 
overturning 
computation was performed where the manufactured home is loaded with a 
vertical roof 
load of 15 psf and a horizontal wall load of 25 psf. The uplift forces at the 
floor-to-wall 
connections are determined using moment equilibrium. A nominal factor of 
safety of 1.6 
(COV = 25%) is assumed to apply to the connection capacity for modeling the 
initial 
failure, and a 20% increase in this value is assumed to apply for the 
condition describing 
a complete failure of the floor-wall connection. 

In cases where the roof fails, a wall is considered to fail if the load 
acting on the wall 
exceeds 12.5 psf (COV = 25%) (i.e., half the value of load used to design the 
manufactured home in shear). The above values all apply to post-HUD 
construction. In 
the case of pre-HUD code manufactured homes, the nominal resistances for the 
connections noted above are reduced by 20%. 

Foundation and Tie Down Modeling. The primary method for anchoring 
manufactured 
homes against lateral and uplift loads is soil anchors. These systems have 
often proven to 
be unreliable and a major source of damage. Soil types and conditions, anchor 
types, 
installation practices, and maintenance all have a tremendous influence on 
the pullout 
capacity of the anchors. Kovacs and Yokel (1979) issued a report comparing 
pullout 
capacity of common anchors in various soil types with theoretical solutions 
based on the 
principles of soil mechanics. They concluded that the soil mechanics theories 
did not 
adequately predict pullout capacity. Given the failure of the theoretical 
approach, Yokel 
et al. (1982) performed a more systematic, empirical study. It was concluded 
that typical 
manufactured home installation practices did not yield anchor performance as 
required by 
the standards, and the capacity of the anchors varies significantly with the 
depth of the 

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?
anchor, and of course, the soil type. Knowledge of the anchor pullout 
capacity, the 
quality of the installation of the anchors and the capacity and corrosion 
states of the 
hardware connecting the anchor to the manufactured home unit is critical in 
the 
prediction of the performance of manufactured homes in severe wind storms. 

The anchor capacity used here is derived from the Yokel, et al. (1982) data 
as given in 
Vasquez (1994), where data are given for the pullout capacity of anchors in 
sandy soils, 
silty soils and clay soils, for a range of penetration depths, and direction 
of applied load. 
The results of the Yokel, et al. (1982) study are summarized in Tables 6.17 
through 6.19. 
As indicated in Tables 6.17 through 6.19, there is significant variability in 
the ultimate 
pullout capacity. Note that in Tables 6.17 through 6.19 the angles, _, 
represent the angle 
of the direction of the pullout force, with 900 representing a vertical load. 
The selection 
of the representative resistance characteristics of the manufactured home 
anchor capacity, 
which may be expected to realistically model the in-service characteristics 
of the anchor 
system, clearly requires significant judgment. 

Table 6.17. Results of Field Pull-Out Tests on Soil Anchors Installed and 
Loaded at 
Various Angles (Yokel, et al., 1982) 

Soil 
Type 
Angles 
_1/_2 
(degrees) 
Load at 102 mm (4.) (lbf) Ultimate Load (lbf) Maximum Displacement (inches) 
Mean COV Mean COV Mean COV 
Silt 60/90 2,967 0.29 7,565 0.15 10.07 0.35 
Silt 45/90 1,350 0.19 7,933 0.02 13.85 0.09 
Sand 40/90 2,583 0.20 6,187 0.05 9.08 0.07 
Clay 40/90 767 0.15 3,267 0.21 18.60 0.20 
Silt 60/135 433 0.13 3,387 0.13 25.15 0.04 
Silt 45/135 413 0.08 4,243 0.03 33.80 0.04 
Silt 15/135 433 0.07 4,775 0.14 54.00 0.05 

Notes: Each case based on 3 tests. All tests conducted under moist soil 
conditions. 

Table 6.18. Results of Field Pull-Out Tests on Fully Embedded Soil Anchors: 
_1 = 900, _2 = 900 (Yokel, et al., 1982) 

Soil Type 
No. of 
Tests 
Load at 51 mm (2.) (lbf) Ultimate Load (lbf) 
Maximum Displacement 
(inches) 
Mean COV Mean COV Mean COV 
Moist Silt 11 4,295 0.20 5,173 0.10 7.17 0.53 
Wet Silt 5 2,320 0.40 3,640 0.17 11.04 0.35 
Moist Sand 6 4,488 0.13 5,063 0.13 3.82 0.35 
Wet Sand 3 5,100 0.17 5,953 0.18 4.35 0.25 
Moist Clay 3 3,067 0.22 3,433 0.16 5.83 0.49 

Table 6.19. Results of Field Pull-Out Tests on Fully Embedded Soil Anchors: 
_1 = 450, _2 = 1050 (Pearson, et al., 1991) 

Stabilizer 
Plates 
No. of 
Tests 
Load at 102 mm (4.) (lbf) Ultimate Load (lbf) 
Maximum Displacement (inches) 
Horizontal Vertical 
Mean COV Mean COV Mean COV Mean COV 
No 5 662 0.18 2490 0.26 11.3 0.17 3.2 0.63 
Yes 19 805 0.41 2578 0.22 16.1 0.26 5.1 0.57 

Chapter 6. Physical Damage Modeling 

?
The anchor tie down system used herein, is modeled with a mean capacity of 
1500 
lbs/anchor with a coefficient of variation of 35%. No reduction in the 
foundation capacity 
is used to model the foundations for pre-HUD manufactured homes. The 
computation of 
the forces required to initiate sliding or overturning are made using a 
static equilibrium 
analysis following the approach described in Vasquez (1994) and Marshall 
(1995). A 
total of five anchors are assumed to be installed for a 60' long unit. In the 
stability 
analysis, the weight of the unit is modeled using a mean value of 25 psf (COV 
= 10%) 
and a value of 10 psf (COV = 20%) to define the weight of the contents. The 
combined 
unit weight of 35 psf is less than the 41 psf value assumed by Vasquez 
(1994), but higher 
than the 25 psf value assumed by Marshall (1995). The failure modes 
considered in the 
foundation failure analysis include sliding and overturning. Following the 
definition of 
manufactured home damage states suggested by Vann and McDonald (1978), the 
sliding 
mode of failure is broken into minor and major categories. Major sliding is 
considered to 
have occurred if the wind load required to initiate sliding exceeds 1.2 times 
the nominal 
sliding resistance. 

Components and Cladding. The cladding components modeled include metal 
siding, 
windows, roof sheathing (plywood or OSB) and roof cover (shingles). The same 
shingle 
model used on single family homes is used to model the shingles on 
manufactured 
homes. The metal siding is assumed to have a mean resistance of 25 psf (COV = 
15%). 
Failure of the cladding is considered for the case of negative loads only. 
The nominal 
resistance of the windows is taken as 32 psf (COV = 18%) for both positive 
and negative 
loads. The mean uplift capacity of the roof sheathing is taken as 45 psf. 

Summary of Failure Modes Considered for Manufactured Homes. The complete list 
of 
failure modes considered for manufactured homes is as follows: 

1. Roof Cover Loss 
2. Roof Sheathing Loss 
3. Window Breakage (Pressure and Windborne Debris) 
4. Siding Failure 
5. Roof-Wall Connection Failure (Minor) 
6. Roof-Wall Connection Failure (Major) 
7. Wall Failure Following Roof Failure 
8. Floor-Wall Connection Failure (Minor) 
9. Floor-Wall Connection Failure (Major) 
10. Foundation Failure (Minor Sliding) 
11. Foundation Failure (Major Sliding) 
12. Foundation Failure (Overturning) 
6.5.2 Damage Model Validation for Manufactured Homes 
The manufactured home damage model has been validated through comparisons of 
damage states predicted from the above model to the damage states predicted 
from the 
models of Vann and McDonald (1978) and Vasquez (1994), as well as through 

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?
comparisons of observed damage from Hurricane Bertha (1996) along the North 
Carolina 
coast, Hurricane Andrew (1992) in Dade County Florida and Hurricane Elena 
(1986) 
along the Central Gulf Coast. 

Comparisons of Wind Speed vs. Damage Predictions to the Vann and McDonald 
Model and the Vasquez Model. Table 6.20 presents a comparison of the 
threshold wind 
speeds (peak gust, 10m open terrain) associated with various damage states 
obtained 
from the load and resistance model described herein, to those given by Vann 
and 
McDonald (1978) and Vasquez (1994). The damage states given in Table 6.20 are 
valid 
for units located in open terrain. The first column in Table 6.20 presents 
the damage 
identification codes given in Vasquez (1994). The Vann and McDonald damage 
states are 
applicable to pre-HUD units only, whereas the Vasquez data likely includes a 
mix of pre-
HUD and HUD units. Both models have been derived from wind speed versus 
damage 
data collected from post-storm damage surveys. In the case of the modeled 
damages, the 
threshold wind speeds are defined as the wind speeds where approximately 5% 
of the 
modeled units experience the indicated damage. 

Table 6.20. Comparison of Threshold Velocities for Various Manufactured Home 
Damage States 

Damage 
Code Damage Description 
Vann and 
McDonald Vasquez 
ModelPre-
HUD ModelHUD 
1.1A (MI) 
1.1A (MA) 
1.1B (MA) 
1.1B (DE) 
1.1C (MA) 
1.1C (DE) 
1.2A (MA) 
1.2B (DE) 
Minor Sliding Damage - No Anchors 
Major Sliding Damage - No Anchors 
Major Vaulting Damage - No Anchors 
Total Vaulting Damage - No Anchors 
Major Overturning Damage - No Anchors 
Total Overturning Damage - No Anchors 
Total Overturning Damage - Anchor Pull Out 
Total Overturning Damage - Tie Failure 
60-70 
65-90 
60-70 
90-125 
120-130 
120-130 
<85-100 
<100-115 
<85-100 
<85-100 
<120-130 
<120-130 
90-100 
115-125 
110-120 
130-140 
130-140 
90-100 
115-125 
110-120 
130-140 
130-140 
2A (MI) 
2A (MA) 
2A (DE) 
2B (MA) 
2B (DE) 
2C (MA) 
2C (DE) 
2D (MA) 
2D (DE) 
Roof to Wall Connection Failure - Minor 
Roof to Wall Connection Failure - Major 
Roof to Wall Connection Failure - Destruction 
Floor to Wall Connection Failure - Major 
Floor to Wall Connection Failure - Destruction 
Structural Member Failure   Major 
Structural Member Failure   Total 
Tree or Large Missile Impact   Major 
Tree or Large Missile Impact   Total 
60-70 
90-125 
120-130 
120-130 
120-130 
120-130 
120-130 
110-120 
<120-130 
100-110 
<120-130 
<120-130 
110-110 
<120-130 
100-110 
100-110 
100-110 
95-105 
100-110 
110-120 
120-130 
100-110 
3A (MI) 
3B (MI) 
3C (MI) 
3C (MA) 
3C (DE) 
3D (MI) 
3D (MI) 
3D (DE) 
3E (MI) 
3E (MA) 
Window Damage   Minor 
Door Damage   Minor 
Roof Covering Damage   Minor 
Roof Covering Damage   Major 
Roof Covering Damage   Total 
Wall Panel Damage   Minor 
Wall Panel Damage   Major 
Wall Panel Damage   Total 
Small Debris Damage   Minor 
Small Debris Damage   Major 
65-90 
120-130 
120-130 
<110-120 
<120-130 
85-95 
85-95 
<120-130 
85-95 
85-95 
<120-130 
100-110 
<85-95 
100-110 
70-80 
120-130 
120-130 
90-100 
120-130 
100-110 
70-80 
120-130 
120-130 
90-100 
120-130 

The general observation that can be made by comparing the threshold wind 
speeds given 
in Table 6.20 is that the model developed here tends to yield higher 
threshold wind 
speeds for foundation failures, but lower threshold wind speeds for the 
initiation of the 

Chapter 6. Physical Damage Modeling 

?
roof-wall connection failures. The threshold wind speeds associated with the 
initiation of 
wall-floor connection failures are comparable to those given in the Vasquez 
model, but 
lower than those suggested by the Vann and McDonald model. The wind speeds 
associated with the initiation of cladding failures are generally comparable 
between 
models, with the exception that the load-resistance model developed here 
yields higher 
estimates of the wind speeds required to initiate wall cladding loss. 

Hurricane Bertha Validation. Following Hurricane Bertha (1996), ARA sent a 
team of 
engineers to perform a post-storm damage survey quantifying the wind induced 
damage 
to buildings along the North Carolina coast. During this damage survey, 114 
manufactured home units around the Surf City area on Topsail Island were 
surveyed. All 
of the manufactured homes surveyed were tied down, and it was not determined 
what 
fraction of the homes were pre-HUD and HUD homes. The survey recorded the 
condition 
of the manufactured homes, noting damage to windows, siding and the roof 
system. The 
overall damage states of the 114 buildings are given in Table 6.21, along 
with the 
modeled damage produced by simulating the full time series of wind speed and 
direction 
produced by Hurricane Bertha at Surf City. The terrain around the 
manufactured home 
parks was typically fairly open, and thus a surface roughness of 0.1 m is 
used to model 
the basic terrain. The modeled damage states in Table 6.21 are given for both 
the HUD 
and pre-HUD model manufactured homes. The peak gust wind speed (open terrain, 
10 m) 
resulting from the Hurricane Bertha simulation was 103 mph. 

Table 6.21. Comparison of Observed and Modeled Manufactured Home Damage 
States on Topsail Island Produced by Hurricane Bertha (1996) 

Observed Damage State Percent Observed HUD Home Simulation Pre-HUD Home 
Simulation 
No Damage 85% 85% 85% 
Loss of One Metal Siding Panel 7% 10% 10% 
Partial Roof Deck Failure 2.6% 0.3% 0.3% 
Whole Roof Failure 1.8% 2.2% 3.7% 
One Broken Window 6% 2.4% 2.4% 
More than One Broken Window 0.9% 2.2% 2.2% 

The comparison of the simulated and observed damage states shown in Table 
6.21 
indicates reasonable agreement between the modeled and observed damage 
states, with 
the model tending to slightly overestimate the number of units with minor 
siding damage 
and the number of units with whole roof damage. The overestimate of the whole 
roof 
damage is somewhat offset by the underestimate of the number of units 
experiencing 
partial roof loss. The model tends to underestimate the number of units with 
window 
damage, but the prediction of the number of units expected to sustain no 
damage is 
correct. 

Hurricane Andrew Validation. Following hurricane Andrew, a report was 
prepared for 
the Manufacturing Housing Institute by Ferguson and Cardwell (1992) 
describing the 
intensity and type of damage experienced by manufactured home units located 
throughout the region impacted by the storm. The damage to the units in each 
of the 
surveyed parks was characterized using the damage classification system 
developed by 
Vann and McDonald (1978). To further validate the manufactured home damage 
model, 

HAZUS-MH MR3 Technical Manual 

?
comparisons of simulated and observed damage states in six of the parks 
surveyed by 
Ferguson and Caldwell are performed. In order to facilitate these 
comparisons, the 
damage states predicted by the damage model had to be mapped to correspond to 
the 
damage classification scheme developed by Vann and McDonald. Table 6.22 
describes 
the damage states associated with each of the five damage classes suggested 
by Vann and 
McDonald, along with the damage states predicted using the load and 
resistance based 
manufactured home damage model for each class. It is noteworthy that there is 
a fine line 
between the damage associated with Classes 3 and 4, and it is likely that 
that in both the 
observations and the mapping between modeled damage to Class 3 and 4 damage, 
there 
will be a number of miss-classifications. 

Table 6.22. Damage Classes Described by Vann and McDonald and the 
Corresponding Damage States from the Damage Simulation Model

Damage 
Class Description of Damage (Vann and McDonald, 1978) 
0 
No Damage or Very Minor Damage - Little or no visible damage from the 
outside. Slight shifting on 
the blocks that would suggest re-leveling, but not off the blocks. Some 
cracked windows, but no resulting 
water damage. 
1 
Minor Damage - Shifting off the blocks or so that blocks press up to the 
floor; re-leveling required. 
Walls, doors, etc., buckled slightly, but able to be corrected by re-
leveling. Minor eave and upper wall 
damage, with slight water damage but roof not pulled all the way back. Minor 
pulling away of siding with 
slight water damage. Minor missile and/or tree damage. Slight window breakage 
and attendant water 
damage. 
2 
Moderate Damage (Still Livable) - Severe shifting off the blocks with some 
attendant floor and 
superstructure damage (punching, racking, etc). Roof removed over a portion 
or all of the home but the 
joists remain intact, walls not collapsed. Missile and/or tree damage to a 
section of the wall or roof, 
including deep dents or punctures. Serious water damage from holes in roof, 
walls, windows, doors or 
floors. 
3 
Severe Damage (Not Livable, but Repairable) - Unit rolled onto side but frame 
intact. Extreme shifting 
causing severe racking and separations in the superstructure. Roof off, 
joists damaged or removed, walls 
damaged from lack of lateral support at top. Severe tree damage, including 
crushing of one wall or roof 
section. Superstructure partially separated from under frame. 
4 
Destruction (Not Livable) - Unit rolled onto top or rolled several times. 
Unit tossed or vaulted through 
the air. Superstructure separated from the underframe or collapsed to side of 
the underframe. Roof off, 
joists removed and walls collapsed. Destruction of a major section by a 
falling tree. 
Modeled Damage States 
(If any one of the conditions in the shaded cells of a given row is true, the 
mobile home is placed in that damage state) 
Damage 
Class 
Roof Cover 
Damage 
Siding 
Damage 
Window and 
Door 
Failures 
Roof 
Sheathing 
Failures 
Roof to Wall and/or 
Wall to Foundation 
Connection Failures 
Foundation to 
Ground Anchor 
Failures 
0 =10% =1 Panel None None None None 
1 >10% to =25% >1 to =25% 1 or 2 None None Minor Sliding 
2 >25% >25% >2 to =50% >0% to =25% Minor Typically Minor 
Sliding 
3 Typically >25% Typically 
>25% >50% >25% Typically Minor Major Sliding 
4 Typically >25% Typically 
>25% 
Typically 
>50% 
Typically 
>25% Major Overturning or 
Uplift 

The location of each manufactured home park surveyed by Ferguson and Caldwell 
was 
noted only approximately. For example, the location of the park referred to 
as University 
Lakes is given as  South side of U.S. 41, 1 mile west of the Florida 
Turnpike.  The 
locations given by Ferguson and Caldwell are accurate enough to place the 
parks for the 

Chapter 6. Physical Damage Modeling 

?
purposes of performing a site specific wind speed simulation, but not 
accurate enough to 
place the parks for the purposes of estimating a surface roughness length. 
Damage 
simulations have been performed for each location using surface roughness 
lengths of 0.1 
m and 0.3 m. All damage simulations were performed using a site specific 
simulation of 
the hurricane wind velocities, but the orientation of the units was taken as 
random, with a 
total of 1000 damage simulations performed for each park. 

Table 6.23 presents the results of the damage simulations, along with a 
comparison of the 
damage descriptions for each site given by Ferguson and Caldwell. Also given 
in Table 

6.23 are the simulated maximum peak gust wind speeds (10 m, open terrain) at 
each site 
obtained from the simulation of Hurricane Andrew. 
Table 6.23. Comparison of Modeled and Observed Manufactured Home Damage 
States Following Hurricane Andrew in South Florida 

Modeled Percent of Manufactured Homes Predicted in 
Each Damage Class 
z0 = 0.10 m z0 = 0.30m 
Park Name and 
Number of 
Units 
Description of Park and Damage Along 
with Estimated Peak Gust Wind Speed 
(10m, Open Terrain) 
Unit 
Class 0 1 2 3 4 0 1 2 3 4 
Pre-HUD 85 7 3 <1 5 95 3 1 <1 1Courtly Manor 
(521 Spaces) 
50% HUD-labeled; Some Class 0 and 1 
damage; 1 pre-HUD Class 3 damage; 
Peak gust wind speed 104 mph HUD 88 7 2 <1 2 96 3 <1 <1 <1 
Pre-HUD 11 15 8 6 60 41 16 12 5 27University 
Lakes (1100 
spaces) 
40% HUD-labeled; 20 Class 3 or 4 
damage, all pre-HUD; HUD Class 0 or 1 
damage; Peak gust wind speed 135 mph HUD 13 6 10 9 52 44 18 13 6 20 
Pre-HUD 4 2 1 3 94 5 11 5 6 73Un-named 
Park 
Old Park, no HUD units; Class 3 and 4 
damage; Peak Gust Wind Speed 156 
mph HUD 
Pre-HUD 0 1 <1 1 98 11 6 3 4 86Dadeland 
(Estimate 200) 
50% HUD-labeled; All class 2, 3 and 4 
damage; Peak gust wind speed 159 mph HUD 0 1 <1 2 97 11 7 4 8 80 
Pre-HUD 0 <1 <1 1 99 1 6 2 3 88Redlands 
(Estimate 90) 
Older Park, mostly pre-HUD; 1 HUD 
Class 1 damage, else class 3 and 4 
damage; Peak gust wind speed 162 mph HUD 0 <1 <1 2 97 1 7 3 6 84 
Pre-HUD <1 1 1 2 95 2 7 4 4 83 
Isla Gold 
50% RV, 10-20% HUD; Mostly Class 4 
damage; Some HUD Class 2 and 3 
damage; 1 HUD Class 1 damage; Peak 
gust wind speed 162 mph HUD <1 1 2 4 92 2 9 7 8 74 
The overall agreement between the observed and simulated damage states is 
generally 
good, except in the case of the homes located at University Lakes, where the 
model 
overestimated the number of HUD units experiencing Class 3 and 4 level 
damage, and 
the model underestimated the number of units experiencing the lower level 
Class 0 and 1 
damage states. In the low wind speed case (Courtly Manor), the predicted and 
observed 
percentage of units experiencing damage levels in Classes 0 and 1 is good. In 
the high 
wind speed cases (Un-named Park, Dadeland, Redlands and Isla Gold), the lack 
of 
knowledge of the true terrain at the sites, clouds the comparisons, but 
generally, the 
agreement is good. 

Hurricane Elena. The damage data described in McDonald and Vann (1986) is 
used to 
validate the manufactured home damage model using the observed manufactured 
home 
damage surveyed following Hurricane Elena (1985) along the Central Gulf 
Coast. A total 
of ten parks were surveyed, but the location of only eight of the ten are 
given by the 

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?
authors. The description of each of the sites, in terms of the surrounding 
terrain, is limited 
with the statement made that some communities were located in heavily wooded 
areas 
while others were sited on rolling hills that were free of trees and other 
obstructions to the 
wind. The authors go on to say that the site on Dauphin Island and the one 
located on the 
west shore of Mobile Bay were the most exposed to hurricane winds. In the 
damage 
simulations, a surface roughness of 0.3 m was used for all parks, except the 
one located 
on Dauphin Island where a surface roughness of 0.1 m was used. 

In McDonald and Vann (1986), the fraction of anchored homes were noted for 
four of the 
eight parks. The fraction of homes in each damage state resulting from the 
damage 
simulation was presented as a weighted average of the anchored and non-
anchored cases. 
At the remaining four sites, no information is given as to what fraction of 
the units were 
anchored. In these latter four cases, simulated damage states are given 
separately for 
anchored and non-anchored units. The survey of the anchor systems at the 
first four parks 
suggest that, on average, 70% of the units were properly anchored. Also, no 
information 
is given in McDonald and Vann (1986) as to what fraction of the units were 
HUD or pre-
HUD units, although the authors do note that the HUD units performed better 
than the 
pre-HUD units. In the damage simulations, only non-HUD units are modeled, and 
thus 
the model results should yield a bias towards higher than observed damage. 

The comparisons of the simulated and observed damage states are given in 
Table 6.24. 
As in the case of the Hurricane Andrew data, the observed unit damage classes 
are 
defined using the Vann and McDonald damage classification system. The peak 
gust wind 
speeds given in Table 6.24 are those resulting from the simulation of 
Hurricane Elena, 
and are representative of the peak gust wind speed at a height of 10 m in 
open terrain. 

Summary. Considering the uncertainties associated with terrain, anchor 
systems and age 
of the units, the comparison of the simulated and observed damage states 
given above is 
encouraging. Furthermore, following the discussion given in McDonald and Vann 
(1986), comparisons of the damage states should be made through comparisons 
of the 
total fraction of units in Classes 0, 1 and 2 combined (minor damage) and 
those in 
Classes 3 and 4 combined (major damage). With this grouping of damage states, 
the 
overall level of agreement between the simulated and observed damage states 
from the 
three validation studies is seen to be good. 

6.5.3 Damage Model Results for Manufactured Homes 
Using the damage model described earlier combined with the wind speed and 
direction 
traces produced for a 20,000 year simulation of hurricanes for storms in the 
South Florida 
area, estimates of manufactured home damage states as a function of peak 
storm wind 
speed are produced for manufactured homes situated in four different 
terrains. In the 
development of the damage functions, for each simulated hurricane, 90 damage 
simulations are performed. At the start of each of the 90 damage simulations, 
the 
manufactured home is randomly oriented, and the component resistances are 
sampled. 
Upon completion of the damage simulation, all failure modes that occurred 
during the 
passage of the storm are recorded along with the peak gust wind speed 
produced by the 

Chapter 6. Physical Damage Modeling 

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Table 6.24. Comparison of Simulated and Observed Manufactured Home Damage 
States Produced by Hurricane Elena 

Manufactured Home Park 
Observed Percentage of Units in 
Each Damage Class 
Modeled Percentage of Units in Each 
Damage Class1 
0 1 2 3 4 0 1 2 3 4 
Trade Winds, Dauphin Island, AL (20)Peak Gust 
Wind Speed 126 mph 5 35 20 0 40 16 15 7 6 55 
Trav Park, West Shore of Mobile Bay, AL 
(12)Peak Gust Wind Speed 109 mph 75 25 89 6 1 0 4 
Old Fort Village, Gautier, MS (97)Peak Gust 
Wind Speed 122 mph 42 39 4 3 11 54 16 7 3 13 
Isle of Pines North, Gautier, MS (102)Peak Gust 
Wind Speed 124 48 23 6 4 19 44 17 10 4 26 
Isle of Pines South, Gautier, MS (86)Peak Gust 
Wind Speed 124 mph 55 22 5 2 16 3847 1816 910 63 3024 
Imperial Estates, North Biloxi, MS (87)Peak Gust 
Wind Speed 122 mph 89 3 1 3 3 6067 1513 57 40 1219 
Rolling Hills, North Biloxi, MS (175)Peak Gust 
Wind Speed 122 mph 87 10 1 1 1 5664 1612 68 41 1916 
Anchor, Gautier, MS (70)Peak Gust Wind Speed 
126 mph 76 19 1 0 4 3845 1816 912 64 3024 

1

 Two rows of modeled data given at sites where the fraction of anchored units 
is not known. The upper line represents non-

anchored units and the lower line represents anchored units. When a single 
row of modeled data is presented, results represent a 

weighted average of anchored and non-anchored damage states. 

storm. Following all the damage simulations for each simulated storm, the 
resulting 
damage experienced by each modeled manufactured home is categorized into one 
of the 
five damage states as defined in lower portion of Table 6.22. The fraction of 
manufactured homes experiencing a given damage state for a given peak gust 
wind speed 
at a height of 10 m in open terrain is then computed and tabulated. The 
modeled damage 
states are given in wind speed increments of 5 mph, such that the fraction of 
homes 
experiencing a given damage state centered on a particular wind speed are 
used to 
compute the damage statistics for that wind speed. 

The resistance properties of the manufactured homes used in the development 
of the 
damage state curves are summarized in Table 6.25. The resistance values for 
the HUD 
and Pre-HUD buildings have been previously discussed. Note that the windows 
of the 
modeled manufactured homes are situated so that no windows are located in the 
high 
suction end zones along the walls and thus the siding capacity, which is at 
least partially 
located in an end zone exceeds the window capacity for the HUD Wind Zone II 
and 
Wind Zone III cases. 

Appendix B shows the computed damage state curves for all terrain, HUD Code 
and tie-
down combinations examined. Table 6.26 lists the per storm average damage 
states. 

6.6 Roof Covers on Flat Roofs 
This section describes the model used to estimate wind-induced damage to roof 
covers on 
flat roofs. The two roof cover systems modeled are built-up roof covers and 
single-ply 
membrane covers. For both roof types, the failure of roof cover system is 
initiated by 
failure of the flashing. 

HAZUS-MH MR3 Technical Manual 

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Table 6.25. Resistance Parameters Used to Model Manufactured Homes 

Building Component Pre-HUD HUD 
1994 HUD Wind 
Zone I 
1994 HUD Wind 
Zone II 
1994 HUD Wind 
Zone III 
Roof Cover Model Residential 
Shingle Model 
Residential 
Shingle Model 
Residential 
Shingle Model 
1.2 Times 
Residential Shingle 
Model 
1.2 Times 
Residential Shingle 
Model 
Roof Sheathing Capacity (psf) Mean = 
45COV = 12% 
Mean = 
45COV = 12% 
Mean = 
45COV = 12% 
Mean = 
90COV = 12% 
Mean = 
90COV = 12% 
Siding Resistance Mean = 
25COV = 15% 
Mean = 
25COV = 15% 
Mean = 
25COV = 15% 
Mean = 
72COV = 15% 
Mean = 
88COV = 15% 
Window Resistance Mean = 
32COV = 18% 
Mean = 
32 COV = 18% 
Mean = 
32COV = 18% 
Mean = 
57COV = 18% 
Mean = 
72COV = 18% 
Design Uplift Load (psf) 15 15 9 27 32 
Design Drag Load (psf) 25 25 15 39 47 
Roof-Wall Connection Safety 
Factor 
Mean = 
1.5COV = 25% 
Mean = 
1.2COV = 25% 
Mean = 
1.5COV = 25% 
Mean = 
1.5COV = 25% 
Mean = 
1.5COV = 25% 
Floor-Wall Connection Safety 
Factor 
Mean = 
1.6COV = 24% 
Mean = 
1.6COV = 24% 
Mean = 
1.6COV = 24% 
Mean = 
1.6COV = 24% 
Mean = 
1.6COV = 24% 
Anchor Pull Out Capacity (lb) Mean = 
1500COV = 35% 
Mean = 
1500COV = 35% 
Mean = 
1500COV = 35% 
Mean = 
1500COV = 35% 
Mean = 
1500COV = 35% 

Table 6.26. Per Storm Average Damage States 

Mobile Home Construction 
Terrain Surface Roughness (m) 
z0 = 0.03 z0 = 0.15 z0 = 0.35 z0 = 0.7 z0 = 1.0 
Pre-HUD, Not Tied Down 0.641 0.480 0.354 0.264 0.226 
Pre-HUD, Tied Down 0.623 0.466 0.339 0.249 0.211 
HUD, Not Tied Down 0.618 0.449 0.330 0.247 0.211 
HUD, Tied Down 0.594 0.428 0.309 0.226 0.190 
1994 HUD   Wind Zone I, Tied Down 0.697 0.537 0.402 0.306 0.262 
1994 HUD   Wind Zone II, Tied Down 0.269 0.234 0.162 0.111 0.091 
1994 HUD   Wind Zone III, Tied Down 0.249 0.214 0.151 0.106 0.087 

Built-up Roof Covers. Built-up roof (BUR) covers are composed of multiple 
plies of 
roofing felts adhered to each other and to the insulation substrate with a 
full mop of hot 
asphalt, coal tar or cold adhesive. The number of plies of roofing felt 
ranges from three to 
five. Roofing felts are commonly made of polyester, organic or glass-based 
materials. 
The surfacing on BUR covers is most often gravel or slag. 

Single-ply Membrane Covers. Single-ply membrane (SPM) covers are normally 
attached 
to the insulation substrate by adhesives (hot asphalt or cold applied 
materials) or by 
mechanical fasteners. Adhered SPM covers can be fully adhered or partially 
adhered. The 
adhesive in partially adhered SPM covers will typically have 50% coverage in 
the central 
portions of the roof and greater coverage at or near the edges and corners of 
the roof. 
Common membranes are thermoplastic membranes, thermoset membranes, modified 
bitumen membranes and liquid applied membranes. 

6.6.1 Resistance Model for Roof Covers on Flat Roofs 
Most wind-induced failures of BUR and SPM covers are initiated by failure of 
the 
perimeter flashing. When the flashing fails due to wind suctions, the wind 
can peel back 
the roof cover membrane at the newly exposed edge and, depending on how well 
the roof 
cover is attached to the substrate, the peeling failure can continue 
throughout the storm 
and result in large-scale damage to the roof cover system. Another failure 
mode is 

Chapter 6. Physical Damage Modeling 

?
bubbling, where the roof cover is separated from the substrate by wind-
induced suctions. 
If the bubbled area expands to an area where the roof cover is torn or 
missing, the airflow 
may  balloon  the bubbled area and the roofing membrane may tear, provided 
the 
resultant forces exceed the tearing strength of the membrane. Subsequent 
damage may 
then occur due to peeling at the newly exposed edges of the roof membrane. A 
bubbled 
section of the roof membrane may also become breached by impact from flying 
debris or 
by tearing at the perimeter of the bubble. 

The capacity of the flashing varies significantly between manufactures and 
mainly 
depends on the flashing shape and size, how the flashing is fastened to the 
roof (e.g., 
cleated versus uncleated), and the quality of installation. The flashing on a 
properly 
designed roof system complying with the minimum Factory Mutual Research 
Corporation roof system uplift rating (i.e., FM 1-60), should have a 
horizontal resistance 
equal to 45 psf. This equates to 22.5 plf (pounds per linear foot) for 
typical 6. wide 
flashing. For the purposes of modeling flashing failure, the resistance of 
the flashing to 
horizontal wind suction is taken as having a mean value of 22.5 plf and a 
coefficient of 
variation of 30%. 

The roof cover damage model samples resistances from normal distributions 
with the 
means and standard deviations shown in Table 6.27. These roof cover 
resistances are 
based on a combination of engineering judgment and on data obtained from a 
survey on 
roof cover types conducted in Palm-Beach, Broward and Miami-Dade counties. 
The 
survey showed that the majority of the roof covers complied only with the 
minimum 
Factory Mutual Research Corporation roof system uplift rating. The minimum 
uplift 
rating (i.e., FM 1-60) corresponds to a static test pressure of 60 psf. 

Table 6.27. Roof Cover Damage Model - Normal Distribution Parameters 

Roof Cover System 
Roof CoverPeeling 
Resistance 
Roof CoverBubbling 
Resistance FlashingResistance 
Mean(psf) COV(%) Mean(psf) COV(%) Mean(plf) COV(%) 
BUR Cover 50 15 150 15 22.5 30 
SPM Cover (adhesive) 40 15 60 15 22.5 30 
SPM Cover (mech. fasteners) 40 15 60 15 22.5 30 

The flashing resistance and roof cover resistances shown in Table 6.27 are 
used for 
 average  quality construction. Another result of the roof cover survey 
mentioned above 
is that approximately 50% of the existing roof covers in Miami-Dade, Broward 
and Palm 
Beach counties are classified as being of  poor  quality. Owing to the 
variability in the 
quality of materials and installation of existing roof cover systems, an 
additional 
resistance model was added to represent  poor  quality roof covers. The 
resistances 
associated with the  poor  quality roof cover system are 30% lower than those 
listed in 
Table 6.27. Also, an additional model was added to represent  good  quality 
roof cover 
systems, which are modeled with failure capacities 50% higher than those 
associated with 
 average  quality roof covers listed in Table 6.27. Note that this 50% 
increase is 
consistent with the increase in the test pressure associated with moving the 
Factory 
Mutual Research Corporation uplift rating from the minimum rating (FM 1-60) 
to the 
next discrete level (FM 1-90). 

HAZUS-MH MR3 Technical Manual 

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In the roof cover damage model, the roof area is divided into square elements 
of equal 
area (e.g., 4 sq ft). The square elements are assigned directionally 
dependent loads 
according to the ASCE wind loading provisions. The loads assigned to the 
flashing are 
set according to the vertical suction loads assigned to the adjacent roof 
cover elements 
multiplied by a factor. The factor transforms the vertical suction acting 
upward on the 
roof near the edge to a horizontal suction acting outward on the wall near 
the same edge. 
A factor of one is chosen for all flashing, whether located at a corner or at 
edges away 
from the corners. The factor of unity is a simplified approximation based on 
full-scale 
measurements of flashing loads made at Texas Tech University (McDonald, et 
al., 1997). 
In the full scale flashing tests, the ratio of the horizontal force to the 
vertical force 
estimated near the roof edge varied significantly between different flashing 
styles. 

The final estimate of both the effective loads and the resistance of the roof 
flashing, is 
subject to significant uncertainties, but the limited comparisons of 
simulated and modeled 
roof damage states performed to date, show the model yields reasonable 
results. 

In the damage model, a failed roof cover element implies that it has been 
removed and 
the associated roof substrate area is exposed to rainwater. On the other 
hand, bubbled 
roof cover areas will not expose the underneath roof surface area to 
rainwater. The roof 
cover damage algorithm performs the following four checks at each time step 
within a 
storm simulation: 

1. All non-failed flashing is checked for failure. When a piece of flashing 
fails, 
the adjacent roof cover elements also fail. 
2. All non-damaged roof cover elements adjacent to failed roof cover elements 
are checked for peeling failure. 
3. All non-damaged roof cover elements are checked for bubbling. 
4. All bubbled areas that have migrated to a failed portion of the roof cover 
are 
failed. 
The roof cover damage model yields failed roof cover only when the flashing 
fails. The 
only damage that results if the flashing remains intact is bubbling of the 
roof cover, 
which, as stated above, will not expose the roof substrate to rainwater. Upon 
further 
development of the roof cover damage model, bubbled roof cover areas will 
also be 
checked for tearing at the bubble perimeter and for membrane breaches caused 
by 
windborne debris impacts. 

6.6.2 Damage Model Validation for Roof Covers on Flat Roofs 
Two detailed validation studies have been performed for Hurricane Andrew 
(1992). 

Hurricane Andrew (1992) - Dixie Highway. The first validation study uses an 
aerial 
photograph taken after Hurricane Andrew in a commercial area located in 
Miami-Dade 
county in the vicinity of Dixie Highway and SW 184th Street. The photograph 
shows an 
area approximately 1600'W4100'. The estimated peak gust wind speed for this 
area at a 
height of 10 m above ground is 129 mph. This translates to about 163 mph at 
the same 

Chapter 6. Physical Damage Modeling 

?
height in open country terrain. Twenty-seven buildings were identified with 
BUR covers 
and the percent roof cover damage was estimated for each of them. It is 
likely that the 
roof cover materials and installation methods vary over the buildings used in 
this 
validation study. Of the 27 buildings identified, 13 are two story commercial 
buildings 
and fourteen are one story residential structures. The majority of the two 
story buildings 
are rectangular in plan with several of them being square or comprised of two 
or more 
rectangular elements (i.e., L-shaped). The one story buildings are all 
rectangular in plan. 
The simulation is performed on a two story rectangular building (aspect ratio 
of 2.25) and 
a one story rectangular building (aspect ratio of 1.5). The aspect ratios are 
consistent with 
the those of the surveyed buildings. The building stock considered in the 
analysis was 
assumed to comprise 40% of the buildings with roof sheathing attached with 6d 
nails and 
60% with 8d nails. 

Figure 6.43 shows the histogram of observed BUR cover damage in comparison to 
that 
predicted by the model. The one-story residential buildings were modeled with 
 average  
and  poor  quality roof cover systems while the two-story commercial building 
were 
modeled with  average  and  good  quality roof cover systems. It can be seen 
in Figure 

6.43 that the one-story residential buildings performed consistent to that 
predicted using 
the  poor  quality BUR cover model and the two-story commercial buildings 
performed 
consistent to that predicted using the  good  quality BUR cover model. 
 Average  Quality BUR Cover  Average  Quality BUR Cover 

10 0 

10 0 

0% >0 -5% 5-10 % 10 -20% 20-50 % 50-100% 
Covering Damage 
Sheathing Nails 
Relative Frequency (%)
0 

0% >0 -5% 5-10% 10 -20% 20-50 % 50-100% 
Covering Damage 
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
 Poor  Quality BUR Cover  Good  Quality BUR Cover 

Relative Frequency (%)

Data 

Simulated, 40% 6d & 60% 8d 
80 

80 

60 

60 

40 

40 

20 

20 

0 
20 
40 
60 
80 
10 0 
Relative Frequency (%)
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
0 
20 
40 
60 
80 
10 0 
Relative Frequency (%)
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
0% >0 -5% 5-10% 10 -20% 20-50% 50-100% 0% >0 -5% 5-10% 10 -20% 20-50% 50-100% 

Covering Damage Covering Damage 

Single Story Residential Structures Two Story Commercial Structures 

Figure 6.43. BUR Cover Damage for Hurricane Andrew - Dixie Highway. 

HAZUS-MH MR3 Technical Manual 

?
Hurricane Andrew (1992) - Aerial Photograph 3. The second validation study 
for 
Hurricane Andrew (1992) uses damage statistics observed from Aerial 
Photograph 3 (see 
description in Section 6.3). This photograph showed 15 one-story units with 
square plan 
dimensions and 15 one-story units with rectangular plan dimensions (the 
aspect ratios 
range from approximately 3 to 5). The residential buildings were built with 
4'W8' 
plywood roof sheathing and BUR covers. The simulated BUR cover damage 
statistics are 
derived from 5,000 simulations using both a one-story square structure and a 
one-story 
rectangular structure (aspect ratio equal to 4). The simulations are 
performed considering 
a 40%/60% mixture of buildings with 6d and 8d sheathing nails, respectively. 

The comparisons given in Figure 6.44 show that the model under-predicts the 
BUR cover 
damage observed in the aerial photograph using the  average  quality BUR 
cover model 
and performs reasonably well using the  poor  quality model. 

 Average  Quality BUR Cover  Poor  Quality BUR Cover 

0 
20 
40 
60 
80 
10 0 
Relative Frequency (%)
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
0 
20 
40 
60 
80 
10 0 
Relative Frequency (%)
Data Simulated, 40% 6d & 60% 8d 
Sheathing Nails 
0% >0 -5% 5-10% 10 -20% 20-50% 50-100% 0% >0 -5% 5-10% 10 -20% 20-50% 50-100% 

Covering Damage Covering Damage 

Figure 6.44. BUR Cover Damage for Hurricane Andrew - Aerial Photograph 3. 

6.7 Open-Web Steel Joist Roof System 
Light weight Open-Web Steel Joist (OWSJ) roof systems represent an efficient 
and 
inexpensive means to design and build a roof system to resist gravity loads. 
The joists 
consist of steel members that make up the top chord, bottom chord, and the 
web. The top 
and bottom chords are usually a set of two equal or unequal angle members. 
The web 
may either be angle or bar members depending on the span and depth of the 
steel joist. 
Under normal loading, the steel joist is designed for compression in the top 
chord and 
tension in the bottom chord (Steel Joist Institute, 1998). The top 
compression member is 
often braced by the roof deck connected to the upper chord of the joist, 
preventing 
buckling of the top chord. If the uplift pressures are greater than the dead 
loads of the 
steel joist, then the joist members undergo a stress reversal. The bottom 
chord members, 
which were designed for tension, now reverse to compression. If the bracing 
of the 
bottom chord of the joist is inadequate, this stress reversal may cause the 
bottom chord to 
buckle, failing the steel joist. The buckling of the bottom chord of an OWSJ 
is one of the 
two more commonly observed wind-induced failure modes associated with OWSJ 
roof 
systems. The other is the failure of the roof-wall connection (uplift 
failure), which is 
often associated with inadequate anchoring. 

Chapter 6. Physical Damage Modeling 

?
Steel joists are commonly used in light weight commercial structures, such as 
shopping 
malls, storage facilities, schools, warehouses, etc., and are usually simply 
supported. The 
OWSJ roof system can be used in combination with all steel buildings as well 
as 
buildings where the joists are supported by reinforced or unreinforced 
masonry walls. 
Different types of roof-wall connections are used to anchor steel joists, 
depending on the 
type of wall construction. The most common types of construction and 
anchorage are as 
follows: 

Steel Frame Construction. For steel frame buildings, the open-web joists are 
usually 
welded to the building frame at both ends of the joist. The welded connection 
is designed 
such that the uplift capacity of the connection exceeds the design uplift 
load, but in no 
cases shall the welded connection be less than two 1.W1/8. fillet welds. 
Open-web joists 
may also be bolted at each end using a minimum of two 1/2. bolts on each side 
of the 
joist. Steel bearing plates are not usually used in fastening steel joist to 
steel frame 
construction. 

Reinforced Masonry Construction. In buildings constructed with reinforced 
masonry 
walls, the open-web joists are anchored to a tie beam or bond beam. The 
anchorage 
consist of a minimum of 1/2. diameter anchor bolts or 1/2. (#4) reinforcing 
bars not 
spaced more than 6' on center. If the beam is at least 8. deep and has a 
minimum 
continuous reinforcement of 0.2 square inches at the top of the wall, then 
the anchorage is 
either welded to or hooked tightly around the longitudinal reinforcement in 
the beam. The 
bolts or reinforcing bars need to be extended into the wall a minimum of 6.. 
To complete 
the roof-wall connection, either the joist is bolted to the anchor bolts or 
the reinforcing 
bars are bent over each side of the joist and then welded. Steel bearing 
plates are not 
usually used when connecting steel joists to reinforced masonry walls. If the 
tie beam or 
bond beam is less than 8. deep, then the masonry wall is considered 
unreinforced 
masonry. 

Unreinforced Masonry. In buildings constructed with unreinforced masonry 
walls, steel 
bearing plates are more common due to the fact that the bearing capacity of 
unreinforced 
masonry walls is less than that of either steel frame or reinforced masonry 
construction. 
The joist is usually anchored to the wall using J-bolts with a length of 6. 
or 1/2. bolts 
with a length of 15.. A steel plate is attached to the head of the 15. bolt 
with a minimum 
surface area of 6 square inches which is fully embedded into the masonry. If 
bearing 
plates are used, the plates are welded to the joist and then anchored to the 
masonry wall 
using the 6. J-bolts or the 15. bolts. The welding of the joist to the steel 
plate follows the 
same requirements mentioned in steel frame construction. 

The uplift capacity of OWSJ anchored to unreinforced masonry walls is much 
less than 
the resistance of the joists anchored to reinforced masonry walls since the 
walls do not 
contain bond beams or tie beams with reinforcement to which the anchorage can 
be 
welded to or wrapped around (NRC, 1991). 

HAZUS-MH MR3 Technical Manual 

?
6.7.1 Resistance Model for Open-Web Steel Joists 
As discussed earlier, most steel joist failures occur due to inadequate roof-
wall anchoring 
or from buckling of the bottom chord of the steel joist. The uplift damage 
model 
developed herein considers the failure of the roof-wall connection at the 
weld connecting 
the joist to either a steel bearing plate or a larger steel beam and the 
failure of 
unreinforced masonry walls at the mortar interface between blocks. In the 
case of 
reinforced masonry walls, the failure of the wall associated with the uplift 
loads acting on 
the joist is not considered (i.e., the welded connection will always fail 
before the wall 
fails). 

Uplift Resistance. The possible failure modes modeled for the steel joist 
roof-wall 
connection are the failure of the welds between the steel joist and the steel 
bearing plate 
and the anchorage failure between the bearing plate and the unreinforced 
masonry wall. If 
a bearing plate is not present, then the failure mode is between the steel 
joist and the 
unreinforced masonry wall. 

Steel Joist and Steel Bearing Plate. The steel joist is welded to the steel 
bearing plate 
using a minimum of two 1.W1/8. fillet welds. Larger steel joists may require 
larger 
welds. The resistance of the joist/plate connection is based on the design 
strength of the 
welds using AISC Manual of Steel Construction (LRFD method) Second Edition 
Specifications (AISC, 1995). 

The weld metal, the plate metal or the joist metal are the three possible 
failure modes for 
this type of connection. Each are calculated using the number of welds, the 
length of each 
weld and the size of the weld. The lesser of the following three resistances 
is used to 
calculate the combined resistance for this type of connection. All capacities 
are based on 
the design procedure as given in AISI 8-129. 

Resistance of the Weld Metal. The nominal resistance of one weld is: 

Rn =Fw 7 Aw (6.4) 

where Fw is the nominal tensile strength of the weld material (Fw = 0.60 FEXX 
in pounds 
per square inch), Aw is the effective area of the weld (Aw = LengthWThroat) 
in square 
inches, FEXX is the classification strength of the weld metal for a EXX 
electrode in 
pounds per square inch, Throat is the effective throat thickness (Throat = 
0.7077Size) in 
inches, Size is the size of the fillet weld in inches. 

Resistance of the Plate Base Metal. The nominal resistance of the plate metal 
is: 

Rn =Fbm 7 Abm (6.5) 

where Fbm is the nominal tensile strength of the plate material in pounds per 
square inch, 
Abm is the effective cross sectional area of the plate (Abm = Effective Width 
of Base 
MetalWThickness) in square inches. 

Chapter 6. Physical Damage Modeling 

?
Resistance of the Steel Joist Base Metal. The nominal resistance of the joist 
metal is: 

Rn =Fbm 7 Abm (6.6) 

where Fbm is the nominal tensile strength of the joist material in pounds per 
square inch, 
Abm is the effective cross sectional area of the joist metal (Abm = Effective 
Width of Base 
MetalWThickness of Base Metal) in square inches. 

The steel bearing plate is usually A36 steel with a yield strength of 36,000 
psi and the 
joist steel yield strength is 50,000 psi. The type of weld electrode commonly 
used with 
A36 steel is an E70 Electrode. 

Example Minimum Uplift Capacity. A typical open-web steel joist with a span 
of 40' 
normally has a chord thickness equal to 3/16.. The steel bearing plate has a 
thickness of 
3/16.. An E70 Electrodes was used to produce 1/8. fillet welds with a total 
length of 2., 
1. on each side of the joist. The resistances of each of the three cases are 
as follows: 

Resistance of the Weld Metal 7,424 lbs 
Resistance of the Plate Base Metal 9,000 lbs 
Resistance of the Steel Joist Base Metal 12,500 lbs 

In this example, the limiting failure mode is the failure of the weld metal 
at a nominal 
value of 7,424 lbs. 

Steel Bearing Plate/Steel Joist and the Wall Anchorage. The resistance of the 
wall 
anchorage for both the steel bearing plate and the steel joist without 
bearing plate are 
based on the same procedure. Experimental uplift failure tests have been 
performed at 
Clemson University (Leland, 1988) on anchors with embedment depths of 6. and 
15. 
into walls 6' in length. The wall length of 6' was to simulate the Standard 
Building Code 
requirements for a maximum spacing not more than 6' on centers. The 
resistances of this 
type of connection is highly variable because it relies on the tensile bond 
strength 
between mortar and masonry units (Leland, 1988). Table 6.28 presents a 
summary of the 
uplift test results. 

Table 6.28. Uplift Resistances of an Unreinforced Roof-Wall Anchor, 6' O.C. 

Type of Roof-Wall Anchor Mean COV 
6. embedment J-Bolt Anchors 3312 lbs 42% 
15. embedment Anchor with 6 in2 endplate 4868 lbs 46% 

Bending Resistance. In the case of bending failures, there is not enough 
information on 
member sizes, depth of the joist, etc., to permit a detailed engineering 
estimate of the 
capacity of the joist, which would require examining the various bending 
failure modes 
(i.e., bottom flange buckling, bottom chord yielding and web members yielding 
or 
buckling). The capacity of the OWSJ to resist moments associated with the 
wind induced 
loads are based on the maximum uplift moments computed using the Standard 
Building 

HAZUS-MH MR3 Technical Manual 

?
Code (UBC) wind load requirements on a simply supported steel joist. No 
information on 
the actual mode of bending failure is known, although buckling probably 
represents the 
actual limit state. 

Prediction of Joist Failures. The premise behind the joist failure model is 
the same as 
the other failure models described herein, in that at each time step during a 
hurricane 
simulation the loads acting on the joists are compared to the sampled 
resistances, and if 
the load exceeds the sampled resistance, the joist is assumed to fail. In the 
case of 
moment failures, if the computed wind induced bending moment exceeds the 
sampled 
moment resistance, the joist is assumed to have buckled and can carry no more 
load. The 
portion of the wind load acting on the joist that exceeds the buckling load 
is then 
distributed to the neighboring joists. Each joist is checked to determine if 
failure has 
occurred. If failure has occurred, the excess load is redistributed. After 
all joists have 
been checked for failure, the checking process is repeated, taking into 
account the 
redistributed loads. The load redistribution process continues until all 
redistributed loads 
have been proportioned to joists that have not failed, or all joists have 
failed. In the case 
of an uplift connection failure, the joist is not able to carry any load, and 
thus all of the 
wind loads acting on the joist must be distributed to the neighboring joists. 

At any time during a damage simulation, the uplift reaction loads (RA and RB) 
at either 
end of the joist, and the bending moment (M) at the center, are computed from 
the 
following: 

RA =qHCFAlw -(L + D)lw / 2 + piwl / 2 (6.7) 

RB =qHCFBlw -(L + D)lw / 2 + piwl / 2 (6.8) 

M =qHCM l2w -(L + D)wl2 / 8 + piwl2 / 8 (6.9) 

where qH is the dynamic pressure at roof height (based on the peak gust wind 
speed at 
roof height), CFA and CFB are the directionally dependent uplift coefficients 
for the 
reaction at each end of the joist, CM is the directionally dependent mid-
point moment 
coefficient, l is the span, w is the distance between joists, L and D are the 
live and dead 
loads, and pi is the internal pressure. The uplift and moment coefficients 
are computed 
using the influence line approach described earlier. 

Example OWSJ Failure Probabilities. No quantitative statistical studies 
examining the 
performance of buildings with OWSJ roofs in hurricane winds have been 
performed to 
enable the failure model to be quantitatively evaluated. Thus the modeled 
results 
presented here are not compared to any full scale performance data, and are 
given only to 
show the reasonableness of the predicted failure rates. 

In the examples given, two simple rectangular buildings were modeled. Each 
building has 
a length of 180' and a width of 40'. The 40' width is spanned by OWSJs spaced 
at 6' on 
center. One building has a roof height of 12' and the other building has a 
roof height of 
20'. On the front side of the building, there are a total of twelve windows 
having 

Chapter 6. Physical Damage Modeling 

?
dimensions of 6' high and 12' wide, and six glass entry doors, each of which 
is 6'8. tall 
and 3' wide. At the rear of the building there are six non-glazed entry 
doors. The roof 
deck is assumed to be comprised of light weight metal with a BUR cover. The 
roof 
system is designed to resist the wind loads as prescribed in the SBC Wind 
Load 
Provisions for a structure located in the 100 mph (fastest mile) wind zone. 
Joists designed 
to meet the loads applied in the more highly loaded end-zones are assumed to 
be used 
throughout the roof system. The dead and live loads used in the design of the 
joists are 

6.25 and 25 psf, respectively. The buildings are assumed to be constructed in 
a terrain 
described by a typical suburban environment (i.e., z0 = 0.35 m), with the 
windborne 
debris environment represented with the residential model. In the damage 
simulation, if a 
window is breached, the internal pressures are assumed to act over the entire 
underside of 
the roof. The mean value of the bending resistance of the joist is taken as 
1.3 times the 
design value. The COV is assumed to be equal to 20%. The resistance factor 
associated 
with the welded connection for uplift is assigned a mean value of 1.5 with a 
COV of 
20%. 
The probability of joist failure as a function of wind speed is developed by 
passing 
hurricane wind speed traces past the buildings. The hurricane wind speed 
traces used to 
develop the joist failure probabilities shown in Figure 6.45 and 6.46 are the 
traces derived 
from a 20,000 year simulation of hurricanes in the South Florida area. 
Results are given 
for both the reinforced and unreinforced masonry wall case. Note that for 
modeling 
purposes, the performance of joists on steel frame buildings is the same as 
the 
performance of joists on reinforced masonry buildings. In the damage 
simulation model, 
the number of failed joists and the peak wind speed produced by the storm is 
retained and 
used to develop the damage state curves given in Figures 6.45 and 6.46. 
Damage states 1, 
2 and 3, noted in Figures 6.45 and 6.46, represent the following: 

Damage State 1 at least one failed joist, but less than 25% failed joists 
Damage State 2 at least 25% failed joists but less than 50% failed joists 
Damage State 3 more than 50% failed joists 

Note that for a given building, the damage state curves, given in Figures 
6.45 and 6.46, 
are very close together, indicating that once a single joist has failed, the 
likelihood of 
other joists failing immediately afterwards is high, implying a system with 
little 
redundancy. 

6.7.2 Resistance Model for Metal Deck on Open-Web Steel Joists 
Metal decks on steel joists are frequently used as the primary roof deck 
system for both 
steel buildings and concrete/masonry buildings. The performance of these 
metal decks in 
high wind events varies with deck size, thickness, attachment method, joist 
spacing, 
quality of installation and number of fasteners. The approach taken here to 
estimate the 
uplift capacity of a metal deck panel is to design some typical systems using 
the 
requirements given in AISI (1996) and then perform a failure analysis using a 
finite 
element model. 

HAZUS-MH MR3 Technical Manual 

?
Un-Reinforced Masonry Walls, H=12' 

90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 

Peak Gust Wind Speed (mph) in Open Terrain 

Reinforced Masonry Walls, H=12' 

90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 

Peak Gust Wind Speed (mph) in Open Terrain 

Figure 6.45. OWSJ Roof Damage States for a 12' High Building in Suburban 
Terrain. 

The approach involves the selection of the fastener requirements to meet the 
design loads, 
coupled with a review of recent experimental studies examining the resistance 
of screwed 
and welded connections. Given these inputs, a stochastic finite element study 
is used to 
determine the failure loads. 

0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
P(Ds>=ds|v) 
Damage State1 
Damage State 2 
Damage State 3 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
P(Ds>=ds|v) 
Damage State1 
Damage State 2 
Damage State 3 
Chapter 6. Physical Damage Modeling 

?
Un-Reinforced Masonry Walls, H=20' 

90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 

Peak Gust Wind Speed (mph) in Open Terrain 

Reinforced Masonry Walls, H=20' 

90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 

Peak Gust Wind Speed (mph) in Open Terrain 

Figure 6.46. OWSJ Roof Damage States for a 20' High Building in Suburban 
Terrain. 

This section describes the methodology used to estimate the uplift capacity 
for two 
example cases; however, the approach is general and can be applied to any 
configuration 
of metal deck roof systems. 

An Overview of Metal Decking. The metal deck is made from cold formed 
structural 
grade sheet steel that conforms to ASTM Designations A611 Grade C, D, or E 
(for 

P(Ds>=ds|v) 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Damage State1 
Damage State 2 
Damage State 3 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
P(Ds>=ds|v) 
Damage State 1 
Damage State 2 
Damage State 3 
HAZUS-MH MR3 Technical Manual 

?
painted deck) or A653 Grade 33, 40, 50 or 80 (for galvanized deck) (USD, 
1997). The 
minimum yield strength of the steel is 33 ksi. The metal deck can be 
categorized into 4 
types (i.e., A, B, F, and N) according to different profiles of the ribs. 
Table 6.29 lists the 
profiles, nominal thickness ranges and weight ranges of these 4 deck types. 
Standard 
deck widths vary from 12. to 36. with an increment of 6. (the length will 
vary depending 
on the spacing of bar joists). Typical thicknesses of the metal deck are 16, 
18, 20, and 22 
gage. 

Table 6.29. Nominal Deck Information, from SDI (1992) 

Deck Type Name (profile) Nominal Thickness Range Weight Range 
Type A 1-1/2.W6. Wide Rib (WR) 0.03. to 0.06. 2 psf to 4 psf 
Type B 1-1/2.W6. Intermediate Rib (IR) 0.03. to 0.06. 2 psf to 4 psf 
Type F 1-1/2.W6. Narrow Rib (NR) 0.03. to 0.06. 2 psf to 4 psf 
Type N 3.W8. Deep Rib (WR) 0.03. to 0.06. 2 psf to 4 psf 

The metal decks are attached to the building frame with arc puddle welds, 
self-drilling 
screws, or powder-actuated or pneumatically-driven pins. Sheet to sheet 
fastening is 
performed with screws, button punching (crimping) or welds. The deck is end-
lapped a 
minimum of 2. with the overlapping occurring at the location of the supports. 
The 
minimum end bearing is 1-1/2.. According to SDI (1992b), the common 
connection 
layout patterns are: 

36. (width) deck: 36/9, 36/7, 36/5, 36/4 for 2.or 3. composite, and 36/3 

30. (width) deck: 30/6, 30/4 and 30/3 

24. (width) deck: 24/4 

where, for example, 36/N represents the number of fasteners, N, across the 
36. width of 
the panel. 

Screwed Decks. In determining the parameters for the case studies, two types 
of fasteners 
were considered: screws and welds. Screws are most often #12 or 1/4. diameter 
when 
fastening the roof deck to structural members. Sheet to sheet connections 
(also known as 
stitch connections) usually use self-drilling #8 or 1/4. diameter screws. 
Table 6.30 
presents the properties of typical screws used to fasten the steel decks to 
the underlying 
structural roof system. Screws can be installed either at the valley or at 
the crest of the 
metal deck with or without washers (in this investigation, all the screws are 
assumed to 
be valley-fixed). 

Table 6.30. Typical Screw Properties 

Screw Size d, Diameter (in) Dw, Nominal Head Diameter (in) Avg. Tested 
Tensile Strength (kips) 
10 0.190 0.415 or 0.400 2.56 
12 0.210 0.430 or 0.400 3.62 
1/4 0.250 0.480 or 0.520 4.81 

Chapter 6. Physical Damage Modeling 

?
For a screw-connected roof panel, post storm damage investigations following 
extreme 
wind events have shown that the two most common steel deck failure modes are: 
(i) a 
pull over failure, where the metal deck pulls over the fastener and (ii) a 
pull out failure 
where the screw pulls out from the structural steel member. The thin steel 
sheathing often 
pulls through (pulls over) the screw heads due to either the large stress 
concentration 
around the fastener holes or fatigue under wind fluctuations. Pull through 
failures can 
occur when very thin high-strength steel battens or purlins are used and the 
screw 
fasteners can be pulled out of the steel battens or purlins. The American 
standard (AISI, 
1996) provides two different formulas for these two failure modes: 

The pull-over strength, Pnov (kips), is given as: 

Pnov =1.5t1dwFult (6.10) 

where t1 = thickness of the deck (in); dw = diameter of the washer or head of 
screw (in); 
Fult = tensile strength of the deck metal (ksi). 

The pull-out strength, Pnot (kips), is given as: 

Pnot = 0.85tcdFult (6.11) 

where tc = thickness of the support member (in); d = diameter of the screw 
shaft (in); 
Fult = tensile strength of the support member (ksi). 

A number of researchers have investigated pull-over and pull-out failures for 
a wide 
variety of base metal materials, deck profiles, load conditions, and test 
procedures 
(Ellifritt and Burnette, 1990; Pekoz, 1990; Garcma, 1994; Smith, 1995; 
Mahendran, 
1994,1997; Mahendran and Tang, 1998). In this investigation, the results 
summarized by 
Pekoz (1990) and Mahendran and Tang (1998) were used to derive the statistics 
of the 
mean to nominal ratios of the screw resistances computed using Equations 6.10 
and 6.11 
for the pull-over failure and pull-out failures, respectively. 

Pekoz (1990) investigated more than 360 pull-over failure cases and proposed 
a design 
formula (Equation 6.10) that has been adopted by the American standard (AISI, 
1996). 
The reported mean-to-nominal and COV are 1.027 and 0.235, respectively. 

Mahendran and Tang (1998) conducted a series of pull-out tests for a range of 
steel 
battens, purlins, girts and screw fasteners commonly used in the building 
industry. The 
test results were compared with the predicted values based on current design 
formula 
(Equation 6.11) using both the measured properties and the specified 
properties. It was 
found that the current design formula used with specified properties (nominal 
value) 
provide better agreement with the test results than those predicted using the 
measured 
properties. A mean-to-nominal ratio of 1.20 and a (COV) of 0.21 were 
determined using 
all 592 test cases. The mean-to-nominal values and COVs for pull-out and 
pull-over 
failure modes form the basis for sampling screw resistances in the stochastic 
finite 
element analysis described later. 

HAZUS-MH MR3 Technical Manual 

?
Welded Decks. In this investigation, only arc spot weld connections were 
considered and 
modeled in the finite element analysis. The supporting members were assumed 
to be the 
top chords of open-web steel joists (however, the results are applicable to 
hot-rolled steel 
beams or girders). The study by LaBoube and Yu (1993) was used to determine 
the 
resistances of the weld connections. Based on more than 260 tension tests of 
arc spot 
weld connections with variations in steel thickness, tensile strength, weld 
process, 
loading condition, and geometry, LaBoube and Yu (1993) developed equations to 
predict 
the tension capacity of an arc spot weld connection. The following equations 
have been 
adopted in the current American standard (AISI, 1996): 

when Fu/E < 0.00187 

Pn =
..
6.59 - 3150.
. 
Fu ..
..
tdaFu = 1.46tdaFu (6.12)
.. E .. 

when Fu/E=0.00187 

Pn = 0.70tdaFu (6.13) 

where Pn = nominal tension capacity; Fu = tensile strength of the sheet 
steel; t = sheet 
steel thickness; da = average weld diameter at mid-thickness of t, where da = 
(d t) for a 
single sheet and (d 2t) for a double sheet; E = Young s modulus of the sheet 
steel. Based 
on concentric loading cases, LaBoube and Yu (1993) found the ratio of the 
failure load 
(Pu) to the predicted load (Pn) to have a mean value of 1.18 and a COV of 
0.242. 

At the perimeter of a steel deck roof system where there may be strong 
spatial gradients 
in the uplift loads, the arc spot welds may experience eccentric loading 
conditions, and 
the weld capacities in these cases can be significantly lower than those 
under concentric 
loads. Based on the 34 eccentric test results reported by LaBoube and Yu 
(1993), a mean-
to-nominal value of 0.617 and a COV of 0.242 were determined, indicating a 
strength 
reduction of about 40% compared to the behavior of a concentrically loaded 
connection. 
In the finite element analysis, for welded connections, the relative location 
of the metal 
deck panel was considered and the resistances were sampled from the eccentric 
load 
conditions for panels located at the edge of a roof system. 

Case Studies. For the purpose of estimating the resistances of metal deck 
configurations 
used in engineering practice, the two most widely used building codes in 
southeastern 
United States (ASCE 7-88 and SBCCI 1988) were adopted to design the metal 
deck roofs 
of two baseline buildings (a one-story building and a two-story building). 
These two 
buildings were assumed to be located in hurricane-prone regions with design 
fastest mile 
wind speeds of 90 mph, 100 mph, and 110 mph. The mean roof heights for the 
flat-roof 
model buildings were 15' (one-story) and 25' (two-story). The metal roof 
system is 
comprised of 3' by 8' metal deck panels and supported by open-web steel 
joists spaced at 
4'. Table 6.31 lists the calculated design wind pressures obtained from ASCE 
7-88 and 
SBCCI 1988. An importance factor of 1.05 was used with the ASCE loads. 

Chapter 6. Physical Damage Modeling 

?
Table 6.31. Design Wind Pressures Calculated Using ASCE 7-88 and SBCCI 1988 

Code 
Number of 
Stories Zone 
Wind Pressure for Components & Cladding (psf) 
90 mph 100 mph 110 mph 
1 -28.71 -35.45 -42.89 
1-Story 2 -44.26 -54.64 -66.12 
ASCE 3 -59.44 -73.38 -88.79 
1 -33.38 -41.21 -49.86 
2-Story 2 -51.45 -63.52 -76.86 
3 -69.10 -85.31 -103.22 
r -20.74 -25.62 -30.50 
1-Story s -28.90 -35.70 -42.50 
SBCCI 
z -39.10 -48.30 -57.50 
2-Story 
r -24.40 -29.59 -35.99 
s -34.00 -41.23 -50.15 
z -46.00 -55.78 -67.85 

The variables considered in the design were fastener size, layout, steel deck 
gage, steel 
deck strength, base metal thickness and base metal strength. The current 
American 
standard for cold-formed steel structures (AISI, 1996) was used to select 
appropriate 
designs for the calculated design loads. The deck resistances were calculated 
for six 
different layouts of fastener attachment schemes for both welded and screwed 
connections. Both load and resistance factor design (LRFD) and allowable 
stress design 
(ASD) were used to select the appropriate fastener layout. Tables 6.32 and 
6.33 lists the 
six different attachment layouts for the screw connections and for the welded 
connections, respectively. 

Table 6.32. Determined Case Studies for Screw Connections 

Case 
Screw 
Size Layout Installation 
Steel Deck Base Metal 
Gage 
Fy(ksi 
) 
Fu(ks 
i) 
Thickness 
(in.) 
Fy(ksi 
) Fu(ksi) 
s1 #10 36/3 Valley-Fixed 22 40 55 0.125 33 45 
s2 #10 36/4 Valley-Fixed 22 50 60 0.125 33 45 
s3 #10 36/5 Valley-Fixed 22 50 60 0.125 33 45 
s4 #12 36/5 Valley-Fixed 20 50 60 0.125 40 55 
s5 #10 36/7 Valley-Fixed 20 50 60 0.125 40 55 
s6 #12 36/7 Valley-Fixed 20 50 60 0.125 40 55 

Table 6.33. Determined Case Studies for Weld Connections 

Case 
WeldSi 
ze Layout Installation 
Steel Deck Base Metal 
Gage 
Fy(ksi 
) 
Fu(ks 
i) 
Thickness 
(in.) 
Fy(ksi 
) Fu(ksi) 
w1 0.500 36/3 Valley-Fixed 22 40 55 0.125 33 45 
w2 0.500 36/3 Valley-Fixed 22 50 60 0.125 33 45 
w3 0.625 36/3 Valley-Fixed 22 50 60 0.125 33 45 
w4 0.625 36/3 Valley-Fixed 20 50 60 0.125 40 55 
w5 0.625 36/4 Valley-Fixed 20 50 60 0.125 40 55 
w6 0.850 36/4 Valley-Fixed 20 50 60 0.125 40 55 

HAZUS-MH MR3 Technical Manual 

?
A total of 12 cases were used in the finite element analysis (6 screw 
connections and 6 
weld connections). Recall the layout pattern of 36/N means N fasteners across 
the 36. 
width of the metal deck panel. Since a 3' by 8' panel covers two spans of the 
bar joists, 
there will be a total of 3N+4 fasteners on the panel (4 fasteners connecting 
adjacent roof 
panels at the middle of the span). The deck profile was assumed to be an 
intermediate rib 
type B and the deck gage was selected from the manufacture s manual. Figures 
6.47 
through 6.50 show the metal deck profile and the different fastener patterns 
(layouts) 
used in the case studies. 

Figure 6.47. Metal Deck Profile with 3 Fasteners. 

Figure 6.48. Metal Deck Profile with 4 Fasteners. 

Figure 6.49. Metal Deck Profile with 5 Fasteners. 

Figure 6.50. Metal Deck Profile with 7 Fasteners. 

Probabilistic Finite Element Analyses. Isoparametric shell elements were used 
to model 
the metal deck. The shell element was a four-node quadrilateral element with 
six degrees 
of freedom at each node. The 3'W8' metal deck panel was divided into 1600 
(32W50) shell 
elements, detailed enough to give accurate fastener forces and still allowing 
the analysis 
to be run in a relatively short time period. Figure 6.51 shows the plan view 
of the mesh 
grids for the metal deck panel along with the fastener locations (denoted 
using stars) for a 
36/4 fastener pattern. A previous study (Mahendran, 1994) suggested that 
despite the 
occasional local metal deck failure around the fasteners, the majority of the 
metal deck 
was still in elastic range when the fasteners failed, and thus the metal deck 
was modeled 
here as a linear elastic material with a Young s modulus of 29000 ksi and a 
Poisson s 
ratio of 0.29. 

The fasteners used to attach the deck panel to the bar joists were modeled as 
a fixed 
support with restrictions in the x, y and z directions. No moment resistance 
was assumed 
for these fasteners. For the fasteners on the lap joints, the movements in 
the x and y 
directions were restrained, but the fasteners were allowed to move freely in 
the z 
direction. 

Chapter 6. Physical Damage Modeling 

?
yyyy
x 

z 

Figure 6.51. Finite Element Model of the 3' by 8' Panel. 

The metal deck was subjected to a uniform pressure with both the ultimate 
tensile 
strength and serviceability being considered in determining the failure of a 
panel. Failures 
caused by shear and local buckling were not modeled in the finite element 
analysis. If 
screws are used, both pull-out and pull-over strengths were sampled and the 
resistance 
was taken as the smaller of the two sampled strengths. Failure of a fastener 
occurs when 
the computed fastener load exceeds the sampled fastener resistance. For the 
complete 
failure of the whole panel, the failure criteria are: (1) less than 3 
fasteners remain on the 
panel (all the other fasteners have failed); or (2) all the remaining 
fasteners align in a 
pattern such that the panel can rotate; or (3) the maximum deflection of the 
panel is 
greater than 2'. 

A Monte Carlo simulation was used to determine the statistics of the failure 
pressure for 
the metal deck (considering the randomness in the material properties and the 
fastener 
strengths). Figure 6.52 shows the flowchart of the probabilistic finite 
element analysis. In 
the simulation, for each fastener, the nominal value (i.e., design value) was 
calculated 
first using the appropriate design formula. The statistics (i.e., mean and 
standard 
deviation) of the fastener resistance were obtained by multiplying the 
calculated design 
value by the mean-to-nominal ratio statistics. The resistance of each screw 
was then 
obtained by sampling from the resulting normally distributed resistance. For 
screw 
connections, both pull-out strength and pull-over strength were sampled and 
the 
resistance was taken as the smaller of the two values. Similar procedures 
were also 
applied to the base metal properties of the metal deck (e.g., Fy and Fu). The 
finite element 
mesh, material properties, boundary conditions, fastener s strengths, and 
initial loads on 
the metal deck were generated automatically by a program written specifically 
for the 
Monte Carlo simulation. Then, a full finite element analysis was carried out 
to calculate 
the reaction in each fastener and the maximum deformation of the metal deck. 
Failure 
criteria were then checked against the calculated reactions and deformation. 
If the failure 
state was reached, the load (pressure) was then recorded as the failure load 
and a new 
simulation would start. Otherwise, failure of individual fasteners were 
checked and any 

HAZUS-MH MR3 Technical Manual 

?
= 
= 
Stop 
yes 
no 
yes 
no 
yes 
no 
yes 
no 
Increase 
pressure 
but 
maintain 
distribution 
Set pressure distribution 
(normalize maximum pressure to 1.0) 
Num. of simu =Num. ofsimu + 1 
Set initial fastener location 
Sample fastener resistances 
from appropriate distributions 
Generate metal deck geometry 
Num. of simu =0 
Run finite element analysis 
Query fastener reactions and 
maximum metal deck deflection 
Fastener forces > 
fastener resistances? 
Remove failed fasteners and re-generate 
the input file for finite element analysis 
Num. of remaining fasteners < 3 
(or) rigid body movement? 
Maximum deflection > 
allowable deflection? 
Num. of simu > specified 
simulation number? 
Stopyesnoyesnoyesnoyesno
Figure 6.52. Flowchart of the Probabilistic Finite Element Analysis. 

Chapter 6. Physical Damage Modeling 

?
failed fasteners were removed from the analysis. The boundary conditions were 
then regenerated 
and a new finite element analysis was carried on the modified sets of the 
fasteners while the pressure remained the same (i.e., considering progressive 
failure). If 
no more fasteners fail, the pressure was increased and the procedure was 
repeated until 
failure of the metal deck panel was reached. The output from the finite 
element analysis 
were the initial failure pressure (i.e., the pressure at which any one of the 
fasteners fails) 
and the final failure pressure (i.e., the pressure at which the whole metal 
deck panel fails 
according to the failure criteria). After 100 simulations had been performed, 
the 
simulation results were used to characterize the statistics of the metal deck 
uplift 
capacity. 

FEM Results. For each example, 100 finite element analyses were performed, 
from 
which the statistics of the failure pressure were computed. Table 6.34 lists 
the resulting 
means and standard deviations of the failure loads for selected case studies 
(associated 
with the design loads for different regions of the baseline model buildings). 
Note that the 
design pressures listed in Table 6.34 are different from the wind pressures 
calculated 
previously (see Table 6.31) because of the inclusion of dead load (for both 
ASD and 
LRFD) and the application of load factors (for LRFD only) in calculating the 
design 
pressures. In most cases, a lognormal distribution was found to provide the 
best fit to the 
failure pressures. Figures 6.53 and 6.54 show the CDFs of the failure 
pressures for the 
metal deck with screw connections and welded connections, respectively. For 
screw 
connections, the increase in resistance with higher design values and 
stronger designs (in 
a sequence of case 1 to case 6) is clear. The coefficient of variation of the 
failure loads is 
seen to be relatively constant throughout the case studies (in a range of 10-
15%). 

However, for welded connections, the resistances do not increase 
monotonically with 
stronger designs, and cases 3 and 6 have larger variations than the other 
cases. Note that 
for welded connections, cases 1 and 2 are assumed to be in the middle of a 
roof system, 
cases 3 and 4 are assumed to be along one edge of a roof system, and cases 5 
and 6 are 
assumed to be at the corner of a roof system. The welds along the edge of a 
roof system 
will experience eccentric loads which have lower resistances than the welds 
experiencing 
concentric loads. Therefore, the relative location of the roof deck panel has 
a significant 
impact on the panel uplift capacity. 

Note that due to the relatively small coefficient of variation (less than 
20%) of the 

simulated failure pressures, the potential sampling error of the estimated 
mean failure 
pressure will be less than 2% of the mean value (i.e., 20% /n = 20% /100 = 
2%). 
Therefore, 100 simulations provide sufficient accuracy. 

Summary. A damage model for metal decks on open-web steel joists has been 
developed 
by combining a finite element analysis of a deck subject to a uniform 
pressure load, with 
the results of experimental data for fastener uplift resistance. Note that a 
number of 
parameters or factors that may contribute to the failure probability, such as 
fatigue, local 
buckling, membrane strength, workmanship, and deterioration were not 
explicitly 
considered. The potential impact of the reduction in deck capacity due to 
fatigue, 
corrosion, etc. is addressed in the building damage simulation results. 

HAZUS-MH MR3 Technical Manual 

?
Table 6.34. Simulated Failure Loads for Different Designs 

Code 
No. of 
Story 
Wind 
Speed 
(mph) Zone 
Design 
Pressure (psf) 
Screw Design Weld Design 
Case Mean (psf) Std (psf) Case Mean (psf) Std (psf) 
1 -34.6 s1 187.3 24.7 w1 168.8 22.3 
90 2 -54.8 s1 187.3 24.7 w1 168.8 22.3 
3 -74.6 s2 212.4 23.0 w1 168.8 22.3 
1 -43.4 s1 187.3 24.7 w1 168.8 22.3 
1 100 2 -68.3 s2 212.4 23.0 w1 168.8 22.3 
3 -92.7 s3 233.5 30.9 w1 168.8 22.3 
1 -53.1 s1 187.3 24.7 w1 168.8 22.3 
110 2 -83.3 s3 233.5 30.9 w1 168.8 22.3 
ASCE 7-88 
3 -112.7 s4 299.0 37.1 w3 222.0 40.3 
1 -40.7 s1 187.3 24.7 w1 168.8 22.3 
90 2 -64.2 s2 212.4 23.0 w1 168.8 22.3 
3 -87.1 s3 233.5 30.9 w1 168.8 22.3 
1 -50.9 s1 187.3 24.7 w1 168.8 22.3 
2 100 2 -79.9 s3 233.5 30.9 w1 168.8 22.3 
3 -108.2 s4 299.0 37.1 w3 222.0 40.3 
1 -62.1 s2 212.4 23.0 w1 168.8 22.3 
110 2 -97.2 s4 299.0 37.1 w3 222.0 40.3 
3 -131.5 s5 435.8 42.0 w4 305.2 40.4 
r -17.7 s1 187.3 24.7 w1 168.8 22.3 
90 s -25.9 s1 187.3 27.7 w1 168.8 22.3 
z -36.1 s1 187.3 24.7 w1 168.8 22.3 
r -22.6 s1 187.3 24.7 w1 168.8 22.3 
1 100 s -32.7 s1 187.3 24.7 w1 168.8 22.3 
z -45.3 s1 187.3 24.7 w1 168.8 22.3 
r -27.5 s1 187.3 24.7 w1 168.8 22.3 
110 s -39.5 s1 187.3 24.7 w1 168.8 22.3 
SBCCI 
z -54.5 s2 212.4 23.0 w1 168.8 22.3 
2 
90 
r -21.4 s1 187.3 24.7 w1 168.8 22.3 
s -31.0 s1 187.3 24.7 w1 168.8 22.3 
z -43.0 s1 187.3 24.7 w1 168.8 22.3 
100 
r -26.6 s1 187.3 24.7 w1 168.8 22.3 
s -38.2 s1 187.3 24.7 w1 168.8 22.3 
z -52.8 s2 212.4 23.0 w1 168.8 22.3 
110 
r -33.0 s1 187.3 24.7 w1 168.8 22.3 
s -47.2 s1 187.3 24.7 w1 168.8 22.3 
z -64.9 s2 212.4 23.0 w1 168.8 22.3 

Chapter 6. Physical Damage Modeling 

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6-95 
HAZUS-MH MR3 Technical Manual 
0.0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.0 
0 100 200 300 400 500 600 700 800 
Failure Pressure (psf) 
Case1 
Case2 
Case3 
Case4 
Case5 
Case6 
Figure 6.53. CDFs of Metal Deck Failure Pressures (Screw Connections). 
0.0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.0 
0 100 200 300 400 500 600 
Failure Pressure (psf) 
Case1 
Case2 
Case3 
Case4 
Case5 
Case6 
Figure 6.54. CDFs of Metal Deck Failure Pressures (Weld Connections). 
6.8 Pre-Engineered Metal Buildings 
Because of their fast erection, flexible expansion capability, and low 
maintenance, metal 
building systems dominate the low-rise non-residential market. For example, 
according to 
MBMA, 65% of the new one- and two-story buildings were built using metal 
building 
structures in 1995. Pre-engineered metal building systems are found in many 
applications, including warehouses, small office buildings, garages, 
supermarkets, retail 
stores, industrial factories, schools, town halls, and even churches (Newman, 
1997). A 
?
metal building system is typically comprised of primary frames (hot-rolled 
wide-flange 
beams and girders, hot-rolled steel rigid frame, or steel trusses), secondary 
frames 
(purlins and girts), endwall posts, roof system, wall system, foundation, and 
bracing. 

In this analysis, as in the low-rise residential building analysis, the 
physical damage 
modeling for metal buildings is limited to modeling the damage to components 
and 
cladding, specifically the metal wall system, the metal roof system, and the 
entry doors 
and overhead doors. 

6.8.1 Resistance Model for Metal Building Wall System 
Wall panels on metal buildings are normally supported on cold-formed C or Z 
girts. The 
thickness of the galvanized-steel siding ranges from 18 to 29 gage, with the 
midrange 
gages being the most popular (Newman, 1997). Exposed-fastener wall panels 
behave 
similarly to through-fastened roofing and some products can be used for both 
wall and 
roof systems. Figure 6.55 shows one section of a typical exposed-fastener 
wall system. 
The wall panels are attached to the supporting members using self-drilling 
screws. Two 
adjacent panels are overlapped and joined together with side-lap fasteners, 
which are 
typically spaced at 24. O.C. 

The development of the damage model for metal wall system follows a similar 
procedure 
to that used for the metal deck systems. That is, the model development 
entails case study 
selections, probabilistic finite element analyses, and evaluation of 
simulation results. Two 
case studies (both are one-story buildings) were selected for the metal wall 
system: one 
with a design pressure of 20 psf and the other with a design pressure of 40 
psf. The wall 
section for these two case studies is composed of two over-lapped corrugated 
USD C36 
panels with exposed fasteners (screw connections). Note that this kind of 
panel can be 
used for both wall systems and roof systems. Figure 6.56 shows the profile of 
the metal 
panel. Each panel is 3' wide by 10' tall with a supporting girt at 7' from 
the ground (see 
Figure 6.55). Each panel in case 1 has 3 size 10 screws (12. O.C.) at both 
ends and 3 size 
10 screws (12. O.C.) on the girt. Each panel in case 2 has 5 size-10 screws 
(7.2. O.C.) at 
both ends and 3 size 10 screws (12.O.C.) on the girt. The panel steel 
conforms to ASTM 
A653 Grade 33 sheet steel (Fy = 33 ksi and Fu = 50 ksi) for case 1 and Grade 
37 sheet 
steel (Fy = 37 ksi and Fu = 58 ksi) for case 2. The girt was assumed to be a 
gage 16 (0.06. 
thickness) cold-formed Z section with a 10. depth. The ultimate strength of 
the base 
metal was slightly less than that of the panel. The two panels for the metal 
wall section 
are joined together using 6 lap-joint screws spaced at 24. O.C. 

Finite Element Model. A major concern regarding the finite element analysis 
of the wall 
system lies in how to model the lap joints. When subjected to a pressure 
load, the two 
panels of the wall section tend to move away from each other, resulting in 
significant 
tension forces at the lap joints. Because of the restraints provided by the 
lap-joint screws, 
the upper and bottom panels around the screw will move together until yield 
stress is 
reached. To simulate this phenomenon, a number of methods were investigated, 
including 
slave nodes, rigid beams, and rigid shell elements. The final modeling of the 
lap joint was 
accomplished by placing a small steel member with the same properties as 
those of 

Chapter 6. Physical Damage Modeling 

?
Figure 6.55. Example of a Metal Wall System with Exposed Fasteners 
(Newman, 1997). 

Figure 6.56. Metal Deck Profile for Wall and Roof Systems. 

HAZUS-MH MR3 Technical Manual 

?
the panel connecting the two separate panels. Figure 6.57 shows the finite 
element mesh 
of the wall section. Note that there is a gap between the two wall panels 
(i.e., no 
connection between the wall panels except at the lap joints). Mesh grid 
refinement was 
made at each fastener and at each lap joint. Figure 6.58 shows the refined 
mesh around 
one fastener. Four nodes surrounding each fastener were fixed to model the 
boundary 
conditions (denoted as stars in Figure 6.58). 

In the FEM analysis, once an element reaches yield stress, its modulus of 
elasticity (E) is 
reduced a certain percentage to simulate the plastic behavior of the element. 
If during the 
following runs that element reaches yield stress again, another percentage of 
reduction is 
applied until the whole wall section fails. However, for the lap joint 
elements that reach 
yield stress, the modulus of elasticity is dropped to near zero (to model the 
weak link 
between the two panels). 

Figure 6.57. Finite Element Mesh of the Wall Panels. 

Chapter 6. Physical Damage Modeling 

?
Figure 6.58. Refined Mesh around the Fastener. 

As in the FEM analysis of the metal deck on steel joists, the panel-to-
support connection 
failure was modeled as a major failure mode. Both pull-over and pull-out 
strengths were 
considered and modeled as random variables with the resistance of the 
fastener taken as 
the smaller of the two sampled values. All the other properties, such as the 
steel strength 
and panel dimensions are assumed to be deterministic (due to their relatively 
small 
variation). Once again, a number of other parameters (such as workmanship, 
fatigue and 
corrosion) that may produce significant effects on the pressure resistance of 
the panel, 
were not explicitly considered. 

The wall section is considered to have failed once any of the following 
criteria are met: 

1. less than 3 fasteners remain on the panel (all the other fasteners have 
failed); 
or 
2. all the remaining fasteners align in a pattern such that the panel can 
rotate; or 
3. the maximum vertical deflection (z direction) of the panel is greater than 
1'; or 
4. the maximum horizontal deflection (x direction) of the panel is greater 
than 
0.5'. 
A more stringent restriction on the vertical deflection was applied to wall 
systems 
compared to the metal deck on steel joists. Horizontal deflection was added 
to the failure 
criteria for the consideration of internal pressurization if an opening 
develops between the 
wall panels. 

Simulation Results. Following the procedures described in Section 6.7.2 and 
shown with 
the flowchart in Figure 6.52, probabilistic finite element analyses were 
performed for the 
two example cases. Figure 6.59 shows one example of the stress distribution 
computed in 
the wall panels under a uniform suction of 200 psf. The stress concentrations 
around the 
fasteners are obvious. Moderate stresses are also seen around one or two lap 
joints. Under 
this high pressure, a number of elements have reached their yield stress and 
most of the 
screws have already failed. 

HAZUS-MH MR3 Technical Manual 

?
Figure 6.59. Stress Distribution of Wall Panels with Superimposed 
Deformation. 

A total of 50 simulations were performed for each case and the statistics of 
the wall panel 
failure pressures were determined from the simulation results. As expected, 
the load 
resistance capability of case 1 is smaller than that of case 2. The mean and 
the standard 
deviation of the computed failure pressure for case 1 are 84.0 psf and 7.5 
psf, 
respectively. The mean and the standard deviation of the computed failure 
pressure for 
case 2 are 124.2 psf and 16.5 psf, respectively. Figure 6.60 shows the CDFs 
of the failure 
pressures for these two case studies, which form the basis for the 
development of the 
damage model for metal wall systems. 

1.0 

0.9 

0.8 

0.7 
0.6 
0.5 
0.4 
0.3 
0.2 
0.1 
0.0 
0 20 40 60 80 100 120 140 160 180 200 
Case1 
Case2 
Failure Pressure (psf) 

Figure 6.60. CDFs of Wall System Failure Pressures. 

Chapter 6. Physical Damage Modeling 

?
6.8.2 Resistance Model for Metal Building Roof System 
The metal roofs investigated herein are similar to the metal decks on open-
web steel 
joists except that the metal roof is fastened to C or Z purlins rather than 
steel joists. The 
1996 Metal Building Manufacturers Association (MBMA) design manual was used 
to 
determine the design loads for two example buildings. The metal roof panel 
was assumed 
to be a USD C36 roof panel (USD, 1998) 3' wide and 10' long (covering 2 
spans). Refer 
to Figure 6.56 for its profile. The purlin was assumed to be a cold-formed Z 
section with 
a 10. depth, a gage of 14 (0.075. thickness), and spaced at 5' on the primary 
frame. As 
noted by Newman (1997), purlin spacing is controlled by the load-carrying 
capacity of 
the roof panels, with a 5' spacing being the most common. Table 6.35 shows 
the 
calculated design pressures for the two example buildings. The metal roof 
failure analysis 
followed the same procedure used to develop the model for decks on OWSJ, with 
the 
joist properties replaced by the purlin properties. The statistics of the 
metal roof failure 
pressures from 100 probabilistic finite element runs are given in Table 6.35. 
The CDFs of 
the failure pressures for the seven screw connection design cases (Table 
6.36) used in the 
two example buildings are shown in Figure 6.61. Note that the failure 
pressures for cases 
4 and 5 are almost identical, since the only difference between case 4 and 
case 5 is the 
metal panel gage. 

Table 6.35. Calculated Design Pressure Using MBMA and Simulated Failure 
Pressure Statistics from FEM Analysis 

Building Roof Wind Speed Design Pressure Screw Failure Pressure (psf) 
Code Height (ft) (mph) Zone (psf) Design1 Mean Std 
r -21.7 s1 88.5 9.7 
90 s -29.0 s1 88.5 9.7 
c -51.0 s2 104.4 11.6 
r -27.2 s1 88.5 9.7 
15 100 s -36.1 s1 88.5 9.7 
c -63.1 s3 119.8 14.3 
r -33.3 s1 88.5 9.7 
110 s -44.2 s1 88.5 9.7 
MBMA c -76.8 s4 151.4 16.1 
30 
90 
r -25.3 s1 88.5 9.7 
s -33.7 s1 88.5 9.7 
c -58.9 s3 119.8 14.3 
100 
r -31.0 s1 88.5 9.7 
s -41.2 s1 88.5 9.7 
c -71.7 s5 151.0 16.2 
110 
r -38.9 s1 88.5 9.7 
s -51.5 s6 109.1 13.6 
c -89.2 s7 176.9 18.6 

 See Table 6.36 

HAZUS-MH MR3 Technical Manual 

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6-102 
Chapter 6. Physical Damage Modeling 
Table 6.36. Connection Layout for Example Metal Roofs 
Roof Panel Girt 
Design 
Case 
Screw 
Size Layout Installation Thickness Fy (ksi) Fu (ksi) 
Thickness 
(in.) Fy (ksi) Fu (ksi) 
s1 #10 36/3 Valley-Fixed 0.024 (gage 24) 40 55 0.075 33 45 
s2 #10 36/3 Valley-Fixed 0.024 (gage 24) 50 60 0.075 40 55 
s3 #12 36/3 Valley-Fixed 0.030 (gage 22) 50 60 0.075 40 55 
s4 #10 36/5 Valley-Fixed 0.036 (gage 20) 40 55 0.075 33 45 
s5 #10 36/5 Valley-Fixed 0.030 (gage 22) 40 55 0.075 33 45 
s6 #10 36/3 Valley-Fixed 0.030 (gage 22) 50 60 0.075 40 55 
s7 #10 36/5 Valley-Fixed 0.036 (gage 20) 50 60 0.075 40 55 
0.0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1.0 
0 50 100 150 200 250 300 
Failure Pressure (psf) 
Case1 
Case2 
Case3 
Case4 
Case5 
Case6 
Case7 
Figure 6.61. CDFs of Metal Roof Failure Pressures. 
6.9 Damage Model Results for Marginally- or Non-Engineered Hotel/Motel and 
Multi-Family Residential Buildings 
Nine different geometric representations of marginally-engineered or non-
engineered 
hotels/motels and multi-family residential buildings were developed, as shown 
pictorially 
in Figure 6.62. Using the wind loading models and the resistance models 
described 
earlier, damage states for each building as a function of wind speed have 
been estimated. 
All buildings with sloped roofs have been modeled as having asphalt shingle 
roofs, and 
the flat roof buildings are modeled as having either a built-up roof (BUR) or 
a single ply 
EPDM roof. The BUR and EPDM roofs are each modeled as being of  poor  quality 
and 
 average  quality. All windows are treated as being comprised of single pane 
annealed 
glass, and all sliding glass doors are modeled with tempered glass. In the 
case of gable 
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6-103 
HAZUS-MH MR3 Technical Manual 
Flat Roof Gable Roof Hip Roof 
One-Story Buildings   80'W40' Plan, 10' Eave Height 
Flat Roof Gable Roof Hip Roof 
Two-Story Buildings   80'W40' Plan, 20' Eave Height 
Flat Roof Gable Roof Hip Roof 
Three-Story Buildings   80'W40' Plan, 30' Eave Height 
Flat Roof Gable Roof Hip Roof 
Four-Story Buildings   80'W40' Plan, 40' Eave Height 
Figure 6.62. Model Building Geometries Used in Building Simulation Study. 
and flat roof structures, the connections between the roof and the wall are 
assumed to 
exist at the base of every truss with no connections along the short end 
wall. In the case 
of the hip roof structures, connections between the roof and the wall are 
assumed to exist 
along the entire perimeter of the wall. Whole roof failure is modeled in such 
a way that it 
fails as an entire unit. Each building has eight independent occupancy units 
on each floor 
(four at the front and four at the back). If a window or door breach occurs 
in any unit, the 
internal pressures are constrained to that unit. Each wall is modeled as 
having a 
horizontal span of 20' and a vertical span of 8', and is unsupported within 
the spans. 
?
The parameters varied in the buildings are as follows: 

Roof Shape: Hip, Gable and Flat 
Roof Cover: Asphalt Shingles (Hip and Gable Roofs) and BUR/EPDM (Flat 
Roofs, Examined for  Average  and  Poor  Quality) 
Wall Construction: Wood Frame (All Buildings) and Unreinforced/ Reinforced 

Masonry (One, Two and Three Story Buildings) 
Roof Deck Attach.: 6d and 8d Nails 
Roof/Wall Conn.: Strapped and Toe-Nailed 
Number of Stories: One, Two, Three and Four

The assumed component resistances used in the simulations are given in Table 
6.37. 
Damage simulations for each basic building type were performed for terrains 
described 
with z0 = 0.03 m (open terrain), z0 = 0.35 m (typical suburban terrain), z0 = 
0.7 m 
(suburban terrain with some trees), and z0 = 1.0 m (treed suburban terrain). 
All buildings 
are assumed to be exposed to a residential missile environment. 

Table 6.37. Component Resistance Values Used to Model Marginally-Engineered 
or 
Non-Engineered Hotel/Motel and Multi-Family Residential Buildings 

Component Distribution Distribution Parameters 
Sheathing Panel (6d) 
Sheathing Panel (8d) 
Annealed Glass Impact 
Tempered Glass Impact 
Window/SG Door Pressure 
Entry Door Pressure 
Strap Up-Lift Resistance 
Toe-Nail Uplift Resistance 
LogNormal 
LogNormal 
Deterministic 
Deterministic 
Normal 
Normal 
Normal 
Normal 
Mean = 54.6 psf, COV = 0.11 
Mean = 103 psf, COV = 0.11 
50 lb-ft 
100 lb-ft 
Mean = 40 psf, COV = 0.2 
Mean = 50 psf, COV = 0.2 
Mean =1200 lb, COV = 0.30 
Mean = 415 lb, COV = 0.25 

The damage state definitions for marginally-engineered or non-engineered 
hotels/motels 
and multi-family residential buildings are the same as those for the single 
family 
residential buildings, which have been outlined in Table 6.9. Except, to make 
the damage 
state definitions with respect to roof damage (i.e., roof cover and roof 
deck) consistent 
between buildings with different numbers of stories, the threshold damage 
levels defining 
the roof damage states are modified by a factor which is a function of the 
number of 
stories. The concept is that 5% roof deck damage, for example, will have a 
greater effect 
in terms of economic losses on a one-story building compared to a two-story 
building. 
That is, in the case of the two-story building, only the top floor will 
experience similar 
damage due to the water infiltration associated with 5% roof deck loss to 
that experienced 
by the single-story building. The assumption made here for the purpose of the 
roof 
damage state definitions is that the intensity of damage on the bottom floor 
of the two- to 
the interior of the single-story building is arbitrarily set to 1, then the 
damage intensity to 
the two-story building will be (1+0.5)/2 = 0.75 (i.e., 1 represents the 
damage intensity 
story building will be half as much as that on the top floor. Thus, if the 
damage intensity 
of the top floor and 0.5 represents the damage intensity of the bottom 
floor). Therefore, to 
achieve a similar damage intensity to that associated with 5% roof deck 
damage on a 
single-story building, an otherwise similar two-story building would require 
6.7% roof 
deck damage (i.e., 5%/0.75). The thresholds used to define the roof damage 
states listed 

Chapter 6. Physical Damage Modeling 

?
in Table 6.9 were modified in the same manner as described above for the two-
, three- 
and four-story buildings. The multiplicative factors are 1.33, 1.71 and 2.13, 
respectively. 
It can be seen, for example, that the four-story building cannot achieve a 
building damage 
state 3 (i.e., severe damage) based on roof cover loss since the 50% 
threshold increases to 
more than 100% (i.e., 2.13*50% > 100%). Also note that the two-and three-
story 
buildings will be placed in damage state 3 when the roof cover loss exceeds 
67% and 
86%, respectively. 

Damage state versus wind speed plots have been developed using the same 
approach 
used for the single family residential buildings. Example building and 
component damage 
state plots are given in Appendix C. The average per storm damage states are 
given in 
Tables 6.38 through 6.40 and the impacts of the various building features on 
the average 
per storm damage are summarized in Table 6.41. 

The results summarized in Table 6.41 indicate that the two parameters having 
the greatest 
impact on the average damage state for these buildings are the number of 
stories and, in 
the case of a flat roof, the change from modeling the roof cover as  average  
quality 
versus  poor  quality. In the case of the poor quality roof, the change from 
a built-up roof 
cover to a single ply membrane has a significant impact on the building 
damage state. 
Recall, however, that both the poor quality roof cover model and the average 
quality roof 
cover model used in modeling both the BUR and the EPDM have been derived 
empirically using limited information obtained from post-storm damage data. 
The 
performance of either a built-up roof or a single ply roof in high wind 
events is subject to 
significant uncertainty, and is heavily influenced by the quality of 
installation. In the case 
of a poor quality flat roof cover, the results given here indicate that the 
performance of 
the single ply membrane roof cover model governs the final damage state of 
the building. 

The change from gable to flat (flat with a built up roof) has an effect that 
is dependent on 
the number of stories and the assumed quality of the flat roof cover. A 
direct comparison 
of the performance of the flat roof building to that of the gable or hip 
buildings is made 
difficult because of the different roof covers; however, in general (for 
buildings other 
than single story) the flat roof buildings tend to perform worse than the 
gable or hip roof 
buildings. 

6.10 Damage Model Results for Low-Rise Masonry Strip Mall Buildings 
Small office and retail buildings are often constructed with units separated 
by fire walls, 
each having separate roof structures but a common roof cover. These low-rise 
buildings 
typically have flat roofs and are one story high. They are usually built with 
a masonry or 
concrete wall system. The roof structures are typically constructed using 
wood trusses 
and a plywood deck, open web steel joists and a metal deck, or a cast-in-
place concrete 
deck. Because the units are separated by firewalls and have separate roof 
systems, 
window breaches in one unit do not impact either the internal pressure or the 
water 
infiltration of the adjacent unit(s). This building class is treated here as 
a separate 
building class from those constructed as a single unit, but of comparable 
size and usage. 
The damage state definitions for low-rise masonry strip mall buildings type 
are described 
in Table 6.42. 

HAZUS-MH MR3 Technical Manual 

?
Table 6.38. Average Building Damage States   Marginally- or Non-Engineered 
Hotel/Motel and Multi-Family Residential Buildings   Gable/Hip Roof with 
Shingles. 

Building Characteristics 
Strapped Roof-Wall Connections 
Terrain Surface Roughness (m) 
Toe-Nailed Roof-Wall Connections 
Terrain Surface Roughness (m) 
No. of 
Stories 
Wall 
Const. 
Roof 
Shape 
Roof 
Deck 
Nails 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
One 
Wood 
Frame 
Gable 6d 0.565 
0.494 
0.455 
0.428 
0.356 
0.316 
0.284 
0.271 
0.284 
0.244 
0.217 
0.206 
0.250 
0.216 
0.187 
0.178 
0.593 
0.542 
0.476 
0.459 
0.375 
0.349 
0.301 
0.291 
0.298 
0.272 
0.228 
0.221 
0.260 
0.237 
0.197 
0.190 
8d 
Hip 
6d 
8d 
Unreinf. 
Masonry 
Gable 
6d 0.566 
0.488 
0.453 
0.417 
0.358 
0.314 
0.282 
0.267 
0.282 
0.245 
0.217 
0.206 
0.248 
0.216 
0.186 
0.178 
0.594 
0.542 
0.472 
0.456 
0.376 
0.350 
0.300 
0.292 
0.298 
0.273 
0.228 
0.222 
0.260 
0.236 
0.194 
0.189 
8d 
Hip 
6d 
8d 
Reinf. 
Masonry 
Gable 
6d 0.563 
0.488 
0.453 
0.416 
0.357 
0.314 
0.282 
0.267 
0.282 
0.245 
0.218 
0.206 
0.248 
0.216 
0.186 
0.178 
0.592 
0.541 
0.473 
0.456 
0.376 
0.348 
0.298 
0.291 
0.297 
0.270 
0.229 
0.219 
0.260 
0.235 
0.196 
0.189 
8d 
Hip 
6d 
8d 
Two 
Wood 
Frame 
Gable 6d 0.755 
0.674 
0.658 
0.627 
0.522 
0.459 
0.447 
0.422 
0.436 
0.376 
0.366 
0.345 
0.393 
0.337 
0.326 
0.306 
0.788 
0.718 
0.682 
0.662 
0.548 
0.497 
0.471 
0.456 
0.454 
0.407 
0.385 
0.373 
0.410 
0.365 
0.343 
0.330 
8d 
Hip 
6d 
8d 
Unreinf. 
Masonry 
Gable 
6d 0.743 
0.656 
0.641 
0.606 
0.515 
0.450 
0.441 
0.414 
0.431 
0.371 
0.362 
0.336 
0.389 
0.331 
0.323 
0.300 
0.780 
0.711 
0.677 
0.658 
0.545 
0.498 
0.468 
0.458 
0.453 
0.408 
0.381 
0.373 
0.409 
0.366 
0.342 
0.328 
8d 
Hip 
6d 
8d 
Reinf. 
Masonry 
Gable 
6d 0.741 
0.657 
0.643 
0.606 
0.515 
0.452 
0.440 
0.413 
0.429 
0.368 
0.362 
0.336 
0.390 
0.330 
0.323 
0.300 
0.781 
0.713 
0.678 
0.658 
0.544 
0.496 
0.469 
0.458 
0.454 
0.409 
0.385 
0.370 
0.409 
0.366 
0.343 
0.328 
8d 
Hip 
6d 
8d 
Three 
Wood 
Frame 
Gable 6d 0.876 
0.783 
0.810 
0.772 
0.630 
0.553 
0.572 
0.545 
0.543 
0.462 
0.487 
0.460 
0.497 
0.421 
0.442 
0.417 
0.904 
0.831 
0.828 
0.804 
0.655 
0.593 
0.595 
0.575 
0.564 
0.500 
0.503 
0.486 
0.517 
0.455 
0.456 
0.441 
8d 
Hip 
6d 
8d 
Unreinf. 
Masonry 
Gable 
6d 0.847 
0.748 
0.779 
0.728 
0.617 
0.533 
0.558 
0.519 
0.530 
0.449 
0.475 
0.439 
0.487 
0.406 
0.433 
0.401 
0.894 
0.813 
0.817 
0.788 
0.653 
0.587 
0.586 
0.571 
0.560 
0.497 
0.498 
0.479 
0.516 
0.451 
0.456 
0.440 
8d 
Hip 
6d 
8d 
Reinf. 
Masonry 
Gable 
6d 0.848 
0.749 
0.778 
0.729 
0.616 
0.533 
0.558 
0.517 
0.531 
0.447 
0.473 
0.439 
0.488 
0.406 
0.433 
0.398 
0.895 
0.814 
0.819 
0.789 
0.651 
0.587 
0.589 
0.568 
0.558 
0.497 
0.501 
0.481 
0.515 
0.450 
0.456 
0.436 
8d 
Hip 
6d 
8d 
Four Wood 
Frame 
Gable 6d 0.980 
0.884 
0.881 
0.853 
0.727 
0.636 
0.639 
0.609 
0.636 
0.543 
0.549 
0.518 
0.590 
0.498 
0.506 
0.472 
1.005 
0.927 
0.910 
0.881 
0.754 
0.675 
0.657 
0.636 
0.658 
0.581 
0.567 
0.546 
0.611 
0.535 
0.520 
0.502 
8d 
Hip 
6d 
8d 

Chapter 6. Physical Damage Modeling 

?
Table 6.39. Average Building Damage States   Marginally- or Non-Engineered 
Hotel/Motel and Multi-Family Residential Buildings   Flat Roofs with BUR. 

Building Characteristics 
Strapped Roof-Wall Connections 
Terrain Surface Roughness (m) 
Toe-Nailed Roof-Wall Connections 
Terrain Surface Roughness (m) 
No. of 
Stories 
Wall 
Const. 
Roof 
Quality 
Roof 
Deck 
Nails 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
One 
Wood 
Frame 
Average 6d 0.387 
0.342 
0.596 
0.580 
0.242 
0.221 
0.371 
0.362 
0.176 
0.164 
0.290 
0.288 
0.150 
0.137 
0.257 
0.255 
0.397 
0.364 
0.607 
0.602 
0.248 
0.238 
0.377 
0.372 
0.181 
0.169 
0.294 
0.292 
0.153 
0.144 
0.263 
0.261 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.383 
0.338 
0.594 
0.575 
0.240 
0.221 
0.366 
0.360 
0.178 
0.163 
0.290 
0.286 
0.148 
0.137 
0.258 
0.254 
0.393 
0.365 
0.606 
0.602 
0.249 
0.237 
0.377 
0.373 
0.182 
0.171 
0.299 
0.292 
0.155 
0.141 
0.265 
0.258 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.384 
0.336 
0.595 
0.574 
0.240 
0.223 
0.368 
0.360 
0.178 
0.163 
0.290 
0.286 
0.148 
0.137 
0.258 
0.254 
0.396 
0.362 
0.604 
0.601 
0.249 
0.237 
0.377 
0.370 
0.182 
0.171 
0.296 
0.292 
0.155 
0.143 
0.265 
0.258 
8d 
Poor 
6d 
8d 
Two 
Wood 
Frame 
Average 6d 0.654 
0.616 
0.982 
0.976 
0.444 
0.418 
0.698 
0.691 
0.359 
0.334 
0.594 
0.591 
0.315 
0.296 
0.544 
0.539 
0.675 
0.650 
1.008 
1.005 
0.463 
0.451 
0.723 
0.717 
0.376 
0.360 
0.616 
0.612 
0.335 
0.319 
0.561 
0.559 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.642 
0.601 
0.972 
0.962 
0.435 
0.409 
0.691 
0.684 
0.353 
0.330 
0.589 
0.584 
0.312 
0.292 
0.539 
0.535 
0.670 
0.640 
1.004 
1.001 
0.464 
0.446 
0.718 
0.716 
0.373 
0.360 
0.613 
0.612 
0.334 
0.320 
0.560 
0.558 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.644 
0.600 
0.974 
0.965 
0.437 
0.410 
0.691 
0.684 
0.352 
0.332 
0.588 
0.583 
0.315 
0.293 
0.541 
0.532 
0.668 
0.642 
1.005 
0.999 
0.463 
0.446 
0.719 
0.715 
0.376 
0.361 
0.610 
0.611 
0.332 
0.318 
0.560 
0.557 
8d 
Poor 
6d 
8d 
Three 
Wood 
Frame 
Average 6d 0.874 
0.841 
1.311 
1.305 
0.631 
0.603 
1.000 
0.994 
0.539 
0.514 
0.886 
0.884 
0.493 
0.472 
0.827 
0.828 
0.909 
0.881 
1.343 
1.341 
0.661 
0.642 
1.032 
1.032 
0.562 
0.546 
0.913 
0.911 
0.516 
0.496 
0.853 
0.850 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.852 
0.816 
1.288 
1.282 
0.619 
0.592 
0.991 
0.985 
0.532 
0.506 
0.876 
0.877 
0.484 
0.460 
0.819 
0.816 
0.898 
0.872 
1.337 
1.333 
0.659 
0.636 
1.025 
1.025 
0.562 
0.542 
0.910 
0.912 
0.514 
0.497 
0.852 
0.850 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.851 
0.814 
1.289 
1.282 
0.620 
0.593 
0.988 
0.983 
0.530 
0.502 
0.883 
0.876 
0.478 
0.461 
0.819 
0.822 
0.898 
0.871 
1.333 
1.333 
0.656 
0.637 
1.025 
1.029 
0.563 
0.540 
0.911 
0.911 
0.513 
0.496 
0.850 
0.852 
8d 
Poor 
6d 
8d 
Four Wood 
Frame 
Average 6d 0.955 
0.897 
1.291 
1.255 
0.706 
0.650 
1.003 
0.974 
0.609 
0.561 
0.900 
0.864 
0.563 
0.513 
0.847 
0.809 
1.001 
0.968 
1.334 
1.326 
0.745 
0.719 
1.048 
1.038 
0.654 
0.625 
0.939 
0.933 
0.602 
0.579 
0.884 
0.874 
8d 
Poor 
6d 
8d 

HAZUS-MH MR3 Technical Manual 

?
Table 6.40. Average Building Damage States   Marginally- or Non-Engineered 
Hotel/Motel and Multi-Family Residential Buildings   Flat Roofs with EPDM. 

Building Characteristics 
Strapped Roof-Wall Connections 
Terrain Surface Roughness (m) 
Toe-Nailed Roof-Wall Connections 
Terrain Surface Roughness (m) 
No. of 
Stories 
Wall 
Const. 
Roof 
Quality 
Roof 
Deck 
Nails 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
One 
Wood 
Frame 
Average 6d 0.430 
0.405 
0.968 
0.962 
0.264 
0.252 
0.623 
0.623 
0.198 
0.188 
0.515 
0.511 
0.170 
0.164 
0.471 
0.465 
0.442 
0.425 
0.984 
0.983 
0.273 
0.266 
0.632 
0.631 
0.201 
0.198 
0.518 
0.519 
0.174 
0.168 
0.475 
0.472 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.424 
0.395 
0.974 
0.952 
0.264 
0.253 
0.625 
0.616 
0.199 
0.189 
0.517 
0.516 
0.169 
0.163 
0.468 
0.468 
0.437 
0.421 
0.982 
0.980 
0.270 
0.265 
0.630 
0.632 
0.203 
0.196 
0.519 
0.519 
0.174 
0.170 
0.476 
0.472 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.427 
0.395 
0.971 
0.954 
0.265 
0.251 
0.625 
0.616 
0.199 
0.189 
0.517 
0.516 
0.169 
0.163 
0.468 
0.468 
0.437 
0.420 
0.979 
0.979 
0.270 
0.263 
0.630 
0.633 
0.203 
0.196 
0.520 
0.519 
0.174 
0.170 
0.476 
0.472 
8d 
Poor 
6d 
8d 
Two 
Wood 
Frame 
Average 6d 0.732 
0.714 
1.419 
1.421 
0.500 
0.489 
1.054 
1.049 
0.412 
0.403 
0.922 
0.923 
0.370 
0.363 
0.853 
0.854 
0.757 
0.747 
1.443 
1.438 
0.525 
0.519 
1.080 
1.078 
0.433 
0.427 
0.940 
0.940 
0.383 
0.384 
0.878 
0.874 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.722 
0.703 
1.419 
1.409 
0.498 
0.482 
1.049 
1.049 
0.408 
0.402 
0.922 
0.919 
0.366 
0.359 
0.853 
0.851 
0.752 
0.745 
1.444 
1.440 
0.526 
0.515 
1.080 
1.078 
0.433 
0.426 
0.942 
0.941 
0.387 
0.380 
0.873 
0.871 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.727 
0.702 
1.411 
1.409 
0.497 
0.483 
1.052 
1.055 
0.409 
0.402 
0.922 
0.915 
0.366 
0.360 
0.855 
0.849 
0.754 
0.742 
1.441 
1.443 
0.527 
0.517 
1.076 
1.073 
0.431 
0.425 
0.941 
0.943 
0.387 
0.382 
0.867 
0.874 
8d 
Poor 
6d 
8d 
Three 
Wood 
Frame 
Average 6d 1.024 
1.011 
1.767 
1.769 
0.753 
0.749 
1.418 
1.417 
0.654 
0.649 
1.282 
1.281 
0.602 
0.593 
1.211 
1.204 
1.051 
1.043 
1.805 
1.801 
0.782 
0.775 
1.446 
1.445 
0.675 
0.673 
1.312 
1.307 
0.627 
0.621 
1.233 
1.232 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 1.000 
0.985 
1.754 
1.746 
0.743 
0.734 
1.406 
1.405 
0.646 
0.635 
1.270 
1.272 
0.594 
0.585 
1.202 
1.199 
1.046 
1.044 
1.795 
1.797 
0.779 
0.771 
1.439 
1.445 
0.676 
0.672 
1.305 
1.306 
0.623 
0.622 
1.231 
1.231 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.997 
0.989 
1.748 
1.745 
0.741 
0.730 
1.405 
1.408 
0.647 
0.638 
1.272 
1.269 
0.592 
0.584 
1.200 
1.196 
1.045 
1.035 
1.795 
1.798 
0.780 
0.776 
1.446 
1.444 
0.665 
0.672 
1.309 
1.309 
0.613 
0.624 
1.233 
1.235 
8d 
Poor 
6d 
8d 
Four Wood 
Frame 
Average 6d 1.046 
1.005 
1.591 
1.543 
0.782 
0.747 
1.294 
1.260 
0.685 
0.650 
1.181 
1.149 
0.638 
0.603 
1.119 
1.088 
1.089 
1.076 
1.631 
1.620 
0.824 
0.814 
1.335 
1.328 
0.727 
0.716 
1.216 
1.213 
0.675 
0.664 
1.154 
1.148 
8d 
Poor 
6d 
8d 

Chapter 6. Physical Damage Modeling 

?
Table 6.41. Percent Increases in the Per Storm Average Building Damage State 
Due 
to Changes in Building Parameters (Minimum/Average/Maximum) - Marginally-
or Non-Engineered Hotel/Motel and Multi-Family Residential Buildings. 

Gable and Hip Roofs With Singles 
Parameter 
Number of Stories 
All One Two Three Four 
Unreinforced Masonry 
to Wood Frame Walls -1% / 1% / 6% -1% / 0% / 3% 0% / 1% / 3% 0% / 2% / 6% 
N/AReinforced Masonry to 
Unreinforced Masonry 
Walls 
-1% / 0% / 1% -1% / 0% / 1% -1% / 0% / 1% -1% / 0% / 1% 
Hip to Gable Roof 
Shape 0% / 14% / 34% 16% / 24% / 34% 7% / 13% / 21% 0% / 7% / 13% 4% / 10% / 
17% 
8d to 6d Roof Deck 
Nails 2% / 9% / 20% 2% / 8% / 16% 2% / 9% / 18% 3% / 10% / 20% 3% / 9% / 19% 
Strapped to Toe-Nailed 
Roof/Wall Connections 2% / 7% / 11% 4% / 7% / 11% 4% / 7% / 11% 2% / 7% / 11% 
3% / 4% / 7% 
One to Two Stories 31% / 53% / 76% 
N/ATwo to Three Stories 14% / 24% / 36% 
Three to Four Stories 9% / 14% / 19% 
Flat Roofs With BUR 
Parameter 
Number of Stories 
All One Two Three Four 
Unreinforced Masonry 
to Wood Frame Walls -1% / 1% / 3% -1% / 0% / 2% 0% / 1% / 3% 0% / 1% / 3% 
N/AReinforced Masonry to 
Unreinforced Masonry 
Walls 
-1% / 0% / 1% -1% / 0% / 1% -1% / 0% / 1% -1% / 0% / 1% 
Average to Poor Quality 
Cover 33% / 62% / 86% 51% / 66% / 86% 49% / 64% / 83% 48% / 63% / 78% 33% / 
45% / 58% 
8d to 6d Roof Deck 
Nails 0% / 3% / 14% 1% / 5% / 14% 0% / 3% / 7% 0% / 2% / 6% 1% / 4% / 10% 
Strapped to Toe-Nailed 
Roof/Wall Connections 1% / 5% / 13% 1% / 3% / 8% 3% / 5% / 9% 2% / 5% / 8% 3% 
/ 7% / 13% 
One to Two Stories 64% / 94% / 127% 
N/ATwo to Three Stories 32% / 45% / 60% 
Three to Four Stories -4% / 6% / 17% 
Flat Roofs With EPDM 
Parameter 
Number of Stories 
All One Two Three Four 
Unreinforced Masonry 
to Wood Frame Walls -1% / 0% / 3% -1% / 0% / 3% -1% / 0% / 2% 0% / 1% / 3% 
N/AReinforced Masonry to 
Unreinforced Masonry 
Walls 
-1% / 0% / 2% -1% / 0% / 1% -1% / 0% / 1% -1% / 0% / 2% 
Average to Poor Quality 
Cover 50% / 114% / 188% 123% / 153% / 188% 91% / 115% / 137% 72% / 90% / 105% 
50% / 66% / 80% 
8d to 6d Roof Deck 
Nails -2% / 1% / 8% -1% / 2% / 8% -1% / 1% / 4% -2% / 0% / 2% 0% / 2% / 6% 
Strapped to Toe-Nailed 
Roof/Wall Connections 0% / 4% / 10% 0% / 3% / 7% 1% / 4% / 7% 2% / 4% / 7% 2% 
/ 6% / 10% 
One to Two Stories 45% / 85% / 128% 
N/ATwo to Three Stories 24% / 43% / 64% 
Three to Four Stories -13% / -2% / 8% 

HAZUS-MH MR3 Technical Manual 

?
Table 6.42. Damage States for Low-Rise Masonry Strip Mall Construction 

Damage 
State Qualitative Damage Description 
Roof 
Cover 
Failure 
Window 
and 
Door 
Failures 
Roof 
Deck 
Failure 
Whole 
Roof 
Failure 
Joist 
Failure 
Wall 
Failure 
6 Units 
1 Unit 
6 Units 
1 Unit 
0 
No Damage or Very Minor Damage 
Little or no visible damage from the 
outside. No broken windows, or failed 
roof deck. Minimal loss of roof cover, 
with no or very limited water 
penetration. 
=2% No No 
No 
No 
No 
No 
No 
1 
Minor Damage 
Maximum of one broken window or 
door. Moderate roof cover loss that 
can be covered to prevent additional 
water entering the building. Marks or 
dents on walls requiring painting or 
patching for repair. 
>2% 
to=15% 
1 
window 
or door 
failure 
No 
No 
No 
No 
No 
No 
2 
Moderate Damage 
Major roof cover damage, moderate 
window breakage. Minor roof 
sheathing failure. Some resulting 
damage to interior of building from 
water. 
>15%to 
=50% 
2 to= the 
greater 
of 20% 
and 3 
1 to 3 
panels 
No 
No 
No 
No 
No 
3 
Severe Damage 
Major window damage or roof 
sheathing loss. Major roof cover loss. 
Extensive damage to interior from 
water. Limited, local joist failures. 
Failure of one wall. 
>50% 
> the 
greater 
of 20% 
and 3 to 
=50% 
>3 
to=25% 
1 unit 
No 
1 Unit to 
1/3 of the 
units with 
joist 
failures 
>0% to 
=25% joist 
failures 
1 wall 
4 
Destruction 
Complete roof failure on 1/3 or more 
of the units and/or failure of more 
than one wall. Loss of more than 25% 
of roof sheathing. 
Typically 
>50% >50% >25% 
2 or more 
units 
>1/3 of the 
units with 
joist 
failures 
2 or 
more 
walls 
Yes >25% joist 
failures 

The representative low-rise building used for small offices and retail is 
modeled here 
with masonry walls (either reinforced or unreinforced) and with either a 
light weight 
open web steel joist (OWSJ) roof system or a wood roof system. The model 
buildings 
have a length of 184', a width of 40' and a roof height of either 12' or 20'. 
The overall 
building geometry, along with the placement and relative size of the windows 
and doors 
is shown in Figure 6.63 for the building with a 12' roof height. The building 
with the 20' 
roof height is otherwise the same as that shown in Figure 6.63. The different 
building 
configurations (with respect to building height, number of units and roof 
system) 
modeled in this section are shown in Table 6.43. 

Chapter 6. Physical Damage Modeling 

?
Figure 6.63. Model Building for One Story Low-Rise Masonry Strip Mall 
Building 
with Roof Height = 12'. 

Table 6.43. Description of Low-Rise Masonry Strip Mall Buildings 

Building 
Designation 
Roof 
Height (ft) 
No. of 
Units Roof System(s) 
A 12 6 
Steel Deck on Steel Roof JoistsJoist Spacing = 4' Joist Span = 30' (5 
Units) and 34' (1 Unit)Joists Parallel to Long Building Edge 
Wood Deck on Wood Roof Trusses 
B 20 6 
Steel Deck on Steel Roof Joists Joist Spacing = 4'Joist Span = 30' (5 
Units) and 34' (1 Unit)Joists Parallel to Long Building Edge 
Wood Deck on Wood Roof Trusses 
C 20 6 Steel Deck on Steel Roof JoistsJoist Spacing = 6'Joist Span = 30' (5 
Units) and 34' (1 Unit)Joists Parallel to Long Building Edge 
D 20 1 Steel Deck on Steel Roof JoistsJoist Spacing = 6'Joist Span = 
40'Joists 
Parallel to Short Building Edge 

The multi-unit buildings have five units with a width of 30' and one end-unit 
with a width 
of 34'. Each of these units has three 6'W4' tempered glass windows and one 
tempered 
double glass entry door. The rear entry doors are modeled as standard 
residential type 
metal doors. The entire building has a total of 30 fenestrations, 24 of which 
have glazing 
and therefore can be broken by missile impact. The roof cover is modeled as 
BUR or as 
EPDM single ply membrane ( average  quality in both cases). 

For the buildings with wood roof structures, the wood roof trusses are spaced 
at 2' on 
center and the roof-wall connections are modeled using either toe-nailed 
connections or 
strapped connections. The plywood roof sheathing is modeled as being attached 
to the 
roof trusses using either 6d or 8d nails with a standard 6./12. spacing. 

The OWSJs are designed to resist the uplift forces and moments computed using 
both 
ASCE-7-88 and the 1988 Version of SBC, as described in Section 6.7.2, for a 
design 
wind speed of 100 mph. In the case of the ASCE-7 loads, the building is 
assumed to be 
designed for open terrain (Exposure C) conditions. The metal deck is modeled 
as being 
connected to the joists using either screws or welds. The weld and screw 
designs are 

HAZUS-MH MR3 Technical Manual 

?
assumed to meet the loading requirements as given in ASCE-7 and SBC and are 
designed 
using the methods outlined in Section 6.7.3. 

The assumed component resistances are given in Table 6.44. The very high 
uplift 
capacities associated with the steel deck are brought about by the minimum 
requirements 
for connection fastening, and the short (4') distance between joists. These 
uplift values 
are for the as-installed roof, but a sensitivity study was performed where 
the uplift 
capacities of the screwed and welded connections were reduced by 50% to allow 
for the 
effect of age and fatigue. This 50% reduction is comparable to the reduction 
in the 
pullout capacity of fasteners in metal decks as reported by Baskaran and Dutt 
(1995). 

Four different missile environments are considered in the analysis. The 
missile 
environments include combinations of residential-type missiles (i.e., roof 
shingles, roof 
tiles and roof sheathing) and commercial-type missiles (i.e., roof gravel). 
Details on the 
windborne debris models are given in Chapter 5. In the case of the high-
density 
commercial missile environment, the buildings from which the roof gravel 
originate are 
modeled as having a mean roof height of about 15' and mean building spacing 
of about 
200'. The different missile environments are examined to determine the impact 
of the 
missile modeling on the building damage states. Table 6.45 gives descriptions 
of the four 
missile environments considered in the analysis. 

Example building and component damage state plots are given in Appendix D. 
The storm 
average damage states are given in Tables 6.46 and 6.47 for buildings with a 
wood roof 
system and Tables 6.48 through 6.50 for buildings with a steel roof system. 

The effects of the various building parameters on the storm-average damage 
states for the 
buildings with a wood roof system are summarized in Table 6.52. It is evident 
that the 
missile environment has a large impact on the overall building damage state, 
increasing 
the mean per storm damage by 23% to 106% for the 12' high building and by 9% 
to 46% 
for the 20' high building when the missile environment changes from no 
windborne 
debris (Missile Environment D) to a mixed residential/commercial type 
environment 
(Missile Environment A). The effect of using toe-nails for the roof wall 
connections 
(versus straps) is a 4% to 29% increase in damage. Using 6d roof sheathing 
nails in place 
of 8d nails increases the storm average damage state by up to 15%. The 
relatively small 
effect of roof nails in these examples is associated with the fact that other 
severe damage 
states, which drive the overall building damage state, have already occurred 
before a 
significant amount of roof sheathing is lost. The effect of EPDM roof cover 
versus BUR 
roof cover is a 4% to 32% increase in the average damage state. This increase 
in damage 
is attributed to the fact that the modeling of the EPDM roof allows the 
entire roof 
membrane to fail more readily than the BUR roof, once the initial local 
failure has 
occurred. 

The building parameter sensitivity results for buildings with steel roof 
systems are shown 
in Table 6.53. The results show that when the uplift capacity of the steel 
roof deck is 
reduced by 50% to account for age and fatigue, the storm average damage state 
increases 
by as much as 31% when the joist spacing is 6' (Buildings C and D) and has a 
negligible 

Chapter 6. Physical Damage Modeling 

?
Table 6.44. Component Resistance Values Used for Strip-Mall Type Building 

Fenestrations 
Component Distribution Distribution Parameters 
Tempered Glass Small Missile Impact 
Tempered Glass Large Missile Impact 
Window/SG Door System Pressure 
Entry Door System Pressure 
Tempered Glass Pressure (Windows) 
Tempered Glass Pressure (SG Doors) 
Deterministic 
Deterministic 
Normal 
Normal 
Weibull 
Weibull 
0.01 slug-ft/sec (Momentum) 
100 lb-ft (Energy) 
Mean = 40 psf, COV = 0.2 
Mean = 50 psf, COV = 0.2 
C = 168 psf, k = 4.8 
C = 175 psf, k = 6.1 
Wood Deck on Wooden Roof Trusses 
Component Distribution Distribution Parameters 
Sheathing Panel (6d) 
Sheathing Panel (8d) 
Strap Uplift Resistance 
Toe-Nail Uplift Resistance 
LogNormal 
LogNormal 
Normal 
Normal 
Mean = 54.6 psf, COV = 0.11 
Mean = 103 psf, COV = 0.11 
Mean = 1200 lb, COV = 0.3 
Mean = 415 lb, COV = 0.25 
Steel Deck on Steel Roof Joists   Building Height = 12', Joist Spacing = 4' 
Component Distribution 
Distribution Parameters 
(Field, Edge, Corner) 
Screwed Steel Deck   SBCCI 100mph 
Screwed Steel Deck   ASCE 100mph 
Welded Steel Deck   SBCCI 100mph 
Welded Steel Deck   ASCE 100mph 
LogNormal 
LogNormal 
LogNormal 
LogNormal 
Mean = 187, 187, 187 psf, 
COV = 0.13, 0.13, 0.13 
Mean = 187, 187, 212 psf, 
COV = 0.13, 0.13, 0.11 
Mean = 169, 169, 169 psf, 
COV = 0.13, 0.13, 0.13 
Mean = 169, 169, 222 psf, 
COV = 0.13, 0.13, 0.18 
Steel Deck on Steel Roof Joists   Building Height = 20', Joist Spacing = 4' 
Component Distribution 
Distribution Parameters 
(Field, Edge, Corner) 
Screwed Steel Deck   SBCCI 100mph 
Screwed Steel Deck   ASCE 100mph 
Welded Steel Deck   SBCCI 100mph 
Welded Steel Deck   ASCE 100mph 
LogNormal 
LogNormal 
LogNormal 
LogNormal 
Mean = 187, 187, 187 psf, 
COV = 0.13, 0.13, 0.13 
Mean = 187, 187, 212 psf, 
COV = 0.13, 0.13, 0.11 
Mean = 169, 169, 169 psf, 
COV = 0.13, 0.13, 0.13 
Mean = 169, 169, 222 psf, 
COV = 0.13, 0.13, 0.18 
Steel Deck on Steel Roof Joists   Building Height = 20', Joist Spacing = 6' 
Component Distribution 
Distribution Parameters 
(Field, Edge, Corner) 
Screwed Steel Deck   SBCCI 100mph 
Screwed Steel Deck   ASCE 100mph 
Welded Steel Deck   SBCCI 100mph 
Welded Steel Deck   ASCE 100mph 
LogNormal 
LogNormal 
LogNormal 
LogNormal 
Mean = 109, 109, 109 psf, 
COV = 0.21, 0.21, 0.21 
Mean = 109, 127, 165 psf, 
COV = 0.21, 0.21, 0.21 
Mean = 91, 91, 97 psf, 
COV = 0.19, 0.19, 0.19 
Mean = 91, 123, 155 psf, 
COV = 0.19, 0.19, 0.19 

HAZUS-MH MR3 Technical Manual 

?
Table 6.45. Description of Missile Environments 

Missile 
Environment 
Missile Source EnvironmentC = Commercial Type Missiles, R = Residential Type 
Missiles,M = 
Equal Mix of Commercial and Residential Type Missiles, N = No Missiles 
Center of 450 Directional Sector 
Designation N NE E SE S SW W NW 
A M M M M M M M M 
B R C R R C R R R 
C R R R R R R R R 
D N N N N N N N N 

effect when the joist spacing is 4' (Buildings A and B). The effect of using 
welds versus 
screws to fasten the steel roof deck to the joists ranges from a 2% decrease 
to a 9% 
increase in the average damage for the buildings having a 6' joist spacing. 
The effect of 
the roof deck fastener is negligible when the joist spacing is 4'. Similarly, 
the result of 
using the SBCCI versus the ACSE metal roof design criteria (both with a 100 
mph design 
speed) ranges from a 1% decrease to a 23% increase in the average damage 
state for the 
buildings having a 6' joist spacing. The effect of the metal deck design 
criteria is 
negligible when the joist spacing is 4'. The impact of metal deck quality, 
installation and 
design criteria on the storm average damage states, as discussed above, is 
more 
significant when the joist spacing is larger because the larger spacing 
reduces the uplift 
capacity of the roof panels (see Table 6.54) and therefore the metal deck 
damage has a 
larger weight in the overall damage state. 

The use of EPDM roof cover versus BUR roof cover increases the overall storm 
average 
damage states by 5% to 34%. The effect of the roof cover type is similar for 
each of the 
four buildings examined. Walls constructed with unreinforced masonry versus 
reinforced 
masonry yield mean damage states which are up to 15% larger for the buildings 
with a 
roof height of 20' (Buildings B, C and D) and has a much less significant 
effect (2% 
decrease to 4% increase) when the roof height is 12' (Building A). Again, the 
different 
missile environments have a large impact on the storm average damage states 
as seen 
from the last three rows of Table 6.53. The largest effect is found when 
changing from no 
windborne missiles (Missile Environment D) to a mixed residential/commercial 
type 
environment (Missile Environment A) where the storm average damage states 
increased 
from between 24% and 115% for the 12' high building cases and from between 8% 
and 
39% for the 20' high building cases. The elimination of glass breakage 
associated with 
windborne debris (i.e., as produced when changing the modeled Missile 
Environment 
from A to D) has a smaller impact on the overall damage state for the 20' 
high building 
cases compared to the 12' high building cases since the overall damage state 
is more 
influenced by roof damage compared to fenestration damage for the taller 
buildings. 

Table 6.54 summarizes the influence of building height, joist spacing and 
number of units 
on the storm average damage state. The building height is shown to have the 
most 
significant effect, increasing the damage states from between 25% and 126% 
when the 
building height increases from 12' to 20'. Increasing the joist spacing from 
4' to 6' 

Chapter 6. Physical Damage Modeling 

?
Table 6.46. Average Damage States for Building A - Twelve Foot High Strip 
Mall 
Building with Six Units and Wood Deck with Two Foot Truss Spacing 

Building Characteristics 
Toe-Nailed Roof-Wall Connection Strapped Roof-Wall Connection 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ. 
Wood 
Deck Nail 
Size 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
6d 
EPDM 
BUR 
0.694 
0.660 
0.688 
0.641 
0.459 
0.440 
0.454 
0.431 
0.340 
0.319 
0.338 
0.314 
0.285 
0.267 
0.282 
0.263 
0.571 
0.531 
0.555 
0.506 
0.425 
0.405 
0.414 
0.392 
0.314 
0.296 
0.310 
0.288 
0.267 
0.248 
0.260 
0.239 
8d 
EPDM 
BUR 
B 
6d 
EPDM 
BUR 
0.600 
0.560 
0.594 
0.545 
0.391 
0.369 
0.386 
0.359 
0.291 
0.271 
0.291 
0.263 
0.249 
0.226 
0.246 
0.221 
0.525 
0.478 
0.508 
0.449 
0.361 
0.332 
0.353 
0.318 
0.267 
0.245 
0.264 
0.233 
0.230 
0.206 
0.222 
0.193 
8d 
EPDM 
BUR 
C 
6d 
EPDM 
BUR 
0.518 
0.468 
0.508 
0.452 
0.331 
0.301 
0.324 
0.291 
0.249 
0.225 
0.247 
0.211 
0.214 
0.186 
0.209 
0.180 
0.475 
0.423 
0.459 
0.390 
0.288 
0.254 
0.275 
0.232 
0.221 
0.190 
0.211 
0.173 
0.190 
0.163 
0.182 
0.148 
8d 
EPDM 
BUR 
D 
6d 
EPDM 
BUR 
0.483 
0.428 
0.478 
0.408 
0.267 
0.227 
0.261 
0.213 
0.211 
0.177 
0.206 
0.164 
0.188 
0.155 
0.180 
0.142 
0.459 
0.408 
0.450 
0.372 
0.253 
0.215 
0.246 
0.192 
0.198 
0.164 
0.190 
0.147 
0.177 
0.146 
0.167 
0.129 
8d 
EPDM 
BUR 
Reinforced Masonry 
A 
6d 
EPDM 
BUR 
0.683 
0.655 
0.686 
0.644 
0.458 
0.432 
0.453 
0.429 
0.340 
0.320 
0.333 
0.316 
0.284 
0.269 
0.279 
0.261 
0.565 
0.530 
0.548 
0.499 
0.429 
0.404 
0.417 
0.391 
0.315 
0.298 
0.311 
0.287 
0.268 
0.247 
0.259 
0.239 
8d 
EPDM 
BUR 
B 
6d 
EPDM 
BUR 
0.595 
0.553 
0.589 
0.539 
0.388 
0.363 
0.387 
0.358 
0.290 
0.266 
0.288 
0.260 
0.246 
0.226 
0.244 
0.217 
0.519 
0.474 
0.499 
0.444 
0.355 
0.330 
0.344 
0.315 
0.270 
0.244 
0.262 
0.232 
0.229 
0.205 
0.222 
0.193 
8d 
EPDM 
BUR 
C 
6d 
EPDM 
BUR 
0.510 
0.463 
0.505 
0.442 
0.327 
0.302 
0.324 
0.288 
0.248 
0.219 
0.245 
0.214 
0.213 
0.187 
0.208 
0.179 
0.470 
0.417 
0.451 
0.381 
0.284 
0.254 
0.271 
0.230 
0.219 
0.190 
0.210 
0.171 
0.191 
0.163 
0.182 
0.147 
8d 
EPDM 
BUR 
D 
6d 
EPDM 
BUR 
0.475 
0.419 
0.468 
0.396 
0.263 
0.223 
0.258 
0.209 
0.209 
0.176 
0.204 
0.162 
0.186 
0.153 
0.180 
0.142 
0.457 
0.402 
0.443 
0.367 
0.253 
0.213 
0.242 
0.189 
0.199 
0.164 
0.190 
0.147 
0.179 
0.145 
0.166 
0.126 
8d 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 6.47. Average Damage States for Building B - Twenty Foot High Strip 
Mall 
Building with Six Units and Wood Deck with Two Foot Truss Spacing 

Building Characteristics 
Toe-Nailed Roof-Wall Connection Strapped Roof-Wall Connection 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ. 
Wood 
Deck 
Nail Size 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
6d 
EPDM 
BUR 
0.933 
0.863 
0.928 
0.852 
0.666 
0.620 
0.665 
0.613 
0.531 
0.485 
0.532 
0.477 
0.463 
0.422 
0.460 
0.415 
0.835 
0.757 
0.822 
0.733 
0.585 
0.533 
0.579 
0.523 
0.466 
0.419 
0.465 
0.407 
0.414 
0.365 
0.409 
0.350 
8d 
EPDM 
BUR 
B 
6d 
EPDM 
BUR 
0.861 
0.795 
0.859 
0.775 
0.596 
0.546 
0.601 
0.537 
0.486 
0.433 
0.483 
0.421 
0.430 
0.380 
0.428 
0.373 
0.792 
0.715 
0.785 
0.692 
0.546 
0.486 
0.539 
0.468 
0.443 
0.390 
0.438 
0.372 
0.392 
0.341 
0.387 
0.326 
8d 
EPDM 
BUR 
C 
6d 
EPDM 
BUR 
0.800 
0.719 
0.798 
0.703 
0.548 
0.484 
0.543 
0.470 
0.448 
0.393 
0.444 
0.378 
0.400 
0.346 
0.401 
0.337 
0.763 
0.673 
0.757 
0.651 
0.503 
0.435 
0.496 
0.418 
0.415 
0.355 
0.412 
0.335 
0.374 
0.316 
0.369 
0.301 
8d 
EPDM 
BUR 
D 
6d 
EPDM 
BUR 
0.789 
0.705 
0.785 
0.681 
0.510 
0.441 
0.506 
0.425 
0.428 
0.365 
0.427 
0.353 
0.390 
0.330 
0.386 
0.316 
0.758 
0.667 
0.753 
0.649 
0.490 
0.416 
0.484 
0.396 
0.409 
0.343 
0.404 
0.326 
0.368 
0.307 
0.364 
0.290 
8d 
EPDM 
BUR 
Reinforced Masonry 
A 
6d 
EPDM 
BUR 
0.903 
0.838 
0.897 
0.821 
0.659 
0.609 
0.653 
0.598 
0.524 
0.479 
0.523 
0.472 
0.460 
0.412 
0.457 
0.404 
0.789 
0.708 
0.769 
0.674 
0.567 
0.516 
0.560 
0.493 
0.458 
0.406 
0.448 
0.382 
0.401 
0.352 
0.396 
0.337 
8d 
EPDM 
BUR 
B 
6d 
EPDM 
BUR 
0.832 
0.764 
0.829 
0.746 
0.590 
0.534 
0.585 
0.521 
0.480 
0.425 
0.475 
0.415 
0.426 
0.372 
0.424 
0.365 
0.751 
0.667 
0.735 
0.630 
0.526 
0.466 
0.514 
0.441 
0.431 
0.372 
0.418 
0.352 
0.381 
0.325 
0.374 
0.308 
8d 
EPDM 
BUR 
C 
6d 
EPDM 
BUR 
0.761 
0.684 
0.763 
0.666 
0.533 
0.470 
0.531 
0.459 
0.440 
0.382 
0.436 
0.370 
0.397 
0.340 
0.392 
0.332 
0.719 
0.631 
0.708 
0.596 
0.484 
0.417 
0.471 
0.383 
0.403 
0.342 
0.394 
0.314 
0.361 
0.303 
0.355 
0.282 
8d 
EPDM 
BUR 
D 
6d 
EPDM 
BUR 
0.755 
0.661 
0.745 
0.641 
0.497 
0.423 
0.492 
0.411 
0.417 
0.358 
0.413 
0.344 
0.382 
0.324 
0.378 
0.309 
0.718 
0.626 
0.706 
0.590 
0.468 
0.393 
0.458 
0.367 
0.395 
0.327 
0.383 
0.303 
0.359 
0.295 
0.349 
0.273 
8d 
EPDM 
BUR 

Chapter 6. Physical Damage Modeling 

?
Table 6.48. Average Damage States for Building A - Twelve Foot High Strip 
Mall 
Building with Six Units and Metal Deck with Four Foot Joist Spacing

Building Characteristics 
No Reduction in Metal Deck 
Capacity 
50% Reduction in Metal Deck 
Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.540 
0.490 
0.538 
0.487 
0.540 
0.490 
0.536 
0.488 
0.421 
0.391 
0.419 
0.393 
0.419 
0.394 
0.420 
0.394 
0.312 
0.287 
0.312 
0.286 
0.313 
0.287 
0.310 
0.286 
0.259 
0.237 
0.260 
0.240 
0.262 
0.236 
0.260 
0.237 
0.537 
0.489 
0.540 
0.487 
0.539 
0.487 
0.537 
0.488 
0.418 
0.392 
0.419 
0.392 
0.418 
0.394 
0.416 
0.394 
0.310 
0.284 
0.312 
0.286 
0.309 
0.288 
0.312 
0.289 
0.261 
0.235 
0.261 
0.237 
0.258 
0.236 
0.261 
0.236 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.494 
0.433 
0.489 
0.431 
0.489 
0.429 
0.491 
0.431 
0.349 
0.310 
0.346 
0.315 
0.350 
0.314 
0.348 
0.315 
0.262 
0.229 
0.261 
0.230 
0.260 
0.231 
0.262 
0.231 
0.223 
0.194 
0.222 
0.194 
0.221 
0.195 
0.223 
0.194 
0.489 
0.432 
0.491 
0.433 
0.489 
0.428 
0.489 
0.431 
0.348 
0.314 
0.346 
0.314 
0.346 
0.315 
0.348 
0.313 
0.261 
0.231 
0.262 
0.231 
0.260 
0.231 
0.261 
0.230 
0.223 
0.196 
0.222 
0.191 
0.220 
0.194 
0.223 
0.195 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.441 
0.372 
0.441 
0.370 
0.441 
0.369 
0.439 
0.368 
0.269 
0.230 
0.271 
0.227 
0.268 
0.227 
0.269 
0.226 
0.208 
0.172 
0.208 
0.168 
0.209 
0.169 
0.209 
0.170 
0.181 
0.145 
0.181 
0.144 
0.180 
0.145 
0.179 
0.145 
0.441 
0.369 
0.442 
0.373 
0.439 
0.369 
0.439 
0.369 
0.270 
0.228 
0.271 
0.226 
0.270 
0.227 
0.270 
0.228 
0.207 
0.170 
0.207 
0.171 
0.209 
0.171 
0.207 
0.171 
0.180 
0.146 
0.180 
0.145 
0.183 
0.144 
0.180 
0.146 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.430 
0.359 
0.431 
0.356 
0.431 
0.354 
0.432 
0.354 
0.240 
0.188 
0.241 
0.187 
0.240 
0.187 
0.239 
0.187 
0.191 
0.145 
0.191 
0.146 
0.191 
0.145 
0.191 
0.145 
0.170 
0.128 
0.170 
0.128 
0.170 
0.128 
0.170 
0.128 
0.431 
0.359 
0.431 
0.358 
0.433 
0.353 
0.432 
0.354 
0.242 
0.188 
0.239 
0.189 
0.239 
0.187 
0.240 
0.187 
0.190 
0.147 
0.191 
0.147 
0.189 
0.146 
0.190 
0.146 
0.169 
0.129 
0.168 
0.129 
0.169 
0.128 
0.169 
0.129 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 6.48. Average Damage States for Building A - Twelve Foot High Strip 
Mall 
Building with Six Units and Metal Deck with Four Foot Joist Spacing 
(concluded) 

Building Characteristics 
No Reduction in Metal Deck 
Capacity 
50% Reduction in Metal Deck 
Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.532 
0.481 
0.534 
0.479 
0.533 
0.478 
0.533 
0.482 
0.418 
0.391 
0.419 
0.390 
0.417 
0.391 
0.418 
0.390 
0.313 
0.289 
0.310 
0.286 
0.308 
0.287 
0.313 
0.284 
0.260 
0.237 
0.262 
0.236 
0.258 
0.237 
0.258 
0.237 
0.531 
0.483 
0.534 
0.484 
0.535 
0.482 
0.533 
0.483 
0.416 
0.390 
0.420 
0.391 
0.420 
0.390 
0.418 
0.392 
0.310 
0.287 
0.311 
0.283 
0.310 
0.286 
0.311 
0.283 
0.260 
0.240 
0.259 
0.236 
0.261 
0.236 
0.258 
0.237 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.485 
0.423 
0.484 
0.424 
0.481 
0.425 
0.485 
0.423 
0.347 
0.312 
0.347 
0.311 
0.346 
0.313 
0.346 
0.311 
0.262 
0.230 
0.260 
0.227 
0.261 
0.228 
0.261 
0.229 
0.217 
0.191 
0.221 
0.191 
0.222 
0.190 
0.219 
0.192 
0.483 
0.424 
0.483 
0.425 
0.483 
0.423 
0.487 
0.426 
0.346 
0.314 
0.343 
0.313 
0.346 
0.313 
0.345 
0.311 
0.263 
0.231 
0.260 
0.229 
0.261 
0.228 
0.260 
0.228 
0.221 
0.193 
0.220 
0.195 
0.220 
0.192 
0.222 
0.191 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.433 
0.360 
0.429 
0.360 
0.433 
0.360 
0.432 
0.361 
0.266 
0.226 
0.267 
0.226 
0.269 
0.226 
0.268 
0.225 
0.207 
0.167 
0.205 
0.169 
0.207 
0.169 
0.206 
0.168 
0.179 
0.144 
0.179 
0.144 
0.180 
0.144 
0.179 
0.144 
0.432 
0.361 
0.431 
0.363 
0.433 
0.361 
0.433 
0.363 
0.268 
0.224 
0.267 
0.226 
0.268 
0.226 
0.269 
0.224 
0.207 
0.169 
0.205 
0.168 
0.208 
0.168 
0.208 
0.171 
0.180 
0.144 
0.180 
0.145 
0.178 
0.144 
0.178 
0.144 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.423 
0.345 
0.425 
0.346 
0.425 
0.347 
0.421 
0.344 
0.239 
0.184 
0.239 
0.185 
0.237 
0.184 
0.237 
0.183 
0.189 
0.144 
0.189 
0.143 
0.189 
0.143 
0.189 
0.143 
0.167 
0.126 
0.167 
0.125 
0.167 
0.126 
0.167 
0.126 
0.422 
0.348 
0.427 
0.350 
0.424 
0.346 
0.421 
0.349 
0.237 
0.183 
0.236 
0.182 
0.237 
0.184 
0.237 
0.184 
0.188 
0.144 
0.188 
0.144 
0.187 
0.143 
0.191 
0.143 
0.168 
0.126 
0.167 
0.125 
0.167 
0.127 
0.168 
0.127 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 6. Physical Damage Modeling 

?
Table 6.49. Average Damage States for Building B - Twenty Foot High Strip 
Mall 
Building with Six Units and Metal Deck with Four Foot Joist Spacing

Building Characteristics 
No Reduction in Metal Deck 
Capacity 
50% Reduction in Metal Deck 
Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.813 
0.724 
0.815 
0.726 
0.814 
0.725 
0.814 
0.725 
0.574 
0.519 
0.574 
0.517 
0.577 
0.517 
0.578 
0.517 
0.463 
0.406 
0.457 
0.402 
0.461 
0.405 
0.463 
0.408 
0.406 
0.352 
0.407 
0.350 
0.407 
0.351 
0.408 
0.350 
0.813 
0.724 
0.809 
0.725 
0.814 
0.726 
0.812 
0.723 
0.575 
0.515 
0.576 
0.519 
0.579 
0.517 
0.581 
0.518 
0.464 
0.404 
0.464 
0.404 
0.465 
0.404 
0.462 
0.404 
0.406 
0.352 
0.406 
0.350 
0.409 
0.348 
0.410 
0.352 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.781 
0.680 
0.776 
0.682 
0.775 
0.680 
0.772 
0.681 
0.532 
0.465 
0.534 
0.466 
0.535 
0.464 
0.536 
0.467 
0.435 
0.366 
0.433 
0.366 
0.433 
0.367 
0.434 
0.367 
0.385 
0.325 
0.387 
0.324 
0.383 
0.322 
0.385 
0.321 
0.773 
0.677 
0.772 
0.684 
0.775 
0.680 
0.775 
0.683 
0.534 
0.464 
0.537 
0.466 
0.534 
0.465 
0.535 
0.461 
0.434 
0.368 
0.431 
0.368 
0.434 
0.369 
0.433 
0.369 
0.387 
0.323 
0.384 
0.322 
0.386 
0.323 
0.387 
0.322 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.745 
0.641 
0.742 
0.640 
0.742 
0.637 
0.744 
0.643 
0.491 
0.408 
0.492 
0.408 
0.491 
0.411 
0.489 
0.412 
0.408 
0.334 
0.409 
0.333 
0.409 
0.332 
0.407 
0.336 
0.363 
0.296 
0.365 
0.296 
0.366 
0.297 
0.364 
0.298 
0.746 
0.641 
0.743 
0.640 
0.744 
0.641 
0.742 
0.640 
0.492 
0.408 
0.493 
0.408 
0.493 
0.410 
0.491 
0.410 
0.406 
0.333 
0.406 
0.335 
0.406 
0.333 
0.403 
0.335 
0.364 
0.295 
0.364 
0.298 
0.367 
0.297 
0.365 
0.294 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.747 
0.633 
0.744 
0.635 
0.749 
0.635 
0.743 
0.637 
0.480 
0.392 
0.477 
0.391 
0.478 
0.392 
0.478 
0.392 
0.400 
0.323 
0.399 
0.322 
0.397 
0.322 
0.398 
0.322 
0.362 
0.289 
0.362 
0.286 
0.363 
0.286 
0.361 
0.286 
0.740 
0.636 
0.743 
0.637 
0.744 
0.632 
0.742 
0.637 
0.478 
0.391 
0.479 
0.391 
0.479 
0.391 
0.479 
0.392 
0.399 
0.323 
0.401 
0.323 
0.400 
0.319 
0.399 
0.321 
0.365 
0.290 
0.358 
0.289 
0.359 
0.287 
0.360 
0.287 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 6.49. Average Damage States for Building B - Twenty Foot High Strip 
Mall 
Building with Six Units and Metal Deck with Four Foot Joist Spacing 
(concluded) 

Building Characteristics 
No Reduction in Metal Deck 
Capacity 
50% Reduction in Metal Deck 
Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.739 
0.634 
0.739 
0.638 
0.739 
0.640 
0.735 
0.640 
0.555 
0.489 
0.556 
0.492 
0.554 
0.490 
0.554 
0.491 
0.443 
0.384 
0.443 
0.384 
0.443 
0.386 
0.445 
0.383 
0.394 
0.330 
0.392 
0.331 
0.392 
0.332 
0.392 
0.332 
0.739 
0.638 
0.738 
0.640 
0.736 
0.640 
0.740 
0.640 
0.555 
0.491 
0.556 
0.492 
0.554 
0.494 
0.555 
0.492 
0.445 
0.382 
0.444 
0.383 
0.444 
0.385 
0.443 
0.384 
0.393 
0.332 
0.389 
0.334 
0.395 
0.332 
0.393 
0.332 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.707 
0.601 
0.708 
0.602 
0.704 
0.601 
0.711 
0.601 
0.506 
0.438 
0.509 
0.433 
0.508 
0.434 
0.508 
0.435 
0.415 
0.344 
0.414 
0.345 
0.413 
0.346 
0.416 
0.347 
0.369 
0.301 
0.371 
0.302 
0.368 
0.305 
0.369 
0.302 
0.706 
0.599 
0.708 
0.602 
0.707 
0.602 
0.710 
0.603 
0.507 
0.438 
0.508 
0.435 
0.506 
0.434 
0.506 
0.432 
0.417 
0.345 
0.415 
0.345 
0.415 
0.346 
0.414 
0.344 
0.371 
0.304 
0.369 
0.303 
0.369 
0.303 
0.369 
0.300 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.677 
0.561 
0.679 
0.562 
0.679 
0.562 
0.677 
0.562 
0.457 
0.370 
0.457 
0.374 
0.458 
0.371 
0.456 
0.371 
0.384 
0.303 
0.382 
0.306 
0.380 
0.304 
0.383 
0.305 
0.345 
0.273 
0.346 
0.274 
0.347 
0.273 
0.344 
0.272 
0.677 
0.562 
0.680 
0.565 
0.677 
0.562 
0.679 
0.565 
0.460 
0.372 
0.457 
0.375 
0.461 
0.373 
0.459 
0.371 
0.384 
0.304 
0.382 
0.305 
0.380 
0.306 
0.381 
0.303 
0.345 
0.273 
0.348 
0.273 
0.345 
0.274 
0.346 
0.272 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.676 
0.555 
0.679 
0.558 
0.677 
0.557 
0.680 
0.559 
0.446 
0.356 
0.448 
0.355 
0.446 
0.357 
0.446 
0.355 
0.378 
0.297 
0.378 
0.296 
0.379 
0.294 
0.378 
0.295 
0.347 
0.267 
0.345 
0.268 
0.346 
0.268 
0.346 
0.268 
0.674 
0.557 
0.676 
0.561 
0.677 
0.558 
0.678 
0.559 
0.452 
0.356 
0.447 
0.357 
0.447 
0.355 
0.446 
0.355 
0.378 
0.295 
0.376 
0.296 
0.375 
0.296 
0.377 
0.296 
0.345 
0.266 
0.344 
0.267 
0.344 
0.267 
0.343 
0.267 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 6. Physical Damage Modeling 

?
Table 6.50. Average Damage States for Building C - Twenty Foot High Strip 
Mall 
Building with Six Units and Metal Deck with Six Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck 
Capacity 
50% Reduction in Metal Deck 
Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.816 
0.729 
0.816 
0.729 
0.810 
0.725 
0.812 
0.722 
0.577 
0.521 
0.579 
0.523 
0.578 
0.520 
0.580 
0.517 
0.462 
0.403 
0.461 
0.402 
0.465 
0.402 
0.462 
0.402 
0.408 
0.351 
0.407 
0.349 
0.409 
0.351 
0.405 
0.350 
0.852 
0.792 
0.880 
0.827 
0.827 
0.747 
0.827 
0.750 
0.602 
0.559 
0.620 
0.587 
0.587 
0.529 
0.584 
0.526 
0.487 
0.444 
0.502 
0.468 
0.472 
0.415 
0.470 
0.414 
0.430 
0.389 
0.445 
0.412 
0.416 
0.364 
0.415 
0.362 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.778 
0.682 
0.775 
0.679 
0.772 
0.677 
0.774 
0.679 
0.536 
0.464 
0.535 
0.467 
0.535 
0.466 
0.536 
0.462 
0.436 
0.368 
0.436 
0.369 
0.431 
0.368 
0.429 
0.368 
0.387 
0.322 
0.387 
0.324 
0.388 
0.325 
0.386 
0.321 
0.814 
0.747 
0.836 
0.782 
0.790 
0.702 
0.789 
0.696 
0.563 
0.513 
0.574 
0.540 
0.544 
0.481 
0.542 
0.475 
0.462 
0.412 
0.476 
0.437 
0.442 
0.382 
0.441 
0.377 
0.409 
0.365 
0.424 
0.387 
0.395 
0.336 
0.390 
0.330 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.747 
0.643 
0.746 
0.641 
0.748 
0.642 
0.747 
0.643 
0.493 
0.414 
0.496 
0.414 
0.490 
0.412 
0.490 
0.411 
0.405 
0.335 
0.407 
0.333 
0.409 
0.333 
0.408 
0.334 
0.368 
0.296 
0.366 
0.296 
0.366 
0.297 
0.364 
0.294 
0.784 
0.712 
0.805 
0.746 
0.756 
0.665 
0.753 
0.659 
0.521 
0.465 
0.540 
0.493 
0.501 
0.430 
0.498 
0.425 
0.433 
0.382 
0.449 
0.408 
0.417 
0.349 
0.414 
0.348 
0.393 
0.343 
0.404 
0.366 
0.373 
0.313 
0.371 
0.308 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.746 
0.635 
0.744 
0.636 
0.744 
0.634 
0.740 
0.637 
0.479 
0.391 
0.477 
0.391 
0.479 
0.392 
0.478 
0.391 
0.399 
0.322 
0.399 
0.320 
0.397 
0.323 
0.397 
0.321 
0.361 
0.288 
0.359 
0.288 
0.363 
0.289 
0.360 
0.288 
0.780 
0.709 
0.798 
0.742 
0.754 
0.658 
0.748 
0.653 
0.509 
0.447 
0.526 
0.475 
0.488 
0.409 
0.486 
0.405 
0.428 
0.371 
0.443 
0.399 
0.407 
0.336 
0.405 
0.333 
0.389 
0.335 
0.401 
0.359 
0.367 
0.302 
0.369 
0.295 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 6.50. Average Damage States for Building C - Twenty Foot High Strip 
Mall 
Building with Six Units and Metal Deck with Six Foot Joist Spacing 
(concluded) 

Building Characteristics 
No Reduction in Metal Deck 
Capacity 
50% Reduction in Metal Deck 
Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.738 
0.638 
0.737 
0.644 
0.740 
0.640 
0.738 
0.642 
0.554 
0.493 
0.557 
0.494 
0.553 
0.492 
0.554 
0.489 
0.443 
0.382 
0.448 
0.384 
0.445 
0.384 
0.445 
0.385 
0.395 
0.333 
0.393 
0.333 
0.395 
0.332 
0.392 
0.333 
0.811 
0.746 
0.847 
0.793 
0.776 
0.694 
0.786 
0.697 
0.587 
0.544 
0.607 
0.569 
0.565 
0.510 
0.566 
0.509 
0.474 
0.431 
0.492 
0.457 
0.456 
0.401 
0.455 
0.401 
0.418 
0.379 
0.437 
0.402 
0.404 
0.350 
0.402 
0.348 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.707 
0.603 
0.706 
0.602 
0.708 
0.604 
0.708 
0.601 
0.511 
0.434 
0.508 
0.437 
0.508 
0.436 
0.509 
0.436 
0.413 
0.347 
0.414 
0.347 
0.413 
0.346 
0.417 
0.345 
0.372 
0.305 
0.372 
0.304 
0.371 
0.305 
0.370 
0.303 
0.771 
0.702 
0.805 
0.752 
0.743 
0.646 
0.748 
0.655 
0.546 
0.495 
0.564 
0.526 
0.523 
0.458 
0.522 
0.458 
0.449 
0.401 
0.464 
0.425 
0.428 
0.366 
0.433 
0.363 
0.399 
0.353 
0.413 
0.379 
0.382 
0.323 
0.380 
0.318 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.679 
0.564 
0.678 
0.560 
0.674 
0.564 
0.675 
0.563 
0.461 
0.374 
0.461 
0.375 
0.460 
0.375 
0.459 
0.374 
0.382 
0.308 
0.381 
0.307 
0.382 
0.307 
0.382 
0.306 
0.347 
0.273 
0.343 
0.275 
0.346 
0.274 
0.347 
0.274 
0.741 
0.671 
0.773 
0.712 
0.713 
0.611 
0.714 
0.613 
0.504 
0.445 
0.526 
0.484 
0.480 
0.406 
0.481 
0.404 
0.419 
0.369 
0.439 
0.398 
0.402 
0.330 
0.402 
0.332 
0.380 
0.330 
0.398 
0.358 
0.362 
0.294 
0.361 
0.294 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.679 
0.559 
0.676 
0.560 
0.676 
0.562 
0.678 
0.556 
0.447 
0.356 
0.448 
0.357 
0.446 
0.358 
0.446 
0.357 
0.379 
0.295 
0.379 
0.296 
0.376 
0.296 
0.376 
0.296 
0.344 
0.267 
0.345 
0.269 
0.343 
0.268 
0.343 
0.267 
0.741 
0.660 
0.768 
0.708 
0.709 
0.606 
0.709 
0.607 
0.490 
0.428 
0.510 
0.463 
0.464 
0.382 
0.467 
0.380 
0.414 
0.356 
0.432 
0.388 
0.390 
0.317 
0.392 
0.318 
0.375 
0.325 
0.390 
0.348 
0.356 
0.289 
0.358 
0.285 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 6. Physical Damage Modeling 

?
Table 6.51. Average Damage States for Building D - Twenty Foot High Strip 
Mall 
Building with One Unit and Metal Deck with Six Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck 
Capacity 
50% Reduction in Metal Deck 
Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.829 
0.744 
0.832 
0.746 
0.831 
0.746 
0.828 
0.743 
0.583 
0.521 
0.583 
0.522 
0.581 
0.524 
0.583 
0.521 
0.468 
0.411 
0.467 
0.408 
0.469 
0.408 
0.466 
0.410 
0.410 
0.354 
0.409 
0.353 
0.412 
0.355 
0.409 
0.352 
0.876 
0.813 
0.896 
0.851 
0.845 
0.771 
0.844 
0.765 
0.612 
0.569 
0.628 
0.594 
0.592 
0.540 
0.587 
0.533 
0.495 
0.450 
0.508 
0.473 
0.474 
0.421 
0.472 
0.419 
0.435 
0.395 
0.453 
0.417 
0.418 
0.368 
0.416 
0.364 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.792 
0.700 
0.791 
0.701 
0.796 
0.697 
0.788 
0.704 
0.542 
0.472 
0.542 
0.472 
0.538 
0.472 
0.542 
0.470 
0.436 
0.375 
0.437 
0.374 
0.436 
0.375 
0.438 
0.375 
0.392 
0.327 
0.391 
0.329 
0.388 
0.328 
0.387 
0.329 
0.832 
0.766 
0.856 
0.807 
0.806 
0.722 
0.807 
0.718 
0.572 
0.525 
0.588 
0.545 
0.548 
0.490 
0.546 
0.486 
0.466 
0.420 
0.482 
0.442 
0.447 
0.390 
0.445 
0.383 
0.415 
0.372 
0.428 
0.391 
0.398 
0.342 
0.397 
0.338 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.763 
0.666 
0.764 
0.662 
0.763 
0.661 
0.762 
0.661 
0.500 
0.422 
0.501 
0.422 
0.499 
0.419 
0.498 
0.420 
0.416 
0.341 
0.411 
0.345 
0.413 
0.342 
0.415 
0.340 
0.372 
0.305 
0.370 
0.299 
0.374 
0.303 
0.370 
0.303 
0.805 
0.729 
0.817 
0.765 
0.775 
0.684 
0.771 
0.681 
0.532 
0.476 
0.554 
0.506 
0.511 
0.441 
0.507 
0.434 
0.442 
0.391 
0.455 
0.413 
0.423 
0.356 
0.422 
0.355 
0.397 
0.350 
0.414 
0.374 
0.378 
0.317 
0.379 
0.318 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.763 
0.656 
0.760 
0.655 
0.764 
0.660 
0.762 
0.655 
0.488 
0.403 
0.487 
0.400 
0.484 
0.398 
0.486 
0.399 
0.405 
0.330 
0.408 
0.329 
0.409 
0.330 
0.407 
0.329 
0.370 
0.298 
0.370 
0.298 
0.367 
0.295 
0.369 
0.296 
0.798 
0.722 
0.818 
0.755 
0.773 
0.674 
0.766 
0.670 
0.515 
0.458 
0.532 
0.482 
0.495 
0.416 
0.491 
0.414 
0.433 
0.377 
0.446 
0.403 
0.413 
0.343 
0.411 
0.341 
0.391 
0.342 
0.401 
0.363 
0.372 
0.307 
0.372 
0.303 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 6.51. Average Damage States for Building D - Twenty Foot High Strip 
Mall 
Building with One Unit and Metal Deck with Six Foot Joist Spacing (concluded) 

Building Characteristics 
No Reduction in Metal Deck 
Capacity 
50% Reduction in Metal Deck 
Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.757 
0.660 
0.752 
0.660 
0.752 
0.658 
0.752 
0.657 
0.563 
0.499 
0.560 
0.499 
0.562 
0.499 
0.560 
0.502 
0.452 
0.389 
0.452 
0.388 
0.448 
0.386 
0.448 
0.388 
0.399 
0.340 
0.394 
0.337 
0.397 
0.336 
0.395 
0.337 
0.827 
0.760 
0.865 
0.812 
0.788 
0.707 
0.798 
0.714 
0.598 
0.550 
0.615 
0.580 
0.572 
0.518 
0.571 
0.516 
0.480 
0.438 
0.499 
0.464 
0.460 
0.406 
0.456 
0.403 
0.426 
0.384 
0.442 
0.405 
0.404 
0.355 
0.405 
0.349 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.722 
0.622 
0.726 
0.622 
0.721 
0.620 
0.722 
0.617 
0.519 
0.443 
0.515 
0.443 
0.519 
0.443 
0.517 
0.445 
0.419 
0.354 
0.421 
0.352 
0.420 
0.349 
0.414 
0.350 
0.374 
0.310 
0.375 
0.307 
0.376 
0.310 
0.371 
0.309 
0.789 
0.720 
0.824 
0.768 
0.752 
0.669 
0.764 
0.669 
0.550 
0.503 
0.573 
0.532 
0.529 
0.465 
0.530 
0.465 
0.454 
0.406 
0.470 
0.432 
0.432 
0.372 
0.433 
0.372 
0.407 
0.361 
0.422 
0.382 
0.389 
0.327 
0.387 
0.328 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.694 
0.584 
0.694 
0.583 
0.694 
0.585 
0.691 
0.579 
0.468 
0.381 
0.467 
0.386 
0.467 
0.383 
0.468 
0.384 
0.392 
0.314 
0.391 
0.314 
0.388 
0.312 
0.388 
0.313 
0.350 
0.282 
0.350 
0.280 
0.353 
0.279 
0.350 
0.279 
0.758 
0.685 
0.794 
0.729 
0.730 
0.631 
0.735 
0.632 
0.512 
0.455 
0.536 
0.488 
0.489 
0.414 
0.490 
0.416 
0.428 
0.374 
0.447 
0.406 
0.405 
0.339 
0.406 
0.340 
0.384 
0.339 
0.402 
0.364 
0.366 
0.302 
0.366 
0.302 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.696 
0.582 
0.698 
0.585 
0.691 
0.576 
0.691 
0.574 
0.457 
0.367 
0.456 
0.367 
0.458 
0.365 
0.455 
0.365 
0.385 
0.306 
0.385 
0.306 
0.384 
0.304 
0.382 
0.305 
0.349 
0.275 
0.348 
0.273 
0.348 
0.274 
0.349 
0.274 
0.754 
0.673 
0.779 
0.716 
0.722 
0.621 
0.727 
0.621 
0.497 
0.434 
0.517 
0.469 
0.474 
0.393 
0.474 
0.390 
0.415 
0.362 
0.436 
0.394 
0.399 
0.328 
0.398 
0.324 
0.378 
0.325 
0.391 
0.355 
0.360 
0.294 
0.359 
0.290 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 6. Physical Damage Modeling 

?
Table 6.52. Percent Increases in the Per Storm Average Building Damage State 
due 
to Changes in Building Parameters (Minimum/Average/Maximum) - Strip Mall 
Building with Wood Roof System 

Building Parameter 
Building Designation 
Building A Building B 
8d to 6d Roof Deck Nails 0% / 4% / 15% -1% / 3% / 9% 
Strap to Toe-Nail Roof/Wall Connections 4% / 12% / 29% 4% / 12% / 24% 
Built-up to Single ply Membrane Roof Cover 4% / 13% / 32% 7% / 15% / 28% 
Reinforced to Unreinforced Masonry Walls -2% / 1% / 3% 1% / 4% / 10% 
Missile Environment D to C 2% / 16% / 38% 0% / 4% / 12% 
Missile Environment C to B 10% / 22% / 37% 4% / 9% / 15% 
Missile Environment B to A 9% / 18% / 24% 5% / 9% / 15% 

Table 6.53. Percent Increases in the Per Storm Average Building Damage State 
due 
to Changes in Building Parameters (Minimum/Average/Maximum) - Strip Mall 
Building with Steel Roof System 

Building Parameter Building Designation 
Building A Building B Building C Building D 
0% to 50% Reduction in Metal 
Roof Deck Resistance -2% / 0% / 2% -1% / 0% / 1% 1% / 9% / 31% 1% / 9% / 30% 
Screwed to Welded Metal Roof 
Deck -2% / 0% / 2% -2% / 0% / 1% -2% / 1% / 9% -2% / 1% / 9% 
ASCE to SBCCI Metal Roof 
Deck Design Criteria -2% / 0% / 2% -2% / 0% / 2% -1% / 5% / 22% -1% / 5% / 
23% 
Built-up to Single ply Membrane 
Roof Cover 6% / 18% / 34% 11% / 20% / 30% 6% / 17% / 29% 5% / 16% / 27% 
Reinforced to Unreinforced 
Masonry Walls -2% / 1% / 4% 3% / 8% / 14% 1% / 6% / 15% 1% / 6% / 15% 
Missile Environment D to C 1% / 11% / 24% -1% / 2% / 5% -1% / 2% / 6% -1% / 
2% / 7% 
Missile Environment C to B 11% / 27% / 40% 4% / 9% / 18% 3% / 8% / 17% 3% / 
7% / 16% 
Missile Environment B to A 9% / 19% / 26% 3% / 8% / 14% 4% / 8% / 14% 4% / 7% 
/ 13% 

Table 6.54. Percent Increases in the Per Storm Average Building Damage State 
Due 
to Changes in Building Configuration (Minimum/Average/Maximum) 

Building Configuration 
Increase in Average Annual 
Building Loss 
Building A to B   Wood Roof System (12' to 20' Roof Height) 26% / 67% / 125% 
Building A to B   Steel Roof System (12' to 20' Roof Height) 25% / 69% / 126% 
Building B to C   Steel Roof System (4' to 6' Joist Spacing) -1% / 5% / 31% 
Building C to D   Steel Roof System (6 Units to 1 Unit) -1% / 2% / 4% 

increases the mean damage state by up to 31%. It is also shown in Table 6.54 
that by 
going from the multi-unit buildings to the single unit buildings, the mean 
per storm 
damage state increases by no more than 4%. 

6.11 Damage Model Results for Pre-Engineered Metal Buildings 
Three metal buildings were modeled as shown in Figure 6.64. The building 
damage states 
associated with the metal buildings are defined in Table 6.55. Note that no 
frame failures 

HAZUS-MH MR3 Technical Manual 

?
Small Metal Building, L = 100', W = 40', H = 14' 

Medium Metal Building, L = 250', W = 200', H = 25' 

Figure 6.64. Geometries of Metal Buildings Used in Study. 

Chapter 6. Physical Damage Modeling 

?
Large Metal Building, L = 1000', W = 500', H = 30' 

Figure 6.64 . Geometries of Metal Buildings Used in Study (concluded). 
Table 6.55. Damage States Metal Buildings 

Damage 
State Qualitative Damage Description 
Entry/Overhead 
Door 
Failures 
Metal Roof 
Deck 
Failures 
Metal Wall 
Siding 
Failures 
Missile 
Impacts on 
Walls 
0 
No Damage or Very Minor Damage 
Little or no visible damage from the outside. 
No broken windows, or failed roof deck. 
None or very limited water penetration. 
No No No No 
1 
Minor Damage 
Maximum of one broken window or, door or 
wall panel. Marks or dents on walls requiring 
painting or patching for repair. 
One door No One panel Typically<5 
impacts 
2 
Moderate Damage 
Moderate fenestration failures. Minor roof 
panel failures, or wall panel failures. Some 
resulting damage to interior of building from 
water. 
>One 
to=33% 
One totwo 
panels 
>One 
to=15% 
Typically5 
to 10 
impacts 
3 
Severe Damage 
Major window damage or roof sheathing 
loss. Extensive damage to the interior from 
water. Some frame damage likely. 
>33% 
to=75% >2 to=10% >15% 
to=33% 
Typically 10 
to 20 
impacts 
4 
Destruction 
Significant failures of fenestrations, 
significant roof and wall panel failures. 
Significant frame damage likely. 
>75% >10% >33% Typically>2 
0 impacts 

HAZUS-MH MR3 Technical Manual 

?
are modeled, with the entire performance of the building governed by the 
performance of 
the cladding and the fenestrations. The degrees of roof sheathing damage 
required to 
move the building from one damage state to another are similar to those used 
for the 
residential buildings. The fenestration damage requirements are less 
stringent than those 
used in the case of residential buildings, where here, 75% of the 
fenestrations must fail to 
enter damage state four, versus only 50% in the residential building case. 
The resistance 
parameters are given for all components in Table 6.56. 

Table 6.56. Component Resistances for Pre-Engineered Metal Buildings 

Component Distribution Distribution Parameters 
Metal Roof Deck - 100mph Design Speed (Corner, Edge, Field) Normal Mean = 
151,89,89 psf, COV = 
0.11,0.11,0.11 
Metal Roof Deck - 90 mph Design Speed (Corner, Edge, Field) Normal Mean = 
104,89,89 psf, COV = 
0.11,0.11,0.11 
Metal Wall Cladding Normal Mean = 84 psf, COV = 0.09 
Overhead Roll-Up Doors Normal Mean = 15 psf, COV = 0.2 
Entry Door Pressure Normal Mean = 50 psf, COV = 0.2 

Two analyses were performed for each building, one with the resistance of the 
metal 
cladding modeled as given in Table 6.56, and the second analysis with the 
resistance of 
the metal components reduced by 50%, to account for aging and fatigue. 

Example building and component damage state plots are given in Appendix E. 
The 
results presented in Appendix E indicate that the performance of the model 
buildings is 
governed by the performance of the fenestrations. This is particularly 
evident in the case 
of the large metal building, which has more doors than either the small or 
medium-sized 
building. The failure sequence of the doors is such that more than one door 
often fails 
during the storm, and there is consequently little distinction between damage 
states 1, 2 
and 3. In the examples presented herein, the weak overhead doors (modeled as 
having a 
mean failure capacity of 15 psf) represent the first mode of failure. The 
wall cladding 
failures are infrequent compared to field observations, suggesting the 
modeled wall 
capacity may be too high. 

The average per storm damage states are summarized in Table 6.57. The impact 
of 
changing the various building parameters is summarized in Table 6.58. The 
results 
indicate that the size of the building has a significant impact on the 
average per storm 
damage state. On average, the per storm average damage states increased by 
20% for the 
medium-sized building versus the small building and 57% for the large 
building versus 
the medium-sized building. Note that the size effect is coupled with the 
effect of the 
increased number of overhead doors associated with the larger buildings. 
Metal roof 
decks which have experienced age and fatigue (modeled with a 50% reduction in 
the 
uplift capacity) were found to have average per storm damage states 6% to 53% 
higher 
than new metal roof decks with all else being the same. Of the building 
parameters 
considered, the roof design code was found to have the smallest impact on 
building 
damage (see Table 6.58). 

Chapter 6. Physical Damage Modeling 

?
Table 6.57. Average Per Storm Damage States for Pre-Engineered Metal 
Buildings 

No Reduction in Metal Panel Capacity 
50% Reduction in Metal Panel 
Capacity 
Building Characteristics Terrain Surface Roughness (m) Terrain Surface 
Roughness (m) 
Design Speed Plan Size 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Small 0.765 0.382 0.287 0.245 0.843 0.450 0.350 0.305 
Med. 0.763 0.427 0.332 0.291 0.964 0.607 0.497 0.44690 mph 
Large 1.087 0.709 0.588 0.525 1.232 0.835 0.710 0.647 
Small 0.765 0.385 0.289 0.245 0.813 0.433 0.335 0.287 
Med. 0.761 0.424 0.332 0.289 0.890 0.534 0.433 0.386100 mph 
Large 1.086 0.704 0.588 0.526 1.225 0.827 0.698 0.638 
Table 6.58. Percent Increases in the Per Storm Average Building Damage State 
Due 
to Changes in Building Parameters (Minimum/Average/Maximum) - Pre-
Engineered Metal Buildings 

Parameter Change in Average Per Storm Damage 
Small to Medium-sized Building -1% / 20% / 46% 
Medium-sized to Large Building 28% / 57% / 82% 
100 mph to 90 mph Roof Design Code -1% / 3% / 16% 
0% to 50% Reduction in Metal Deck Capacity (i.e., Age and Fatigue) 6% / 23% / 
53% 

6.12 Damage Model Results for Engineered Residential and Commercial Buildings 
The fully engineered buildings being considered have a structural system 
comprised of 
either concrete or steel. In the case of the concrete buildings it has been 
assumed that the 
roof slab is a poured concrete slab that can not be penetrated by water if 
the roof cover 
fails. Thus, in the case of concrete buildings, the damage state of the 
building is driven 
entirely by the performance of the windows. The damage state definitions 
proposed for 
the steel buildings are given in Table 6.59. 

The steel buildings are modeled as having an open web steel joist roof system 
with a 
metal deck welded to the joists. The uplift capacities of the metal roof 
panels are 
estimated based on the ASCE design code for a 100 mph fastest mile design 
speed (see 
Section 6.7.3). The roof cover is modeled as either a good quality single ply 
membrane or 
a good quality built-up roof. The prime variables being altered for the 
engineered 
buildings are the fraction of the walls covered by glass (nominally 20%, 33% 
and 50%) 
and the number of stories (two, five and eight). The building models are 
shown in Figures 

6.65 through 6.67. The environmental variables considered in the analysis are 
missile 
source environment and terrain exposure. The four missile environments used 
for the 
strip-mall study (see Table 6.45) are used again here for the engineered-
building study. 
The four terrain environments considered are open (z0 = 0.03 m), suburban (z0 
= 0.35 m), 
lightly treed suburban (z0 = 0.7 m), and heavily treed suburban (z0 = 1.0 m). 
HAZUS-MH MR3 Technical Manual 

?
Table 6.59. Damage State Definitions for Engineered Steel Buildings 

Damage 
State Qualitative Damage Description 
Roof 
Cover 
Failure 
Window/ 
Door 
Failures 
Roof 
Deck 
Failure 
Missile 
Impacts 
on Walls 
Joist 
Failures 
0 
No Damage or Very Minor Damage 
Little or no visible damage from the outside. No 
broken windows, or failed roof deck. Minimal loss 
of roof cover, with no or very limited water 
penetration. 
=2% No No No No 
1 
Minor Damage 
Maximum of one broken window or door. 
Moderate roof cover loss that can be covered to 
prevent additional water entering the building. 
Marks or dents on walls requiring painting or 
patching for repair. 
>2% 
to=15% 
One 
windowor 
door 
No 
Typically 
<5 
impacts 
No 
2 
Moderate Damage 
Major roof cover damage, moderate window 
breakage. Minor roof deck failure. Some resulting 
damage to interior of building from water. 
>15% 
to=50% 
>Oneto 
=2% 
One or 
two 
panels 
Typically 
5 to 10 
impacts 
No 
3 
Severe Damage 
Major window damage or roof sheathing loss. 
Major roof cover loss. Extensive damage to 
interior from water. Limited, local joist failures. 
>50% >2% to 
=25% 
>Two 
to 
=25% 
Typically 
10 to 20 
impacts 
One 
Joistto 
=25% 
4 
Destruction 
Essentially complete roof failure and/or of more 
than 25% of roof sheathing. Significant amount of 
the wall envelope opened through windows failure. 
Extensive damage to interior 
Typicall 
y >50% >25% >25% 
Typically 
>20 
impacts 
>25% 

The residential buildings have eight separate units per floor and the 
commercial buildings 
have a single unit per floor. The internal pressure associated with glass 
breakage is 
confined to the unit in which the window damage occurs. That is, it is 
assumed that no air 
leakage occurs between the adjacent units on the same floor and that no air 
leakage 
occurs between the adjacent units on different floors. 

All glass is modeled as single pane (non-insulated) tempered glass. The 
window frame 
system is modeled with a mean failure pressure of 75 psf and a coefficient of 
variation of 
20%. The metal deck failure pressures are modeled with normal distributions. 
The 
distribution parameters are listed in Table 6.60. 

The results of the damage simulations show that the building performance is 
strongly 
driven by the performance of the roof cover. Recalling that the single ply 
membrane and 
the built-up roof cover models were developed using limited empirical data, 
it appears 
that the damage results given here overestimate the failure rates for these 
roof covers. To 
obtain a better understanding of the effect of the other parameters (window 
area, missile 
environment, number of units per floor, etc.) on the average per storm damage 
states, the 
effect of the roof cover damage was removed from the building damage state 
definitions 
and the damage analysis was re-run. Damage state results have been presented 
either 
including or excluding the roof cover effect on the overall damage state. 

Chapter 6. Physical Damage Modeling 

?
(a) 21% Glazing Coverage (b) 32% Glazing Coverage 
(c) 46% Glazing Coverage 
Figure 6.65. Modeled Two-Story Fully-Engineered Buildings. 

To make the damage state definitions with respect to roof damage (i.e., roof 
cover, roof 
deck and roof joists) consistent between the buildings with a different 
number of stories, 
the threshold damage levels defining the damage states are modified by a 
factor which is 
a function of the number of stories. The concept is that 5% roof deck damage, 
for 
example, will have a greater effect in terms of economic losses on a one-
story building 
compared to a two-story building. That is, in the case of the two-story 
building, only the 
top floor will experience similar damage due to the water infiltration 
associated with 5% 
roof deck loss to that experienced by the single-story building. The 
assumption made here 
for the purpose of the roof damage state definitions is that the intensity of 
damage on the 
bottom floor of the two-story building will be half as much as that on the 
top floor. Thus, 
if the damage intensity to the interior of the single-story building is 
arbitrarily set to 1, 
then the damage intensity to the two-story building will be (1+0.5)/2 = 0.75 
(i.e., 1 
represents the damage intensity of the top floor and 0.5 represents the 
damage intensity of 
the bottom floor). Therefore, to achieve a similar damage intensity to that 
associated with 
5% roof deck damage on a single-story building, an otherwise similar two-
story building 
would require 6.7% roof deck damage (i.e., 5%/0.75). The thresholds used to 
define the 
roof damage states listed in Table 6.59 where modified in the same manner as 
described 

HAZUS-MH MR3 Technical Manual 

?
6-132 
Chapter 6. Physical Damage Modeling 
(a) 20% Glazing Coverage (b) 36% Glazing Coverage 
(c) 54% Glazing Coverage 
Figure 6.66. Modeled Five-Story Fully-Engineered Buildings. 
above for the two-, five- and eight-story buildings. The multiplicative 
factors are 1.33, 
2.58 and 4.02, respectively. It can be seen, for example, that the five- and 
eight-story 
building cannot achieve a building damage state 3 (i.e., severe damage) based 
on roof 
cover loss since the 50% threshold increases to more than a 100% in both 
cases. Also 
note that the two-story building will be placed in damage state 3 when the 
roof cover loss 
exceeds 67%. 
Example building and component damage state plots are given in Appendix F. 
Tables 
6.61 through 6.63 list the average per storm damage states for the two-, 
five- and eightstory 
buildings, respectively. A summary of the effects of the various parameters 
on the 
average per storm damage states is given in Table 6.64. The average per storm 
damage 
states presented in Tables 6.61 through 6.62 were computed with the roof 
cover damage 
criteria included and excluded. As noted above, the roof cover was found to 
govern the 
overall building damage state in many of the cases and thus, to better 
examine the effects 
of the other variables, both sets of damage states have been given. 
The average per storm damage states are found to increase by up to 33% when 
the roof 
cover is modeled with a EPDM single ply membrane versus a built-up roof 
cover. The 
effect of having a single unit per floor (commercial usage) compared to 
multi-units per 
floor (residential usage) is at most an 8% increase in the mean per storm 
damage state. 
?
6-133 
HAZUS-MH MR3 Technical Manual 
(a) 20% Glazing Coverage (b) 36% Glazing Coverage 
(c) 54% Glazing Coverage 
Figure 6.67. Modeled Eight-Story Fully-Engineered Buildings. 
Table 6.60. Failure Pressure Models for Metal Deck on Steel Joists 
Mean Failure Pressure (Standard Deviation), psf 
Number of ACSE Design, 100 mph Wind Speed Zone, Welded Deck 
Stories Roof Zone 1 Roof Zone 2 Roof Zone 3 Roof Zone 4* 
2 91 (17) 145 (27) 155 (29) - 
5 123 (23) 155 (29) 214 (41) - 
8 145 (27) 155 (29) 214 (41) 285 (45) 
* Roof Zone 4 only applicable for buildings more than 60' high 
The average per storm damage states increased by as much as 13% when the 
glazing 
coverage changed from 20% to 33% and by as much as 18% when the glazing 
coverage 
changed from 33% to 50%. The impact of the missile environment on building 
damage 
states is more pronounced on the two-story buildings than it is on either the 
five- or eightstory 
buildings. This trend results from the fact that the modeled average heights 
of the 
buildings from which the commercial and residential type missiles originate 
are lower 
than the five- and eight-story engineered buildings. Thus, the glazing on the 
upper floors 
of the five- and eight-story buildings are less susceptible to missile damage 
than the 
glazing on the lower floors. 
?
Table 6.61. Average Building Damage States - Two-Story Engineered Building 

Building Characteristics 
Residential Buildings 
(8 Units per Floor) 
Commercial Buildings 
(1 Unit per Floor) 
Roof Terrain Roughness (m) Terrain Roughness (m) 
Cover 
Effect 
Missile 
Environ. 
Glazing 
Coverage 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.00 
Included 
A 
20% BUR 
EPDM 
0.607 
0.629 
0.684 
0.699 
0.769 
0.776 
0.542 
0.548 
0.593 
0.598 
0.663 
0.667 
0.393 
0.403 
0.433 
0.439 
0.484 
0.488 
0.322 
0.335 
0.356 
0.365 
0.397 
0.403 
0.610 
0.629 
0.684 
0.695 
0.768 
0.774 
0.541 
0.547 
0.593 
0.598 
0.664 
0.669 
0.392 
0.401 
0.434 
0.439 
0.485 
0.489 
0.322 
0.334 
0.356 
0.364 
0.396 
0.405 
33% BUR 
EPDM 
50% BUR 
EPDM 
B 
20% BUR 
EPDM 
0.470 
0.512 
0.525 
0.557 
0.595 
0.620 
0.415 
0.433 
0.458 
0.472 
0.510 
0.520 
0.303 
0.322 
0.333 
0.351 
0.370 
0.387 
0.252 
0.272 
0.277 
0.291 
0.305 
0.323 
0.476 
0.516 
0.529 
0.560 
0.597 
0.624 
0.413 
0.435 
0.455 
0.468 
0.512 
0.521 
0.304 
0.325 
0.333 
0.351 
0.373 
0.387 
0.250 
0.272 
0.275 
0.293 
0.308 
0.323 
33% BUR 
EPDM 
50% BUR 
EPDM 
C 
20% BUR 
EPDM 
0.364 
0.422 
0.400 
0.448 
0.450 
0.487 
0.312 
0.339 
0.346 
0.371 
0.386 
0.403 
0.231 
0.260 
0.254 
0.281 
0.284 
0.304 
0.194 
0.223 
0.213 
0.237 
0.233 
0.256 
0.373 
0.430 
0.409 
0.457 
0.456 
0.492 
0.312 
0.339 
0.349 
0.368 
0.388 
0.405 
0.232 
0.260 
0.255 
0.279 
0.282 
0.303 
0.192 
0.222 
0.211 
0.239 
0.235 
0.258 
33% BUR 
EPDM 
50% BUR 
EPDM 
D 
20% BUR 
EPDM 
0.279 
0.356 
0.279 
0.359 
0.291 
0.363 
0.181 
0.238 
0.182 
0.237 
0.187 
0.243 
0.152 
0.202 
0.153 
0.202 
0.158 
0.204 
0.137 
0.182 
0.138 
0.183 
0.143 
0.184 
0.284 
0.360 
0.286 
0.362 
0.305 
0.376 
0.182 
0.238 
0.185 
0.242 
0.196 
0.249 
0.153 
0.201 
0.153 
0.203 
0.161 
0.208 
0.138 
0.184 
0.139 
0.185 
0.147 
0.189 
33% BUR 
EPDM 
50% BUR 
EPDM 
A 
20% 0.580 
0.665 
0.755 
0.530 
0.583 
0.654 
0.378 
0.419 
0.473 
0.305 
0.340 
0.384 
0.584 
0.666 
0.754 
0.529 
0.583 
0.655 
0.377 
0.420 
0.474 
0.304 
0.341 
0.383 
33% 
50% 
Excluded 
B 
20% 
N/A 
0.409 
0.479 
0.562 
0.390 
0.437 
0.493 
0.272 
0.308 
0.350 
0.217 
0.248 
0.282 
0.419 
0.484 
0.565 
0.387 
0.434 
0.495 
0.275 
0.307 
0.352 
0.217 
0.247 
0.285 
33% 
50% 
C 
20% 0.270 
0.326 
0.397 
0.272 
0.315 
0.361 
0.184 
0.217 
0.254 
0.144 
0.171 
0.200 
0.285 
0.339 
0.403 
0.273 
0.317 
0.364 
0.188 
0.217 
0.254 
0.144 
0.171 
0.203 
33% 
50% 
D 
20% 0.092 
0.096 
0.145 
0.041 
0.041 
0.075 
0.030 
0.030 
0.060 
0.025 
0.025 
0.050 
0.098 
0.104 
0.160 
0.042 
0.043 
0.085 
0.031 
0.032 
0.067 
0.025 
0.026 
0.059 
33% 
50% 

6.13 Damage Model Results for Industrial Buildings 
Large industrial buildings typically have flat roofs and are usually built 
with a masonry 
wall system. The roof structure is typically constructed using open web steel 
joists and a 
metal deck. The damage states for large industrial buildings are described in 
Table 6.65. 

The representative industrial building is modeled here with masonry walls 
(either 
reinforced or unreinforced) and with a light weight open web steel joist 
(OWSJ) roof 
system. The model building has a length of 200', a width of 120' and a roof 
height of 20'. 

Chapter 6. Physical Damage Modeling 

?
Table 6.62. Average Building Damage States - Five-Story Engineered Building 

Building Characteristics 
Residential Buildings 
(8 Units per Floor) 
Commercial Buildings 
(1 Unit per Floor) 
Roof Terrain Roughness (m) Terrain Roughness (m) 
Cover 
Effect 
Missile 
Environ 
Glazing 
Coverage 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.00 
Included 
A 
20% BUR 
EPDM 
0.628 
0.667 
0.711 
0.741 
0.795 
0.817 
0.562 
0.591 
0.618 
0.644 
0.677 
0.695 
0.428 
0.467 
0.469 
0.501 
0.512 
0.542 
0.367 
0.410 
0.399 
0.437 
0.436 
0.470 
0.643 
0.681 
0.724 
0.754 
0.805 
0.826 
0.566 
0.597 
0.624 
0.645 
0.681 
0.697 
0.433 
0.471 
0.472 
0.506 
0.518 
0.546 
0.372 
0.411 
0.399 
0.442 
0.440 
0.474 
33% BUR 
EPDM 
50% BUR 
EPDM 
B 
20% BUR 
EPDM 
0.542 
0.598 
0.604 
0.649 
0.682 
0.719 
0.477 
0.516 
0.524 
0.560 
0.576 
0.608 
0.372 
0.420 
0.407 
0.448 
0.446 
0.484 
0.324 
0.375 
0.351 
0.400 
0.384 
0.426 
0.557 
0.612 
0.623 
0.668 
0.700 
0.734 
0.482 
0.523 
0.528 
0.565 
0.580 
0.612 
0.376 
0.426 
0.409 
0.453 
0.452 
0.490 
0.331 
0.379 
0.355 
0.403 
0.392 
0.432 
33% BUR 
EPDM 
50% BUR 
EPDM 
C 
20% BUR 
EPDM 
0.481 
0.546 
0.534 
0.594 
0.599 
0.648 
0.416 
0.467 
0.459 
0.505 
0.506 
0.543 
0.334 
0.388 
0.364 
0.415 
0.399 
0.442 
0.297 
0.351 
0.318 
0.370 
0.349 
0.398 
0.502 
0.566 
0.554 
0.610 
0.616 
0.670 
0.425 
0.474 
0.466 
0.509 
0.508 
0.549 
0.341 
0.398 
0.369 
0.417 
0.404 
0.448 
0.302 
0.361 
0.324 
0.376 
0.355 
0.403 
33% BUR 
EPDM 
50% BUR 
EPDM 
D 
20% BUR 
EPDM 
0.400 
0.489 
0.407 
0.496 
0.447 
0.528 
0.302 
0.376 
0.305 
0.378 
0.342 
0.411 
0.268 
0.337 
0.269 
0.338 
0.303 
0.369 
0.250 
0.317 
0.254 
0.318 
0.285 
0.345 
0.417 
0.503 
0.425 
0.511 
0.479 
0.563 
0.304 
0.379 
0.311 
0.384 
0.368 
0.437 
0.273 
0.342 
0.275 
0.345 
0.324 
0.391 
0.254 
0.320 
0.256 
0.321 
0.303 
0.364 
33% BUR 
EPDM 
50% BUR 
EPDM 
A 
20% 0.572 
0.669 
0.763 
0.522 
0.586 
0.650 
0.373 
0.423 
0.475 
0.301 
0.343 
0.388 
0.589 
0.682 
0.774 
0.527 
0.591 
0.654 
0.378 
0.425 
0.480 
0.307 
0.345 
0.393 
33% 
50% 
Excluded 
B 
20% 
N/A 
0.448 
0.529 
0.627 
0.412 
0.472 
0.533 
0.292 
0.338 
0.390 
0.235 
0.272 
0.320 
0.466 
0.551 
0.645 
0.418 
0.476 
0.537 
0.295 
0.340 
0.396 
0.239 
0.277 
0.326 
33% 
50% 
C 
20% 0.355 
0.433 
0.523 
0.332 
0.392 
0.450 
0.234 
0.280 
0.330 
0.185 
0.223 
0.272 
0.378 
0.456 
0.543 
0.339 
0.398 
0.454 
0.239 
0.283 
0.335 
0.192 
0.230 
0.278 
33% 
50% 
D 
20% 0.151 
0.160 
0.273 
0.083 
0.088 
0.188 
0.066 
0.069 
0.164 
0.057 
0.059 
0.150 
0.164 
0.179 
0.303 
0.086 
0.094 
0.216 
0.068 
0.073 
0.185 
0.059 
0.063 
0.170 
33% 
50% 

The overall building geometry, along with the placement and relative size of 
the overhead 
rollup doors and entry doors is shown in Figure 6.68. The roof is divided 
into three 
sections each being 200' long and 40' wide. The joists span 40' with their 
ends supported 
by a perimeter wall or a main structural beam and are spaced at 6'. Note that 
the 
entrydoors and overhead rollup doors are not glazed and thus no envelope 
breaches can 
be caused by missile impacts. The roof cover is modeled as an average quality 
EPDM 
single ply membrane. 

The OWSJs are designed to resist the uplift forces and moments computed using 
ASCE7-
88, as described in Section 6.7.2, for a fastest-mile design wind speed of 
100 mph. The 
building is assumed to be designed for open terrain (Exposure C) conditions. 
The metal 

HAZUS-MH MR3 Technical Manual 

?
Table 6.63. Average Building Damage States - Eight-Story Engineered Building 

Building Characteristics 
Residential Buildings 
(8 Units per Floor) 
Commercial Buildings 
(1 Unit per Floor) 
Roof Terrain Roughness (m) Terrain Roughness (m) 
Cover 
Effect 
Missile 
Environ. 
Glazing 
Coverage 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.00 
Included 
A 
20% BUR 
EPDM 
0.761 
0.857 
0.809 
0.900 
0.896 
0.978 
0.684 
0.770 
0.724 
0.801 
0.780 
0.862 
0.598 
0.691 
0.620 
0.711 
0.667 
0.752 
0.557 
0.653 
0.575 
0.666 
0.613 
0.704 
0.768 
0.861 
0.821 
0.912 
0.922 
1.001 
0.682 
0.773 
0.727 
0.811 
0.802 
0.879 
0.599 
0.692 
0.625 
0.712 
0.690 
0.771 
0.560 
0.652 
0.577 
0.668 
0.635 
0.728 
33% BUR 
EPDM 
50% BUR 
EPDM 
B 
20% BUR 
EPDM 
0.729 
0.832 
0.759 
0.856 
0.833 
0.929 
0.640 
0.734 
0.673 
0.762 
0.722 
0.811 
0.574 
0.670 
0.591 
0.686 
0.634 
0.724 
0.545 
0.640 
0.556 
0.647 
0.593 
0.684 
0.732 
0.839 
0.773 
0.870 
0.866 
0.960 
0.640 
0.737 
0.676 
0.765 
0.747 
0.831 
0.574 
0.671 
0.594 
0.689 
0.656 
0.750 
0.547 
0.639 
0.558 
0.653 
0.614 
0.707 
33% BUR 
EPDM 
50% BUR 
EPDM 
C 
20% BUR 
EPDM 
0.706 
0.814 
0.729 
0.836 
0.792 
0.898 
0.616 
0.717 
0.641 
0.737 
0.683 
0.780 
0.562 
0.661 
0.574 
0.672 
0.610 
0.709 
0.535 
0.634 
0.540 
0.639 
0.577 
0.674 
0.711 
0.818 
0.743 
0.850 
0.827 
0.928 
0.619 
0.717 
0.642 
0.737 
0.706 
0.797 
0.565 
0.663 
0.577 
0.675 
0.636 
0.732 
0.537 
0.633 
0.547 
0.645 
0.604 
0.696 
33% BUR 
EPDM 
50% BUR 
EPDM 
D 
20% BUR 
EPDM 
0.685 
0.801 
0.694 
0.808 
0.738 
0.856 
0.590 
0.694 
0.590 
0.697 
0.627 
0.735 
0.551 
0.652 
0.552 
0.655 
0.588 
0.686 
0.530 
0.628 
0.533 
0.631 
0.563 
0.663 
0.688 
0.806 
0.708 
0.823 
0.785 
0.902 
0.590 
0.696 
0.596 
0.704 
0.669 
0.775 
0.552 
0.654 
0.557 
0.656 
0.627 
0.727 
0.532 
0.629 
0.532 
0.629 
0.599 
0.697 
33% BUR 
EPDM 
50% BUR 
EPDM 
A 
20% 0.554 
0.631 
0.746 
0.496 
0.559 
0.637 
0.368 
0.411 
0.482 
0.307 
0.343 
0.406 
0.560 
0.645 
0.776 
0.496 
0.561 
0.660 
0.370 
0.414 
0.501 
0.312 
0.346 
0.430 
33% 
50% 
Excluded 
B 
20% 
N/A 
0.458 
0.521 
0.631 
0.405 
0.459 
0.537 
0.309 
0.344 
0.415 
0.265 
0.292 
0.357 
0.464 
0.538 
0.667 
0.408 
0.462 
0.561 
0.309 
0.346 
0.437 
0.266 
0.293 
0.381 
33% 
50% 
C 
20% 0.388 
0.444 
0.547 
0.342 
0.390 
0.468 
0.267 
0.300 
0.367 
0.235 
0.256 
0.322 
0.394 
0.461 
0.587 
0.345 
0.395 
0.489 
0.270 
0.302 
0.390 
0.234 
0.259 
0.350 
33% 
50% 
D 
20% 0.278 
0.286 
0.382 
0.220 
0.223 
0.306 
0.199 
0.200 
0.277 
0.187 
0.188 
0.263 
0.281 
0.304 
0.433 
0.221 
0.227 
0.347 
0.201 
0.204 
0.320 
0.188 
0.190 
0.301 
33% 
50% 

deck is modeled as being connected to the joists using welds. The weld design 
is assumed 
to meet the loading requirements as given in ASCE-7 and is designed using the 
methods 
outlined in Section 6.7.3. 

The assumed component resistances are given in Table 6.66. The uplift values 
are for the 
as-installed roof, but a sensitivity study was performed where the uplift 
capacities of the 
welded connections were reduced by 50% to allow for the effect of age and 
fatigue. This 
50% reduction is comparable to the reduction in the pullout capacity of 
fasteners in metal 
decks as reported by Baskaran and Dutt (1995). 

Chapter 6. Physical Damage Modeling 

?
Example building and component damage state plots are given in Appendix G. 
The 
average per storm damage states are given in Table 6.67. Reviewing the data 
in Table 
6.67, it can be seen that per storm average building damage states vary by no 
more than 
5% over the entire range of building parameters examined. As expected there 
are 
negligible differences between the per storm average building damage states 
due to the 
different missile environments since the structure does not have any glazing. 
The 
different missile environments were included, however, since they will have 
an impact on 
the losses associated with refinishing the masonry wall surfaces and 
replacing damaged 
overhead/entry doors due to damage by missile impacts. It is evident from the 
damage 
state plots shown in Appendix F that building damage state 1 is driven 
primarily by roof 
cover damage and that building damage states 3 and 4 are driven primarily by 
entry/overhead door damage. The entry/overhead door damage states are 
primarily 
governed by the overhead garage doors which are modeled as having a mean 
pressure 
resistance of 15 psf. 

Table 6.64. Percent Increases in the Per Storm Average Building Damage State 
Due 
to Changes in Building Parameters (Minimum/Average/Maximum) - Engineered 
Residential and Commercial Buildings 

Building Parameter Roof Cover Effect Included Roof Cover Effect Excluded 
Two-Story Engineered Buildings 
Residential to Commercial Building Class -1% / 1% / 5% -1% / 2% / 16% 
Built-up to Single Ply Membrane Roof Cover 1% / 12% / 33% N/A 
20% to 33% Glazing Coverage 0% / 7% / 13% -2% / 11% / 21% 
33% to 50% Glazing Coverage 0% / 9% / 13% 12% / 34% / 128% 
Missile Environment D to C 19% / 49% / 107% 152% / 406% / 664% 
Missile Environment C to B 20% / 28% / 33% 36% / 43% / 52% 
Missile Environment B to A 22% / 27% / 31% 32% / 36% / 42% 
Five-Story Engineered Buildings 
Residential to Commercial Building Class 0% / 2% / 8% 0% / 4% / 15% 
Built-up to Single Ply Membrane Roof Cover 2% / 13% / 26% N/A 
20% to 33% Glazing Coverage 0% / 7% / 13% 3% / 14% / 23% 
33% to 50% Glazing Coverage 6% / 10% / 18% 11% / 43% / 171% 
Missile Environment D to C 11% / 25% / 51% 63% / 195% / 349% 
Missile Environment C to B 5% / 10% / 15% 17% / 21% / 27% 
Missile Environment B to A 9% / 14% / 18% 20% / 24% / 30% 
Eight-Story Engineered Buildings 
Residential to Commercial Building Class 0% / 2% / 7% -1% / 3% / 16% 
Built-up to Single Ply Membrane Roof Cover 9% / 15% / 19% N/A 
20% to 33% Glazing Coverage 0% / 3% / 7% 0% / 10% / 17% 
33% to 50% Glazing Coverage 5% / 8% / 13% 14% / 28% / 58% 
Missile Environment D to C 0% / 3% / 9% 15% / 42% / 75% 
Missile Environment C to B 1% / 3% / 6% 9% / 15% / 19% 
Missile Environment B to A 2% / 5% / 8% 13% / 19% / 22% 
All Engineered Buildings 
Two to Five Stories 2% / 36% / 105% -2% / 41% / 198% 
Five to Eight Stories 13% / 54% / 112% -6% / 35% / 233% 

HAZUS-MH MR3 Technical Manual 

?
Table 6.65. Damage States for Industrial Buildings 

Damage 
State Qualitative Damage Description 
Roof Cover 
Failure 
Door 
Failures 
Roof 
Deck 
Failures 
Missile 
Impacts on 
Walls 
Joist 
Failures 
Wall 
Failures 
0 
No Damage or Very Minor Damage 
Little or no visible damage from the 
outside. No failed doors or roof deck. 
Minimal loss of roof cover, with no or very 
limited water penetration. 
=2% No No No No No 
1 
Minor Damage 
Maximum of one failed door. Moderate 
roof cover loss that can be covered to 
prevent additional water entering the 
building. Marks or dents on walls requiring 
painting or patching for repair. 
>2% 
to=15% 
1 door No Typically 
<5 impacts No No 
2 
Moderate Damage 
Major roof cover damage, moderate 
window breakage. Minor roof sheathing 
failure. Some resulting damage to interior 
of building from water. 
>15%to 
=50% 
2 to = the 
greater of 
15% & 3 
1 or 2 
panels 
Typically 
5 to 10 
impacts 
No No 
3 
Severe Damage 
Major window damage or roof sheathing 
loss. Major roof cover loss. Extensive 
damage to interior from water. Limited, 
local joist failures. Failure of one wall. 
>50% 
> the 
greater of 
15% & 3 
to =50% 
3 to 
=25% 
Typically 
10 to 20 
impacts 
1 joistto 
=25% 1 wall 
4 
Destruction 
Complete roof failure on 1/3 or more of the 
units and/or failure of more than one wall. 
Loss of more than 25% of roof sheathing. 
Typically 
>50% >50% >25% 
Typically 
>20 
impacts 
>25% 2 or more 

Figure 6.68. Model Building for Large Industrial Building - 200'W120'W20' 
High. 

Chapter 6. Physical Damage Modeling 

?
Table 6.66. Component Resistance Values Used for Strip-Mall Type Building 

Fenestrations 
Component Distribution Distribution Parameters 
Entry Door Pressure 
Overhead Rollup Garage Door Pressure 
Normal 
LogNormal 
Mean = 50 psf, COV = 0.2 
Mean = 15 psf, COV = 0.2 
Steel Deck on Steel Roof Joists   Building Height = 20', Joist Spacing = 6' 
Component Distribution Distribution Parameters (Field, Edge, Corner) 
Welded Steel Deck   ASCE 100mph Normal Mean = 91, 123, 155 psf, 
COV = 0.19, 0.19, 0.19 

Table 6.67. Per Storm Average Building Damage States   Industrial Building 

Building Characteristics 100% Roof Deck Capacity 50% Roof Deck Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Construction 
Missile 
Environment 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced 
Masonry 
A 
B 
C 
D 
1.152 
1.155 
1.149 
1.151 
0.688 
0.691 
0.689 
0.690 
0.560 
0.559 
0.558 
0.561 
0.498 
0.496 
0.499 
0.497 
1.157 
1.157 
1.159 
1.159 
0.695 
0.696 
0.694 
0.695 
0.564 
0.564 
0.566 
0.565 
0.504 
0.501 
0.506 
0.505 
Reinforced 
Masonry 
A 
B 
C 
D 
1.105 
1.108 
1.103 
1.107 
0.678 
0.674 
0.678 
0.674 
0.554 
0.549 
0.553 
0.553 
0.493 
0.493 
0.492 
0.492 
1.115 
1.112 
1.112 
1.113 
0.680 
0.683 
0.682 
0.684 
0.560 
0.558 
0.555 
0.561 
0.498 
0.502 
0.502 
0.499 

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6-140 

Chapter 6. Physical Damage Modeling 

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Chapter 7. Direct Economic Losses 

7.1 Introduction 
The loss model is a physically-based, damage-to-loss model that computes 
direct 
economic losses using a combination of explicit and implicit costing 
techniques. The loss 
model subdivides buildings into costing subassemblies. This approach provides 
significant costing flexibility and the capability to process a wide range of 
building types. 
The loss model is designed to process detailed building envelope damage 
states, and it 
provides the added capability of estimating building interior and content 
dollar losses by 
directly taking into account the volume of water penetrating through failed 
fenestrations 
(windows, doors, garage doors, etc.). The modeling approach is also well-
suited for 
estimating loss of use and repair time. 

The methodology, validation and results of the loss model are described in 
this section of 
the technical manual. The order of presentation parallels the discussion of 
the damage 
models in Chapter 6. Sections 7.2 through 7.7 deal with residential 
buildings, Section 7.8 
covers manufactured homes, and Sections 7.9 through 7.14 deal with commercial 
buildings. 

7.2 Residential Input Parameters 
A schematic representation of the residential loss model is given in Figure 
7.1. Cost data 
are obtained from RSMeans Residential Cost Data (2001). These data are based 
on 
national averages and require certain adjustments that take into account the 
specific 
geographical location of the loss, the level of work difficulty associated 
with repair and 
remodeling work, and contractor s overhead and profit. Cost adjustment 
indices are not 
applied to content or loss of use dollars. Default subassembly cost ratios 
are modified by 
the building characterization inputs and reflect actual building materials 
and construction 
types. 

Loss model input parameters are grouped into two basic categories: building 
characterization parameters and building envelope damage parameters. Building 
characterization parameters include building material, building construction, 
fenestration, 
basic geometry, and building/content value information. Building envelope 
damage 
parameters include roof cover, roof deck, window damage, building missile 
hits, etc. 
Table 7.1 lists model input parameters as a function of input category. 

The roof type input parameter is used to categorize the overall roof geometry 
and 
modifies the base square foot costs of installed roofing materials. The roof 
type parameter 
takes into account the decrease in labor output as the level of work 
difficulty increases. 
Roof type cost adjustment factors are listed in Table 7.2. The roof slope 
parameter 
modifies the base cost of installed roofing materials and also reflects the 
increase in work 
difficulty. 

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on 
-
  
  
  
D        
    
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onon-
  
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onon-
 
Building 
Characterization 
Building 
Damage State 
Default 
Sub-Assembly 
Cost Configuration 
onInterior 
ConstructionSpecialties 
Framing 
Exterior 
Walls 
Building Specific 
Sub-Assembly 
Cost Configuration 
irect 
Economic 
Loss 
   
Direct 
EconomicLoss      
    
   
Direct 
EconomicLossRoof Covering 
 Roof Sheathing 
 Exterior Cladding 
 Windows 
 Sliding Glass Doors 
 Entrance Doors 
 Garage Doors 
 Protection 
 Interior 
 Specialties 
 Mechanical 
 Electrical 
Costing 
Explicit Implicit 
Electrical 
Foundation 
Site Work 
Roofing 
Mechanical 
 Content 
 Loss Of Use 
 Repair Time 
Direct 
EconomicLossRepair & Remodeling 
Cost Index 
Overhead & Profit 
Location Cost Index 
Direct 
EconomicLoss
Figure 7.1. Schematic Representation of the Residential Damage-to-Loss Model. 
Table 7.1. Model Parameters 

Building Characterization Damage State 
ZIP Code Percent Roof Covering Damaged 
Value Group Percent Roof Sheathing Damaged 
Number of Floors Missile Hits to Exterior Cladding 
Plan Area Volume of Water Penetrating Envelope 
Wall Area Door 
Roof Type Window 
Roof Slope Sliding Glass Door 
Roof Area Skylight 
Roof Cover Material Garage Door 
Exterior Construction Protection 
Exterior Cladding 
Entrance Door 
Window 
Sliding Glass Door 
Skylight 
Garage Door 
Protection (e.g., Shutters) 

Table 7.2. Roof Installed Materials Cost Modification Factors 

Roof Type Difficulty Index 
Shed 0.90 
Gable 1.00 
Gambrel 1.14 
Hip 1.26 
Mansard 1.49 
Complex 1.49 

Chapter 7. Direct Economic Losses 

?
7.3 Residential Subassembly Costs 
Residential structures are grouped into four classes according to square foot 
value: 
economy, average, custom, and luxury. The square foot value grouping of 
residential 
structures is primarily used to reflect quality of construction, uniqueness 
of design, 
overall living area, and cost. The loss model can estimate the cost of 
residential structures 
falling into any of the above value groups. This is possible since the large 
majority of all 
residential structures, regardless of square foot value classification, 
consist of nine basic 
subassemblies: site work, foundation, framing, exterior walls, roofing, 
interiors, 
specialties, mechanical, and electrical. These nine basic subassemblies are 
used 
throughout the industry for both the costing of installed materials and the 
scheduling of 
labor and tasks. 

The loss model is designed to take advantage of the construction subassembly 
concept by 
assigning a cost ratio to each of the subassemblies within the default 
building. The cost 
ratio of a subassembly is defined as the ratio of the cost to complete the 
subassembly to 
the total cost of the entire residential building. 

7.3.1 Default Residence 
The default economy house is a masonry single story building having a living 
area of 
approximately 1200 sq ft and an overall value of $68,940, or $57.45 per sq ft 
of living 
space. The economy residence is mass-produced from stock plans. The materials 
and 
workmanship are sufficient only to satisfy minimum building codes. Low 
construction 
cost is more important than distinctive features. The design is seldom other 
than square or 
rectangular. 

Site work consists of preparation for a poured slab and excavation of a 4' 
deep trench for 
a foundation wall. The foundation is typically a continuous concrete footing 
8. deep by 
18. wide. The foundation wall is built with 8. thick concrete block and is 4' 
deep. A 4. 
trowel finished concrete slab is poured onto a 4. crushed stone base. 

Roof framing costs are based on a 4:12 pitch roof using 2W4 wood trusses 
spaced 24. on 
center with 3/8. plywood sheathing. Exterior walls are constructed of 8. 
concrete block 
sealed and painted on the exterior with furring on the interior for drywall. 
The residence 
will typically have 2 flush solid core wood exterior doors and aluminum or 
wood awning 
windows. 

Roof covering consists of 14 squares of asphalt shingles with 15 lb roofing 
paper, 
aluminum flashing, and attic insulation. 

The interiors are constructed with =. drywall taped and finished and painted 
with primer 
and one finish coat. Softwood baseboard and trim are used and painted with 
primer and 
one finish coat. Floor finishes consists of rubber backed carpeting over 80% 
of floor area 
and tile over the remainder. There are typically 15 to 20 hollow core wood 
interior doors. 

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7-4 

Specialties include 6 linear ft of kitchen wall and base cabinets with 
laminated plastic 
counter top. 

Mechanical considerations consist of one wall hung lavatory, one water 
closet, one 
porcelain enamel steel bathtub, one stainless steel kitchen sink, a 30 gallon 
gas fired 
water heater, and gas fired forced hot air heat. 

The electrical subassembly consists of a 100 ampere service with romex 
wiring, 
incandescent lighting fixtures, switches, and receptacles. 

7.3.2 Subassembly Cost Ratios 
Default residential subassembly cost ratios are given as a function of the 
number of 
stories in Table 7.3. For residential structures falling outside of the 
default building 
definition, cost ratios are easily modified to accommodate departures from 
the default 
material and construction configurations. A comparison between subassembly 
cost ratios 
for homes having tile versus asphalt shingle roof covering is provided in 
Table 7.4. For 
one story homes with tile roofing, the cost ratio increases significantly for 
the roofing 
subassembly and the ratios for other subassemblies decrease. The impact of 
this roofing 
material change is less for two and three story residences. 

Table 7.3. Default Residential Subassembly Cost Ratios 

Subassembly 1 Story 2 Story 3 Story 
Site Work 1% 1% 1% 
Foundation 13% 8% 5% 
Framing 13% 11% 14% 
Exterior Wall 22% 25% 26% 
Roofing 3% 2% 1% 
Interiors 32% 38% 39% 
Specialties 4% 4% 4% 
Mechanical 9% 8% 7% 
Electrical 3% 3% 3% 
Total 100% 100% 100% 

Table 7.4. Subassembly Cost Ratios - Tiles versus Shingle 

Assembly 
1 Story 2 Story 3 Story 
Shingle Tile Shingle Tile Shingle Tile 
Site Work 1% 1% 1% 1% 1% 1% 
Foundation 13% 11% 8% 7% 5% 5% 
Framing 13% 13% 11% 11% 14% 11% 
Exterior Wall 22% 17% 25% 23% 26% 26% 
Roofing 3% 13% 2% 7% 1% 5% 
Interiors 32% 30% 38% 36% 39% 38% 
Specialties 4% 4% 4% 4% 4% 4% 
Mechanical 9% 8% 8% 8% 7% 7% 
Electrical 3% 3% 3% 3% 3% 3% 
Total 100% 100% 100% 100% 100% 100% 

Chapter 7. Direct Economic Losses 

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Table 7.5 shows a comparison between cost ratios for the default single story 
home and a 
modified single story home (brick veneer and tile roof). Using preprocessed 
cost data, the 
square foot cost of exterior wall and roofing subassemblies are adjusted to 
take into 
account the increase in cost of using brick and tile. Modified cost ratios 
are then easily 
computed as shown. 

Table 7.5. Cost Ratio Comparison for Brick and Tile Modification 

Subassembly Default Modified Default Modified 
Site Work $0.60 $0.60 1% 1% 
Foundation $7.19 $7.19 13% 10% 
Framing $7.83 $7.83 13% 11% 
Exterior Wall $12.10 $18.60 22% 28% 
Roofing $1.79 $7.17 3% 10% 
Interiors $18.42 $18.42 32% 27% 
Specialties $2.52 $2.52 4% 4% 
Mechanical $4.92 $4.92 9% 7% 
Electrical $1.65 $1.65 3% 2% 
Sub $57.02 $68.90 100% 100% 
Profit & Overhead $2.86 $3.46 
Total $59.88 $72.36 100% 100% 

If the modified building of Table 7.5 has a value of $65,000 and were to 
suffer damage to 
50% of its interior subassembly, the base cost to repair the interior 
subassembly only 
(excluding city cost index, repair and remodeling adjustments, and overhead 
and profit) is 
approximately 0.5W0.27W$65,000 or $8,775. 

Using the above approach, all residential structures can easily be sub-
divided into their 
subassemblies and their respective cost ratios computed. For buildings 
falling outside the 
scope of the default building definition, modified cost ratios are easily 
computed as 
shown in Table 7.5. This approach provides the loss model with an 
extraordinary degree 
of flexibility for costing. Once the cost ratios are computed the building 
value is used to 
assign dollar values to the various subassemblies. Given a damage state to a 
particular 
subassembly, the cost to repair is easily computed as follows: 

C =D7CR 7V (7.1) 

where C is the base cost to repair, D is a the fraction of the subassembly to 
be replaced or 
repaired, CR is the cost ratio for the subassembly, and V is the building 
value. The base 
cost, C, is then adjusted to take into account city cost index, repair and 
remodeling 
adjustments, and overhead and profit. 

7.4 Explicit Costing of Residential Losses 
The explicit costing method directly calculates the repair and replacement 
costs for 
damaged building envelope components using a combination of RSMeans unit, 
linear 
foot, and square foot costs. Explicit costing is used within the model for 
several building 
envelope components, such as roof covering, roof sheathing, windows, entrance 
doors, 

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garage doors, fenestration protection, etc. Dollar losses associated with 
these components 
are directly calculated using actual building damage states and damage 
replacement 
thresholds. Damage replacement thresholds are the minimum damage levels at 
which 
building components are completely replaced. Replacement thresholds are an 
important 
model parameter and can significantly impact final losses, this is especially 
true of tile 
roof coverings. Replacement thresholds will typically vary between building 
component 
type and insurance companies; however, in the present loss model, damage 
replacement 
thresholds are currently set at a 0.5% damage state for both asphalt roof 
covering and tile 
roof covering. A 5% threshold for roof sheathing is used. In addition to 
replacement 
thresholds, the loss model also takes into account building component 
replacement based 
on serviceability considerations. For those building envelope components 
(windows, 
entrance doors, garage doors, and fenestration protection) that do not fail 
under applied 
load, they may still require replacement after undergoing thousands of load 
cycles as they 
become prone to permanent set. This is especially true when load cycles 
approach the 
ultimate capacity of the component. The loss model takes serviceability 
considerations 
into account by replacing those components that sustain loads exceeding 85% 
of their 
ultimate capacity. 

The cost data used in the explicit costing portion of the loss model is 
derived from the 
RSMeans Residential Cost Data and is based on national averages for new 
construction 
materials and installation. Continuous deterministic functions are used to 
cost windows, 
doors, protection, garage doors, skylights, etc., as a function of type, 
material, and size. 
For example, for double hung wood windows, the following functions are used 
to 
estimate the replacement cost: 

Standard glass: Replacement Cost = 10.00 7 (AREA   12.00)+225.00 

Insulating glass: Replacement Cost = 14.40 7 (AREA   12.00)+270.00 

where AREA is the overall area of the window unit. All building envelope 
fenestration 
(windows, entrance doors, garage doors, and protection) have conceptually 
similar 
functions. Roof covering costs are calculated as 

RC =RA 7(CC + CO ) (7.2) 

where RC is the base roof cost, RA is the roof area to replace, CC is the 
covering cost, and 
CO are other roofing costs. Roof sheathing costs are determined similarly. 

The loss model cost data are obtained from new construction RSMeans 
Residential Cost 
Data. These data are based on national averages and require certain 
adjustments which 
take into account the specific geographical location of the loss, the level 
of work 
difficulty, and contractor s overhead and profit. Consequently, the model 
uses a city cost 
index to modify losses as a function of ZIP Code. A partial listing of city 
cost indices is 
provided in Table 7.6. Furthermore, loss model cost data are based on new 
construction 
costs and it is essential to adjust the computed base costs to take into 
account the repair 
and remodeling nature of the work. Costs are typically increased due to the 
decrease in 
labor output typically associated with repair and remodeling work. Additional 
costs also 

Chapter 7. Direct Economic Losses 

?
Table 7.6. Location Index Factor   Florida 

ZIP Code City Index 
320, 322 Jacksonville 0.85 
321 Daytona Beach 0.89 
323 Tallahassee 0.78 
324 Panama City 0.72 
325 Pensacola 0.87 
326 Gainesville 0.86 
327, 328, 347 Orlando 0.88 
329 Melbourne 0.89 
330, 331, 332, 340 Miami 0.85 
333 Fort Lauderdale 0.85 
334, 349 West Palm Beach 0.86 
335, 336, 346 Tampa 0.82 
337 St. Petersburg 0.83 
338 Lakeland 0.82 
339 Fort Myers 0.82 
342 Sarasota 0.81 

arise when protecting existing work. To account for these increases in cost, 
base costs are 
adjusted using a repair and remodeling cost adjustment factor of 1.25. This 
factor is 
determined using expert judgment, field observation, and literature review 
(RSMeans). 

7.5 Modeling of Residential Interior and Content Losses and Loss of Use 
Unlike the case of the building exterior, where the cost associated with the 
predicted or 
modeled damage can be explicitly estimated, the interior damage and, hence, 
resulting 
loss, is estimated using an implicit model. In this model, the economic loss 
is estimated 
using simple functions developed primarily on the basis of experience and 
judgment. The 
economic damage to the interior of the building is a function of the damage 
to the roof 
cover, roof sheathing, roof structure and the windows and doors. The basic 
premise used 
in the development of these simple models is that once the envelope is 
breached, most of 
the damage to the interior of the building is a function of the amount of 
water that enters 
the building. 

7.5.1 Economic Damage to the Building Interior 
The economic damage to the interior of the building is taken as the maximum 
of the 
economic damage estimated to be a result of roof cover damage, roof sheathing 
damage 
and window damage. The interior economic damage associated with loss of roof 
cover, 
LRC, is given as: 

LRC =f1 (RRC )(1- f2 (ARC )) f3(RRC )VI (7.3) 

where, RRC, is the fraction of failed roof cover, ARC, is the area of failed 
roof cover 
(square feet) and VI is the value of the interior of the building. The 
functions, f1, f2, and f3 
are described below. 

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The function f1 is expressed as: 

f1 (RRC )=1.11RRC , for RR=0.9 

f1 (RRC )=1.0 , for RR>0.9 (7.4) 

where RC is the fraction of failed or missing roof cover. The function f1 
represents the 
fractional amount of the interior area affected by the loss of a fraction of 
the roof cover. 

The function f2 is expressed as: 

f2 (ARC )=1- 0.005ARC , for ARC=200 ft2 

f2 (ARC )=0 , for ARC>200 ft2 (7.5) 

where ARC is the area of failed roof cover. The function f2 represents a term 
that accounts 
for the fact that when the amount of roof cover damage is relatively small, 
in many cases 
water is not able to enter the building because the underlayment remains 
intact, or no 
gaps in the roof sheathing are exposed. 

The third function, f3 is expressed as: 

f3 (RRC )= 0.1, for RRC=0.05 

f3 (RRC )= 2.0RRC , for 0.05 < RRC = 0.5 (7.6) 

f3 (RRC )= 1.0 , for RRC<0.5 

The function f3 represents a term that accounts for the fact that the 
resulting interior 
economic damage becomes more severe as the area of interior damage becomes 
larger. 
Examples of severe damage include ceiling sheet rock collapsing and damaging, 
flooring, 
cabinets, etc., or water migrating behind walls and damaging the electrical 
systems, etc. 
The three functions noted above are shown in graphical form in Figure 7.2. 

The implicit economic damage caused by failure of the roof sheathing is 
modeled in the 
form: 

LS =(3.6RS + 0.1)VI + RSVRF , for 0<Rs<0.25 (7.7) 

where LS is the economic damage associated with loss of roof sheathing, RS is 
the fraction 
of missing roof sheathing and VRF is the value of the roof framing (VRF 
typically 
represents only 4%-5% of the total value of a house). This equation, derived 
primarily 
from experience, in addition to the damage and loss data for individual 
buildings given in 
Bhinderwala (1995), yields an interior loss of approximately 10-15% 
(depending on the 
size of the house and the size of the sheathing) following failure of the 
first piece of 
sheathing. The interior of the house is assumed to require replacement when 
25% of the 
roof sheathing has failed. These two points (the first sheathing failure and 
the 25% loss of 
sheathing) were used to bound the linear model for the prediction of interior 
loss given 
sheathing failure. 

Chapter 7. Direct Economic Losses 

?
0 
1 
f1 
0 
1 
f2 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 50 100 150 200 
Fraction of Roof Cover Failed Area of Failed Roof Cover (sqf) 

0 
1 
0.1 
f3 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Fraction of Roof Cover Failed 

Figure 7.2. Implicit Loss Functions Associated with Damage to the Roof Cover. 

The economic damage produced by the failure of windows and doors is modeled 
as 

LF =(4DW )VI , for 0.=DW=0.25. (7.8) 

where DW is the depth of water, in inches, averaged over the floor area of 
the building. 
The simple loss model associated with water entering through failed 
fenestration was 
developed with the assumption that the losses increase linearly with an 
increasing amount 
of water entering the building, and that 1/4. of water (distributed uniformly 
over the floor 
area of the house) is sufficient to produce a 100% loss to the interior of 
the building. 

The estimates of losses produced to the interior of the building are adjusted 
to take into 
account the additional costs associated with repair and replacement, and to 
include an 
allowance for overhead and profit. The total economic loss of the building, 
including 
both the exterior losses and the interior losses is constrained not to exceed 
the assumed 
value of the building. 

7.5.2 Economic Damage to Contents 
Content losses are modeled using the assumption that the damage to contents 
is highly 
correlated with damage to the interior of the building, but begins to occur 
after at least 
some damage has been done to the interior of the building. The model assumes 
that losses 
are associated with water entering the building. Figures 7.3 and 7.4 show 
examples of 
content damage produced by roof and window failures. Note that in the roof 
failure 
example (Figure 7.3), the damage to the contents is ultimately caused by 
failure of the 
interior (i.e., ceiling) of the building. 

HAZUS-MH MR3 Technical Manual 

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Figure 7.3. Content and Interior Losses Associated with Roof Sheathing 
Failure. 

Figure 7.4. Content Losses Due to Fenestration Breach. 

Content losses associated with roof cover loss are accrued at one half the 
rate associated 
with losses to the interior of the building, except that in the case of roof 
cover loss, no 
contents are damaged until a threshold of 20% roof cover loss has occurred. 
This jump at 
20% is based on the premise that there must be water collected above the 
ceiling to cause 
the ceiling to fail, and initiate content damage. The content loss associated 
with 
fenestration failure is accrued at the same rate as the economic loss 
associated with the 
interior of the building. The content loss associated with sheathing loss is 
given as: 

Chapter 7. Direct Economic Losses 

?
LC = 4RS , RS<0.25 

LC = 1.0 , RS>0.25 (7.9) 

The economic loss associated with damage to the contents of the building is 
taken to be 
the maximum among the losses produced by roof cover damage, roof sheathing 
damage 
or fenestration damage. 

7.5.3 Loss of Use for Residential Buildings 
Loss of use estimates for residential buildings are based on the approach 
used in the 
HAZUS Earthquake Model for estimating recovery (reconstruction) time as a 
function of 
the building damage state (FEMA, 1999). Five damage states (none, slight, 
moderate, 
extensive, and complete) are employed in the earthquake model, with damage 
states 
defined as 0%, 2%, 10%, 50%, and 100%, respectively. In the hurricane model, 
the loss 
ratio is used as the indicator of reconstruction time, providing a continuous 
curve varying 
between 0% and 100%. 

The recovery time for the 5 key damage states in the Earthquake Model are 0, 
5, 120, 
360, and 720 days, respectively, and these points define the recovery time 
for the 
Hurricane Model, with loss used instead of damage. Linear interpolation is 
used to obtain 
the actual recovery time for buildings with loss ratios other than the 5 key 
values. 

The economic losses associated with building recovery time consider the 
amount of time 
persons in a damaged home will require temporary lodging. A loss of use 
multiplier is 
applied to the obtained recovery time to take into account the fact that 
homeowners can 
remain in their homes when buildings have slight to moderate damage. The loss 
of use 
multipliers for none, slight, moderate, extensive, and complete damage states 
are 0, 0, 
0.5, 1, and 1, respectively (FEMA, 1999). 

7.6 Residential Loss Model Validation Examples 
The residential damage-to-loss model has been validated using a set of 
residential claim 
folders which document wind-induced economic losses following Hurricanes 
Erin, Opal, 
Bertha, and Fran. The houses analyzed are screened from over 140 individual 
claim 
folders. Individual claim folders were considered only if there exist a 
minimal degree of 
uncertainty with respect to the characterization of the building and the 
associated loss, 
that is, insured value, roof type, roof area, plan area, etc., building 
damage state, and 
dollar loss. Files containing information which suggests flooding may have 
occurred are 
rejected as are those files having insufficient data to explicitly rule out 
the possibility of 
flooding. The initial selection criteria were constrained to include only 
those claim 
folders having a damage state limited to roof covering and roof sheathing. 

Model losses were obtained by running the model with observed damage states 
and 
building-specific information such as insured value, roof type, roof area, 
plan area, etc. 
These data are gathered by carrying out a careful study of the claim folder 
documentation. The documentation typically includes loss estimatics, claim 
folder 
damage pictures, a written description of the damage state, and sketches 
showing a plan 

HAZUS-MH MR3 Technical Manual 

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view of the building and the damage locations on the roof. A rigorous 
investigation of the 
claim pictures provides an estimate of the building envelop damage and 
characterization 
of the building in general. The insurance policy provides the building 
insured value while 
the estimatics are used to obtain the actual dollar loss information. 

Table 7.7 summarizes the compiled data and associated losses reported as a 
ratio of 
building value. The average prediction error ratio (defined as the actual 
loss divided by 
the predicted loss) is 0.83. This suggests reasonable agreement between the 
predicted and 
actual losses. Figure 7.5 compares predicted losses and actual loss data. In 
some cases the 
actual loss dollars are adjusted to eliminate the contribution of loss 
dollars from 
components which are not explicitly modeled. For example, costs associated 
with outside 
deck repairs are omitted since the residential damage and loss models do not 
model these 
components. Hence, if the total loss for a given building was $10,000 but 
$3500 of the 
loss originated from deck and sprinkler system damage, the overall dollar 
loss would be 
adjusted by subtracting from the total loss those loss dollars associated 
with non-modeled 
components (i.e., deck and sprinkler). 

Table 7.7. Predicted and Actual Losses 

Case 
Number 
Building Characteristics Building Losses Content Losses 
Value ($) Roof Area Roof Cover* 
Damage 
(%) Actual Model Ratio Actual Model 
1 281,000 3,125 AS 2.5 0.037 0.023 1.59 0.00 0.00 
2 57,600 770 BUR 50.0 0.502 0.493 1.02 0.00 0.14 
3 199,000 3,300 AS 1.0 0.017 0.029 0.60 0.00 0.00 
4 230,000 3,200 AS 2.5 0.029 0.022 1.31 0.00 0.00 
5 191,000 2,100 AS 2.5 0.025 0.020 1.25 0.00 0.00 
6 452,200 3,500 AS 2.5 0.016 0.016 0.98 0.00 0.00 
7 375,000 3,500 AS 7.5 0.041 0.027 1.53 0.00 0.00 
8 226,000 2,150 AS 0.5 0.019 0.018 1.03 0.00 0.00 
9 212,000 4,800 AS 4.0 0.039 0.038 1.03 0.00 0.00 
10 506,000 3,100 AS 0.5 0.009 0.011 0.82 0.00 0.00 
11 302,200 3,600 AS 2.0 0.012 0.019 0.61 ? 0.00 
12 152,000 2,750 AS 2.5 0.027 0.028 0.94 0.00 0.00 
13 74,737 1,205 AS 2.5 0.013 0.025 0.52 0.00 0.00 
14 245,000 2,640 TILE 2.5 0.048 0.068 0.70 0.00 0.00 
15 137,000 1,650 AS 2.5 0.004 0.019 0.24 0.00 0.00 
16 68,000 1,800 AS 10.0 0.066 0.057 1.14 0.00 0.00 
17 153,400 1,900 AS 10.0 0.030 0.037 0.81 0.00 0.00 
18 81,000 1,900 AS 5.0 0.054 0.038 1.42 ? 0.00 
19 253,000 4,000 AS 3.0 0.057 0.026 2.20 0.00 0.00 
20 537,000 7,000 WS 3.0 0.058 0.050 1.15 0.00 0.00 
21 508,000 2,000 AS 0.5 0.006 0.006 1.05 0.00 0.00 
22 178,000 2,000 BUR 5.0 0.077 0.024 3.19 0.00 0.00 
23 120,000 2,000 BUR 60.0 0.154 0.592 0.26 ? 0.17 
24 119,000 1,100 BUR 20.0 0.095 0.093 1.03 0.06 0.06 
25 156,000 2,300 AS 1.0 0.059 0.023 2.59 0.00 0.00 
Average 0.060 0.072 0.83 0.003 0.014 

*AS = Asphalt Shingle, BUR = Built-Up Roof, Tile = Tile Roof 

Chapter 7. Direct Economic Losses 

?
RESIDENTIAL LOSS MODEL 

y = 1.1653x - 498.87 
0 
10000 
20000 
30000 
40000 
50000 
60000 
70000 
80000 
Model Loss 
One-to-One 
Linear (Model Loss) 
0 5000 10000 15000 20000 25000 30000 35000 

Actual $ 

Figure 7.5. Actual and Predicted Losses. 

For the 25 validation cases considered, Table 7.7 lists the actual and 
predicted loss ratios 
for both building and content losses. The case-by-case variability is not 
unexpected given 
that the majority of cases are at the low end of the loss curve. In such 
circumstances, the 
governing damage state is usually roof cover damage and is generally limited 
to less 
than10%. The variability seen in Table 7.7 is primarily linked to the total 
replacement 
threshold. If the computed cost is less than the default minimum repair cost 
of $250, the 
default minimum is registered. Roof cover damage states which do not exceed 
0.5% will 
typically correspond to roof cover tab damage in the case of shingles, or in 
the case of tile 
roofs, scattered individual tiles, typically located at the ridges. These 
types of damage can 
usually be repaired with minimal effort. However, if the roof cover damage 
exceeds 
0.5%, claim folder data has suggested that the repair costs are often based 
on a complete 
or partial re-roofing of the entire roof surface. For example, a gable 
receiving minimal 
damage to one side may have the entire cover replaced. The decision to 
replace the entire 
roof cover or a portion of the roof cover is at the discretion of the claim 
adjuster. Factors 
which are taken into account when making this decision are the age of the 
roof, color 
compatibility issues, discontinuation of or availability of the existing 
roofing materials, 
etc. As a result, the adjuster may or may not sanction a total or partial 
replacement of 
roofing materials depending on the overall circumstances. The loss model 
replaces the 
total roof surface given a damage state exceeding 0.5%. 

Table 7.7 also list comparisons of actual and predicted content losses. The 
table entries 
denoted by the question mark symbol (?), correspond to cases where the 
content loss was 
ambiguous. The model accurately predicted zero content losses for the 
majority of cases. 
For case 23, a 17% of building value content loss was predicted. The claim 
folder did not 
report the actual content dollar loss since the content losses were below the 
15% 

HAZUS-MH MR3 Technical Manual 

?
deductible. However, it was reported that there was extensive content damage 
which 
approached the 15% deductible, suggesting the predicted 17% content loss is 
not 
unreasonable. 

7.7 Loss Model Results for Residential Buildings 
Using the same hurricane wind speed and direction data used in the 
development of the 
damage curves for residential buildings, per storm losses were estimated 
using the 
building and content loss models described earlier (and validated on an end-
to-end basis 
as described in Chapter 9). Loss functions have been produced for the basic, 
unmitigated 
buildings (one story or two story, hip or gable, 6d or 8d sheathing nails, 
straps or toenails, 
wood frame, reinforced masonry or unreinforced masonry, no garage, 20 psf 
garage 
door or 10 psf garage door) for the four basic terrain categories. 

Example plots of building loss versus wind speed are given in Appendix H 
where the 
building loss is expressed as a fraction of the total value of the building, 
and thus ranges 
between zero and one. Also included in Appendix H are loss functions 
associated with 
contents loss expressed as a fraction of the assumed content value (taken as 
50% of the 
value of the building). 

Tables 7.8, 7.9 and 7.10 present the average annual building loss, content 
loss and total 
loss, respectively, for all of the 144 residential buildings. Note that the 
losses are 
normalized by the building value in all cases and that the total loss 
represents the sum of 
building, contents and alternate living expenses. The average annual loss is 
simply the 
sum of all losses produced during the 20,000-year simulation of hurricanes 
divided by 
20,000 years. Again, the values given in Tables 7.8 through 7.10 reflect the 
average 
annual losses for structures located in the South Florida area. 

Mitigation. In order to examine the effect of mitigation on residential 
building losses, 
five different mitigation cases were examined as indicated below: 

1. Install shutters. 
2. Upgrade roof (roof sheathing and roof cover). 
3. Upgrade roof and apply secondary water resistance. 
4. Install shutters and upgrade roof. 
5. Install shutters, upgrade roof and apply secondary water resistance. 
The upgraded roof is modeled assuming that the existing roof (nailed with 
either 6d nails 
or 8d nails with a 6./12. spacing), is upgraded by adding 8d nails so the 
deck is 
connected to the roof trusses with a 6./6. pattern. At the same time it is 
assumed that a 
more wind resistant roof shingles are installed. These upgraded shingles are 
modeled by 
increasing the resistance of the basic shingles by 20%. 

Secondary water resistance is provided by applying a waterproof seal, or 
cover, over the 
spaces between the roof sheathing panels that prevents water from entering 
the building 
through the roof if the roof cover fails in a storm. Methods for applying 
secondary water 

Chapter 7. Direct Economic Losses 

?
Table 7.8. Average Annual Building Loss Normalized by Building Value 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/Wall 
Conn. 
Sheath 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d 
None 
Strong 
Weak 
0.0172 
0.0167 
0.0178 
0.0092 
0.0084 
0.0089 
0.0064 
0.0058 
0.0060 
0.0053 
0.0047 
0.0049 
0.0195 
0.0185 
0.0199 
0.0107 
0.0095 
0.0102 
0.0078 
0.0068 
0.0071 
0.0065 
0.0056 
0.0059 
8d 
None 
Strong 
Weak 
0.0162 
0.0158 
0.0164 
0.0083 
0.0081 
0.0083 
0.0058 
0.0055 
0.0056 
0.0047 
0.0044 
0.0045 
0.0175 
0.0171 
0.0177 
0.0089 
0.0086 
0.0088 
0.0063 
0.0059 
0.0060 
0.0051 
0.0047 
0.0049 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0186 
0.0186 
0.0217 
0.0107 
0.0095 
0.0106 
0.0074 
0.0065 
0.0073 
0.0060 
0.0053 
0.0058 
0.0214 
0.0216 
0.0281 
0.0138 
0.0119 
0.0139 
0.0097 
0.0083 
0.0096 
0.0080 
0.0069 
0.0078 
8d 
None 
Strong 
Weak 
0.0184 
0.0186 
0.0217 
0.0106 
0.0094 
0.0106 
0.0073 
0.0064 
0.0073 
0.0058 
0.0052 
0.0057 
0.0206 
0.0210 
0.0280 
0.0133 
0.0117 
0.0137 
0.0093 
0.0081 
0.0094 
0.0076 
0.0066 
0.0075 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.0240 
0.0241 
0.0264 
0.0136 
0.0138 
0.0146 
0.0103 
0.0103 
0.0109 
0.0087 
0.0087 
0.0093 
0.0279 
0.0268 
0.0281 
0.0163 
0.0156 
0.0163 
0.0125 
0.0119 
0.0124 
0.0108 
0.0103 
0.0108 
8d 
None 
Strong 
Weak 
0.0229 
0.0232 
0.0254 
0.0129 
0.0132 
0.0140 
0.0096 
0.0098 
0.0104 
0.0082 
0.0083 
0.0087 
0.0264 
0.0253 
0.0259 
0.0148 
0.0146 
0.0150 
0.0112 
0.0110 
0.0113 
0.0094 
0.0095 
0.0096 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0268 
0.0268 
0.0304 
0.0159 
0.0152 
0.0164 
0.0119 
0.0114 
0.0124 
0.0101 
0.0096 
0.0105 
0.0307 
0.0304 
0.0388 
0.0201 
0.0178 
0.0216 
0.0152 
0.0136 
0.0163 
0.0130 
0.0116 
0.0136 
8d 
None 
Strong 
Weak 
0.0265 
0.0267 
0.0303 
0.0159 
0.0151 
0.0164 
0.0118 
0.0112 
0.0122 
0.0100 
0.0094 
0.0104 
0.0300 
0.0301 
0.0382 
0.0195 
0.0175 
0.0211 
0.0147 
0.0132 
0.0159 
0.0126 
0.0114 
0.0135 
Unreinforced Masonry 
One 
Straps 
6d 
None 
Strong 
Weak 
0.0172 
0.0166 
0.0177 
0.0092 
0.0085 
0.0089 
0.0064 
0.0058 
0.0060 
0.0052 
0.0047 
0.0049 
0.0196 
0.0185 
0.0198 
0.0107 
0.0096 
0.0102 
0.0078 
0.0067 
0.0071 
0.0065 
0.0056 
0.0059 
8d 
None 
Strong 
Weak 
0.0163 
0.0159 
0.0164 
0.0083 
0.0081 
0.0083 
0.0057 
0.0055 
0.0056 
0.0047 
0.0044 
0.0045 
0.0176 
0.0171 
0.0177 
0.0089 
0.0086 
0.0089 
0.0062 
0.0058 
0.0060 
0.0052 
0.0047 
0.0049 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0187 
0.0187 
0.0219 
0.0106 
0.0095 
0.0106 
0.0074 
0.0065 
0.0073 
0.0059 
0.0053 
0.0058 
0.0214 
0.0215 
0.0282 
0.0137 
0.0119 
0.0139 
0.0098 
0.0084 
0.0097 
0.0079 
0.0069 
0.0079 
8d 
None 
Strong 
Weak 
0.0185 
0.0186 
0.0217 
0.0106 
0.0095 
0.0106 
0.0073 
0.0065 
0.0072 
0.0058 
0.0052 
0.0057 
0.0206 
0.0210 
0.0277 
0.0133 
0.0116 
0.0136 
0.0094 
0.0082 
0.0095 
0.0076 
0.0066 
0.0076 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.0240 
0.0241 
0.0263 
0.0136 
0.0138 
0.0146 
0.0103 
0.0103 
0.0110 
0.0087 
0.0087 
0.0092 
0.0277 
0.0267 
0.0280 
0.0163 
0.0155 
0.0162 
0.0125 
0.0119 
0.0125 
0.0108 
0.0102 
0.0107 
8d 
None 
Strong 
Weak 
0.0229 
0.0231 
0.0254 
0.0129 
0.0132 
0.0140 
0.0096 
0.0098 
0.0103 
0.0082 
0.0082 
0.0086 
0.0263 
0.0251 
0.0259 
0.0148 
0.0145 
0.0149 
0.0112 
0.0110 
0.0113 
0.0094 
0.0094 
0.0095 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0266 
0.0268 
0.0304 
0.0159 
0.0152 
0.0166 
0.0118 
0.0113 
0.0123 
0.0101 
0.0096 
0.0104 
0.0308 
0.0304 
0.0386 
0.0199 
0.0177 
0.0215 
0.0153 
0.0135 
0.0162 
0.0129 
0.0117 
0.0136 
8d 
None 
Strong 
Weak 
0.0267 
0.0268 
0.0304 
0.0159 
0.0151 
0.0164 
0.0118 
0.0111 
0.0123 
0.0100 
0.0094 
0.0104 
0.0300 
0.0300 
0.0384 
0.0195 
0.0175 
0.0212 
0.0147 
0.0132 
0.0159 
0.0124 
0.0114 
0.0135 
Reinforced Masonry 
One 
Straps 
6d 
None 
Strong 
Weak 
0.0173 
0.0167 
0.0177 
0.0091 
0.0084 
0.0089 
0.0064 
0.0058 
0.0060 
0.0052 
0.0047 
0.0049 
0.0195 
0.0185 
0.0199 
0.0108 
0.0096 
0.0101 
0.0077 
0.0068 
0.0072 
0.0065 
0.0056 
0.0059 
8d 
None 
Strong 
Weak 
0.0162 
0.0159 
0.0163 
0.0083 
0.0081 
0.0083 
0.0057 
0.0055 
0.0056 
0.0047 
0.0044 
0.0045 
0.0175 
0.0171 
0.0177 
0.0089 
0.0086 
0.0088 
0.0062 
0.0059 
0.0061 
0.0051 
0.0047 
0.0048 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0185 
0.0187 
0.0218 
0.0106 
0.0095 
0.0106 
0.0074 
0.0066 
0.0073 
0.0060 
0.0053 
0.0059 
0.0214 
0.0215 
0.0282 
0.0138 
0.0119 
0.0139 
0.0097 
0.0084 
0.0096 
0.0080 
0.0068 
0.0078 
8d 
None 
Strong 
Weak 
0.0185 
0.0185 
0.0217 
0.0105 
0.0094 
0.0105 
0.0073 
0.0064 
0.0072 
0.0058 
0.0052 
0.0058 
0.0205 
0.0211 
0.0280 
0.0133 
0.0116 
0.0137 
0.0094 
0.0081 
0.0095 
0.0077 
0.0066 
0.0076 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.0239 
0.0242 
0.0263 
0.0136 
0.0137 
0.0146 
0.0102 
0.0103 
0.0109 
0.0086 
0.0087 
0.0092 
0.0277 
0.0267 
0.0281 
0.0162 
0.0156 
0.0163 
0.0125 
0.0119 
0.0125 
0.0108 
0.0102 
0.0107 
8d 
None 
Strong 
Weak 
0.0229 
0.0230 
0.0253 
0.0129 
0.0131 
0.0139 
0.0096 
0.0098 
0.0103 
0.0081 
0.0082 
0.0086 
0.0263 
0.0251 
0.0258 
0.0150 
0.0146 
0.0150 
0.0112 
0.0110 
0.0112 
0.0095 
0.0094 
0.0095 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0265 
0.0267 
0.0304 
0.0159 
0.0151 
0.0165 
0.0119 
0.0114 
0.0124 
0.0099 
0.0096 
0.0105 
0.0308 
0.0305 
0.0386 
0.0199 
0.0177 
0.0217 
0.0153 
0.0137 
0.0163 
0.0131 
0.0116 
0.0138 
8d 
None 
Strong 
Weak 
0.0266 
0.0266 
0.0305 
0.0160 
0.0149 
0.0164 
0.0118 
0.0112 
0.0123 
0.0100 
0.0094 
0.0104 
0.0302 
0.0300 
0.0383 
0.0196 
0.0175 
0.0215 
0.0147 
0.0133 
0.0160 
0.0124 
0.0113 
0.0135 

HAZUS-MH MR3 Technical Manual 

?
Table 7.9. Average Annual Content Loss Normalized by Building Value 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/Wall 
Conn. 
Sheath 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d 
None 
Strong 
Weak 
0.0086 
0.0087 
0.0092 
0.0041 
0.0041 
0.0042 
0.0023 
0.0023 
0.0024 
0.0016 
0.0016 
0.0017 
0.0099 
0.0100 
0.0106 
0.0049 
0.0047 
0.0050 
0.0031 
0.0028 
0.0030 
0.0023 
0.0020 
0.0022 
8d 
None 
Strong 
Weak 
0.0082 
0.0083 
0.0086 
0.0038 
0.0039 
0.0040 
0.0020 
0.0022 
0.0022 
0.0013 
0.0015 
0.0015 
0.0093 
0.0095 
0.0098 
0.0043 
0.0043 
0.0045 
0.0024 
0.0025 
0.0026 
0.0017 
0.0017 
0.0018 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0101 
0.0108 
0.0132 
0.0059 
0.0053 
0.0062 
0.0037 
0.0033 
0.0039 
0.0027 
0.0024 
0.0028 
0.0119 
0.0130 
0.0180 
0.0081 
0.0072 
0.0086 
0.0053 
0.0046 
0.0057 
0.0041 
0.0036 
0.0043 
8d 
None 
Strong 
Weak 
0.0100 
0.0108 
0.0132 
0.0059 
0.0053 
0.0062 
0.0036 
0.0033 
0.0040 
0.0026 
0.0024 
0.0029 
0.0117 
0.0128 
0.0180 
0.0079 
0.0071 
0.0086 
0.0052 
0.0046 
0.0056 
0.0041 
0.0036 
0.0043 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.0142 
0.0141 
0.0162 
0.0075 
0.0077 
0.0084 
0.0052 
0.0053 
0.0058 
0.0041 
0.0041 
0.0045 
0.0170 
0.0161 
0.0168 
0.0096 
0.0091 
0.0094 
0.0069 
0.0066 
0.0068 
0.0057 
0.0055 
0.0057 
8d 
None 
Strong 
Weak 
0.0138 
0.0139 
0.0159 
0.0073 
0.0075 
0.0081 
0.0049 
0.0050 
0.0056 
0.0039 
0.0040 
0.0043 
0.0167 
0.0156 
0.0161 
0.0092 
0.0088 
0.0090 
0.0066 
0.0063 
0.0064 
0.0053 
0.0052 
0.0052 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0162 
0.0165 
0.0195 
0.0096 
0.0091 
0.0102 
0.0068 
0.0064 
0.0073 
0.0055 
0.0051 
0.0059 
0.0191 
0.0192 
0.0257 
0.0125 
0.0112 
0.0140 
0.0092 
0.0082 
0.0103 
0.0077 
0.0068 
0.0084 
8d 
None 
Strong 
Weak 
0.0161 
0.0165 
0.0195 
0.0096 
0.0091 
0.0102 
0.0067 
0.0063 
0.0073 
0.0054 
0.0050 
0.0059 
0.0190 
0.0192 
0.0253 
0.0124 
0.0111 
0.0138 
0.0091 
0.0081 
0.0101 
0.0076 
0.0068 
0.0084 
Unreinforced Masonry 
One 
Straps 
6d 
None 
Strong 
Weak 
0.0085 
0.0087 
0.0092 
0.0041 
0.0041 
0.0043 
0.0023 
0.0024 
0.0024 
0.0016 
0.0016 
0.0017 
0.0100 
0.0100 
0.0105 
0.0049 
0.0047 
0.0050 
0.0031 
0.0028 
0.0030 
0.0023 
0.0021 
0.0022 
8d 
None 
Strong 
Weak 
0.0082 
0.0084 
0.0086 
0.0038 
0.0039 
0.0040 
0.0020 
0.0022 
0.0022 
0.0013 
0.0015 
0.0015 
0.0094 
0.0095 
0.0098 
0.0043 
0.0043 
0.0045 
0.0024 
0.0025 
0.0026 
0.0017 
0.0017 
0.0018 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0102 
0.0109 
0.0133 
0.0059 
0.0053 
0.0062 
0.0036 
0.0032 
0.0039 
0.0026 
0.0024 
0.0029 
0.0120 
0.0129 
0.0180 
0.0081 
0.0072 
0.0086 
0.0054 
0.0047 
0.0057 
0.0041 
0.0036 
0.0044 
8d 
None 
Strong 
Weak 
0.0101 
0.0109 
0.0132 
0.0059 
0.0053 
0.0062 
0.0037 
0.0033 
0.0039 
0.0026 
0.0024 
0.0028 
0.0118 
0.0127 
0.0178 
0.0079 
0.0071 
0.0085 
0.0053 
0.0046 
0.0057 
0.0040 
0.0035 
0.0044 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.0142 
0.0141 
0.0161 
0.0076 
0.0077 
0.0084 
0.0052 
0.0052 
0.0058 
0.0041 
0.0041 
0.0045 
0.0170 
0.0161 
0.0167 
0.0095 
0.0091 
0.0094 
0.0069 
0.0066 
0.0068 
0.0057 
0.0054 
0.0056 
8d 
None 
Strong 
Weak 
0.0139 
0.0139 
0.0159 
0.0073 
0.0075 
0.0082 
0.0049 
0.0050 
0.0056 
0.0039 
0.0039 
0.0043 
0.0167 
0.0156 
0.0161 
0.0092 
0.0088 
0.0090 
0.0067 
0.0063 
0.0065 
0.0053 
0.0052 
0.0052 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0161 
0.0165 
0.0195 
0.0096 
0.0091 
0.0103 
0.0067 
0.0063 
0.0073 
0.0055 
0.0051 
0.0059 
0.0192 
0.0193 
0.0255 
0.0124 
0.0111 
0.0140 
0.0092 
0.0082 
0.0102 
0.0076 
0.0069 
0.0084 
8d 
None 
Strong 
Weak 
0.0162 
0.0165 
0.0195 
0.0096 
0.0091 
0.0103 
0.0067 
0.0063 
0.0073 
0.0055 
0.0050 
0.0059 
0.0190 
0.0191 
0.0255 
0.0125 
0.0111 
0.0139 
0.0091 
0.0081 
0.0101 
0.0075 
0.0068 
0.0084 
Reinforced Masonry 
One 
Straps 
6d 
None 
Strong 
Weak 
0.0086 
0.0087 
0.0092 
0.0041 
0.0041 
0.0042 
0.0023 
0.0023 
0.0024 
0.0016 
0.0016 
0.0017 
0.0099 
0.0099 
0.0105 
0.0049 
0.0047 
0.0049 
0.0030 
0.0028 
0.0030 
0.0023 
0.0021 
0.0022 
8d 
None 
Strong 
Weak 
0.0082 
0.0083 
0.0085 
0.0038 
0.0039 
0.0040 
0.0020 
0.0022 
0.0022 
0.0013 
0.0015 
0.0015 
0.0093 
0.0095 
0.0098 
0.0043 
0.0043 
0.0045 
0.0024 
0.0025 
0.0026 
0.0017 
0.0017 
0.0017 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0100 
0.0109 
0.0133 
0.0059 
0.0053 
0.0062 
0.0037 
0.0033 
0.0039 
0.0027 
0.0024 
0.0029 
0.0119 
0.0129 
0.0180 
0.0081 
0.0071 
0.0086 
0.0053 
0.0047 
0.0057 
0.0041 
0.0036 
0.0044 
8d 
None 
Strong 
Weak 
0.0100 
0.0108 
0.0132 
0.0058 
0.0054 
0.0062 
0.0037 
0.0032 
0.0039 
0.0026 
0.0024 
0.0029 
0.0117 
0.0128 
0.0180 
0.0079 
0.0070 
0.0086 
0.0053 
0.0046 
0.0057 
0.0041 
0.0036 
0.0043 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.0142 
0.0141 
0.0161 
0.0076 
0.0076 
0.0084 
0.0052 
0.0052 
0.0058 
0.0040 
0.0041 
0.0045 
0.0169 
0.0161 
0.0168 
0.0095 
0.0092 
0.0095 
0.0069 
0.0066 
0.0068 
0.0057 
0.0055 
0.0056 
8d 
None 
Strong 
Weak 
0.0140 
0.0139 
0.0159 
0.0073 
0.0074 
0.0082 
0.0049 
0.0050 
0.0056 
0.0039 
0.0039 
0.0043 
0.0167 
0.0156 
0.0161 
0.0093 
0.0088 
0.0091 
0.0066 
0.0063 
0.0064 
0.0053 
0.0052 
0.0052 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0161 
0.0164 
0.0195 
0.0096 
0.0090 
0.0102 
0.0068 
0.0064 
0.0073 
0.0054 
0.0051 
0.0059 
0.0192 
0.0193 
0.0255 
0.0124 
0.0111 
0.0141 
0.0092 
0.0083 
0.0103 
0.0077 
0.0068 
0.0085 
8d 
None 
Strong 
Weak 
0.0162 
0.0164 
0.0196 
0.0097 
0.0090 
0.0102 
0.0067 
0.0063 
0.0073 
0.0055 
0.0051 
0.0059 
0.0191 
0.0192 
0.0254 
0.0125 
0.0111 
0.0140 
0.0091 
0.0081 
0.0102 
0.0075 
0.0067 
0.0084 

Chapter 7. Direct Economic Losses 

?
Table 7.10. Average Annual Total Loss Normalized by Building Value 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/Wall 
Conn. 
Sheath 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d 
None 
Strong 
Weak 
0.0283 
0.0278 
0.0295 
0.0145 
0.0137 
0.0144 
0.0095 
0.0089 
0.0093 
0.0076 
0.0069 
0.0073 
0.0322 
0.0311 
0.0333 
0.0172 
0.0155 
0.0166 
0.0120 
0.0105 
0.0111 
0.0097 
0.0083 
0.0089 
8d 
None 
Strong 
Weak 
0.0266 
0.0264 
0.0273 
0.0132 
0.0132 
0.0134 
0.0085 
0.0084 
0.0085 
0.0066 
0.0065 
0.0066 
0.0293 
0.0291 
0.0301 
0.0144 
0.0141 
0.0145 
0.0095 
0.0092 
0.0094 
0.0075 
0.0071 
0.0073 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0313 
0.0322 
0.0381 
0.0181 
0.0161 
0.0183 
0.0121 
0.0107 
0.0121 
0.0095 
0.0084 
0.0094 
0.0364 
0.0377 
0.0503 
0.0238 
0.0208 
0.0245 
0.0164 
0.0142 
0.0166 
0.0133 
0.0114 
0.0132 
8d 
None 
Strong 
Weak 
0.0311 
0.0322 
0.0381 
0.0179 
0.0161 
0.0183 
0.0118 
0.0106 
0.0122 
0.0092 
0.0082 
0.0093 
0.0354 
0.0370 
0.0503 
0.0231 
0.0205 
0.0243 
0.0158 
0.0139 
0.0164 
0.0127 
0.0111 
0.0129 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.0417 
0.0418 
0.0465 
0.0231 
0.0235 
0.0251 
0.0169 
0.0171 
0.0182 
0.0140 
0.0141 
0.0151 
0.0491 
0.0469 
0.0490 
0.0282 
0.0270 
0.0281 
0.0212 
0.0203 
0.0209 
0.0181 
0.0172 
0.0180 
8d 
None 
Strong 
Weak 
0.0402 
0.0405 
0.0450 
0.0221 
0.0225 
0.0241 
0.0159 
0.0162 
0.0174 
0.0132 
0.0134 
0.0142 
0.0471 
0.0447 
0.0458 
0.0262 
0.0255 
0.0262 
0.0195 
0.0188 
0.0193 
0.0161 
0.0160 
0.0161 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0470 
0.0473 
0.0544 
0.0278 
0.0265 
0.0290 
0.0204 
0.0194 
0.0215 
0.0170 
0.0161 
0.0179 
0.0545 
0.0542 
0.0704 
0.0357 
0.0316 
0.0389 
0.0266 
0.0237 
0.0290 
0.0226 
0.0201 
0.0241 
8d 
None 
Strong 
Weak 
0.0466 
0.0471 
0.0543 
0.0279 
0.0263 
0.0291 
0.0202 
0.0192 
0.0213 
0.0169 
0.0158 
0.0178 
0.0535 
0.0539 
0.0694 
0.0349 
0.0312 
0.0381 
0.0260 
0.0232 
0.0284 
0.0221 
0.0198 
0.0240 
Unreinforced Masonry 
One 
Straps 
6d 
None 
Strong 
Weak 
0.0281 
0.0277 
0.0294 
0.0145 
0.0137 
0.0144 
0.0095 
0.0089 
0.0093 
0.0075 
0.0068 
0.0073 
0.0325 
0.0312 
0.0332 
0.0172 
0.0156 
0.0167 
0.0119 
0.0104 
0.0111 
0.0097 
0.0084 
0.0089 
8d 
None 
Strong 
Weak 
0.0268 
0.0265 
0.0273 
0.0132 
0.0131 
0.0133 
0.0084 
0.0083 
0.0086 
0.0066 
0.0064 
0.0065 
0.0294 
0.0291 
0.0301 
0.0144 
0.0142 
0.0145 
0.0094 
0.0091 
0.0094 
0.0075 
0.0071 
0.0072 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0316 
0.0323 
0.0384 
0.0179 
0.0162 
0.0183 
0.0120 
0.0106 
0.0122 
0.0093 
0.0084 
0.0095 
0.0365 
0.0375 
0.0504 
0.0238 
0.0208 
0.0245 
0.0165 
0.0143 
0.0169 
0.0131 
0.0116 
0.0134 
8d 
None 
Strong 
Weak 
0.0312 
0.0322 
0.0381 
0.0180 
0.0161 
0.0183 
0.0119 
0.0106 
0.0121 
0.0092 
0.0082 
0.0093 
0.0354 
0.0369 
0.0497 
0.0231 
0.0204 
0.0241 
0.0160 
0.0140 
0.0165 
0.0127 
0.0110 
0.0131 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.0418 
0.0419 
0.0463 
0.0231 
0.0234 
0.0251 
0.0169 
0.0170 
0.0183 
0.0140 
0.0141 
0.0150 
0.0488 
0.0468 
0.0489 
0.0282 
0.0270 
0.0279 
0.0212 
0.0203 
0.0211 
0.0181 
0.0171 
0.0179 
8d 
None 
Strong 
Weak 
0.0402 
0.0404 
0.0449 
0.0222 
0.0226 
0.0242 
0.0159 
0.0162 
0.0173 
0.0132 
0.0132 
0.0141 
0.0470 
0.0445 
0.0458 
0.0263 
0.0254 
0.0260 
0.0195 
0.0189 
0.0194 
0.0160 
0.0158 
0.0160 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0468 
0.0473 
0.0544 
0.0279 
0.0264 
0.0293 
0.0202 
0.0193 
0.0214 
0.0170 
0.0160 
0.0178 
0.0546 
0.0543 
0.0700 
0.0353 
0.0314 
0.0387 
0.0268 
0.0237 
0.0288 
0.0224 
0.0203 
0.0240 
8d 
None 
Strong 
Weak 
0.0469 
0.0473 
0.0545 
0.0278 
0.0264 
0.0291 
0.0203 
0.0189 
0.0213 
0.0169 
0.0158 
0.0178 
0.0536 
0.0537 
0.0697 
0.0349 
0.0313 
0.0382 
0.0260 
0.0233 
0.0283 
0.0218 
0.0198 
0.0239 
Reinforced Masonry 
One 
Straps 
6d 
None 
Strong 
Weak 
0.0283 
0.0278 
0.0295 
0.0144 
0.0137 
0.0143 
0.0095 
0.0089 
0.0093 
0.0075 
0.0068 
0.0073 
0.0322 
0.0312 
0.0332 
0.0172 
0.0156 
0.0165 
0.0118 
0.0106 
0.0112 
0.0097 
0.0084 
0.0089 
8d 
None 
Strong 
Weak 
0.0267 
0.0265 
0.0271 
0.0131 
0.0131 
0.0134 
0.0085 
0.0084 
0.0086 
0.0066 
0.0064 
0.0065 
0.0293 
0.0290 
0.0300 
0.0144 
0.0141 
0.0145 
0.0094 
0.0091 
0.0094 
0.0074 
0.0070 
0.0072 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0311 
0.0323 
0.0382 
0.0180 
0.0161 
0.0184 
0.0120 
0.0107 
0.0122 
0.0094 
0.0084 
0.0096 
0.0363 
0.0375 
0.0504 
0.0239 
0.0208 
0.0245 
0.0164 
0.0143 
0.0167 
0.0132 
0.0114 
0.0133 
8d 
None 
Strong 
Weak 
0.0311 
0.0320 
0.0381 
0.0178 
0.0161 
0.0183 
0.0120 
0.0105 
0.0122 
0.0092 
0.0083 
0.0095 
0.0352 
0.0371 
0.0502 
0.0231 
0.0203 
0.0243 
0.0159 
0.0139 
0.0165 
0.0128 
0.0112 
0.0131 
Two 
Straps 
6d 
None 
Strong 
Weak 
0.0416 
0.0419 
0.0463 
0.0231 
0.0234 
0.0251 
0.0168 
0.0170 
0.0183 
0.0139 
0.0141 
0.0150 
0.0488 
0.0468 
0.0491 
0.0281 
0.0271 
0.0281 
0.0213 
0.0203 
0.0211 
0.0181 
0.0172 
0.0178 
8d 
None 
Strong 
Weak 
0.0402 
0.0403 
0.0448 
0.0220 
0.0224 
0.0241 
0.0158 
0.0162 
0.0173 
0.0131 
0.0133 
0.0141 
0.0470 
0.0445 
0.0457 
0.0265 
0.0255 
0.0263 
0.0193 
0.0189 
0.0193 
0.0161 
0.0160 
0.0161 
Toe-nails 
6d 
None 
Strong 
Weak 
0.0466 
0.0471 
0.0545 
0.0278 
0.0263 
0.0291 
0.0204 
0.0195 
0.0214 
0.0167 
0.0161 
0.0179 
0.0547 
0.0544 
0.0699 
0.0353 
0.0315 
0.0390 
0.0267 
0.0240 
0.0291 
0.0227 
0.0201 
0.0243 
8d 
None 
Strong 
Weak 
0.0469 
0.0471 
0.0547 
0.0280 
0.0261 
0.0290 
0.0202 
0.0191 
0.0213 
0.0169 
0.0158 
0.0177 
0.0538 
0.0538 
0.0696 
0.0351 
0.0311 
0.0387 
0.0260 
0.0233 
0.0286 
0.0218 
0.0197 
0.0239 

HAZUS-MH MR3 Technical Manual 

?
resistance include hot-mopping the entire roof deck with tar prior to the 
application of the 
roof cover, or covering the spaces between sheathing panels with bitumous 
strips, usually 
manufactured for use as ice guards. 

There are several products on the market now that are used in roofing of 
commercial and 
residential buildings to stop the formation of ice dams. This class of 
products is correctly 
referred to as  Self Adhering Modified Bitumen  products, but is generically 
referred to 
as Ice & Water shields. These products are an asphalt product with a polymer 
modifier 
added to give it a rubber gasket texture. The backing has an adhesive with 
paper covering 
that is removed upon application. The top of the sheet is often a sand 
roughened surface 
or plastic film that keeps the product from sticking to itself during 
shipping and also 
provides a skid resistant surface to work on after application. 

This product is installed after the old roofing material is removed down to 
the deck and 
before the new roofing material is installed. It should be installed along 
all edges of the 
roof and along all joints between the plywood sheets. This will prevent water 
from 
penetrating through the joints after loss of the roof covering occurs. The 
product is 
manufactured in various widths. An economical use of the product for plywood 
joint 
sealing is the 6. width rather than the 3' width typical of many of the 
products. An 
example of the application of this type of secondary water protection is 
shown in 
Figure 7.6. 

In order to model the effect of secondary water resistance, it is assumed 
that this 
mitigation approach is 95% effective, thus when the roof cover fails, the 
ensuing losses 
associated with roof cover failure (described in Section 7.6) are reduced to 
5% of the 
baseline (no secondary water resistance) estimates. 

Figure 7.6. Example of Self-Adhering Waterproof Underlayment Used to Tape 
Plywood Joints, Ridges and Eaves. Photo Depicts Use Around Fenestrations. 

Chapter 7. Direct Economic Losses 

?
The loss analysis performed for the unmitigated buildings is repeated for the 
mitigated 
buildings to produce loss versus wind speed curves as well as average annual 
losses. 
Example loss function plots are given in Appendix H. Average annual losses 
(building, 
contents and total) are given in Tables 7.11 through 7.25 for the five 
mitigation strategies 
listed above. Table 7.26 summarizes the effect of the various building 
parameters on the 
average annual building loss and Table 7.27 gives ranges of the reductions in 
total loss 
associated with the five mitigation strategies. 

7.8 Manufactured Home Loss Model 
The manufactured home (MH) loss model is a physically-based damage-to-loss 
model 
that computes building economic losses using a combination of explicit and 
implicit 
costing techniques. The loss model uses the same modeling approach as the 
residential 
loss model (i.e., subdividing the structure into costing subassemblies). 
Eight basic 
subassemblies are used, they are foundation, framing, exterior walls, 
roofing, interiors, 
specialties, mechanical, and electrical. The prototypical MH is defined as 
having an 
average cost of $35 per sq ft. 

7.8.1 Manufactured Home Loss Model Costing 
Losses Associated with Exterior Damage. Losses associated with damage to the 
building 
exterior are computed using an explicit costing technique. The explicit 
costing method 
directly calculates the repair and replacement costs for damaged MH envelope 
components using a combination of RSMeans unit, linear foot, and square foot 
costs. 
Explicit costing within the model is carried out for several envelope 
components such as 
roof covering, roof decking, windows, entrance doors, fenestration 
protection, exterior 
siding, etc. Dollar losses associated with these components are directly 
calculated using 
actual damage states and damage replacement thresholds. Damage replacement 
thresholds are the minimum damage levels at which a building component is 
completely 
replaced. Damage replacement thresholds are currently set at a 2.5% damage 
state for 
roof covering (rather than 0.5% for site built homes) and 5% for roof 
sheathing, which 
are determined from field observations and judgment. In addition to 
replacement 
thresholds, the loss model also takes into account building component 
replacement based 
on serviceability considerations. For those MH envelope components (windows, 
entrance 
doors, garage doors, and fenestration protection) that do not fail under 
applied load, they 
may still require replacement after undergoing thousands of load cycles as 
they become 
prone to permanent set. The loss model takes serviceability considerations 
into account 
by replacing those components that sustain loads exceeding 85% of their 
ultimate 
capacity. 

As in the residential case, the cost data used in the explicit costing 
portion of the loss 
model are derived from the RSMeans Residential Cost Data and are based on 
national 
averages for new construction materials and installation. These data are 
adjusted to take 
into account the specific geographical location of the loss, the level of 
work difficulty, 
and contractor s overhead and profit. 

HAZUS-MH MR3 Technical Manual 

?
Table 7.11. Average Annual Building Loss Normalized by Building Value   
Install 
Shutters 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0131 
0.0122 
0.0072 
0.0066 
0.0053 
0.0049 
0.0045 
0.0041 
0.0154 
0.0137 
0.0086 
0.0076 
0.0066 
0.0058 
0.0057 
0.0049 
8d 
None 
Strong 
0.0122 
0.0116 
0.0065 
0.0061 
0.0049 
0.0045 
0.0042 
0.0038 
0.0128 
0.0119 
0.0071 
0.0065 
0.0053 
0.0048 
0.0045 
0.0041 
Toe-nails 
6d 
None 
Strong 
0.0135 
0.0128 
0.0077 
0.0072 
0.0057 
0.0052 
0.0048 
0.0044 
0.0159 
0.0145 
0.0093 
0.0084 
0.0070 
0.0063 
0.0060 
0.0054 
8d 
None 
Strong 
0.0129 
0.0125 
0.0074 
0.0071 
0.0054 
0.0051 
0.0045 
0.0043 
0.0143 
0.0137 
0.0084 
0.0079 
0.0062 
0.0058 
0.0053 
0.0050 
Two 
Straps 
6d 
None 
Strong 
0.0169 
0.0168 
0.0106 
0.0107 
0.0085 
0.0085 
0.0074 
0.0074 
0.0197 
0.0186 
0.0127 
0.0120 
0.0102 
0.0096 
0.0091 
0.0084 
8d 
None 
Strong 
0.0163 
0.0160 
0.0100 
0.0100 
0.0079 
0.0079 
0.0070 
0.0069 
0.0169 
0.0164 
0.0108 
0.0105 
0.0085 
0.0082 
0.0076 
0.0072 
Toe-nails 
6d 
None 
Strong 
0.0173 
0.0172 
0.0112 
0.0110 
0.0088 
0.0087 
0.0077 
0.0078 
0.0208 
0.0200 
0.0136 
0.0129 
0.0110 
0.0105 
0.0098 
0.0093 
8d 
None 
Strong 
0.0170 
0.0170 
0.0110 
0.0107 
0.0086 
0.0086 
0.0075 
0.0075 
0.0193 
0.0189 
0.0126 
0.0121 
0.0100 
0.0097 
0.0089 
0.0087 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0131 
0.0121 
0.0072 
0.0066 
0.0054 
0.0049 
0.0045 
0.0041 
0.0154 
0.0136 
0.0087 
0.0076 
0.0066 
0.0058 
0.0058 
0.0049 
8d 
None 
Strong 
0.0122 
0.0114 
0.0065 
0.0061 
0.0048 
0.0045 
0.0041 
0.0039 
0.0128 
0.0117 
0.0070 
0.0064 
0.0053 
0.0048 
0.0045 
0.0041 
Toe-nails 
6d 
None 
Strong 
0.0133 
0.0128 
0.0077 
0.0072 
0.0056 
0.0052 
0.0048 
0.0045 
0.0158 
0.0144 
0.0093 
0.0085 
0.0069 
0.0064 
0.0060 
0.0053 
8d 
None 
Strong 
0.0129 
0.0125 
0.0075 
0.0070 
0.0054 
0.0051 
0.0045 
0.0043 
0.0144 
0.0136 
0.0085 
0.0079 
0.0062 
0.0059 
0.0053 
0.0051 
Two 
Straps 
6d 
None 
Strong 
0.0168 
0.0168 
0.0107 
0.0107 
0.0085 
0.0084 
0.0074 
0.0075 
0.0195 
0.0186 
0.0127 
0.0119 
0.0103 
0.0096 
0.0091 
0.0085 
8d 
None 
Strong 
0.0161 
0.0160 
0.0100 
0.0100 
0.0080 
0.0079 
0.0070 
0.0069 
0.0167 
0.0164 
0.0107 
0.0104 
0.0085 
0.0082 
0.0074 
0.0072 
Toe-nails 
6d 
None 
Strong 
0.0174 
0.0173 
0.0112 
0.0111 
0.0088 
0.0088 
0.0077 
0.0077 
0.0207 
0.0200 
0.0136 
0.0129 
0.0110 
0.0105 
0.0097 
0.0092 
8d 
None 
Strong 
0.0170 
0.0168 
0.0110 
0.0108 
0.0086 
0.0085 
0.0075 
0.0075 
0.0192 
0.0187 
0.0125 
0.0121 
0.0100 
0.0098 
0.0088 
0.0087 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0131 
0.0122 
0.0072 
0.0066 
0.0053 
0.0049 
0.0045 
0.0041 
0.0153 
0.0135 
0.0087 
0.0076 
0.0066 
0.0057 
0.0057 
0.0049 
8d 
None 
Strong 
0.0122 
0.0114 
0.0065 
0.0062 
0.0048 
0.0045 
0.0042 
0.0039 
0.0128 
0.0119 
0.0070 
0.0065 
0.0053 
0.0048 
0.0046 
0.0041 
Toe-nails 
6d 
None 
Strong 
0.0134 
0.0127 
0.0076 
0.0072 
0.0056 
0.0052 
0.0048 
0.0044 
0.0159 
0.0144 
0.0093 
0.0085 
0.0070 
0.0063 
0.0059 
0.0054 
8d 
None 
Strong 
0.0129 
0.0123 
0.0075 
0.0070 
0.0054 
0.0050 
0.0046 
0.0043 
0.0143 
0.0136 
0.0085 
0.0080 
0.0063 
0.0059 
0.0053 
0.0050 
Two 
Straps 
6d 
None 
Strong 
0.0168 
0.0168 
0.0107 
0.0107 
0.0084 
0.0084 
0.0074 
0.0074 
0.0196 
0.0186 
0.0127 
0.0119 
0.0103 
0.0095 
0.0091 
0.0085 
8d 
None 
Strong 
0.0160 
0.0160 
0.0100 
0.0100 
0.0079 
0.0080 
0.0070 
0.0068 
0.0167 
0.0164 
0.0108 
0.0104 
0.0085 
0.0082 
0.0075 
0.0072 
Toe-nails 
6d 
None 
Strong 
0.0173 
0.0173 
0.0112 
0.0110 
0.0087 
0.0088 
0.0077 
0.0077 
0.0206 
0.0198 
0.0136 
0.0129 
0.0109 
0.0105 
0.0097 
0.0092 
8d 
None 
Strong 
0.0170 
0.0169 
0.0110 
0.0108 
0.0086 
0.0085 
0.0075 
0.0075 
0.0192 
0.0189 
0.0125 
0.0120 
0.0101 
0.0098 
0.0090 
0.0084 

Chapter 7. Direct Economic Losses 

?
Table 7.12. Average Annual Content Loss Normalized by Building Value   
Install 
Shutters

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0040 
0.0038 
0.0021 
0.0019 
0.0013 
0.0012 
0.0010 
0.0009 
0.0054 
0.0048 
0.0029 
0.0025 
0.0020 
0.0017 
0.0016 
0.0013 
8d 
None 
Strong 
0.0031 
0.0032 
0.0015 
0.0015 
0.0008 
0.0009 
0.0007 
0.0007 
0.0035 
0.0036 
0.0019 
0.0017 
0.0011 
0.0010 
0.0008 
0.0008 
Toe-nails 
6d 
None 
Strong 
0.0047 
0.0050 
0.0029 
0.0029 
0.0018 
0.0018 
0.0014 
0.0014 
0.0064 
0.0063 
0.0039 
0.0038 
0.0027 
0.0026 
0.0021 
0.0021 
8d 
None 
Strong 
0.0043 
0.0049 
0.0027 
0.0029 
0.0017 
0.0018 
0.0012 
0.0014 
0.0059 
0.0061 
0.0036 
0.0036 
0.0024 
0.0025 
0.0019 
0.0020 
Two 
Straps 
6d 
None 
Strong 
0.0061 
0.0061 
0.0038 
0.0037 
0.0026 
0.0026 
0.0022 
0.0021 
0.0080 
0.0074 
0.0051 
0.0047 
0.0038 
0.0034 
0.0033 
0.0029 
8d 
None 
Strong 
0.0054 
0.0054 
0.0032 
0.0031 
0.0021 
0.0021 
0.0017 
0.0016 
0.0062 
0.0060 
0.0039 
0.0037 
0.0027 
0.0025 
0.0022 
0.0021 
Toe-nails 
6d 
None 
Strong 
0.0070 
0.0071 
0.0046 
0.0046 
0.0033 
0.0033 
0.0027 
0.0028 
0.0099 
0.0097 
0.0065 
0.0062 
0.0050 
0.0049 
0.0044 
0.0043 
8d 
None 
Strong 
0.0068 
0.0070 
0.0045 
0.0044 
0.0032 
0.0032 
0.0026 
0.0026 
0.0095 
0.0092 
0.0062 
0.0060 
0.0048 
0.0046 
0.0042 
0.0040 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0039 
0.0037 
0.0021 
0.0019 
0.0013 
0.0012 
0.0010 
0.0009 
0.0054 
0.0047 
0.0029 
0.0025 
0.0020 
0.0016 
0.0016 
0.0013 
8d 
None 
Strong 
0.0029 
0.0031 
0.0015 
0.0016 
0.0008 
0.0009 
0.0006 
0.0007 
0.0034 
0.0034 
0.0018 
0.0017 
0.0011 
0.0010 
0.0008 
0.0008 
Toe-nails 
6d 
None 
Strong 
0.0045 
0.0050 
0.0029 
0.0029 
0.0018 
0.0018 
0.0014 
0.0014 
0.0064 
0.0063 
0.0040 
0.0038 
0.0026 
0.0027 
0.0021 
0.0021 
8d 
None 
Strong 
0.0043 
0.0048 
0.0028 
0.0028 
0.0016 
0.0018 
0.0012 
0.0014 
0.0059 
0.0060 
0.0037 
0.0036 
0.0024 
0.0025 
0.0019 
0.0020 
Two 
Straps 
6d 
None 
Strong 
0.0061 
0.0061 
0.0038 
0.0037 
0.0027 
0.0026 
0.0021 
0.0022 
0.0078 
0.0074 
0.0050 
0.0046 
0.0038 
0.0034 
0.0033 
0.0029 
8d 
None 
Strong 
0.0052 
0.0053 
0.0031 
0.0031 
0.0021 
0.0021 
0.0017 
0.0016 
0.0061 
0.0059 
0.0039 
0.0036 
0.0027 
0.0025 
0.0022 
0.0020 
Toe-nails 
6d 
None 
Strong 
0.0070 
0.0071 
0.0046 
0.0046 
0.0033 
0.0033 
0.0027 
0.0027 
0.0099 
0.0096 
0.0065 
0.0062 
0.0051 
0.0048 
0.0044 
0.0042 
8d 
None 
Strong 
0.0068 
0.0069 
0.0046 
0.0044 
0.0032 
0.0031 
0.0027 
0.0026 
0.0095 
0.0091 
0.0063 
0.0059 
0.0048 
0.0047 
0.0042 
0.0040 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0039 
0.0037 
0.0021 
0.0019 
0.0013 
0.0011 
0.0010 
0.0009 
0.0054 
0.0047 
0.0029 
0.0025 
0.0020 
0.0016 
0.0016 
0.0013 
8d 
None 
Strong 
0.0030 
0.0030 
0.0015 
0.0016 
0.0008 
0.0009 
0.0007 
0.0007 
0.0033 
0.0034 
0.0018 
0.0017 
0.0011 
0.0010 
0.0008 
0.0008 
Toe-nails 
6d 
None 
Strong 
0.0046 
0.0049 
0.0029 
0.0029 
0.0018 
0.0018 
0.0014 
0.0014 
0.0064 
0.0063 
0.0039 
0.0038 
0.0027 
0.0026 
0.0021 
0.0021 
8d 
None 
Strong 
0.0043 
0.0046 
0.0028 
0.0028 
0.0017 
0.0017 
0.0012 
0.0014 
0.0059 
0.0060 
0.0037 
0.0037 
0.0024 
0.0025 
0.0019 
0.0020 
Two 
Straps 
6d 
None 
Strong 
0.0060 
0.0062 
0.0038 
0.0037 
0.0026 
0.0026 
0.0021 
0.0022 
0.0079 
0.0074 
0.0051 
0.0046 
0.0038 
0.0034 
0.0033 
0.0029 
8d 
None 
Strong 
0.0052 
0.0052 
0.0031 
0.0031 
0.0021 
0.0021 
0.0017 
0.0016 
0.0061 
0.0060 
0.0039 
0.0036 
0.0027 
0.0025 
0.0022 
0.0020 
Toe-nails 
6d 
None 
Strong 
0.0069 
0.0071 
0.0046 
0.0045 
0.0033 
0.0033 
0.0028 
0.0028 
0.0098 
0.0094 
0.0066 
0.0063 
0.0050 
0.0049 
0.0044 
0.0042 
8d 
None 
Strong 
0.0068 
0.0069 
0.0045 
0.0044 
0.0032 
0.0032 
0.0026 
0.0026 
0.0094 
0.0092 
0.0062 
0.0059 
0.0049 
0.0046 
0.0043 
0.0038 

HAZUS-MH MR3 Technical Manual 

?
Table 7.13. Average Annual Total Loss Normalized by Building Value   Install 
Shutters 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0189 
0.0176 
0.0102 
0.0094 
0.0073 
0.0067 
0.0061 
0.0056 
0.0229 
0.0203 
0.0127 
0.0111 
0.0094 
0.0082 
0.0080 
0.0068 
8d 
None 
Strong 
0.0169 
0.0165 
0.0089 
0.0084 
0.0063 
0.0059 
0.0053 
0.0050 
0.0181 
0.0172 
0.0098 
0.0091 
0.0070 
0.0064 
0.0059 
0.0054 
Toe-nails 
6d 
None 
Strong 
0.0199 
0.0195 
0.0116 
0.0110 
0.0082 
0.0077 
0.0067 
0.0064 
0.0245 
0.0229 
0.0145 
0.0134 
0.0107 
0.0097 
0.0089 
0.0083 
8d 
None 
Strong 
0.0190 
0.0191 
0.0111 
0.0110 
0.0078 
0.0076 
0.0063 
0.0063 
0.0222 
0.0217 
0.0132 
0.0126 
0.0094 
0.0091 
0.0079 
0.0077 
Two 
Straps 
6d 
None 
Strong 
0.0254 
0.0254 
0.0159 
0.0159 
0.0123 
0.0123 
0.0106 
0.0105 
0.0305 
0.0287 
0.0197 
0.0184 
0.0155 
0.0144 
0.0136 
0.0125 
8d 
None 
Strong 
0.0240 
0.0237 
0.0146 
0.0146 
0.0111 
0.0110 
0.0096 
0.0095 
0.0255 
0.0248 
0.0162 
0.0157 
0.0124 
0.0118 
0.0108 
0.0103 
Toe-nails 
6d 
None 
Strong 
0.0267 
0.0267 
0.0174 
0.0172 
0.0134 
0.0132 
0.0114 
0.0117 
0.0338 
0.0326 
0.0221 
0.0209 
0.0176 
0.0168 
0.0156 
0.0149 
8d 
None 
Strong 
0.0262 
0.0264 
0.0171 
0.0166 
0.0131 
0.0130 
0.0111 
0.0111 
0.0317 
0.0309 
0.0206 
0.0199 
0.0163 
0.0157 
0.0143 
0.0139 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0187 
0.0175 
0.0102 
0.0094 
0.0073 
0.0067 
0.0061 
0.0055 
0.0230 
0.0202 
0.0128 
0.0112 
0.0095 
0.0082 
0.0081 
0.0069 
8d 
None 
Strong 
0.0167 
0.0161 
0.0089 
0.0085 
0.0062 
0.0059 
0.0053 
0.0050 
0.0179 
0.0168 
0.0097 
0.0090 
0.0070 
0.0065 
0.0059 
0.0054 
Toe-nails 
6d 
None 
Strong 
0.0196 
0.0196 
0.0116 
0.0110 
0.0082 
0.0078 
0.0067 
0.0064 
0.0244 
0.0227 
0.0146 
0.0135 
0.0105 
0.0099 
0.0089 
0.0081 
8d 
None 
Strong 
0.0188 
0.0190 
0.0112 
0.0108 
0.0077 
0.0076 
0.0063 
0.0062 
0.0222 
0.0215 
0.0133 
0.0126 
0.0094 
0.0091 
0.0079 
0.0078 
Two 
Straps 
6d 
None 
Strong 
0.0253 
0.0253 
0.0160 
0.0159 
0.0123 
0.0121 
0.0105 
0.0106 
0.0302 
0.0287 
0.0195 
0.0183 
0.0156 
0.0144 
0.0137 
0.0126 
8d 
None 
Strong 
0.0236 
0.0235 
0.0145 
0.0145 
0.0112 
0.0111 
0.0096 
0.0094 
0.0252 
0.0247 
0.0161 
0.0155 
0.0125 
0.0119 
0.0106 
0.0103 
Toe-nails 
6d 
None 
Strong 
0.0269 
0.0269 
0.0174 
0.0172 
0.0133 
0.0133 
0.0114 
0.0115 
0.0336 
0.0325 
0.0221 
0.0210 
0.0178 
0.0168 
0.0155 
0.0147 
8d 
None 
Strong 
0.0262 
0.0261 
0.0171 
0.0167 
0.0130 
0.0128 
0.0111 
0.0111 
0.0315 
0.0306 
0.0206 
0.0197 
0.0163 
0.0159 
0.0143 
0.0140 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0187 
0.0175 
0.0103 
0.0094 
0.0072 
0.0066 
0.0060 
0.0055 
0.0228 
0.0201 
0.0128 
0.0112 
0.0095 
0.0081 
0.0081 
0.0069 
8d 
None 
Strong 
0.0167 
0.0159 
0.0088 
0.0086 
0.0063 
0.0059 
0.0053 
0.0050 
0.0178 
0.0169 
0.0098 
0.0091 
0.0070 
0.0064 
0.0060 
0.0054 
Toe-nails 
6d 
None 
Strong 
0.0198 
0.0194 
0.0115 
0.0110 
0.0081 
0.0077 
0.0067 
0.0064 
0.0245 
0.0228 
0.0145 
0.0135 
0.0106 
0.0098 
0.0088 
0.0082 
8d 
None 
Strong 
0.0189 
0.0186 
0.0112 
0.0107 
0.0078 
0.0074 
0.0064 
0.0062 
0.0222 
0.0216 
0.0133 
0.0127 
0.0095 
0.0091 
0.0079 
0.0077 
Two 
Straps 
6d 
None 
Strong 
0.0252 
0.0254 
0.0160 
0.0159 
0.0122 
0.0122 
0.0106 
0.0106 
0.0303 
0.0286 
0.0196 
0.0182 
0.0156 
0.0143 
0.0137 
0.0126 
8d 
None 
Strong 
0.0235 
0.0235 
0.0145 
0.0145 
0.0111 
0.0112 
0.0096 
0.0094 
0.0252 
0.0247 
0.0163 
0.0155 
0.0124 
0.0119 
0.0107 
0.0102 
Toe-nails 
6d 
None 
Strong 
0.0266 
0.0269 
0.0174 
0.0172 
0.0133 
0.0132 
0.0115 
0.0115 
0.0334 
0.0321 
0.0221 
0.0211 
0.0175 
0.0168 
0.0154 
0.0147 
8d 
None 
Strong 
0.0262 
0.0262 
0.0170 
0.0167 
0.0129 
0.0129 
0.0111 
0.0112 
0.0314 
0.0308 
0.0205 
0.0196 
0.0165 
0.0158 
0.0145 
0.0135 

Chapter 7. Direct Economic Losses 

?
Table 7.14. Average Annual Building Loss Normalized by Building Value   
Upgrade Roof 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0133 
0.0133 
0.0063 
0.0063 
0.0038 
0.0037 
0.0028 
0.0027 
0.0146 
0.0147 
0.0070 
0.0068 
0.0042 
0.0041 
0.0032 
0.0030 
8d 
None 
Strong 
0.0134 
0.0132 
0.0063 
0.0063 
0.0038 
0.0037 
0.0028 
0.0027 
0.0145 
0.0147 
0.0069 
0.0068 
0.0042 
0.0041 
0.0032 
0.0030 
Toe-nails 
6d 
None 
Strong 
0.0158 
0.0167 
0.0090 
0.0082 
0.0058 
0.0052 
0.0043 
0.0039 
0.0181 
0.0194 
0.0120 
0.0106 
0.0080 
0.0071 
0.0063 
0.0054 
8d 
None 
Strong 
0.0159 
0.0167 
0.0090 
0.0082 
0.0058 
0.0052 
0.0043 
0.0039 
0.0181 
0.0194 
0.0120 
0.0107 
0.0079 
0.0071 
0.0063 
0.0055 
Two 
Straps 
6d 
None 
Strong 
0.0204 
0.0205 
0.0106 
0.0110 
0.0073 
0.0074 
0.0058 
0.0059 
0.0242 
0.0229 
0.0129 
0.0126 
0.0092 
0.0091 
0.0075 
0.0074 
8d 
None 
Strong 
0.0203 
0.0205 
0.0106 
0.0110 
0.0074 
0.0075 
0.0058 
0.0059 
0.0242 
0.0229 
0.0129 
0.0126 
0.0092 
0.0090 
0.0075 
0.0074 
Toe-nails 
6d 
None 
Strong 
0.0244 
0.0243 
0.0142 
0.0132 
0.0099 
0.0093 
0.0080 
0.0076 
0.0280 
0.0284 
0.0181 
0.0160 
0.0132 
0.0117 
0.0109 
0.0100 
8d 
None 
Strong 
0.0244 
0.0243 
0.0141 
0.0132 
0.0099 
0.0093 
0.0080 
0.0076 
0.0281 
0.0284 
0.0181 
0.0160 
0.0133 
0.0118 
0.0109 
0.0099 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0133 
0.0132 
0.0063 
0.0062 
0.0038 
0.0037 
0.0027 
0.0027 
0.0146 
0.0147 
0.0069 
0.0068 
0.0042 
0.0041 
0.0031 
0.0030 
8d 
None 
Strong 
0.0133 
0.0132 
0.0063 
0.0063 
0.0038 
0.0037 
0.0028 
0.0027 
0.0146 
0.0147 
0.0069 
0.0068 
0.0042 
0.0041 
0.0032 
0.0030 
Toe-nails 
6d 
None 
Strong 
0.0158 
0.0167 
0.0091 
0.0081 
0.0058 
0.0052 
0.0043 
0.0040 
0.0182 
0.0194 
0.0120 
0.0105 
0.0081 
0.0071 
0.0063 
0.0056 
8d 
None 
Strong 
0.0159 
0.0166 
0.0091 
0.0082 
0.0058 
0.0052 
0.0043 
0.0040 
0.0181 
0.0194 
0.0119 
0.0106 
0.0080 
0.0070 
0.0063 
0.0056 
Two 
Straps 
6d 
None 
Strong 
0.0201 
0.0205 
0.0106 
0.0110 
0.0073 
0.0074 
0.0057 
0.0060 
0.0241 
0.0227 
0.0129 
0.0125 
0.0091 
0.0091 
0.0074 
0.0074 
8d 
None 
Strong 
0.0201 
0.0204 
0.0105 
0.0109 
0.0072 
0.0075 
0.0058 
0.0059 
0.0240 
0.0227 
0.0129 
0.0126 
0.0092 
0.0090 
0.0074 
0.0074 
Toe-nails 
6d 
None 
Strong 
0.0242 
0.0246 
0.0140 
0.0132 
0.0099 
0.0094 
0.0081 
0.0075 
0.0281 
0.0285 
0.0180 
0.0161 
0.0133 
0.0118 
0.0109 
0.0099 
8d 
None 
Strong 
0.0242 
0.0246 
0.0140 
0.0132 
0.0098 
0.0094 
0.0081 
0.0076 
0.0279 
0.0285 
0.0181 
0.0160 
0.0132 
0.0118 
0.0109 
0.0098 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0134 
0.0132 
0.0063 
0.0063 
0.0038 
0.0037 
0.0028 
0.0027 
0.0146 
0.0146 
0.0069 
0.0068 
0.0042 
0.0041 
0.0031 
0.0030 
8d 
None 
Strong 
0.0133 
0.0131 
0.0062 
0.0063 
0.0038 
0.0037 
0.0028 
0.0027 
0.0146 
0.0146 
0.0069 
0.0068 
0.0042 
0.0041 
0.0032 
0.0030 
Toe-nails 
6d 
None 
Strong 
0.0159 
0.0168 
0.0091 
0.0081 
0.0058 
0.0051 
0.0043 
0.0039 
0.0181 
0.0194 
0.0119 
0.0106 
0.0080 
0.0071 
0.0063 
0.0055 
8d 
None 
Strong 
0.0159 
0.0168 
0.0091 
0.0081 
0.0058 
0.0051 
0.0043 
0.0039 
0.0183 
0.0195 
0.0119 
0.0106 
0.0080 
0.0071 
0.0063 
0.0055 
Two 
Straps 
6d 
None 
Strong 
0.0202 
0.0203 
0.0106 
0.0110 
0.0072 
0.0075 
0.0057 
0.0060 
0.0240 
0.0229 
0.0128 
0.0126 
0.0092 
0.0089 
0.0074 
0.0074 
8d 
None 
Strong 
0.0200 
0.0204 
0.0105 
0.0108 
0.0073 
0.0075 
0.0057 
0.0060 
0.0240 
0.0228 
0.0129 
0.0125 
0.0091 
0.0090 
0.0074 
0.0073 
Toe-nails 
6d 
None 
Strong 
0.0242 
0.0247 
0.0140 
0.0132 
0.0099 
0.0093 
0.0080 
0.0076 
0.0281 
0.0284 
0.0181 
0.0160 
0.0132 
0.0118 
0.0109 
0.0099 
8d 
None 
Strong 
0.0242 
0.0247 
0.0140 
0.0133 
0.0099 
0.0093 
0.0080 
0.0076 
0.0280 
0.0284 
0.0180 
0.0159 
0.0131 
0.0117 
0.0109 
0.0099 

HAZUS-MH MR3 Technical Manual 

?
Table 7.15. Average Annual Content Loss Normalized by Building Value   
Upgrade Roof 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0078 
0.0081 
0.0036 
0.0037 
0.0019 
0.0020 
0.0012 
0.0013 
0.0089 
0.0092 
0.0041 
0.0041 
0.0022 
0.0023 
0.0014 
0.0015 
8d 
None 
Strong 
0.0079 
0.0080 
0.0036 
0.0037 
0.0019 
0.0020 
0.0011 
0.0013 
0.0088 
0.0092 
0.0041 
0.0041 
0.0021 
0.0023 
0.0014 
0.0015 
Toenails 
6d 
None 
Strong 
0.0096 
0.0107 
0.0057 
0.0053 
0.0035 
0.0032 
0.0024 
0.0022 
0.0113 
0.0126 
0.0078 
0.0071 
0.0051 
0.0046 
0.0039 
0.0034 
8d 
None 
Strong 
0.0097 
0.0106 
0.0057 
0.0053 
0.0035 
0.0032 
0.0024 
0.0023 
0.0114 
0.0126 
0.0078 
0.0071 
0.0050 
0.0046 
0.0039 
0.0035 
Two 
Straps 
6d 
None 
Strong 
0.0136 
0.0136 
0.0070 
0.0072 
0.0047 
0.0047 
0.0035 
0.0036 
0.0164 
0.0154 
0.0090 
0.0086 
0.0063 
0.0061 
0.0050 
0.0049 
8d 
None 
Strong 
0.0136 
0.0136 
0.0070 
0.0072 
0.0047 
0.0048 
0.0035 
0.0036 
0.0164 
0.0154 
0.0091 
0.0086 
0.0063 
0.0060 
0.0050 
0.0049 
Toenails 
6d 
None 
Strong 
0.0160 
0.0160 
0.0094 
0.0088 
0.0064 
0.0060 
0.0050 
0.0048 
0.0187 
0.0191 
0.0123 
0.0110 
0.0089 
0.0079 
0.0073 
0.0067 
8d 
None 
Strong 
0.0160 
0.0160 
0.0094 
0.0088 
0.0064 
0.0061 
0.0051 
0.0049 
0.0187 
0.0191 
0.0123 
0.0110 
0.0090 
0.0080 
0.0073 
0.0066 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0078 
0.0080 
0.0036 
0.0037 
0.0018 
0.0020 
0.0011 
0.0013 
0.0089 
0.0092 
0.0041 
0.0041 
0.0022 
0.0023 
0.0014 
0.0015 
8d 
None 
Strong 
0.0078 
0.0080 
0.0036 
0.0037 
0.0018 
0.0020 
0.0011 
0.0013 
0.0089 
0.0092 
0.0041 
0.0041 
0.0022 
0.0023 
0.0014 
0.0015 
Toenails 
6d 
None 
Strong 
0.0097 
0.0107 
0.0057 
0.0053 
0.0034 
0.0032 
0.0024 
0.0023 
0.0114 
0.0126 
0.0078 
0.0070 
0.0051 
0.0046 
0.0039 
0.0035 
8d 
None 
Strong 
0.0097 
0.0106 
0.0057 
0.0053 
0.0034 
0.0032 
0.0024 
0.0023 
0.0114 
0.0126 
0.0077 
0.0070 
0.0050 
0.0045 
0.0039 
0.0035 
Two 
Straps 
6d 
None 
Strong 
0.0136 
0.0136 
0.0070 
0.0073 
0.0047 
0.0047 
0.0035 
0.0037 
0.0165 
0.0153 
0.0090 
0.0085 
0.0063 
0.0061 
0.0050 
0.0049 
8d 
None 
Strong 
0.0136 
0.0135 
0.0070 
0.0072 
0.0046 
0.0048 
0.0035 
0.0036 
0.0164 
0.0153 
0.0090 
0.0086 
0.0063 
0.0061 
0.0050 
0.0049 
Toenails 
6d 
None 
Strong 
0.0158 
0.0162 
0.0093 
0.0089 
0.0064 
0.0061 
0.0051 
0.0048 
0.0187 
0.0191 
0.0122 
0.0110 
0.0090 
0.0080 
0.0073 
0.0066 
8d 
None 
Strong 
0.0158 
0.0162 
0.0093 
0.0089 
0.0064 
0.0061 
0.0051 
0.0048 
0.0186 
0.0191 
0.0123 
0.0109 
0.0089 
0.0080 
0.0073 
0.0066 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0079 
0.0080 
0.0036 
0.0037 
0.0018 
0.0020 
0.0011 
0.0013 
0.0089 
0.0092 
0.0041 
0.0041 
0.0022 
0.0023 
0.0014 
0.0015 
8d 
None 
Strong 
0.0078 
0.0079 
0.0035 
0.0037 
0.0018 
0.0020 
0.0011 
0.0013 
0.0088 
0.0091 
0.0041 
0.0041 
0.0022 
0.0023 
0.0014 
0.0015 
Toenails 
6d 
None 
Strong 
0.0097 
0.0108 
0.0057 
0.0053 
0.0034 
0.0031 
0.0024 
0.0023 
0.0114 
0.0126 
0.0078 
0.0070 
0.0051 
0.0046 
0.0039 
0.0035 
8d 
None 
Strong 
0.0097 
0.0108 
0.0057 
0.0053 
0.0034 
0.0032 
0.0024 
0.0022 
0.0115 
0.0127 
0.0078 
0.0070 
0.0051 
0.0046 
0.0039 
0.0035 
Two 
Straps 
6d 
None 
Strong 
0.0136 
0.0135 
0.0071 
0.0073 
0.0046 
0.0047 
0.0035 
0.0036 
0.0164 
0.0154 
0.0089 
0.0086 
0.0063 
0.0060 
0.0050 
0.0049 
8d 
None 
Strong 
0.0135 
0.0136 
0.0070 
0.0071 
0.0047 
0.0048 
0.0035 
0.0037 
0.0164 
0.0153 
0.0090 
0.0085 
0.0063 
0.0061 
0.0050 
0.0049 
Toenails 
6d 
None 
Strong 
0.0158 
0.0163 
0.0093 
0.0088 
0.0065 
0.0061 
0.0051 
0.0048 
0.0187 
0.0190 
0.0123 
0.0109 
0.0089 
0.0080 
0.0073 
0.0066 
8d 
None 
Strong 
0.0158 
0.0163 
0.0093 
0.0089 
0.0065 
0.0061 
0.0051 
0.0048 
0.0187 
0.0191 
0.0122 
0.0109 
0.0088 
0.0079 
0.0073 
0.0067 

Chapter 7. Direct Economic Losses 

?
Table 7.16. Average Annual Total Loss Normalized by Building Value   Upgrade 
Roof

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0229 
0.0232 
0.0107 
0.0108 
0.0062 
0.0062 
0.0043 
0.0043 
0.0257 
0.0261 
0.0120 
0.0119 
0.0069 
0.0070 
0.0050 
0.0049 
8d 
None 
Strong 
0.0232 
0.0232 
0.0107 
0.0109 
0.0062 
0.0062 
0.0043 
0.0043 
0.0255 
0.0261 
0.0119 
0.0118 
0.0069 
0.0070 
0.0050 
0.0049 
Toe-nails 
6d 
None 
Strong 
0.0278 
0.0299 
0.0160 
0.0146 
0.0101 
0.0091 
0.0074 
0.0066 
0.0321 
0.0350 
0.0216 
0.0193 
0.0143 
0.0128 
0.0111 
0.0096 
8d 
None 
Strong 
0.0278 
0.0298 
0.0160 
0.0147 
0.0101 
0.0091 
0.0074 
0.0067 
0.0322 
0.0349 
0.0216 
0.0194 
0.0141 
0.0128 
0.0111 
0.0098 
Two 
Straps 
6d 
None 
Strong 
0.0370 
0.0372 
0.0192 
0.0198 
0.0130 
0.0132 
0.0102 
0.0104 
0.0444 
0.0417 
0.0238 
0.0231 
0.0169 
0.0165 
0.0136 
0.0135 
8d 
None 
Strong 
0.0369 
0.0371 
0.0192 
0.0198 
0.0131 
0.0134 
0.0102 
0.0103 
0.0442 
0.0418 
0.0239 
0.0231 
0.0169 
0.0164 
0.0136 
0.0133 
Toe-nails 
6d 
None 
Strong 
0.0441 
0.0441 
0.0257 
0.0240 
0.0178 
0.0167 
0.0142 
0.0135 
0.0510 
0.0518 
0.0332 
0.0294 
0.0241 
0.0214 
0.0199 
0.0181 
8d 
None 
Strong 
0.0441 
0.0440 
0.0256 
0.0240 
0.0178 
0.0167 
0.0143 
0.0136 
0.0511 
0.0519 
0.0331 
0.0294 
0.0243 
0.0215 
0.0198 
0.0180 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0230 
0.0231 
0.0107 
0.0108 
0.0060 
0.0062 
0.0042 
0.0043 
0.0256 
0.0260 
0.0119 
0.0118 
0.0069 
0.0070 
0.0049 
0.0048 
8d 
None 
Strong 
0.0230 
0.0231 
0.0107 
0.0109 
0.0061 
0.0062 
0.0043 
0.0043 
0.0257 
0.0261 
0.0119 
0.0119 
0.0069 
0.0070 
0.0050 
0.0049 
Toe-nails 
6d 
None 
Strong 
0.0278 
0.0298 
0.0161 
0.0146 
0.0100 
0.0090 
0.0073 
0.0068 
0.0322 
0.0350 
0.0216 
0.0191 
0.0144 
0.0127 
0.0111 
0.0099 
8d 
None 
Strong 
0.0278 
0.0298 
0.0161 
0.0147 
0.0100 
0.0092 
0.0073 
0.0068 
0.0322 
0.0350 
0.0214 
0.0192 
0.0142 
0.0125 
0.0111 
0.0099 
Two 
Straps 
6d 
None 
Strong 
0.0367 
0.0372 
0.0191 
0.0198 
0.0131 
0.0132 
0.0100 
0.0105 
0.0443 
0.0415 
0.0238 
0.0229 
0.0167 
0.0165 
0.0135 
0.0134 
8d 
None 
Strong 
0.0367 
0.0370 
0.0191 
0.0197 
0.0128 
0.0134 
0.0101 
0.0104 
0.0440 
0.0415 
0.0238 
0.0231 
0.0169 
0.0164 
0.0135 
0.0134 
Toe-nails 
6d 
None 
Strong 
0.0437 
0.0446 
0.0255 
0.0241 
0.0178 
0.0169 
0.0144 
0.0134 
0.0511 
0.0520 
0.0330 
0.0295 
0.0243 
0.0216 
0.0199 
0.0180 
8d 
None 
Strong 
0.0437 
0.0445 
0.0255 
0.0241 
0.0177 
0.0169 
0.0144 
0.0136 
0.0508 
0.0519 
0.0332 
0.0293 
0.0240 
0.0216 
0.0198 
0.0179 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0231 
0.0231 
0.0107 
0.0108 
0.0061 
0.0062 
0.0042 
0.0044 
0.0256 
0.0260 
0.0119 
0.0119 
0.0069 
0.0069 
0.0049 
0.0049 
8d 
None 
Strong 
0.0230 
0.0229 
0.0106 
0.0109 
0.0060 
0.0062 
0.0043 
0.0043 
0.0255 
0.0259 
0.0119 
0.0119 
0.0070 
0.0069 
0.0050 
0.0049 
Toe-nails 
6d 
None 
Strong 
0.0279 
0.0301 
0.0161 
0.0146 
0.0100 
0.0090 
0.0073 
0.0067 
0.0322 
0.0350 
0.0214 
0.0192 
0.0142 
0.0128 
0.0110 
0.0098 
8d 
None 
Strong 
0.0279 
0.0301 
0.0161 
0.0146 
0.0100 
0.0090 
0.0073 
0.0066 
0.0324 
0.0351 
0.0215 
0.0192 
0.0143 
0.0127 
0.0110 
0.0098 
Two 
Straps 
6d 
None 
Strong 
0.0368 
0.0368 
0.0192 
0.0198 
0.0129 
0.0133 
0.0101 
0.0104 
0.0440 
0.0418 
0.0237 
0.0231 
0.0169 
0.0163 
0.0135 
0.0133 
8d 
None 
Strong 
0.0365 
0.0371 
0.0190 
0.0195 
0.0130 
0.0133 
0.0101 
0.0105 
0.0440 
0.0415 
0.0238 
0.0229 
0.0168 
0.0164 
0.0134 
0.0133 
Toe-nails 
6d 
None 
Strong 
0.0437 
0.0447 
0.0254 
0.0240 
0.0179 
0.0167 
0.0144 
0.0135 
0.0511 
0.0518 
0.0331 
0.0293 
0.0241 
0.0216 
0.0198 
0.0180 
8d 
None 
Strong 
0.0437 
0.0447 
0.0254 
0.0242 
0.0179 
0.0167 
0.0143 
0.0135 
0.0510 
0.0519 
0.0330 
0.0292 
0.0239 
0.0213 
0.0199 
0.0181 

HAZUS-MH MR3 Technical Manual 

?
Table 7.17. Average Annual Building Loss Normalized by Building Value   
Upgrade Roof and Add Secondary Water Resistance 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0127 
0.0127 
0.0059 
0.0059 
0.0034 
0.0033 
0.0023 
0.0023 
0.0140 
0.0142 
0.0065 
0.0064 
0.0037 
0.0037 
0.0026 
0.0026 
8d 
None 
Strong 
0.0129 
0.0127 
0.0059 
0.0059 
0.0034 
0.0033 
0.0023 
0.0023 
0.0139 
0.0142 
0.0064 
0.0063 
0.0037 
0.0037 
0.0026 
0.0025 
Toe-nails 
6d 
None 
Strong 
0.0154 
0.0165 
0.0089 
0.0080 
0.0056 
0.0050 
0.0041 
0.0036 
0.0176 
0.0191 
0.0118 
0.0105 
0.0078 
0.0069 
0.0061 
0.0052 
8d 
None 
Strong 
0.0154 
0.0164 
0.0089 
0.0080 
0.0056 
0.0050 
0.0041 
0.0037 
0.0177 
0.0191 
0.0118 
0.0105 
0.0077 
0.0069 
0.0061 
0.0053 
Two 
Straps 
6d 
None 
Strong 
0.0199 
0.0200 
0.0101 
0.0105 
0.0067 
0.0069 
0.0051 
0.0054 
0.0238 
0.0224 
0.0125 
0.0121 
0.0088 
0.0086 
0.0070 
0.0069 
8d 
None 
Strong 
0.0198 
0.0200 
0.0101 
0.0105 
0.0068 
0.0070 
0.0051 
0.0053 
0.0238 
0.0224 
0.0126 
0.0121 
0.0087 
0.0085 
0.0070 
0.0068 
Toe-nails 
6d 
None 
Strong 
0.0240 
0.0240 
0.0138 
0.0129 
0.0095 
0.0089 
0.0075 
0.0072 
0.0277 
0.0281 
0.0179 
0.0158 
0.0129 
0.0115 
0.0106 
0.0097 
8d 
None 
Strong 
0.0240 
0.0240 
0.0138 
0.0129 
0.0095 
0.0089 
0.0076 
0.0072 
0.0277 
0.0282 
0.0178 
0.0158 
0.0130 
0.0115 
0.0106 
0.0096 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0127 
0.0126 
0.0059 
0.0058 
0.0033 
0.0033 
0.0023 
0.0023 
0.0140 
0.0141 
0.0064 
0.0063 
0.0037 
0.0037 
0.0026 
0.0025 
8d 
None 
Strong 
0.0128 
0.0126 
0.0059 
0.0059 
0.0033 
0.0033 
0.0023 
0.0023 
0.0140 
0.0142 
0.0064 
0.0063 
0.0037 
0.0037 
0.0026 
0.0025 
Toe-nails 
6d 
None 
Strong 
0.0154 
0.0164 
0.0089 
0.0080 
0.0056 
0.0050 
0.0041 
0.0037 
0.0177 
0.0192 
0.0118 
0.0104 
0.0078 
0.0069 
0.0061 
0.0054 
8d 
None 
Strong 
0.0154 
0.0164 
0.0089 
0.0080 
0.0055 
0.0050 
0.0041 
0.0037 
0.0176 
0.0191 
0.0117 
0.0104 
0.0077 
0.0068 
0.0061 
0.0054 
Two 
Straps 
6d 
None 
Strong 
0.0196 
0.0200 
0.0100 
0.0105 
0.0067 
0.0069 
0.0051 
0.0054 
0.0237 
0.0222 
0.0125 
0.0120 
0.0087 
0.0086 
0.0069 
0.0068 
8d 
None 
Strong 
0.0196 
0.0199 
0.0100 
0.0104 
0.0066 
0.0070 
0.0051 
0.0053 
0.0236 
0.0222 
0.0125 
0.0121 
0.0087 
0.0085 
0.0069 
0.0068 
Toe-nails 
6d 
None 
Strong 
0.0238 
0.0243 
0.0137 
0.0129 
0.0095 
0.0090 
0.0076 
0.0071 
0.0277 
0.0282 
0.0177 
0.0158 
0.0130 
0.0115 
0.0106 
0.0096 
8d 
None 
Strong 
0.0238 
0.0242 
0.0137 
0.0129 
0.0094 
0.0090 
0.0076 
0.0072 
0.0276 
0.0282 
0.0178 
0.0157 
0.0129 
0.0115 
0.0106 
0.0095 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0128 
0.0126 
0.0059 
0.0059 
0.0033 
0.0033 
0.0023 
0.0023 
0.0140 
0.0141 
0.0064 
0.0064 
0.0037 
0.0036 
0.0026 
0.0025 
8d 
None 
Strong 
0.0127 
0.0125 
0.0059 
0.0059 
0.0033 
0.0033 
0.0023 
0.0023 
0.0140 
0.0141 
0.0064 
0.0063 
0.0037 
0.0036 
0.0026 
0.0025 
Toe-nails 
6d 
None 
Strong 
0.0155 
0.0165 
0.0089 
0.0079 
0.0056 
0.0049 
0.0041 
0.0037 
0.0177 
0.0192 
0.0117 
0.0104 
0.0078 
0.0070 
0.0060 
0.0053 
8d 
None 
Strong 
0.0155 
0.0165 
0.0089 
0.0080 
0.0056 
0.0049 
0.0041 
0.0036 
0.0178 
0.0192 
0.0117 
0.0104 
0.0078 
0.0069 
0.0060 
0.0053 
Two 
Straps 
6d 
None 
Strong 
0.0196 
0.0198 
0.0101 
0.0105 
0.0066 
0.0069 
0.0051 
0.0053 
0.0236 
0.0223 
0.0124 
0.0121 
0.0087 
0.0084 
0.0069 
0.0068 
8d 
None 
Strong 
0.0195 
0.0199 
0.0100 
0.0103 
0.0067 
0.0069 
0.0051 
0.0054 
0.0235 
0.0222 
0.0125 
0.0120 
0.0087 
0.0085 
0.0068 
0.0068 
Toe-nails 
6d 
None 
Strong 
0.0238 
0.0243 
0.0136 
0.0129 
0.0096 
0.0089 
0.0076 
0.0072 
0.0277 
0.0281 
0.0178 
0.0157 
0.0129 
0.0115 
0.0106 
0.0096 
8d 
None 
Strong 
0.0238 
0.0243 
0.0136 
0.0130 
0.0095 
0.0089 
0.0076 
0.0072 
0.0277 
0.0282 
0.0178 
0.0157 
0.0128 
0.0114 
0.0106 
0.0096 

Chapter 7. Direct Economic Losses 

?
Table 7.18. Average Annual Content Loss Normalized by Building Value - 
Upgrade Roof and Add Secondary Water Resistance 

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0077 
0.0080 
0.0035 
0.0037 
0.0018 
0.0019 
0.0011 
0.0012 
0.0088 
0.0091 
0.0041 
0.0041 
0.0021 
0.0022 
0.0014 
0.0014 
8d 
None 
Strong 
0.0078 
0.0079 
0.0035 
0.0037 
0.0018 
0.0019 
0.0011 
0.0012 
0.0087 
0.0091 
0.0040 
0.0041 
0.0021 
0.0022 
0.0013 
0.0014 
Toe-nails 
6d 
None 
Strong 
0.0096 
0.0106 
0.0057 
0.0053 
0.0034 
0.0032 
0.0024 
0.0022 
0.0112 
0.0126 
0.0078 
0.0070 
0.0050 
0.0046 
0.0039 
0.0034 
8d 
None 
Strong 
0.0096 
0.0106 
0.0057 
0.0053 
0.0034 
0.0032 
0.0024 
0.0022 
0.0113 
0.0126 
0.0078 
0.0071 
0.0050 
0.0046 
0.0038 
0.0034 
Two 
Straps 
6d 
None 
Strong 
0.0135 
0.0135 
0.0070 
0.0071 
0.0046 
0.0046 
0.0035 
0.0036 
0.0164 
0.0153 
0.0089 
0.0085 
0.0063 
0.0060 
0.0050 
0.0049 
8d 
None 
Strong 
0.0135 
0.0135 
0.0070 
0.0072 
0.0046 
0.0047 
0.0034 
0.0035 
0.0163 
0.0153 
0.0090 
0.0085 
0.0062 
0.0060 
0.0050 
0.0048 
Toe-nails 
6d 
None 
Strong 
0.0159 
0.0160 
0.0094 
0.0088 
0.0064 
0.0060 
0.0050 
0.0048 
0.0186 
0.0190 
0.0123 
0.0109 
0.0089 
0.0079 
0.0072 
0.0066 
8d 
None 
Strong 
0.0159 
0.0160 
0.0093 
0.0088 
0.0064 
0.0060 
0.0050 
0.0048 
0.0186 
0.0190 
0.0122 
0.0109 
0.0089 
0.0079 
0.0072 
0.0066 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0077 
0.0079 
0.0035 
0.0036 
0.0018 
0.0019 
0.0011 
0.0012 
0.0088 
0.0091 
0.0040 
0.0041 
0.0021 
0.0022 
0.0013 
0.0014 
8d 
None 
Strong 
0.0078 
0.0079 
0.0035 
0.0037 
0.0018 
0.0019 
0.0011 
0.0012 
0.0088 
0.0091 
0.0040 
0.0041 
0.0021 
0.0022 
0.0014 
0.0014 
Toe-nails 
6d 
None 
Strong 
0.0096 
0.0106 
0.0057 
0.0053 
0.0034 
0.0031 
0.0024 
0.0023 
0.0113 
0.0126 
0.0078 
0.0070 
0.0051 
0.0045 
0.0039 
0.0035 
8d 
None 
Strong 
0.0096 
0.0106 
0.0057 
0.0053 
0.0034 
0.0032 
0.0024 
0.0023 
0.0113 
0.0126 
0.0077 
0.0070 
0.0050 
0.0045 
0.0039 
0.0035 
Two 
Straps 
6d 
None 
Strong 
0.0135 
0.0135 
0.0069 
0.0072 
0.0046 
0.0046 
0.0034 
0.0036 
0.0164 
0.0152 
0.0089 
0.0084 
0.0062 
0.0060 
0.0050 
0.0048 
8d 
None 
Strong 
0.0135 
0.0135 
0.0069 
0.0071 
0.0045 
0.0047 
0.0035 
0.0035 
0.0163 
0.0152 
0.0089 
0.0085 
0.0063 
0.0060 
0.0050 
0.0048 
Toe-nails 
6d 
None 
Strong 
0.0157 
0.0162 
0.0093 
0.0088 
0.0064 
0.0061 
0.0050 
0.0047 
0.0187 
0.0191 
0.0122 
0.0110 
0.0089 
0.0080 
0.0072 
0.0066 
8d 
None 
Strong 
0.0157 
0.0161 
0.0093 
0.0088 
0.0063 
0.0061 
0.0050 
0.0048 
0.0186 
0.0191 
0.0123 
0.0109 
0.0088 
0.0080 
0.0072 
0.0065 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0078 
0.0079 
0.0035 
0.0037 
0.0018 
0.0019 
0.0011 
0.0013 
0.0088 
0.0091 
0.0040 
0.0041 
0.0021 
0.0022 
0.0013 
0.0014 
8d 
None 
Strong 
0.0077 
0.0078 
0.0035 
0.0037 
0.0018 
0.0019 
0.0011 
0.0012 
0.0087 
0.0090 
0.0040 
0.0041 
0.0022 
0.0022 
0.0014 
0.0014 
Toe-nails 
6d 
None 
Strong 
0.0096 
0.0107 
0.0057 
0.0052 
0.0034 
0.0031 
0.0024 
0.0022 
0.0113 
0.0126 
0.0077 
0.0070 
0.0050 
0.0046 
0.0038 
0.0034 
8d 
None 
Strong 
0.0096 
0.0107 
0.0057 
0.0053 
0.0034 
0.0031 
0.0024 
0.0022 
0.0114 
0.0126 
0.0077 
0.0070 
0.0051 
0.0045 
0.0038 
0.0034 
Two 
Straps 
6d 
None 
Strong 
0.0135 
0.0134 
0.0070 
0.0072 
0.0045 
0.0047 
0.0034 
0.0035 
0.0163 
0.0153 
0.0089 
0.0085 
0.0063 
0.0059 
0.0049 
0.0048 
8d 
None 
Strong 
0.0134 
0.0135 
0.0069 
0.0071 
0.0046 
0.0047 
0.0034 
0.0036 
0.0163 
0.0152 
0.0090 
0.0085 
0.0062 
0.0060 
0.0049 
0.0048 
Toe-nails 
6d 
None 
Strong 
0.0158 
0.0162 
0.0092 
0.0088 
0.0064 
0.0060 
0.0051 
0.0047 
0.0187 
0.0190 
0.0122 
0.0109 
0.0089 
0.0079 
0.0072 
0.0066 
8d 
None 
Strong 
0.0158 
0.0162 
0.0092 
0.0089 
0.0064 
0.0060 
0.0050 
0.0048 
0.0186 
0.0190 
0.0122 
0.0109 
0.0087 
0.0079 
0.0072 
0.0066 

HAZUS-MH MR3 Technical Manual 

?
Table 7.19. Average Annual Total Loss Normalized by Building Value   Upgrade 
Roof and Add Secondary Water Resistance

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0222 
0.0225 
0.0102 
0.0103 
0.0056 
0.0057 
0.0037 
0.0037 
0.0248 
0.0254 
0.0115 
0.0113 
0.0063 
0.0064 
0.0043 
0.0043 
8d 
None 
Strong 
0.0225 
0.0224 
0.0102 
0.0104 
0.0056 
0.0057 
0.0036 
0.0038 
0.0247 
0.0254 
0.0113 
0.0113 
0.0062 
0.0064 
0.0042 
0.0043 
Toe-nails 
6d 
None 
Strong 
0.0272 
0.0296 
0.0158 
0.0144 
0.0098 
0.0089 
0.0071 
0.0063 
0.0314 
0.0346 
0.0213 
0.0191 
0.0140 
0.0125 
0.0108 
0.0094 
8d 
None 
Strong 
0.0273 
0.0295 
0.0158 
0.0144 
0.0098 
0.0089 
0.0071 
0.0064 
0.0316 
0.0346 
0.0213 
0.0191 
0.0138 
0.0125 
0.0108 
0.0095 
Two 
Straps 
6d 
None 
Strong 
0.0364 
0.0365 
0.0186 
0.0191 
0.0123 
0.0125 
0.0093 
0.0097 
0.0438 
0.0410 
0.0233 
0.0224 
0.0163 
0.0158 
0.0129 
0.0127 
8d 
None 
Strong 
0.0363 
0.0365 
0.0186 
0.0192 
0.0124 
0.0126 
0.0093 
0.0096 
0.0437 
0.0411 
0.0235 
0.0224 
0.0162 
0.0157 
0.0130 
0.0126 
Toe-nails 
6d 
None 
Strong 
0.0436 
0.0436 
0.0253 
0.0236 
0.0173 
0.0162 
0.0136 
0.0130 
0.0505 
0.0515 
0.0328 
0.0291 
0.0238 
0.0211 
0.0195 
0.0177 
8d 
None 
Strong 
0.0436 
0.0436 
0.0252 
0.0236 
0.0173 
0.0162 
0.0137 
0.0131 
0.0506 
0.0515 
0.0327 
0.0291 
0.0239 
0.0212 
0.0194 
0.0177 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0223 
0.0223 
0.0102 
0.0103 
0.0055 
0.0057 
0.0036 
0.0038 
0.0248 
0.0253 
0.0114 
0.0113 
0.0063 
0.0063 
0.0042 
0.0042 
8d 
None 
Strong 
0.0223 
0.0223 
0.0102 
0.0103 
0.0055 
0.0057 
0.0037 
0.0038 
0.0248 
0.0254 
0.0113 
0.0113 
0.0063 
0.0064 
0.0043 
0.0043 
Toe-nails 
6d 
None 
Strong 
0.0272 
0.0295 
0.0158 
0.0144 
0.0097 
0.0088 
0.0070 
0.0065 
0.0316 
0.0346 
0.0213 
0.0189 
0.0141 
0.0125 
0.0108 
0.0096 
8d 
None 
Strong 
0.0273 
0.0294 
0.0158 
0.0145 
0.0097 
0.0089 
0.0070 
0.0065 
0.0315 
0.0346 
0.0211 
0.0189 
0.0139 
0.0123 
0.0108 
0.0096 
Two 
Straps 
6d 
None 
Strong 
0.0361 
0.0365 
0.0185 
0.0192 
0.0123 
0.0125 
0.0092 
0.0097 
0.0437 
0.0408 
0.0233 
0.0223 
0.0161 
0.0159 
0.0129 
0.0127 
8d 
None 
Strong 
0.0360 
0.0363 
0.0184 
0.0190 
0.0120 
0.0126 
0.0093 
0.0096 
0.0435 
0.0408 
0.0233 
0.0225 
0.0163 
0.0157 
0.0128 
0.0127 
Toe-nails 
6d 
None 
Strong 
0.0431 
0.0442 
0.0251 
0.0237 
0.0173 
0.0164 
0.0138 
0.0129 
0.0507 
0.0516 
0.0327 
0.0292 
0.0239 
0.0212 
0.0195 
0.0176 
8d 
None 
Strong 
0.0431 
0.0440 
0.0250 
0.0237 
0.0172 
0.0165 
0.0138 
0.0130 
0.0504 
0.0516 
0.0329 
0.0290 
0.0237 
0.0212 
0.0194 
0.0175 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0224 
0.0223 
0.0102 
0.0103 
0.0055 
0.0057 
0.0036 
0.0039 
0.0248 
0.0252 
0.0113 
0.0113 
0.0063 
0.0063 
0.0042 
0.0043 
8d 
None 
Strong 
0.0223 
0.0221 
0.0102 
0.0103 
0.0055 
0.0056 
0.0037 
0.0037 
0.0247 
0.0252 
0.0113 
0.0113 
0.0064 
0.0063 
0.0043 
0.0043 
Toe-nails 
6d 
None 
Strong 
0.0274 
0.0297 
0.0159 
0.0143 
0.0098 
0.0087 
0.0070 
0.0064 
0.0316 
0.0347 
0.0212 
0.0190 
0.0139 
0.0126 
0.0107 
0.0095 
8d 
None 
Strong 
0.0274 
0.0297 
0.0159 
0.0144 
0.0098 
0.0088 
0.0070 
0.0063 
0.0318 
0.0347 
0.0212 
0.0190 
0.0140 
0.0124 
0.0107 
0.0095 
Two 
Straps 
6d 
None 
Strong 
0.0361 
0.0361 
0.0185 
0.0192 
0.0121 
0.0126 
0.0093 
0.0096 
0.0435 
0.0411 
0.0232 
0.0224 
0.0163 
0.0156 
0.0128 
0.0126 
8d 
None 
Strong 
0.0359 
0.0364 
0.0184 
0.0189 
0.0123 
0.0126 
0.0093 
0.0097 
0.0434 
0.0408 
0.0233 
0.0223 
0.0162 
0.0157 
0.0127 
0.0126 
Toe-nails 
6d 
None 
Strong 
0.0431 
0.0443 
0.0249 
0.0236 
0.0174 
0.0162 
0.0138 
0.0129 
0.0507 
0.0514 
0.0328 
0.0290 
0.0238 
0.0212 
0.0194 
0.0176 
8d 
None 
Strong 
0.0431 
0.0443 
0.0249 
0.0238 
0.0174 
0.0162 
0.0137 
0.0130 
0.0506 
0.0515 
0.0327 
0.0289 
0.0235 
0.0210 
0.0195 
0.0177 

Chapter 7. Direct Economic Losses 

?
Table 7.20. Average Annual Building Loss Normalized by Building Value   
Install 
Shutters and Upgrade Roof

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0073 
0.0070 
0.0037 
0.0036 
0.0024 
0.0022 
0.0019 
0.0018 
0.0079 
0.0075 
0.0040 
0.0038 
0.0027 
0.0025 
0.0022 
0.0020 
8d 
None 
Strong 
0.0073 
0.0071 
0.0036 
0.0035 
0.0024 
0.0022 
0.0019 
0.0017 
0.0079 
0.0075 
0.0040 
0.0038 
0.0027 
0.0025 
0.0022 
0.0019 
Toe-nails 
6d 
None 
Strong 
0.0085 
0.0087 
0.0050 
0.0047 
0.0032 
0.0032 
0.0025 
0.0025 
0.0104 
0.0102 
0.0060 
0.0059 
0.0041 
0.0041 
0.0033 
0.0033 
8d 
None 
Strong 
0.0085 
0.0086 
0.0050 
0.0048 
0.0032 
0.0031 
0.0026 
0.0025 
0.0104 
0.0101 
0.0061 
0.0059 
0.0041 
0.0040 
0.0034 
0.0034 
Two 
Straps 
6d 
None 
Strong 
0.0105 
0.0103 
0.0060 
0.0061 
0.0044 
0.0044 
0.0036 
0.0036 
0.0113 
0.0111 
0.0069 
0.0067 
0.0050 
0.0048 
0.0041 
0.0040 
8d 
None 
Strong 
0.0105 
0.0104 
0.0061 
0.0061 
0.0044 
0.0044 
0.0036 
0.0036 
0.0113 
0.0110 
0.0069 
0.0067 
0.0050 
0.0048 
0.0042 
0.0040 
Toe-nails 
6d 
None 
Strong 
0.0118 
0.0118 
0.0075 
0.0072 
0.0054 
0.0054 
0.0045 
0.0044 
0.0150 
0.0147 
0.0095 
0.0093 
0.0074 
0.0071 
0.0064 
0.0062 
8d 
None 
Strong 
0.0116 
0.0117 
0.0074 
0.0073 
0.0054 
0.0053 
0.0046 
0.0044 
0.0150 
0.0147 
0.0095 
0.0093 
0.0074 
0.0071 
0.0064 
0.0062 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0072 
0.0069 
0.0036 
0.0035 
0.0024 
0.0022 
0.0019 
0.0018 
0.0078 
0.0073 
0.0041 
0.0038 
0.0027 
0.0025 
0.0022 
0.0019 
8d 
None 
Strong 
0.0071 
0.0069 
0.0036 
0.0035 
0.0023 
0.0022 
0.0019 
0.0018 
0.0077 
0.0073 
0.0040 
0.0038 
0.0026 
0.0025 
0.0022 
0.0020 
Toe-nails 
6d 
None 
Strong 
0.0084 
0.0086 
0.0051 
0.0049 
0.0033 
0.0032 
0.0026 
0.0025 
0.0102 
0.0102 
0.0062 
0.0059 
0.0042 
0.0041 
0.0033 
0.0033 
8d 
None 
Strong 
0.0084 
0.0086 
0.0050 
0.0049 
0.0032 
0.0031 
0.0025 
0.0026 
0.0102 
0.0101 
0.0060 
0.0060 
0.0041 
0.0041 
0.0033 
0.0034 
Two 
Straps 
6d 
None 
Strong 
0.0102 
0.0102 
0.0060 
0.0060 
0.0043 
0.0043 
0.0036 
0.0036 
0.0112 
0.0108 
0.0069 
0.0067 
0.0051 
0.0048 
0.0042 
0.0040 
8d 
None 
Strong 
0.0102 
0.0102 
0.0061 
0.0061 
0.0043 
0.0043 
0.0036 
0.0036 
0.0111 
0.0109 
0.0069 
0.0066 
0.0050 
0.0048 
0.0042 
0.0040 
Toe-nails 
6d 
None 
Strong 
0.0116 
0.0117 
0.0074 
0.0073 
0.0054 
0.0052 
0.0045 
0.0045 
0.0149 
0.0145 
0.0096 
0.0092 
0.0074 
0.0071 
0.0063 
0.0063 
8d 
None 
Strong 
0.0115 
0.0118 
0.0074 
0.0073 
0.0054 
0.0053 
0.0045 
0.0045 
0.0148 
0.0146 
0.0096 
0.0093 
0.0073 
0.0071 
0.0064 
0.0063 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0072 
0.0069 
0.0036 
0.0035 
0.0024 
0.0022 
0.0019 
0.0018 
0.0077 
0.0073 
0.0040 
0.0038 
0.0027 
0.0024 
0.0022 
0.0019 
8d 
None 
Strong 
0.0071 
0.0069 
0.0036 
0.0035 
0.0024 
0.0022 
0.0019 
0.0018 
0.0077 
0.0073 
0.0040 
0.0038 
0.0027 
0.0025 
0.0022 
0.0019 
Toe-nails 
6d 
None 
Strong 
0.0084 
0.0085 
0.0050 
0.0049 
0.0032 
0.0032 
0.0025 
0.0025 
0.0103 
0.0101 
0.0061 
0.0059 
0.0041 
0.0042 
0.0033 
0.0033 
8d 
None 
Strong 
0.0084 
0.0086 
0.0049 
0.0048 
0.0033 
0.0032 
0.0026 
0.0025 
0.0102 
0.0101 
0.0060 
0.0059 
0.0042 
0.0041 
0.0033 
0.0033 
Two 
Straps 
6d 
None 
Strong 
0.0102 
0.0103 
0.0060 
0.0060 
0.0043 
0.0043 
0.0035 
0.0036 
0.0111 
0.0109 
0.0069 
0.0066 
0.0051 
0.0048 
0.0042 
0.0040 
8d 
None 
Strong 
0.0102 
0.0102 
0.0061 
0.0061 
0.0043 
0.0043 
0.0036 
0.0035 
0.0112 
0.0108 
0.0069 
0.0066 
0.0050 
0.0048 
0.0042 
0.0040 
Toe-nails 
6d 
None 
Strong 
0.0116 
0.0118 
0.0073 
0.0073 
0.0054 
0.0054 
0.0045 
0.0045 
0.0148 
0.0146 
0.0095 
0.0093 
0.0074 
0.0071 
0.0064 
0.0061 
8d 
None 
Strong 
0.0117 
0.0118 
0.0074 
0.0073 
0.0054 
0.0054 
0.0045 
0.0045 
0.0149 
0.0147 
0.0095 
0.0094 
0.0073 
0.0071 
0.0064 
0.0060 

HAZUS-MH MR3 Technical Manual 

?
Table 7.21. Average Annual Content Loss Normalized by Building Value   
Install 
Shutters and Upgrade Roof

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0022 
0.0024 
0.0011 
0.0011 
0.0005 
0.0005 
0.0003 
0.0003 
0.0026 
0.0027 
0.0013 
0.0013 
0.0007 
0.0006 
0.0004 
0.0004 
8d 
None 
Strong 
0.0023 
0.0024 
0.0010 
0.0012 
0.0005 
0.0005 
0.0003 
0.0003 
0.0026 
0.0027 
0.0013 
0.0013 
0.0007 
0.0006 
0.0004 
0.0004 
Toenails 
6d 
None 
Strong 
0.0036 
0.0042 
0.0024 
0.0024 
0.0013 
0.0015 
0.0009 
0.0011 
0.0053 
0.0055 
0.0032 
0.0033 
0.0021 
0.0022 
0.0015 
0.0017 
8d 
None 
Strong 
0.0036 
0.0042 
0.0024 
0.0025 
0.0013 
0.0015 
0.0010 
0.0011 
0.0052 
0.0055 
0.0033 
0.0033 
0.0020 
0.0021 
0.0016 
0.0017 
Two 
Straps 
6d 
None 
Strong 
0.0043 
0.0042 
0.0024 
0.0023 
0.0014 
0.0014 
0.0010 
0.0010 
0.0050 
0.0050 
0.0032 
0.0030 
0.0020 
0.0018 
0.0015 
0.0014 
8d 
None 
Strong 
0.0043 
0.0042 
0.0025 
0.0024 
0.0014 
0.0014 
0.0010 
0.0010 
0.0050 
0.0049 
0.0032 
0.0029 
0.0020 
0.0018 
0.0015 
0.0014 
Toenails 
6d 
None 
Strong 
0.0058 
0.0060 
0.0039 
0.0037 
0.0026 
0.0026 
0.0021 
0.0020 
0.0086 
0.0084 
0.0056 
0.0055 
0.0043 
0.0041 
0.0037 
0.0035 
8d 
None 
Strong 
0.0057 
0.0059 
0.0038 
0.0038 
0.0026 
0.0025 
0.0021 
0.0020 
0.0086 
0.0084 
0.0057 
0.0055 
0.0043 
0.0041 
0.0037 
0.0036 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0020 
0.0023 
0.0010 
0.0011 
0.0005 
0.0005 
0.0003 
0.0003 
0.0025 
0.0025 
0.0013 
0.0013 
0.0007 
0.0006 
0.0004 
0.0004 
8d 
None 
Strong 
0.0020 
0.0022 
0.0010 
0.0011 
0.0005 
0.0005 
0.0003 
0.0003 
0.0024 
0.0026 
0.0013 
0.0013 
0.0006 
0.0006 
0.0004 
0.0004 
Toenails 
6d 
None 
Strong 
0.0035 
0.0041 
0.0024 
0.0025 
0.0014 
0.0015 
0.0009 
0.0011 
0.0051 
0.0055 
0.0033 
0.0033 
0.0021 
0.0022 
0.0016 
0.0017 
8d 
None 
Strong 
0.0035 
0.0041 
0.0024 
0.0025 
0.0013 
0.0014 
0.0009 
0.0011 
0.0052 
0.0055 
0.0032 
0.0034 
0.0021 
0.0022 
0.0015 
0.0017 
Two 
Straps 
6d 
None 
Strong 
0.0041 
0.0041 
0.0024 
0.0023 
0.0014 
0.0014 
0.0010 
0.0010 
0.0050 
0.0048 
0.0031 
0.0029 
0.0021 
0.0018 
0.0015 
0.0014 
8d 
None 
Strong 
0.0040 
0.0041 
0.0025 
0.0024 
0.0014 
0.0014 
0.0010 
0.0010 
0.0049 
0.0048 
0.0032 
0.0029 
0.0020 
0.0018 
0.0016 
0.0014 
Toenails 
6d 
None 
Strong 
0.0057 
0.0059 
0.0038 
0.0038 
0.0025 
0.0026 
0.0021 
0.0021 
0.0086 
0.0083 
0.0057 
0.0054 
0.0043 
0.0041 
0.0036 
0.0036 
8d 
None 
Strong 
0.0056 
0.0059 
0.0038 
0.0038 
0.0026 
0.0026 
0.0020 
0.0021 
0.0085 
0.0084 
0.0057 
0.0055 
0.0043 
0.0041 
0.0038 
0.0036 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0020 
0.0023 
0.0010 
0.0011 
0.0005 
0.0005 
0.0003 
0.0003 
0.0024 
0.0026 
0.0013 
0.0013 
0.0006 
0.0006 
0.0004 
0.0004 
8d 
None 
Strong 
0.0020 
0.0022 
0.0010 
0.0011 
0.0005 
0.0005 
0.0003 
0.0003 
0.0024 
0.0026 
0.0013 
0.0013 
0.0007 
0.0006 
0.0004 
0.0004 
Toenails 
6d 
None 
Strong 
0.0034 
0.0041 
0.0024 
0.0025 
0.0013 
0.0015 
0.0009 
0.0011 
0.0052 
0.0054 
0.0033 
0.0033 
0.0021 
0.0022 
0.0016 
0.0017 
8d 
None 
Strong 
0.0034 
0.0041 
0.0024 
0.0025 
0.0014 
0.0015 
0.0009 
0.0011 
0.0051 
0.0055 
0.0032 
0.0033 
0.0021 
0.0022 
0.0015 
0.0017 
Two 
Straps 
6d 
None 
Strong 
0.0040 
0.0042 
0.0024 
0.0023 
0.0014 
0.0014 
0.0010 
0.0010 
0.0049 
0.0048 
0.0032 
0.0028 
0.0021 
0.0018 
0.0016 
0.0014 
8d 
None 
Strong 
0.0041 
0.0041 
0.0024 
0.0024 
0.0014 
0.0014 
0.0011 
0.0010 
0.0050 
0.0048 
0.0032 
0.0029 
0.0020 
0.0018 
0.0016 
0.0014 
Toenails 
6d 
None 
Strong 
0.0057 
0.0059 
0.0038 
0.0037 
0.0026 
0.0026 
0.0020 
0.0021 
0.0085 
0.0084 
0.0057 
0.0055 
0.0043 
0.0041 
0.0037 
0.0035 
8d 
None 
Strong 
0.0057 
0.0059 
0.0038 
0.0038 
0.0026 
0.0026 
0.0020 
0.0021 
0.0086 
0.0085 
0.0057 
0.0055 
0.0043 
0.0041 
0.0037 
0.0034 

Chapter 7. Direct Economic Losses 

?
Table 7.22. Average Annual Total Loss Normalized by Building Value   Install 
Shutters and Upgrade Roof

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0104 
0.0104 
0.0052 
0.0051 
0.0031 
0.0030 
0.0024 
0.0023 
0.0115 
0.0112 
0.0059 
0.0056 
0.0037 
0.0034 
0.0028 
0.0026 
8d 
None 
Strong 
0.0105 
0.0105 
0.0050 
0.0051 
0.0031 
0.0030 
0.0024 
0.0022 
0.0116 
0.0112 
0.0059 
0.0056 
0.0037 
0.0034 
0.0029 
0.0026 
Toe-nails 
6d 
None 
Strong 
0.0131 
0.0141 
0.0080 
0.0078 
0.0050 
0.0051 
0.0038 
0.0038 
0.0171 
0.0171 
0.0101 
0.0101 
0.0067 
0.0069 
0.0052 
0.0055 
8d 
None 
Strong 
0.0132 
0.0140 
0.0080 
0.0080 
0.0049 
0.0050 
0.0038 
0.0038 
0.0170 
0.0171 
0.0102 
0.0101 
0.0067 
0.0067 
0.0055 
0.0056 
Two 
Straps 
6d 
None 
Strong 
0.0162 
0.0160 
0.0093 
0.0092 
0.0064 
0.0063 
0.0051 
0.0051 
0.0180 
0.0177 
0.0111 
0.0106 
0.0077 
0.0073 
0.0062 
0.0060 
8d 
None 
Strong 
0.0163 
0.0160 
0.0094 
0.0093 
0.0064 
0.0064 
0.0051 
0.0050 
0.0179 
0.0175 
0.0110 
0.0106 
0.0077 
0.0073 
0.0063 
0.0059 
Toe-nails 
6d 
None 
Strong 
0.0192 
0.0195 
0.0124 
0.0120 
0.0088 
0.0087 
0.0072 
0.0070 
0.0259 
0.0253 
0.0166 
0.0162 
0.0127 
0.0122 
0.0110 
0.0106 
8d 
None 
Strong 
0.0190 
0.0193 
0.0123 
0.0121 
0.0087 
0.0086 
0.0073 
0.0070 
0.0258 
0.0253 
0.0166 
0.0162 
0.0128 
0.0123 
0.0110 
0.0107 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0101 
0.0101 
0.0051 
0.0051 
0.0031 
0.0030 
0.0024 
0.0023 
0.0113 
0.0108 
0.0059 
0.0056 
0.0037 
0.0034 
0.0029 
0.0026 
8d 
None 
Strong 
0.0099 
0.0100 
0.0050 
0.0051 
0.0031 
0.0030 
0.0024 
0.0023 
0.0111 
0.0108 
0.0058 
0.0055 
0.0036 
0.0034 
0.0029 
0.0026 
Toe-nails 
6d 
None 
Strong 
0.0130 
0.0139 
0.0082 
0.0081 
0.0050 
0.0051 
0.0038 
0.0039 
0.0168 
0.0172 
0.0104 
0.0101 
0.0068 
0.0069 
0.0054 
0.0054 
8d 
None 
Strong 
0.0129 
0.0140 
0.0081 
0.0080 
0.0049 
0.0050 
0.0038 
0.0040 
0.0168 
0.0170 
0.0101 
0.0102 
0.0067 
0.0068 
0.0053 
0.0056 
Two 
Straps 
6d 
None 
Strong 
0.0157 
0.0158 
0.0092 
0.0091 
0.0063 
0.0063 
0.0051 
0.0051 
0.0178 
0.0172 
0.0110 
0.0105 
0.0078 
0.0072 
0.0063 
0.0059 
8d 
None 
Strong 
0.0156 
0.0157 
0.0094 
0.0093 
0.0063 
0.0062 
0.0051 
0.0050 
0.0177 
0.0172 
0.0111 
0.0105 
0.0077 
0.0073 
0.0064 
0.0059 
Toe-nails 
6d 
None 
Strong 
0.0189 
0.0192 
0.0123 
0.0122 
0.0087 
0.0085 
0.0072 
0.0072 
0.0257 
0.0249 
0.0167 
0.0160 
0.0127 
0.0123 
0.0108 
0.0108 
8d 
None 
Strong 
0.0188 
0.0193 
0.0122 
0.0122 
0.0088 
0.0086 
0.0072 
0.0073 
0.0255 
0.0251 
0.0166 
0.0161 
0.0126 
0.0123 
0.0111 
0.0108 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0102 
0.0101 
0.0051 
0.0051 
0.0031 
0.0030 
0.0024 
0.0024 
0.0112 
0.0108 
0.0058 
0.0056 
0.0036 
0.0033 
0.0029 
0.0026 
8d 
None 
Strong 
0.0100 
0.0100 
0.0050 
0.0051 
0.0031 
0.0030 
0.0024 
0.0023 
0.0111 
0.0109 
0.0058 
0.0055 
0.0037 
0.0034 
0.0029 
0.0025 
Toe-nails 
6d 
None 
Strong 
0.0129 
0.0138 
0.0081 
0.0080 
0.0050 
0.0051 
0.0038 
0.0039 
0.0169 
0.0169 
0.0103 
0.0101 
0.0068 
0.0070 
0.0053 
0.0055 
8d 
None 
Strong 
0.0129 
0.0138 
0.0080 
0.0080 
0.0050 
0.0051 
0.0038 
0.0039 
0.0167 
0.0171 
0.0101 
0.0101 
0.0068 
0.0069 
0.0053 
0.0055 
Two 
Straps 
6d 
None 
Strong 
0.0157 
0.0159 
0.0093 
0.0092 
0.0063 
0.0063 
0.0050 
0.0051 
0.0176 
0.0173 
0.0111 
0.0104 
0.0078 
0.0073 
0.0063 
0.0059 
8d 
None 
Strong 
0.0157 
0.0158 
0.0093 
0.0093 
0.0063 
0.0063 
0.0052 
0.0050 
0.0177 
0.0171 
0.0111 
0.0104 
0.0077 
0.0073 
0.0064 
0.0059 
Toe-nails 
6d 
None 
Strong 
0.0189 
0.0193 
0.0122 
0.0120 
0.0087 
0.0087 
0.0071 
0.0072 
0.0256 
0.0251 
0.0166 
0.0161 
0.0128 
0.0121 
0.0110 
0.0105 
8d 
None 
Strong 
0.0190 
0.0193 
0.0123 
0.0122 
0.0088 
0.0088 
0.0071 
0.0072 
0.0257 
0.0253 
0.0166 
0.0163 
0.0127 
0.0122 
0.0110 
0.0103 

HAZUS-MH MR3 Technical Manual 

?
Table 7.23. Average Annual Building Loss Normalized by Building Value   
Install 
Shutters, Upgrade Roof and Add Secondary Water Resistance

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0048 
0.0048 
0.0025 
0.0025 
0.0014 
0.0014 
0.0011 
0.0010 
0.0054 
0.0053 
0.0028 
0.0027 
0.0017 
0.0015 
0.0012 
0.0011 
8d 
None 
Strong 
0.0048 
0.0048 
0.0024 
0.0025 
0.0014 
0.0014 
0.0011 
0.0010 
0.0051 
0.0050 
0.0027 
0.0027 
0.0016 
0.0015 
0.0012 
0.0010 
Toe-nails 
6d 
None 
Strong 
0.0068 
0.0074 
0.0042 
0.0041 
0.0026 
0.0027 
0.0020 
0.0020 
0.0090 
0.0092 
0.0054 
0.0054 
0.0035 
0.0037 
0.0027 
0.0029 
8d 
None 
Strong 
0.0068 
0.0074 
0.0042 
0.0043 
0.0026 
0.0026 
0.0020 
0.0020 
0.0090 
0.0091 
0.0054 
0.0054 
0.0035 
0.0035 
0.0028 
0.0029 
Two 
Straps 
6d 
None 
Strong 
0.0072 
0.0073 
0.0041 
0.0041 
0.0025 
0.0025 
0.0018 
0.0020 
0.0083 
0.0083 
0.0051 
0.0049 
0.0032 
0.0031 
0.0025 
0.0024 
8d 
None 
Strong 
0.0072 
0.0072 
0.0041 
0.0042 
0.0025 
0.0026 
0.0018 
0.0019 
0.0081 
0.0081 
0.0050 
0.0048 
0.0031 
0.0031 
0.0024 
0.0023 
Toe-nails 
6d 
None 
Strong 
0.0095 
0.0099 
0.0061 
0.0060 
0.0042 
0.0042 
0.0033 
0.0033 
0.0136 
0.0133 
0.0086 
0.0085 
0.0065 
0.0063 
0.0056 
0.0054 
8d 
None 
Strong 
0.0094 
0.0098 
0.0060 
0.0061 
0.0041 
0.0042 
0.0033 
0.0033 
0.0136 
0.0133 
0.0086 
0.0085 
0.0065 
0.0063 
0.0056 
0.0055 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0045 
0.0046 
0.0024 
0.0025 
0.0014 
0.0013 
0.0011 
0.0010 
0.0051 
0.0049 
0.0028 
0.0027 
0.0016 
0.0015 
0.0012 
0.0011 
8d 
None 
Strong 
0.0044 
0.0045 
0.0024 
0.0025 
0.0014 
0.0013 
0.0011 
0.0010 
0.0047 
0.0048 
0.0027 
0.0026 
0.0015 
0.0014 
0.0012 
0.0010 
Toe-nails 
6d 
None 
Strong 
0.0067 
0.0073 
0.0043 
0.0043 
0.0026 
0.0027 
0.0020 
0.0021 
0.0088 
0.0092 
0.0055 
0.0054 
0.0035 
0.0036 
0.0028 
0.0028 
8d 
None 
Strong 
0.0066 
0.0073 
0.0043 
0.0043 
0.0026 
0.0026 
0.0020 
0.0021 
0.0089 
0.0091 
0.0053 
0.0055 
0.0035 
0.0036 
0.0027 
0.0029 
Two 
Straps 
6d 
None 
Strong 
0.0069 
0.0071 
0.0040 
0.0041 
0.0024 
0.0025 
0.0018 
0.0019 
0.0082 
0.0080 
0.0050 
0.0049 
0.0033 
0.0030 
0.0025 
0.0024 
8d 
None 
Strong 
0.0067 
0.0070 
0.0041 
0.0041 
0.0024 
0.0025 
0.0018 
0.0018 
0.0080 
0.0079 
0.0050 
0.0048 
0.0032 
0.0030 
0.0024 
0.0023 
Toe-nails 
6d 
None 
Strong 
0.0093 
0.0097 
0.0061 
0.0061 
0.0041 
0.0042 
0.0033 
0.0034 
0.0135 
0.0131 
0.0086 
0.0084 
0.0065 
0.0063 
0.0055 
0.0055 
8d 
None 
Strong 
0.0093 
0.0097 
0.0060 
0.0061 
0.0041 
0.0042 
0.0033 
0.0035 
0.0134 
0.0133 
0.0086 
0.0084 
0.0064 
0.0063 
0.0057 
0.0055 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0046 
0.0046 
0.0024 
0.0025 
0.0014 
0.0014 
0.0011 
0.0010 
0.0051 
0.0050 
0.0028 
0.0027 
0.0016 
0.0015 
0.0012 
0.0011 
8d 
None 
Strong 
0.0044 
0.0045 
0.0024 
0.0024 
0.0014 
0.0013 
0.0011 
0.0010 
0.0047 
0.0048 
0.0027 
0.0026 
0.0016 
0.0015 
0.0012 
0.0010 
Toe-nails 
6d 
None 
Strong 
0.0066 
0.0072 
0.0043 
0.0043 
0.0026 
0.0027 
0.0020 
0.0020 
0.0089 
0.0091 
0.0055 
0.0054 
0.0035 
0.0037 
0.0027 
0.0028 
8d 
None 
Strong 
0.0066 
0.0073 
0.0042 
0.0043 
0.0026 
0.0027 
0.0020 
0.0020 
0.0088 
0.0091 
0.0054 
0.0054 
0.0035 
0.0036 
0.0027 
0.0029 
Two 
Straps 
6d 
None 
Strong 
0.0068 
0.0072 
0.0040 
0.0041 
0.0024 
0.0025 
0.0018 
0.0019 
0.0081 
0.0081 
0.0050 
0.0047 
0.0033 
0.0031 
0.0025 
0.0024 
8d 
None 
Strong 
0.0068 
0.0071 
0.0040 
0.0041 
0.0024 
0.0025 
0.0018 
0.0018 
0.0080 
0.0079 
0.0050 
0.0047 
0.0032 
0.0030 
0.0024 
0.0023 
Toe-nails 
6d 
None 
Strong 
0.0093 
0.0098 
0.0060 
0.0061 
0.0041 
0.0043 
0.0032 
0.0034 
0.0134 
0.0133 
0.0086 
0.0084 
0.0065 
0.0062 
0.0056 
0.0053 
8d 
None 
Strong 
0.0094 
0.0098 
0.0060 
0.0061 
0.0041 
0.0043 
0.0032 
0.0034 
0.0135 
0.0134 
0.0086 
0.0085 
0.0064 
0.0063 
0.0056 
0.0053 

Chapter 7. Direct Economic Losses 

?
Table 7.24. Average Annual Content Loss Normalized by Building Value   
Install 
Shutters, Upgrade Roof and Add Secondary Water Resistance

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0017 
0.0019 
0.0009 
0.0009 
0.0003 
0.0003 
0.0001 
0.0002 
0.0021 
0.0023 
0.0011 
0.0011 
0.0005 
0.0004 
0.0002 
0.0002 
8d 
None 
Strong 
0.0017 
0.0020 
0.0008 
0.0010 
0.0003 
0.0003 
0.0001 
0.0002 
0.0020 
0.0022 
0.0010 
0.0011 
0.0004 
0.0004 
0.0002 
0.0002 
Toe-nails 
6d 
None 
Strong 
0.0032 
0.0039 
0.0022 
0.0023 
0.0012 
0.0014 
0.0008 
0.0010 
0.0050 
0.0053 
0.0031 
0.0032 
0.0019 
0.0021 
0.0014 
0.0016 
8d 
None 
Strong 
0.0032 
0.0039 
0.0022 
0.0024 
0.0012 
0.0014 
0.0009 
0.0010 
0.0049 
0.0053 
0.0031 
0.0032 
0.0019 
0.0020 
0.0015 
0.0017 
Two 
Straps 
6d 
None 
Strong 
0.0036 
0.0035 
0.0020 
0.0019 
0.0011 
0.0010 
0.0007 
0.0007 
0.0044 
0.0044 
0.0028 
0.0026 
0.0016 
0.0014 
0.0012 
0.0011 
8d 
None 
Strong 
0.0036 
0.0036 
0.0021 
0.0020 
0.0010 
0.0010 
0.0007 
0.0006 
0.0043 
0.0043 
0.0027 
0.0025 
0.0016 
0.0014 
0.0012 
0.0010 
Toe-nails 
6d 
None 
Strong 
0.0053 
0.0055 
0.0036 
0.0034 
0.0023 
0.0023 
0.0018 
0.0018 
0.0083 
0.0082 
0.0055 
0.0053 
0.0041 
0.0039 
0.0035 
0.0034 
8d 
None 
Strong 
0.0052 
0.0055 
0.0035 
0.0035 
0.0023 
0.0023 
0.0018 
0.0018 
0.0083 
0.0081 
0.0055 
0.0054 
0.0041 
0.0040 
0.0035 
0.0034 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0014 
0.0017 
0.0008 
0.0009 
0.0003 
0.0003 
0.0001 
0.0002 
0.0019 
0.0020 
0.0011 
0.0011 
0.0004 
0.0004 
0.0002 
0.0002 
8d 
None 
Strong 
0.0014 
0.0017 
0.0008 
0.0009 
0.0003 
0.0003 
0.0001 
0.0002 
0.0017 
0.0020 
0.0010 
0.0010 
0.0004 
0.0004 
0.0002 
0.0002 
Toe-nails 
6d 
None 
Strong 
0.0031 
0.0038 
0.0023 
0.0024 
0.0012 
0.0014 
0.0008 
0.0010 
0.0048 
0.0053 
0.0032 
0.0032 
0.0019 
0.0021 
0.0015 
0.0016 
8d 
None 
Strong 
0.0031 
0.0039 
0.0023 
0.0024 
0.0012 
0.0013 
0.0008 
0.0010 
0.0049 
0.0052 
0.0031 
0.0033 
0.0019 
0.0021 
0.0014 
0.0017 
Two 
Straps 
6d 
None 
Strong 
0.0034 
0.0034 
0.0019 
0.0019 
0.0010 
0.0010 
0.0007 
0.0006 
0.0043 
0.0042 
0.0027 
0.0025 
0.0017 
0.0014 
0.0011 
0.0010 
8d 
None 
Strong 
0.0032 
0.0034 
0.0020 
0.0020 
0.0010 
0.0010 
0.0006 
0.0006 
0.0043 
0.0042 
0.0028 
0.0025 
0.0016 
0.0015 
0.0012 
0.0010 
Toe-nails 
6d 
None 
Strong 
0.0052 
0.0054 
0.0035 
0.0035 
0.0023 
0.0023 
0.0018 
0.0018 
0.0083 
0.0080 
0.0055 
0.0053 
0.0041 
0.0040 
0.0034 
0.0034 
8d 
None 
Strong 
0.0052 
0.0054 
0.0035 
0.0035 
0.0023 
0.0024 
0.0018 
0.0019 
0.0082 
0.0081 
0.0055 
0.0053 
0.0041 
0.0040 
0.0036 
0.0034 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0015 
0.0018 
0.0008 
0.0009 
0.0003 
0.0003 
0.0001 
0.0002 
0.0019 
0.0021 
0.0011 
0.0011 
0.0004 
0.0004 
0.0002 
0.0002 
8d 
None 
Strong 
0.0014 
0.0017 
0.0008 
0.0009 
0.0003 
0.0003 
0.0001 
0.0002 
0.0018 
0.0020 
0.0010 
0.0010 
0.0004 
0.0004 
0.0002 
0.0002 
Toe-nails 
6d 
None 
Strong 
0.0031 
0.0038 
0.0023 
0.0024 
0.0012 
0.0014 
0.0008 
0.0010 
0.0049 
0.0052 
0.0032 
0.0032 
0.0019 
0.0021 
0.0014 
0.0016 
8d 
None 
Strong 
0.0031 
0.0038 
0.0022 
0.0024 
0.0012 
0.0014 
0.0008 
0.0010 
0.0048 
0.0053 
0.0031 
0.0032 
0.0019 
0.0021 
0.0014 
0.0016 
Two 
Straps 
6d 
None 
Strong 
0.0033 
0.0035 
0.0020 
0.0019 
0.0010 
0.0010 
0.0006 
0.0006 
0.0042 
0.0042 
0.0027 
0.0024 
0.0017 
0.0015 
0.0012 
0.0010 
8d 
None 
Strong 
0.0034 
0.0034 
0.0020 
0.0020 
0.0010 
0.0010 
0.0007 
0.0006 
0.0043 
0.0041 
0.0028 
0.0025 
0.0016 
0.0014 
0.0012 
0.0010 
Toe-nails 
6d 
None 
Strong 
0.0052 
0.0054 
0.0035 
0.0035 
0.0023 
0.0024 
0.0017 
0.0018 
0.0083 
0.0081 
0.0055 
0.0053 
0.0042 
0.0039 
0.0035 
0.0033 
8d 
None 
Strong 
0.0052 
0.0055 
0.0035 
0.0035 
0.0023 
0.0024 
0.0017 
0.0018 
0.0083 
0.0082 
0.0055 
0.0054 
0.0041 
0.0039 
0.0035 
0.0033 

HAZUS-MH MR3 Technical Manual 

?
Table 7.25. Average Annual Total Loss Normalized by Building Value   Install 
Shutters, Upgrade Roof and Add Secondary Water Resistance

Building Characteristics Hip Roof Gable Roof 
Wall 
Const. 
No. of 
Stories 
Roof/ 
Wall 
Conn. 
Deck 
Nails 
Garage 
Door 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Wood Frame 
One 
Straps 
6d None 
Strong 
0.0071 
0.0074 
0.0037 
0.0037 
0.0018 
0.0018 
0.0013 
0.0012 
0.0081 
0.0082 
0.0042 
0.0041 
0.0023 
0.0021 
0.0015 
0.0014 
8d 
None 
Strong 
0.0071 
0.0074 
0.0035 
0.0037 
0.0018 
0.0018 
0.0013 
0.0012 
0.0077 
0.0079 
0.0040 
0.0041 
0.0021 
0.0020 
0.0015 
0.0014 
Toe-nails 
6d 
None 
Strong 
0.0108 
0.0123 
0.0070 
0.0070 
0.0041 
0.0044 
0.0030 
0.0032 
0.0152 
0.0157 
0.0092 
0.0094 
0.0059 
0.0063 
0.0045 
0.0049 
8d 
None 
Strong 
0.0108 
0.0122 
0.0070 
0.0072 
0.0040 
0.0043 
0.0031 
0.0032 
0.0151 
0.0156 
0.0093 
0.0094 
0.0058 
0.0061 
0.0047 
0.0050 
Two 
Straps 
6d 
None 
Strong 
0.0117 
0.0117 
0.0066 
0.0065 
0.0038 
0.0038 
0.0027 
0.0028 
0.0139 
0.0139 
0.0086 
0.0082 
0.0053 
0.0049 
0.0040 
0.0038 
8d 
None 
Strong 
0.0118 
0.0117 
0.0067 
0.0067 
0.0038 
0.0039 
0.0026 
0.0027 
0.0136 
0.0134 
0.0084 
0.0080 
0.0051 
0.0049 
0.0039 
0.0037 
Toe-nails 
6d 
None 
Strong 
0.0161 
0.0168 
0.0105 
0.0103 
0.0071 
0.0071 
0.0055 
0.0056 
0.0239 
0.0234 
0.0153 
0.0150 
0.0116 
0.0111 
0.0099 
0.0095 
8d 
None 
Strong 
0.0160 
0.0166 
0.0104 
0.0104 
0.0069 
0.0070 
0.0056 
0.0055 
0.0239 
0.0234 
0.0154 
0.0151 
0.0116 
0.0112 
0.0099 
0.0097 
Unreinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0064 
0.0068 
0.0035 
0.0037 
0.0018 
0.0018 
0.0013 
0.0012 
0.0076 
0.0076 
0.0042 
0.0040 
0.0022 
0.0021 
0.0016 
0.0014 
8d 
None 
Strong 
0.0062 
0.0067 
0.0035 
0.0037 
0.0018 
0.0018 
0.0013 
0.0012 
0.0070 
0.0074 
0.0040 
0.0039 
0.0021 
0.0020 
0.0014 
0.0013 
Toe-nails 
6d 
None 
Strong 
0.0106 
0.0121 
0.0072 
0.0073 
0.0042 
0.0044 
0.0030 
0.0033 
0.0149 
0.0157 
0.0095 
0.0093 
0.0059 
0.0062 
0.0046 
0.0048 
8d 
None 
Strong 
0.0105 
0.0122 
0.0072 
0.0073 
0.0040 
0.0043 
0.0030 
0.0034 
0.0150 
0.0156 
0.0091 
0.0095 
0.0059 
0.0062 
0.0045 
0.0050 
Two 
Straps 
6d 
None 
Strong 
0.0111 
0.0115 
0.0064 
0.0065 
0.0037 
0.0038 
0.0026 
0.0027 
0.0137 
0.0133 
0.0085 
0.0081 
0.0054 
0.0049 
0.0039 
0.0037 
8d 
None 
Strong 
0.0108 
0.0113 
0.0066 
0.0066 
0.0036 
0.0037 
0.0026 
0.0026 
0.0133 
0.0132 
0.0084 
0.0079 
0.0052 
0.0049 
0.0039 
0.0036 
Toe-nails 
6d 
None 
Strong 
0.0158 
0.0165 
0.0104 
0.0105 
0.0069 
0.0071 
0.0055 
0.0057 
0.0238 
0.0231 
0.0154 
0.0148 
0.0116 
0.0112 
0.0097 
0.0098 
8d 
None 
Strong 
0.0158 
0.0165 
0.0104 
0.0105 
0.0070 
0.0071 
0.0055 
0.0058 
0.0236 
0.0233 
0.0153 
0.0149 
0.0115 
0.0112 
0.0101 
0.0097 
Reinforced Masonry 
One 
Straps 
6d None 
Strong 
0.0065 
0.0069 
0.0035 
0.0037 
0.0018 
0.0018 
0.0013 
0.0012 
0.0075 
0.0077 
0.0042 
0.0041 
0.0022 
0.0020 
0.0015 
0.0014 
8d 
None 
Strong 
0.0063 
0.0067 
0.0035 
0.0036 
0.0018 
0.0018 
0.0013 
0.0012 
0.0070 
0.0074 
0.0040 
0.0039 
0.0021 
0.0021 
0.0015 
0.0013 
Toe-nails 
6d 
None 
Strong 
0.0105 
0.0120 
0.0071 
0.0073 
0.0041 
0.0044 
0.0030 
0.0033 
0.0150 
0.0155 
0.0094 
0.0094 
0.0059 
0.0063 
0.0045 
0.0048 
8d 
None 
Strong 
0.0105 
0.0120 
0.0070 
0.0072 
0.0042 
0.0044 
0.0030 
0.0033 
0.0148 
0.0156 
0.0092 
0.0094 
0.0059 
0.0062 
0.0044 
0.0049 
Two 
Straps 
6d 
None 
Strong 
0.0110 
0.0116 
0.0065 
0.0066 
0.0037 
0.0038 
0.0026 
0.0027 
0.0135 
0.0134 
0.0085 
0.0078 
0.0054 
0.0050 
0.0040 
0.0037 
8d 
None 
Strong 
0.0110 
0.0114 
0.0064 
0.0066 
0.0037 
0.0037 
0.0026 
0.0026 
0.0134 
0.0131 
0.0084 
0.0078 
0.0052 
0.0048 
0.0039 
0.0035 
Toe-nails 
6d 
None 
Strong 
0.0158 
0.0165 
0.0103 
0.0104 
0.0070 
0.0072 
0.0054 
0.0056 
0.0236 
0.0233 
0.0154 
0.0149 
0.0116 
0.0110 
0.0099 
0.0094 
8d 
None 
Strong 
0.0159 
0.0166 
0.0104 
0.0105 
0.0070 
0.0073 
0.0054 
0.0057 
0.0237 
0.0235 
0.0153 
0.0151 
0.0114 
0.0111 
0.0099 
0.0094 

Chapter 7. Direct Economic Losses 

?
Table 7.26. Percent Increases in the Average Annual Total Loss Due to Changes 
in 
Building Parameters (Minimum/Average/Maximum)   Residential Buildings 

Parameter Increase in Average Annual Building Loss 
Treed to Open Terrain 141% / 230% / 320% 
One Story to Two Story 38% / 73% / 129% 
8d to 6d Roof Sheathing Nails -1% / 5% / 31% 
Strapped to Toe-Nailed Roof/Wall Connections 10% / 31% / 82% 
Hip to Gable Roof 2% / 22% / 41% 
No Garage to Strong Garage Door -14% / -5% / 5% 
No Garage to Weak Garage Door -8% / 6% / 43% 
Reinforced Masonry to Unreinforced Masonry Walls -2% / 0% / 2% 
Unreinforced Masonry to Wood Frame Walls -2% / 0% / 2% 

Table 7.27. Percent Decreases in the Average Annual Total Loss Due to 
Mitigation 
Parameters (Minimum/Average/Maximum)   Residential Buildings 

Mitigation Strategy Decrease in Average Annual Building Loss 
Install Shutters 17% / 33% / 46% 
Upgrade Roof 3% / 16% / 49% 
Add Secondary Water Resistance 3% / 12% / 35% 
Install Shutters and Upgrade Roof 46% / 59% / 71% 
Install Shutters, Upgrade Roof, and Add Secondary Water Resistance 51% / 68% 
/ 85% 
Upgrade Roof and Add Secondary Water Resistance 4% / 19% / 57% 

Losses Associated with Frame Damage or Foundation Failure. Loss ratios 
associated 
with the failure of roof-wall connections, wall failure following partial or 
whole roof 
failure, floor-wall connection failure and foundation failure through sliding 
or 
overturning, are summarized in Table 7.28. The values listed in Table 7.28 
are 
percentages of the MH replacement cash value or the content cash value. 
Hence, a MH 
sustaining minor sliding will have a building loss of 10% and a content loss 
of 0%. The 
values used in Table 7.28 are compiled from field observations and 
engineering 
judgment. 

Table 7.28. Building and Content Losses Associated with Manufactured Home 
Frame Damage or Foundation Failure 

Modeled Structure Damage State 
Structure Loss (% of 
Building Value) 
Content Loss (% of 
Content Value) 
Minor Roof-Wall Connection Failure 
Major Roof-Wall Connection Failure 
Wall Failure Following roof Failure 
Minor Floor-Wall Connection Failure 
Major Floor-Wall Connection Failure 
Minor Sliding Failure 
Major Sliding Failure 
Overturning 
20 
100 
100 
30 
100 
10 
100 
100 
0 
100 
100 
0 
100 
0 
100 
100 

HAZUS-MH MR3 Technical Manual 

?
7-36 

Interior and Content Losses. The implicit costing method calculates losses to 
the interior 
and framing of the building as a function of water volume penetrating the 
building 
envelope. The functions which relate the volume of water to building losses 
are based on 
engineering judgment and field observations and are identical to the 
residential case. 

7.8.2 Fast Running Manufactured Home Loss Functions 
Using the hurricane wind speed and direction data used to develop the damage 
function 
curves for manufactured homes, per storm losses were estimated through the 
combination 
of the loss model described above and the storm-by-storm damage estimates, to 
produce 
estimates of economic loss as a function of peak gust wind speed in open 
terrain. The 
resulting loss functions, computed separately for the economic losses to the 
building and 
to the contents, are given in Appendix I. Table 7.29 lists the average annual 
loss data for 
each of the manufactured homes analyzed. 

Table 7.29. Annual Average Losses   Manufactured Homes 

Average Annual Building Loss Normalized by Building Value 
Mobile Home Construction 
Terrain Surface Roughness (m) 
z0 = 0.03 z0 = 0.15 z0 = 0.35 z0 = 0.7 z0 = 1.0 
Pre-HUD, Not Tied Down 0.0303 0.0225 0.0167 0.0126 0.0110 
Pre-HUD, Tied Down 0.0277 0.0203 0.0147 0.0106 0.0090 
HUD, Not Tied Down 0.0294 0.0211 0.0157 0.0119 0.0104 
HUD, Tied Down 0.0262 0.0183 0.0131 0.0094 0.0079 
1994 HUD   Wind Zone I, Tied Down 0.0314 0.0240 0.0179 0.0136 0.0117 
1994 HUD   Wind Zone II, Tied Down 0.0129 0.0099 0.0069 0.0048 0.0040 
1994 HUD   Wind Zone III, Tied Down 0.0121 0.0091 0.0064 0.0046 0.0038 
Average Annual Content Loss Normalized by Building Value 
Mobile Home Construction 
Terrain Surface Roughness (m) 
z0 = 0.03 z0 = 0.15 z0 = 0.35 z0 = 0.7 z0 = 1.0 
Pre-HUD, Not Tied Down 0.0139 0.0102 0.0075 0.0055 0.0047 
Pre-HUD, Tied Down 0.0121 0.0088 0.0061 0.0041 0.0034 
HUD, Not Tied Down 0.0134 0.0095 0.0070 0.0052 0.0044 
HUD, Tied Down 0.0113 0.0077 0.0052 0.0034 0.0027 
1994 HUD   Wind Zone I, Tied Down 0.0139 0.0107 0.0077 0.0057 0.0047 
1994 HUD   Wind Zone II, Tied Down 0.0049 0.0039 0.0025 0.0016 0.0013 
1994 HUD   Wind Zone III, Tied Down 0.0044 0.0034 0.0023 0.0015 0.0012 

7.9 Economic Loss Model for Commercial Buildings 
Similar to the residential and manufactured home (MH) loss models, the 
commercial loss 
model is also developed using explicit and implicit costing techniques. The 
commercial 
loss model differs from the residential and MH loss models in how the total 
cost of the 
building is distributed amongst the subassemblies. Due to the scope of the 
current 
commercial model, detailed unit costs are not used to derive the explicit 
loss for failed 
components. Rather, subassemblies are cost directly from their damage ratios 
calculated 
from the damage model. Changes in cost distribution among subassemblies 
associated 
with differences in exterior wall types and common additives are ignored. It 
is also noted 

Chapter 7. Direct Economic Losses 

?
that the commercial model described herein uses different influence functions 
or cost 
functions from those used in residential loss model. 

Due to the scarcity of high quality loss data, limited calibration has been 
done with the 
commercial loss model, which has been developed primarily on the basis of 
experience 
and judgment. 

7.9.1 Subassembly Cost Ratios 
The subassembly cost ratios for the model the buildings are based on the 
RSMeans 
Square Foot Costs for new construction. Seventy commercial/industrial 
building types 
are listed in RSMeans, including apartments, bank, church, factory, hotel, 
motel, 
department store, etc. The HAZUS model buildings, which are selected to 
represent 
certain building classes, are mapped to one of those 70 building types with 
possible 
adjustments to the RSMeans cost ratios according to the number of stories, 
percentage of 
exterior door area to the total exterior wall area, and percentage of windows 
and glazed 
wall area to the total exterior wall area of the model building. 

Cost Ratio Adjustment - Number of Stories. As indicated in RSMeans, for 
similar 
building types, the cost ratios for foundations, substructure, exterior 
closure, roofing and 
mechanical decrease with the increase in number of stories, whereas the cost 
ratios for 
interior construction and conveying increase with the increase in number of 
stories. The 
changes in superstructure and electrical are not monotonic. Since these 
changes can be 
significant, adjustments must be made to the cost ratios if the number of 
stories for 
selected model building is different from that for the model building listed 
in RSMeans. 
Based on the data from RSMeans, the functions shown in Equations 7.10 through 
7.14 
are developed to adjust the cost ratio according to the story number of the 
selected model 
building. 

1. Foundation, substructure, and roofing: 
Rc' = Rc .
.
1- 2.0.
. Nm -1...
. 
, for Nm<N 
.. N ..

Rc' = Rc .
.
1 - 0.4.
. Nm -1..
.
. 
, for Nm>N (7.10)
.. N .. 

2. Superstructure: 
Rc' = Rc .
.
1+ 0.6.
. Nm -1..
.
. 
, for Nm<N 
.. N ..

Rc' = Rc .
.
1- 0.08.
. 
Nm -1..
.
. 
, for Nm>N (7.11)
.. N .. 

HAZUS-MH MR3 Technical Manual 

?
3. Exterior closure and mechanical: 
.

N

.

R' R

cc 

=

for Nm<N

1

0.2
-

m

-

,

1 
Rc' Rc , for Nm>N (7.12) 

1 

1

...

...
...

...
4. Interior construction: 
...

= 

.
.

.
.

N

.

N

.

...
-

R' R

cc 

=

for Nm<N

1

-

m

.
.

.
.

0.2
+

,

N

.

N

.

R' R

cc 

=

, for Nm>N

1

(7.13)
m

.
.

.
.

0.133
+ 

N

5. Conveying: 
.

N

.

1 

...

1

...

...
NNwhere =number of stories for selected model building, =number of stories 
for themRRbuilding listed in RSMeans, =unadjusted cost ratio, '=adjusted cost 
ratio. Nocc 
adjustments are made for the other subassemblies. 
Cost Ratio Adjustment Door, Window, and Glazed Wall Area. Since exterior 
closure- 
plays an important role in determining losses, adjustments are made to the 
cost 
distributions among all the subassemblies based on the difference in exterior 
closure 
between HAZUS model building and RSMeans model building. Specifically, the 
costs 
per square foot for walls, doors, windows and glazed walls are adjusted based 
on the 
percentage of the wall area of each subassembly. For example, if the RSMeans 
model 
building has 14% of the wall area with windows and glazed walls (cost per 
square 
foot=$1.37) and a HAZUS model building has 20% of the wall area with windows 
and 
glazed walls, the adjusted cost per square foot will be 
$1.37/0.14*0.20=$1.96. Note that 
since changing cost per square foot for one subassembly will affect the cost 
ratio for all 
other subassemblies (because the subtotal changes), the cost ratios for all 
other 
Even though other parameters, such as square footage of floor area, perimeter 
length, 
exterior wall type, and frame type can also change the cost ratio 
distribution, it is 
assumed that their influences can be neglected. Table 7.30 lists the 
comparison between 
the cost ratios from RSMeans (motel, 2-3 stories) and adjusted cost ratios 
for the selected 

subassemblies must be updated accordingly. 

model building (a 4-story motel). For some subassemblies, the changes in cost 
ratio can 
be substantial (e.g., roofing). 

...
-

R' R

cc 

=

for Nm<N

1

-

m

.
.

.
.

0.6
+

,

N

.

N

.

R' R

cc 

=

, for Nm>N

1

(7.14)
m

.
.

.
.

0.133
+ 

N

Chapter 7. Direct Economic Losses 

?
7.9.2 Building Loss Model 
The damage state parameters required for the commercial loss model include 
damage to 
windows, doors, roof covering, roof decking, and roof framing, damaged wall 
surface 
area, water from fenestration damage, water from roof cover damage, and water 
from 
roof sheathing damage. It is assumed that the explicit losses are caused 
primarily by the 
damage to windows, doors, roofs, and walls while water entering the building 
is the 
major cause of losses to the interior, mechanical, electrical, and conveying 
systems (i.e., 
implicit losses). To reflect thedifferent damage-to-loss propagation 
mechanisms for 
different components, a set of costing functions are developed based on 
experience and 
judgment. Tables 7.31 and 7.32 show the influence matrixes for explicit loss 
and implicit 
loss, respectively. Each row in Tables 7.31 and 7.32 indicates a subassembly 
and each 
column indicates an input damage state. The value in each cell of Table 7.31 
or 7.32 is a 
cost function index, which indicates the function type used to map damage to 
loss for a 
given damage parameter. For example, damage to the roof cover, roof deck, and 
roof 
frame all can result in insulation losses. However, the insulation loss 
ratios due to the 
same damage ratios from roof cover, roof deck, and roof frame are different. 
The cost 
functions for explicit insulation losses due to roof cover, roof deck, and 
roof frame are 
determined to be type 1, type 4, and type 4, respectively (see Table 7.31 - 
Insulation). 
The cost function indices as well as their associated formulae are shown in 
Table 7.33. 
Figure 7.7 shows the plots of these influence functions. In determining the 
loss ratios 
associated with roof cover damage, the influence functions for mechanical, 
electrical, and 
special construction are multiplied by 0.1, 0.2, and 0.3, respectively, to 
account for the 
reduced damage severity than the interior construction (based on limited 
calibration 
studies). 

Several repair and replacement thresholds are used in the current commercial 
model to 
reflect the fact that when a certain percentage of damage to a subassembly is 
achieved, 
the entire subassembly will be replaced. These thresholds are 5%, 10%, and 
30% for roof 
cover, roof deck, and wall cladding, respectively. 

For the subassemblies with more than one damage variable involved, the 
possibility of 
overlapping is considered using their joint probability. Hence, the resulted 
loss ratio (R) 
for a subassembly with two contributing variables is approximated by 
R=R1+R2 R1R2, 
with R1=loss ratio resulted from the first variable only and R2=loss ratio 
resulted from the 
second variable only. After the loss ratio for each subassembly has been 
calculated, the 
total building loss ratio is obtained by simply adding up the loss ratios for 
the 
subassemblies. The building loss ratio is then multiplied by a repair and 
replacement 
factor of 1.25 and an overhead and profit factor of 1.20 to account for the 
additional costs 
associated with the repair and remodeling. The total economic loss of the 
building is also 
constrained not to exceed the assumed insured value. 

Figure 7.8 shows a scatter plot of the calculated building loss ratio as a 
function of open 
terrain peak gust wind speed based on 20,000 year simulation for a 4-story 
motel in the 
Miami area. The contribution to the total building loss ratio from each 
damage state 
considered is also shown in this figure. Implicit losses (interior, 
mechanical, and 

HAZUS-MH MR3 Technical Manual 

?
Table 7.30. Cost Ratio - RSMeans Model Building and HAZUS Model Building 

Damage Variables 
RSMeans Model Building(3-Story 
Motel) 
HAZUS Model Building(4Story 
Motel) 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
2.0% 
0.0% 
0.5% 
2.5% 
1.7% 
0.0% 
0.5% 
2.2% 
Substructures 
Slab on Grade 
Special Substructures 
1.7% 
0.0% 
1.7% 1.5% 
0.0% 
1.5% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
0.0% 
2.6% 
6.6% 
3.2% 
1.4% 
13.8% 
0.0% 
2.5% 
6.5% 
3.2% 
1.4% 
13.6% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
4.7% 
0.0% 
3.9% 
1.8% 
10.3% 
4.3% 
0.0% 
3.9% 
0.6% 
8.8% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
1.1% 
0.7% 
0.3% 
2.1% 
1.0% 
0.6% 
0.2% 
1.8% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
8.9% 
7.9% 
3.7% 
9.3% 
4.6% 
1.2% 
35.6% 
9.4% 
8.3% 
3.9% 
9.8% 
4.9% 
1.3% 
37.6% 
Conveying 
Elevators 
Special Conveyors 
2.7% 
0.0% 
2.7% 2.8% 
0.0% 
2.8% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
15.9% 
1.8% 
0.0% 
5.2% 
0.0% 
22.9% 
16.1% 
1.8% 
0.0% 
5.3% 
0.0% 
23.2% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
0.5% 
7.4% 
0.5% 
8.4% 
0.5% 
7.5% 
0.5% 
8.5% 
Special Construction 
Specialties (& Additives) 0.0% 0.0% 0.0% 0.0% 
Total 100% 100% 100% 100% 

Chapter 7. Direct Economic Losses 

?
Table 7.31. Influence Matrix for Explicit Building Loss 

Roof Roof Roof Wall Surf. Wall Surf. Wall 
Damage Variables Windows Doors Covering Decking Framing by Missile by 
Pressure Structure 
Superstructure 
Columns and Beams 0 0 0 0 0 0 0 5 
Structural Walls 0 0 0 0 0 0 0 1 
Elevated Floors/Diaphragms 0 0 0 0 0 0 0 5 
Roof Decking/Framing 0 0 0 2 1 0 0 0 
Stairs 0 0 0 0 0 0 0 0 
Exterior Closure 
Walls 0 0 0 0 0 9 1 1 
Exterior Wall Finishes 0 0 0 0 0 9 1 1 
Doors 0 1 0 0 0 0 0 1 
Windows & Glazed Walls 1 0 0 0 0 0 0 1 
Roofing 
Roof Covering 0 0 3 0 0 0 0 0 
Insulation 0 0 1 4 4 0 0 0 
Openings & Specialties 0 0 1 1 4 0 0 0 

Table 7.32. Influence Matrix for Implicit Building Loss 

Damage Variables 
Water Depth 
by Fen 
Water Depth 
by Cover 
Water Depth 
by Sheath 
Wet Area by 
Cover 
Wet Area by 
Sheath 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
6 
6 
6 
6 
6 
6 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
7 
7 
7 
7 
7 
7 
8 
8 
8 
8 
8 
8 
Conveying 
Elevators 
Special Conveyors 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
6 
6 
6 
6 
6 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
7 
7 
7 
7 
7 
8 
8 
8 
8 
8 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
6 
6 
6 
0 
0 
0 
0 
0 
0 
7 
7 
7 
8 
8 
8 
Special Construction 
Specialties (& Additives) 6 0 0 7 8 

HAZUS-MH MR3 Technical Manual 

?
Table 7.33. Influence Functions 

Function Index Formula* 
1 y = x; 
2 
3 
4 
5 
6 
7 
8 
9 
1)( etan2 y 0.8 x1 - 
p 
= - 
1 )( etan2 y 
1.550.0x1 - 
p 
= - 
1)( etan2 y 5.0x1 - 
p 
= - 
1)( etan2 y 0.4 x1 - 
p 
= - 
1 )( etan2 y 10.0x1 - 
p 
= - 
1)( etan2 y 
2.03.1x1 - 
p 
= - 
1)( etan2 y 8.0x1 - 
p 
= - 
1)( etan2 y 1.4 x1 - 
p 
= - 

* Note: x = component damage ratio (0%   100%) and y = 
calculated loss ratio (0%   100%) 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
1 
2 
3 
4 
5 
6 
7 
8 
9 
1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 
0.1 
0 
Figure 7.7. Plots of Influence Functions. 

Chapter 7. Direct Economic Losses 

?
Figure 7.8. Relationship between Open Terrain Gust Wind Speed and Building 
Loss Ratio for a 4-Story Motel. 

electrical losses due to building envelop breaches) are seen to contribute 
most to the total 
building loss. The explicit losses caused by damage to roofs, walls, and 
fenestration 
account for less than 20% of the total building loss. 

7.9.3 Content Loss Model 
The content loss model for commercial buildings is almost identical to the 
content loss 
model for residential buildings developed previously. Content losses are 
assumed to be 
highly correlated with the damage to the interior of the building, but begin 
to occur after 
at least some damage has been done to the interior of the building. Water 
entering the 
building is also believed to be the major cause of content loss. Three 
modeled damage 
variables (water depth due to fenestration damage, wetted interior area due 
to roof cover 
damage, and wetted interior area due to roof sheathing damage) are used to 
model the 
content loss. Note that all these three variables are related to the water 
entering a building 
after the breach of the building envelop. 

Content losses due to fenestration damage are modeled as: 

Lc1=4.0Dfen, for Dfen<0.25. 

Lc1=1.0, for Dfen=0.25. (7.15) 

where Lc1 = content loss ratio due to fenestration damage; Dfen = accumulated 
water depth 
due to fenestration damage. Content losses due to roof cover damage are 
assumed to be 
accrued at one half the rate associated with losses to the interior of the 
building. It is also 
assumed that no contents are damaged until a threshold of 20% of roof cover 
damage has 
occurred. A linear approximation is made to the costing function of the 
interior damage 
due to roof cover damage to derive the costing function for content loss: 

HAZUS-MH MR3 Technical Manual 

?
Lc2=0.0, for Rcover<0.2 

Lc2=1.67(Rcover   0.2)(0.5), for Rcover=0.2 (7.16) 

where Lc2 = content loss ratio due to roof over damage; Rcover = wetted 
interior ratio due 
to roof cover damage. The content loss associated with roof sheathing damage 
is modeled 
as: 

Lc3=4.0Rsheath, for Rsheath<0.25 

Lc3=1.0, for Rsheath=0.25 (7.17) 

where Lc3=content loss ratio due to roof sheathing damage; Rsheath=wetted 
interior ratio 
due to roof sheathing damage. 

The final content loss is taken to be the maximum among the losses produced 
by 
fenestration damage, roof cover damage or roof sheathing damage, i.e., 
max(Lc1, Lc2, Lc3). 
Figure 7.9 shows the relationship between building loss ratio and content 
loss ratio for a 
4-story motel located in the Miami area using 20,000 year simulation. 

Figure 7.9. Relationship between Building Loss Ratio and Content Loss Ratio 
for a 
4-Story Motel. 

7.9.4 Business Interruption Model 
The business interruption model for commercial buildings follows the business 
interruption model developed for earthquakes in HAZUS-99 (FEMA, 1999). The 
model 
applies a multiplier to expected loss of use to estimate the duration of 
business 
interruption. The model for expected loss of use follows the approach 
presented in 
Section 7.5.3 for single-family buildings. The default multipliers for 
estimating business 

Chapter 7. Direct Economic Losses 

?
interruption are provided in Table 7.34. As explained in HAZUS-99 Technical 
Manual, 
the business interruption multiplier reflects the fact that business 
interruption is not 
directly related to loss of use for certain types of business (since they can 
rent alternative 
space or use spare industrial/commercial capacity elsewhere). The output from 
the 
business interruption model is the expected number of days for a business to 
be fully 
recovered. The relationship between the business interruption, N (days), and 
building loss 
ratio can be expressed as: 

Table 7.34. Business Interruption Time Table 

Label Occupancy Class 
Business Interruption Time 
Multiplier 
Building Loss Ratio 
0% 2% 10% 50% 100% 
RES3 Multi-Family Dwelling 0 0 0.5 1 1 
RES4 Temporary Lodging 0 0 0.5 1 1 
RES5 Institutional Dormitory 0 0 0.5 1 1 
RES6 Nursing Home 0 0 0.5 1 1 
COM1 Retail Trade 0.5 0.1 0.1 0.3 0.4 
COM2 Wholesale Trade 0.5 0.1 0.2 0.3 0.4 
COM3 Personal and Repair Service 0.5 0.1 0.2 0.3 0.4 
COM4 Professional/Technical/Business Services 0.5 0.1 0.1 0.2 0.3 
COM5 Banks/Financial Institutions 0.5 0.1 0.05 0.03 0.03 
COM6 Hospital 0.5 0.1 0.5 0.5 0.5 
COM7 Medical Office/Clinic 0.5 0.1 0.5 0.5 0.5 
COM8 Entertainment & Recreation 0.5 0.1 1 1 1 
COM9 Theaters 0.5 0.1 1 1 1 
COM10 Parking 0.1 0.1 1 1 1 
IND1 Heavy Industrial Factory 0.5 0.5 1 1 1 
IND2 Light Industrial Factory 0.5 0.1 0.2 0.3 0.4 
IND3 Food/Drug/Chemicals Factory 0.5 0.2 0.2 0.3 0.4 
IND4 Metals/Minerals Processing Factory 0.5 0.2 0.2 0.3 0.4 
IND5 High Technology Factory 0.5 0.2 0.2 0.3 0.4 
IND6 Construction 0.5 0.1 0.2 0.3 0.4 
AGR Agriculture 0 0 0.05 0.1 0.2 
REL Church/Membership Organization 1 0.2 0.05 0.03 0.03 
GOV1 General Service 0.5 0.1 0.02 0.03 0.03 
GOV2 Emergency Response 0.5 0.1 0.02 0.03 0.03 
ED1 Schools/Libraries 0.5 0.1 0.02 0.05 0.05 
ED2 College/University 0.5 0.1 0.02 0.03 0.03 

N =Nlou (Rbuilding ) 7 Mod (Rbuilding ) (7.18) 

where Nlou()=building loss of use (days) taking into account of delays in 
decision making, 
fiancing, inspection, etc. and Mod()=business interruption multiplier from 
Table 7.34. 
Both loss of use and business interruption multiplier are modeled as 
functions of building 
loss ratio (Rbuilding). Figure 7.10 illustrates the relationship between loss 
of use, business 
interruption, and building loss for a motel (RES4). 

HAZUS-MH MR3 Technical Manual 

?
0 
100 
200 
300 
400 
500 
Days 
Loss of Use 
Business Interruption 
0 10 20 30 40 50 60 70 80 90 100 

Building Loss (%) 

Figure 7.10. Relationship between Building Loss, Loss of Use, and Business 
Interruption for a Motel. 

7.10 Loss Model Results for Marginally-Engineered or Non-Engineered 
Hotel/Motel and Multi-Family Residential Buildings 
The commercial loss model described in Section 7.9 has been coupled with the 
damage 
results for the multi-family residential buildings to produce estimates of 
losses as a 
function of wind speed. The same 20,000-year storm simulation used to produce 
the loss 
plots for the single family residential buildings was used for the multi-
family buildings. 
The assumed subassembly cost distributions are listed in Tables 7.35, 7.36 
and 7.37 for 
one-story motels, two- to three-story motels and four- to seven-story hotels, 
respectively. 

Example building and content loss functions are given in Appendix J. Tables 
7.38 
through 7.40 present the average annual building loss (expressed as a 
fraction of the total 
building value) for gable/hip roofs with shingles, flat roofs with built-up 
roof cover and 
flat roofs with single ply membrane roof covers, respectively. Tables 7.41 
through 
7.43present the average annual content loss (also expressed as a fraction of 
the building 
value) for the same three building classes, respectively. Table 7.44 presents 
a summary of 
the effects of various building parameters on average annual building loss. 

7.11 Loss Model Results for Low-Rise Masonry Strip Mall Buildings 
Loss functions have been developed for low-rise masonry strip mall buildings 
using the 
same hurricane wind speed and direction data (20,000 years of storm 
simulation) used in 
the development of the damage curves. The model buildings were assumed to be 
used as 
department stores. The assumed subassembly cost distributions are listed in 
Table 7.45. 

Chapter 7. Direct Economic Losses 

?
Table 7.35. Subassembly Cost Distributions for One-Story Motels 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
8.1% 
0.0% 
1.6% 
9.8% 
Substructures 
Slab on Grade 
Special Substructures 
4.7% 
0.0% 
4.7% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
0.0% 
0.0% 
0.0% 
6.6% 
0.0% 
6.6% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
10.1% 
0.0% 
4.0% 
4.2% 
18.4% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
1.6% 
1.3% 
0.7% 
3.6% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
6.5% 
1.7% 
2.7% 
8.8% 
4.4% 
1.7% 
25.8% 
Conveying 
Elevators 
Special Conveyors 
0.0% 
0.0% 
0.0% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
15.8% 
3.1% 
0.0% 
4.7% 
0.0% 
23.5% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
0.8% 
6.4% 
0.4% 
7.6% 
Special Construction 
Specialties (& Additives) 0.0% 0.0% 
Total 100% 100% 

HAZUS-MH MR3 Technical Manual 

?
Table 7.36. Subassembly Cost Distributions for Two- and Three-Story Motels 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
2.0% 
0.0% 
0.5% 
2.5% 
Substructures 
Slab on Grade 
Special Substructures 
1.7% 
0.0% 
1.7% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
0.0% 
2.6% 
6.6% 
3.2% 
1.4% 
13.8% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
4.7% 
0.0% 
3.9% 
1.8% 
10.3% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
1.1% 
0.7% 
0.3% 
2.1% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
8.9% 
7.9% 
3.7% 
9.3% 
4.6% 
1.2% 
35.6% 
Conveying 
Elevators 
Special Conveyors 
2.7% 
0.0% 
2.7% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
15.9% 
1.8% 
0.0% 
5.2% 
0.0% 
22.9% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
0.5% 
7.4% 
0.5% 
8.4% 
Special Construction 
Specialties (& Additives) 0.0% 0.0% 
Total 100% 100% 

Chapter 7. Direct Economic Losses 

?
Table 7.37. Subassembly Cost Distributions for Four- to Seven-Story Hotels 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
1.6% 
0.0% 
0.2% 
1.8% 
Substructures 
Slab on Grade 
Special Substructures 
0.7% 
0.0% 
0.7% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
0.0% 
0.0% 
12.1% 
1.1% 
0.5% 
13.7% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
7.0% 
0.0% 
0.2% 
2.9% 
10.1% 
Roofing 
Roof Covering 
Insulation 
Openings & Speci alties 
0.5% 
0.3% 
0.1% 
0.9% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
6.0% 
8.3% 
2.8% 
5.9% 
1.8% 
1.0% 
25.8% 
Conveying 
Elevators 
Special Conveyors 
5.1% 
0.0% 
5.1% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
14.5% 
1.9% 
3.7% 
10.9% 
0.0% 
31.2% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
1.1% 
7.2% 
2.5% 
10.7% 
Special Construction 
Specialties (& Additives) 0.0% 0.0% 
Total 100% 100% 

HAZUS-MH MR3 Technical Manual 

?
Table 7.38. Average Annual Building Loss Normalized by Building Value   
Marginally- or Non-Engineered Hotel/Motel and Multi-Family Residential 
Buildings   Gable/Hip Roofs with Shingles 

Building Characteristics 
Strapped Roof-Wall Connections 
Terrain Surface Roughness (m) 
Toe-Nailed Roof-Wall Connections 
Terrain Surface Roughness (m) 
No. of 
Stories 
Wall 
Constr. 
Roof 
Shape 
Roof 
Deck 
Nails 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
One 
Wood 
Frame 
Gable 6d 0.0120 
0.0093 
0.0090 
0.0077 
0.0070 
0.0050 
0.0051 
0.0041 
0.0050 
0.0034 
0.0035 
0.0028 
0.0041 
0.0028 
0.0028 
0.0022 
0.0151 
0.0141 
0.0119 
0.0117 
0.0089 
0.0083 
0.0068 
0.0065 
0.0065 
0.0059 
0.0046 
0.0044 
0.0053 
0.0048 
0.0037 
0.0035 
8d 
Hip 
6d 
8d 
Unreinf. 
Masonry 
Gable 
6d 0.0113 
0.0084 
0.0085 
0.0067 
0.0069 
0.0048 
0.0050 
0.0040 
0.0050 
0.0034 
0.0035 
0.0028 
0.0041 
0.0028 
0.0028 
0.0022 
0.0137 
0.0127 
0.0107 
0.0104 
0.0085 
0.0079 
0.0065 
0.0063 
0.0062 
0.0056 
0.0044 
0.0042 
0.0050 
0.0045 
0.0035 
0.0033 
8d 
Hip 
6d 
8d 
Reinf. 
Masonry 
Gable 
6d 0.0113 
0.0084 
0.0084 
0.0067 
0.0069 
0.0048 
0.0050 
0.0040 
0.0050 
0.0034 
0.0035 
0.0028 
0.0041 
0.0028 
0.0028 
0.0022 
0.0137 
0.0126 
0.0107 
0.0103 
0.0085 
0.0079 
0.0064 
0.0062 
0.0061 
0.0055 
0.0044 
0.0042 
0.0050 
0.0044 
0.0035 
0.0033 
8d 
Hip 
6d 
8d 
Two 
Wood 
Frame 
Gable 6d 0.0184 
0.0157 
0.0155 
0.0137 
0.0114 
0.0092 
0.0094 
0.0078 
0.0089 
0.0068 
0.0072 
0.0057 
0.0075 
0.0056 
0.0061 
0.0048 
0.0220 
0.0206 
0.0189 
0.0184 
0.0142 
0.0131 
0.0120 
0.0116 
0.0110 
0.0101 
0.0092 
0.0088 
0.0095 
0.0086 
0.0077 
0.0075 
8d 
Hip 
6d 
8d 
Unreinf. 
Masonry 
Gable 
6d 0.0175 
0.0146 
0.0146 
0.0129 
0.0110 
0.0088 
0.0092 
0.0075 
0.0085 
0.0065 
0.0070 
0.0055 
0.0074 
0.0054 
0.0059 
0.0046 
0.0205 
0.0191 
0.0176 
0.0171 
0.0135 
0.0125 
0.0113 
0.0112 
0.0104 
0.0096 
0.0086 
0.0084 
0.0090 
0.0083 
0.0074 
0.0070 
8d 
Hip 
6d 
8d 
Reinf. 
Masonry 
Gable 
6d 0.0173 
0.0145 
0.0145 
0.0127 
0.0111 
0.0088 
0.0092 
0.0075 
0.0085 
0.0065 
0.0070 
0.0055 
0.0074 
0.0054 
0.0059 
0.0046 
0.0205 
0.0191 
0.0174 
0.0170 
0.0134 
0.0124 
0.0114 
0.0111 
0.0104 
0.0097 
0.0087 
0.0083 
0.0090 
0.0082 
0.0074 
0.0070 
8d 
Hip 
6d 
8d 
Three 
Wood 
Frame 
Gable 6d 0.0217 
0.0186 
0.0195 
0.0171 
0.0139 
0.0113 
0.0123 
0.0102 
0.0110 
0.0086 
0.0098 
0.0078 
0.0096 
0.0073 
0.0085 
0.0067 
0.0261 
0.0248 
0.0235 
0.0229 
0.0170 
0.0160 
0.0153 
0.0148 
0.0137 
0.0127 
0.0122 
0.0116 
0.0122 
0.0112 
0.0106 
0.0102 
8d 
Hip 
6d 
8d 
Unreinf. 
Masonry 
Gable 
6d 0.0203 
0.0171 
0.0182 
0.0155 
0.0132 
0.0105 
0.0118 
0.0095 
0.0105 
0.0081 
0.0093 
0.0073 
0.0093 
0.0069 
0.0082 
0.0063 
0.0241 
0.0226 
0.0217 
0.0210 
0.0161 
0.0149 
0.0142 
0.0138 
0.0130 
0.0119 
0.0114 
0.0108 
0.0115 
0.0105 
0.0101 
0.0096 
8d 
Hip 
6d 
8d 
Reinf. 
Masonry 
Gable 
6d 0.0201 
0.0169 
0.0179 
0.0152 
0.0131 
0.0105 
0.0117 
0.0094 
0.0105 
0.0080 
0.0092 
0.0073 
0.0092 
0.0068 
0.0082 
0.0062 
0.0239 
0.0225 
0.0216 
0.0207 
0.0161 
0.0150 
0.0142 
0.0137 
0.0127 
0.0119 
0.0115 
0.0109 
0.0115 
0.0104 
0.0100 
0.0094 
8d 
Hip 
6d 
8d 
Four Wood 
Frame 
Gable 6d 0.0225 
0.0195 
0.0197 
0.0176 
0.0148 
0.0122 
0.0126 
0.0107 
0.0120 
0.0094 
0.0100 
0.0083 
0.0106 
0.0081 
0.0088 
0.0070 
0.0275 
0.0261 
0.0247 
0.0240 
0.0185 
0.0172 
0.0161 
0.0157 
0.0151 
0.0140 
0.0130 
0.0126 
0.0135 
0.0126 
0.0115 
0.0111 
8d 
Hip 
6d 
8d 

Chapter 7. Direct Economic Losses 

?
Table 7.39. Average Annual Building Loss Normalized by Building Value   
Marginally- or Non-Engineered Hotel/Motel and Multi-Family Residential 
Buildings   Flat Roofs with Built-up Roof Cover 

Building Characteristics 
Strapped Roof-Wall Connections 
Terrain Surface Roughness (m) 
Toe-Nailed Roof-Wall Connections 
Terrain Surface Roughness (m) 
No. of 
Stories 
Wall 
Constr. 
Roof 
Quality 
Roof 
Deck 
Nails 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
One 
Wood 
Frame 
Average 6d 0.0083 
0.0067 
0.0119 
0.0105 
0.0046 
0.0036 
0.0069 
0.0060 
0.0031 
0.0024 
0.0050 
0.0044 
0.0025 
0.0019 
0.0043 
0.0037 
0.0105 
0.0096 
0.0138 
0.0131 
0.0057 
0.0052 
0.0079 
0.0074 
0.0037 
0.0033 
0.0056 
0.0052 
0.0030 
0.0025 
0.0047 
0.0043 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0078 
0.0059 
0.0114 
0.0098 
0.0045 
0.0035 
0.0069 
0.0059 
0.0031 
0.0024 
0.0050 
0.0044 
0.0025 
0.0019 
0.0043 
0.0037 
0.0095 
0.0087 
0.0129 
0.0123 
0.0055 
0.0049 
0.0076 
0.0072 
0.0037 
0.0032 
0.0055 
0.0051 
0.0029 
0.0025 
0.0046 
0.0042 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0077 
0.0059 
0.0114 
0.0097 
0.0045 
0.0035 
0.0069 
0.0059 
0.0031 
0.0024 
0.0050 
0.0044 
0.0025 
0.0019 
0.0043 
0.0037 
0.0095 
0.0086 
0.0128 
0.0122 
0.0055 
0.0049 
0.0077 
0.0072 
0.0037 
0.0032 
0.0055 
0.0051 
0.0029 
0.0025 
0.0046 
0.0042 
8d 
Poor 
6d 
8d 
Two 
Wood 
Frame 
Average 6d 0.0177 
0.0158 
0.0243 
0.0227 
0.0110 
0.0095 
0.0160 
0.0147 
0.0084 
0.0070 
0.0129 
0.0117 
0.0070 
0.0059 
0.0115 
0.0104 
0.0206 
0.0199 
0.0269 
0.0262 
0.0133 
0.0128 
0.0181 
0.0176 
0.0103 
0.0097 
0.0146 
0.0141 
0.0089 
0.0083 
0.0129 
0.0125 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0167 
0.0149 
0.0234 
0.0218 
0.0104 
0.0091 
0.0157 
0.0143 
0.0081 
0.0068 
0.0127 
0.0115 
0.0069 
0.0057 
0.0112 
0.0102 
0.0193 
0.0184 
0.0257 
0.0250 
0.0127 
0.0122 
0.0174 
0.0170 
0.0098 
0.0093 
0.0141 
0.0137 
0.0084 
0.0079 
0.0125 
0.0121 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0167 
0.0149 
0.0234 
0.0218 
0.0105 
0.0091 
0.0156 
0.0144 
0.0080 
0.0068 
0.0126 
0.0115 
0.0069 
0.0057 
0.0113 
0.0101 
0.0192 
0.0183 
0.0256 
0.0248 
0.0126 
0.0121 
0.0175 
0.0170 
0.0098 
0.0092 
0.0141 
0.0137 
0.0084 
0.0079 
0.0125 
0.0120 
8d 
Poor 
6d 
8d 
Three 
Wood 
Frame 
Average 6d 0.0245 
0.0220 
0.0327 
0.0301 
0.0163 
0.0141 
0.0231 
0.0209 
0.0133 
0.0113 
0.0195 
0.0175 
0.0118 
0.0099 
0.0176 
0.0158 
0.0285 
0.0276 
0.0364 
0.0355 
0.0193 
0.0187 
0.0260 
0.0252 
0.0159 
0.0151 
0.0220 
0.0213 
0.0142 
0.0134 
0.0200 
0.0193 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0231 
0.0205 
0.0312 
0.0287 
0.0157 
0.0135 
0.0224 
0.0203 
0.0129 
0.0108 
0.0190 
0.0171 
0.0114 
0.0094 
0.0173 
0.0154 
0.0265 
0.0256 
0.0346 
0.0337 
0.0184 
0.0176 
0.0249 
0.0242 
0.0151 
0.0144 
0.0212 
0.0206 
0.0136 
0.0128 
0.0194 
0.0187 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0229 
0.0203 
0.0311 
0.0285 
0.0157 
0.0135 
0.0223 
0.0203 
0.0128 
0.0107 
0.0189 
0.0170 
0.0113 
0.0095 
0.0172 
0.0154 
0.0264 
0.0255 
0.0342 
0.0334 
0.0183 
0.0176 
0.0248 
0.0243 
0.0152 
0.0143 
0.0212 
0.0204 
0.0135 
0.0128 
0.0193 
0.0187 
8d 
Poor 
6d 
8d 
Four Wood 
Frame 
Average 6d 0.0247 
0.0221 
0.0300 
0.0275 
0.0168 
0.0144 
0.0212 
0.0192 
0.0137 
0.0115 
0.0180 
0.0158 
0.0122 
0.0101 
0.0164 
0.0142 
0.0302 
0.0296 
0.0352 
0.0348 
0.0212 
0.0204 
0.0256 
0.0249 
0.0178 
0.0171 
0.0219 
0.0214 
0.0161 
0.0155 
0.0201 
0.0195 
8d 
Poor 
6d 
8d 

HAZUS-MH MR3 Technical Manual 

?
Table 7.40. Average Annual Building Loss Normalized by Building Value   
Marginally- or Non-Engineered Hotel/Motel and Multi-Family Residential 
Buildings   Flat Roofs with Single Ply Membrane Roof Cover 

Building Characteristics 
Strapped Roof-Wall Connections 
Terrain Surface Roughness (m) 
Toe-Nailed Roof-Wall Connections 
Terrain Surface Roughness (m) 
No. of 
Stories 
Wall 
Constr. 
Roof 
Quality 
Roof 
Deck 
Nails 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
One 
Wood 
Frame 
Average 6d 0.0095 
0.0081 
0.0206 
0.0193 
0.0054 
0.0043 
0.0128 
0.0120 
0.0037 
0.0030 
0.0101 
0.0094 
0.0031 
0.0025 
0.0090 
0.0084 
0.0115 
0.0109 
0.0223 
0.0216 
0.0064 
0.0059 
0.0136 
0.0132 
0.0043 
0.0039 
0.0105 
0.0102 
0.0035 
0.0031 
0.0093 
0.0090 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0089 
0.0072 
0.0203 
0.0186 
0.0053 
0.0043 
0.0129 
0.0119 
0.0037 
0.0030 
0.0101 
0.0095 
0.0031 
0.0025 
0.0089 
0.0085 
0.0105 
0.0098 
0.0214 
0.0207 
0.0061 
0.0056 
0.0134 
0.0130 
0.0041 
0.0038 
0.0104 
0.0101 
0.0034 
0.0030 
0.0092 
0.0089 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0089 
0.0072 
0.0202 
0.0186 
0.0053 
0.0043 
0.0129 
0.0119 
0.0037 
0.0030 
0.0101 
0.0095 
0.0031 
0.0025 
0.0089 
0.0085 
0.0104 
0.0097 
0.0213 
0.0207 
0.0061 
0.0056 
0.0134 
0.0130 
0.0041 
0.0038 
0.0104 
0.0101 
0.0034 
0.0030 
0.0092 
0.0089 
8d 
Poor 
6d 
8d 
Two 
Wood 
Frame 
Average 6d 0.0200 
0.0182 
0.0347 
0.0333 
0.0126 
0.0113 
0.0244 
0.0230 
0.0099 
0.0087 
0.0204 
0.0193 
0.0086 
0.0075 
0.0185 
0.0175 
0.0227 
0.0221 
0.0371 
0.0363 
0.0149 
0.0145 
0.0263 
0.0258 
0.0117 
0.0112 
0.0220 
0.0215 
0.0101 
0.0098 
0.0200 
0.0195 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0191 
0.0174 
0.0340 
0.0324 
0.0123 
0.0110 
0.0240 
0.0228 
0.0097 
0.0086 
0.0203 
0.0191 
0.0085 
0.0073 
0.0184 
0.0173 
0.0215 
0.0209 
0.0360 
0.0353 
0.0144 
0.0137 
0.0257 
0.0253 
0.0113 
0.0108 
0.0216 
0.0211 
0.0098 
0.0093 
0.0196 
0.0191 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0191 
0.0173 
0.0339 
0.0323 
0.0123 
0.0110 
0.0240 
0.0229 
0.0097 
0.0085 
0.0203 
0.0190 
0.0085 
0.0074 
0.0184 
0.0173 
0.0215 
0.0207 
0.0359 
0.0352 
0.0144 
0.0137 
0.0257 
0.0251 
0.0113 
0.0107 
0.0216 
0.0211 
0.0098 
0.0093 
0.0195 
0.0192 
8d 
Poor 
6d 
8d 
Three 
Wood 
Frame 
Average 6d 0.0282 
0.0256 
0.0411 
0.0387 
0.0191 
0.0171 
0.0305 
0.0285 
0.0159 
0.0140 
0.0265 
0.0245 
0.0143 
0.0123 
0.0244 
0.0224 
0.0318 
0.0307 
0.0449 
0.0440 
0.0221 
0.0212 
0.0334 
0.0327 
0.0184 
0.0177 
0.0290 
0.0283 
0.0166 
0.0159 
0.0267 
0.0260 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0266 
0.0240 
0.0398 
0.0372 
0.0185 
0.0164 
0.0299 
0.0278 
0.0153 
0.0134 
0.0259 
0.0240 
0.0139 
0.0119 
0.0240 
0.0221 
0.0300 
0.0291 
0.0430 
0.0422 
0.0210 
0.0202 
0.0324 
0.0317 
0.0177 
0.0169 
0.0282 
0.0275 
0.0159 
0.0153 
0.0260 
0.0254 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0264 
0.0239 
0.0396 
0.0370 
0.0185 
0.0163 
0.0298 
0.0278 
0.0154 
0.0134 
0.0259 
0.0240 
0.0138 
0.0119 
0.0239 
0.0220 
0.0297 
0.0288 
0.0428 
0.0420 
0.0210 
0.0203 
0.0324 
0.0316 
0.0173 
0.0169 
0.0282 
0.0276 
0.0156 
0.0153 
0.0261 
0.0254 
8d 
Poor 
6d 
8d 
Four Wood 
Frame 
Average 6d 0.0268 
0.0244 
0.0350 
0.0324 
0.0184 
0.0164 
0.0258 
0.0237 
0.0154 
0.0132 
0.0224 
0.0203 
0.0139 
0.0118 
0.0205 
0.0185 
0.0321 
0.0315 
0.0403 
0.0399 
0.0229 
0.0222 
0.0302 
0.0297 
0.0193 
0.0188 
0.0262 
0.0257 
0.0176 
0.0170 
0.0243 
0.0237 
8d 
Poor 
6d 
8d 

Chapter 7. Direct Economic Losses 

?
Table 7.41. Average Annual Content Loss Normalized by Building Value   
Marginally- or Non-Engineered Hotel/Motel and Multi-Family Residential 
Buildings   Gable/Hip Roofs with Shingles 

Building Characteristics 
Strapped Roof-Wall Connections 
Terrain Surface Roughness (m) 
Toe-Nailed Roof-Wall Connections 
Terrain Surface Roughness (m) 
No. of 
Stories 
Wall 
Constr. 
Roof 
Shape 
Roof 
Deck 
Nails 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
One 
Wood 
Frame 
Gable 6d 0.0047 
0.0030 
0.0031 
0.0022 
0.0023 
0.0010 
0.0013 
0.0007 
0.0016 
0.0006 
0.0008 
0.0004 
0.0013 
0.0004 
0.0006 
0.0003 
0.0065 
0.0059 
0.0049 
0.0047 
0.0036 
0.0033 
0.0025 
0.0024 
0.0026 
0.0022 
0.0016 
0.0015 
0.0021 
0.0018 
0.0013 
0.0012 
8d 
Hip 
6d 
8d 
Unreinf. 
Masonry 
Gable 
6d 0.0045 
0.0027 
0.0028 
0.0017 
0.0023 
0.0009 
0.0013 
0.0006 
0.0016 
0.0005 
0.0008 
0.0003 
0.0013 
0.0004 
0.0006 
0.0002 
0.0065 
0.0059 
0.0049 
0.0046 
0.0036 
0.0032 
0.0025 
0.0024 
0.0026 
0.0023 
0.0016 
0.0015 
0.0021 
0.0018 
0.0012 
0.0012 
8d 
Hip 
6d 
8d 
Reinf. 
Masonry 
Gable 
6d 0.0045 
0.0027 
0.0028 
0.0017 
0.0023 
0.0009 
0.0013 
0.0006 
0.0016 
0.0005 
0.0008 
0.0003 
0.0013 
0.0004 
0.0006 
0.0002 
0.0065 
0.0059 
0.0049 
0.0046 
0.0036 
0.0033 
0.0025 
0.0024 
0.0025 
0.0022 
0.0016 
0.0015 
0.0021 
0.0017 
0.0012 
0.0012 
8d 
Hip 
6d 
8d 
Two 
Wood 
Frame 
Gable 6d 0.0076 
0.0061 
0.0061 
0.0049 
0.0044 
0.0031 
0.0033 
0.0023 
0.0033 
0.0021 
0.0024 
0.0014 
0.0027 
0.0016 
0.0019 
0.0011 
0.0098 
0.0092 
0.0082 
0.0079 
0.0062 
0.0057 
0.0050 
0.0048 
0.0047 
0.0043 
0.0037 
0.0036 
0.0040 
0.0036 
0.0031 
0.0030 
8d 
Hip 
6d 
8d 
Unreinf. 
Masonry 
Gable 
6d 0.0075 
0.0058 
0.0058 
0.0047 
0.0043 
0.0030 
0.0032 
0.0022 
0.0032 
0.0020 
0.0023 
0.0013 
0.0027 
0.0016 
0.0019 
0.0010 
0.0097 
0.0090 
0.0081 
0.0078 
0.0061 
0.0056 
0.0049 
0.0049 
0.0046 
0.0043 
0.0036 
0.0036 
0.0040 
0.0036 
0.0031 
0.0029 
8d 
Hip 
6d 
8d 
Reinf. 
Masonry 
Gable 
6d 0.0074 
0.0059 
0.0058 
0.0046 
0.0044 
0.0030 
0.0032 
0.0021 
0.0032 
0.0020 
0.0023 
0.0014 
0.0027 
0.0015 
0.0019 
0.0010 
0.0097 
0.0091 
0.0080 
0.0078 
0.0061 
0.0056 
0.0050 
0.0049 
0.0046 
0.0043 
0.0037 
0.0035 
0.0040 
0.0036 
0.0031 
0.0029 
8d 
Hip 
6d 
8d 
Three 
Wood 
Frame 
Gable 6d 0.0092 
0.0077 
0.0079 
0.0065 
0.0056 
0.0042 
0.0046 
0.0033 
0.0043 
0.0030 
0.0036 
0.0023 
0.0037 
0.0025 
0.0030 
0.0019 
0.0119 
0.0113 
0.0104 
0.0101 
0.0076 
0.0071 
0.0065 
0.0063 
0.0060 
0.0056 
0.0051 
0.0049 
0.0053 
0.0049 
0.0044 
0.0042 
8d 
Hip 
6d 
8d 
Unreinf. 
Masonry 
Gable 
6d 0.0089 
0.0073 
0.0075 
0.0060 
0.0054 
0.0040 
0.0044 
0.0031 
0.0042 
0.0028 
0.0034 
0.0022 
0.0036 
0.0023 
0.0029 
0.0017 
0.0116 
0.0109 
0.0101 
0.0097 
0.0074 
0.0069 
0.0063 
0.0061 
0.0060 
0.0055 
0.0050 
0.0047 
0.0052 
0.0047 
0.0044 
0.0041 
8d 
Hip 
6d 
8d 
Reinf. 
Masonry 
Gable 
6d 0.0088 
0.0072 
0.0075 
0.0059 
0.0053 
0.0040 
0.0044 
0.0031 
0.0041 
0.0028 
0.0034 
0.0022 
0.0036 
0.0023 
0.0029 
0.0017 
0.0116 
0.0109 
0.0102 
0.0097 
0.0075 
0.0070 
0.0063 
0.0061 
0.0058 
0.0055 
0.0051 
0.0047 
0.0052 
0.0047 
0.0043 
0.0040 
8d 
Hip 
6d 
8d 
Four Wood 
Frame 
Gable 6d 0.0102 
0.0087 
0.0087 
0.0075 
0.0063 
0.0051 
0.0051 
0.0041 
0.0050 
0.0038 
0.0040 
0.0030 
0.0044 
0.0031 
0.0035 
0.0025 
0.0131 
0.0124 
0.0116 
0.0113 
0.0085 
0.0079 
0.0073 
0.0071 
0.0069 
0.0064 
0.0058 
0.0056 
0.0062 
0.0057 
0.0051 
0.0049 
8d 
Hip 
6d 
8d 

HAZUS-MH MR3 Technical Manual 

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Table 7.42. Average Annual Content Loss Normalized by Building Value   
Marginally- or Non-Engineered Hotel/Motel and Multi-Family Residential 
Buildings   Flat Roofs with Built-up Roof Cover 

Building Characteristics 
Strapped Roof-Wall Connections 
Terrain Surface Roughness (m) 
Toe-Nailed Roof-Wall Connections 
Terrain Surface Roughness (m) 
No. of 
Stories 
Wall 
Constr. 
Roof 
Quality 
Roof 
Deck 
Nails 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
One 
Wood 
Frame 
Average 6d 0.0030 
0.0020 
0.0035 
0.0024 
0.0013 
0.0005 
0.0016 
0.0008 
0.0008 
0.0003 
0.0010 
0.0005 
0.0006 
0.0002 
0.0009 
0.0004 
0.0043 
0.0037 
0.0046 
0.0042 
0.0020 
0.0017 
0.0023 
0.0019 
0.0012 
0.0009 
0.0014 
0.0012 
0.0009 
0.0006 
0.0011 
0.0009 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0028 
0.0016 
0.0032 
0.0021 
0.0013 
0.0005 
0.0015 
0.0008 
0.0008 
0.0003 
0.0010 
0.0005 
0.0006 
0.0002 
0.0008 
0.0004 
0.0042 
0.0037 
0.0046 
0.0041 
0.0020 
0.0016 
0.0022 
0.0019 
0.0012 
0.0009 
0.0014 
0.0011 
0.0009 
0.0007 
0.0011 
0.0009 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0028 
0.0016 
0.0033 
0.0021 
0.0013 
0.0005 
0.0015 
0.0008 
0.0008 
0.0003 
0.0010 
0.0005 
0.0006 
0.0002 
0.0008 
0.0004 
0.0042 
0.0036 
0.0046 
0.0041 
0.0020 
0.0016 
0.0022 
0.0019 
0.0012 
0.0009 
0.0014 
0.0012 
0.0009 
0.0007 
0.0011 
0.0009 
8d 
Poor 
6d 
8d 
Two 
Wood 
Frame 
Average 6d 0.0068 
0.0055 
0.0074 
0.0063 
0.0039 
0.0028 
0.0043 
0.0034 
0.0028 
0.0019 
0.0032 
0.0023 
0.0022 
0.0014 
0.0027 
0.0019 
0.0087 
0.0083 
0.0094 
0.0089 
0.0055 
0.0051 
0.0059 
0.0056 
0.0041 
0.0037 
0.0045 
0.0042 
0.0035 
0.0031 
0.0038 
0.0036 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0066 
0.0054 
0.0073 
0.0061 
0.0037 
0.0028 
0.0043 
0.0033 
0.0027 
0.0018 
0.0032 
0.0023 
0.0022 
0.0014 
0.0026 
0.0018 
0.0087 
0.0081 
0.0093 
0.0089 
0.0054 
0.0051 
0.0059 
0.0056 
0.0041 
0.0037 
0.0045 
0.0042 
0.0034 
0.0031 
0.0038 
0.0035 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0066 
0.0054 
0.0073 
0.0061 
0.0037 
0.0028 
0.0042 
0.0033 
0.0027 
0.0018 
0.0031 
0.0023 
0.0022 
0.0014 
0.0027 
0.0018 
0.0086 
0.0080 
0.0093 
0.0088 
0.0054 
0.0051 
0.0059 
0.0056 
0.0040 
0.0037 
0.0045 
0.0042 
0.0034 
0.0031 
0.0038 
0.0035 
8d 
Poor 
6d 
8d 
Three 
Wood 
Frame 
Average 6d 0.0092 
0.0077 
0.0101 
0.0085 
0.0057 
0.0043 
0.0064 
0.0050 
0.0044 
0.0032 
0.0050 
0.0038 
0.0038 
0.0026 
0.0044 
0.0032 
0.0120 
0.0115 
0.0128 
0.0123 
0.0078 
0.0074 
0.0085 
0.0080 
0.0063 
0.0058 
0.0069 
0.0065 
0.0055 
0.0051 
0.0061 
0.0058 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0088 
0.0073 
0.0097 
0.0081 
0.0055 
0.0041 
0.0061 
0.0048 
0.0043 
0.0030 
0.0049 
0.0037 
0.0037 
0.0024 
0.0043 
0.0030 
0.0116 
0.0111 
0.0125 
0.0120 
0.0077 
0.0072 
0.0083 
0.0079 
0.0062 
0.0058 
0.0068 
0.0064 
0.0055 
0.0051 
0.0061 
0.0057 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0088 
0.0073 
0.0097 
0.0081 
0.0055 
0.0041 
0.0062 
0.0048 
0.0043 
0.0030 
0.0049 
0.0037 
0.0037 
0.0025 
0.0043 
0.0031 
0.0116 
0.0112 
0.0125 
0.0120 
0.0077 
0.0073 
0.0083 
0.0080 
0.0062 
0.0058 
0.0068 
0.0064 
0.0055 
0.0051 
0.0060 
0.0057 
8d 
Poor 
6d 
8d 
Four Wood 
Frame 
Average 6d 0.0105 
0.0090 
0.0112 
0.0098 
0.0066 
0.0053 
0.0072 
0.0060 
0.0052 
0.0040 
0.0058 
0.0046 
0.0046 
0.0034 
0.0051 
0.0040 
0.0138 
0.0135 
0.0144 
0.0142 
0.0093 
0.0090 
0.0099 
0.0095 
0.0078 
0.0074 
0.0082 
0.0080 
0.0070 
0.0067 
0.0075 
0.0072 
8d 
Poor 
6d 
8d 

Chapter 7. Direct Economic Losses 

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Table 7.43. Average Annual Content Loss Normalized by Building Value   
Marginally- or Non-Engineered Hotel/Motel and Multi-Family Residential 
Buildings   Flat Roofs with Single Ply Membrane Roof Cover 

Building Characteristics 
Strapped Roof-Wall Connections 
Terrain Surface Roughness (m) 
Toe-Nailed Roof-Wall Connections 
Terrain Surface Roughness (m) 
No. of 
Stories 
Wall 
Constr. 
Roof 
Quality 
Roof 
Deck 
Nails 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
One 
Wood 
Frame 
Average 6d 0.0032 
0.0022 
0.0047 
0.0038 
0.0014 
0.0006 
0.0024 
0.0017 
0.0009 
0.0004 
0.0018 
0.0013 
0.0007 
0.0003 
0.0015 
0.0011 
0.0044 
0.0039 
0.0059 
0.0054 
0.0021 
0.0017 
0.0031 
0.0027 
0.0013 
0.0010 
0.0021 
0.0019 
0.0010 
0.0007 
0.0018 
0.0015 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0029 
0.0018 
0.0046 
0.0034 
0.0014 
0.0006 
0.0024 
0.0016 
0.0009 
0.0004 
0.0018 
0.0013 
0.0007 
0.0003 
0.0015 
0.0011 
0.0043 
0.0038 
0.0058 
0.0053 
0.0020 
0.0017 
0.0030 
0.0027 
0.0013 
0.0010 
0.0021 
0.0019 
0.0010 
0.0007 
0.0018 
0.0015 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0029 
0.0018 
0.0045 
0.0034 
0.0014 
0.0006 
0.0024 
0.0016 
0.0009 
0.0004 
0.0018 
0.0013 
0.0007 
0.0003 
0.0015 
0.0011 
0.0043 
0.0038 
0.0058 
0.0053 
0.0020 
0.0017 
0.0031 
0.0027 
0.0013 
0.0010 
0.0021 
0.0019 
0.0010 
0.0007 
0.0018 
0.0015 
8d 
Poor 
6d 
8d 
Two 
Wood 
Frame 
Average 6d 0.0070 
0.0058 
0.0086 
0.0074 
0.0040 
0.0031 
0.0053 
0.0043 
0.0029 
0.0020 
0.0040 
0.0031 
0.0024 
0.0016 
0.0034 
0.0027 
0.0089 
0.0086 
0.0105 
0.0100 
0.0056 
0.0054 
0.0069 
0.0065 
0.0042 
0.0039 
0.0054 
0.0050 
0.0035 
0.0033 
0.0046 
0.0043 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0068 
0.0056 
0.0085 
0.0073 
0.0039 
0.0030 
0.0052 
0.0042 
0.0028 
0.0020 
0.0040 
0.0032 
0.0024 
0.0016 
0.0034 
0.0026 
0.0088 
0.0085 
0.0105 
0.0101 
0.0056 
0.0052 
0.0068 
0.0066 
0.0042 
0.0039 
0.0053 
0.0050 
0.0035 
0.0033 
0.0046 
0.0043 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0069 
0.0056 
0.0085 
0.0073 
0.0039 
0.0029 
0.0052 
0.0043 
0.0028 
0.0020 
0.0040 
0.0031 
0.0024 
0.0016 
0.0034 
0.0026 
0.0089 
0.0084 
0.0105 
0.0100 
0.0057 
0.0052 
0.0068 
0.0065 
0.0042 
0.0039 
0.0053 
0.0049 
0.0036 
0.0032 
0.0046 
0.0043 
8d 
Poor 
6d 
8d 
Three 
Wood 
Frame 
Average 6d 0.0097 
0.0081 
0.0110 
0.0094 
0.0059 
0.0047 
0.0072 
0.0059 
0.0047 
0.0035 
0.0058 
0.0045 
0.0041 
0.0028 
0.0051 
0.0039 
0.0123 
0.0118 
0.0137 
0.0132 
0.0081 
0.0076 
0.0093 
0.0089 
0.0066 
0.0062 
0.0076 
0.0072 
0.0058 
0.0055 
0.0068 
0.0064 
8d 
Poor 
6d 
8d 
Unreinf. 
Masonry 
Average 
6d 0.0092 
0.0076 
0.0106 
0.0090 
0.0058 
0.0045 
0.0070 
0.0056 
0.0045 
0.0033 
0.0056 
0.0044 
0.0040 
0.0027 
0.0050 
0.0038 
0.0121 
0.0115 
0.0134 
0.0129 
0.0079 
0.0075 
0.0091 
0.0087 
0.0065 
0.0061 
0.0076 
0.0071 
0.0057 
0.0053 
0.0067 
0.0064 
8d 
Poor 
6d 
8d 
Reinf. 
Masonry 
Average 
6d 0.0092 
0.0076 
0.0106 
0.0090 
0.0058 
0.0045 
0.0069 
0.0056 
0.0045 
0.0033 
0.0056 
0.0044 
0.0039 
0.0027 
0.0049 
0.0038 
0.0120 
0.0115 
0.0134 
0.0129 
0.0080 
0.0075 
0.0092 
0.0087 
0.0063 
0.0061 
0.0075 
0.0072 
0.0056 
0.0054 
0.0068 
0.0064 
8d 
Poor 
6d 
8d 
Four Wood 
Frame 
Average 6d 0.0108 
0.0094 
0.0119 
0.0104 
0.0068 
0.0056 
0.0078 
0.0066 
0.0055 
0.0043 
0.0064 
0.0052 
0.0048 
0.0037 
0.0057 
0.0046 
0.0140 
0.0137 
0.0152 
0.0150 
0.0096 
0.0092 
0.0105 
0.0103 
0.0079 
0.0077 
0.0089 
0.0086 
0.0072 
0.0069 
0.0080 
0.0078 
8d 
Poor 
6d 
8d 

HAZUS-MH MR3 Technical Manual 

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Table 7.44. Percent Increases in the Average Annual Total Loss Due to Changes 
in 
Building Parameters (Minimum/Average/Maximum) - Marginally- or Non-
Engineered Hotel/Motel and Multi-Family Residential Buildings 

Parameter 
Number of Stories 
All One Two Three Four 
Gable and Hip Roofs With Singles 
Unreinforced Masonry to 
Wood Frame Walls 0% / 5% / 18% 0% / 4% / 18% 1% / 4% / 7% 3% / 6% / 10% 
N/AReinforced Masonry to 
Unreinforced Masonry Walls -2% / 0% / 3% -1% / 0% / 3% -2% / 0% / 2% -1% / 1% 
/ 2% 
Hip to Gable Roof Shape 9% / 23% / 59% 22% / 37% / 59% 13% / 21% / 30% 9% / 
13% / 17% 9% / 16% / 22% 
8d to 6d Roof Deck Nails 2% / 21% / 71% 2% / 28% / 71% 2% / 19% / 46% 2% / 
18% / 42% 3% / 14% / 34% 
Strapped to Toe-Nailed 
Roof/Wall Connections 21% / 48% / 103% 27% / 61% / 103% 21% / 45% / 79% 22% / 
42% / 72% 24% / 42% / 69% 
One to Two Stories 48% / 91% / 135% 
N/ATwo to Three Stories 17% / 28% / 49% 
Three to Four Stories 3% / 9% / 14% 
Flat Roofs With BUR 
Unreinforced Masonry to 
Wood Frame Walls 0% / 4% / 15% 0% / 3% / 15% 1% / 3% / 6% 2% / 4% / 7% 
N/AReinforced Masonry to 
Unreinforced Masonry Walls -1% / 0% / 2% -1% / 0% / 2% -1% / 0% / 1% -1% / 0% 
/ 1% 
Average to Poor Quality 
Cover 13% / 40% / 98% 24% / 53% / 98% 24% / 40% / 69% 21% / 35% / 56% 13% / 
21% / 35% 
8d to 6d Roof Deck Nails 1% / 15% / 50% 6% / 24% / 50% 3% / 11% / 29% 3% / 
11% / 28% 1% / 10% / 25% 
Strapped to Toe-Nailed 
Roof/Wall Connections 12% / 31% / 68% 12% / 33% / 68% 14% / 29% / 56% 14% / 
28% / 52% 21% / 37% / 65% 
One to Two Stories 97% / 167% / 261% 
N/ATwo to Three Stories 31% / 49% / 70% 
Three to Four Stories -4% / 5% / 20% 
Flat Roofs With EPDM 
Unreinforced Masonry to 
Wood Frame Walls -1% / 3% / 14% -1% / 2% / 14% 0% / 2% / 5% 2% / 3% / 7% 
N/AReinforced Masonry to 
Unreinforced Masonry Walls -1% / 0% / 2% 0% / 0% / 1% -1% / 0% / 1% -1% / 0% 
/ 2% 
Average to Poor Quality 
Cover 20% / 87% / 246% 77% / 146% / 246% 50% / 80% / 124% 33% / 51% / 77% 20% 
/ 32% / 48% 
8d to 6d Roof Deck Nails 1% / 11% / 37% 4% / 16% / 37% 2% / 8% / 22% 2% / 9% 
/ 23% 1% / 8% / 20% 
Strapped to Toe-Nailed 
Roof/Wall Connections 5% / 23% / 54% 5% / 21% / 52% 9% / 21% / 44% 12% / 22% 
/ 42% 18% / 32% / 54% 
One to Two Stories 69% / 136% / 247% 
N/ATwo to Three Stories 16% / 40% / 67% 
Three to Four Stories -12% / -1% / 12% 

Chapter 7. Direct Economic Losses 

?
Table 7.45. Subassembly Cost Distributions for Low-Rise Masonry Strip Mall 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
2.2% 
0.0% 
2.0% 
4.2% 
Substructures 
Slab on Grade 
Special Substructures 
6.4% 
0.0% 
6.4% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
1.1% 
0.0% 
0.0% 
28.1% 
0.0% 
29.2% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
6.2% 
0.0% 
0.5% 
1.4% 
8.1% 
Roofing 
Roof Covering 
Insulation 
Openings & Speci alties 
3.6% 
2.6% 
0.2% 
6.4% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
1.3% 
1.8% 
0.4% 
8.6% 
2.6% 
0.9% 
15.7% 
Conveying 
Elevators 
Special Conveyors 
0.0% 
0.0% 
0.0% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
1.3% 
2.8% 
0.0% 
13.6% 
0.0% 
18.2% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
1.1% 
10.6% 
0.6% 
12.3% 
Special Construction 
Specialties (& Additives) 0.0% 0.0% 
Total 100% 100% 

HAZUS-MH MR3 Technical Manual 

?
Example building and content loss functions are given in Appendix K. The 
average 
annual building and content losses (both normalized by the total building 
value) for all 
the buildings examined are presented in Tables 7.46 through 7.57. The average 
annual 
loss is obtained by summing all losses produced during the 20,000-year 
hurricane 
simulation period and then dividing by 20,000 years. The values given in the 
tables 
reflect the average annual losses for low-rise strip mall buildings located 
in the South 
Florida area. Tables 7.46 and 7.47 list building loss ratios for the 
buildings with a wood 
roof system and Tables 7.48 through 7.51 list building loss ratios for the 
buildings with a 
metal roof system. Tables 7.52 through 7.57 are the corresponding tables 
listing the 
content loss ratios. 

Table 7.46. Average Annual Building Loss Normalized by Building Value for 
Building A - Twelve Foot High Strip Mall Building with Six Units and Wood 
Deck 
with Two Foot Truss Spacing 

Building Characteristics 
Toe-Nailed Roof-Wall Connection Strapped Roof-Wall Connection 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Wood 
Deck 
Nail Size 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
6d EPDMBUR 
0.0240 
0.0232 
0.0237 
0.0227 
0.0136 
0.0131 
0.0134 
0.0130 
0.0094 
0.0089 
0.0093 
0.0089 
0.0075 
0.0071 
0.0075 
0.0071 
0.0140 
0.0130 
0.0125 
0.0115 
0.0071 
0.0064 
0.0057 
0.0051 
0.0049 
0.0043 
0.0037 
0.0032 
0.0040 
0.0035 
0.0030 
0.0024 
8d EPDMBUR 
B 
6d EPDMBUR 
0.0197 
0.0188 
0.0194 
0.0187 
0.0114 
0.0110 
0.0112 
0.0109 
0.0078 
0.0075 
0.0078 
0.0074 
0.0064 
0.0059 
0.0063 
0.0058 
0.0131 
0.0122 
0.0120 
0.0110 
0.0067 
0.0060 
0.0057 
0.0049 
0.0046 
0.0040 
0.0036 
0.0031 
0.0037 
0.0032 
0.0029 
0.0023 
8d EPDMBUR 
C 
6d EPDMBUR 
0.0161 
0.0151 
0.0157 
0.0150 
0.0101 
0.0096 
0.0098 
0.0094 
0.0071 
0.0066 
0.0069 
0.0063 
0.0056 
0.0051 
0.0055 
0.0051 
0.0126 
0.0116 
0.0118 
0.0106 
0.0064 
0.0057 
0.0053 
0.0045 
0.0044 
0.0038 
0.0035 
0.0029 
0.0036 
0.0031 
0.0028 
0.0022 
8d EPDMBUR 
D 
6d EPDMBUR 
0.0138 
0.0129 
0.0137 
0.0127 
0.0060 
0.0055 
0.0059 
0.0053 
0.0045 
0.0039 
0.0043 
0.0036 
0.0038 
0.0032 
0.0036 
0.0030 
0.0120 
0.0111 
0.0112 
0.0100 
0.0050 
0.0043 
0.0042 
0.0034 
0.0035 
0.0028 
0.0027 
0.0021 
0.0029 
0.0024 
0.0022 
0.0016 
8d EPDMBUR 
Reinforced Masonry 
A 
6d EPDMBUR 
0.0229 
0.0224 
0.0229 
0.0223 
0.0135 
0.0128 
0.0132 
0.0127 
0.0093 
0.0088 
0.0090 
0.0089 
0.0074 
0.0070 
0.0072 
0.0069 
0.0134 
0.0123 
0.0119 
0.0108 
0.0070 
0.0063 
0.0055 
0.0048 
0.0048 
0.0043 
0.0037 
0.0031 
0.0040 
0.0034 
0.0029 
0.0023 
8d EPDMBUR 
B 
6d EPDMBUR 
0.0189 
0.0181 
0.0187 
0.0178 
0.0111 
0.0105 
0.0110 
0.0105 
0.0076 
0.0072 
0.0076 
0.0071 
0.0062 
0.0058 
0.0061 
0.0056 
0.0126 
0.0115 
0.0113 
0.0104 
0.0066 
0.0059 
0.0053 
0.0047 
0.0045 
0.0040 
0.0036 
0.0029 
0.0037 
0.0032 
0.0028 
0.0022 
8d EPDMBUR 
C 
6d EPDMBUR 
0.0154 
0.0145 
0.0152 
0.0141 
0.0099 
0.0094 
0.0097 
0.0091 
0.0069 
0.0063 
0.0068 
0.0063 
0.0056 
0.0051 
0.0054 
0.0049 
0.0121 
0.0110 
0.0110 
0.0099 
0.0062 
0.0055 
0.0050 
0.0044 
0.0044 
0.0037 
0.0034 
0.0027 
0.0036 
0.0030 
0.0027 
0.0022 
8d EPDMBUR 
D 
6d EPDMBUR 
0.0132 
0.0121 
0.0127 
0.0118 
0.0059 
0.0052 
0.0056 
0.0049 
0.0043 
0.0037 
0.0040 
0.0034 
0.0037 
0.0031 
0.0034 
0.0029 
0.0114 
0.0104 
0.0105 
0.0094 
0.0048 
0.0041 
0.0040 
0.0032 
0.0034 
0.0028 
0.0027 
0.0020 
0.0029 
0.0023 
0.0022 
0.0016 
8d EPDMBUR 

Chapter 7. Direct Economic Losses 

?
Table 7.47. Average Annual Building Loss Normalized by Building Value for 
Building B - Twenty Foot High Strip Mall Building with Six Units and Wood 
Deck 
with Two Foot Truss Spacing 

Building Characteristics 
Toe-Nailed Roof-Wall Connection Strapped Roof-Wall Connection 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Wood 
Deck 
Nail Size 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
6d EPDM 
BUR 
0.0337 
0.0320 
0.0336 
0.0319 
0.0235 
0.0225 
0.0234 
0.0224 
0.0178 
0.0169 
0.0179 
0.0167 
0.0151 
0.0142 
0.0149 
0.0142 
0.0244 
0.0224 
0.0231 
0.0212 
0.0146 
0.0133 
0.0132 
0.0118 
0.0112 
0.0100 
0.0100 
0.0087 
0.0098 
0.0085 
0.0084 
0.0072 
8d EPDM 
BUR 
B 
6d EPDM 
BUR 
0.0301 
0.0288 
0.0300 
0.0285 
0.0200 
0.0190 
0.0201 
0.0189 
0.0155 
0.0143 
0.0154 
0.0141 
0.0132 
0.0121 
0.0132 
0.0122 
0.0232 
0.0212 
0.0221 
0.0201 
0.0138 
0.0124 
0.0126 
0.0112 
0.0107 
0.0094 
0.0095 
0.0081 
0.0091 
0.0079 
0.0080 
0.0067 
8d EPDM 
BUR 
C 
6d EPDM 
BUR 
0.0268 
0.0251 
0.0267 
0.0250 
0.0175 
0.0161 
0.0173 
0.0159 
0.0135 
0.0124 
0.0134 
0.0123 
0.0117 
0.0106 
0.0117 
0.0105 
0.0224 
0.0205 
0.0215 
0.0196 
0.0132 
0.0117 
0.0121 
0.0107 
0.0102 
0.0089 
0.0092 
0.0078 
0.0088 
0.0076 
0.0077 
0.0065 
8d EPDM 
BUR 
D 
6d EPDM 
BUR 
0.0257 
0.0238 
0.0254 
0.0235 
0.0147 
0.0133 
0.0145 
0.0131 
0.0118 
0.0105 
0.0116 
0.0104 
0.0105 
0.0093 
0.0103 
0.0091 
0.0220 
0.0199 
0.0210 
0.0192 
0.0120 
0.0104 
0.0109 
0.0094 
0.0094 
0.0080 
0.0083 
0.0069 
0.0081 
0.0068 
0.0070 
0.0056 
8d EPDM 
BUR 
Reinforced Masonry 
A 
6d EPDM 
BUR 
0.0310 
0.0295 
0.0306 
0.0291 
0.0225 
0.0213 
0.0223 
0.0210 
0.0170 
0.0160 
0.0170 
0.0161 
0.0146 
0.0134 
0.0143 
0.0133 
0.0218 
0.0197 
0.0200 
0.0181 
0.0134 
0.0119 
0.0116 
0.0099 
0.0105 
0.0091 
0.0088 
0.0072 
0.0091 
0.0078 
0.0075 
0.0062 
8d EPDM 
BUR 
B 
6d EPDM 
BUR 
0.0275 
0.0262 
0.0274 
0.0259 
0.0192 
0.0180 
0.0190 
0.0177 
0.0148 
0.0137 
0.0147 
0.0135 
0.0127 
0.0116 
0.0126 
0.0115 
0.0207 
0.0187 
0.0193 
0.0173 
0.0127 
0.0112 
0.0111 
0.0096 
0.0099 
0.0086 
0.0084 
0.0070 
0.0086 
0.0073 
0.0071 
0.0058 
8d EPDM 
BUR 
C 
6d EPDM 
BUR 
0.0241 
0.0224 
0.0240 
0.0221 
0.0165 
0.0152 
0.0165 
0.0150 
0.0129 
0.0116 
0.0127 
0.0115 
0.0113 
0.0100 
0.0111 
0.0100 
0.0200 
0.0181 
0.0189 
0.0168 
0.0122 
0.0107 
0.0109 
0.0092 
0.0096 
0.0082 
0.0083 
0.0068 
0.0082 
0.0070 
0.0070 
0.0057 
8d EPDM 
BUR 
D 
6d 
EPDM 
BUR 
0.0228 
0.0208 
0.0224 
0.0206 
0.0136 
0.0120 
0.0134 
0.0119 
0.0110 
0.0097 
0.0108 
0.0096 
0.0099 
0.0087 
0.0096 
0.0084 
0.0195 
0.0176 
0.0183 
0.0163 
0.0108 
0.0093 
0.0095 
0.0079 
0.0086 
0.0072 
0.0072 
0.0058 
0.0076 
0.0063 
0.0062 
0.0048 
8d 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 7.48. Average Annual Building Loss Normalized by Building Value for 
Building A - Twelve Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Four Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0100 
0.0089 
0.0100 
0.0088 
0.0097 
0.0086 
0.0096 
0.0086 
0.0051 
0.0044 
0.0051 
0.0044 
0.0050 
0.0043 
0.0051 
0.0043 
0.0035 
0.0028 
0.0035 
0.0028 
0.0034 
0.0028 
0.0034 
0.0028 
0.0028 
0.0022 
0.0028 
0.0022 
0.0028 
0.0022 
0.0027 
0.0022 
0.0100 
0.0089 
0.0102 
0.0092 
0.0098 
0.0087 
0.0099 
0.0088 
0.0052 
0.0044 
0.0053 
0.0045 
0.0051 
0.0044 
0.0052 
0.0045 
0.0035 
0.0029 
0.0036 
0.0029 
0.0035 
0.0028 
0.0035 
0.0029 
0.0028 
0.0022 
0.0029 
0.0023 
0.0028 
0.0022 
0.0028 
0.0023 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0098 
0.0087 
0.0097 
0.0085 
0.0094 
0.0082 
0.0095 
0.0083 
0.0049 
0.0041 
0.0048 
0.0041 
0.0048 
0.0040 
0.0048 
0.0040 
0.0033 
0.0027 
0.0033 
0.0027 
0.0032 
0.0026 
0.0032 
0.0026 
0.0026 
0.0021 
0.0027 
0.0021 
0.0026 
0.0021 
0.0026 
0.0021 
0.0098 
0.0088 
0.0100 
0.0089 
0.0095 
0.0084 
0.0096 
0.0085 
0.0049 
0.0043 
0.0051 
0.0043 
0.0048 
0.0041 
0.0049 
0.0042 
0.0033 
0.0027 
0.0034 
0.0028 
0.0033 
0.0027 
0.0034 
0.0027 
0.0027 
0.0021 
0.0027 
0.0022 
0.0027 
0.0021 
0.0027 
0.0021 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0095 
0.0083 
0.0095 
0.0084 
0.0092 
0.0080 
0.0092 
0.0080 
0.0045 
0.0038 
0.0045 
0.0037 
0.0044 
0.0036 
0.0044 
0.0036 
0.0031 
0.0025 
0.0031 
0.0024 
0.0031 
0.0024 
0.0031 
0.0024 
0.0025 
0.0019 
0.0025 
0.0019 
0.0025 
0.0019 
0.0025 
0.0019 
0.0095 
0.0084 
0.0097 
0.0086 
0.0093 
0.0081 
0.0094 
0.0082 
0.0046 
0.0038 
0.0047 
0.0039 
0.0045 
0.0037 
0.0045 
0.0038 
0.0031 
0.0025 
0.0032 
0.0026 
0.0031 
0.0024 
0.0031 
0.0025 
0.0025 
0.0020 
0.0026 
0.0020 
0.0025 
0.0019 
0.0026 
0.0020 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0092 
0.0081 
0.0092 
0.0080 
0.0089 
0.0077 
0.0089 
0.0077 
0.0037 
0.0029 
0.0037 
0.0029 
0.0036 
0.0028 
0.0036 
0.0028 
0.0025 
0.0019 
0.0026 
0.0019 
0.0025 
0.0019 
0.0025 
0.0019 
0.0022 
0.0015 
0.0021 
0.0016 
0.0021 
0.0015 
0.0021 
0.0015 
0.0093 
0.0082 
0.0094 
0.0082 
0.0090 
0.0078 
0.0091 
0.0079 
0.0038 
0.0030 
0.0038 
0.0030 
0.0036 
0.0029 
0.0037 
0.0029 
0.0026 
0.0020 
0.0027 
0.0020 
0.0026 
0.0019 
0.0026 
0.0019 
0.0022 
0.0016 
0.0022 
0.0016 
0.0022 
0.0016 
0.0022 
0.0016 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 7. Direct Economic Losses 

?
Table 7.48. Average Annual Building Loss Normalized by Building Value for 
Building A - Twelve Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Four Foot Joist Spacing (concluded) 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0077 
0.0066 
0.0078 
0.0066 
0.0078 
0.0066 
0.0078 
0.0067 
0.0046 
0.0039 
0.0047 
0.0039 
0.0046 
0.0039 
0.0046 
0.0039 
0.0032 
0.0026 
0.0032 
0.0026 
0.0032 
0.0026 
0.0033 
0.0026 
0.0026 
0.0021 
0.0027 
0.0020 
0.0026 
0.0021 
0.0026 
0.0020 
0.0087 
0.0076 
0.0093 
0.0081 
0.0088 
0.0075 
0.0092 
0.0079 
0.0048 
0.0041 
0.0050 
0.0043 
0.0049 
0.0041 
0.0050 
0.0042 
0.0034 
0.0027 
0.0034 
0.0028 
0.0034 
0.0027 
0.0034 
0.0028 
0.0027 
0.0022 
0.0028 
0.0022 
0.0027 
0.0021 
0.0028 
0.0022 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0075 
0.0063 
0.0075 
0.0063 
0.0076 
0.0065 
0.0076 
0.0065 
0.0043 
0.0036 
0.0044 
0.0035 
0.0043 
0.0036 
0.0043 
0.0036 
0.0031 
0.0024 
0.0031 
0.0024 
0.0030 
0.0024 
0.0031 
0.0024 
0.0025 
0.0019 
0.0025 
0.0019 
0.0025 
0.0019 
0.0025 
0.0019 
0.0086 
0.0073 
0.0090 
0.0078 
0.0085 
0.0073 
0.0089 
0.0078 
0.0046 
0.0038 
0.0047 
0.0040 
0.0046 
0.0039 
0.0047 
0.0040 
0.0032 
0.0025 
0.0033 
0.0026 
0.0032 
0.0025 
0.0032 
0.0026 
0.0026 
0.0020 
0.0027 
0.0021 
0.0026 
0.0020 
0.0027 
0.0021 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0073 
0.0061 
0.0072 
0.0061 
0.0073 
0.0061 
0.0073 
0.0062 
0.0039 
0.0032 
0.0039 
0.0032 
0.0039 
0.0032 
0.0040 
0.0032 
0.0028 
0.0021 
0.0028 
0.0022 
0.0028 
0.0022 
0.0028 
0.0022 
0.0024 
0.0017 
0.0024 
0.0018 
0.0024 
0.0018 
0.0024 
0.0018 
0.0083 
0.0071 
0.0087 
0.0075 
0.0082 
0.0070 
0.0086 
0.0075 
0.0042 
0.0034 
0.0044 
0.0036 
0.0042 
0.0034 
0.0043 
0.0035 
0.0030 
0.0023 
0.0030 
0.0024 
0.0030 
0.0023 
0.0031 
0.0024 
0.0025 
0.0018 
0.0025 
0.0019 
0.0024 
0.0019 
0.0025 
0.0019 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0070 
0.0057 
0.0070 
0.0058 
0.0070 
0.0058 
0.0071 
0.0058 
0.0032 
0.0024 
0.0032 
0.0024 
0.0032 
0.0024 
0.0032 
0.0024 
0.0024 
0.0017 
0.0024 
0.0017 
0.0024 
0.0017 
0.0023 
0.0017 
0.0020 
0.0014 
0.0020 
0.0014 
0.0020 
0.0014 
0.0020 
0.0014 
0.0080 
0.0068 
0.0084 
0.0072 
0.0079 
0.0068 
0.0082 
0.0071 
0.0034 
0.0026 
0.0035 
0.0027 
0.0034 
0.0026 
0.0035 
0.0027 
0.0025 
0.0018 
0.0025 
0.0019 
0.0024 
0.0018 
0.0026 
0.0018 
0.0021 
0.0015 
0.0021 
0.0015 
0.0021 
0.0015 
0.0021 
0.0015 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 7.49. Average Annual Building Loss Normalized by Building Value for 
Building B - Twenty Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Four Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0192 
0.0173 
0.0193 
0.0173 
0.0188 
0.0168 
0.0188 
0.0168 
0.0113 
0.0099 
0.0114 
0.0100 
0.0111 
0.0097 
0.0112 
0.0097 
0.0087 
0.0073 
0.0086 
0.0073 
0.0084 
0.0071 
0.0085 
0.0071 
0.0073 
0.0061 
0.0074 
0.0061 
0.0072 
0.0060 
0.0072 
0.0060 
0.0192 
0.0173 
0.0195 
0.0175 
0.0189 
0.0169 
0.0190 
0.0170 
0.0113 
0.0099 
0.0115 
0.0102 
0.0112 
0.0097 
0.0114 
0.0099 
0.0087 
0.0074 
0.0088 
0.0076 
0.0085 
0.0072 
0.0087 
0.0073 
0.0074 
0.0062 
0.0075 
0.0063 
0.0073 
0.0060 
0.0074 
0.0062 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0188 
0.0166 
0.0187 
0.0166 
0.0182 
0.0162 
0.0181 
0.0162 
0.0108 
0.0094 
0.0109 
0.0095 
0.0107 
0.0092 
0.0107 
0.0093 
0.0084 
0.0069 
0.0082 
0.0069 
0.0081 
0.0067 
0.0082 
0.0067 
0.0070 
0.0058 
0.0071 
0.0058 
0.0069 
0.0057 
0.0069 
0.0057 
0.0187 
0.0166 
0.0188 
0.0170 
0.0183 
0.0163 
0.0185 
0.0165 
0.0110 
0.0095 
0.0112 
0.0096 
0.0108 
0.0093 
0.0109 
0.0093 
0.0083 
0.0070 
0.0084 
0.0070 
0.0082 
0.0068 
0.0083 
0.0069 
0.0072 
0.0059 
0.0071 
0.0059 
0.0070 
0.0057 
0.0071 
0.0058 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0182 
0.0161 
0.0182 
0.0162 
0.0178 
0.0157 
0.0177 
0.0158 
0.0105 
0.0089 
0.0105 
0.0089 
0.0103 
0.0088 
0.0103 
0.0087 
0.0081 
0.0066 
0.0081 
0.0066 
0.0079 
0.0064 
0.0079 
0.0065 
0.0068 
0.0055 
0.0069 
0.0055 
0.0067 
0.0054 
0.0067 
0.0054 
0.0183 
0.0163 
0.0183 
0.0164 
0.0177 
0.0159 
0.0179 
0.0159 
0.0106 
0.0090 
0.0107 
0.0091 
0.0104 
0.0088 
0.0104 
0.0088 
0.0080 
0.0066 
0.0081 
0.0068 
0.0079 
0.0065 
0.0079 
0.0066 
0.0069 
0.0056 
0.0070 
0.0057 
0.0068 
0.0055 
0.0068 
0.0055 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0180 
0.0159 
0.0179 
0.0158 
0.0175 
0.0154 
0.0175 
0.0155 
0.0096 
0.0080 
0.0096 
0.0080 
0.0094 
0.0078 
0.0094 
0.0078 
0.0073 
0.0060 
0.0073 
0.0059 
0.0072 
0.0058 
0.0072 
0.0058 
0.0063 
0.0050 
0.0063 
0.0050 
0.0063 
0.0049 
0.0062 
0.0049 
0.0180 
0.0160 
0.0181 
0.0160 
0.0175 
0.0154 
0.0177 
0.0156 
0.0097 
0.0081 
0.0097 
0.0082 
0.0094 
0.0078 
0.0095 
0.0080 
0.0074 
0.0060 
0.0075 
0.0061 
0.0073 
0.0058 
0.0074 
0.0059 
0.0064 
0.0051 
0.0064 
0.0052 
0.0063 
0.0049 
0.0063 
0.0050 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 7. Direct Economic Losses 

?
Table 7.49. Average Annual Building Loss Normalized by Building Value for 
Building B - Twenty Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Four Foot Joist Spacing (concluded) 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0131 
0.0108 
0.0132 
0.0110 
0.0132 
0.0111 
0.0133 
0.0111 
0.0087 
0.0071 
0.0087 
0.0071 
0.0087 
0.0071 
0.0087 
0.0071 
0.0067 
0.0053 
0.0067 
0.0053 
0.0067 
0.0054 
0.0068 
0.0054 
0.0059 
0.0045 
0.0058 
0.0045 
0.0059 
0.0045 
0.0058 
0.0045 
0.0152 
0.0129 
0.0158 
0.0136 
0.0150 
0.0129 
0.0157 
0.0135 
0.0094 
0.0079 
0.0098 
0.0082 
0.0094 
0.0079 
0.0097 
0.0082 
0.0073 
0.0058 
0.0075 
0.0062 
0.0072 
0.0058 
0.0075 
0.0060 
0.0063 
0.0049 
0.0064 
0.0052 
0.0063 
0.0049 
0.0064 
0.0051 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0130 
0.0107 
0.0131 
0.0108 
0.0131 
0.0109 
0.0132 
0.0110 
0.0084 
0.0068 
0.0084 
0.0068 
0.0084 
0.0068 
0.0084 
0.0068 
0.0066 
0.0051 
0.0066 
0.0051 
0.0066 
0.0052 
0.0066 
0.0052 
0.0057 
0.0043 
0.0058 
0.0044 
0.0057 
0.0045 
0.0057 
0.0044 
0.0150 
0.0128 
0.0156 
0.0134 
0.0150 
0.0128 
0.0156 
0.0133 
0.0092 
0.0076 
0.0095 
0.0079 
0.0092 
0.0076 
0.0094 
0.0078 
0.0071 
0.0057 
0.0073 
0.0059 
0.0071 
0.0057 
0.0073 
0.0058 
0.0061 
0.0048 
0.0063 
0.0050 
0.0061 
0.0048 
0.0063 
0.0049 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0127 
0.0105 
0.0128 
0.0106 
0.0129 
0.0107 
0.0130 
0.0107 
0.0081 
0.0064 
0.0081 
0.0065 
0.0081 
0.0064 
0.0081 
0.0064 
0.0064 
0.0048 
0.0064 
0.0049 
0.0063 
0.0049 
0.0064 
0.0049 
0.0056 
0.0042 
0.0056 
0.0043 
0.0056 
0.0042 
0.0056 
0.0042 
0.0148 
0.0126 
0.0154 
0.0132 
0.0147 
0.0125 
0.0153 
0.0131 
0.0089 
0.0072 
0.0092 
0.0076 
0.0089 
0.0072 
0.0092 
0.0075 
0.0069 
0.0054 
0.0071 
0.0057 
0.0068 
0.0054 
0.0071 
0.0056 
0.0060 
0.0046 
0.0062 
0.0048 
0.0059 
0.0046 
0.0061 
0.0047 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0125 
0.0102 
0.0126 
0.0103 
0.0126 
0.0104 
0.0127 
0.0104 
0.0073 
0.0056 
0.0074 
0.0057 
0.0074 
0.0057 
0.0074 
0.0057 
0.0059 
0.0044 
0.0059 
0.0044 
0.0060 
0.0044 
0.0060 
0.0044 
0.0053 
0.0039 
0.0052 
0.0039 
0.0052 
0.0039 
0.0053 
0.0039 
0.0145 
0.0122 
0.0150 
0.0130 
0.0145 
0.0122 
0.0150 
0.0127 
0.0082 
0.0065 
0.0084 
0.0068 
0.0081 
0.0064 
0.0083 
0.0066 
0.0064 
0.0049 
0.0066 
0.0051 
0.0063 
0.0049 
0.0065 
0.0050 
0.0056 
0.0042 
0.0057 
0.0044 
0.0056 
0.0042 
0.0057 
0.0044 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 7.50. Average Annual Building Loss Normalized by Building Value for 
Building C - Twenty Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Six Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck Capacity 50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0199 
0.0179 
0.0200 
0.0181 
0.0196 
0.0176 
0.0197 
0.0176 
0.0116 
0.0102 
0.0118 
0.0104 
0.0116 
0.0101 
0.0117 
0.0101 
0.0088 
0.0075 
0.0089 
0.0076 
0.0088 
0.0074 
0.0088 
0.0074 
0.0075 
0.0063 
0.0076 
0.0064 
0.0075 
0.0063 
0.0074 
0.0062 
0.0242 
0.0221 
0.0265 
0.0245 
0.0228 
0.0209 
0.0240 
0.0219 
0.0150 
0.0135 
0.0169 
0.0154 
0.0139 
0.0124 
0.0146 
0.0132 
0.0116 
0.0103 
0.0133 
0.0119 
0.0108 
0.0094 
0.0114 
0.0100 
0.0100 
0.0088 
0.0114 
0.0102 
0.0093 
0.0079 
0.0098 
0.0085 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0193 
0.0174 
0.0193 
0.0174 
0.0190 
0.0170 
0.0190 
0.0170 
0.0112 
0.0097 
0.0112 
0.0098 
0.0111 
0.0097 
0.0111 
0.0095 
0.0085 
0.0071 
0.0086 
0.0072 
0.0084 
0.0070 
0.0084 
0.0070 
0.0072 
0.0059 
0.0073 
0.0060 
0.0072 
0.0059 
0.0071 
0.0059 
0.0229 
0.0208 
0.0249 
0.0230 
0.0219 
0.0197 
0.0227 
0.0205 
0.0141 
0.0126 
0.0156 
0.0142 
0.0131 
0.0115 
0.0138 
0.0121 
0.0110 
0.0096 
0.0124 
0.0110 
0.0101 
0.0087 
0.0105 
0.0091 
0.0094 
0.0082 
0.0107 
0.0094 
0.0087 
0.0074 
0.0091 
0.0077 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0188 
0.0168 
0.0189 
0.0169 
0.0186 
0.0165 
0.0186 
0.0166 
0.0108 
0.0093 
0.0109 
0.0093 
0.0106 
0.0091 
0.0106 
0.0090 
0.0082 
0.0068 
0.0082 
0.0069 
0.0081 
0.0067 
0.0081 
0.0068 
0.0070 
0.0057 
0.0071 
0.0058 
0.0069 
0.0057 
0.0069 
0.0056 
0.0219 
0.0199 
0.0237 
0.0218 
0.0209 
0.0188 
0.0215 
0.0195 
0.0133 
0.0118 
0.0148 
0.0132 
0.0125 
0.0110 
0.0130 
0.0114 
0.0103 
0.0091 
0.0116 
0.0103 
0.0097 
0.0083 
0.0101 
0.0087 
0.0091 
0.0077 
0.0101 
0.0089 
0.0083 
0.0070 
0.0086 
0.0073 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0185 
0.0165 
0.0185 
0.0164 
0.0182 
0.0162 
0.0183 
0.0163 
0.0098 
0.0082 
0.0099 
0.0083 
0.0097 
0.0081 
0.0097 
0.0082 
0.0075 
0.0061 
0.0075 
0.0061 
0.0074 
0.0060 
0.0074 
0.0060 
0.0064 
0.0051 
0.0065 
0.0052 
0.0064 
0.0051 
0.0063 
0.0051 
0.0214 
0.0196 
0.0231 
0.0212 
0.0204 
0.0184 
0.0210 
0.0190 
0.0120 
0.0104 
0.0133 
0.0118 
0.0112 
0.0095 
0.0115 
0.0099 
0.0095 
0.0080 
0.0106 
0.0093 
0.0086 
0.0072 
0.0089 
0.0075 
0.0083 
0.0070 
0.0094 
0.0082 
0.0075 
0.0062 
0.0078 
0.0064 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 7. Direct Economic Losses 

?
Table 7.50. Average Annual Building Loss Normalized by Building Value for 
Building C - Twenty Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Six Foot Joist Spacing (concluded) 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0148 
0.0125 
0.0156 
0.0136 
0.0145 
0.0122 
0.0148 
0.0127 
0.0092 
0.0077 
0.0097 
0.0081 
0.0090 
0.0075 
0.0092 
0.0076 
0.0070 
0.0057 
0.0074 
0.0061 
0.0070 
0.0055 
0.0071 
0.0057 
0.0061 
0.0048 
0.0065 
0.0051 
0.0060 
0.0047 
0.0060 
0.0048 
0.0221 
0.0200 
0.0245 
0.0224 
0.0208 
0.0185 
0.0219 
0.0197 
0.0141 
0.0125 
0.0162 
0.0145 
0.0128 
0.0112 
0.0137 
0.0121 
0.0110 
0.0096 
0.0126 
0.0113 
0.0100 
0.0085 
0.0107 
0.0093 
0.0095 
0.0082 
0.0111 
0.0097 
0.0086 
0.0073 
0.0092 
0.0079 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0146 
0.0124 
0.0155 
0.0133 
0.0143 
0.0121 
0.0147 
0.0125 
0.0091 
0.0074 
0.0095 
0.0079 
0.0088 
0.0072 
0.0090 
0.0074 
0.0069 
0.0055 
0.0072 
0.0059 
0.0068 
0.0054 
0.0070 
0.0055 
0.0060 
0.0047 
0.0063 
0.0049 
0.0059 
0.0046 
0.0060 
0.0046 
0.0209 
0.0188 
0.0230 
0.0212 
0.0198 
0.0174 
0.0207 
0.0185 
0.0133 
0.0116 
0.0149 
0.0134 
0.0122 
0.0106 
0.0128 
0.0112 
0.0104 
0.0089 
0.0118 
0.0104 
0.0094 
0.0081 
0.0100 
0.0085 
0.0090 
0.0076 
0.0102 
0.0091 
0.0081 
0.0069 
0.0086 
0.0072 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0144 
0.0122 
0.0152 
0.0129 
0.0139 
0.0118 
0.0144 
0.0123 
0.0087 
0.0070 
0.0092 
0.0075 
0.0085 
0.0068 
0.0087 
0.0070 
0.0068 
0.0053 
0.0071 
0.0056 
0.0066 
0.0051 
0.0067 
0.0052 
0.0058 
0.0045 
0.0061 
0.0048 
0.0057 
0.0044 
0.0058 
0.0044 
0.0200 
0.0181 
0.0219 
0.0199 
0.0190 
0.0168 
0.0196 
0.0175 
0.0127 
0.0111 
0.0142 
0.0127 
0.0117 
0.0100 
0.0122 
0.0106 
0.0098 
0.0084 
0.0112 
0.0099 
0.0091 
0.0076 
0.0095 
0.0080 
0.0086 
0.0072 
0.0098 
0.0084 
0.0078 
0.0065 
0.0081 
0.0068 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0142 
0.0119 
0.0150 
0.0128 
0.0137 
0.0116 
0.0143 
0.0119 
0.0079 
0.0062 
0.0083 
0.0067 
0.0077 
0.0061 
0.0079 
0.0062 
0.0063 
0.0048 
0.0066 
0.0050 
0.0061 
0.0046 
0.0062 
0.0047 
0.0055 
0.0041 
0.0057 
0.0043 
0.0054 
0.0040 
0.0054 
0.0041 
0.0195 
0.0172 
0.0212 
0.0192 
0.0184 
0.0162 
0.0190 
0.0169 
0.0111 
0.0095 
0.0125 
0.0109 
0.0102 
0.0086 
0.0106 
0.0089 
0.0089 
0.0075 
0.0102 
0.0087 
0.0080 
0.0066 
0.0083 
0.0069 
0.0078 
0.0065 
0.0089 
0.0077 
0.0070 
0.0057 
0.0073 
0.0059 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 7.51. Average Annual Building Loss Normalized by Building Value for 
Building D - Twenty Foot High Strip Mall Building with One Unit and Metal 
Deck 
with Six Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0211 
0.0192 
0.0211 
0.0193 
0.0210 
0.0192 
0.0209 
0.0191 
0.0121 
0.0106 
0.0122 
0.0107 
0.0120 
0.0107 
0.0120 
0.0106 
0.0092 
0.0079 
0.0092 
0.0079 
0.0091 
0.0078 
0.0091 
0.0079 
0.0077 
0.0066 
0.0078 
0.0066 
0.0078 
0.0065 
0.0077 
0.0065 
0.0255 
0.0235 
0.0281 
0.0262 
0.0239 
0.0221 
0.0252 
0.0231 
0.0153 
0.0136 
0.0172 
0.0157 
0.0140 
0.0126 
0.0147 
0.0132 
0.0118 
0.0104 
0.0135 
0.0121 
0.0109 
0.0095 
0.0113 
0.0100 
0.0102 
0.0089 
0.0118 
0.0105 
0.0093 
0.0081 
0.0098 
0.0086 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0205 
0.0187 
0.0206 
0.0187 
0.0204 
0.0186 
0.0203 
0.0187 
0.0118 
0.0103 
0.0119 
0.0104 
0.0117 
0.0103 
0.0118 
0.0103 
0.0089 
0.0076 
0.0090 
0.0076 
0.0089 
0.0076 
0.0089 
0.0076 
0.0076 
0.0063 
0.0077 
0.0064 
0.0075 
0.0063 
0.0075 
0.0063 
0.0245 
0.0226 
0.0268 
0.0249 
0.0232 
0.0213 
0.0242 
0.0222 
0.0148 
0.0133 
0.0164 
0.0148 
0.0135 
0.0121 
0.0142 
0.0128 
0.0114 
0.0100 
0.0129 
0.0116 
0.0105 
0.0092 
0.0110 
0.0095 
0.0098 
0.0086 
0.0111 
0.0099 
0.0090 
0.0078 
0.0094 
0.0081 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0201 
0.0182 
0.0202 
0.0182 
0.0199 
0.0182 
0.0200 
0.0182 
0.0114 
0.0098 
0.0115 
0.0100 
0.0113 
0.0098 
0.0114 
0.0098 
0.0087 
0.0072 
0.0087 
0.0074 
0.0086 
0.0073 
0.0086 
0.0072 
0.0073 
0.0061 
0.0074 
0.0061 
0.0074 
0.0060 
0.0073 
0.0061 
0.0239 
0.0218 
0.0255 
0.0238 
0.0226 
0.0206 
0.0233 
0.0214 
0.0142 
0.0127 
0.0159 
0.0143 
0.0132 
0.0116 
0.0138 
0.0122 
0.0110 
0.0097 
0.0123 
0.0110 
0.0102 
0.0088 
0.0106 
0.0092 
0.0095 
0.0083 
0.0108 
0.0096 
0.0087 
0.0074 
0.0091 
0.0079 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0198 
0.0178 
0.0198 
0.0180 
0.0197 
0.0178 
0.0196 
0.0178 
0.0105 
0.0090 
0.0106 
0.0091 
0.0104 
0.0088 
0.0105 
0.0088 
0.0080 
0.0067 
0.0081 
0.0067 
0.0081 
0.0067 
0.0080 
0.0066 
0.0070 
0.0057 
0.0070 
0.0057 
0.0069 
0.0056 
0.0069 
0.0056 
0.0231 
0.0212 
0.0248 
0.0229 
0.0221 
0.0201 
0.0226 
0.0207 
0.0127 
0.0112 
0.0139 
0.0124 
0.0118 
0.0103 
0.0121 
0.0107 
0.0099 
0.0085 
0.0111 
0.0098 
0.0092 
0.0078 
0.0094 
0.0081 
0.0086 
0.0074 
0.0098 
0.0086 
0.0079 
0.0067 
0.0081 
0.0068 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 7. Direct Economic Losses 

?
Table 7.51. Average Annual Building Loss Normalized by Building Value for 
Building D - Twenty Foot High Strip Mall Building with One Unit and Metal 
Deck 
with Six Foot Joist Spacing (concluded) 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0160 
0.0139 
0.0165 
0.0144 
0.0152 
0.0131 
0.0155 
0.0134 
0.0100 
0.0084 
0.0103 
0.0087 
0.0097 
0.0081 
0.0098 
0.0082 
0.0076 
0.0061 
0.0078 
0.0064 
0.0073 
0.0059 
0.0074 
0.0061 
0.0065 
0.0051 
0.0066 
0.0053 
0.0063 
0.0049 
0.0063 
0.0050 
0.0234 
0.0212 
0.0264 
0.0242 
0.0216 
0.0194 
0.0231 
0.0208 
0.0145 
0.0127 
0.0166 
0.0150 
0.0131 
0.0116 
0.0139 
0.0122 
0.0112 
0.0099 
0.0131 
0.0117 
0.0102 
0.0087 
0.0106 
0.0093 
0.0098 
0.0084 
0.0114 
0.0100 
0.0087 
0.0075 
0.0093 
0.0079 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0157 
0.0136 
0.0164 
0.0142 
0.0151 
0.0128 
0.0153 
0.0131 
0.0097 
0.0081 
0.0100 
0.0084 
0.0094 
0.0078 
0.0095 
0.0079 
0.0074 
0.0060 
0.0077 
0.0062 
0.0072 
0.0057 
0.0071 
0.0058 
0.0063 
0.0049 
0.0065 
0.0052 
0.0062 
0.0048 
0.0061 
0.0049 
0.0225 
0.0203 
0.0249 
0.0228 
0.0209 
0.0189 
0.0220 
0.0198 
0.0139 
0.0123 
0.0157 
0.0141 
0.0128 
0.0111 
0.0134 
0.0118 
0.0108 
0.0095 
0.0124 
0.0110 
0.0098 
0.0084 
0.0104 
0.0090 
0.0094 
0.0081 
0.0109 
0.0095 
0.0085 
0.0071 
0.0090 
0.0076 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0155 
0.0133 
0.0161 
0.0139 
0.0148 
0.0126 
0.0151 
0.0128 
0.0092 
0.0075 
0.0096 
0.0080 
0.0089 
0.0072 
0.0090 
0.0074 
0.0071 
0.0056 
0.0074 
0.0059 
0.0068 
0.0054 
0.0069 
0.0054 
0.0061 
0.0048 
0.0063 
0.0050 
0.0060 
0.0045 
0.0060 
0.0046 
0.0217 
0.0195 
0.0238 
0.0217 
0.0205 
0.0184 
0.0214 
0.0191 
0.0135 
0.0118 
0.0151 
0.0135 
0.0124 
0.0108 
0.0131 
0.0114 
0.0105 
0.0090 
0.0120 
0.0105 
0.0095 
0.0081 
0.0101 
0.0086 
0.0091 
0.0078 
0.0104 
0.0092 
0.0082 
0.0069 
0.0086 
0.0074 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0152 
0.0130 
0.0159 
0.0137 
0.0145 
0.0123 
0.0147 
0.0125 
0.0084 
0.0068 
0.0088 
0.0071 
0.0081 
0.0064 
0.0082 
0.0066 
0.0066 
0.0051 
0.0069 
0.0054 
0.0064 
0.0049 
0.0064 
0.0050 
0.0057 
0.0044 
0.0059 
0.0045 
0.0056 
0.0042 
0.0056 
0.0043 
0.0210 
0.0188 
0.0225 
0.0206 
0.0197 
0.0177 
0.0204 
0.0184 
0.0118 
0.0102 
0.0132 
0.0116 
0.0108 
0.0092 
0.0112 
0.0095 
0.0092 
0.0079 
0.0106 
0.0093 
0.0085 
0.0070 
0.0088 
0.0073 
0.0082 
0.0068 
0.0093 
0.0081 
0.0074 
0.0060 
0.0076 
0.0063 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 7.52. Average Annual Content Loss Normalized by Building Value for 
Building A - Twelve Foot High Strip Mall Building with Six Units and Wood 
Deck 
with Two Foot Truss Spacing 

Building Characteristics 
Toe-Nailed Roof-Wall Connection Strapped Roof-Wall Connection 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ. 
Wood 
Deck 
Nail Size 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
6d 
EPDM 
BUR 
0.0096 
0.0094 
0.0094 
0.0092 
0.0056 
0.0055 
0.0055 
0.0054 
0.0037 
0.0036 
0.0037 
0.0036 
0.0029 
0.0028 
0.0029 
0.0029 
0.0052 
0.0050 
0.0044 
0.0043 
0.0022 
0.0022 
0.0016 
0.0016 
0.0014 
0.0014 
0.0009 
0.0009 
0.0011 
0.0011 
0.0006 
0.0005 
8d 
EPDM 
BUR 
B 
6d 
EPDM 
BUR 
0.0075 
0.0074 
0.0074 
0.0073 
0.0043 
0.0043 
0.0042 
0.0042 
0.0028 
0.0028 
0.0028 
0.0027 
0.0022 
0.0022 
0.0022 
0.0021 
0.0047 
0.0046 
0.0041 
0.0040 
0.0021 
0.0020 
0.0015 
0.0014 
0.0013 
0.0012 
0.0008 
0.0008 
0.0010 
0.0009 
0.0006 
0.0005 
8d 
EPDM 
BUR 
C 
6d 
EPDM 
BUR 
0.0058 
0.0056 
0.0056 
0.0055 
0.0035 
0.0035 
0.0034 
0.0034 
0.0024 
0.0023 
0.0022 
0.0022 
0.0018 
0.0017 
0.0018 
0.0017 
0.0044 
0.0044 
0.0040 
0.0038 
0.0019 
0.0018 
0.0013 
0.0012 
0.0012 
0.0011 
0.0007 
0.0006 
0.0009 
0.0009 
0.0005 
0.0004 
8d 
EPDM 
BUR 
D 
6d 
EPDM 
BUR 
0.0050 
0.0049 
0.0049 
0.0048 
0.0019 
0.0019 
0.0018 
0.0017 
0.0013 
0.0012 
0.0012 
0.0011 
0.0011 
0.0010 
0.0010 
0.0009 
0.0042 
0.0042 
0.0038 
0.0036 
0.0014 
0.0013 
0.0010 
0.0009 
0.0009 
0.0008 
0.0005 
0.0004 
0.0007 
0.0006 
0.0004 
0.0003 
8d 
EPDM 
BUR 
Reinforced Masonry 
A 
6d 
EPDM 
BUR 
0.0091 
0.0091 
0.0091 
0.0090 
0.0055 
0.0053 
0.0053 
0.0053 
0.0036 
0.0036 
0.0035 
0.0036 
0.0028 
0.0028 
0.0028 
0.0027 
0.0050 
0.0048 
0.0041 
0.0040 
0.0022 
0.0021 
0.0015 
0.0014 
0.0014 
0.0013 
0.0008 
0.0008 
0.0011 
0.0010 
0.0006 
0.0005 
8d 
EPDM 
BUR 
B 
6d 
EPDM 
BUR 
0.0072 
0.0072 
0.0072 
0.0070 
0.0041 
0.0040 
0.0041 
0.0040 
0.0027 
0.0027 
0.0027 
0.0026 
0.0021 
0.0021 
0.0021 
0.0020 
0.0046 
0.0044 
0.0039 
0.0038 
0.0020 
0.0019 
0.0013 
0.0013 
0.0012 
0.0012 
0.0007 
0.0007 
0.0010 
0.0009 
0.0005 
0.0004 
8d 
EPDM 
BUR 
C 
6d 
EPDM 
BUR 
0.0054 
0.0054 
0.0054 
0.0052 
0.0034 
0.0034 
0.0033 
0.0032 
0.0022 
0.0022 
0.0022 
0.0021 
0.0017 
0.0017 
0.0017 
0.0016 
0.0043 
0.0041 
0.0037 
0.0035 
0.0018 
0.0017 
0.0011 
0.0011 
0.0012 
0.0010 
0.0006 
0.0005 
0.0009 
0.0008 
0.0004 
0.0004 
8d 
EPDM 
BUR 
D 
6d 
EPDM 
BUR 
0.0047 
0.0045 
0.0045 
0.0044 
0.0018 
0.0017 
0.0016 
0.0015 
0.0012 
0.0011 
0.0010 
0.0010 
0.0010 
0.0009 
0.0009 
0.0008 
0.0040 
0.0039 
0.0035 
0.0034 
0.0013 
0.0012 
0.0009 
0.0008 
0.0008 
0.0008 
0.0005 
0.0004 
0.0007 
0.0006 
0.0003 
0.0002 
8d 
EPDM 
BUR 

Chapter 7. Direct Economic Losses 

?
Table 7.53. Average Annual Content Loss Normalized by Building Value for 
Building B - Twenty Foot High Strip Mall Building with Six Units and Wood 
Deck 
with Two Foot Truss Spacing 

Building Characteristics 
Toe-Nailed Roof-Wall Connection Strapped Roof-Wall Connection 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ. 
Wood 
Deck 
Nail Size 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
6d 
EPDM 
BUR 
0.0128 
0.0126 
0.0128 
0.0125 
0.0093 
0.0091 
0.0093 
0.0091 
0.0068 
0.0068 
0.0069 
0.0066 
0.0057 
0.0056 
0.0056 
0.0056 
0.0094 
0.0090 
0.0088 
0.0084 
0.0053 
0.0051 
0.0047 
0.0045 
0.0039 
0.0037 
0.0033 
0.0032 
0.0033 
0.0031 
0.0027 
0.0025 
8d 
EPDM 
BUR 
B 
6d 
EPDM 
BUR 
0.0112 
0.0111 
0.0112 
0.0110 
0.0075 
0.0074 
0.0075 
0.0074 
0.0056 
0.0054 
0.0056 
0.0054 
0.0047 
0.0045 
0.0047 
0.0046 
0.0087 
0.0084 
0.0081 
0.0078 
0.0047 
0.0046 
0.0043 
0.0041 
0.0035 
0.0033 
0.0030 
0.0027 
0.0029 
0.0027 
0.0024 
0.0022 
8d 
EPDM 
BUR 
C 
6d 
EPDM 
BUR 
0.0098 
0.0095 
0.0097 
0.0094 
0.0061 
0.0059 
0.0060 
0.0058 
0.0046 
0.0044 
0.0045 
0.0044 
0.0039 
0.0037 
0.0039 
0.0037 
0.0082 
0.0079 
0.0078 
0.0075 
0.0044 
0.0042 
0.0039 
0.0037 
0.0032 
0.0031 
0.0027 
0.0025 
0.0027 
0.0025 
0.0022 
0.0020 
8d 
EPDM 
BUR 
D 
6d 
EPDM 
BUR 
0.0094 
0.0091 
0.0092 
0.0089 
0.0050 
0.0048 
0.0050 
0.0047 
0.0039 
0.0037 
0.0038 
0.0036 
0.0034 
0.0033 
0.0034 
0.0032 
0.0080 
0.0076 
0.0076 
0.0073 
0.0039 
0.0037 
0.0034 
0.0031 
0.0029 
0.0027 
0.0024 
0.0021 
0.0024 
0.0022 
0.0019 
0.0017 
8d 
EPDM 
BUR 
Reinforced Masonry 
A 
6d 
EPDM 
BUR 
0.0120 
0.0118 
0.0118 
0.0115 
0.0089 
0.0087 
0.0089 
0.0086 
0.0066 
0.0064 
0.0065 
0.0065 
0.0055 
0.0052 
0.0054 
0.0052 
0.0081 
0.0078 
0.0070 
0.0067 
0.0044 
0.0042 
0.0034 
0.0031 
0.0033 
0.0031 
0.0024 
0.0021 
0.0028 
0.0026 
0.0019 
0.0017 
8d 
EPDM 
BUR 
B 
6d 
EPDM 
BUR 
0.0103 
0.0102 
0.0103 
0.0101 
0.0072 
0.0070 
0.0071 
0.0069 
0.0053 
0.0052 
0.0053 
0.0051 
0.0044 
0.0043 
0.0045 
0.0042 
0.0074 
0.0072 
0.0067 
0.0063 
0.0040 
0.0038 
0.0032 
0.0029 
0.0030 
0.0028 
0.0022 
0.0019 
0.0025 
0.0023 
0.0017 
0.0015 
8d 
EPDM 
BUR 
C 
6d 
EPDM 
BUR 
0.0087 
0.0085 
0.0086 
0.0083 
0.0056 
0.0054 
0.0056 
0.0053 
0.0042 
0.0040 
0.0042 
0.0040 
0.0036 
0.0034 
0.0036 
0.0034 
0.0070 
0.0068 
0.0064 
0.0060 
0.0038 
0.0035 
0.0030 
0.0027 
0.0028 
0.0025 
0.0021 
0.0018 
0.0023 
0.0021 
0.0016 
0.0014 
8d 
EPDM 
BUR 
D 
6d 
EPDM 
BUR 
0.0082 
0.0079 
0.0081 
0.0078 
0.0045 
0.0043 
0.0044 
0.0042 
0.0035 
0.0033 
0.0035 
0.0032 
0.0031 
0.0029 
0.0030 
0.0028 
0.0069 
0.0066 
0.0062 
0.0059 
0.0032 
0.0030 
0.0025 
0.0023 
0.0024 
0.0022 
0.0017 
0.0014 
0.0020 
0.0018 
0.0013 
0.0011 
8d 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 7.54. Average Annual Content Loss Normalized by Building Value for 
Building A - Twelve Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Four Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0035 
0.0034 
0.0035 
0.0033 
0.0034 
0.0033 
0.0034 
0.0033 
0.0014 
0.0013 
0.0013 
0.0013 
0.0013 
0.0013 
0.0013 
0.0013 
0.0008 
0.0007 
0.0007 
0.0007 
0.0007 
0.0007 
0.0007 
0.0007 
0.0005 
0.0005 
0.0005 
0.0005 
0.0005 
0.0005 
0.0005 
0.0004 
0.0035 
0.0034 
0.0036 
0.0035 
0.0034 
0.0033 
0.0034 
0.0033 
0.0014 
0.0013 
0.0014 
0.0013 
0.0013 
0.0013 
0.0014 
0.0013 
0.0008 
0.0007 
0.0008 
0.0007 
0.0008 
0.0007 
0.0008 
0.0007 
0.0005 
0.0005 
0.0006 
0.0005 
0.0005 
0.0005 
0.0005 
0.0005 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0034 
0.0032 
0.0033 
0.0032 
0.0032 
0.0031 
0.0032 
0.0031 
0.0012 
0.0011 
0.0012 
0.0011 
0.0012 
0.0010 
0.0012 
0.0011 
0.0007 
0.0006 
0.0007 
0.0006 
0.0006 
0.0006 
0.0006 
0.0006 
0.0004 
0.0004 
0.0005 
0.0004 
0.0004 
0.0004 
0.0004 
0.0004 
0.0034 
0.0032 
0.0034 
0.0033 
0.0032 
0.0031 
0.0033 
0.0032 
0.0012 
0.0012 
0.0013 
0.0012 
0.0012 
0.0011 
0.0012 
0.0011 
0.0007 
0.0006 
0.0007 
0.0006 
0.0006 
0.0006 
0.0007 
0.0006 
0.0005 
0.0004 
0.0005 
0.0004 
0.0005 
0.0004 
0.0005 
0.0004 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0032 
0.0030 
0.0032 
0.0031 
0.0031 
0.0029 
0.0031 
0.0029 
0.0010 
0.0009 
0.0010 
0.0009 
0.0009 
0.0008 
0.0009 
0.0008 
0.0005 
0.0005 
0.0005 
0.0004 
0.0005 
0.0004 
0.0005 
0.0004 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
0.0032 
0.0031 
0.0033 
0.0032 
0.0031 
0.0029 
0.0032 
0.0030 
0.0010 
0.0009 
0.0010 
0.0009 
0.0010 
0.0009 
0.0010 
0.0009 
0.0006 
0.0005 
0.0006 
0.0005 
0.0005 
0.0004 
0.0006 
0.0005 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0031 
0.0030 
0.0031 
0.0030 
0.0030 
0.0028 
0.0030 
0.0029 
0.0008 
0.0008 
0.0009 
0.0007 
0.0008 
0.0007 
0.0008 
0.0007 
0.0005 
0.0004 
0.0005 
0.0004 
0.0004 
0.0004 
0.0004 
0.0004 
0.0003 
0.0002 
0.0003 
0.0003 
0.0003 
0.0002 
0.0003 
0.0002 
0.0031 
0.0030 
0.0032 
0.0030 
0.0030 
0.0029 
0.0031 
0.0030 
0.0009 
0.0008 
0.0009 
0.0008 
0.0008 
0.0007 
0.0008 
0.0007 
0.0005 
0.0004 
0.0005 
0.0004 
0.0005 
0.0004 
0.0005 
0.0004 
0.0003 
0.0003 
0.0004 
0.0003 
0.0003 
0.0003 
0.0004 
0.0003 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 7. Direct Economic Losses 

?
Table 7.54. Average Annual Content Loss Normalized by Building Value for 
Building A - Twelve Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Four Foot Joist Spacing (concluded) 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0026 
0.0025 
0.0026 
0.0025 
0.0026 
0.0025 
0.0027 
0.0025 
0.0011 
0.0010 
0.0011 
0.0010 
0.0011 
0.0010 
0.0011 
0.0010 
0.0006 
0.0005 
0.0006 
0.0005 
0.0006 
0.0005 
0.0006 
0.0005 
0.0004 
0.0004 
0.0004 
0.0004 
0.0004 
0.0004 
0.0004 
0.0004 
0.0028 
0.0027 
0.0030 
0.0029 
0.0028 
0.0027 
0.0030 
0.0028 
0.0011 
0.0011 
0.0012 
0.0011 
0.0012 
0.0011 
0.0012 
0.0011 
0.0006 
0.0006 
0.0007 
0.0006 
0.0006 
0.0006 
0.0007 
0.0006 
0.0004 
0.0004 
0.0005 
0.0004 
0.0005 
0.0004 
0.0005 
0.0004 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0025 
0.0024 
0.0025 
0.0024 
0.0025 
0.0024 
0.0025 
0.0024 
0.0009 
0.0008 
0.0009 
0.0008 
0.0009 
0.0008 
0.0009 
0.0008 
0.0005 
0.0004 
0.0005 
0.0004 
0.0005 
0.0004 
0.0005 
0.0004 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
0.0027 
0.0025 
0.0029 
0.0027 
0.0027 
0.0026 
0.0028 
0.0027 
0.0010 
0.0009 
0.0010 
0.0010 
0.0010 
0.0009 
0.0010 
0.0010 
0.0005 
0.0005 
0.0006 
0.0005 
0.0005 
0.0005 
0.0006 
0.0005 
0.0004 
0.0003 
0.0004 
0.0004 
0.0004 
0.0003 
0.0004 
0.0003 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0024 
0.0022 
0.0023 
0.0022 
0.0024 
0.0022 
0.0023 
0.0022 
0.0007 
0.0006 
0.0007 
0.0006 
0.0006 
0.0006 
0.0007 
0.0006 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
0.0025 
0.0024 
0.0027 
0.0026 
0.0025 
0.0024 
0.0027 
0.0026 
0.0008 
0.0007 
0.0008 
0.0007 
0.0007 
0.0007 
0.0008 
0.0007 
0.0004 
0.0004 
0.0005 
0.0004 
0.0004 
0.0003 
0.0005 
0.0004 
0.0003 
0.0002 
0.0004 
0.0003 
0.0003 
0.0002 
0.0003 
0.0003 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0023 
0.0021 
0.0023 
0.0021 
0.0023 
0.0021 
0.0023 
0.0021 
0.0006 
0.0005 
0.0006 
0.0005 
0.0006 
0.0005 
0.0006 
0.0005 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
0.0025 
0.0024 
0.0026 
0.0025 
0.0025 
0.0023 
0.0026 
0.0025 
0.0007 
0.0005 
0.0007 
0.0006 
0.0006 
0.0005 
0.0007 
0.0006 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
0.0004 
0.0003 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
0.0003 
0.0002 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 7.55. Average Annual Content Loss Normalized by Building Value for 
Building B - Twenty Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Four Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0078 
0.0074 
0.0078 
0.0074 
0.0077 
0.0074 
0.0077 
0.0074 
0.0039 
0.0038 
0.0040 
0.0038 
0.0040 
0.0037 
0.0040 
0.0038 
0.0028 
0.0025 
0.0028 
0.0026 
0.0028 
0.0025 
0.0028 
0.0025 
0.0022 
0.0020 
0.0023 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0077 
0.0074 
0.0077 
0.0075 
0.0077 
0.0074 
0.0078 
0.0075 
0.0040 
0.0038 
0.0041 
0.0038 
0.0040 
0.0037 
0.0040 
0.0037 
0.0028 
0.0026 
0.0028 
0.0026 
0.0028 
0.0025 
0.0028 
0.0026 
0.0023 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0074 
0.0070 
0.0074 
0.0070 
0.0074 
0.0070 
0.0073 
0.0070 
0.0036 
0.0034 
0.0036 
0.0034 
0.0036 
0.0034 
0.0036 
0.0034 
0.0025 
0.0023 
0.0025 
0.0023 
0.0025 
0.0023 
0.0025 
0.0023 
0.0020 
0.0018 
0.0020 
0.0018 
0.0020 
0.0018 
0.0020 
0.0018 
0.0074 
0.0070 
0.0073 
0.0070 
0.0073 
0.0070 
0.0073 
0.0070 
0.0036 
0.0034 
0.0037 
0.0034 
0.0036 
0.0034 
0.0036 
0.0033 
0.0025 
0.0023 
0.0025 
0.0024 
0.0026 
0.0023 
0.0025 
0.0023 
0.0020 
0.0018 
0.0020 
0.0018 
0.0020 
0.0018 
0.0020 
0.0018 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0072 
0.0069 
0.0072 
0.0069 
0.0072 
0.0068 
0.0072 
0.0069 
0.0032 
0.0030 
0.0032 
0.0030 
0.0032 
0.0030 
0.0032 
0.0029 
0.0022 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0018 
0.0016 
0.0018 
0.0016 
0.0018 
0.0016 
0.0018 
0.0016 
0.0072 
0.0069 
0.0072 
0.0069 
0.0072 
0.0068 
0.0073 
0.0069 
0.0033 
0.0030 
0.0033 
0.0030 
0.0032 
0.0029 
0.0033 
0.0030 
0.0023 
0.0020 
0.0023 
0.0020 
0.0022 
0.0020 
0.0023 
0.0020 
0.0018 
0.0016 
0.0018 
0.0016 
0.0018 
0.0016 
0.0018 
0.0016 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0078 
0.0074 
0.0078 
0.0074 
0.0077 
0.0074 
0.0077 
0.0074 
0.0039 
0.0038 
0.0040 
0.0038 
0.0040 
0.0037 
0.0040 
0.0038 
0.0028 
0.0025 
0.0028 
0.0026 
0.0028 
0.0025 
0.0028 
0.0025 
0.0022 
0.0020 
0.0023 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0077 
0.0074 
0.0077 
0.0075 
0.0077 
0.0074 
0.0078 
0.0075 
0.0040 
0.0038 
0.0041 
0.0038 
0.0040 
0.0037 
0.0040 
0.0037 
0.0028 
0.0026 
0.0028 
0.0026 
0.0028 
0.0025 
0.0028 
0.0026 
0.0023 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 7. Direct Economic Losses 

?
Table 7.55. Average Annual Content Loss Normalized by Building Value for 
Building B - Twenty Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Four Foot Joist Spacing (concluded) 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0045 
0.0042 
0.0045 
0.0041 
0.0045 
0.0042 
0.0045 
0.0042 
0.0022 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0022 
0.0019 
0.0014 
0.0012 
0.0014 
0.0012 
0.0014 
0.0012 
0.0014 
0.0012 
0.0011 
0.0009 
0.0011 
0.0009 
0.0011 
0.0009 
0.0011 
0.0009 
0.0049 
0.0046 
0.0051 
0.0048 
0.0049 
0.0045 
0.0051 
0.0048 
0.0024 
0.0022 
0.0026 
0.0023 
0.0024 
0.0022 
0.0025 
0.0023 
0.0016 
0.0014 
0.0017 
0.0015 
0.0016 
0.0014 
0.0017 
0.0015 
0.0013 
0.0011 
0.0014 
0.0012 
0.0013 
0.0011 
0.0014 
0.0012 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0044 
0.0040 
0.0044 
0.0041 
0.0044 
0.0041 
0.0044 
0.0041 
0.0020 
0.0018 
0.0020 
0.0018 
0.0020 
0.0018 
0.0020 
0.0018 
0.0013 
0.0011 
0.0013 
0.0011 
0.0013 
0.0011 
0.0013 
0.0011 
0.0010 
0.0008 
0.0011 
0.0008 
0.0010 
0.0009 
0.0011 
0.0008 
0.0048 
0.0044 
0.0050 
0.0047 
0.0048 
0.0044 
0.0050 
0.0046 
0.0022 
0.0020 
0.0024 
0.0022 
0.0022 
0.0020 
0.0024 
0.0021 
0.0015 
0.0013 
0.0016 
0.0014 
0.0015 
0.0013 
0.0016 
0.0014 
0.0012 
0.0010 
0.0013 
0.0011 
0.0012 
0.0010 
0.0013 
0.0011 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0042 
0.0039 
0.0042 
0.0039 
0.0043 
0.0039 
0.0043 
0.0039 
0.0018 
0.0015 
0.0018 
0.0015 
0.0018 
0.0015 
0.0018 
0.0015 
0.0012 
0.0009 
0.0012 
0.0010 
0.0012 
0.0009 
0.0012 
0.0009 
0.0010 
0.0007 
0.0010 
0.0007 
0.0010 
0.0007 
0.0010 
0.0007 
0.0047 
0.0043 
0.0049 
0.0046 
0.0046 
0.0043 
0.0049 
0.0045 
0.0020 
0.0018 
0.0022 
0.0019 
0.0020 
0.0018 
0.0022 
0.0019 
0.0014 
0.0011 
0.0015 
0.0013 
0.0014 
0.0011 
0.0015 
0.0013 
0.0011 
0.0009 
0.0012 
0.0010 
0.0011 
0.0009 
0.0012 
0.0010 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0042 
0.0038 
0.0042 
0.0039 
0.0042 
0.0039 
0.0042 
0.0039 
0.0017 
0.0014 
0.0017 
0.0014 
0.0017 
0.0014 
0.0017 
0.0014 
0.0011 
0.0009 
0.0012 
0.0009 
0.0012 
0.0009 
0.0012 
0.0009 
0.0009 
0.0007 
0.0009 
0.0007 
0.0009 
0.0007 
0.0009 
0.0007 
0.0046 
0.0042 
0.0048 
0.0045 
0.0046 
0.0042 
0.0048 
0.0044 
0.0019 
0.0017 
0.0021 
0.0018 
0.0019 
0.0017 
0.0021 
0.0018 
0.0013 
0.0011 
0.0014 
0.0012 
0.0013 
0.0011 
0.0014 
0.0012 
0.0011 
0.0008 
0.0011 
0.0009 
0.0011 
0.0008 
0.0011 
0.0009 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 7.56. Average Annual Content Loss Normalized by Building Value for 
Building C - Twenty Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Six Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0083 
0.0080 
0.0083 
0.0080 
0.0082 
0.0080 
0.0083 
0.0079 
0.0045 
0.0043 
0.0045 
0.0043 
0.0044 
0.0043 
0.0045 
0.0042 
0.0031 
0.0030 
0.0031 
0.0029 
0.0031 
0.0029 
0.0031 
0.0029 
0.0025 
0.0023 
0.0025 
0.0024 
0.0025 
0.0023 
0.0025 
0.0023 
0.0096 
0.0093 
0.0106 
0.0103 
0.0090 
0.0088 
0.0095 
0.0092 
0.0056 
0.0054 
0.0064 
0.0062 
0.0051 
0.0049 
0.0054 
0.0052 
0.0041 
0.0040 
0.0049 
0.0047 
0.0037 
0.0036 
0.0041 
0.0038 
0.0034 
0.0033 
0.0041 
0.0040 
0.0031 
0.0029 
0.0034 
0.0032 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0078 
0.0075 
0.0078 
0.0075 
0.0078 
0.0074 
0.0078 
0.0074 
0.0040 
0.0038 
0.0040 
0.0038 
0.0040 
0.0038 
0.0040 
0.0038 
0.0028 
0.0026 
0.0028 
0.0026 
0.0028 
0.0026 
0.0027 
0.0025 
0.0022 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0088 
0.0085 
0.0096 
0.0094 
0.0085 
0.0081 
0.0088 
0.0084 
0.0050 
0.0048 
0.0057 
0.0055 
0.0046 
0.0044 
0.0049 
0.0046 
0.0037 
0.0035 
0.0044 
0.0042 
0.0033 
0.0032 
0.0036 
0.0033 
0.0031 
0.0029 
0.0037 
0.0035 
0.0028 
0.0026 
0.0030 
0.0027 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0074 
0.0071 
0.0075 
0.0071 
0.0074 
0.0070 
0.0074 
0.0071 
0.0036 
0.0034 
0.0037 
0.0034 
0.0036 
0.0034 
0.0036 
0.0033 
0.0025 
0.0023 
0.0025 
0.0024 
0.0025 
0.0022 
0.0025 
0.0023 
0.0020 
0.0018 
0.0021 
0.0018 
0.0020 
0.0018 
0.0020 
0.0018 
0.0083 
0.0081 
0.0090 
0.0088 
0.0079 
0.0076 
0.0081 
0.0078 
0.0046 
0.0044 
0.0052 
0.0050 
0.0042 
0.0040 
0.0044 
0.0042 
0.0033 
0.0032 
0.0040 
0.0038 
0.0031 
0.0029 
0.0033 
0.0030 
0.0029 
0.0027 
0.0034 
0.0032 
0.0025 
0.0023 
0.0027 
0.0025 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0072 
0.0069 
0.0072 
0.0069 
0.0072 
0.0069 
0.0072 
0.0069 
0.0032 
0.0029 
0.0033 
0.0030 
0.0032 
0.0030 
0.0033 
0.0030 
0.0023 
0.0020 
0.0023 
0.0020 
0.0022 
0.0020 
0.0022 
0.0020 
0.0018 
0.0016 
0.0019 
0.0016 
0.0018 
0.0016 
0.0018 
0.0016 
0.0081 
0.0079 
0.0088 
0.0086 
0.0078 
0.0074 
0.0079 
0.0076 
0.0040 
0.0038 
0.0046 
0.0044 
0.0037 
0.0034 
0.0038 
0.0036 
0.0030 
0.0028 
0.0035 
0.0034 
0.0027 
0.0024 
0.0028 
0.0026 
0.0025 
0.0024 
0.0031 
0.0029 
0.0022 
0.0020 
0.0023 
0.0021 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 7. Direct Economic Losses 

?
Table 7.56. Average Annual Content Loss Normalized by Building Value for 
Building C - Twenty Foot High Strip Mall Building with Six Units and Metal 
Deck 
with Six Foot Joist Spacing (concluded) 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0048 
0.0044 
0.0051 
0.0048 
0.0047 
0.0043 
0.0048 
0.0045 
0.0023 
0.0021 
0.0025 
0.0023 
0.0023 
0.0021 
0.0024 
0.0021 
0.0015 
0.0013 
0.0017 
0.0015 
0.0015 
0.0013 
0.0015 
0.0013 
0.0012 
0.0010 
0.0013 
0.0011 
0.0012 
0.0010 
0.0012 
0.0010 
0.0083 
0.0080 
0.0097 
0.0093 
0.0076 
0.0072 
0.0083 
0.0079 
0.0048 
0.0045 
0.0059 
0.0056 
0.0041 
0.0039 
0.0047 
0.0045 
0.0035 
0.0034 
0.0045 
0.0043 
0.0031 
0.0028 
0.0035 
0.0033 
0.0030 
0.0028 
0.0039 
0.0037 
0.0025 
0.0024 
0.0030 
0.0027 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0047 
0.0043 
0.0049 
0.0046 
0.0046 
0.0043 
0.0047 
0.0044 
0.0022 
0.0019 
0.0024 
0.0021 
0.0021 
0.0019 
0.0022 
0.0019 
0.0014 
0.0012 
0.0016 
0.0014 
0.0014 
0.0012 
0.0015 
0.0012 
0.0012 
0.0009 
0.0013 
0.0011 
0.0011 
0.0009 
0.0011 
0.0009 
0.0077 
0.0074 
0.0088 
0.0086 
0.0071 
0.0067 
0.0076 
0.0073 
0.0043 
0.0041 
0.0052 
0.0051 
0.0038 
0.0036 
0.0042 
0.0040 
0.0032 
0.0030 
0.0040 
0.0038 
0.0027 
0.0026 
0.0031 
0.0028 
0.0027 
0.0025 
0.0034 
0.0033 
0.0023 
0.0021 
0.0026 
0.0024 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0045 
0.0042 
0.0048 
0.0044 
0.0044 
0.0040 
0.0046 
0.0042 
0.0020 
0.0017 
0.0021 
0.0019 
0.0019 
0.0016 
0.0020 
0.0017 
0.0013 
0.0011 
0.0015 
0.0012 
0.0013 
0.0010 
0.0013 
0.0011 
0.0011 
0.0008 
0.0012 
0.0010 
0.0010 
0.0008 
0.0011 
0.0008 
0.0072 
0.0070 
0.0082 
0.0079 
0.0067 
0.0063 
0.0071 
0.0067 
0.0040 
0.0038 
0.0048 
0.0046 
0.0036 
0.0032 
0.0039 
0.0036 
0.0029 
0.0027 
0.0036 
0.0035 
0.0026 
0.0023 
0.0029 
0.0026 
0.0025 
0.0023 
0.0031 
0.0030 
0.0021 
0.0019 
0.0023 
0.0021 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0045 
0.0041 
0.0048 
0.0044 
0.0044 
0.0040 
0.0046 
0.0042 
0.0018 
0.0016 
0.0020 
0.0018 
0.0018 
0.0015 
0.0019 
0.0016 
0.0013 
0.0010 
0.0014 
0.0011 
0.0012 
0.0010 
0.0013 
0.0010 
0.0010 
0.0008 
0.0011 
0.0009 
0.0010 
0.0007 
0.0010 
0.0008 
0.0070 
0.0067 
0.0079 
0.0076 
0.0065 
0.0061 
0.0068 
0.0065 
0.0034 
0.0032 
0.0041 
0.0040 
0.0030 
0.0028 
0.0032 
0.0030 
0.0026 
0.0024 
0.0032 
0.0030 
0.0022 
0.0019 
0.0024 
0.0021 
0.0022 
0.0020 
0.0028 
0.0026 
0.0018 
0.0016 
0.0020 
0.0018 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Table 7.57. Average Annual Content Loss Normalized by Building Value for 
Building D - Twenty Foot High Strip Mall Building with One Unit and Metal 
Deck 
with Six Foot Joist Spacing 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0101 
0.0099 
0.0101 
0.0099 
0.0101 
0.0099 
0.0101 
0.0098 
0.0053 
0.0050 
0.0053 
0.0051 
0.0052 
0.0051 
0.0052 
0.0050 
0.0036 
0.0035 
0.0036 
0.0035 
0.0037 
0.0035 
0.0036 
0.0035 
0.0029 
0.0027 
0.0029 
0.0027 
0.0029 
0.0028 
0.0029 
0.0027 
0.0110 
0.0107 
0.0119 
0.0116 
0.0105 
0.0103 
0.0109 
0.0106 
0.0059 
0.0057 
0.0067 
0.0066 
0.0056 
0.0054 
0.0058 
0.0056 
0.0043 
0.0042 
0.0051 
0.0049 
0.0040 
0.0038 
0.0042 
0.0040 
0.0036 
0.0034 
0.0043 
0.0042 
0.0033 
0.0031 
0.0035 
0.0033 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0097 
0.0094 
0.0098 
0.0094 
0.0097 
0.0094 
0.0097 
0.0095 
0.0049 
0.0047 
0.0049 
0.0048 
0.0049 
0.0047 
0.0049 
0.0047 
0.0034 
0.0032 
0.0034 
0.0032 
0.0034 
0.0032 
0.0034 
0.0032 
0.0027 
0.0025 
0.0027 
0.0025 
0.0027 
0.0025 
0.0027 
0.0025 
0.0103 
0.0101 
0.0112 
0.0110 
0.0100 
0.0098 
0.0103 
0.0100 
0.0056 
0.0054 
0.0063 
0.0061 
0.0052 
0.0050 
0.0054 
0.0053 
0.0040 
0.0039 
0.0047 
0.0046 
0.0037 
0.0036 
0.0039 
0.0037 
0.0033 
0.0032 
0.0039 
0.0038 
0.0031 
0.0029 
0.0032 
0.0031 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0094 
0.0091 
0.0094 
0.0091 
0.0093 
0.0091 
0.0094 
0.0091 
0.0046 
0.0043 
0.0046 
0.0043 
0.0045 
0.0043 
0.0045 
0.0043 
0.0032 
0.0029 
0.0031 
0.0030 
0.0031 
0.0030 
0.0031 
0.0030 
0.0025 
0.0023 
0.0025 
0.0023 
0.0025 
0.0023 
0.0025 
0.0023 
0.0099 
0.0097 
0.0105 
0.0104 
0.0097 
0.0094 
0.0098 
0.0096 
0.0052 
0.0050 
0.0060 
0.0057 
0.0049 
0.0047 
0.0051 
0.0049 
0.0038 
0.0037 
0.0044 
0.0043 
0.0035 
0.0033 
0.0037 
0.0035 
0.0032 
0.0030 
0.0037 
0.0037 
0.0029 
0.0027 
0.0031 
0.0029 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0092 
0.0088 
0.0092 
0.0089 
0.0092 
0.0089 
0.0092 
0.0089 
0.0042 
0.0040 
0.0042 
0.0040 
0.0041 
0.0039 
0.0042 
0.0039 
0.0029 
0.0027 
0.0029 
0.0027 
0.0029 
0.0027 
0.0029 
0.0027 
0.0023 
0.0021 
0.0023 
0.0021 
0.0023 
0.0021 
0.0023 
0.0021 
0.0096 
0.0094 
0.0102 
0.0100 
0.0094 
0.0091 
0.0095 
0.0092 
0.0046 
0.0045 
0.0052 
0.0050 
0.0044 
0.0042 
0.0045 
0.0043 
0.0033 
0.0032 
0.0039 
0.0038 
0.0032 
0.0030 
0.0032 
0.0031 
0.0028 
0.0026 
0.0033 
0.0032 
0.0025 
0.0024 
0.0026 
0.0025 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

Chapter 7. Direct Economic Losses 

?
Table 7.57. Average Annual Content Loss Normalized by Building Value for 
Building D - Twenty Foot High Strip Mall Building with One Unit and Metal 
Deck 
with Six Foot Joist Spacing (concluded) 

Building Characteristics 
No Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Deck Capacity 
Terrain Surface Roughness (m) 
Wall 
Type 
Missile 
Environ 
Design 
Code 
Metal 
Deck 
Attach. 
Roof 
Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Reinforced Masonry 
A 
SBCCI 
Screws 
EPDM 
BUR 
0.0055 
0.0053 
0.0056 
0.0054 
0.0053 
0.0051 
0.0054 
0.0051 
0.0028 
0.0026 
0.0029 
0.0027 
0.0027 
0.0026 
0.0028 
0.0026 
0.0018 
0.0016 
0.0019 
0.0017 
0.0018 
0.0016 
0.0018 
0.0016 
0.0014 
0.0012 
0.0015 
0.0013 
0.0013 
0.0011 
0.0014 
0.0012 
0.0089 
0.0086 
0.0107 
0.0103 
0.0080 
0.0077 
0.0090 
0.0086 
0.0048 
0.0046 
0.0061 
0.0059 
0.0042 
0.0041 
0.0048 
0.0045 
0.0036 
0.0035 
0.0046 
0.0045 
0.0031 
0.0029 
0.0034 
0.0033 
0.0030 
0.0028 
0.0040 
0.0038 
0.0025 
0.0024 
0.0029 
0.0027 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
B 
SBCCI 
Screws 
EPDM 
BUR 
0.0054 
0.0051 
0.0055 
0.0053 
0.0052 
0.0049 
0.0053 
0.0049 
0.0026 
0.0024 
0.0027 
0.0025 
0.0025 
0.0023 
0.0025 
0.0023 
0.0017 
0.0015 
0.0018 
0.0016 
0.0016 
0.0014 
0.0016 
0.0014 
0.0013 
0.0011 
0.0014 
0.0012 
0.0012 
0.0010 
0.0012 
0.0011 
0.0085 
0.0081 
0.0099 
0.0096 
0.0078 
0.0075 
0.0085 
0.0081 
0.0046 
0.0044 
0.0057 
0.0054 
0.0041 
0.0038 
0.0045 
0.0043 
0.0034 
0.0032 
0.0043 
0.0041 
0.0029 
0.0027 
0.0033 
0.0031 
0.0028 
0.0027 
0.0037 
0.0035 
0.0024 
0.0022 
0.0027 
0.0025 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
C 
SBCCI 
Screws 
EPDM 
BUR 
0.0052 
0.0049 
0.0053 
0.0050 
0.0050 
0.0047 
0.0051 
0.0048 
0.0022 
0.0020 
0.0023 
0.0021 
0.0021 
0.0019 
0.0022 
0.0019 
0.0015 
0.0012 
0.0016 
0.0014 
0.0014 
0.0012 
0.0014 
0.0012 
0.0012 
0.0010 
0.0012 
0.0010 
0.0011 
0.0009 
0.0011 
0.0009 
0.0081 
0.0078 
0.0093 
0.0090 
0.0076 
0.0072 
0.0082 
0.0078 
0.0043 
0.0042 
0.0053 
0.0051 
0.0038 
0.0036 
0.0044 
0.0041 
0.0032 
0.0030 
0.0041 
0.0039 
0.0028 
0.0026 
0.0032 
0.0029 
0.0027 
0.0025 
0.0034 
0.0033 
0.0023 
0.0021 
0.0026 
0.0024 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 
D 
SBCCI 
Screws 
EPDM 
BUR 
0.0051 
0.0049 
0.0054 
0.0051 
0.0050 
0.0047 
0.0050 
0.0047 
0.0021 
0.0019 
0.0023 
0.0020 
0.0020 
0.0017 
0.0020 
0.0018 
0.0014 
0.0012 
0.0015 
0.0013 
0.0013 
0.0011 
0.0014 
0.0011 
0.0011 
0.0009 
0.0012 
0.0010 
0.0011 
0.0008 
0.0011 
0.0009 
0.0079 
0.0076 
0.0088 
0.0086 
0.0073 
0.0070 
0.0078 
0.0075 
0.0038 
0.0036 
0.0045 
0.0044 
0.0033 
0.0031 
0.0036 
0.0034 
0.0027 
0.0026 
0.0034 
0.0034 
0.0024 
0.0022 
0.0026 
0.0024 
0.0023 
0.0021 
0.0029 
0.0028 
0.0019 
0.0017 
0.0021 
0.0019 
Welds 
EPDM 
BUR 
ASCE 
Screws 
EPDM 
BUR 
Welds 
EPDM 
BUR 

HAZUS-MH MR3 Technical Manual 

?
Tables 7.58 through 7.60 present a summary of the effects of various building 
parameters 
on average annual total loss. The configuration of model buildings (and 
consequently the 
roof height and the joist spacing) plays a significant role in determining 
the average 
annual losses. For example, the differences in average annual total loss 
between Building 
A (Height=12') and Building B (Height=20'), with all other parameters being 
equal, 
range from 30% to 277% for the wood roof sytem and from 65% to 266% for the 
steel 
roof system. Changing from a 4' joist spacing to a 6' joist spacing results 
in a 1% to 
109% increase in average annual total loss. The effect of 6 unit versus 1 
unit is a 1% 
decrease to an 17% increase in average annual total loss. Besides the 
building 
configuration, the missiles environment has the greatest impact on the 
average annual 
loss, increasing the average annual loss by up to 182% when the model 
buildings are 
situated in the commercial/residential missile environment (D) compared to 
the 
environment with no windborne debris (A). 

Table 7.58. Percent Increases in the Average Annual Total Loss Due to Changes 
in 
Building Parameters (Minimum/Average/Maximum) - Strip Mall Building with 
Wood Roof System 

Building Parameter 
Increase in Average Annual Building Loss 
Building A Building B 
8d to 6d Roof Deck Nails -1% / 17% / 64% -1% / 9% / 37% 
Strap to Toe-Nail Roof/Wall Connections 15% / 92% / 239% 17% / 57% / 141% 
Built-up to Single ply Membrane Roof Cover 1% / 11% / 39% 3% / 10% / 26% 
Reinforced to Unreinforced Masonry Walls -1% / 4% / 9% 3% / 12% / 27% 
Missile Environment D to C 4% / 42% / 92% 2% / 13% / 26% 
Missile Environment C to B 2% / 13% / 29% 2% / 11% / 21% 
Missile Environment B to A 2% / 15% / 28% 4% / 12% / 21% 

Table 7.59. Percent Increases in the Average Annual Total Loss Due to Changes 
in 
Building Parameters (Minimum/Average/Maximum) - Strip Mall Building with 
Steel Roof System 

Building Parameter 
Increase in Average Annual Building Loss 
Building A Building B Building C Building D 
0% to 50% Reduction in Metal Roof 
Deck Resistance -1% / 6% / 22% -1% / 7% / 23% 11% / 46% / 
115% 9% / 42% / 109% 
Screwed to Welded Metal Roof Deck -3% / 2% / 7% -2% / 1% / 7% -2% / 6% / 22% 
-2% / 6% / 23% 
ASCE to SBCCI Metal Roof Deck 
Design Criteria -3% / 1% / 6% -2% / 1% / 4% 0% / 9% / 34% -2% / 9% / 33% 
Built-up to Single ply Membrane Roof 
Cover 9% / 22% / 44% 9% / 20% / 35% 6% / 16% / 34% 5% / 14% / 31% 
Reinforced to Unreinforced Masonry 
Walls 2% / 14% / 41% 19% / 39% / 68% 4% / 24% / 55% 3% / 27% / 60% 
Missile Environment D to C 2% / 18% / 33% 1% / 8% / 13% 1% / 9% / 19% 1% / 9% 
/ 20% 
Missile Environment C to B 2% / 10% / 18% 1% / 5% / 9% 2% / 5% / 8% 2% / 5% / 
11% 
Missile Environment B to A 2% / 7% / 14% 1% / 5% / 10% 1% / 6% / 12% 1% / 4% 
/ 7% 

Chapter 7. Direct Economic Losses 

?
Table 7.60. Percent Increases in the Average Annual Total Loss Due to Changes 
in 
Building Configuration (Minimum/Average/Maximum)

Building Configuration Increase in Average Annual Building Loss 
Building A to B   Wood Roof System (12' to 20' Roof Height) 30% / 116% / 277% 
Building A to B   Steel Roof System (12' to 20' Roof Height) 65% / 145% / 
266% 
Building B to C   Steel Roof System (4' to 6' Joist Spacing) 1% / 25% / 109% 
Building C to D   Steel Roof System (6 Units to 1 Unit) -1% / 8% / 17% 

7.12 Loss Model Results for Pre-Engineered Metal Buildings 
Using the same hurricane wind speed and direction data (20,000 years of storm 
simulation) used in the development of the damage curves for the pre-
engineered metal 
buildings, average annual losses were estimated using the commercial loss 
model 
described in Section 7.9. The metal buildings are assumed to be used as 
warehouses. The 
assumed subassembly cost distributions are listed in Table 7.61. Even though 
other 
occupancy types are applicable and slightly different results may be 
obtained, a 
warehouse is believed to be an appropriate usage type for the model buildings 
used in the 
present simulation. 

Example building and content loss functions are given in Appendix L. The 
avarage 
annual building and content losses (both normalized by the total building 
value) for all 
metal buildings examined are presented in Tables 7.62 and 7.63, respectively. 
The 
average annual loss is obtained by summing all losses produced during the 
20,000-year 
hurricane simulation period and then dividing by 20,000 years. The values 
given in 
Tables 7.62 and 7.63 reflect the average annual losses for metal buildings 
located in the 
South Florida area. 

Table 7.64 presents a summary of the effects of the various building 
parameters on 
average annual building loss. The configuration of model buildings (and 
consequently the 
size of building and the percentage of wall area covered by fenestrations) 
plays a 
significant role in determining the average annual loss. For example, the 
decreases in 
average annual total loss between small metal buildings and medium-sized 
metal 
buildings (with all other parameters being equal) range from 39% to 56%. 
Changing from 
the medium-sized metal building to the large metal buildings results in a 4% 
to 43% 
increase in average annual loss. The reduction in the metal roof panel 
resistance (to 
account for aging and fatigue) also has substantial effects on the simulated 
losses, where 
an increase of up to 55% was observed. However, the differences in total loss 
using 
different design wind speeds (90 mph vs. 100 mph) are minimal. The 12% 
difference in 
simulated loss can be taken as randomness involved in the modeling process. 

7.13 Loss Model Results for Engineered Residential and Commercial Buildings 
Similar to the other model building classes, loss functions for engineered 
residential and 
commercial buildings have been developed using 20,000 years of hurricane 
simulations 
in the South Florida area. Model buildings with eight units per floor are 
assumed to be 

HAZUS-MH MR3 Technical Manual 

?
Table 7.61. Subassembly Cost Distributions for Pre-Engineered Metal Buildings 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
6.0% 
0.0% 
2.6% 
8.6% 
Substructures 
Slab on Grade 
Special Substructures 
16.6% 
0.0% 
16.6% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
0.0% 
0.0% 
3.0% 
9.2% 
0.9% 
13.1% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
10.8% 
0.0% 
1.5% 
0.0% 
12.4% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
5.1% 
3.3% 
0.8% 
9.2% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
1.1% 
0.3% 
0.4% 
2.9% 
0.8% 
2.8% 
8.3% 
Conveying 
Elevators 
Special Conveyors 
0.0% 
0.0% 
0.0% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
3.7% 
4.7% 
7.9% 
1.9% 
0.0% 
18.2% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
0.8% 
7.9% 
0.7% 
9.4% 
Special Construction 
Specialties (& Additives) 4.3% 4.3% 
Total 100% 100% 

Chapter 7. Direct Economic Losses 

?
Table 7.62. Average Annual Building Loss Normalized by Building Value for 
Pre-
Engineered Metal Buildings 

Building Characteristics 
No Reduction in Metal Panel Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Panel Capacity 
Terrain Surface Roughness (m) 
Plan Size Design Speed (mph) 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Small 90 0.0402 0.0201 0.0147 0.0125 0.0412 0.0209 0.0155 0.0130 
Med. 90 0.0187 0.0103 0.0072 0.0056 0.0222 0.0127 0.0093 0.0076 
Large 90 0.0200 0.0117 0.0088 0.0071 0.0254 0.0155 0.0125 0.0109 
Small 100 0.0402 0.0203 0.0149 0.0125 0.0406 0.0205 0.0155 0.0128 
Med. 100 0.0187 0.0103 0.0071 0.0056 0.0220 0.0126 0.0092 0.0076 
Large 100 0.0200 0.0117 0.0088 0.0072 0.0254 0.0158 0.0124 0.0108 

Table 7.63. Average Annual Content Loss Normalized by Building Value for Pre-
Engineered Metal Buildings 

Building Characteristics 
No Reduction in Metal Panel Capacity 
Terrain Surface Roughness (m) 
50% Reduction in Metal Panel Capacity 
Terrain Surface Roughness (m) 
Plan Size Design Speed (mph) 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Small 90 0.0162 0.0080 0.0059 0.0050 0.0166 0.0084 0.0062 0.0052 
Med. 90 0.0080 0.0041 0.0028 0.0022 0.0091 0.0050 0.0037 0.0030 
Large 90 0.0080 0.0045 0.0034 0.0027 0.0102 0.0062 0.0050 0.0044 
Small 100 0.0162 0.0081 0.0059 0.0050 0.0164 0.0082 0.0062 0.0051 
Med. 100 0.0080 0.0041 0.0028 0.0022 0.0091 0.0050 0.0036 0.0030 
Large 100 0.0080 0.0045 0.0034 0.0028 0.0103 0.0063 0.0050 0.0044 

Table 7.64. Percent Increases in the Average Annual Total Loss due to Changes 
in 
Building Parameters (Minimum/Average/Maximum) - Pre-Engineered Metal 
Buildings 

Parameter Change in Average Building Loss Ratio 
Small Building to Medium Building -56% / -47% / -39% 
Medium Building to Large Building 4% / 23% / 43% 
100 mph to 90 mph Roof Design Speed -2% / 0% / 2% 
0% to 50% Reduction in Roof Deck Resistance 1% / 24% / 55% 

used as apartments while model buildings with one unit per floor are assumed 
to be used 
as office buildings. Tables 7.65 through 7.70 show the assumed subassembly 
cost 
distributions for two-, five- and 8-story apartments and office buildings, 
respectively. The 
subassembly cost ratios listed in Tables 7.65 through 7.70 have already been 
adjusted for 
number of stories and glazing area according to the commercial loss model 
described in 
Section 7.9. The values included in Tables 7.65 through 7.70 correspond to 
the model 
buildings with 32% glazing coverage for two-story buildings and 36% glazing 
coverage 
for five- and eight-story buildings. Slightly different subassembly cost 
ratios can be 
expected for model buildings with higher or lower glazing coverage. 

HAZUS-MH MR3 Technical Manual 

?
Table 7.65. Subassembly Cost Distributions for Two-Story Apartments 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
5.1% 
0.0% 
0.8% 
5.9% 
Substructures 
Slab on Grade 
Special Substructures 
2.4% 
0.0% 2.4% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
0.9% 
0.0% 
7.1% 
1.7% 
1.3% 
11.0% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
8.3% 
0.0% 
0.3% 
3.9% 
12.4% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
2.0% 
1.0% 
0.0% 
3.0% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
3.7% 
5.7% 
2.2% 
5.3% 
3.6% 
1.6% 
22.2% 
Conveying 
Elevators 
Special Conveyors 
3.0% 
0.0% 3.0% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
12.9% 
2.4% 
6.6% 
8.5% 
0.0% 
30.4% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
1.1% 
5.9% 
0.9% 
7.9% 
Special Construction 
Specialties (& Additives) 1.7% 1.7% 
Total 100% 100% 

Chapter 7. Direct Economic Losses 

?
Table 7.66. Subassembly Cost Distributions for Five-Story Apartments 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
2.1% 
0.0% 
0.3% 
2.4% 
Substructures 
Slab on Grade 
Special Substructures 
1.0% 
0.0% 1.0% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
1.9% 
0.0% 
12.1% 
1.0% 
1.4% 
16.6% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
7.0% 
0.0% 
0.2% 
4.6% 
11.8% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
0.8% 
0.4% 
0.0% 
1.2% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
4.9% 
7.1% 
2.8% 
5.5% 
3.7% 
1.6% 
25.6% 
Conveying 
Elevators 
Special Conveyors 
5.2% 
0.0% 5.2% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
11.3% 
2.3% 
5.6% 
7.5% 
0.0% 
26.7% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
1.2% 
5.9% 
0.5% 
7.6% 
Special Construction 
Specialties (& Additives) 2.0% 2.0% 
Total 100% 100% 

HAZUS-MH MR3 Technical Manual 

?
Table 7.67. Subassembly Cost Distributions for Eight-Story Apartments 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
1.9% 
0.0% 
0.2% 
2.1% 
Substructures 
Slab on Grade 
Special Substructures 
0.5% 
0.0% 0.5% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
2.5% 
0.0% 
8.3% 
0.2% 
1.1% 
12.2% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
6.9% 
0.0% 
1.6% 
4.3% 
12.8% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
0.4% 
0.2% 
0.0% 
0.7% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
10.2% 
6.4% 
2.5% 
4.9% 
3.3% 
1.3% 
28.6% 
Conveying 
Elevators 
Special Conveyors 
5.2% 
0.0% 5.2% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
11.8% 
2.8% 
5.7% 
7.6% 
0.0% 
27.8% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
0.6% 
6.1% 
1.8% 
8.5% 
Special Construction 
Specialties (& Additives) 1.8% 1.8% 
Total 100% 100% 

Chapter 7. Direct Economic Losses 

?
Table 7.68. Subassembly Cost Distributions for Two-Story Office Buildings 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
3.3% 
0.0% 
1.0% 
4.3% 
Substructures 
Slab on Grade 
Special Substructures 
3.1% 
0.0% 3.1% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
0.9% 
0.0% 
7.3% 
1.7% 
0.9% 
10.7% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
8.8% 
0.0% 
0.4% 
3.4% 
12.6% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
2.1% 
1.3% 
0.0% 
3.3% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
2.5% 
4.5% 
1.3% 
8.1% 
5.3% 
1.4% 
23.0% 
Conveying 
Elevators 
Special Conveyors 
3.1% 
0.0% 3.1% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
2.9% 
0.4% 
0.0% 
22.1% 
0.0% 
25.4% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
1.6% 
12.6% 
0.3% 
14.6% 
Special Construction 
Specialties (& Additives) 0.0% 0.0% 
Total 100% 100% 

HAZUS-MH MR3 Technical Manual 

?
Table 7.69. Subassembly Cost Distributions for Five-Story Office Buildings 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
2.9% 
0.0% 
0.4% 
3.3% 
Substructures 
Slab on Grade 
Special Substructures 
1.1% 
0.0% 1.1% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
2.5% 
0.0% 
11.2% 
0.6% 
1.1% 
15.5% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
8.4% 
0.0% 
0.2% 
6.2% 
14.8% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
0.8% 
0.5% 
0.0% 
1.3% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
2.2% 
2.1% 
1.0% 
7.5% 
4.9% 
1.6% 
19.2% 
Conveying 
Elevators 
Special Conveyors 
7.6% 
0.0% 7.6% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
2.0% 
0.2% 
0.0% 
20.8% 
0.0% 
23.0% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
1.3% 
11.8% 
1.2% 
14.2% 
Special Construction 
Specialties (& Additives) 0.0% 0.0% 
Total 100% 100% 

Chapter 7. Direct Economic Losses 

?
Table 7.70. Subassembly Cost Distributions for Eight-Story Office Buildings 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
1.6% 
0.0% 
0.2% 
1.8% 
Substructures 
Slab on Grade 
Special Substructures 
0.6% 
0.0% 0.6% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
3.1% 
0.0% 
14.0% 
0.8% 
1.4% 
19.3% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
7.6% 
0.0% 
0.2% 
5.8% 
13.5% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
0.5% 
0.3% 
0.0% 
0.7% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
2.3% 
2.2% 
1.1% 
7.8% 
5.1% 
1.6% 
20.1% 
Conveying 
Elevators 
Special Conveyors 
9.4% 
0.0% 9.4% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
1.8% 
0.2% 
0.0% 
18.7% 
0.0% 
20.7% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
1.3% 
11.4% 
1.1% 
13.8% 
Special Construction 
Specialties (& Additives) 0.0% 0.0% 
Total 100% 100% 

Example building and content loss functions are given in Appendix M. Note 
that the loss 
functions cross over between open terrain (z0=0.03 m) and suburban terrain 
(z0=0.35 m) 
for a number of cases. As explained in Section 6.12, wind-driven missiles are 
the primary 
cause of damage for engineered residential/commercial buildings. In an open 
terrain, 
virtually no missiles can be produced from the surrounding area of a 
building. On the 
other hand, in a suburban terrain, once the wind speed reaches a certain 
threshold, 

HAZUS-MH MR3 Technical Manual 

?
significant amounts of wind-driven missiles can be produced as a result of 
damage 
sustained by surrounding buildings. Therefore, higher damages, and 
accordingly losses, 
can be expected for a building located in a suburban terrain compared to the 
same 
building situated in an open terrain (even though the local wind speed is 
higher in open 
terrain than in suburban terrain). 

The average annual building losses (normalized by the total building value) 
for the two-, 
five- and eight-story engineered residential and commercial buildings are 
presented in 
Tables 7.71 through 7.73, respectively. The corresponding average annual 
content losses 
(also normalized by the total building value) are given in Tables 7.74 
through 7.76. The 
average annual loss is obtained by summing all losses produced during the 
20,000-year 
hurricane simulation period and then dividing the sum by 20,000 years. The 
average 
annual losses presented in Tables 7.71 through 7.72 reflect the wind climate 
of South 
Florida. 

Table 7.77 presents a summary of the effects of the various building 
parameters on 
average annual total loss. Considering the buildings constructed with a metal 
roof deck 
on steel joists, the average annual losses are found to increase by up to 46% 
when the 
roof cover is modeled with a EPDM single ply membrane versus a built-up roof 
cover. 
The corresponding increase in total loss for buildings constructed with a 
concrete roof 
deck is found to be about half this value (i.e., 25%) since, in the case of 
the concrete 
deck, there is no deck failure contributing to the roof cover failure and no 
water can 
permeate the concrete deck (even when the cover has failed) to damage the 
interior of the 
building. The effect of using a metal roof deck versus a impervious concrete 
roof deck is 
an increase by up to 458% in the average annual loss. 

The following discussion pertains to the engineered buildings constructed 
with metal roof 
deck on steel joists. The effect of having a single unit per floor 
(commercial usage) 
compared to multi-units per floor (residential usage) is at most a 45% 
increase in average 
annual total loss. The average annual total loss increased by as much as 28% 
when the 
glazing coverage changed from 20% to 33% and by as much as 145% when the 
glazing 
coverage changed from 33% to 50%. Also, as can be seen in Table 7.77, the 
surrounding 
missile environment has its largest impact on average annual losses for the 
two-story 
engineered buildings. This trend results from the fact that the modeled 
average heights of 
the buildings from which the commercial and residential type missiles 
originate are lower 
than the five- and eight-story engineered buildings. Thus, the glazing on the 
upper floors 
of the five- and eight-story buildings are less susceptible to missile damage 
than the 
glazing on the lower floors. 

In terms of average annual loss, the effects of glazing coverage, building 
usage and 
missile environment are found to be more pronounced on the buildings 
constructed with 
concrete roof decks than they are on the buildings constructed with metal 
roof decks. 
This relates to the fact that roof deck damage is eliminated, roof cover 
damage is reduced 
(since deck failures will not contribute to roof cover failure) and interior 
damage is 
reduced (since no water infiltration through the roof system can occur) for 
concrete roof 

Chapter 7. Direct Economic Losses 

?
Table 7.71. Average Annual Building Loss Normalized by Building Value   Two-
Story Engineered Building 

Building Characteristics 
Residential Buildings(8 Units per 
Floor) 
Commercial Buildings(1 Unit per 
Floor) 
Roof Missile Glazing Roof Terrain Roughness (m) Terrain Roughness (m) 
Deck Environ. Coverage Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.00 
Metal 
A 
20% BUR 
EPDM 
0.0088 
0.0101 
0.0109 
0.0120 
0.0138 
0.0147 
0.0084 
0.0092 
0.0100 
0.0107 
0.0124 
0.0130 
0.0057 
0.0064 
0.0068 
0.0075 
0.0085 
0.0091 
0.0045 
0.0051 
0.0054 
0.0059 
0.0068 
0.0073 
0.0125 
0.0136 
0.0151 
0.0159 
0.0181 
0.0187 
0.0114 
0.0120 
0.0138 
0.0142 
0.0159 
0.0163 
0.0078 
0.0083 
0.0094 
0.0099 
0.0110 
0.0114 
0.0060 
0.0066 
0.0074 
0.0078 
0.0087 
0.0091 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0074 
0.0087 
0.0090 
0.0101 
0.0113 
0.0122 
0.0071 
0.0078 
0.0083 
0.0090 
0.0099 
0.0105 
0.0048 
0.0055 
0.0056 
0.0063 
0.0068 
0.0074 
0.0038 
0.0044 
0.0044 
0.0050 
0.0054 
0.0060 
0.0100 
0.0112 
0.0119 
0.0130 
0.0144 
0.0153 
0.0086 
0.0094 
0.0103 
0.0109 
0.0121 
0.0126 
0.0058 
0.0065 
0.0070 
0.0076 
0.0083 
0.0088 
0.0045 
0.0051 
0.0054 
0.0060 
0.0065 
0.0070 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0063 
0.0076 
0.0072 
0.0084 
0.0090 
0.0101 
0.0055 
0.0064 
0.0062 
0.0071 
0.0076 
0.0084 
0.0038 
0.0045 
0.0042 
0.0050 
0.0052 
0.0060 
0.0030 
0.0037 
0.0033 
0.0041 
0.0042 
0.0049 
0.0084 
0.0097 
0.0098 
0.0109 
0.0119 
0.0129 
0.0069 
0.0077 
0.0081 
0.0088 
0.0095 
0.0102 
0.0046 
0.0054 
0.0054 
0.0061 
0.0064 
0.0070 
0.0035 
0.0043 
0.0042 
0.0049 
0.0050 
0.0057 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0045 
0.0059 
0.0047 
0.0061 
0.0058 
0.0072 
0.0025 
0.0034 
0.0025 
0.0035 
0.0033 
0.0042 
0.0020 
0.0028 
0.0020 
0.0028 
0.0027 
0.0034 
0.0017 
0.0025 
0.0018 
0.0026 
0.0023 
0.0030 
0.0051 
0.0065 
0.0054 
0.0067 
0.0078 
0.0090 
0.0026 
0.0036 
0.0027 
0.0037 
0.0044 
0.0053 
0.0020 
0.0029 
0.0021 
0.0029 
0.0034 
0.0042 
0.0018 
0.0026 
0.0018 
0.0026 
0.0030 
0.0037 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
Concrete 
A 
20% BUR 
EPDM 
0.0064 
0.0067 
0.0090 
0.0092 
0.0126 
0.0128 
0.0074 
0.0075 
0.0093 
0.0094 
0.0119 
0.0121 
0.0048 
0.0049 
0.0061 
0.0062 
0.0081 
0.0082 
0.0036 
0.0036 
0.0046 
0.0047 
0.0063 
0.0064 
0.0110 
0.0112 
0.0140 
0.0142 
0.0174 
0.0176 
0.0108 
0.0110 
0.0134 
0.0135 
0.0157 
0.0158 
0.0071 
0.0072 
0.0090 
0.0091 
0.0107 
0.0109 
0.0054 
0.0055 
0.0070 
0.0070 
0.0084 
0.0085 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0049 
0.0050 
0.0069 
0.0070 
0.0098 
0.0099 
0.0058 
0.0059 
0.0072 
0.0073 
0.0092 
0.0093 
0.0037 
0.0038 
0.0046 
0.0047 
0.0061 
0.0062 
0.0027 
0.0028 
0.0035 
0.0035 
0.0047 
0.0049 
0.0082 
0.0084 
0.0105 
0.0107 
0.0135 
0.0137 
0.0077 
0.0078 
0.0097 
0.0098 
0.0117 
0.0118 
0.0049 
0.0050 
0.0063 
0.0064 
0.0078 
0.0079 
0.0036 
0.0037 
0.0047 
0.0048 
0.0060 
0.0061 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0035 
0.0037 
0.0048 
0.0049 
0.0073 
0.0074 
0.0040 
0.0041 
0.0050 
0.0051 
0.0068 
0.0069 
0.0024 
0.0025 
0.0031 
0.0032 
0.0044 
0.0046 
0.0017 
0.0018 
0.0022 
0.0023 
0.0034 
0.0035 
0.0064 
0.0066 
0.0082 
0.0084 
0.0109 
0.0110 
0.0058 
0.0059 
0.0073 
0.0074 
0.0089 
0.0090 
0.0035 
0.0037 
0.0045 
0.0047 
0.0058 
0.0059 
0.0025 
0.0026 
0.0033 
0.0034 
0.0044 
0.0045 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0016 
0.0018 
0.0019 
0.0021 
0.0038 
0.0039 
0.0006 
0.0007 
0.0007 
0.0008 
0.0018 
0.0020 
0.0005 
0.0006 
0.0005 
0.0006 
0.0015 
0.0016 
0.0004 
0.0005 
0.0004 
0.0005 
0.0012 
0.0013 
0.0027 
0.0029 
0.0032 
0.0034 
0.0063 
0.0065 
0.0008 
0.0010 
0.0010 
0.0011 
0.0033 
0.0035 
0.0006 
0.0007 
0.0006 
0.0008 
0.0025 
0.0026 
0.0005 
0.0006 
0.0005 
0.0006 
0.0022 
0.0022 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 

HAZUS-MH MR3 Technical Manual 

?
Table 7.72. Average Annual Building Loss Normalized by Building Value   Five-
Story Engineered Building 

Building Characteristics 
Residential Buildings(8 Units per 
Floor) 
Commercial Buildings(1 Unit per 
Floor) 
Roof Missile Glazing Roof Terrain Roughness (m) Terrain Roughness (m) 
Deck Environ. Coverage Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.00 
Metal 
A 
20% BUR 
EPDM 
0.0078 
0.0091 
0.0090 
0.0103 
0.0116 
0.0128 
0.0069 
0.0080 
0.0080 
0.0090 
0.0101 
0.0111 
0.0052 
0.0062 
0.0059 
0.0069 
0.0077 
0.0087 
0.0044 
0.0054 
0.0050 
0.0059 
0.0067 
0.0076 
0.0104 
0.0115 
0.0124 
0.0133 
0.0155 
0.0165 
0.0094 
0.0103 
0.0109 
0.0117 
0.0130 
0.0138 
0.0068 
0.0077 
0.0079 
0.0087 
0.0098 
0.0106 
0.0056 
0.0064 
0.0065 
0.0073 
0.0083 
0.0091 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0076 
0.0089 
0.0087 
0.0099 
0.0112 
0.0124 
0.0067 
0.0077 
0.0076 
0.0086 
0.0095 
0.0105 
0.0050 
0.0060 
0.0057 
0.0067 
0.0074 
0.0083 
0.0043 
0.0052 
0.0048 
0.0057 
0.0064 
0.0073 
0.0097 
0.0107 
0.0114 
0.0124 
0.0145 
0.0154 
0.0084 
0.0093 
0.0096 
0.0104 
0.0117 
0.0125 
0.0061 
0.0070 
0.0070 
0.0078 
0.0090 
0.0097 
0.0050 
0.0058 
0.0058 
0.0065 
0.0077 
0.0084 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0075 
0.0088 
0.0086 
0.0099 
0.0110 
0.0121 
0.0067 
0.0078 
0.0076 
0.0086 
0.0094 
0.0104 
0.0050 
0.0060 
0.0057 
0.0066 
0.0073 
0.0082 
0.0043 
0.0052 
0.0047 
0.0057 
0.0063 
0.0072 
0.0094 
0.0103 
0.0110 
0.0120 
0.0139 
0.0150 
0.0080 
0.0089 
0.0092 
0.0099 
0.0110 
0.0118 
0.0059 
0.0067 
0.0067 
0.0074 
0.0085 
0.0093 
0.0048 
0.0056 
0.0055 
0.0063 
0.0073 
0.0080 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0063 
0.0076 
0.0067 
0.0080 
0.0088 
0.0101 
0.0039 
0.0050 
0.0041 
0.0052 
0.0061 
0.0071 
0.0033 
0.0043 
0.0034 
0.0044 
0.0053 
0.0062 
0.0030 
0.0039 
0.0031 
0.0040 
0.0049 
0.0057 
0.0067 
0.0078 
0.0074 
0.0084 
0.0113 
0.0123 
0.0038 
0.0047 
0.0041 
0.0050 
0.0080 
0.0088 
0.0031 
0.0039 
0.0033 
0.0042 
0.0068 
0.0076 
0.0027 
0.0035 
0.0029 
0.0037 
0.0063 
0.0070 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
Concrete 
A 
20% BUR 
EPDM 
0.0044 
0.0045 
0.0059 
0.0060 
0.0093 
0.0093 
0.0044 
0.0045 
0.0058 
0.0059 
0.0083 
0.0085 
0.0029 
0.0030 
0.0039 
0.0039 
0.0061 
0.0062 
0.0022 
0.0023 
0.0030 
0.0031 
0.0051 
0.0051 
0.0085 
0.0086 
0.0107 
0.0108 
0.0144 
0.0144 
0.0078 
0.0079 
0.0096 
0.0097 
0.0120 
0.0121 
0.0053 
0.0054 
0.0066 
0.0067 
0.0088 
0.0089 
0.0041 
0.0042 
0.0052 
0.0053 
0.0073 
0.0075 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0042 
0.0043 
0.0056 
0.0056 
0.0088 
0.0089 
0.0042 
0.0043 
0.0054 
0.0054 
0.0078 
0.0079 
0.0027 
0.0028 
0.0036 
0.0037 
0.0058 
0.0058 
0.0021 
0.0021 
0.0028 
0.0028 
0.0048 
0.0049 
0.0077 
0.0078 
0.0097 
0.0099 
0.0134 
0.0135 
0.0068 
0.0069 
0.0083 
0.0084 
0.0108 
0.0109 
0.0045 
0.0046 
0.0057 
0.0058 
0.0080 
0.0081 
0.0035 
0.0036 
0.0045 
0.0045 
0.0067 
0.0068 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0041 
0.0042 
0.0055 
0.0057 
0.0086 
0.0087 
0.0042 
0.0043 
0.0054 
0.0055 
0.0078 
0.0079 
0.0027 
0.0028 
0.0036 
0.0037 
0.0058 
0.0058 
0.0021 
0.0022 
0.0027 
0.0028 
0.0047 
0.0048 
0.0074 
0.0074 
0.0094 
0.0095 
0.0128 
0.0130 
0.0064 
0.0065 
0.0079 
0.0080 
0.0101 
0.0102 
0.0043 
0.0044 
0.0054 
0.0054 
0.0076 
0.0077 
0.0033 
0.0034 
0.0042 
0.0043 
0.0064 
0.0065 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0026 
0.0028 
0.0033 
0.0035 
0.0062 
0.0063 
0.0011 
0.0012 
0.0014 
0.0015 
0.0040 
0.0041 
0.0008 
0.0009 
0.0010 
0.0011 
0.0035 
0.0035 
0.0007 
0.0008 
0.0009 
0.0009 
0.0031 
0.0031 
0.0045 
0.0046 
0.0054 
0.0056 
0.0099 
0.0100 
0.0019 
0.0020 
0.0024 
0.0025 
0.0068 
0.0069 
0.0013 
0.0014 
0.0017 
0.0018 
0.0058 
0.0059 
0.0011 
0.0012 
0.0013 
0.0014 
0.0053 
0.0053 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 

Chapter 7. Direct Economic Losses 

?
Table 7.73. Average Annual Building Loss Normalized by Building Value   
Eight-
Story Engineered Building 

Building Characteristics 
Residential Buildings(8 Units per 
Floor) 
Commercial Buildings(1 Unit per 
Floor) 
Roof Missile Glazing Roof Terrain Roughness (m) Terrain Roughness (m) 
Deck Environ. Coverage Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.00 
Metal 
A 
20% BUR 
EPDM 
0.0106 
0.0118 
0.0116 
0.0128 
0.0148 
0.0161 
0.0088 
0.0099 
0.0096 
0.0106 
0.0124 
0.0135 
0.0074 
0.0085 
0.0079 
0.0090 
0.0106 
0.0117 
0.0067 
0.0078 
0.0071 
0.0081 
0.0097 
0.0107 
0.0113 
0.0122 
0.0128 
0.0138 
0.0170 
0.0179 
0.0095 
0.0103 
0.0106 
0.0114 
0.0141 
0.0149 
0.0076 
0.0084 
0.0084 
0.0092 
0.0119 
0.0127 
0.0066 
0.0074 
0.0073 
0.0081 
0.0108 
0.0116 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0103 
0.0115 
0.0112 
0.0124 
0.0143 
0.0155 
0.0085 
0.0096 
0.0091 
0.0102 
0.0118 
0.0130 
0.0071 
0.0082 
0.0076 
0.0086 
0.0102 
0.0112 
0.0065 
0.0075 
0.0069 
0.0078 
0.0094 
0.0104 
0.0108 
0.0117 
0.0122 
0.0130 
0.0163 
0.0173 
0.0088 
0.0097 
0.0098 
0.0106 
0.0134 
0.0142 
0.0071 
0.0079 
0.0078 
0.0086 
0.0114 
0.0122 
0.0063 
0.0070 
0.0069 
0.0076 
0.0105 
0.0113 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0102 
0.0114 
0.0110 
0.0123 
0.0140 
0.0153 
0.0083 
0.0094 
0.0089 
0.0100 
0.0116 
0.0127 
0.0070 
0.0081 
0.0074 
0.0085 
0.0101 
0.0111 
0.0064 
0.0074 
0.0067 
0.0077 
0.0093 
0.0104 
0.0105 
0.0114 
0.0117 
0.0126 
0.0161 
0.0169 
0.0085 
0.0093 
0.0094 
0.0102 
0.0130 
0.0138 
0.0069 
0.0077 
0.0075 
0.0083 
0.0112 
0.0120 
0.0060 
0.0068 
0.0066 
0.0074 
0.0104 
0.0111 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0096 
0.0108 
0.0102 
0.0115 
0.0134 
0.0146 
0.0070 
0.0081 
0.0074 
0.0085 
0.0104 
0.0114 
0.0062 
0.0073 
0.0065 
0.0076 
0.0093 
0.0104 
0.0058 
0.0069 
0.0061 
0.0071 
0.0089 
0.0099 
0.0094 
0.0103 
0.0103 
0.0112 
0.0150 
0.0160 
0.0065 
0.0073 
0.0069 
0.0078 
0.0118 
0.0126 
0.0056 
0.0064 
0.0060 
0.0068 
0.0108 
0.0115 
0.0052 
0.0060 
0.0055 
0.0062 
0.0101 
0.0109 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
Concrete 
A 
20% BUR 
EPDM 
0.0051 
0.0051 
0.0064 
0.0065 
0.0105 
0.0106 
0.0040 
0.0040 
0.0049 
0.0050 
0.0086 
0.0086 
0.0028 
0.0029 
0.0036 
0.0036 
0.0070 
0.0071 
0.0023 
0.0024 
0.0029 
0.0030 
0.0063 
0.0063 
0.0080 
0.0080 
0.0098 
0.0098 
0.0146 
0.0147 
0.0063 
0.0064 
0.0076 
0.0077 
0.0119 
0.0119 
0.0045 
0.0046 
0.0056 
0.0056 
0.0098 
0.0098 
0.0037 
0.0037 
0.0045 
0.0046 
0.0087 
0.0088 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0048 
0.0049 
0.0060 
0.0060 
0.0100 
0.0100 
0.0036 
0.0037 
0.0045 
0.0046 
0.0080 
0.0081 
0.0025 
0.0026 
0.0032 
0.0033 
0.0066 
0.0067 
0.0021 
0.0021 
0.0027 
0.0027 
0.0060 
0.0060 
0.0075 
0.0075 
0.0091 
0.0091 
0.0139 
0.0140 
0.0057 
0.0058 
0.0068 
0.0069 
0.0112 
0.0112 
0.0041 
0.0041 
0.0050 
0.0050 
0.0093 
0.0094 
0.0033 
0.0033 
0.0041 
0.0041 
0.0084 
0.0085 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0047 
0.0047 
0.0058 
0.0059 
0.0096 
0.0098 
0.0035 
0.0035 
0.0043 
0.0043 
0.0078 
0.0078 
0.0025 
0.0025 
0.0031 
0.0032 
0.0065 
0.0065 
0.0020 
0.0020 
0.0025 
0.0026 
0.0058 
0.0060 
0.0072 
0.0072 
0.0087 
0.0087 
0.0136 
0.0136 
0.0054 
0.0054 
0.0065 
0.0065 
0.0108 
0.0109 
0.0038 
0.0039 
0.0046 
0.0047 
0.0091 
0.0092 
0.0030 
0.0032 
0.0038 
0.0039 
0.0084 
0.0083 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0040 
0.0041 
0.0049 
0.0052 
0.0090 
0.0091 
0.0021 
0.0022 
0.0027 
0.0027 
0.0066 
0.0066 
0.0016 
0.0017 
0.0021 
0.0022 
0.0057 
0.0059 
0.0014 
0.0014 
0.0018 
0.0019 
0.0055 
0.0054 
0.0060 
0.0061 
0.0072 
0.0072 
0.0126 
0.0127 
0.0032 
0.0033 
0.0039 
0.0040 
0.0096 
0.0096 
0.0025 
0.0025 
0.0031 
0.0031 
0.0086 
0.0086 
0.0021 
0.0022 
0.0027 
0.0027 
0.0080 
0.0081 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 

HAZUS-MH MR3 Technical Manual 

?
Table 7.74. Average Annual Content Loss Normalized by Building Value   Two-
Story Engineered Building 

Building Characteristics 
Residential Buildings (8 Units per 
Floor) 
Commercial Buildings(1 Unit per 
Floor) 
Roof Missile Glazing Roof Terrain Roughness (m) Terrain Roughness (m) 
Deck Environ. Coverage Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.00 
Metal 
A 
20% BUR 
EPDM 
0.0037 
0.0039 
0.0049 
0.0050 
0.0068 
0.0069 
0.0034 
0.0035 
0.0044 
0.0044 
0.0059 
0.0060 
0.0022 
0.0022 
0.0028 
0.0029 
0.0039 
0.0040 
0.0016 
0.0017 
0.0021 
0.0021 
0.0030 
0.0031 
0.0052 
0.0053 
0.0068 
0.0068 
0.0086 
0.0086 
0.0048 
0.0048 
0.0064 
0.0064 
0.0078 
0.0078 
0.0030 
0.0031 
0.0042 
0.0042 
0.0052 
0.0052 
0.0022 
0.0022 
0.0032 
0.0032 
0.0040 
0.0040 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0029 
0.0031 
0.0039 
0.0040 
0.0054 
0.0055 
0.0027 
0.0028 
0.0035 
0.0035 
0.0046 
0.0047 
0.0017 
0.0018 
0.0022 
0.0023 
0.0030 
0.0031 
0.0012 
0.0013 
0.0016 
0.0017 
0.0023 
0.0024 
0.0040 
0.0041 
0.0051 
0.0051 
0.0066 
0.0066 
0.0032 
0.0032 
0.0044 
0.0044 
0.0055 
0.0055 
0.0019 
0.0020 
0.0027 
0.0028 
0.0036 
0.0036 
0.0013 
0.0014 
0.0020 
0.0020 
0.0026 
0.0027 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0022 
0.0024 
0.0028 
0.0030 
0.0041 
0.0042 
0.0018 
0.0019 
0.0022 
0.0023 
0.0033 
0.0033 
0.0011 
0.0011 
0.0014 
0.0014 
0.0021 
0.0022 
0.0008 
0.0008 
0.0010 
0.0011 
0.0016 
0.0017 
0.0032 
0.0033 
0.0040 
0.0041 
0.0053 
0.0053 
0.0023 
0.0023 
0.0031 
0.0031 
0.0040 
0.0040 
0.0013 
0.0014 
0.0018 
0.0019 
0.0025 
0.0025 
0.0009 
0.0009 
0.0013 
0.0013 
0.0018 
0.0019 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0013 
0.0015 
0.0014 
0.0016 
0.0023 
0.0025 
0.0005 
0.0006 
0.0005 
0.0006 
0.0011 
0.0012 
0.0003 
0.0005 
0.0003 
0.0005 
0.0008 
0.0009 
0.0003 
0.0004 
0.0003 
0.0004 
0.0007 
0.0008 
0.0016 
0.0018 
0.0018 
0.0020 
0.0034 
0.0036 
0.0005 
0.0006 
0.0005 
0.0006 
0.0017 
0.0018 
0.0003 
0.0004 
0.0003 
0.0004 
0.0013 
0.0014 
0.0002 
0.0003 
0.0003 
0.0004 
0.0011 
0.0012 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
Concrete 
A 
20% BUR 
EPDM 
0.0030 
0.0030 
0.0044 
0.0044 
0.0065 
0.0065 
0.0032 
0.0032 
0.0042 
0.0043 
0.0059 
0.0059 
0.0020 
0.0020 
0.0027 
0.0027 
0.0039 
0.0039 
0.0014 
0.0014 
0.0020 
0.0020 
0.0030 
0.0030 
0.0049 
0.0049 
0.0066 
0.0066 
0.0085 
0.0085 
0.0048 
0.0048 
0.0064 
0.0064 
0.0078 
0.0078 
0.0030 
0.0030 
0.0042 
0.0042 
0.0052 
0.0052 
0.0022 
0.0021 
0.0032 
0.0031 
0.0040 
0.0040 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0022 
0.0021 
0.0033 
0.0032 
0.0050 
0.0050 
0.0025 
0.0024 
0.0033 
0.0032 
0.0045 
0.0045 
0.0015 
0.0015 
0.0020 
0.0020 
0.0029 
0.0029 
0.0010 
0.0010 
0.0015 
0.0014 
0.0022 
0.0022 
0.0036 
0.0036 
0.0048 
0.0048 
0.0065 
0.0065 
0.0030 
0.0031 
0.0043 
0.0043 
0.0055 
0.0055 
0.0018 
0.0018 
0.0027 
0.0027 
0.0036 
0.0036 
0.0012 
0.0012 
0.0019 
0.0019 
0.0026 
0.0026 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0014 
0.0014 
0.0021 
0.0021 
0.0037 
0.0036 
0.0014 
0.0014 
0.0019 
0.0019 
0.0031 
0.0031 
0.0008 
0.0007 
0.0011 
0.0011 
0.0019 
0.0020 
0.0005 
0.0005 
0.0007 
0.0008 
0.0015 
0.0015 
0.0027 
0.0027 
0.0037 
0.0037 
0.0052 
0.0051 
0.0021 
0.0020 
0.0030 
0.0030 
0.0040 
0.0040 
0.0011 
0.0011 
0.0017 
0.0017 
0.0025 
0.0025 
0.0007 
0.0007 
0.0012 
0.0012 
0.0018 
0.0018 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0005 
0.0005 
0.0007 
0.0007 
0.0018 
0.0018 
0.0001 
0.0001 
0.0001 
0.0001 
0.0008 
0.0008 
0.0000 
0.0000 
0.0000 
0.0000 
0.0006 
0.0006 
0.0000 
0.0000 
0.0000 
0.0000 
0.0005 
0.0005 
0.0011 
0.0011 
0.0014 
0.0014 
0.0032 
0.0032 
0.0002 
0.0002 
0.0002 
0.0002 
0.0016 
0.0016 
0.0001 
0.0001 
0.0001 
0.0001 
0.0012 
0.0012 
0.0000 
0.0000 
0.0001 
0.0001 
0.0010 
0.0010 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 

Chapter 7. Direct Economic Losses 

?
Table 7.75. Average Annual Content Loss Normalized by Building Value   Five-
Story Engineered Building 

Building Characteristics 
Residential Buildings(8 Units per 
Floor) 
Commercial Buildings(1 Unit per 
Floor) 
Roof Missile Glazing Roof Terrain Roughness (m) Terrain Roughness (m) 
Deck Environ. Coverage Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.00 
Metal 
A 
20% BUR 
EPDM 
0.0031 
0.0032 
0.0038 
0.0040 
0.0055 
0.0056 
0.0025 
0.0026 
0.0031 
0.0033 
0.0046 
0.0047 
0.0017 
0.0018 
0.0022 
0.0023 
0.0034 
0.0035 
0.0013 
0.0015 
0.0017 
0.0018 
0.0029 
0.0030 
0.0050 
0.0051 
0.0062 
0.0063 
0.0083 
0.0085 
0.0042 
0.0043 
0.0052 
0.0054 
0.0067 
0.0069 
0.0029 
0.0030 
0.0036 
0.0037 
0.0050 
0.0051 
0.0022 
0.0024 
0.0028 
0.0030 
0.0041 
0.0043 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0029 
0.0032 
0.0037 
0.0038 
0.0053 
0.0054 
0.0024 
0.0025 
0.0030 
0.0031 
0.0043 
0.0044 
0.0016 
0.0018 
0.0020 
0.0022 
0.0032 
0.0034 
0.0013 
0.0014 
0.0016 
0.0017 
0.0027 
0.0028 
0.0045 
0.0047 
0.0056 
0.0058 
0.0078 
0.0079 
0.0035 
0.0036 
0.0044 
0.0045 
0.0059 
0.0060 
0.0024 
0.0025 
0.0030 
0.0031 
0.0044 
0.0045 
0.0018 
0.0020 
0.0024 
0.0025 
0.0037 
0.0038 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0029 
0.0031 
0.0036 
0.0038 
0.0052 
0.0053 
0.0023 
0.0025 
0.0029 
0.0031 
0.0043 
0.0044 
0.0016 
0.0017 
0.0021 
0.0022 
0.0032 
0.0033 
0.0013 
0.0014 
0.0016 
0.0017 
0.0027 
0.0028 
0.0044 
0.0045 
0.0054 
0.0056 
0.0074 
0.0076 
0.0032 
0.0033 
0.0041 
0.0042 
0.0054 
0.0055 
0.0022 
0.0023 
0.0028 
0.0029 
0.0041 
0.0042 
0.0017 
0.0018 
0.0022 
0.0023 
0.0035 
0.0036 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0023 
0.0025 
0.0026 
0.0028 
0.0040 
0.0042 
0.0011 
0.0012 
0.0012 
0.0014 
0.0026 
0.0027 
0.0008 
0.0010 
0.0009 
0.0010 
0.0022 
0.0023 
0.0007 
0.0008 
0.0008 
0.0009 
0.0020 
0.0021 
0.0031 
0.0033 
0.0036 
0.0038 
0.0061 
0.0063 
0.0013 
0.0015 
0.0016 
0.0018 
0.0042 
0.0043 
0.0010 
0.0011 
0.0012 
0.0013 
0.0035 
0.0036 
0.0008 
0.0009 
0.0009 
0.0011 
0.0032 
0.0032 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
Concrete 
A 
20% BUR 
EPDM 
0.0021 
0.0021 
0.0030 
0.0030 
0.0050 
0.0050 
0.0019 
0.0019 
0.0027 
0.0027 
0.0043 
0.0043 
0.0012 
0.0012 
0.0017 
0.0017 
0.0031 
0.0031 
0.0009 
0.0009 
0.0013 
0.0013 
0.0026 
0.0026 
0.0045 
0.0045 
0.0058 
0.0058 
0.0081 
0.0081 
0.0038 
0.0038 
0.0050 
0.0050 
0.0066 
0.0066 
0.0025 
0.0025 
0.0033 
0.0033 
0.0048 
0.0048 
0.0019 
0.0019 
0.0026 
0.0026 
0.0040 
0.0040 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0020 
0.0020 
0.0029 
0.0028 
0.0048 
0.0048 
0.0018 
0.0017 
0.0025 
0.0024 
0.0040 
0.0040 
0.0011 
0.0011 
0.0016 
0.0016 
0.0029 
0.0029 
0.0008 
0.0008 
0.0012 
0.0012 
0.0025 
0.0024 
0.0040 
0.0040 
0.0053 
0.0053 
0.0076 
0.0075 
0.0031 
0.0031 
0.0041 
0.0041 
0.0057 
0.0057 
0.0020 
0.0020 
0.0028 
0.0028 
0.0043 
0.0043 
0.0015 
0.0015 
0.0021 
0.0021 
0.0036 
0.0036 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0019 
0.0019 
0.0028 
0.0028 
0.0047 
0.0047 
0.0017 
0.0017 
0.0025 
0.0025 
0.0040 
0.0040 
0.0011 
0.0011 
0.0016 
0.0016 
0.0029 
0.0029 
0.0008 
0.0008 
0.0012 
0.0012 
0.0024 
0.0024 
0.0038 
0.0038 
0.0051 
0.0051 
0.0072 
0.0073 
0.0028 
0.0028 
0.0038 
0.0038 
0.0053 
0.0053 
0.0018 
0.0018 
0.0025 
0.0025 
0.0040 
0.0040 
0.0013 
0.0013 
0.0019 
0.0019 
0.0033 
0.0033 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0013 
0.0013 
0.0017 
0.0017 
0.0034 
0.0034 
0.0004 
0.0004 
0.0006 
0.0006 
0.0022 
0.0022 
0.0002 
0.0003 
0.0004 
0.0004 
0.0019 
0.0018 
0.0002 
0.0002 
0.0003 
0.0003 
0.0017 
0.0016 
0.0026 
0.0026 
0.0032 
0.0032 
0.0058 
0.0059 
0.0009 
0.0009 
0.0012 
0.0012 
0.0039 
0.0039 
0.0006 
0.0006 
0.0008 
0.0008 
0.0033 
0.0033 
0.0004 
0.0004 
0.0006 
0.0006 
0.0030 
0.0030 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 

HAZUS-MH MR3 Technical Manual 

?
Table 7.76. Average Annual Content Loss Normalized by Building Value   Eight-
Story Engineered Building 

Building Characteristics 
Residential Buildings(8 Units per 
Floor) 
Commercial Buildings(1 Unit per 
Floor) 
Roof Missile Glazing Roof Terrain Roughness (m) Terrain Roughness (m) 
Deck Environ. Coverage Cover 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.00 
Metal 
A 
20% 
BUR 
EPDM 
0.0038 
0.0040 
0.0044 
0.0045 
0.0063 
0.0065 
0.0028 
0.0029 
0.0032 
0.0033 
0.0050 
0.0051 
0.0022 
0.0023 
0.0025 
0.0027 
0.0042 
0.0043 
0.0019 
0.0021 
0.0022 
0.0023 
0.0038 
0.0039 
0.0054 
0.0055 
0.0064 
0.0066 
0.0093 
0.0095 
0.0041 
0.0042 
0.0049 
0.0051 
0.0074 
0.0076 
0.0031 
0.0033 
0.0037 
0.0038 
0.0062 
0.0064 
0.0026 
0.0027 
0.0031 
0.0032 
0.0056 
0.0057 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0037 
0.0038 
0.0042 
0.0044 
0.0060 
0.0062 
0.0026 
0.0027 
0.0030 
0.0031 
0.0047 
0.0049 
0.0021 
0.0022 
0.0024 
0.0025 
0.0040 
0.0041 
0.0019 
0.0019 
0.0021 
0.0022 
0.0036 
0.0037 
0.0051 
0.0052 
0.0061 
0.0062 
0.0089 
0.0091 
0.0037 
0.0038 
0.0043 
0.0045 
0.0070 
0.0072 
0.0028 
0.0029 
0.0033 
0.0034 
0.0059 
0.0060 
0.0024 
0.0025 
0.0028 
0.0029 
0.0054 
0.0055 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0036 
0.0038 
0.0041 
0.0043 
0.0058 
0.0061 
0.0025 
0.0026 
0.0029 
0.0030 
0.0046 
0.0047 
0.0020 
0.0021 
0.0023 
0.0024 
0.0039 
0.0040 
0.0018 
0.0019 
0.0020 
0.0021 
0.0036 
0.0037 
0.0049 
0.0051 
0.0058 
0.0059 
0.0088 
0.0089 
0.0034 
0.0035 
0.0041 
0.0042 
0.0067 
0.0069 
0.0026 
0.0027 
0.0031 
0.0032 
0.0057 
0.0059 
0.0022 
0.0023 
0.0026 
0.0027 
0.0053 
0.0054 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0034 
0.0036 
0.0038 
0.0040 
0.0057 
0.0058 
0.0021 
0.0023 
0.0024 
0.0025 
0.0042 
0.0043 
0.0018 
0.0019 
0.0020 
0.0021 
0.0037 
0.0038 
0.0016 
0.0018 
0.0018 
0.0019 
0.0035 
0.0036 
0.0045 
0.0047 
0.0051 
0.0053 
0.0083 
0.0085 
0.0026 
0.0028 
0.0030 
0.0032 
0.0063 
0.0064 
0.0021 
0.0023 
0.0024 
0.0026 
0.0056 
0.0057 
0.0019 
0.0020 
0.0022 
0.0023 
0.0052 
0.0054 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
Concrete 
A 
20% 
BUR 
EPDM 
0.0024 
0.0024 
0.0031 
0.0031 
0.0053 
0.0054 
0.0016 
0.0015 
0.0021 
0.0021 
0.0042 
0.0042 
0.0011 
0.0011 
0.0015 
0.0015 
0.0034 
0.0035 
0.0008 
0.0009 
0.0012 
0.0012 
0.0031 
0.0031 
0.0046 
0.0046 
0.0057 
0.0058 
0.0089 
0.0089 
0.0033 
0.0033 
0.0042 
0.0042 
0.0070 
0.0071 
0.0023 
0.0023 
0.0030 
0.0030 
0.0058 
0.0058 
0.0018 
0.0018 
0.0024 
0.0024 
0.0052 
0.0052 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
B 
20% 
BUR 
EPDM 
0.0022 
0.0023 
0.0029 
0.0029 
0.0051 
0.0051 
0.0014 
0.0014 
0.0019 
0.0019 
0.0039 
0.0039 
0.0009 
0.0009 
0.0013 
0.0013 
0.0033 
0.0033 
0.0008 
0.0007 
0.0011 
0.0010 
0.0029 
0.0029 
0.0043 
0.0043 
0.0054 
0.0053 
0.0084 
0.0085 
0.0029 
0.0028 
0.0036 
0.0036 
0.0066 
0.0066 
0.0020 
0.0020 
0.0026 
0.0026 
0.0055 
0.0055 
0.0016 
0.0016 
0.0021 
0.0021 
0.0050 
0.0050 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
C 
20% 
BUR 
EPDM 
0.0022 
0.0021 
0.0028 
0.0028 
0.0049 
0.0050 
0.0013 
0.0013 
0.0017 
0.0017 
0.0038 
0.0038 
0.0009 
0.0009 
0.0012 
0.0012 
0.0032 
0.0032 
0.0007 
0.0007 
0.0010 
0.0010 
0.0029 
0.0029 
0.0041 
0.0041 
0.0051 
0.0051 
0.0083 
0.0083 
0.0026 
0.0026 
0.0034 
0.0034 
0.0063 
0.0063 
0.0018 
0.0018 
0.0024 
0.0024 
0.0053 
0.0054 
0.0014 
0.0014 
0.0019 
0.0019 
0.0050 
0.0049 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 
D 
20% 
BUR 
EPDM 
0.0020 
0.0020 
0.0025 
0.0026 
0.0047 
0.0047 
0.0009 
0.0009 
0.0013 
0.0012 
0.0034 
0.0034 
0.0006 
0.0007 
0.0009 
0.0009 
0.0029 
0.0030 
0.0005 
0.0005 
0.0008 
0.0008 
0.0028 
0.0027 
0.0037 
0.0037 
0.0044 
0.0044 
0.0078 
0.0079 
0.0018 
0.0018 
0.0023 
0.0023 
0.0058 
0.0058 
0.0013 
0.0013 
0.0017 
0.0017 
0.0052 
0.0052 
0.0011 
0.0011 
0.0015 
0.0014 
0.0048 
0.0048 
33% 
BUR 
EPDM 
50% 
BUR 
EPDM 

Chapter 7. Direct Economic Losses 

?
Table 7.77. Percent Increases in the Average Annual Total Loss due to Changes 
in 
Building Parameters (Minimum/Average/Maximum) - Engineered Residential and 
Commercial Buildings 

Two-Story Engineered Buildings 
Building Parameter Metal Roof Deck Concrete Roof Deck 
Residential to Commercial Building Class 0% / 24% / 42% 23% / 46% / 87% 
Built-up to Single Ply Membrane Roof Cover 2% / 14% / 46% -1% / 4% / 25% 
20% to 33% Glazing Coverage 0% / 16% / 28% 5% / 28% / 44% 
33% to 50% Glazing Coverage 17% / 32% / 99% 18% / 90% / 435% 
Missile Environment D to C 37% / 103% / 253% 66% / 368% / 808% 
Missile Environment C to B 18% / 29% / 42% 25% / 44% / 72% 
Missile Environment B to A 18% / 28% / 44% 28% / 37% / 58% 
Concrete to Metal Roof Deck 1% / 66% / 458% 
Five-Story Engineered Buildings 
Building Parameter Metal Roof Deck Concrete Roof Deck 
Residential to Commercial Building Class -6% / 25% / 45% 30% / 65% / 98% 
Built-up to Single Ply Membrane Roof Cover 4% / 12% / 29% -1% / 2% / 13% 
20% to 33% Glazing Coverage 3% / 14% / 21% 21% / 32% / 40% 
33% to 50% Glazing Coverage 21% / 43% / 145% 28% / 92% / 321% 
Missile Environment D to C 14% / 56% / 130% 18% / 142% / 294% 
Missile Environment C to B 0% / 3% / 7% -2% / 4% / 9% 
Missile Environment B to A 2% / 8% / 15% 4% / 10% / 21% 
Concrete to Metal Roof Deck 6% / 58% / 395% 
Eight-Story Engineered Buildings 
Building Parameter Metal Roof Deck Concrete Roof Deck 
Residential to Commercial Building Class -7% / 12% / 26% 47% / 61% / 76% 
Built-up to Single Ply Membrane Roof Cover 4% / 9% / 16% -3% / 1% / 6% 
20% to 33% Glazing Coverage 5% / 10% / 15% 19% / 27% / 37% 
33% to 50% Glazing Coverage 29% / 48% / 100% 51% / 109% / 221% 
Missile Environment D to C 2% / 13% / 35% 2% / 29% / 61% 
Missile Environment C to B 1% / 3% / 6% 1% / 5% / 10% 
Missile Environment B to A 3% / 5% / 10% 3% / 9% / 14% 
Concrete to Metal Roof Deck 12% / 82% / 334% 
All Engineered Buildings 
Building Parameter Metal Roof Deck Concrete Roof Deck 
Two to Five Stories -23% / 25% / 130% -40% / 32% / 242% 
Five to Eight Stories -4% / 33% / 105% -25% / 18% / 122% 

decks, and thus losses are primarily driven by glazing damage, which is 
influenced by 
both usage, since internal pressure is confined to smaller internal areas 
when there are 
multiple units, and missile environment. 

The number of stories also has a significant impact on the normalized average 
annual 
total losses (see last two rows of Table 7.77). For example, with all else 
being the same, 
the predicted average annual losses for the five-story engineered buildings 
are up to 
242% higher than those for the two-story buildings engineered buildings. 

7.14 Loss Model Results for Industrial Buildings 
Loss functions for industrial buildings have been developed following the 
same 
procedure as the other model building classes (based on 20,000 years of 
hurricane 

HAZUS-MH MR3 Technical Manual 

?
simulation). The model buildings are assumed to be used as factories. The 
assumed 
subassembly cost distributions are listed in Table 7.78. 

Example building and content loss functions are given in Appendix N. The 
average 
annual building and content losses (both normalized by the total building 
value) for all 
industrial buildings examined are presented in Tables 7.79 and 7.80, 
respectively. The 
average annual loss is obtained by summing all losses produced during the 
20,000-year 
hurricane simulation period and then dividing by 20,000 years. The values 
given in 
Tables 7.79 and 7.80 reflect the average annual losses for industrial 
buildings located in 
the South Florida area. 

Table 7.81 presents a summary of the effects of the various building 
parameters on 
normalized average annual total loss. The wall construction has the largest 
impact on 
average annual loss with an increase of 34% on average for unreinforced 
masonry walls 
versus reinforced masonry walls. This is due to the weaker resistance of the 
joist/wall 
connections for unreinforced masonry versus reinforced masonry walls since 
unreinforced masonry walls do not contain bond beams or tie beams with 
reinforcement 
to which the anchorage can be welded to or wrapped around (NRC, 1991). 
Average 
annual total losses are shown to increase by 16% on average when the metal 
roof deck 
has aged and fatigued, which has been modeled with a 50% reduction in the 
base uplift 
resistance. The effect of the surrounding missile environment on building 
losses is small 
since the modeled industrial building does not comprise glazing. However, 
small 
increases were observed (up to 3%) when the missile environment changed from 
the no-
missile environment (D) to the environment associated with a mixture of 
commercial and 
residential type missiles (A) since costs are associated with re-finishing 
wall surfaces and 
replacing entry and/or overhead doors that have been impacted by windborne 
debris. 

Chapter 7. Direct Economic Losses 

?
Table 7.78. Subassembly Cost Distributions for Industrial Buildings 

Subassemblies Cost Ratios 
Foundations 
Footings & Foundations 
Piles & Caissons 
Excavation & Backfill 
4.8% 
0.0% 
2.2% 
7.0% 
Substructures 
Slab on Grade 
Special Substructures 
6.8% 
0.0% 6.8% 
Superstructure 
Columns and Beams 
Structural Walls 
Elevated Floors/Diaphragms 
Roof Decking/Framing 
Stairs 
0.0% 
0.0% 
0.0% 
10.5% 
0.0% 
10.5% 
Exterior Closure 
Walls 
Exterior Wall Finishes 
Doors 
Windows & Glazed Walls 
4.7% 
0.0% 
1.4% 
0.0% 
6.1% 
Roofing 
Roof Covering 
Insulation 
Openings & Specialties 
4.5% 
2.7% 
0.7% 
7.9% 
Interior Construction 
Partitions 
Interior Doors 
Wall Finishes 
Floor Finishes 
Ceiling Finishes 
Interior Surface of Exterior Walls 
4.0% 
1.9% 
1.0% 
0.6% 
0.7% 
1.6% 
9.8% 
Conveying 
Elevators 
Special Conveyors 
0.0% 
0.0% 0.0% 
Mechanical 
Plumbing 
Fire Protection 
Heating 
Cooling 
Special Systems 
6.2% 
4.4% 
11.8% 
14.8% 
0.0% 
37.2% 
Electrical 
Service & Distribution 
Lighting & Power 
Special Electrical 
1.7% 
12.5% 
0.6% 
14.8% 
Special Construction 
Specialties (& Additives) 0.0% 0.0% 
Total 100% 100% 

HAZUS-MH MR3 Technical Manual 

?
Table 7.79. Average Annual Building Loss Normalized by Building Value   
Industrial Building 

Building Characteristics 
100% Roof Deck Capacity 50% Roof Deck Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall Construction Missile Environment 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced 
Masonry 
A 
B 
C 
D 
0.0286 
0.0286 
0.0286 
0.0285 
0.0147 
0.0147 
0.0148 
0.0145 
0.0107 
0.0107 
0.0107 
0.0106 
0.0088 
0.0088 
0.0089 
0.0087 
0.0297 
0.0296 
0.0297 
0.0295 
0.0155 
0.0155 
0.0157 
0.0153 
0.0115 
0.0116 
0.0117 
0.0114 
0.0096 
0.0097 
0.0098 
0.0096 
Reinforced 
Masonry 
A 
B 
C 
D 
0.0200 
0.0201 
0.0201 
0.0200 
0.0108 
0.0109 
0.0110 
0.0106 
0.0081 
0.0081 
0.0081 
0.0079 
0.0067 
0.0068 
0.0068 
0.0066 
0.0231 
0.0231 
0.0231 
0.0230 
0.0130 
0.0131 
0.0132 
0.0128 
0.0100 
0.0099 
0.0100 
0.0098 
0.0084 
0.0085 
0.0086 
0.0084 

Table 7.80. Average Annual Content Loss Normalized by Building Value   
Industrial Building 

Building Characteristics 
100% Roof Deck Capacity 50% Roof Deck Capacity 
Terrain Surface Roughness (m) Terrain Surface Roughness (m) 
Wall Construction Missile Environment 0.03 0.35 0.70 1.0 0.03 0.35 0.70 1.0 
Unreinforced 
Masonry 
A 
B 
C 
D 
0.0126 
0.0126 
0.0126 
0.0126 
0.0057 
0.0057 
0.0057 
0.0057 
0.0038 
0.0038 
0.0038 
0.0038 
0.0029 
0.0029 
0.0030 
0.0029 
0.0128 
0.0128 
0.0128 
0.0128 
0.0059 
0.0059 
0.0060 
0.0059 
0.0041 
0.0041 
0.0041 
0.0041 
0.0032 
0.0033 
0.0033 
0.0033 
Reinforced 
Masonry 
A 
B 
C 
D 
0.0069 
0.0069 
0.0069 
0.0069 
0.0031 
0.0031 
0.0031 
0.0031 
0.0021 
0.0021 
0.0021 
0.0021 
0.0016 
0.0016 
0.0016 
0.0016 
0.0085 
0.0086 
0.0086 
0.0086 
0.0044 
0.0044 
0.0044 
0.0044 
0.0032 
0.0031 
0.0032 
0.0032 
0.0026 
0.0026 
0.0027 
0.0027 

Table 7.81. Percent Increase in Average Annual Total Loss Due to Changes in 
Building Parameters (Minimum/Average/Maximum)   Industrial Buildings 

Parameter Increase in Building Loss 
Reinforced Masonry to Unreinforced Masonry Walls 16% / 34% / 53% 
Missile Environment D to A 0% / 1% / 3% 
0% to 50% Reduction in Roof Deck Resistance 3% / 16% / 34% 

Chapter 7. Direct Economic Losses 

?
8-1 

Chapter 8. Building Stock Classification Methods 

8.1 Introduction 
In this section, two case studies are provided to demonstrate methods used to 
characterize 
the building stock in Southeast Florida. In Section 8.2, we show how aerial 
photography 
can be used to classify building geometries. In Section 8.3, we illustrate 
the use of a 
contractor survey to gather data on roof cover types. 

8.2 Aerial Photography Samples 
Commercial Building Stock. The classification of commercial buildings in 
Dade, 
Broward and Palm Beach counties is achieved by inspection of aerial 
photographs 
showing samples of the building stock within the three counties. Twenty-five 
aerial 
photographs were sampled from the three counties. The twenty-five photographs 
include 
ten random samples from areas classified as  Commercial and Services,  ten 
random 
samples from areas classified as  Industrial,  and five random samples from 
areas 
classified as  Institutional.  The types of facilities included in these 
three land use 
categories are listed in Table 8. 1. Table 8.2 shows the photograph coverage 
area and the 
number of buildings shown within each of the randomly selected areas. Figure 
8.1 shows 
the locations of the selected areas on a map of South Florida. 

Table 8.1. Number of Aerial Photographs and Facility Type Examples by Land 
Use 
Category 

Land Use Category 
Number of 
Aerial 
Photographs Facility Type Examples 
Commercial and Services 10 Retail Sales and Services, Wholesale Sales and 
Services, Professional 
Services, Cultural and Entertainment, Tourist Services, and Oil and 
Gas Storage 
Industrial 10 Food Processing, Timber Processing, Mineral Processing, and Oil 
and 
Gas Processing. 
Institutional 5 Educational, Religious, Military, Medical and Health Care, 
Governmental, Correctional, and Commercial Child Care. 

The South Florida Water Management District Land Use/Land Cover data set is 
used to 
generate the random sample. The data is in the form of a GIS polygon data 
layer. For 
example, there are 1148 polygons associated with the  Commercial and 
Services  
category. Each of these polygons are assigned an integer number identifier 
from 1 to 
1148. A random number generator was then used to select ten distinct integer 
numbers 
between 1 and 1148 (each number having an equal probability of being chosen) 
and the 
ten polygons associated with the randomly selected integer numbers were used. 

HAZUS-MH MR3 Technical Manual 

?
Table 8.2. Coverage Area and Number of Buildings Within Each Photograph 

Land Use Category Photograph ID Area(*1000 sq. ft.) Number of Buildings 
Commercialand 
Services 
COMM01 
COMM02 
COMM03 
COMM04 
COMM05 
COMM06 
COMM07 
COMM08 
COMM09 
COMM10 
5800 
4400 
9000 
6600 
4400 
4400 
4400 
4600 
18480 
5000 
3 
18 
28 
27 
24 
15 
5 
27 
51 
14 
Industrial 
INDU01 
INDU02 
INDU03 
INDU04 
INDU05 
INDU06 
INDU07 
INDU08 
INDU09 
INDU10 
9600 
9800 
6800 
4800 
22680 
9900 
5400 
6800 
8200 
12600 
42 
10 
12 
33 
90 
62 
32 
25 
43 
38 
Institutional 
INST01 
INST02 
INST03 
INST04 
INST05 
4600 
5000 
4800 
4600 
5600 
53 
23 
14 
11 
23 
Total 188260 723 

Table 8.3 presents a breakdown of the percentage of buildings having shapes 
defined as 
L, T, C, Rectangular, Circular, Z, H, X and Triangular. Overall, the 
rectangular shape 
accounts for over three quarters of the building shapes. The rectangular 
shape, combined 
with the relatively simple L shape account for over 80% of the building 
shapes. The 
observation that the vast majority of the buildings can be described by these 
simple 
shapes indicates that modeling the overall building stock in a damage 
simulation can be 
adequately accomplished using the simple rectangular-shaped building models 
described 
in Chapters 6 and 7. 

Residential Building Stock. Using the aerial photographs used to estimate 
roof cover 
damage in the Miami area, we estimated the fraction of one and two story 
houses, and the 
fraction of homes with either hip or gable roofs. We did not attempt to 
characterize the 
plan shapes of the residential buildings. 

From these photographs a total of 1633 homes could be seen. Two story homes 
comprised 21% of the population, all of which were observed to have gable 
roofs. In the 
case of the one story homes, 23% had hip roofs, with the remainder having 
gable roofs. 
No flat-roofed homes were observed in these photographs. In cases where the 
homes had 
a roof which was a combination of a hip section and a flat section it was 
classified as a 
hip roof building. The same classification scheme was used for homes with 
combined flat 
and gable roofs. In cases where the roof was a hip/gable combination, the 
building was 
classified by the roof style that was dominant. 

Chapter 8. Building Stock Classification Methods 

?
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
#Y 
Commercial and Services 
Industrial 
Institutional 
Palm Beach 
Broward 
Miami-Dade 
Figure 8.1. Commercial Building Stock Aerial Photograph Locations. 
Table 8.3. Shapes of Institutional, Commercial and Industrial Buildings 

Building Shape 
% of Buildings Associated with Each Building Shape 
Institutional Commercial Industrial All 
C 
L 
T 
Complex 
Rectangular 
Circular 
Z 
H 
X 
Triangular 
2.4 
10.5 
1.6 
4.0 
81.4 
-
-
-
-
7.5 
7.5 
2.1 
7.1 
68.4 
0.5 
-
-
0.6 
0.6 
1.5 
9.3 
1.4 
4.3 
78.3 
0.9 
3.5 
0.9 
-
-
2.5 
8.8 
1.5 
7.9 
75.8 
0.6 
1.8 
0.6 
0.1 
0.1 

The building shape data base developed by ARA during the Florida Residential 
Construction Mitigation Program (RCMP) is comprised of 1103 homes. The roof 
shapes 
of these homes consists of 29% hip roof, 56% gable, 10% combination (e.g., 
hip/gable, 
hip/flat, etc.) and 5% other (e.g., flat, mansard). Single story homes 
comprised 85% of 
the total population of buildings in the RCMP building characteristics 
database. These 
proportions are comparable to the aerial photography results. 

HAZUS-MH MR3 Technical Manual 

?
The HUD post-Andrew damage survey of 466 homes indicates that 80% are single 
story, 
and gable roofs comprise 80% of the population of one story homes. Of the two 
story 
homes, in excess of 95% had gable roofs. 

The suggested simplified default building stock geometry for single family 
residential 
construction in Southeast Florida is: 

Single Story Gable 60% 
Single Story Hip 20% 
Two Story Gable 20% 

8.3 Contractor Survey 
In order to estimate the distribution of roof cover types expected in the 
Southeast Florida 
area, a survey form was developed by Mr. Tom Smith, and sent to over twenty 
roofing 
contractors who do business in Palm Beach, Broward and Dade Counties. The 
survey 
form sent to the roofing contractors is shown in Figure 8.2. A total of 
eleven responses 
were received from the roofing contractors. In the brief discussion of the 
survey results, 
commercial/ industrial/institutional roof covers and residential roof cover 
responses are 
treated separately. 

Commercial/Industrial/Institutional Roof Covers. The results of the survey 
with respect 
to existing roof covers and newly installed roof covers are given in Tables 
8.4 and 8.5, 
respectively. For the existing population of buildings, the survey results 
clearly indicate 
that built-up and modified bitumen roof covers are the most commonly applied 
roof 
cover for non-residential buildings in Southeast Florida. These roof covers 
are expected 
to constitute approximately 85% of the existing population of non-residential 
buildings. 
As discussed earlier, the roof cover modeling methodology treats the failure 
of modified 
bitumen and built up roofs in the same manner. 

The results of the survey with respect to new roof cover installations 
indicates the total 
combined population of built-up and modified bitumen roofs is about the same 
as for the 
existing population of roof covers, but there is a trend towards using more 
modified 
bitumen roofs than built-up with aggregate surface. 

As seen in Table 8.6, the results of the survey show that existing roof 
covers were not 
typically installed such the resistance of the roofs would be higher in the 
more highly 
loaded corner regions. 

The uplift capacity of the roof covers on commercial/industrial/and 
institutional buildings 
is implied by the results given in Table 8.7. Only two of the eleven 
respondents indicated 
that they felt roof covers were installed such that they met the Factory 
Mutual FM 1-90 
standards. 

Quality of installation is addressed in Table 8.8, where it is suggested that 
on average, 
prior to 1993, only 50% of the roofs were well installed. The contractors 
indicate that the 
quality of installation has improved beginning in about 1993-1994, about the 
time the 
newest edition of the South Florida Building Code was issued. 

Chapter 8. Building Stock Classification Methods 

?
HAZUS Roof Covering Survey
Broward, Dade and Palm Beach
January 1999

Contact: name ___________________________________________ 

Company: ____________________________________________ 

Phone number __________________________ 

1. Please check which County(s) you are reporting on: Broward ___, Dade ___, 
Palm Beach ___. 
2. What percentage of the following types of roof coverings occur on the 
existing population of roofs on: 
  Commercial/Institutional/Industrial Buildings (total percent = 100): 
___ Built-up, aggregate surface 

___ Built-up, cap sheet or smooth surface

 ___ Liquid-applied

 ___ Metal panel, architectural

 ___ Metal panel, structural

 ___ Modified bitumen

 ___ Single-ply (e.g., EPDM, PVC, TPO, CSPE), aggregate ballasted

 ___ Single-ply, paver ballasted

 ___ Single-ply, air-pressure equalized (e.g., Kelly system)

 ___ Single-ply, fully adhered

 ___ Single-ply, mechanically attached

 ___ Sprayed polyurethane foam

 ___ Other 

  Residential Buildings (including apartments and condominiums): 
___ Asphalt shingles

 ___ Cement-fiber

 ___ Metal panel, architectural

 ___ Metal panel, structural

 ___ Metal shingles

 ___ Slate

 ___ Tile

 ___ Wood shakes/shingles 

Other 

  What is the current replacement rate (i.e., what percentage of the existing 
roofs are reroofed each year), 
as a function of the following types of roof systems: 
___ Low-slope membranes (e.g., built-up, modified bitumen, single-ply) 
___ Asphalt shingles 
___ Metal panels 
___ Tile 
___ Wood shakes/shingles

  What will be the average service life (in years) of the existing roofs, by 
system type 
and time of installation: 
1. Roofs installed around 1980: 
___ Built-up 
___ Modified bitumen 
___ Single-ply 
___ Sprayed polyurethane foam

 ___ Asphalt shingles 
___ Cement fiber 
___ Metal panel 
___ Metal shingles 
___ Slate 
___ Tile 
___ Wood shakes/shingles

  Roofs installed around 1990: 
___ Built-up 
___ Modified bitumen 
___ Single-ply 
___ Sprayed polyurethane foam

 ___ Asphalt shingles 
___ Cement fiber 
___ Metal panel 
___ Metal shingles 
___ Slate 
___ Tile 
___ Wood shakes/shingles

3. What percentage of the following types of new and reroofing systems are 
being 
stalled on: 
  Commercial/Institutional/Industrial Buildings (total percent = 100) Roofs 
currently beinginstalled: 
___ Built-up, aggregate surface
___ Built-up, cap sheet or smooth surface 
___ Liquid-applied
___ Metal panel, architectural
___ Metal panel, structural
___ Modified bitumen
___ Single-ply (e.g., EPDM, PVC, TPO, CSPE), aggregate ballasted
___ Single-ply, paver ballasted
___ Single-ply, air-pressure equalized (e.g., Kelly system)
___ Single-ply, fully adhered 
___ Single-ply, mechanically attached
___ Sprayed polyurethane foam 
___ Other

1. Residential Buildings (including apartments and condominiums): 
___ Asphalt shingles 
___ Cement-fiber
___ Metal panel, architectural
___ Metal panel, structural
___ Metal shingles 
___ Slate 
___ Tile
___ Wood shakes/shingles 
___ Other

  What is the average age of all of the existing roofs, by system type: 
___ Built-up
___ Modified bitumen 
___ Single-ply
___ Sprayed polyurethane foam

___ Asphalt shingles
___ Cement fiber
___ Metal panel
___ Metal shingles 
___ Slate 
___ Tile

Wood shakes/shingles 

  Roofs currently being installed: 
___ Built-up 
___ Modified bitumen 
___ Single-ply 
___ Sprayed polyurethane foam

 ___ Asphalt shingles 
___ Cement fiber 
___ Metal panel 
___ Metal shingles 
___ Slate 
___ Tile 
___ Wood shakes/shingles

  What is the quality of workmanship of the existing roofs: 
1. Installed prior to Hurricane Andrew: Good ___ Poor ___ 
  Installed within the first two years after Hurricane Andrew: Good ___ Poor 
___ 
  Installed after the first two years following Andrew: Good ___ Poor ___ 
  Prior to Hurricane Andrew, were the corner areas of the roof typically more 
securely attached than the field of the roof for: 
  The roof covering: Yes ___ No ___ 
  The roof deck: Yes ___ No ___ 
  Prior to Hurricane Andrew, did the low-slope roof systems typically 
comply with: 
  FM 1-60: Yes ___ No ___ Unknown ___ 
  FM 1-90: Yes ___ No ___ Unknown ___ 
. 

Figure 8.2. Survey Form Sent to Roof Contractors to Ascertain Relative 
Frequency 
of Roof Cover Types. 

HAZUS-MH MR3 Technical Manual 

?
Table 8.4. Responses to the Question:  What Percentage of the Following Types 
of 
Roof Coverings Occur on the Existing Populations of 
Commercial/Institutional/Industrial Buildings?  

Roof Cover Type 
Response Number 
1 2 3 4 5 6 7 8 9 10 11 AVE 
Built-up, aggregate surface 
Built-up, cap sheet or smooth 
surface 
Liquid-applied 
Metal panel, architectural 
Metal panel, structural 
Modified bitumen 
Single-ply aggregate ballasted 
Single-ply, paver ballasted 
Single-ply, air-pressure equalized 
Single-ply, fully adhered 
Single-ply, mechanically attached 
Sprayed polyurethane foam 
Other 
20 
40 
0 
0 
0 
40 
0 
0 
0 
0 
0 
0 
0 
30 
8 
1 
5 
3 
25 
10 
2 
1 
5 
5 
5 
0 
50 
5 
0 
5 
0 
30 
0 
0 
0 
5 
5 
0 
0 
60 
20 
0 
0.05 
0.05 
15 
0 
0 
0 
0.05 
2 
0.05 
1 
60 
15 
0 
0 
0 
25 
0 
0 
0 
0 
0 
0 
0 
40 
20 
0 
0 
0 
20 
5 
0 
0 
0 
0 
5 
0 
30 
10 
0 
10 
0 
18 
0 
0 
0 
10 
5 
2 
5 
30 
40 
0 
0 
0 
30 
0 
0 
0 
0 
0 
0 
0 
55 
15 
0 
0 
0 
25 
0 
2 
0 
0 
2 
1 
0 
30 
13 
1 
5 
5 
25 
2 
2 
1 
2 
2 
1 
1 
41.8 
19.2 
0.2 
0.6 
0.8 
26.1 
1.8 
0.6 
0.2 
2.3 
2.2 
1.5 
0.7 
Total 100 100 100 98.2 100 90 90 100 100 90 100 

Table 8.5. Responses to the Question:  What Percentage of the Following Types 
of 
New and Re-roofing Systems are Being Installed on 
Commercial/Institutional/Industrial Buildings?  

Roof Cover Type 
Response Number 
1 2 3 4 5 6 7 8 9 10 11 AVE 
Built-up, aggregate surface 
Built-up, cap sheet or smooth 
surface 
Liquid-applied 
Metal panel, architectural 
Metal panel, structural 
Modified bitumen 
Single-ply aggregate ballasted 
Single-ply, paver ballasted 
Single-ply, air-pressure equalized 
Single-ply, fully adhered 
Single-ply, mechanically attached 
Sprayed polyurethane foam 
Other 
20 
40 
0 
0 
0 
40 
0 
0 
0 
0 
0 
0 
0 
30 
5 
2 
7 
2 
30 
10 
3 
1 
2 
3 
3 
2 
50 
0 
0 
10 
0 
30 
0 
0 
0 
5 
5 
0 
0 
10 
30 
0 
5 
1 
50 
0 
0 
0 
1 
2 
1 
0 
70 
4 
0 
0 
0 
25 
0 
0 
0 
1 
0 
0 
0 
30 
15 
0 
0 
0 
20 
20 
0 
0 
10 
0 
5 
0 
40 
20 
0 
5 
0 
21 
0 
0 
0 
10 
2 
2 
0 
30 
40 
0 
0 
0 
30 
0 
0 
0 
0 
0 
0 
0 
30 
10 
0 
3 
0 
50 
0 
0 
0 
0 
2 
0 
5 
19 
19 
0 
1 
1 
50 
6 
1 
1 
1 
1 
0 
0 
32.9 
18.3 
0.2 
3.1 
0.4 
34.6 
3.6 
0.4 
0.2 
3.0 
1.5 
1.1 
0.7 
Total 100 100 100 100 100 100 100 100 100 100 100 

Table 8.6. Responses to the Question:  Prior to Hurricane Andrew, Were the 
Corner Areas of the Roof Typically More Securely Attached than the Field of 
the 
Roof?  

Response Number 
1 2 3 4 5 6 7 8 9 10 11 
The roof covering 
The roof deck 
No 
No 
No 
No 
No 
No 
No 
No 
Yes 
Yes 
No 
No 
No 
No 
No 
No 
No 
No 
No 
Yes 
No 
No 

Chapter 8. Building Stock Classification Methods 

?
Table 8.7. Responses to the Question:  Prior to Hurricane Andrew, Did the Low
Slope Roof Systems Typically Comply with FM I-60 or FM I-90?  

Response Number 
1 2 3 4 5 6 7 8 9 10 11 
FM 1-60 Yes Yes No Yes U No Yes U U Yes 
FM 1-90 No No Yes No Yes U No No U U No 

U = Unknown 

Table 8.8. Responses to the Question:  What is the Quality of Workmanship of 
the 
Existing Roofs?  

Installation Time 
Response Number Summary 
1 2 3 4 5 6 7 8 9 10 11 
Prior to Hurricane Andrew G G P P G G P G G P P 55% G, 45% P 
Within first two years following Andrew G P G/P P G G P G G P P 50% G, 50% P 
After the first two years following Andrew G P G P G G G G G G G 82% G, 18% P 

Residential Roof Covers. Not surprisingly, the survey results indicate that 
90% of the 
residential roof covers are either tile or shingle, with tile roofs being 
more common than 
shingle roofs (Table 8.9). Wood shakes and metal panels were felt to be the 
next most 
common roof cover type, representing 3.3% and 2.3% of the total roof covers 
in use in 
residential buildings. 

In new construction and re-roofing (Table 8.10), shingle and tiles again are 
estimated to 
comprise about 90% of the roofs. 

The RCMP data on 1100 homes in Dade, Broward, and Palm Beach counties 
indicates 
that about 47% have shingles, 45% tiles, 6% built-up, and the remaining 2% 
are wood 
shakes, slate and metal roof. These data will be used to develop the default 
inventory data 
for the South Florida regional study. 

Table 8.9. Responses to the Question:  What Percentage of the Following Types 
of 
Roof Coverings Occur on the Existing Populations of Residential Buildings 
(Including Apartments and Condominiums)?  

Roof Cover Type 
Response Number 
1 2 3 4 5 6 7 8 9 10 11 AVE 
Asphalt shingles 80 35 40 33 30 10 50 55 40 40 41.3 
Cement-fiber 0 2 0 0 0 0 0 0 0 1 0.3 
Metal panel, architectural 0 5 10 2 0 0 0 0 0 6 2.3 
Metal panel, structural 0 1 0 1 0 0 0 0 0 1 0.3 
Metal shingles 0 3 0 0 0 0 0 0 0 1 0.4 
Slate 0 2 0 0 0 0 0 0 0 1 0.3 
Tile 20 40 40 60 70 90 50 45 40 40 49.5 
Wood shakes/shingles 0 10 10 3 0 0 0 0 0 10 3.3 
Other 0 4 0 1 0 0 0 0 20 0 2.5 
Total 100 102 100 100 100 100 100 100 100 100 100 

HAZUS-MH MR3 Technical Manual 

?
Table 8.10. Responses to the Question:  What Percentage of the Following 
Types of 
New and Re-roofing Systems are Being Installed on Residential Buildings 
(Including 
Apartments and Condominiums)?  

Roof Cover Type 
Response Number 
1 2 3 4 5 6 7 8 9 10 11 AVE 
Asphalt shingles 50 30 10 65 50 5 50 50 40 40 39 
Cement-fiber 0 0 0 0 0 0 0 0 0 0 0 
Metal panel, architectural 0 8 10 3 0 0 0 0 0 0 2.1 
Metal panel, structural 0 1 0 0 0 0 0 0 0 0 0.1 
Metal shingles 0 3 0 0 0 0 0 0 0 0 0.3 
Slate 0 1 0 0 0 0 0 0 0 0 0.1 
Tile 50 45 70 30 50 95 50 50 40 60 54 
Wood shakes/shingles 0 5 10 1 0 0 0 0 0 0 1.6 
Other 0 7 0 1 0 0 0 0 20 0 2.8 
Total 100 100 100 100 100 100 100 100 100 100 0 

Chapter 8. Building Stock Classification Methods 

?
Chapter 9. Loss Model Validation Studies for 
Hurricanes Andrew, Erin, Opal and Hugo 

9.1 Introduction 
The objective of the validation studies is to assess the performance of the 
hazard-loadresistance-damage-loss model through comparisons of simulated and 
observed loss 
statistics. The modeling approach entails selecting a population of buildings 
that are 
representative of those associated with the study regions (in these examples, 
South 
Florida, the Florida Panhandle, and South Carolina), locating these buildings 
within the 
study region, assigning the terrain values, performing the hurricane 
simulation, damaging 
the buildings during the passage of the hurricane and finally computing the 
losses. Each 
of the steps noted above requires some assumptions, particularly with respect 
to the 
building stock. The assumptions regarding the building stock are discussed on 
a case by 
case basis later in this section. 

Loss model validation studies have been completed using the load, damage and 
loss 
models described in Chapters 4 through 7, coupled with the terrain modeling 
described in 
Chapter 3. The end-to-end loss validation studies are performed using ZIP 
Code averaged 
loss data for Hurricane Andrew obtained from two different sources, 
residential loss data 
provided by an insurance company for Hurricanes Erin and Opal, and 
residential loss data 
using ZIP Code averaged data for Hurricane Hugo in South Carolina. 

The loss data provided for the Hurricanes Andrew and Hugo comparisons 
represents the 
total repair and replacement losses associated with the building and the 
contents. The ZIP 
Code averaged loss data are given in Bhinderwala (1995). A second set of 
Hurricane 
Andrew data is also ZIP Code averaged data for building and content losses. 
The loss 
data for Hurricanes Erin and Opal represents losses associated with damage to 
the 
building only. 

Since both the actual and modeled losses are very sensitive to the wind 
speeds 
experienced at a given location, each set of loss comparisons is accompanied 
with a 
discussion of the modeled wind speeds, with comparisons of modeled and 
observed wind 
speeds presented where possible. 

9.2 Example Results   Hurricane Andrew 
For the Hurricane Andrew damage to loss simulation, the building stock is 
modeled 
assuming 80% of the homes were single story (25% hip, 75% gable). All two 
story 
homes are modeled as having gable roofs. 63% of the one story homes and 50% 
of the 
two story homes are assumed to have a garage. 40% of the homes use 6d roof 
sheathing 
nails spaced at 6. on center at the edges and 12. on center in the field, 
with the remaining 
60% of the homes having sheathing nailed with 8d nails using the 6./12. 
spacing. All 
roof-wall connections are assumed to be strapped. The roof cover of the homes 
are 
modeled with 50% of the homes having a shingle roof cover and 50% of the 
homes 

HAZUS-MH MR3 Technical Manual 

?
having tiles as the roof cover. All loss analyses for Hurricane Andrew are 
performed with 
the construction quality factor set equal to one. The average surface 
roughness at the ZIP 
Code level is estimated using both the FWMD data and the MRLC data. 
Therefore, two 
example sets of results are given, one using the FWMD terrain data base and 
the other 
using the MRLC terrain database. The hurricane simulation is performed with 
the wind 
speeds computed at the geographic centroid of each ZIP Code. The houses are 
assumed 
to be randomly oriented within a ZIP Code. 

The loss ratios (total loss divided by the total coverage limits) are plotted 
vs. the 
maximum modeled peak gust wind speed (open terrain) computed at each ZIP Code 
centroid. The value of the contents in this example is taken as 70% of the 
value of the 
structure, and the losses are capped so that they cannot exceed the insured 
value of the 
structure and/or contents. It is worth noting that in many instances the cost 
of rebuilding a 
severely damaged house can exceed the replacement value of the house because 
it is 
more expensive to demolish and replace the building than it is to build a new 
building on 
a site which does not have an existing structure. Total losses exceeding the 
replacement 
value of a building occurred frequently in the heavily damaged areas 
associated with 
Hurricane Andrew. 

Comparisons of the predicted and observed losses are plotted vs. the modeled 
peak gust 
wind speed at the centroid of the ZIP Code in Figure 9.1 for both terrain 
cases. The 
results indicate that using the HAZUS loading, damage and loss models for 
residential 
buildings works reasonably well. 

0 
10 
20 
30 
40 
50 
60 
70 
80 
90 
100 
Model_FWMD 
Model_MRLC 
Bhinderwala 
Other Insurer 
60 80 100 120 140 160 

180 

Peak Gust Wind Speed (mph) in Open Terrain 

Figure 9.1. Comparison of Observed and Simulated ZIP Code Averaged Losses 
Produced by Hurricane Andrew in Dade County, Florida. 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
Figure 9.2 shows an x-y plot comparing modeled and observed losses for the 
two 
different terrain database cases. Results are given for losses in the range 
of 0 to 10%, 0 to 
50% and 0 to 100%, to help visualize how the model performs for various loss 
ranges. To 
facilitate the assessment of the loss sensitivity to the assumed terrain, 
modeled loss ratios 
using the FWMD terrain data and the MRLC terrain data are compared in Figure 
9.3. No 
significant bias is found using either of these two databases. 

0 - 10% 
0 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
Modeled Loss Ratio 
Bhinderwala - FWMD 
Bhinderwala - MRLC 
Other Insurer - FWMD 
Other Insurer - MRLC 
01 234 5678 910 
Observed Loss Ratio 

0 -50% 

0 5 101520253035404550 
Observed Loss Ratio 

0 - 100% 

0 10 20 30 40 50 60 70 80 90 100 
Observed Loss Ratio 

Modeled Loss Ratio 
0 
5 
10 
15 
20 
25 
30 
35 
40 
45 
50 
Bhinderwala - FWMD 
Bhinderwala - MRLC 
Other Insurer - FWMD 
Other Insurer - MRLC 
100 
Modeled Loss Ratio 0 
10 
20 
30 
40 
50 
60 
70 
80 
90 Bhinderwala -FWMD 
Bhinderwala -MRLC 
Other Insurer - FWMD 
Other Insurer - MRLC 
Figure 9.2. Comparison of Modeled vs. Observed Losses. 

HAZUS-MH MR3 Technical Manual 

?
0 
10 
20 
30 
40 
50 
60 
70 
80 
90 
100 
0 10 20 30 40 50 60 70 80 90 100 

Modeled Loss Ratios Using FWMD Terrain (%) 

Figure 9.3. Comparison of Modeled Losses Using FWMD Terrain and MRLC 
Terrain. 

The geographic variation of the observed and modeled losses is shown in 
Figures 9.4 
through 9.7. Comparing the losses given in Figures 9.4 and 9.5 (both actual 
losses) 
provides an indication of the statistical variation that can be expected from 
the data alone, 
without taking into account errors introduced by wind speed modeling, 
terrain, etc. 

Total Losses. The total loss (building plus content), summed over all of Dade 
County, is 
estimated assuming that both the number of policies and value of these 
policies in each 
ZIP Code are the same. In each case, a contribution to the estimated total 
loss was 
computed only if the insurer had policies in the given ZIP Code. This is 
clearly not the 
case in Dade County, but a comparison presented in this manner is valid, and 
helps mask 
the identity of a given insurer. The aggregate losses are given in Table 9.1. 
Note that the 
actual and modeled totals given in Table 9.1 differ between cases because the 
number of 
ZIP Codes having policies is different for the different insurers. 

The results shown in Table 9.1 indicate that the modeled losses resulting 
from the two 
terrain models (FWMD and MRLC) are slightly lower than the observed losses, 
with the 
FWMD terrain model yielding the higher values of the estimated losses. 

Modeled Wind Speeds in Dade County Produced by Hurricane Andrew. Figure 9.8 
shows a contour representation of the maximum peak gust wind speeds produced 
by the 
hurricane model for the Hurricane Andrew simulation. The maximum wind speeds 
produced by the model are of the order of 165 mph, with the maximum wind 
speeds 
occurring in the same area as the maximum losses as shown in Figures 9.4 and 
9.5. 
Figure 9.8 also shows the locations of three anemometers where wind speed and 
directions were recorded during the storm, as well as the location of the 
nine NAHB 
survey sites discussed in Chapter 4, for which wind speed estimates were 
provided by 
NOAA/HRD. 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
( 
) 

Storm Track 
73 
1 
1 
2 
84 
9 
11 
45 
67 
5 
95 
83 
2 
68 
64 
73 
3 
28 
2 
2 2 
2 
1 
81 
1 
39 
2 
2 
2 
37 
4 
2 
4 
3 
8 
63 
2 
3 
10 
8 
12 
4 
3 2 
2 
2 
2 
4 
5 
11 
6 
13 
3 
90 
5 
4 
3 
2 
6 
2 
59 
81 
6 
5 
13 
8 
0 - 5 
5 - 10 
10 - 15 
15 - 20 
20 - 30 
30 - 40 
40 - 50 
50 - 70 
70 - 100 
Hurricane Andrew Loss Ratio 
by Zipcode (from Bhinderwala
Figure 9.4. Loss Ratios by ZIP Code in Dade County (Bhinderwala Data). 

Hurricane Andrew Loss Ratio ( 
by Zipcode (from other insurer 
0 - 5 
5 - 10 
10 - 15 
15 - 20 
20 - 30 
30 - 40 
40 - 50 
50 - 70 
70 - 100 
5 
2 
3 
5 
6 
70 
79 
4 
95 
7 
2 
79 
55 
15 
2 
1 
79 
35 
5 
2 
21 
4 
1 
7 
2 
8 
2 
2 
7 
6 
15 
5 
13 
8 
3 
2 
3 
6 
4 
15 
52 
63 
3 
Storm Track 
Figure 9.5. Loss Ratios by ZIP Code in Dade County (Other Insurer Data). 

HAZUS-MH MR3 Technical Manual 

?
Storm Track 
51 
1 
3 
2 
64 
7 
13 
68 
2 
58 
6 
66 
57 
3 
51 
59 
3 
38 
2 
6 
1 1 
1 
1 
64 
1 
44 
2 
1 
1 
41 
6 
1 
7 
2 
8 
65 
2 
3 
19 
1111 
30 
5 
2 2 
1 
1 
3 
7 
19 
3 
25 
1 
60 
77 
62 
6 
13 
3 
1 
4 
18 
2 
59 
16 
63 
5 
7 
7 
9 
13 
6 7 
42 
7 
0 -5 
5 -10 
10 -15 
15 -20 
20 -30 
30 -40 
40 -50 
50 -70 
70 -100 
Hurricane Andrew Loss Ratio (%) 
by Zipcode (Model FWMD) 
Figure 9.6. Modeled Loss Ratio (FWMD). 

Hurricane Andrew Loss Ratio (%) 
by Zipcode (Model MRLC) 
0 -5 
5 -10 
10 - 15 
15 - 20 
20 - 30 
30 - 40 
40 - 50 
50 - 70 
70 - 100 
Storm Track 
55 
1 
3 
2 
63 
7 
11 
53 
1 
60 
6 
66 
54 
3 
51 
55 
4 
33 
2 
5 
1 1 
1 
1 
57 
1 
36 
2 
1 
1 
40 
6 
1 
7 
1 
9 
64 
2 
3 
16 
1010 
23 
5 
2 2 
0 
1 
3 
7 
16 
3 
19 
1 
61 
77 
50 
6 
11 
3 
2 
5 
19 
2 
49 
13 
72 
5 
7 
7 
8 
20 
7 
56 
126 
Figure 9.7. Modeled Loss Ratio (MRLC). 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
Table 9.1. Comparison of Modeled and Observed Losses from Hurricane Andrew 

Case Actual Loss Ratio(%) 
Modeled Loss Ratio (%) 
(FWMD) 
Modeled Loss Ratio (%) 
(MRLC) 
Bhinderwala 
Other Insurer 
19.1 
15.7 
18.0 
15.3 
17.0 
13.8 

x{ 
x{ 
x{ 
x{ 
x{ 
x{ 
x{
x{ 
x{ 
(^ 
(^ 
(^ 
Storm Track 
160-165 
120-125 
80-85 
ShadingRepresents5 mph Rangesof Peak Gust 
Speeds inOpen Terrain 
Hurricane Andrew, 1992 
100-105 
Virginia Key Sewage Plant 
NGWLMS DARDC 
140-145 "!1"!2 
"!3"!4 "!5 
"!6"!7 
"!8 
"!9 
FWYF1 
Figure 9.8. Swath of Modeled Peak Gust Wind Speeds Produced by Hurricane 
Andrew. 

Figure 9.9 presents comparisons of the simulated and observed peak gust wind 
speeds 
and the mean wind directions at the three anemometer sites. The agreement 
between the 
observed and modeled wind speeds and directions is generally good. One major 
discrepancy in the comparison of the peak gust wind speed recorded at the 
Haulover site 
at 08:00 is associated with an anomalous gust. Note that the wind speeds 
recorded at the 
Virginia Key site are not from a continuous record and, thus, the peak values 
shown may 
not correspond to the maximum in the storm. 

Figures 9.10 through 9.12 show comparisons of the model generated peak gust 
wind 
speeds at the locations of the nine NAHB damage surveys to the estimates of 
wind speeds 
and direction at these same sites derived by NOAA/HRD personnel. The 
comparisons of 
the wind speed traces suggest the NOAA/HRD representation of the wind field 
is 
somewhat broader than that produced by the wind field model. At the two 
northern 
locations (positions 1 and 2 as shown in Figure 9.8), the maximum values of 
the HRD 
estimated peak gust wind speeds are about 10 mph to 15 mph higher than those 
obtained 
from the wind field model. 

HAZUS-MH MR3 Technical Manual 

?
FWFY1 (C-MAN) - Hurricane Andrew 

NGWLMS DARDC - Hurricane Andrew 360 

270 

180 

0

0 

2:00 4:00 6:00 8:00 10:00 12:00 14:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 
175 
175 

Peak Gust Wind Speed (mph) Wind Direction 

Wind Direction 

90 

0 
25 
50 
75 
100 
125 
150 
2:00 4:00 6:00 8:00 10:00 12:00 14:00 
Peak Gust Wind Speed (mph) 
150 

125 

100 

75 

50 

25 

0 

Wind Direction 

2:00 4:00 6:00 8:00 10:00 12:00 14:00 
Time UTC, 24 August 1992 

Time UTC, 24 August 1992 

Virginia Key Sewage Plant - Hurricane Andrew 

360
270
180
90
0

4:00 6:00 8:00 10:00 12:00 14:00 
4:00 6:00 8:00 10:00 12:00 14:00
Time UTC, 24 August 1992
0 
25 
50 
75 
100 
125 
150 
175 
Peak Gust Wind Speed (mph) 
Figure 9.9. Comparison of Simulated and Observed Peak Gust Wind Speeds and 
Directions Produced by Hurricane Andrew. Gust Data at Virginia Key Location 
Derived from One Minute Mean Values. All Wind Speeds are Given for the Actual 
Terrain at a Height of 10 m Above Ground. 

Moving towards the south, the differences in the magnitude of the HRD maximum 
wind 
speeds and the simulated wind speeds reduce, where at the southern most 
points, the 
modeled wind speeds are slightly higher (~10 mph) than the HRD values. 

Overall, the differences between the modeled wind speeds and the HRD 
estimated wind 
speeds are typically less than 10%, a difference which is less than the 
uncertainty 
attributable to adjusting the aircraft measured wind speeds to surface level 
wind speeds as 
indicated in Powell and Houston (1996). 

The wind speed and direction traces from the wind field model and those 
provided by 
HRD are used in the conjunction with the damage and loss models to determine 
the effect 
of these different wind speed estimates on predicted losses. The building 
stock used in 
this damage and loss analysis is the same as described earlier for the basic 
Hurricane 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
9-9 

NAHB Location 1 NAHB Location 2 
360 

360 

315 

270 

225 

Peak Gust Wind Speed (mph) Wind Direction 

Wind Direction 

180 

135 

90 

45 

0 

8.25 8.75 9.25 9.75 10.25 10.75 8.25 8.75 9.25 9.75 10.25 10.75 
Peak Gust Wind Speed (mph) 

160 
140 

80 

60 

40 

20 

0 

180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

8.25 8.75 9.25 9.75 10.25 10.75 
8.25 8.75 9.25 9.75 10.25 10.75 
Time (UTC) 

Time (UTC) 

NAHB Location 3 NAHB Location 4 
360 

360 

Wind Direction 

315 

270 

225 

180 

135 

90 

45 

0 

315 

270 

225 

180 

135 

90 

45

Wind Direction 

0 

8.25 8.75 9.25 9.75 10.25 10.75 8.25 8.75 9.25 9.75 10.25 10.75 
Peak Gust Wind Speed (mph) 

180 
160 
140 
120 
100 
80 

Peak Gust wind Speed (mph) 

180 
160 
140 
120 
100 
80 

60 

60 

40 

40 

20 

20 

0 

0 

8.25 8.75 9.25 9.75 10.25 10.75 8.25 8.75 9.25 9.75 10.25 10.75 
Time (UTC) Time (UTC) 

Figure 9.10. Comparison of Simulated Peak Gust Wind Speeds and Wind Direction 
Obtained Using the Hurricane Wind Field Model (shown as triangles) to Wind 
Speeds Estimated by the Hurricane Research Division (shown as continuous 
line) at 
NAHB Locations 1 through 4 as Indicated in Figure 9.8. 

Andrew loss simulation example. The loss example is performed using two 
surface 
roughness (z0 = 0.15 m and z0 = 0.35 m). Results are given in Table 9.2, 
along with 
estimates of the observed losses near the locations of each of the nine wind 
speed traces 
(compare loss ratio data given in Figures 9.4 and 9.5 to locations 1 through 
9 shown in 
Figure 9.8). The losses predicted using the HRD wind speeds are notably 
higher than 
those predicted using the wind speed trace produced by the wind field model 
at locations 
one and two, simply because the HRD wind speeds are consistently higher. At 
locations 3 
through 9, the losses produced using the HRD wind speed trace are lower than 
those 
obtained using the wind field model generated wind speed trace. The high 
losses 
produced using the modeled wind speed trace are primarily a result of the 
high wind 
speeds occurring for longer periods of time and being associated with larger 
wind 
direction changes than those produced by the HRD trace. 

HAZUS-MH MR3 Technical Manual 

?
9-10 

NAHB Location 5 NAHB Location 6 

360 

360 

315 

270 

225 

Peak Gust Wind Speed (mph) Wind Direction 

Peak Gust wind Speed (mph) Wind Direction 

180 

135 

90 

45 

0 

8.25 8.75 9.25 9.75 10.25 10.75 8.25 8.75 9.25 9.75 10.25 10.75 
100 

80 

60 

40 

20 

0 

180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

8.25 8.75 9.25 9.75 10.25 10.75 
8.25 8.75 9.25 9.75 10.25 10.75 
Time (UTC) Time (UTC) 
NAHB Location 8 

NAHB Location 7 

360

360 

315

315 

270 

225 

180 

135 

90 

45 

Peak Gust Wind Speed (mph) Wind Direction 

Peak Gust Wind Speed (mph) Wind Direction 

270 

225 

180 

135 

90 

45 

0 

0 

8.25 8.75 9.25 9.75 10.25 10.75 8.25 8.75 9.25 9.75 10.25 10.75 
180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

8.25 8.75 9.25 9.75 10.25 10.75 8.25 8.75 9.25 9.75 10.25 10.75 
Time (UTC) Time (UTC) 

Figure 9.11. Comparison of Simulated Peak Gust Wind Speeds and Wind Direction 
Obtained Using the Hurricane Wind Field Model (shown as triangles) to Wind 
Speeds Estimated by the Hurricane Research Division (shown as continuous 
line) at 
NAHB Locations 5 through 8 as Indicated in Figure 9.8. 

9.3 Example Results   Hurricanes Erin and Opal 
Hurricane Erin and Opal Wind Speed Simulations. Figures 9.13 and 9.14 show 
contour 
plots of the foot print of maximum peak gust wind speeds swept out by 
Hurricanes Erin 
and Opal, as obtained from the hurricane wind field model. Figures 9.13 and 
9.14 also 
show the locations of 4 anemometers used in the wind speed validation 
studies. Figures 

9.15 and 9.16 show comparisons of the simulated and observed peak gust wind 
speeds of 
the four anemometer locations of both storms. In the wind speed validation 
examples 
given in Figures 9.15 and 9.16, note that the records from Eglin AFB and the 
Panama 
City Beach stations are not continuous records and, as a result, the maximum 
gust wind 
speeds produced at these sites may not have been recorded. 
Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
NAHB Location 9 

8.25 8.75 9.25 9.75 10.25 10.75 
8.25 8.75 9.25 9.75 10.25 10.75 
Time (UTC) 

Figure 9.12. Comparison of Simulated Peak Gust Wind Speeds and Wind Direction 
Obtained Using the Hurricane Wind Field Model (shown as triangles) to Wind 
Speeds Estimated by the Hurricane Research Division (shown as continuous 
line) at 
NAHB Location 9 as Indicated in Figure 9.8. 

Table 9.2. Comparisons of Loss Ratios Obtained Using the HRD Wind Speed 
Estimates to the Wind Field Model Wind Speed Estimates 

0 
45 
90 
135 
180 
225 
270 
315 
360 
Wind Direction 
0 
20 
40 
60 
80 
100 
120 
140 
160 
180Peak Gust Wind Speed (mph) 
Location 
Wind Field 
Model 
Estimated 
Peak Gust 
Wind Speed 
(mph) 
HRD 
Estimated 
Peak Gust 
Wind Speed 
(mph) 
Loss Ratios 
Predicted 
Using Wind 
Field Model 
Wind Speeds 
(z0=0.15 m) 
Loss Ratios 
Predicted 
Using HRD 
Wind Speeds 
(z0=0.15 m) 
Actual Loss 
Ratios (%) 
Loss Ratios 
Predicted 
Using Wind 
Field Model 
Wind Speeds 
(z0=0.35 m) 
Loss Ratios 
Predicted 
Using HRD 
Wind Speeds 
(z0=0.35 m) 
1 158.5 176.1 66% 85% ~50%-60% 51% 75% 
2 161.4 173.2 71% 86% ~50%-60% 56% 74% 
3 159.6 160.3 68% 61% ~80% 53% 44% 
4 159.4 158.1 64% 55% ~80% 49% 38% 
5 160.4 160.5 70% 61% ~80% 54% 44% 
6 161.2 157.5 66% 54% ~80% 51% 39% 
7 160 155.8 61% 49% ~60%-80% 45% 33% 
8 160.6 154.9 62% 46% ~60%-80% 44% 30% 
9 159.9 153.1 55% 44% ~70% 40% 30% 

HAZUS-MH MR3 Technical Manual 

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9-12 
Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 
(^ 
(^(^ 
(^ 
(^ 
60-70 
90-100 
100-110 
80-90 
70-80 
Shading Represents 
10 mph Ranges of Peak 
Gust Speeds in Open Terrain
Hurricane Erin, 1995 
Storm Track 
Panama City Beach 
Hurlburt 
Eglin AFB-B71 
Pensacola LLWSAS 
Figure 9.13. Swath of Simulated Peak Gust Wind Speeds (10 m in Open Terrain) 
Produced by Hurricane Erin. 
(^ 
(^(^ 
(^ 
(^ 
Shading Represents Storm Track 
10 mph Ranges of 
Peak Gust Speeds 
in Open Terrain Hurricane Opal, 1995 
120-130
100-110 
80-90 
70-80 
60-70 
90-100 
Pensacola LLWSAS Eglin AFB-B71 
Hurlburt 
Panama City Beach 
110-120 
Figure 9.14. Swath of Simulated Peak Gust Wind Speeds (10 m in Open Terrain) 
Produced by Hurricane Opal. 
?
9-13 

Hurlburt, Hurricane Erin Pensacola LLWSAS, Hurricane Erin 

360 

360 

270 

180 

0 

0 

10:00 12:00 14:00 16:00 18:00 20:00 14:00 15:00 16:00 17:00 18:00 19:00 
150 
150 

Peak Gust Wind Speed (mph) Wind Direction Peak Gust Wind Speed (mph) Wind 
Direction 

Peak Gust Wind Speed (mph) Wind Direction Peak Gust Wind Speed (mph) Wind 
Direction 

90 

0 

125 

100 

75 

50 

25 

0 

10:00 12:00 14:00 16:00 18:00 20:00 
14:00 15:00 16:00 17:00 18:00 19:00 
Time UTC, 3 August 1995 Time UTC, 3 August 1995 
Eglin AFB-B71, Hurricane Erin Panama City Beach, Hurricane Erin 
360 

270 

180 

90 

0 

0 

8:00 10:00 12:00 14:00 16:00 18:00 20:00 8:00 10:00 12:00 14:00 16:00 18:00 
20:00 
150 
150 

125 

100 

0 

125 

100 

75 

50 

25 

0 

8:00 10:00 12:00 14:00 16:00 18:00 20:00 
8:00 10:00 12:00 14:00 16:00 18:00 20:00 
Time UTC, 3 August 1995 Time UTC, 3 August 1995 
Figure 9.15. Comparison of Simulated and Observed Peak Gust Wind Speeds and 
Directions Produced by Hurricane Erin. Gust Data at Eglin AFB Location 
Derived 
from One Minute Mean Values. All Wind Speeds are Given for the Actual Terrain 
at a Height of 10 m Above Ground. 

In the case of Hurricane Erin, the comparisons at the Hulburt Field station 
suggest that 
the model may be underestimating the peak gust wind speeds by about 5%, but 
is 
generally good elsewhere. In the case of Hurricane Opal, the agreement 
between modeled 
and observed wind speeds for locations on the right hand side of the storm is 
reasonable, 
but the model is overestimating the wind speeds on the left hand side of the 
storm as 
indicated by the comparison of wind speeds at the Pensacola station. The 
maximum 
simulated peak gust wind speeds are typically within about 5% to 10% of the 
observed 
values. The overall shapes of the simulated traces are similar to the 
observed traces 
indicating that the duration characteristics of the modeled and observed 
storms are 
similar. 

HAZUS-MH MR3 Technical Manual 

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9-14 

Pensacola LLWSAS, Hurricane Opal 

Hurlburt (Record 2), Hurricane Opal 360 

360 

270 

180 

Peak Gust Wind Speed (mph) Wind Direction 
Peak Gust Wind Speed (mph) Wind Direction 

Peak Gust Wind Speed (mph) Wind Direction 

90 

0 

17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 
16:00 18:00 20:00 22:00 0:00 2:00 4:00 
150 
0 
25 
50 
75 
100 
125 
17:00 18:00 19:00 20:00 21:00 22:00 23:00 0:00 
Time UTC, 4 October 1995 
0 

16:00 18:00 20:00 22:00 0:00 2:00 4:00
Time UTC, 4 October 1995
Eglin AFB-B71, Hurricane Opal Panama City Beach, Hurricane Opal 
360 

0 

0 

17:00 19:00 21:00 23:00 1:00 3:00 17:00 19:00 21:00 23:00 1:00 3:00 5:00 
150 
150 

270 

Peak Gust Wind Speed (mph) Wind Direction 

180 

90 

0 
25 
50 
75 
100 
125 
17:00 19:00 21:00 23:00 1:00 3:00 
Time UTC, 4 October 1995 
0 
25 
50 
75 
100 
125 
17:00 19:00 21:00 23:00 1:00 3:00 5:00 
Time UTC, 4 October 1995 
Figure 9.16. Comparison of Simulated and Observed Peak Gust Wind Speeds and 
Directions Produced by Hurricane Opal. All Wind Speeds are Given for the 
Actual 
Terrain at a Height of 10 m Above Ground. 

Comparison of Modeled and Observed Loss Statistics. In the Erin and Opal loss 
validation studies, the building stock is modeled assuming 25% of the single 
story 
buildings have hip roofs and 75% have gable roofs. All two story homes are 
modeled as 
having gable roofs. 70% of the one story homes and 45% of the two story homes 
are 
assumed to have a garage. The roof deck is assumed to be attached to the roof 
trusses 
with 6d nails for 40% of the buildings and with 8d nails for 60% of the 
buildings. Roof-
wall connections are assumed to be strapped in 80% of the cases and toe-
nailed for the 
other 20%. The fraction of one and two story homes is estimated from 
statistical 
information provided by the insurer. All loss analyses for Hurricanes Erin 
and Opal are 
performed with the construction quality factor set equal to one. 
Approximately 40% of 
the homes were able to be geo-coded, and thus their  exact  location and 
hence terrain 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
environment could be established. Thus, for each house that could be geo-
coded, the 
terrain is obtained by determining which land use category the house is 
located, and then 
assigning the appropriate surface roughness using the surface roughness 
tables given in 
Chapter 3. Each has been checked to determine if the unit is located on a 
barrier island or 
on the mainland. In the case of the homes that could not be geo-coded 
(approximately 
50% of the units), the location and terrain characteristics for each house 
are estimated by 
assuming that for a given ZIP Code, the non geo-coded policies belong to the 
same 
population as the geo-coded policies. 

Hurricane Erin and Opal simulations are performed with wind speed traces 
being 
recorded using grid points spaced at 0.100. Since the wind speed grid used in 
the 
simulation of the storms does not correspond to the exact location of each 
home, the wind 
speed experienced by each house is obtained by using the wind speed and 
direction trace 
associated with the nearest grid point. Using this wind speed and direction 
trace, damage 
and ensuing losses are produced for all potential building types in the 
assumed building 
stock. The total loss ratio in each ZIP Code and county examined is estimated 
by 
summing the simulated losses weighted in proportion to the assumed number of 
units 
associated with each combination of deck thickness, roof shape, etc. 

Figures 9.17 and 9.18 show the comparison of modeled and observed loss ratios 
at ZIP 
Code level for Hurricanes Erin and Opal, respectively. Only ZIP Codes with 
more than 
10 policies are included in the comparison. The model is seen to provide 
reasonable 
agreement between modeled and actual loss ratios for Hurricane Erin. However, 
for 
Hurricane Opal, the model significantly underestimates the loss ratios of ZIP 
Codes with 
modeled peak gust wind speeds between 80 and 100 mph and overestimates the 
loss 
ratios of ZIP Codes with modeled peak gust wind speeds greater than 120 mph. 
In the 
comparison of observed and modeled losses averaged over the counties impacted 
by 
Hurricanes Erin and Opal given in Figures 9.19 through 9.22, it can be seen 
that near the 
point of landfall, the model tends to overestimate the losses in Santa Rosa 
County for 
Hurricane Opal and underestimate the losses in Santa Rosa County for 
Hurricane Erin, 
suggesting the wind field model maybe underestimating the Hurricane Erin wind 
speeds 
near the eyewall and overestimating the Hurricane Opal speeds. Besides the 
simulated 
wind speeds, a number of factors, including the assumed building stock, 
terrain, 
assumption of the non geo-coded policies belonging to the same population as 
geo-coded 
ones, can all contribute to the discrepancies between the modeled and actual 
loss ratios. 

9.4 Example Results - Hurricane Hugo 
Figure 9.23 shows the maximum peak gust wind speed contours of Hurricane Hugo 
produced by the hurricane model. The maximum wind speeds predicted by the 
model are 
between 130 mph and 140 mph. Figure 9.23 also shows the locations of ten 
anemometers 
and the actual and modeled maximum peak gust wind speeds at these locations 
where 
wind speeds and directions were recorded during the storm. The mean of the 
modeled-toactual gust wind speed ratios is 1.02 and the COV is 0.08. Figures 
9.24 through 9.26 
show comparisons of the model generated peak gust wind speeds and directions 
to the 
estimates of wind speeds and directions at each of the ten locations. The 
overall 
performance of the hurricane model is, again, satisfactory. 

HAZUS-MH MR3 Technical Manual 

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9-16 

12 

10 

Actual 
Model 
8 

6 

4 

2 

0 
0 20 40 60 80 100 
Gust Wind Speed (mph) 

Figure 9.17. Hurricane Erin Building Loss Ratio Comparison. 

50 
45 
40 
35 
30 
25 
20 
15 
10 

5 
0 
0 20 40 60 80 100 120 140 
Gust Wind Speed (mph) 

Actual 
Model 
Figure 9.18. Hurricane Opal Building Loss Ratio Comparison. 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
Storm Track 
Walton Okaloosa 
Santa Rosa Escambia 
0.241 
7.971 
1.769 
0 
4.161 
Figure 9.19. Actual Building Loss Ratios   Hurricane Erin. 

Storm Track 
Santa Rosa Escambia 
3.578 0.673 
Walton Okaloosa 
0.095 1.425 
Figure 9.20. Modeled Building Loss Ratios   Hurricane Erin. 

HAZUS-MH MR3 Technical Manual 

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Okaloosa 
Santa Rosa Escambia 
8.349 
9.560 
1.613 
Storm Track 
Figure 9.21. Actual Building Loss Ratios   Hurricane Opal. 
Walton 
5.411 
Storm Track 
Santa Rosa 
12.816 
Walton Okaloosa 
Escambia 
1.402 11.197 
0.272 
Figure 9.22. Modeled Building Loss Ratios   Hurricane Opal. 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
130 - 140 
120 - 130 
110 - 120 
100 - 110 
90 -100 
70 -80 
60 -70 50 -60 
70 -80 
60 -70 
50 -60 
StormTrack 
Myrtle Beach AFB 
FBIS1 
Charleston NAS 
Charleston Airport 
Florence 
Shaw AFB 
McEntire AFB 
Columbia Airport 
Charlotte 
Actual: 63 mph 
Modeled: 60 mph 
Actual: 108 mph 
Modeled: 102 mph 
Actual: 103 mph 
Modeled: 105 mph 
Actual: 110 mph 
Modeled: 125 mph 
Actual: 74 mph 
Modeled: 79 mph 
Actual: 123 mph 
Modeled: 115 mph 
Actual: 88 mph 
Modeled: 98 mph 
Actual: 75 mph 
Modeled: 80 mph 
Actual: 86 mph 
Modeled: 87 mph 
Actual: 87 mph 
Modeled: 77 
SVLS1 
Figure 9.23. Swath of Simulated Peak Gust Wind Speeds (10 m in Open Terrain) 
Produced by Hurricane Hugo. 

In the Hugo loss validation study, the building stock is modeled assuming 70% 
of the 
homes are single story and 30% of the homes are two stories. 71% of the one 
story homes 
and 50% of the two story homes are assumed to have a garage. 75% of the homes 
are 
assumed to have gable roofs and 25% of the homes are assumed to have hip 
roofs. All 
roof covers are assumed to be shingles. 30% of the homes are assumed to use 
6d nails for 
roof-deck attachment, and 70% of the homes are assumed to use 8d nails. Roof-
wall 
connections are assumed to be toe-nailed in 90% of the cases and strapped for 
the other 
10%. The construction quality factor is set equal to one in the analysis. The 
average 
surface roughness at each ZIP Code is estimated using MRLC data. The model 
houses 
are assumed to be randomly oriented within a ZIP Codes. The insured value of 
the 
contents is taken as 70% of the insured building value. 

For each ZIP Code and each building configuration, a number of simulations 
are 
performed and the average loss ratio is calculated. Then, by taking into 
account the 
assumed building stock distribution within each ZIP Code, a weighted average 
of the loss 
ratios (for all modeled building configurations) is obtained. Comparisons of 
the predicted 
and observed loss ratios are plotted vs. the modeled peak gust wind speed at 
the centroid 
of the ZIP Code in Figure 9.27, which shows reasonably good agreement between 
the 
two. The total losses (building plus contents) summed over the entire state 
of South 
Carolina was estimated assuming that both the number of policies and value of 
these 

HAZUS-MH MR3 Technical Manual 

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9-20 

Hurricane Hugo, Savanah Light Hurricane Hugo, Folly Beach Island 

360 

360 

270 

180 

90 

Wind Direction 
Wind Direction 

Wind Direction 
Wind Direction

0 

0 

16:00 20:00 0:00 4:00 8:00 12:00 20:00 0:00 4:00 8:00 12:00 
Time UTC, 21-22 September 1989 Time UTC, 21-22 September 1989 
150 

150 

Peak Gust Wind Speed (mph) 

Peak Gust Wind Speed (mph) 

120 

90 

60 

30 

120 

0 

16:00 20:00 0:00 4:00 8:00 12:00 0 
20:00 0:00 4:00 8:00 12:00 
Time UTC, 21-22 September 1989 
Time UTC, 21-22 September 1989 

Hurricane Hugo, Charleston Airport Hurricane Hugo, Charleson Naval Station 
360 

270 

180 

90 

0

0 

20:00 0:00 4:00 8:00 12:00 20:00 0:00 4:00 8:00 12:00 
Time UTC, 21-22 September 1989 Time UTC, 21-22 September 1989 
150 

150 

Peak Gust Wind Speed (mph) 

120 

90 

60 

30

Peak Gust Wind Speed (mph) 

120 

0

0 

20:00 0:00 4:00 8:00 12:00 
20:00 0:00 4:00 8:00 12:00 
Time UTC, 21-22 September 1989 
Time UTC, 21-22 September 1989 
Figure 9.24. Comparison of Simulated and Observed Peak Gust Wind Speeds and 
Directions Produced by Hurricane Hugo. All Wind Speeds are Given for the 
Actual 
Terrain at a Height of 10 m Above Ground. 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
9-21 

Hurricane Hugo, Myrtle Beach AFB Hurricane Hugo, Columbia Airport 

360

360 

270 

0

0 

20:00 0:00 4:00 8:00 12:00 0:00 4:00 8:00 12:00 
Time UTC, 21-22 September 1989 Time UTC, 22 September 1989 
150 

150 

Wind Direction 
Wind Direction 

Wind Direction 
Wind Direction 

180 

90 

Peak Gust Wind Speed (mph) 

Peak Gust Wind Speed (mph) 

120 

120 

90 

60 

30 

0 

0 

20:00 0:00 4:00 8:00 12:00 0:00 4:00 8:00 12:00 
Time UTC, 21-22 September 1989 Time UTC, 22 September 1989 
Hurricane Hugo, McEntire AFB Hurricane Hugo, Florence Airport 

360 

360 

270 

180 

90 

0 

0 

0:00 4:00 8:00 12:00 0:00 4:00 8:00 12:00 
Time UTC, 22 September 1989 Time UTC, 22 September 1989 
150 

150 

Peak Gust Wind Speed (mph) 

Peak Gust Wind Speed (mph) 

120 

90 

60 

30 

120 

0 0 
0:00 4:00 8:00 12:00 0:00 4:00 8:00 12:00 
Time UTC, 22 September 1989 Time UTC, 22 September 1989 

Figure 9.25. Comparison of Simulated and Observed Peak Gust Wind Speeds and 
Directions Produced by Hurricane Hugo. All Wind Speeds are Given for the 
Actual 
Terrain at a Height of 10 m Above Ground. 

HAZUS-MH MR3 Technical Manual 

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Hurricane Hugo, Shaw AFB Hurricane Hugo, Charlotte Airport 

0 
90 
180 
270 
360 
Wind Direction 
0 
90 
180 
270 
360 
Wind Direction 
0:00 4:00 8:00 12:00 0:00 4:00 8:00 12:00 
Time UTC, 22 September 1989 Time UTC, 22 September 1989 
150 

Peak Gust Wind Speed (mph) 

120 

Gust Wind Speed (mph) 

120 

90 

60 

30 

0 0 
0:00 4:00 8:00 12:00 0:00 4:00 8:00 12:00 
Time UTC, 22 September 1989 Time UTC, 22 September 1989 

Figure 9.26. Comparison of Simulated and Observed Peak Gust Wind Speeds and 
Directions Produced by Hurricane Hugo. All Wind Speeds are Given for the 
Actual 
Terrain at a Height of 10 m Above Ground. 

35 

60 70 80 90 100 110 120 130 140 
Peak Gust Wind Speed (mph) in Open Terrain 
Model 
Bhinderwala 
30 
25 
20 
15 
10 
5 
0 

Figure 9.27. Comparison of Observed and Simulated ZIP Code Averaged Losses 
Produced by Hurricane Hugo in South Carolina. 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
policies in each ZIP Code are the same (for the same reason as in Andrew 
validation 
study). The results shown in Table 9.3 indicate that the modeled losses are 
slightly lower 
than the observed losses. The geographic variation of the observed and 
modeled losses 
are shown in Figures 9.28 and 9.29, respectively. 

Table 9.3. Comparison of Modeled and Observed Aggregate Losses from Hurricane 
Hugo 

Case Actual Loss Ratio(%) Modeled Loss Ratio (%) 
Bhinderwala 2.96 2.32 
9.5 Summary 
End-to-end comparisons of modeled and observed insured losses have been 
presented 
using five different sets of insurance loss data, covering four hurricanes 
and three 
different insurers. The model validation study has been performed for 
residential 
buildings only. All modeled losses have been produced using the full load and 
resistance 
based damage and loss methodology described in Chapters 4 through 7 (as 
opposed to the 
fast-running loss curves used in the end product), and include only direct 
wind-induced 
damage to the model buildings, with no modeling of additional damage that 
some 
structures will have experienced due to falling trees, nor the inclusion of 
the effects of 
minor damage associated with wind driven rain entering a structure through 
non-
breached windows, doors etc. 

Figure 9.30 shows a comparison of the modeled and observed losses for the 
five data 
sets, plotted vs. the model estimated value of the maximum peak gust wind 
speed at the 
ZIP Code centroid. The modeled Hurricane Andrew data in Figure 9.30 are for 
the case 
with the terrain modeled using the FWMD LULC database. The losses are given 
as a 
percentage, defined as the total loss of the building and contents divided by 
the insured 
value of the building and contents. The loss ratios are plotted both on a 
linear scale (left 
plot) and on a logarithmic scale (right plot). The agreement between the 
modeled and the 
observed losses is generally good, particularly for wind speeds greater than 
about 100 
mph. Figure 9.31 presents an x-y plot showing modeled losses plotted vs. the 
insurance 
loss data, where again, the agreement between the modeled and the observed 
losses is 
good. Overall, if one assumes that the value of the buildings (and contents, 
if applicable) 
in each ZIP Code plotted in Figure 9.30 is the same, the average model loss 
is 8.15%, 
compared to an observed average of 8.85%, a difference of less than 10%. 

The comparisons given in Figure 9.30 and 9.31 overall show good agreement, 
but suggest 
that the damage and loss models may underestimate the small losses that occur 
at lower 
wind speeds (less than about 100 mph). This underestimate of the losses at 
these lower 
wind speeds is not unexpected since, as noted above, the damage and ensuing 
losses 
produced by tree blowdown is not modeled, nor are some other small losses 
associated 
with damage not explicitly modeled in the damage model, such as minor loss of 
some 
types of wall covering, leaking fenestrations, damage of soffits, chimneys, 
vents, etc. 

HAZUS-MH MR3 Technical Manual 

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Storm Track 
1 
1 
2 
6 
3 
1 
2 
2 
2 
1 
2 
1 
4 
6 
2 
0 
1 
2 
2 
5 
1 
2 
4 
4 
2 
2 
0 
1 
9 
1 
8 
3 
3 
21 
2 
4 
8 
3 
3 
2 
3 
1 
6 
2 
2 
4 
0 
2 
4 
0 
8 
1 
1 
2 
26 
3 
3 
7 
5 
3 
4 
4 
4 
10 
2 
2 
1 
6 
3 
3 
5 
1 
3 
7 
5 1320 
Hurricane Hugo Loss Ratio (%) 

by Zipcode (from Bhinderwala) 

0 -2 

2 -5 

5 -10 

10 -15 

15 -20 

20 -30 

30 -50 

Figure 9.28. Loss Ratios by ZIP Code in South Carolina (Bhinderwala Data). 

Storm Track 
1 
18 
12 
1 
9 
12 
1 
5 
1 
3 
1 
11 
7 
3 
3 
1 
16 
2 
1 
4 
2 
1 
4 
1 
3 
4 
1 
1 
2 
1 
8 
71 
3 
1 
9 
3 
2 
1 
101 
2 
27 
1 
1 
3 
1 
1 
1 
4 
4 
2 
4 
12 
2 
1 
2 
1 
2 
3 
1 
2 
1 
4 
11 
14 
19 
103 4 
Hurricane Hugo Loss Ratio (%) 
by Zipcode (Model) 
0 -2 
2 -5 
5 -10 
10 -15 
15 -20 
20 -30 
30 -50 

Figure 9.29. Modeled Loss Ratio (MRLC). 

Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
9-25 

100 

100.00 

90 

80 

Insurer Loss Data 
10.00 

Insurer Loss Data 
Modeled Losses 
Loss Ratio (%) 

70 
60 
50 
40 
30 

Modeled Loss 
Loss Ratio (%)

1.00 

0.10 

20 
10 
0 

0.01 
50 70 90 110 130 150 170 50 70 90 110 130 150 170 
Peak Gust Wind Speed in Open Terrain (mph) Peak Gust Wind Speed in Open 
Terrain (mph) 

Figure 9.30. Comparison of Modeled and Observed Losses vs. Wind Speed. 

100.00 

10.00 

1.00 

0.10 

0.01 

0.01 0.10 1.00 10.00 100.00 
Insurance Data Loss Ratio (%) 
Figure 9.31. Comparison of Modeled and Observed Losses. 

Modeled Loss Ratio (%) 

Furthermore, in the case of Hurricane Hugo, losses experienced by homeowners 
to 
appurtenant structures (fences, driveways, sheds, decks, etc.) were lumped 
into payments 
made for losses to the structure and cannot be separated. The loss estimation 
model does 
not account for the additional costs associated with these losses. 

In summary, the loss validation studies run with the full development model 
have shown 
that the damage and loss models reproduce the observed losses reasonably 
well. Given 
the recent addition of the tree blowdown methodology (Chapter 12), an updated 
analysis 
is needed to determine whether the the losses predicted with the final 
version of the 
model are adequately estimated. The validation studies should also be re-run 
using the 
end user version of the HAZUS Hurricane Model to assess whether the fast-
running loss 
curves applied at the census tract level adequately encapsulate the details 
considered in 
the full development model (e.g., storm duration, changes in wind direction, 
etc.). 

HAZUS-MH MR3 Technical Manual 

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Chapter 9. Loss Model Validation Studies for Hurricanes Andrew, Erin, Opal 
and Hugo 

?
Chapter 10. Debris Generated from Damaged Buildings 

10.1 Introduction 
Debris generated by severe wind events can be categorized into five general 
types 
(Holmlin, 1993): medical (or bio-hazardous) wastes, hazardous or toxic wastes 
(HTW s), 
household garbage, burnable roadside debris, and construction and demolition 
debris. 
Burnable road side debris, which is primarily trees and other yard wastes, is 
often the 
largest among those five types of debris. Based on the scope of work 
specified by FEMA, 
only debris generated from damaged buildings (i.e., construction and 
demolition debris 
produced immediately after the event and during the rebuilding and repairing 
phases) and 
tree blow down are calculated in the current version of the model. This 
chapter presents 
the building debris model. The tree debris model is presented in Chapter 12. 

Building debris consists of construction and demolition waste that is 
generally nonhazardous 
and not water soluble. Construction and demolition debris can be further 
categorized as wood (which is bio-degradable), masonry, metal (which is 
recyclable), and 
other (which includes gypsum board, carpet, asphalt roofing material, 
insulation, ceiling, 
pipe, etc.). Masonry and other debris are usually disposed of in authorized 
landfills. 
Debris removal is often one of the most costly and challenging operations 
following a 
natural disaster (Holmlin, 1993). Due to contamination with different types 
of waste 
materials, it is also difficult to recycle most of the structural waste 
(debris). Accurate and 
prompt estimating of the total debris generated and its distribution is vital 
to ensure the 
success of a debris removal operation after a hurricane. 

A simple model for estimating the volume and type of debris has been 
developed by the 

U.S. Army Corp of Engineers (USACE) using historical data from hurricanes 
Frederic, 
Hugo, and Andrew. The USACE model was used to estimate debris produced in 
Escambia County for hurricanes Erin and Opal. This simple model is intended 
to be used 
for estimating debris produced by single-family residential buildings only. 
The model 
yields estimates of the expected volumes of burnable debris, landfill debris, 
soil debris, 
and metallic debris based on the category of hurricane winds occurring in a 
county 
combined with factors related to business use, number of households, 
vegetation density 
(light, medium or heavy) and a storm wet/dry multiplier. The model error is 
typically 
within 130% (Moorse, 2001). Recent developments of this model have enabled 
the 
analysis to be done at a census track level, resulting in higher accuracy. 
The major 
limitation of this model is the inability to take into account various 
construction and 
usage classes. For example, the model significantly overestimated the 
building debris 
produced by Typhoon Paka in Guam because the model was not able to take into 
account 
the reduction in building losses associated with the large number of 
residential buildings 
having hardened concrete roofs. 
To overcome the limitations of the USACE model, a new debris estimation model 
has 
been developed based on the damage states for structural and non-structural 
components 
of several model buildings. For each damaged component, the debris generated 
in each 

HAZUS-MH MR3 Technical Manual 

?
category (wood, masonry, metal and other) is calculated based on the 
component s 
damage state and weight statistics. Then, by adding up the debris produced by 
all the 
damaged components, the total debris weight for that model building can be 
estimated. 
The debris volume is simply estimated by dividing the debris weight by its 
density. The 
accuracy of the new model depends heavily on the accuracy of underlying 
databases 
(which includes unit weights of the building components, debris distribution 
matrix, and 
detailed building configuration). Therefore, efforts have been devoted to the 
database 
development for the new debris model. Limited case studies have been 
conducted to test 
and calibrate the new model, and the results are summarized at the end of 
this section. 

10.2 Description of Methodology 
The form of the debris model is: 

n 
D =SWif(di )Di (10.1) 
i=1

where D = debris distribution vector for the model building, which consists 
of debris 
from wood, masonry, metal and other, respectively; Wi = total weight of 
building 
component i; f(7) = damage intensity function, 0 = f(7) = 1; di = damage 
state of building 
component i, which is obtained from the physical damage model; Di = debris 
distribution 
vector for building component i, which specifies the ffactions of total 
component weight 
in wood, masonry, metal and other. Since the building component weights and 
debris 
distribution vectors are available from a number of sources, the only new 
development 
needed is to define the damage intensity functions. 

Similar to the economic loss model, the debris model has to produce debris 
estimation 
based on the damage states provided by the load-resistance physical damage 
model, 
which include number of damaged fenestrations, roof cover damage, roof 
sheathing 
damage, wall damage, and water damage (amount of water entering the 
building). The 
building components can be divided into two categories: those modeled 
explicitly by the 
damage model (primarily damage to the building envelope) and those modeled 
implicitly 
by the damage model (i.e., their damage states have to be estimated based on 
envelope 
damage). For components that are modeled explicitly in the damage model, it 
was 
assumed that f(di) = di. For components that are not modeled explicitly 
(e.g., building 
interior), the damage intensity functions were assumed to be similar in 
format to those 
used in the economic loss model, however with different parameters. For 
example, the 
interior damage due to water entering damaged fenestration was modeled as 1/4 
of the 
rate defined by the damage intensity function in the economic loss model. 
Those 
parameters were developed using a combination of historical data and 
judgment. The 
interior damage intensity functions for roof cover, roof sheathing, and 
fenestration 
damages are defined as: 

..
. 

Chapter 10. Debris Generated from Damaged Buildings 

0.5dcv without secondary water resistance
I

f(d ) 

(10.2)
=

=

cv 

cv 

d with secondary water resistance

0.05
cv 

?
Ish =f(dsh ) = 1.8dsh (10.3) 

I fen = f(d fen ) =1.0d fen (10.4) 

where Icv = interior damage level due to roof cover damage; dcv = percentage 
of roof 
cover damage; Ish = interior damage level due to roof sheathing damage; dsh = 
percentage 
of roof sheathing damage; Ifen = interior damage level due to fenestration 
damage; dfen = 
the amount (in.) of wind driven rain in the interior due to fenestration 
damage. The final 
interior damage level (I) is calculated as the maximum of interior damage 
levels due to 
roof cover, roof sheathing, and fenestration damages, i.e., I = max(Icv, Ish, 
Ifen). The 
damage levels for all interior assemblies, such as partition wall, ceiling, 
floor finish, wall 
finish, content, etc., given an interior damage level, are assumed to be the 
same. 
Therefore, the total interior debris is estimated by (assuming m interior 
components): 

m 
Dint = I SWiDi (10.5) 
i=1 

where Dint = interior debris distribution vector. It is further assumed that, 
for engineered 
buildings, non-modeled load-bearing structural components (such as columns 
and beams, 
concrete or metal deck floors, and load-bearing partition walls) will not be 
damaged and 
therefore no debris could be produced from those components. However, 
finishes on 
those components can be damaged by water entering the building. 

For buildings that reach the destruction damage state (see definitions in 
Chapter 6), the 
entire building will be torn down and the debris produced by demolition will 
equal to the 
total building weight. In the present debris model, global damage indicators 
(such as total 
roof cover damage ratio, total roof deck damage ratio, roof frame failure, 
wall frame 
failure, etc.) are monitored and once the destruction state is reached, the 
total building 
weight will be used as the total debris weight. The total building weight is 
pre-calculated 
based on the specified construction for the model building (in the case when 
construction 
types for certain components are not given, default construction types are 
used in the 
analysis). Note that the weight of foundation is not included in the total 
building weight 
calculation. The default construction characteristics for economy, average, 
custom, and 
luxury residential buildings are defined in the residential economic loss 
model. The 
default construction characteristics for commercial buildings will be 
discussed later. 

After total debris weight in each category (wood, masonry, metal and other) 
is 
determined, the total debris volume is estimated by dividing the total debris 
weight of 
each type by its density. However, since debris can t be fully packed, the 
debris density 
will be much less than its material s density. In the debris model, the 
debris density for 
masonry is assumed to be 2/3 of concrete masonry density (125 pcf) and the 
debris 
densities for the remaining types of debris are assumed to be 1/2 of their 
material 
densities. Due to mixture of different materials, the material density for 
other types of 

HAZUS-MH MR3 Technical Manual 

?
debris is not readily available. It is assumed to be 70 pcf in the present 
debris model 
(between the densities of wood and masonry). 

Component Unit Weight. Building component (or assembly) weights are closely 
related 
to building dead loads, while content weights are closely related to 
sustained live loads. 
Therefore, it is natural to refer to the current building code for this 
information. 
Specifically, the ASCE 7-98 Commentary (1999) is used to obtain the building 
component weights and live load statistics in this study. For items that are 
not included in 
the ASCE Commentary, references are made to the manufacturer s manuals. 
RSMeans 
(2001) is also used to obtain unit weights for a number of building 
assemblies. Table 10.1 
lists the collected average building component weights. The COV for each item 
is 
assumed to be 20%. Table 10.2 lists the sustained live load statistics for 
residential 
buildings and several types of commercial buildings. For building usage types 
that are not 
listed in this table, a mean of 10 psf and standard deviation of 5 psf are 
assumed. Note 
that sustained live load statistics are based on specified areas of 
observation (see Table 
10.2-2). The standard deviation of the average sustained load for an area 
greater than the 
observation area is: 

s

sn =

 
(10.6)
n 

where sn = standard deviation of the sustained load for an area n times of 
the observation 
area and s = observed standard deviation of the sustained load. 

Debris Distribution Matrix. The debris distribution matrix defines how the 
debris is 
distributed among the four debris types (wood, masonry, metal, and other). As 
shown in 
Table 10.3, for each building component, four numbers are given, which 
represent the 
portion of debris in each debris type. For example, for combined wood and 
masonry 
exterior wall, 45% of the debris is wood, 45% of the debris is masonry, 5% of 
the debris 
is metal and the remaining 5% of the debris is other. Even though most of the 
time the 
debris distribution is intuitive and readily determined (such as metal 
shingle, plywood 
deck, and fibrous glass insulation), there are still cases that require 
engineering judgment. 
Further validation of those cases is desirable in the future. 

Default Component Construction Types for Commercial Buildings. For components 
that are not specified in the model building, the default construction types 
are assumed to 
be the same as those specified in the RSMeans (2001). Table 10.4 lists the 
indices of the 
construction types for roof frame, insulation, partition construction, floor 
construction, 
floor finish, ceiling finish, and wall finish. The descriptions and weights 
for related 
indices are listed in Tables 10.5 through 10.11, respectively. The weights 
for floor finish 
and wall finish are calculated based on their finish composition. 

Chapter 10. Debris Generated from Damaged Buildings 

?
Table 10.1. Component Unit Weight Used in the Debris Model 

Category Component Unit Weight (psf) 
Slate, 1/4. 10 
26 gage metal shingle or metal panel 2 
Wood shingles 3 
Three-ply ready roofing 1 
Asbestos-cement shingles 4 
Slate, 3/16. 7 
Single-ply, sheet membranes 0.7 
Roman tile 12 
Ludowici tile 10 
Liquid applied membranes 1 
Roof Cover Five-ply felt and gravel 6 
Corrugated asbestos-cement roofing 4 
Copper or tin 1 
Cement tile 16 
Book tile, 3. 20 
Book tile, 2. 12 
Bituminous, smooth surface membranes 1.5 
Bituminous, gravel-covered membranes 5.5 
Asphalt shingles 2 
Four-ply felt and gravel 5.5 
Spanish 19 
Decking, 2. wood 5 
Wood sheathing (per in. thickness) 3 
Roof Deck Decking, 3. (Douglas fir) 8 
Deck, metal, 20 gage 2.5 
Deck, metal, 18 gage 3 
Plywood (per in. thickness) 3.2 
16. deep @ 6' Bar Joist 17.5 
Roof Frame 
2W4 @24. slope 4/12 2.0 
2W6 @ 48. 2W3 batten@36. 1.2 
Metal truss 12 
Post and beam 8 
Rigid insulation, 1/2. 0.8 
Urethane foam with skin 0.5 
Polystyrene foam insulation 0.2 
Insulation Fibrous glass insulation (4. thick) 4.4 
Fiberboard insulation 1.5 
Cellular glass insulation (4. thick) 2.8 
Perlite insulation 0.8 
Acoustical fiber board 1 
Gypsum Board (1/2. thickness) 2 
Mechanical duct allowance 4 
Ceiling 
Plaster on tile or concrete 5 
Plaster on wood lath 8 
Suspended metal lath and cement plaster 15 
Suspended metal lath and gypsum plaster 10 
Suspended steel channel system 2 
Wood furring suspension system 2.5 
Coverings, Roof, and 
Wall 
Fiberboard, 1/2. 0.8 
Gypsum sheathing, 1/2. 2 
8. medium weight hollow CMU, grout 40. O.C. 45 
Exterior Wall 6. normal weight solid CMU 64 
4. normal weight solid CMU 41 
6. light weight solid CMU 51 

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Table 10.1. Component Unit Weight Used in the Debris Model (continued) 

Category Component Unit Weight (psf) 
6. medium weight hollow CMU, full grout 59 
6. medium weight hollow CMU, grout 16. O.C. 44 
6. medium weight hollow CMU, grout 24. O.C. 39 
6. medium weight hollow CMU, grout 32. O.C. 36 
6. medium weight hollow CMU, grout 40. O.C. 34 
6.. medium weight hollow CMU, grout 48. O.C. 33 
6.. medium weight hollow CMU, no grout 28 
6. medium weight solid CMU 60 
4. medium weight solid CMU 38 
8. clay brick wythes 79 
8. light weight solid CMU 69 
8. medium weight hollow CMU, full grout 81 
8. medium weight hollow CMU, grout 16. O.C. 59 
4. medium weight hollow CMU, no grout 26 
8. medium weight hollow CMU, grout 32. O.C. 47 
2W6 @ 16., 5/8. gypsum, insulated, 3/8. siding 12 
8. medium weight hollow CMU, grout 48. O.C. 44 
8. medium weight hollow CMU, no grout 36 
8. medium weight solid CMU 81 
8. normal weight solid CMU 87 
Exterior stud walls with brick veneer 48 
8. medium weight hollow CMU, grout 24. O.C. 51 
10. normal weight solid CMU 110 
Exterior Wall 10. light weight solid CMU 87 
10. medium weight hollow CMU, full grout 102 
10. medium weight hollow CMU, grout 16. O.C. 73 
10. medium weight hollow CMU, grout 24. O.C. 63 
10. medium weight hollow CMU, grout 32. O.C. 58 
10. medium weight hollow CMU, grout 40. O.C. 56 
10. medium weight hollow CMU, grout 48. O.C. 54 
4-. light weight solid CMU 32 
10. medium weight solid CMU 102 
12. clay brick wythes 115 
12. light weight solid CMU 105 
12. medium weight hollow CMU, full grout 123 
2W4 @ 16., 5/8. gypsum, insulated, 3/8. siding 11 
12. medium weight hollow CMU, grout 24. O.C. 75 
12. medium weight hollow CMU, grout 32. O.C. 68 
4. clay brick wythes 39 
12. medium weight hollow CMU, grout 40. O.C. 65 
12. medium weight hollow CMU, grout 48. O.C. 62 
12. medium weight hollow CMU, no grout 50 
12. medium weight solid CMU 124 
12. normal weight solid CMU 133 
16. clay brick wythes 155 
12. medium weight hollow CMU, grout 16. O.C. 87 
10. medium weight hollow CMU, no grout 44 
Floors and Floor 
Finishes 
Asphalt block (2.), 1/2. mortar 30 
Cement finish (1.) on stone-concrete fill 32 

Chapter 10. Debris Generated from Damaged Buildings 

?
Table 10.1. Component Unit Weight Used in the Debris Model (concluded) 

Category Component Unit Weight (psf) 
Ceramic or quarry tile (3/4.) on 1/2. mortar bed 16 
Ceramic or quarry tile (3/4.) on 1. mortar bed 23 
Hardwood flooring, 7/7. 4 
Linoleum or asphalt tile, 1/4. 1 
Terrazzo (1.) on stone-concrete fill 32 
Concrete floor on steel beam (Commercial) 38 
Steel deck on steel beam (Commercial) 30 
Floor and Floor 
Finishes 
Concrete fill finish (per inch thickness) 12 
Carpet 2 
Wood block (3.) on 1/2. mortar base 16 
Terrazzo (1.) on 2. stone-concrete 32 
Terrazzo (1-1/2.) directly on slab 19 
Subflooring, 3/4. 3 
Solid flat tile on 1. mortar base 23 
Slate (per mm thickness) 15 
Marble and mortar on stone-concrete file 33 
Wood block (3. 0 on mastic, no fill 10 
2W6 joists, 12. spacing double wood floor 6 
2W8 joists, 12. spacing double wood floor 6 
2W8 joists, 24. spacing double wood floor 5 
2W8 joists, 16. spacing double wood floor 8 
2W10 joists, 12. spacing double wood floor 7 
Floors, Wood-Joist 2W10 joists, 16. spacing double wood floor 6 
2W12 joists, 24. spacing double wood floor 6 
2W12 joists, 16. spacing double wood floor 7 
2W12 joists, 12. spacing double wood floor 8 
2W10 joists, 24. spacing double wood floor 6 
2W6 joists, 16. spacing double wood floor 5 
2W6 joists, 24. spacing double wood floor 5 
Movable steel partitions 4 
Wood studs, 2W4, unplastered 4 
Frame Partitions Wood studs, 2W4, plastered two sides 20 
Wood or steel studs, 1/2. gypsum board each side 8 
Wood studs, 2W4, plastered one side 12 
Gypsum board and sound deadening board on wood or steel stud 15 
Doors Interior and Exterior 10 
Garage Doors Regular garage doors 1.6 
Skylight Skylight, metal frame, 3/8. wire glass 8 
Sliders 3/16. tempered sliding glass door 4 
Windows Windows, glass, frame and sash 8 

Table 10.2. Sustained Load Statistics 

Usage Type Occupancy Mean (psf) Std (psf) Area (ft2) 
Office Building Offices 10.9 5.9 200 
Residential Renter 6 2.6 200 
Residential Owner 6 2.6 200 
Residential Attic 2 0.87 200 
Commercial Default 10 5 200 
Hotel Guest room 4.5 1.2 200 
School Classrooms 12 2.7 1000 

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Table 10.3. Debris Distribution Matrix 

Category Type Wood Masonry Metal Other 
Gypsum board (2 mm thickness) 0.00 0.00 0.00 1.00 
Ceiling Gypsum board or plaster on wood furring 0.10 0.00 0.00 0.90 
Suspended metal lath and gypsum plaster 0.00 0.00 0.15 0.85 
Suspended steel channel system 0.00 0.00 1.00 0.00 
Content Commercial 0.30 0.00 0.20 0.50 
Residential 0.60 0.00 0.00 0.40 
Combined wood and masonry 0.45 0.45 0.05 0.05 
Exterior Wall Unreinforced and reinforced Masonry 0.00 0.90 0.05 0.05 
Wood 0.90 0.00 0.05 0.05 
Aluminum siding, metal panel 0.00 0.00 1.00 0.00 
Exterior Wall 
Siding 
Brick veneer, block 0.00 1.00 0.00 0.00 
NA 0.00 0.00 0.00 0.00 
Stone veneer, vinyl, stucco 0.00 0.00 0.00 1.00 
Wood 1.00 0.00 0.00 0.00 
Commercial (concrete) 0.05 0.00 0.05 0.90 
Floors Commercial (steel joist, flat form, concrete) 0.00 0.00 0.40 0.60 
Commercial (steel joist/truss) 0.00 0.00 0.80 0.20 
Residential 0.70 0.00 0.00 0.30 
Garage Doors All 0.00 0.00 1.00 0.00 
Insulation Fibrous glass, All other 0.00 0.00 0.00 1.00 
50% Concrete, 50% Wood Stud 0.10 0.00 0.00 0.90 
Concrete Block 0.00 0.00 0.00 1.00 
Interior Partition Movable Steel Partition 0.00 0.00 0.90 0.10 
Steel stud 0.00 0.00 0.20 0.80 
Wood stud 0.20 0.00 0.00 0.80 
Regular Doors All 0.90 0.00 0.00 0.10 
Roof Cover 
Asphalt shingle, asbestos shingle, flat tile, other tile, 
Slate, built-up roof, single-ply membrane, and Other 
0.00 0.00 0.00 1.00 
Metal shingle, metal panel 0.00 0.00 1.00 0.00 
Wood shake 1.00 0.00 0.00 0.00 
Roof Deck Concrete 0.00 0.00 0.00 1.00 
Plywood, T&G, OSB, Dimensional lumber, Batten 1.00 0.00 0.00 0.00 
Roof Frame Metal truss 0.00 0.00 1.00 0.00 
Wood truss, wood joist, post and beam 1.00 0.00 0.00 0.00 
Skylight All 0.00 0.00 0.10 0.90 
Sliders All 0.00 0.00 0.10 0.90 
Windows All 0.20 0.00 0.10 0.70 

10.3 Validation Studies 
The US Army Corps of Engineers (USACE) is the principal organization 
responsible for 
debris clean-up and removal. Other local and state government agencies may 
also 
coordinate the debris clean-up efforts. USACE after action reports are the 
most readily 
available sources for model validation and testing. These reports contain 
information on 
the total amount (either by volume or by weight) of debris produced by 
hurricanes. 
However, since the after action reports don t differentiate burnable roadside 
debris from 
construction and demolition debris, assumptions have to be made to the ratio 
of these two 
types of debris (recall that only construction and demolition debris is 
estimated by the 

Chapter 10. Debris Generated from Damaged Buildings 

?
Table 10.4. Default Building Component Construction Types 

ID Building Type 
Roof 
Frame Insul. 
Partit. 
Const. 
Floor 
Const. 
Floor 
Finish 
Ceiling 
Finish 
Wall 
Finish 
M010 Apartment, 1-3 Story 6 5 5 7 9 11 13 
M020 Apartment, 4-7 Story 6 5 5 7 9 11 13 
M030 Apartment, 8-24 Story 6 5 6 7 9 11 13 
M040 Auditorium 9 5 4 7 23 14 14 
M050 Bank 1 5 7 10 3 7 5 
M060 Bowling Alley 6 5 3 10 34 14 21 
M070 Bus Terminal 6 5 3 10 6 7 20 
M080 Car Wash 6 5 3 10 36 17 21 
M090 Church 5 7 10 10 33 17 21 
M100 Club, Country 11 10 7 10 1 5 2 
M110 Club, Social 6 5 3 10 10 7 10 
M120 College, Classroom, 2-3 Story 6 5 3 7 22 7 19 
M130 College, Dorm, 2-3 Story 2 5 6 2 24 1 19 
M140 College, Dorm, 4-8 Story 3 5 3 3 24 7 19 
M150 College, Laboratory 6 5 3 5 11 7 8 
M160 College, Student Union 2 5 7 2 5 14 5 
M170 Community Center 6 5 7 10 5 7 21 
M180 Courthouse, 1 Story 1 5 10 10 12 4 12 
M190 Courthouse, 2-3 Story 3 5 10 3 12 4 12 
M200 Factory, 1 Story 6 5 4 10 34 2 21 
M210 Factory, 3 Story 2 5 7 2 31 2 21 
M220 Fire Station, 1 Story 6 5 4 10 8 2 21 
M230 Fire Station, 2 Story 6 5 4 7 8 2 21 
M240 Fraternity/Sorority House 10 3 7 6 16 3 21 
M250 Funeral Home 11 6 5 10 18 2 7 
M260 Garage, Auto Sales 6 5 7 10 8 2 21 
M270 Garage, Parking 12 10 3 10 36 17 21 
M280 Garage, Underground Parking 1 10 3 1 36 17 21 
M290 Garage, Repair 6 5 4 10 31 3 21 
M300 Garage, Service Station 11 5 4 10 34 10 21 
M310 Gymnasium 5 6 4 10 30 7 4 
M320 Hangar, Aircraft 6 2 4 10 36 17 21 
M330 Hospital, 2-3 Story 1 5 6 1 13 15 1 
M340 Hospital, 4-8 Story 6 5 5 3 13 15 1 
M350 Hotel, 4-7 Story 6 5 5 3 24 7 17 
M360 Hotel, 8-24 Story 6 5 5 7 24 11 17 
M370 Jail 3 5 3 3 20 7 21 
M380 Laundromat 6 5 7 10 34 2 21 
M390 Library 4 5 7 4 4 7 21 
M400 Medical Office, 1 Story 11 3 5 10 5 7 5 
M410 Medical Office, 2 Story 6 5 5 7 5 7 6 
M420 Motel, 1 Story 11 3 5 10 27 10 18 
M430 Motel, 2-3 Story 7 5 3 28 8 18 8 
M440 Movie Theatre 6 5 4 4 7 21 7 
M450 Nursing Home 8 5 7 32 9 6 9 
M460 Office, 2-4 Story 6 5 7 9 7 9 7 
M470 Office, 5-10 Story 6 5 7 9 3 9 7 
M480 Office, 11-20 Story 6 5 7 9 3 9 7 
M490 Police Station 6 5 4 20 7 18 7 
M500 Post Office 6 5 4 8 10 21 7 
M510 Racquetball Court 6 5 6 25 7 21 7 

HAZUS-MH MR3 Technical Manual 

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Table 10.4. Default Building Component Construction Types (concluded) 

ID Building Type 
Roof 
Frame Insul. 
Partit. 
Const. 
Floor 
Const. 
Floor 
Finish 
Ceiling 
Finish 
Wall 
Finish 
M520 Religious Education 6 5 4 5 10 21 7 
M530 Restaurant 11 3 7 14 10 16 7 
M540 Restaurant, Fast Food 6 5 7 35 10 21 7 
M550 Rink, Hockey/Indoor Soccer 6 5 3 26 10 21 7 
M560 School, Elementary 6 5 4 15 10 15 7 
M570 School, High, 2-3 Story 2 5 4 21 2 15 7 
M580 School, Jr. High, 2-3 Story 6 5 4 7 7 3 7 
M590 School, Vocational 6 5 4 21 7 3 7 
M600 Store, Convenience 11 3 7 34 10 21 7 
M610 Store, Department, 1 Story 7 5 7 4 10 21 7 
M620 Store, Department, 3 Story 6 5 7 2 3 11 7 
M630 Store, Retail 6 5 7 34 10 21 7 
M640 Supermarket 6 5 2 34 10 21 7 
M650 Swimming Pool, Enclosed 11 5 4 19 10 20 7 
M660 Telephone Exchange 6 5 7 29 10 21 2 
M670 Town hall, 1 Story 6 5 7 17 10 18 7 
M680 Town Hall, 2-3 Story 6 5 7 17 7 18 7 
M690 Warehouse 6 5 3 31 7 21 7 
M700 Warehouse, Mini 6 5 6 36 10 21 17 

Table 10.5. Roof Frame Construction Types and Unit Weights 

ID Construction Type Weight (psf) 
1 Cast-in-place concrete slab 48.0 
2 Concrete flat plate (8.) 96.0 
3 Concrete slab on metal deck and beam 34.0 
4 Concrete waffle slab (10.) 120.0 
5 Laminated wood arches 3.0 
6 Open web steel joist 17.5 
7 Pre-cast concrete beam and plank 80.0 
8 Pre-cast double tees 42.0 
9 Steel Truss 3.0 
10 Wood Rafter 1.2 
11 Wood Truss 2.0 

Table 10.6. Insulation Types and Unit Weights 

ID Construction Type Weight (psf) 
1 Cellular glass insulation (4. thick) 2.8 
2 Fiberboard 1.5 
3 Fiberglass sheets 1.5 
4 Fibrous glass insulation (4. thick) 4.4 
5 Perlite/EPS composite 0.8 
6 Polyisocyanurate sheets 0.4 
7 Polystyrene (2. thick) 0.4 
8 Rigid insulation, 1/2. 0.8 
9 Urethane foam with skin 0.5 

Chapter 10. Debris Generated from Damaged Buildings 

?
Table 10.7. Partition Construction Types and Unit Weights 

ID Construction Type Weight (psf) 
1 2W4 unplastered wood or metal studs 4 
2 50% concrete block, 505 gypsum board on metal studs 22 
3 Concrete block 36 
4 Concrete block and toilet partitions 26 
5 Gypsum board and sound deadening board on wood or metal studs 15 
6 Gypsum board on concrete block and metal studs 32 
7 Gypsum board on wood or metal studs 8 
8 Lightweight concrete block 24 
9 Movable steel partitions 4 
10 One side plaster on wood or metal studs 12 
11 Two sides plaster on wood or metal studs 20 

Table 10.8. Floor Finish Types and Unit Weights 

ID Finish Type Weight (psf) 
1 50% carpet, 30% hardwood, 20% ceramic tile 6.8 
2 50% carpet, 40% marble tile, 10% terrazzo 17.4 
3 50% carpet, 40% vinyl composition tile, 10% quarry tile 3.7 
4 50% carpet, 50% ceramic tile 12.5 
5 50% carpet, 50% vinyl composition tile 1.5 
6 50% quarry tile, 50% vinyl composition tile 12.0 
7 50% vinyl composition tile, 30% carpet, 20% terrazzo 7.5 
8 50% vinyl composition tile, 50% paint 0.5 
9 60% carpet, 30% vinyl composition tile, 10% ceramic tile 3.8 
10 60% carpet, 35% hardwood, 5% ceramic tile 3.8 
11 60% epoxy, 20% carpet, 20% vinyl composition tile 5.0 
12 60% hardwood, 20% carpet, 20% terrazzo 9.2 
13 60% vinyl composition tile, 20% ceramic tile, 20% terrazzo 11.6 
14 65% carpet, 35% quarry tile 9.4 
15 65% vinyl composition tile, 25% carpet, 10% terrazzo 4.4 
16 70% carpet, 10% hardwood, 20% ceramic tile 6.4 
17 70% carpet, 15% terrazzo, 15% vinyl composition tile 6.4 
18 70% carpet, 30% ceramic tile 8.3 
19 70% terrazzo, 30% ceramic tile 29.3 
20 70% vinyl composition tile, 20% carpet, 10% ceramic tile 3.4 
21 70% vinyl composition tile, 20% carpet, 10% terrazzo 4.3 
22 70% vinyl composition tile, 25% carpet, 5% ceramic tile 2.4 
23 70% vinyl composition tile, 30% carpet 1.3 
24 80% carpet, 10% vinyl composition tile, 10% ceramic tile 4.0 
25 80% carpet, 20% ceramic tile 6.2 
26 80% rubber mat, 20% paint 1.6 
27 85% carpet, 15% ceramic tile 5.2 
28 85% carpet, 5% vinyl composition tile, 10% ceramic tile 4.1 
29 90% carpet, 10% terrazzo 5.0 
30 90% hardwood, 10% ceramic tile 5.9 
31 90% metallic hardener, 10% vinyl composition tile 1.9 
32 95% vinyl tile, 5% ceramic tile 2.1 
33 Carpet 2.0 
34 Vinyl composition tile 1.0 
35 Quarry tile 23.0 

HAZUS-MH MR3 Technical Manual 

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Table 10.9. Floor Construction Types and Unit Weights 

ID Construction Type Weight (psf) 
1 Cast-in-place concrete beam and slab 120.0 
2 Concrete flat plate 120.0 
3 Concrete slab with metal deck and beams 38.0 
4 Concrete waffle slab 120.0 
5 Metal deck on open web steel joist 17.5 
6 Wood joist 6.0 
7 Open web steel joists, slab form, concrete 30.0 
8 Pre-cast concrete beam and plank 80.0 
9 Pre-cast double tees with concrete topping 58.0 

Table 10.10. Wall Finish Types and Unit Weights 

ID Construction Type Weight (psf) 
1 40% vinyl wall covering, 35% ceramic tile, 25% epoxy coating 8.5 
2 40% vinyl wall covering, 40% paint, 20% ceramic tile 5.0 
3 50% paint, 40% glazed coating, 10% ceramic tile 2.3 
4 50% paint, 50% ceramic tile 11.5 
5 50% paint, 50% vinyl wall covering 0.5 
6 50% vinyl wall covering, 45% paint, 5% ceramic tile 1.7 
7 50% wallpaper, 25% wood paneling, 25% paint 1.1 
8 60% paint, 40% epoxy coating 0.0 
9 60% vinyl wall covering, 40% paint 0.6 
10 65% paint, 25% vinyl wall covering, 10% ceramic tile 2.6 
11 70% paint, 20% vinyl wall covering, 10% ceramic tile 2.5 
12 70% paint, 20% wood paneling, 10% vinyl wall covering 0.9 
13 70% paint, 25% vinyl wall covering, 5% ceramic tile 1.4 
14 70% paint, 30% epoxy coating 0.0 
15 75% paint, 15% glazed coating, 10% ceramic tile 2.3 
16 75% paint, 25% ceramic tile 5.8 
17 75% vinyl covering, 20% paint, 5% ceramic tile 1.9 
18 90% paint, 10% ceramic tile 2.3 
19 95% paint, 5% ceramic tile 1.2 
20 Glazed coating 0.0 
21 Paint 0.0 

Table 10.11. Ceiling Finish Types and Unit Weights 

ID Finish Type Weight (psf) 
90% paint, 10% suspended fiberglass board 0.2 
Fiberglass board on exposed grid system 3.0 
Gypsum board on wood furring 4.5 
Gypsum plaster on suspended metal lath 10.0 
Gypsum plaster on wood furring 10.0 
Mechanical duct allowance 4.0 
Mineral fiber tile on concealed zee bars 2.0 
Textured finish 0.2 
Painted gypsum board 2.0 
Painted gypsum board on furring 3.0 
Painted gypsum board on resilient channels 2.0 
Plaster on tile or concrete 5.0 
Plaster on wood lath 8.0 
Suspended fiberglass board 1.5 
Suspended metal lath and cement plaster 15.0 
Suspended steel channel system 2.0 

Chapter 10. Debris Generated from Damaged Buildings 

?
present debris model), which is not ideal for a validation study. Therefore, 
references 
were made to a number of published papers on debris removal (Dowd, 1990; 
Tansel, 
1993; Dewberry and Davis, 1993) to identify appropriate cases for model 
validation. 

After Hurricane Andrew, Tansel (1993) carried out a detailed debris analysis 
for five 
zones with significant structural damage in Dade County. Table 10.12 lists 
the zone 
location, number of buildings, exterior wall construction distribution, 
average exterior 
damage ratio, and total structural debris for each of the five zones 
investigated. The 
amount of structural debris was estimated based on an average type of 
residence and 
structural damage states. The damage statistics in each zone were calculated 
using the 
data collected by the Metro-Dade County Building and Zoning Department. 
Structures 
with more than 50% damage were judged uninhabitable and assumed to be 
demolished. 
The debris simulation is performed using model buildings with different 
combinations of 
number of stories (1-story or 2-story), roof shape (hip or gable), wall 
construction (wood 
frame or masonry), nail size (6d or 8d), and roof cover type (shingle or 
tile). The total 
debris produced by each model building under the simulated wind speed in each 
zone is 
calculated and then aggregated within the zone to obtain the average amount 
of debris per 
building (weighted by the assumed building stock). The total amount of 
structural debris 
in each zone is calculated by multiplying the average amount of debris per 
building with 
the total number of buildings in that zone. For Dade County, the building 
stock is 
modeled assuming 80% of the homes are single story. 75% of the homes are 
assumed to 
have gable roofs and 25% of the homes are assumed to have hip roofs. The 
number of 
homes having shingle roof cover is assumed to be the same as that having tile 
roof cover. 
40% of the homes are assumed to use 6d nails on roof deck and the remaining 
homes are 
assumed to use 8d nails. All the homes are assumed to have straps for roof-
to-wall 
connection. The distribution of exterior wall construction types in each zone 
is assumed 
to be the same as that listed in Table 10.12. The exterior wall construction 
distribution for 
Zone 4, which is not given in the paper by Tansel (1993), is assumed to be 
the same as 
that of Zone 5 (in the same zip code). Table 10.12 also shows the simulated 
average 
debris weight per building, average debris volume per building, total 
structural debris 
weight in each zone, and the ratio between simulated and actual debris in 
each zone. The 
mean of the model-to-actual ratios is 1.05 and the standard deviation is 
0.58. The result is 
very promising given that the debris model relies heavily on engineering 
judgment. 

Table 10.13 shows debris comparisons for hurricanes Hugo and Andrew at a 
regional 
level. Dowd (1990) reported the debris removal and channel shoaling of USACE 
in 
hurricane Hugo. Political subdivisions made 359 requests to FEMA and an 
estimated 
15,500,000 cubic yards of debris were removed by or for these subdivisions. 
USACE 
assisted and administrated the debris removal mission in seven counties 
(Berkeley, 
Charleston, Darlington, Dorchester, Lancaster, Orangeburg, and Sumter), with 
a total of 
4,589,559 cubic yards of debris removed. Tansel (1993) estimated that 2.9 
million tons of 
construction and demolition debris were generated by hurricane Andrew. The 
USACE 
after action report (USACE, 1993) gives an estimate of 40 million cubic yards 
of debris 
in Dade County. The debris simulation is performed at a zip code level. 
Similar to the 
first validation study, the average debris weight and volume per building in 
each zip code 

HAZUS-MH MR3 Technical Manual 

?
Table 10.12. Structural Debris Comparison for Hurricane Andrew 

Zone (zip 
code) 
Actual Data Simulation Results 
Model 
Actual 
Number 
of 
Buildings 
Exterior Wall 
Construction 
Distribution 
Average 
Exterior 
Damage 
Ratio 
Structural 
Debris 
(tons) 
Average 
Debris 
Weight per 
Building (lb) 
Average Debris 
Volume per 
Building 
(yard3) 
Total 
Structural 
Debris 
(tons) 
Zone 1 
(33186) 14,000 85% wood frame 63% 182,650 27,252 145 173,215 0.95 
Zone 2 
(33156) 18,000 75% concrete 24% 456,365 25,864 127 211,364 0.46 
Zone 3 
(33156) 604 50% wood frame; 
50% concrete 53% 11,376 24,665 125 6,764 0.59 
Zone 4 
(33031) 2,500 -48% 42,900 53,232 264 60,418 1.41 
Zone 5 
(33031) 1,100 85% wood frame 47% 14,390 53,232 264 26,584 1.85 

Table 10.13. Debris Comparison for Hurricanes Hugo and Andrew 

Storm Region 
Actual 
Weight 
(tons) 
Actual 
Volume 
(yard3) 
Modeled 
Weight 
(tons) 
Modeled 
Volume 
(yard3) 
Model/ 
Actual 
(weight) 
Model/ 
Actual 
(volume) 
Hugo South Carolina -15,500,000 951,009 9,130,843 -0.59 
Hugo USACE Admin. Region -4,589,559 782,480 7,366,394 -1.61 
Andrew Dade County 2,900,000 40,000,000 3,396,991 35,323,080 1.17 0.88 

are simulated first (using the assumed building stock). The total debris 
weight or volume 
in each zip code is then calculated by multiplying the total number of houses 
in each zip 
code with the average debris weight or volume per building. The total amount 
of debris in 
the study region is estimated by adding up the total debris in each zip code. 
The default 
building stock in Dade County is assumed to be the same as that in the first 
validation 
study and the default building stock for South Carolina is assumed to be: 70% 
one-story 
and 30% two-story; 75% gable and 25% hip; 30% using 6d nails and 70% using 8d 
nails; 
10% using straps and 90% using toe-nails for roof-to-wall connection. The 
total number 
of houses in each zip code is estimated using 1990 census data. As shown in 
Table 10.13, 
the debris model underestimated the debris volume in South Carolina and 
overestimated 
debris volume in the regions administrated by USACE (seven counties). 

For hurricane Andrew, both the modeled debris weight and debris volume are 
reasonably 
close to the actual values. It is not clear in the paper by Dowd (1990) 
whether all the 
political subdivisions that made requests to FEMA for debris removal 
assistance are 
within South Carolina. Therefore, the actual study region may cover a larger 
area than the 
state of South Carolina, which may help to explain the underestimation of 
debris volume 
by the model. The overestimation of the debris volume in the USACE 
administrated 
region may likely be to the opposite. The study region may only include 
portions (most 
likely just municipalities) of the seven counties that were mentioned in the 
paper. 

Chapter 10. Debris Generated from Damaged Buildings 

?
10.4 Final Remarks 
A building debris estimation model has been developed based on building 
component 
damage states and building component weight statistics. The model is capable 
of 
providing estimates of the amount (both weight and volume) of construction 
and 
demolition debris in each of the four debris types (wood, masonry, metal and 
other). 
Limited validation studies have shown that the model can produce reasonable 
estimates 
of the total building debris produced by a hurricane. 

HAZUS-MH MR3 Technical Manual 

?
10-16

Chapter 10. Debris Generated from Damaged Buildings 

?
Chapter 11. Short Term Shelter Requirements 

11.1 Introduction 
The model for estimating the number displaced households and short term 
shelter needs 
follows that used for the HAZUS Earthquake Model. The concept and formulation 
are 
described in the HAZUS (Earthquake) Technical Manual (FEMA, 1999). The only 
modification for the Hurricane Model is that building loss ratios, instead of 
building 
damage states, are used to estimate the proportion of uninhabitable housing 
units. 

The shelter model provides two estimates for each census tract: 

1. The number of displaced households due to loss of habitability 
2. The number of people requiring only short-term public shelter. 
Loss of habitability is calculated from modeled damage to residential 
buildings, whose 
severity is expressed in terms of loss ratios due to physical damage, and 
from estimated 
loss of water or power supply to residential buildings or units. 

11.2 Description of Methodology 
The form of the displaced households model is: 

D =(Ua +_Ub ) H , (11.1)

S + M 

where 

D -Number of displaced households, 

Ua -Number of uninhabitable units due to damage (Equation 11.2), 

Ub -Number of uninhabitable units due to loss of water or power (Equation 
11.3), 

H -Total number of households, 

S -Total number of single-family dwelling units, 

M -Total number of dwelling units in multi-family buildings, 

_ -Adjustment factor for household tolerance to loss of power or water (user 
input). 

The ratio H/(S+M) represents the occupancy rate averaged over the single-
family and 
multi-family categories. Ua and Ub are estimated as follows, respectively, 

11 
Ua = S 7. fs( x)7 ws( x) dx + M 7. fm( x)7 wm( x) dx , (11.2) 
00 

HAZUS-MH MR3 Technical Manual 

?
Ub = Ru [(S + M) -Ua ]. (11.3) 

where, 

fs(x) -Probability density function of loss ratio x for single-family 
buildings, 

fm(x) -Probability density function of loss ratio x for multi-family 
buildings, 

ws(x) -Un-inhabitability function in terms of loss ratio x, single-family 
buildings, 

wm(x) - Un-inhabitability function in terms of loss ratio x, multi-family 
buildings, 

Ru -Damage ratio to power and water facilities. 

Examples of the modeled probability density function fs(x), for the case of 
single-family 
buildings for the building stock in North Florida, are shown in Figure 11.1 
in the form of 
probability mass functions. As wind speed increases, the mass of the 
probability moves 
toward unity, indicating that all buildings experience a complete loss. 
Building stock data 
used for the loss ratio computations for Florida are presented in Figure 
11.2. 

Figure 11.3 shows an empirical un-inhabitability function for single-family 
buildings in 
terms of loss ratio x, where below 20% loss a building is considered still 
inhabitable and 
above 50% a building is assumed to be completely uninhabitable, while for 
buildings 
with a loss ratio between these two values a linear proportion is assumed to 
be 
uninhabitable. For multi-family buildings, as shown in Figure 11.4, the 
linear range is 
defined between 10% and 50% empirically, since some of the units in a 
building with 
relatively mild overall damage and loss may already have become 
uninhabitable. 

Examples of computed percentage of household being displaced are shown in 
Figure 11.5. 

Similar to the HAZUS Earthquake Model, the number of people likely seeking 
public 
shelter is estimated based on the number of displaced households, D, 
recognizing that 
only a fraction of the displaced households will likely seek public shelter 
and this fraction 
is a function of several demographic variables such as income and ethnicity, 
as expressed 
below: 

D 7P 5523 
N = SSSS(aijkl 7IiE jOk Al ), (11.4)
Hi=1 j=1 k =1 l=1 

where 

N -Number of people likely seeking public shelter, 

D -Number of displaced households (Equation 11.1), 

P -Population, 

H -Total number of households, 

Chapter 11. Short Term Shelter Requirements 

?
11-3 
HAZUS-MH MR3 Technical Manual 
Modeled Probability Mass Functions of Loss Ratio, Conditional on Peak Gust 
Speed 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9
1 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Loss Ratio 
Probability 
52.5 
57.5 
62.5 
67.5 
72.5 
77.5 
82.5 
87.5 
92.5 
97.5 
Peak Gust 
Speed (mph) 
(a). Lower Peak Gust Wind Speeds (between 50 mph and 100 mph) 
Modeled Probability Mass Functions of Loss Ratio, Conditional on Peak Gust 
Speed 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9
1 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Loss Ratio 
Probability 
102.5 
107.5 
112.5 
117.5 
122.5 
127.5 
132.5 
137.5 
142.5 
147.5 
Peak Gust 
Speed (mph) 
(b). Medium Peak Gust Wind Speeds (between 100 mph and 150 mph) 
Figure 11.1. Modeled Probability Mass Function of Loss Ratios. 
?
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Probability 
152.5 
157.5 
162.5 
167.5 
172.5 
177.5 
182.5 
187.5 
192.5 
197.5 
Peak Gust 
Speed (mph) 
Modeled Probability Mass Functions of Loss Ratio, Conditional on Peak Gust 
Speed 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Loss Ratio 

(c) High Peak Gust Wind Speeds (between 150 mph and 200 mph) 
Figure 11.1. Modeled Probability Mass Function of Loss Ratios (concluded). 

Ii -Percentage of population in the ith income class, 

Ej -Percentage of population in the jth ethnic class, 

Ok -Percentage of population in the kth ownership class, 

Al -Percentage of population in the lth age class, 

aijkl -A fractional coefficient, which is a weighted average of empirical 
fractions of displaced households from various demographic classes who 
seek public shelter: 

()+w () +w (F )+w (F )(11.5)

aijkl =wIFIi EFEj O Ok A Al , 

where the weights and fractions are defined in Tables 11.1 and 11.2, 
respectively, with 
their default values. Note that Equations 11.4 and 11.5 are formulated 
assuming that the 
demographic variables are mutually independent and the displaced households 
are 
distributed among demographic classes in proportion to their number of 
households. An 
example of computed results is shown in Figure 11.6. 

11.3 Simplified Methodology 
The methodology represented by Equation 11.2 requires the full probability 
density 
function of building loss as a function of peak gust wind speed. In an effort 
to reduce the 
data storage requirements and computational requirements imposed by Equation 
11.2, a 

Chapter 11. Short Term Shelter Requirements 

?
11-5 
HAZUS-MH MR3 Technical Manual 
Region I. Southeast Florida 
Region II. South Florida 
Region III. Middle Florida 
Region IV. North Florida 
Region I Region II Region III Region IV 
Roof Shape 
Gable 0.62 0.61 0.75 0.73 
Hip 0.38 0.39 0.25 0.27 
Roof Cover 
Regular Shingle 0.70 1.00 1.00 1.00 
Hurricane Shingle 0.30 0.00 0.00 0.00 
Roof-Wall Connection 
Toe Nail 0.10 0.09 0.25 0.15 
Strap 0.90 0.91 0.75 0.85 
Roof Deck Fastening 
6d@6/12 0.25 0.46 0.29 0.40 
8d@6/12 0.32 0.32 0.29 0.35 
8d@6/6 0.43 0.22 0.41 0.26 
Opening Protection 
No Protection 0.70 0.85 0.95 0.92 
Dade Shutter 0.30 0.15 0.05 0.08 
Figure 11.2. Building Stock Data Used for the Loss Ratio Computation. 
?
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Uninhabitability 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 
Loss Ratio 

Figure 11.3. Empirical Un-Inhabitability Function for Single-Family 
Buildings. 

Uninhabitability 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 
0.1 
0 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Loss Ratio 
Figure 11.4. Empirical Un-Inhabitability Function for Multi-Family Buildings. 

Chapter 11. Short Term Shelter Requirements 

?
Percentage of Household Being Displaced vs Peak Gust Speed at Site 

Example for Florida 
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 
Peak Gust Speed 
Region 1 
Region 2 
Region 3 
Region 4 
Figure 11.5. Example of Computed Percentage of Household Being Displaced as a 
Function of Peak Gust Wind Speed at Site. 

Table 11.1. Default Weights for Demographic Variables 

Percentage of Household 

Symbol Description Default Value 
wI Income Factor Weighting 0.73 
wE Ethnic Factor Weighting 0.27 
wO Ownership Factor Weighting 0.00 
wA Age Factor Weighting 0.00 
(wI+ wE + wO + wA) Total 1.00 

implification has been developed to estimate the integrated uninhabitability 
ratio using 
mean building losses instead of the probability density functions of building 
loss. Mean 
building loss is readily available within HAZUS. 

A study was carried out to examine the relations between the integrated un-
inhabitability 
ratios, which are evaluated by the integrals in Equation 11.2, and the mean 
building loss 
ratios. The results are shown in Figures 11.7 and 11.8 for single-family and 
multi-family 
buildings respectively. It is found that the scatter is very insignificant; 
that is, the mean 
building loss is a good predictor of the un-inhabitability. Fitted mean 
functions are also 
shown in Figures 11.7 and 11.8. These functions are used in HAZUS in place of 
Equation 

11.8 as follows, 
Ua = S 7 FS (XS ) + M 7 FM (XM ) , (11.6) 

where 

HAZUS-MH MR3 Technical Manual 

?
Table 11.2. Default Fractions of Displaced Households Seeking Public Shelter 

Symbol Description Default Value 
Income 
(FI)1 Household Income < $10000 0.62 
(FI)2 $10000 < Household Income < $15000 0.42 
(FI)3 $15000 < Household Income < $25000 0.29 
(FI)4 $25000 < Household Income < $35000 0.22 
(FI)5 $35000 < Household Income 0.13 
Ethnicity 
(FE)1 White 0.24 
(FE)2 Black 0.48 
(FE)3 Hispanic 0.47 
(FE)4 Asian 0.26 
(FE)5 Native American 0.26 
Ownership 
(FO)1 Own Dwelling Unit 0.40 
(FO)2 Rent Dwelling Unit 0.40 
Age 
(FA)1 Population Under 16 Years Old 0.40 
(FA)2 Population Between 16 and 65 Years 0.40 
(FA)3 Population Over 65 Years Old 0.40 

Percentage of Population Seeking Short-Term Public Shelter vs Peak Gust Speed 
at Site 

Example for Florida 

50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 

Peak Gust Speed 

Figure 11.6. Example of Computed Percentage of Population Seeking Short-Term 
Public Shelter as a Function of Peak Gust Wind Speed at Site. 

0% 
5% 
10% 
15% 
20% 
25% 
30% 
35% 
40% 
Percentage of Population 
Region 1 
Region 2 
Region 3 
Region 4 
Chapter 11. Short Term Shelter Requirements 

?
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Uninhabitable Ratio 
SF Uninhabitable Ratio 
SF Fit F(X)=2X^1.6/(1+X^2.6) 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Mean Loss Ratio, X 

Figure 11.7. Uninhabitability as a Function of Mean Building Loss for Single-
Family Buildings. 

0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Uninhabitable Ratio 
MF Uninhabitable Ratio 
MF Fit F(X)=2X^1.3/(1+X^2.2) 
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 
Mean Loss Ratio, X 

Figure 11.8. Uninhabitability as a Function of Mean Building Loss for Multi-
Family Buildings. 

HAZUS-MH MR3 Technical Manual 

?
FS (XS ) =
12 
+ 
XXSS 
1.26.6 (11.7) 

FM (XM ) =
12 
+ 
XXMM 
1.23.2 (11.8) 

and XS and XM denote the mean building loss ratios for single-family and 
multi-family 
buildings respectively. Equations 11.6 and 11.7 are plotted in Figures 11.7 
and 11.8, 
respectively. 

Because of the small amount of scatter in Figures 11.7 and 11.8, results 
computed using 
Equation 11.5 instead of Equation 11.2 are nearly identical to those shown in 
Figures 

11.5 and 11.6. Therefore, the simplified approach is used in the fast-running 
implementation of the HAZUS software to compute displaced households and 
population 
seeking short-term public shelters. 
Chapter 11. Short Term Shelter Requirements 

?
Chapter 12. Tree Blowdown 

12.1 Introduction and Background 
Damage to structures caused by windthrown trees is an ongoing problem in 
forested 
areas. In addition, during Hurricane Hugo, for example, most of the damage to 
the 
electric power distribution system was caused not by the direct action of 
wind but by 
trees falling on the distribution lines and breaking the lines (Cook, 1990). 
Tree debris 
produced by Hurricane Hugo also hampered emergency crews and delayed repairs 
to 
lifelines (Cook, 1990). At Charleston Naval Base, windthrown trees broke 
buried water 
lines, disrupting the water supply (Strehmeyer, 1990). 

Extreme winds associated with thunderstorms and extratropical storms also 
cause 
extensive tree-induced damage. For example, in May 1990, there were in excess 
of 300 
separate reports of downed trees. Of the 150 reported downed power lines, 
approximately 
30% were caused by trees falling across the lines. Of the 100 reports of 
damage to 
structures, approximately 40% were caused by downed trees, with one case 
producing a 
fatality. 

Trees have both positive and negative effects in the presence of extreme 
winds. On the 
positive side, trees provide shelter to structures, reducing the likelihood 
of damage 
produced by the direct action of wind. On the negative side, the existence of 
many trees 
surrounding a structure increases the likelihood of a tree striking and 
damaging the 
structure. 

This chapter describes the tree blowdown methodology implemented in HAZUS and 
the 
two damage/loss models that use the results produced by the tree blowdown 
methodology. The first estimates the quantity of tree debris after a 
hurricane. The second 
estimates the additional economic loss to residential buildings and contents 
caused by 
fallen trees. 

Figure 12.1 shows a high-level flow chart of the data and the models. The 
combination of 
tree data by census tract and a tree blowdown probability model provide the 
elements 
needed for estimating debris quantities, while the tree data, blowdown model, 
hit 
probability and damage model, along with a cost model, yield the estimation 
of damage 
and loss to residential buildings due to tree blowdown, given a defined 
hurricane climate. 

Section 12.2 provides a brief overview of related research. The wind throw 
model is 
described in Section 12.3, and the blowdown probability curves produced by 
the model 
are presented in Section 12.4. The development of the tree inventory database 
is 
presented in Sections 12.5 and 12.6. The tree debris model is described in 
Section 12.7, 
and the damage and loss models for residential buildings and contents are 
presented in 
Sections 12.8 through 12.10. 

HAZUS-MH MR3 Technical Manual 

?
---
Tree Debris 
Weight or 
Volume 
Combined with Losses 
due to Pressure/Missile 
Tree Inventory Data 
(USFS FIA & MRLC NLCD) 
Blow-Down 
Probability 
Building Damage 
Estimation 
Building & Content 
Losses 
Figure 12.1. Estimation of Tree Blow-Down Debris and Damage to Buildings. 

12.2 Related Research 
Virtually all the research related to natural blowdown of trees has been 
performed by the 
forestry industry. This research is prompted by large annual losses of 
harvestable wood in 
many countries. In New Zealand, the average annual losses of softwood trees 
due to 
catastrophic wind events ranges between 0.02% and 3.5% (depending on the 
forest) of 
the total stock. The extent of attritional damage associated with lesser 
winds varies 
between 0% and 1% of the growing stock per annum (Somerville, 1993). Over the 
period 
1981-1990, more than 50% of the total yield in the Czech Republic had to be 
cut down 
due to injuries produced by windthrow or snowbreak (Slodicak, 1993). 

The research performed by the forestry industry (predominately in the U.K. 
and Europe) 
includes full-scale measurement of tree response due to wind action, 
measurements of 
windspeeds within canopies, static pull down tests and relatively simple 
mathematical 
models to estimate the forest blowdown potential. The model described herein 
draws on 
work done by the forestry industry in the U.K. and Europe combined with 
research 
performed in the United States. Very little research in the U. S. has been 
directed toward 
assessing the risk of forest blowdown produced by natural wind; however, the 
most 
relevant research was directed toward assessing tree blowdown probabilities 
associated 
with the effects of nuclear weapons (Twisdale, et al., 1984). The ten-year 
research 
program produced a computer simulation methodology termed BLOWTRAN 
(BLOWdown TRANSport) described in the section  Damage to Forests  in the EM-1 
Nuclear Effects Manual. The BLOWTRAN model is adapted as described herein to 
obtain estimates of tree blowdown associated with natural wind. 

12.3 Wind Throw Model 
12.3.1 Wind Load Response and Breakage Model 
The mathematical model used in BLOWTRAN to determine the drag loads acting on 
a 
tree is based on the model developed by the United States Forest Service 
(USFS) during 
the early 1950s. The drag force, FD, acting on the tree crown is a function 
of the dynamic 
pressure acting on the tree crown combined with the effective surface area 
and the 

Chapter 12. Tree Blowdown 

?
effective drag coefficient. Both the drag coefficient and the effective crown 
area of a tree 
subject to strong winds decrease due to streamlining of the leaves and 
branches, with the 
end result being that the wind force acting on the tree is nearly 
proportional to velocity. 

To determine the effect of streamlining the tree-crown system, and to develop 
a model to 
define the wind loads, the USFS conducted full-scale drag tests on 13 
coniferous trees 
(Sauer, et al., 1951) and 18 broadleaf trees (Lai, 1955). More recent full-
scale 
measurements on over 30 coniferous trees (Frank, et al., 1987; Frank, et al., 
1989; Frank, 
et al., 1991) supplement the USFS data. All of the full-scale test data used 
herein were 
carried out by mounting full-size trees on the rear of a tractor trailer and 
driving at a 
constant velocity. Base overturning moments and shear forces were measured 
for mean 
velocities ranging between 6 m/s and 32 m/s in the more recent tests, and 5 
m/s to 25 m/s 
in the USFS tests. 

The drag data from these full-scale tests are correlated as functional 
relationships of two 
dimensionless parameters that describe the variation in the drag force with 
the bending 
moment at the base of the crown, as a function of the wind force, and the 
tree crown and 
stem characteristics. The drag force, FD, in the USFS model is expressed as 

FD =dc 
31 . (12.1)

1 .U 2 h Wdbf 
D 

2 

where h is the distance between the effective center of pressure and the base 
of the 
crown, dc is the diameter at the base of the crown, Wdbf is the ratio of the 
weight of the 
dry branches to the weight of the dry foliage, and .D is a drag function. The 
wind 
velocity, U, in Equation 12.1 is the relative velocity (i.e., wind velocity 
minus the 
velocity of the tree) and, therefore, aerodynamic damping is inherently 
included in 
Equation 12.1. The drag function, .D, is given as: 

-1.5 

k2k2 ..
. R 
..
. 
.D = .
. 
WdcR 
.
.-1.5 (12.2) 

k1 + k2 .. ..
.Wdc .

where k1 and k2 are drag function parameters, Wdc is the dry crown weight, 
and R is the 
restoring force in the stem at the base of the crown. In the static case, the 
restoring force, 
R, is equal to the drag force, FD. In Equation 12.2, the parameter k1 is 
directly 
proportional to the drag coefficient for a perfectly rigid tree, and k2 is 
the parameter 
responsible for reducing the effective drag force with increases in wind 
speed. Small 
values of k2 describe a tree which streamlines readily; conversely, large 
values of k2 
describe a tree which does not readily streamline. The dry crown weight, Wdc, 
in Equation 

12.2 does not need to be determined explicitly for each tree since it has 
been found to be 
strongly correlated with the height of the crown, Hc, and the stem diameter, 
dc, at the base 
of the crown. Empirical relationships for the dry crown weight have been 
developed for a 
HAZUS-MH MR3 Technical Manual 

?
number of hardwood and coniferous tree species (Storey, Fons, and Sauer, 
1955; Lai, 
1955; Twisdale, et al., 1989) in the form 

ln (WdcHc )= a +bln dc +e (12.3) 

where Hc is the height of the crown, a and b are regression constants, and e 
is a normally 
distributed error term. The r2 values for these species-dependent empirical 
relationships 
exceed 0.93 in all cases. Similar relationships for the parameter Wdbf have 
also been 
developed. 

Because the dry crown weight, Wdc, and the ratio of the weight of the dry 
branches to the 
weight of the dry foliage, Wdf. are determined through empirical 
relationships, the basic 
inputs required for the drag (or loading) side of the model are: 

(i) Tree height, Hbh; 
(ii) Diameter at breast height, dbh (1.3 meters above ground); 
(iii) Species; 
(iv) Stem form parameters, as and cs, which describe the taper in the stem; 
(v) Percent crown; 
(vi) Drag parameters k1 and k2. 
The species-dependent variables, as, cs, k1, and k2, are given for a variety 
of conifers and 
broadleaf trees in Twisdale, et al. (1989) and Vickery, et al. (1993). 

Statistical distributions for the drag parameter, k1, have been developed for 
12 species 
(three conifers and nine broadleaf trees) and a relationship between the 
modulus of 
rupture and k1 was developed so that the value of k1 can be estimated for 
species where 
direct measurements are not available. Figure 12.2 shows the drag parameters 
k1, and k2 
plotted versus the modulus of rupture, sr, for broadleaf trees where it is 
evident that trees 
having higher drag coefficients (as defined using k1) are generally stronger. 
In the case of 
conifers, no trend of increasing k1 with increasing sr was observed; however 
a weak 
positive correlation between sr 
and k2 was observed. For broadleaf trees, the drag 
parameter k1 (as shown in Figure 12.3) is modeled as a lognormal distribution 
where 

mln k =7.777 + 0.04198 sr (12.4a)

1 

sln k = 0.679 (12.4b)

2 

are the logarithmic mean and standard deviation, respectively. The drag 
parameter k2 is 
also lognormally distributed with the logarithmic mean and standard deviation 
given as 

mln k1 
=12.98+ 0.0145s r (12.5a) 

sln k2 
= 0.679 (12.5b) 

Chapter 12. Tree Blowdown 

?
Figure 12.2. Drag Function Parameters k1 and k2 versus sr 
for Green Wood 
(Broadleaf Trees). 

Tree response is modeled using a single degree of freedom model given as 

me&x&+c&x&+ kx = FD(t ) (12.6) 

where an effective mass, me, located at the center of pressure, is used to 
predict the 
dynamic response of the tree subjected to either a static or dynamic wind 
load. The drag 
force is assumed to act as a point load located at the center of pressure (in 
the crown at a 
height, Hcp above breast height). 

The linear spring stiffness for the tree is derived from small deflection 
theory as 

K =3EIbh K.(cs, f1) (12.7)

3

Hbh 

where Ibh is the moment of inertia at breast height, Hbh, is the height of 
the tree above 
breast height, E is the species-dependent Young's modulus, and K is a 
stiffness modulus 
parameter which accounts for the natural variations in the stiffness from the 
reference (or 
theoretical value). Statistical distributions of K used herein have been 
determined from 
static pull-down tests on both conifers and broadleaf trees (Frank, et al., 
1987, 1989; 
Vickery, et al., 1991). The shape function, .(cs,.f1), accounts for the 
variable moment of 
inertia along the tree stem. The shape of the stem of a tree is hyperbolic in 
nature (Behre, 
1927), such that the inside diameter, d, at any point along the stem is 
defined by 

f

d =dbh (12.8)

as ( f + cs ) 

HAZUS-MH MR3 Technical Manual 

?
where as and cs are the species-dependent stem form parameters and f is the 
non-
dimensional distance measured from the top of the tree. The 3EI/H3 term in 
Equation 12.7 
is the stiffness of a uniform cantilever. The shape function modifies the 
stiffness to 
account for the shape of the stem. 

The tree period and effective mass of the equivalent single degree of freedom 
system is 
determined using species-dependent empirical relationships for the tree 
period combined 
with the calculated spring constant, K. The tree period, T, is obtained from 

2 
T = a1+b1 
Hbh +e (12.9)
dbh 

where a1 and b1 are species-dependent regression constants, and e is a 
normally 
distributed error term. 

Bending stresses in the extreme fiber of the tree along the length of the 
stem prior to 
yielding are given by 

Md

s= (12.10)

2I 

. 1 .4 

where I = Ibh .. , d is the inside diameter, and M is the bending moment. 

.as ( f + cs ). 

The region of maximum stress, and hence the point at which the tree will 
likely fail, 
varies with the position of the applied load. In general, trees loaded near 
the top will tend 
to break in the crown, and as the effective load point moves closer to the 
ground, the 
location of maximum stress moves toward the base of the tree. 

In order to determine whether or not failure occurs, a reference deflection, 
yr, at the point 
of application of the load is determined from small deflection theory for a 
reference strain 
equal to sr/E, where sr, the modulus of rupture for green wood. The maximum 
linear 
spring resistance, Rm, is given as 

Rm =RbKr yr (12.11) 

where Rb is a random variable representing the variability in the strength of 
the tree 
stem. Statistical distributions for Rb have been described in Frank, et al. 
(1987; 1989) 
and Vickery, et al. (1993) from the results of static pull-down tests for a 
number of 
broadleaf and coniferous trees. 

In the case of conifers, Rb was found to be negatively correlated with 
diameter at breast 
height for both root and stem failures (i. e., larger diameter trees are less 
likely to be able 
to develop the theoretical maximum resistance moment). This negative 
correlation was 
less pronounced in the case of broadleaf trees. 

Chapter 12. Tree Blowdown 

?
The breakage defection, yb, is obtained from 

yb =5yr (12.12) 

where the ductility, 5., is a random variable whose statistical parameters 
have, again, 
been determined using the results of static tree pull-down experiments for a 
wide range of 
species. Once the breakage deflection has been reached, the tree is assumed 
to have 
failed. The height above ground at which a stem failure occurs is determined 
by sampling 
uniformly over the region where the stem bending stress exceeds 90% of the 
maximum 
calculated value. 

Uprooting failures are treated similarly to stem breakage failures by 
replacing Rb and 5. 
with data derived from pull-down tests, where failure occurred by uprooting. 
Given tree 
size and the modulus of rupture, sr, the single most important (and 
uncertain) parameter 
describing the overall resistance of the tree to blowdown is R. Table 12.1 
summarizes 

b

the basic parameters (and relationships between parameters) necessary for 
modeling the 
response of a tree to wind (or blast) loads. Information on the parameters 
as, cs, T, 5, K , 
Rb , Cp, Wdc, Wdb/f, and k2 are species-dependent and are derived from 
experimental 
studies. The values of these parameters are given in Vickery, Frank, and 
Twisdale (1993) 
for a number of tree species. The information given in Vickery, Frank, and 
Twisdale 
summarizes data from a wide range of sources. In addition to species-
dependent 
parameters, data is given for generic conifers and broadleaf trees. 
Information on tree 
height, Hbh, diameter, dbh, and % crown, fc, varies with species and 
location; however, 
information on typical values of Hbh, dbh, and fc is readily available in the 
forestry 
literature. Values of sr 
and E are given in the USDA Wood Handbook (USDA, 1974). 

12.3.2 Wind Modeling for Simple Terrains 
In the case of relatively open terrain, similar to open country or suburban 
exposures in 
most building codes, where the variation in windspeed with height can be 
adequately 
modeled using logarithmic or power law models, simulating the incident 
windfield is 
relatively straightforward. In these basic cases, a windspeed time history is 
simulated 
using 

U ()t =U +S 
N 
ajcos(2p.fj +fj )t (12.13) 
j=1 

where U(t) is the instantaneous windspeed at time (at the height of the 
center of pressure) 
and U is the mean windspeed at the height of the estimated center of pressure 
within the 
crown of the tree, .f is a frequency increment, fj 
is a random phase angle sampled 
uniformly over the interval 0 =f=2p, and aj is a frequency dependent 
amplitude. The 
amplitude term, aj, is given as 

1
22

aj =[2Su () () fj . fj ].f (12.14) 

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Table 12.1. Input Parameters for Modeling of Trees 

Number Variable Description Functional Relationship Distribution 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 
16 
dbh 
Hbhfc 
as 
cs 
T5(2) 
K (2) 
Rb 
(2) 
Cp 
Wdc 
Wdb/f 
K1, k2 
dc 
sr 
E 
Diameter at breast height 
Height above breast 
% Crown 
Stem form parameter 
Stem form parameter 
Fundamental period 
DuctilityStiffness parameter 
Strength parameter 
Center of pressure (tree in uniform flow) 
Weight of dry crown 
Ratio of dry branches weight to dry foliage 
Drag function parameters 
Diameter of base of crown 
Modulus of rupture for green wood 
Modulus of elasticity for green wood 
User supplied 
User supplied 
User supplied 
User supplied 
1-= 
s 
s a 
a c (1) 
bhbh / dbHaT 2= + (1) 
User supplied(1)
User supplied(1)
bhb b ln daln R = + (1)
User supplied(1)
( ) bhcdc b ln daHln w = + (1) 
( ) bhdc / f b ln daln w = + (1) 
User supplied(1)
( s )cs 
cbh 
c cfa 
fdd 
+ 
= 
User supplied 
User supplied 
Uniform 
Truncated Normal 
Truncated Normal 
Lognormal 
Normal Error Term 
Normal Error Term 
Lognormal 
Lognormal 
Normal Error Term 
Truncated Normal 
Normal Error Term 
Normal Error Term 
Normal Error Term 
NA 
NA(3) 
NA(4) 

(1) Species- (site-) dependent data derived from tests. Data available in 
Vickery, Frank, and Twisdale (1992). 
(2) Separate distributions are given for stem failure and uprooting. 
(3) Natural variation in sr 
is accounted for with Rb distribution. 
(4) Natural variation in E is accounted for with K distribution. 
where Su(fj) is the value of the spectrum of longitudinal turbulence at 
frequency fj and 
.2(fj) is the magnitude of the aerodynamic admittance function at frequency 
fj. 

The velocity spectrum, Su(f), used in this study is based on the ESDU (1975) 
formulation: 

z 

fSu ()= 
s2 
f 
u (1+70.8n2 )6 
5 
4n (12.15) 
where 
n =Lu f / Ux (12.16) 
and the integral length scale x Lu is given as 
Lu 
x =
0.063 
25z0 35. 
(12.17) 

0 

Chapter 12. Tree Blowdown 

?
where the height above ground, z, is equal to the height to the center of 
pressure of the 
crown, and z0 is the aerodynamic surface roughness. 

The aerodynamic admittance function, .2(f), is determined using the coherence 
function 
for vertical separations given in Bowen, Flay, and Panofsky (1983), where the 
square of 
the coherence function is given as 

2 .. 11.z .f.z .

Ruu(z1,z2, f )=exp .-.12 + .. (12.18)

.. z . z . 

and 

.2 ()=..Ruu (z1,z2 ,f dz1,dz2

f ) (12.19) 
Hc 

The integral in Equation 12.19 is solved numerically for various combinations 
of crown 
height, Hc, and Hb (height to base of the crown), with the solution 
approximated as 

.f2 ()= 
1+a1n 
1. 
+ 
0 
axn2 (12.20) 

where 

n =fHc (12.21)

U 

The coefficients a1 and a2 vary with Hc/Hb. They are evaluated for Hc/Hb 
ranging between 

0.1 and 10 and stored for later use. Ignoring the displacement height, d, the 
mean and 
turbulence profiles for these  open  cases are given as (ESDU 1982) 
z

U () ln ...
. 
zz 
0 
...
. 

U ref= 
ln ..
zref .
. 
(12.22) 

..

. z0 . 

and 

.. z ..p 

7.5..0.538 +0.09 ln .. ..
.
su .. .z0 ...
U ()= (12.23)

z . u* .
1+0.156 ln .. ..
.fc z0 .

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?
where zref is the reference height, U(z) is the windspeed at height z, su(z) 
is the RMS 
longitudinal windspeed at height z, U ref is the mean windspeed at the 
reference height, u*, 
is the friction velocity, fc is the coriolus parameter, and 

.=1- 6 fcz and p =.16 (12.24)
u* 

12.3.3 Example Tree Response   Ponderosa Pine 
The wind-induced response of a Ponderosa Pine tree characterized in Table 
12.2 was 
examined in some detail, as described here. Figure 12.3 shows the input 
velocity 
spectrum at the center of pressure in conjunction with the resulting base 
bending moment 
spectrum for mean windspeeds ranging between 10 m/s and 25 m/s. The intensity 
of 
turbulence at the center of pressure is about 25%. A resonant peak is seen 
clearly in 
Figure 12.3; however, the importance of the resonant response diminishes with 
increasing 
windspeed due to increases in the aerodynamic damping. Sensitivity studies 
performed 
where the weight of the dry crown (Wdc) was both increased and decreased 
indicate that 
the resonant portion of the tree response decreases with increasing crown 
weight. For 
very large values of Wdc the tree response is nearly quasi-static. 

Table 12.2. Characteristics of Key Parameters for Example Ponderosa Pine Tree 
Response Estimates 

Parameter Value 
Height (m) 16.4 
Diameter at Breast Height (cm) 18 
% Crown 56 
Dry Crown Weight (N) 90 
Drag Parameter k1 8,669 
Drag Parameter k2 399,528 
Period (seconds) 3.28 

Figure 12.4 shows the mean, RMS, and maximum base bending moments plotted 
versus 
the mean wind speed 10 m above ground, showing the effect of dry crown weight 
(Wdc) 
on tree response. The windspeed is increased to the point where the tree 
fails. The dry 
crown weight has little effect on the base bending moments at low windspeeds. 
In this 
example, for a mean windspeed of 10 m/s, the peak base bending moment is 
proportional 
to Wdc raised to the power of 0.14, whereas for a mean windspeed of 25 m/s, 
the peak 
base bending moment is proportional to Wdc raised to the power 0.4. In this 
example, the 
effect of crown weight is less important than the model of Mayhead, et al. 
(1975), where 
the mass of the crown is included in the drag force model raised to the power 
of 0.67. It is 
noteworthy that the drag model proposed by Mayhead, et al. for Sitka Spruce 
given in the 
form 

FD =A1U 2mc 
0.67 exp{- 0.0009779U2} (12.25) 

Chapter 12. Tree Blowdown 

?
Figure 12.3. Velocity Spectra and Base Moment Spectra for Ponderosa Pine 
(Suburban Exposure). 

HAZUS-MH MR3 Technical Manual 

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Figure 12.4. Mean, RMS, and Peak Base Bending Moment Versus Windspeed 
Showing Effect of Crown Weight on Tree Response. 

(where mc is the live branch weight and A1 is a constant) yields a maximum 
drag force for 
a windspeed equal to 32 m/s. Higher windspeeds result in a drag force, FD, 
which 
decreases. 

Figure 12.5 shows the peak base bending moment for the Ponderosa Pine tree 
characterized in Table 12.2, plotted versus both the mean windspeed at 10 m 
and the peak 
windspeed at the center of the crown for typical open country (z0 = 0.03 m) 
and suburban 
(z0 = 0.3 m) windfields. The response of this example tree is clearly 
governed by the peak 
windspeed, and that the peak base bending moment increases approximately 
linearly with 
increases in the peak windspeed. 

Chapter 12. Tree Blowdown 

?
12-13

Figure 12.5. Mean and Peak Base Bending Moment Versus Peak Windspeed at 
Center of Pressure (Open and Suburban Terrains). 

12.3.4 Wind Modeling in Forested Areas 
Flow within and above plant canopies has been studied by numerous 
investigators over 
the past few decades. These studies include full-scale measurements, wind 
tunnel 
investigations, and mathematical modeling. In the case of forest flows, 
examples of full- 
scale measurements are given in Oliver and Mayhead (1974), Thompson (1979), 
and 
Bergstrom and Hogstrom (1989) for pine forests; Amiro and Davis (1988) for a 
Black 
Spruce forest; Amiro (1990a) for pine, spruce, and aspen forests; Baldocchi 
and Meyers 
(1988) for an Oak-Hickory forest; and Milne (1993) and Gardiner (1994) for a 
Sitka 
Spruce forest. Wind tunnel simulations range from very simplistic models 
using arrays of 
rigid rods to model the vegetation (Seginer, et al., 1976) to detailed 
aeroelastic modeling 
of forests (Stacey, et al., 1994). Mathematical models used to model flow 
within and 
above plant canopies include first-order closure models (e.g., Li, et al., 
1985), second-
order closure models (e.g., Meyers and Paw, 1986), and simplified empirical 
models 
(e.g., Cionco, 1972). A reviews of mathematical techniques used to model flow 
within 
canopies is given in Massman (1987). All of the full-scale studies noted 
above were 
carried out in relatively dense forests, so the results are not directly 
usable for estimating 
flow conditions in relatively lightly forested suburban areas, and no 
published 
measurements of wind flow conditions in lightly forested regions typical of 
suburban 
areas were found. 

In the investigation described herein, the first-order closure model 
described in Li, et al. 
(1985), Li, et al. (1990), and Miller, et al. (1991) was used to describe the 
flow structure 
within and above the  forest  canopy. The computer code was provided by D. R. 
Miller 
of the University of Connecticut. The main inputs to the model include a 
description of 
the Leaf Area Index (LAI) profile of the plant canopy, defined as the leaf 
area per unit 
area of soil, an effective drag coefficient for the vegetation, and two 
windspeeds. Mean 

HAZUS-MH MR3 Technical Manual 

?
windspeed and turbulence intensity profiles resulting from the model were 
compared to 
full-scale measurements in forests for cases where information on the LAI 
profile was 
also available. 

Figure 12.6 shows profiles of LAI along with the measured and simulated mean, 
turbulence intensity, and peak windspeed profiles (peak windspeed is defined 
as the mean 
plus three standard deviations) for data given in Gardiner (1994), Stacey, et 
al. (1994), 
Baldocchi and Meyers (1988), and Amiro (1990). A drag coefficient of 0.16 
(e.g., Meyers 
and Paw (1986) and Amiro (1990b)) was used in all cases. Figure 12.6 
indicates that the 
first order closure model results reproduce the mean velocity profile 
reasonably well, 
through to the underside of the canopy where the secondary maxima produced by 
the 
model is greater than the maxima observed in the full-scale measurements. The 
RMS 
velocity, at height z, su(z), is estimated from 

suz = lz 
.u

()=2u* 2 () (12.26)

.z 

where l(z) is the mixing length at height z above the ground surface. Details 
on the 
mixing length model are given in Miller, et al. (1991). Equation 12.26 is 
valid above the 
displacement height, d, but not beneath z = d. Below z = d the turbulence 
intensity is set 
equal to the value computed at the lowest level in the grid. As indicated in 
Figure 12.6, 
there is no inclination for modeled local turbulence intensities to 
consistently 
overestimate or underestimate the measured intensities within the canopy; 
however, the 
modeled intensities consistently underestimate the observed intensities 
beneath the 
canopy. Within the canopy, the modeled turbulence intensities agree 
surprisingly well 
with the measured intensities. The agreement is better than the agreement 
between 
observed and modeled intensities in a Maize canopy given in Meyers and Paw 
(1986) 
using a second order closure model. 

12.3.4.1 Canopy Modeling 
The LAI distribution for a forested suburban region is modeled in the form 

()= 
2 
B 
pS 
..
..
-12 
..
..
.- 
S 
.p ..
.. 
2 
..
.
. (12.27)

LAI z 

.. 

where S = 0.25, .p is the nondimensional distance to the center of pressure 
measured 
from the base of the crown, and B is a scale factor which is a function of 
LAI and .p . A 
model similar to Equation 12.27 was used by Milne and Brown (1990) to 
describe the 
LAI profile in a Sitka Spruce forest. Mean and turbulence intensity profiles 
were 
developed for values of CdLAI ranging from 0.01 and 0.3 for forests having 
average 
percentage crowns of 30%, 40%, 50%, and 60%. The mean value, .p , is taken as 
being 
equal to 0.5 (i.e., acting in the center of the crown). 

Chapter 12. Tree Blowdown 

?
Figure 12.6. Measured Comparison of Modeled Mean and Turbulence Intensity 
Profiles in Forests. 

HAZUS-MH MR3 Technical Manual 

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Figure 12.7 shows the mean velocity and turbulence intensity profiles 
generated for some 
example CdLAI profiles. Profiles are given for mean crown percentages within 
the 
forested area of 60%. Also shown in Figure 12.7 are the turbulence intensity 
and mean 
velocity profiles generated using the ESDU models for the atmospheric 
boundary layer. 
In developing the velocity profiles given in Figure 12.7, the displacement 
height, d and 
the surface roughness length, zo were obtained by plotting the mean wind 
speed profile in 
semi-logarithmic space with various assumed values of d/H and selecting the 
combination of the two values that best fit the wind speed profile resulting 
from the 
simulation. The resulting the values of d/H and zo/H are consistent with the 
information 
given in Shaw and Pereira (1982) and Massman (1987). The relationships 
between d/H 
and z0/H are presented in Figure 12.8 as a function of CdLAI. The comparison 
of the 
numerical model results with the ESDU model, show remarkable agreement above 
the 
height of the canopy for both the mean velocity and the turbulence intensity. 

Given the mean velocity and turbulence intensity profiles generated for the 
range of 
CdLAI described above, the mean wind speed at a height of (2H-d) coupled with 
the 
estimate of the local zo is used to estimate the open terrain wind speed 
using the 
methodology given in ESDU (1982). The velocity spectrum at the center of 
pressure is 
determined using Equation 12.17, with the height replaced by z -d, and is 
combined with 
the admittance function defined in Equation 12.20 to develop the effective 
wind spectrum 
for use in the response estimates. In the development of the velocity 
profiles used herein, 
the influence of buildings and structures on the flow field is ignored. 

12.3.4.2 Effective Windspeeds in Forested Areas 
Since the tree blowdown model requires the windspeed acting at the effective 
center of 
pressure to determine the wind-induced response, effective values of U and su 
acting at 
the center of pressure must be defined. These effective values are determined 
by 

integrating the product of the windspeed at height z and the frontal area at 
height z 
(assuming Cd is constant over the tree height) and equating this product with 
the product 
of an effective windspeed and the full frontal area of the tree. Effective 
values of the 
mean windspeed and turbulence intensity acting at the center of pressure were 
computing 
using two different approaches, the first of which assumes that the drag 
force is linearly 
proportional to velocity, and a second approach where the drag force is 
assumed to be 
related to the local velocity squared. 

Drag Force Proportional to Velocity. Effective values of the mean velocities 
acting at 
the center of pressure of a tree in a forested area are computed as 

H 
..()z LAI ()z dz 
Ueff =UH 
0 
H (12.28) 
.LAI ()

z dz 

0 

Chapter 12. Tree Blowdown 

?
CdLAI=0.3, Zo/H=0.14, d/H=0.51 CdLAI=0.2, Zo/H=0.11, d/H=0.52 

Turbuelnce IntensityESDU 
Mean Velocity
2.5 

2.5 

2

2 

Turbuelnce Intensity 
ESDU 
Mean Velocity 
Non-Dimensional Height (z/H) 
Non-Dimensional Height (z/H) 

Non-Dimensional Height (z/H) 
Non-Dimensional Height (z/H) 

1.5 

1.5 

1 

1 

0.5 

0.5 

0 

0 

0 0.5 11.5 2 00.5 11.5 2 
Mean Velocity (U(z)/U(H)) or Intensity Mean Velocity (U(z)/U(H)) or Intensity 
CdLAI=0.15, Zo/H=0.09, d/H=0.51 CdLAI=0.1, Zo/H=0.09, d/H=0.40 

Turbuelnce Intensity 
ESDU 
Mean Velocity
2.5 

2.5 

2

2 

0 
Turbuelnce Intensity 
ESDU 
Mean Velocity 
1.5 

1.5 

1 

1 

0.5 

0.5 

0 

0 0.5 11.5 2 00.5 11.5 2 
Mean Velocity (U(z)/U(H)) or Intensity Mean Velocity (U(z)/U(H)) or Intensity 
Figure 12.7. Mean and Turbulence Intensity Profiles for Various Values of 
CdLAI. 

HAZUS-MH MR3 Technical Manual 

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0.25 

fc=0.4 
fc=0.5 
fc=0.6 

0.6 

0.5 

0.2 

0.4 

0.15 

0.3 

0.1 

0.2 

0.05 

0.1 
0 
0 

fc=0.4 
fc=0.5 
fc=0.6 
Zo/H 

d/H

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 
CdLAI CdLAI 

Figure 12.8. zo/H and d/H Plotted vs. CdLAI for Three Different Values of 
Crown 
Fraction. 

where .(z) = U(z)/UH is obtained from the first order closure model and 
LAI(z) is the 
distribution of the leaf density over the height of the tree. The effective 
RMS velocity is 
computed from 

eff 
.sueff .
. 
H . z 1 u dz (12.29)

U ..
1+gU .
=U LAI () () . z (+gI () z )
. eff .

where the peak factor, g, is taken as 3.0, and Iu(z) is the turbulence 
intensity profile 
obtained from the first order closure model. 

As noted earlier, the response of the tree is governed by the peak windspeed, 
not the 
mean windspeed, and as a result the effective location of the center of 
pressure is derived 
using the estimated peak wind profile. 

The effective center of pressure is determined from 

H
UH ..()(+gIu ()z )LAI z zdz

z 1 () 
Hcpeff 
= 0 
H (12.30) 

U  eff .LAI ()z dz 

0 

where U  eff is the effective peak velocity given as 

U  =U (1+gs) (12.31)

eff eff ueff 

Drag Force Proportional to Velocity Squared. The effective value of the mean 
velocity, 
Ueff , acting at the center of pressure is derived from 

Chapter 12. Tree Blowdown 

?
H 

..2 () () 

z LAI z dz 

2 20

U eff =UH H (12.32) 
.LAI ()

z dz 

0 

The effective turbulence intensity, defined as 

sueff

Iueff 
=
U 
(12.33) 
eff 

is derived from 

2 22

.LAI () () .z [+2 u ()z Idz

UH 
z 1 gI +gu ()z ]

H 

20

U eff = 
H (12.34) 
.LAI ()z dz 

0 

2

the effective peak velocity U eff is given as 

U 2 =U 2 .1+2gIu +g 2 I 2 . (12.35)

eff eff ..
eff ueff ..

where again the peak factor, g, is set equal to 3. The effective height at 
the center of 
pressure is determined from 

H
2 
H . 2 ()z (+2giu ()2

U . 1z ) 

H cp = 0 (12.36)

eff H
Ueff .LAI ()

  2 z dz
0

The effective values, Ueff , sueff , and H cpeff determined using the linear 
and quadratic 
dependencies on windspeed typically vary by less than 10%, with the average 
of the two 
approaches being used to compute the tree response. 

12.3.5 Simulation Methodology 
In the simulation process, values of the key tree parameters given in Table 
12.1 are 
obtained from sampling from the appropriate distributions. Using the sampled 
value of 
the center of pressure, the crown shape parameters B and .p are determined, 
after which 
the values of Ueff/UH and su/UH and effective value of center of pressure 
taking into 
account the velocity profile are calculated as described in Section 12.3.4.1. 
Given the 
new value of the center of pressure, combined with the sampled values of Rb , 
for root and 

HAZUS-MH MR3 Technical Manual 

?
stem failure, the failure mode (root failure or stem breakage) is determined. 
Given this 
information, time series of windspeeds (ten minutes in length) are generated, 
having a 
mean windspeed, UH, and turbulence intensity, sr/UH, and the response of the 
tree is 
calculated. Using an iterative interval halving technique, the minimum mean 
windspeed, 
UH, required to fail the tree is determined, after which another tree is 
sampled and the 
process is repeated. The simulation process is repeated 100 times with the 
resulting 
failure windspeeds (converted to equivalent open country mean values) used to 
define the 
probability of failure distribution. Simulations for each tree examined are 
performed for a 
range of forest densities. Using the mean values of CdA calculated for trees 
in a uniform 
wind, the average tree density, ., (stems/Ha) necessary to provide the 
effective CdLAI 
corresponding to the velocity and turbulence intensity profiles used can then 
be 
determined from 

104 Cd LAI

.= stems/Ha (12.37)

Cd A 

The information on failure windspeeds and tree density are used to determine 
the 
probabilities of trees failing and striking a typical residential structure 
as discussed in the 
following sections. 

12.4 Blowdown Results 
12.4.1 Tree Blowdown Curves 
Simulations were performed for values of CdLAI of 0.3, 0.2, 0.15, 0.10, 0.05 
and 0.01. 
For each simulation, the forest canopy was approximately uniform in height 
(COV = 8%) 
and the tree diameters varied by 11.25 cm about the mean value. Figure 12.9 
shows 
example cumulative failure probability distribution for both homogenous 
deciduous and 
coniferous forests for three different mean values of height-diameter 
classes. Information 
on typical height and diameters and relationships between diameter and crown 
weight 
was taken from Storey and Pong (1957) for trees in a mixed hardwood forest in 
North 
Carolina. As noted in Figure 12.9, gust failure windspeed (in open country 
terrain) 
decreases with increasing forest density. 

12.4.2 Tree Blowdown Validation 
A validation study of the tree blowdown curves presented in Section 12.4.1 
was 
undertaken in eight randomly selected subdivisions in eastern North Carolina 
immediately following Hurricane Isabel in 2003. In each of the selected 
residential 
subdivisions the survey teams counted the number of trees on each lot, 
counting the 
number of trees in each of 3 previously defined height ranges (consistent 
with the height 
ranges used in HAZUS), the tree type (evergreen or deciduous) and the 
performance of 
the tree (uproot failure, stem failure or no failure). In one of the surveyed 
subdivisions 
(South Mills, NC) each tree height was estimated and the diameter at breast 
height was 
measured, and the dimensions of each lot was obtained. In all cases the 
address of the lot 
was recorded. 

Chapter 12. Tree Blowdown 

?
12-21 
HAZUS-MH MR3 Technical Manual 
70 Foot Deciduous Tree 
0.00 
0.10 
0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00
0.00 50.00 100.00 150.00 200.00 250.00 
Peak Gust Wind Speed (mph) 
Probability of Blowdown 
CDdLAI = 0.30 
CDdLAI = 0.20 
CDdLAI = 0.15 
CDdLAI = 0.10 
CDdLAI = 0.05 
CDdLAI = 0.01 
70 FootConiferous Tree 
0.00 
0.10 
0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00
0.00 50.00 100.00 150.00 200.00 250.00 
Peak Gust Wind Speed (mph) 
Probability of Blowdown 
CDdLAI = 0.30 
CDdLAI = 0.20 
CDdLAI = 0.15 
CDdLAI = 0.10 
CDdLAI = 0.05 
CDdLAI = 0.01 
50 Foot Deciduous Tree 
0.00 
0.10 
0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00
0.00 50.00 100.00 150.00 200.00 250.00 
Peak Gust Wind Speed (mph) 
Probability of Blowdown 
CDdLAI = 0.30 
CDdLAI = 0.20 
CDdLAI = 0.15 
CDdLAI = 0.10 
CDdLAI = 0.05 
CDdLAI = 0.01 
50 Foot Coniferous Tree 
0.00 
0.10 
0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00
0.00 50.00 100.00 150.00 200.00 250.00 
Peak Gust Wind Speed (mph) 
Probability of Blowdown 
CDdLAI = 0.30 
CDdLAI = 0.20 
CDdLAI = 0.15 
CDdLAI = 0.10 
CDdLAI = 0.05 
CDdLAI = 0.01 
35 Foot Deciduous Tree 
0.00 
0.10 
0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00
0.00 50.00 100.00 150.00 200.00 250.00 
Peak Gust Wind Speed (mph) 
Probability of Blowdown 
CDdLAI = 0.30 
CDdLAI = 0.20 
CDdLAI = 0.15 
CDdLAI = 0.10 
CDdLAI = 0.05 
CDdLAI = 0.01 
35 Foot Coniferous Tree 
0.00 
0.10 
0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00
0.00 50.00 100.00 150.00 200.00 250.00 
Peak Gust Wind Speed (mph) 
Probability of Blowdown 
CDdLAI = 0.30 
CDdLAI = 0.20 
CDdLAI = 0.15 
CDdLAI = 0.10 
CDdLAI = 0.05 
CDdLAI = 0.01 
Figure 12.9. Tree Blowdown Curves. 
Table 12.4 presents a more detailed summary presenting the number of trees in 
each 
height class as well as indicating the number of trees in each height class 
that fail by 
uprooting or through stem failure. Approximately 12% of the conifers were 
blown down 
and about 10% of the deciduous trees were blown down. The deciduous trees 
were more 
likely to fail from uprooting, whereas in the case of the conifers, stem and 
uprooting 
failures were approximately equally likely. 
Table 12.3 presents a summary of the data collected at the eight sites, 
including the 
estimated peak gust wind speed, the number of lots surveyed, the total number 
of trees 
surveyed, the percent of trees blown down, and the total area surveyed. A 
total of 1158 
trees were surveyed, with the sample comprising 628 conifers and 530 
deciduous trees. 
?
Table 12.4 presents a more detailed summary presenting the number of trees in 
each 
height class as well as indicating the number of trees in each height class 
that fail by 
uprooting or through stem failure. Approximately 12% of the conifers were 
blown down 
and about 10% of the deciduous trees were blown down. The deciduous trees 
were more 
likely to fail from uprooting, whereas in the case of the conifers, stem and 
uprooting 
failures were approximately equally likely. 

Table 12.3. Summary of Tree Blowdown Data 

Location 
Peak Gust Wind 
Speed (mph) 
Number of Lots 
Surveyed 
Total Number of 
Trees 
% of Trees 
Blown Down 
Ahoski 1 86 20 54 3.7% 
Ahoski 2 86 28 113 5.3% 
Elizabeth City 1 95 34 171 5.8% 
Elizabeth City 2 95 45 217 8.8% 
Manteo 1 92 9 178 18% 
Manteo 2 92 32 150 11% 
South Mills 92 27 150 19.3% 
Windsor 84 28 125 8.8% 
Total 223 1158 10.8% 

Table 12.4. Summary of Number of Failed Trees by Height Class 

Location 
Conifers Deciduous 
Height Range (feet) Height Range (feet) 
<30 30-40 40-60 > 60 All <30 30-40 40-60 > 60 All 
Ahoski 1 
# Trees 
# Uproot Failures 
# Stem Failures 
Total # Failures 
1 
0 
0 
0 
4 
0 
1 
1 
11 
0 
0 
0 
4 
0 
0 
0 
20 
0 
1 
1 
9 
1 
0 
1 
8 
0 
0 
0 
17 
0 
0 
0 
0 
0 
0 
0 
34 
1 
0 
1 
Ahoski 2 
# Trees 
# Uproot Failures 
# Stem Failures 
Total # Failures 
5 
1 
0 
1 
9 
1 
0 
1 
9 
0 
0 
0 
15 
0 
1 
1 
38 
2 
1 
3 
23 
1 
0 
1 
14 
0 
0 
0 
19 
2 
0 
2 
19 
0 
0 
0 
75 
3 
0 
3 
Elizabeth 
City 1 
# Trees 
# Uproot Failures 
# Stem Failures 
Total # Failures 
0 
0 
0 
0 
10 
0 
1 
1 
41 
0 
0 
0 
39 
2 
0 
2 
90 
2 
1 
3 
17 
1 
0 
1 
22 
1 
0 
1 
38 
4 
1 
5 
4 
0 
0 
0 
81 
6 
1 
7 
Elizabeth 
City 2 
# Trees 
# Uproot Failures 
# Stem Failures 
Total # Failures 
3 
1 
0 
1 
11 
0 
0 
0 
73 
2 
2 
4 
61 
0 
3 
3 
148 
3 
5 
8 
26 
2 
2 
4 
23 
2 
0 
2 
11 
1 
1 
2 
9 
3 
0 
3 
69 
8 
3 
11 
Manteo 1 
# Trees 
# Uproot Failures 
# Stem Failures 
Total # Failures 
2 
0 
0 
0 
4 
1 
2 
3 
91 
6 
13 
19 
0 
0 
0 
0 
97 
7 
15 
22 
26 
4 
0 
4 
45 
3 
3 
6 
10 
0 
0 
0 
0 
0 
0 
0 
81 
7 
3 
10 
Manteo 2 
# Trees 
# Uproot Failures 
# Stem Failures 
Total # Failures 
14 
0 
0 
0 
24 
0 
1 
1 
21 
5 
3 
8 
23 
1 
2 
3 
82 
6 
6 
12 
6 
0 
0 
0 
48 
3 
1 
4 
14 
0 
0 
0 
0 
0 
0 
0 
68 
3 
1 
4 
Windsor 
# Trees 
# Uproot Failures 
# Stem Failures 
Total # Failures 
3 
0 
0 
0 
6 
0 
0 
0 
23 
1 
1 
2 
23 
1 
1 
2 
55 
2 
2 
4 
25 
1 
1 
2 
23 
1 
1 
2 
12 
0 
1 
1 
10 
0 
2 
2 
70 
2 
5 
7 

Chapter 12. Tree Blowdown 

?
South 
Mills 
# Trees 
# Uproot Failures 
# Stem Failures 
Total # Failures 
2 
0 
0 
0 
47 
7 
1 
8 
21 
2 
1 
3 
28 
8 
1 
9 
98 
17 
3 
20 
5 
0 
0 
0 
19 
2 
2 
4 
17 
1 
2 
3 
11 
0 
2 
2 
52 
3 
6 
9 
Total 
# Trees 
# Uproot Failures 
# Stem Failures 
Total # Failures 
30 
2 
0 
2 
115 
9 
6 
15 
290 
16 
20 
36 
193 
12 
8 
20 
628 
39 
34 
73 
137 
10 
3 
13 
202 
12 
7 
19 
138 
8 
5 
13 
53 
3 
4 
7 
530 
33 
19 
52 

Within the HAZUS tree blowdown model, each family of tree blowdown curves 
(probability of blowdown vs. wind speed) is stored as a function for a range 
of tree 
densities (trees/acre) for 3 pre-defined height classes for the coniferous 
and deciduous 
tree types. The original tree blowdown curves were developed by computing the 
probability of blowdown as a function of the effective drag per unit land are 
within the 
modeled  forest  canopy, rather than the number of trees per acre. The 
effective drag per 
unit land area is defined using a parameter referred to as CdLAI, where Cd is 
a drag 
coefficient and LAI is the Leaf Area Index. The value of CdLAI for an area is 
often 
obtained using satellite imagery to estimate the LAI and multiplying by a 
typical drag 
coefficient, but can also be estimated by dividing the effective total drag 
area in the 
forested region, CdA, by the total land area covered by the trees. 

The estimated values of CdLAI at the eight sites range between 0.01 and 0.02. 
The lowest 
two values of CdLAI used in the development of the tree blowdown curves used 
in 
HAZUS are 0.01 and 0.05, and these curves are given in Figure 12.10 along 
with the 
observed blowdown data for each study region. Separate plots are given for 
each height 
class and tree type (deciduous or coniferous). The large diamond shaped point 
on each 
plot represents the weighted average probability of blowdown for all trees of 
the class at 
all sites surveyed. 

From the plots, it is seen that the collected data from Hurricane Isabel 
agree well with the 
probability of blowdown curves for deciduous trees. However, less agreement 
is seen 
when coniferous trees are considered. It appears that for this case, the 
curves 
underestimate the actual probability of blowdown. In light of this 
comparison, the tree 
blowdown probabilities for coniferous trees were shifted to better agree with 
the available 
validation data. 

The blowdown functions have been shifted as follows: 

  All functions for short (< 40 ) conifers were shifted by 30 mph 
  All functions for medium (40  to 60 ) conifers were shifted by 15 mph 
  All functions for tall (> 60 ) conifers were shifted by 10 mph. 
Figure 12.11 shows the resulting shifted functions for coniferous trees along 
with the 
validation data. 

HAZUS-MH MR3 Technical Manual 

?
12-24 

70 Foot Deciduous Tree 70 Foot Coniferous Tree 

1 

1 

CdLAI=0.05 
CdLAI=0.01 
Isabel Tree Data 
0.9 

0.2 

0.2 

0.1 

0.1 

0 

0 
50 60 70 80 90 100 110 120130 140 150 0 20 40 60 80 100 120 140 

CdLAI=0.05 
CdLAI=0.01 
Isabel Data 
Peak Gust Wind Speed (mph) Peak Gust Wind Speed (mph) 

50 Foot Deciduous Tree 50 Foot Coniferous Tree 

1 

1 

0.8 

Probability of Blowdown Probability of Blowdown Probability of Blowdown 

Probability of Blowdown Probability of Blowdown Probability of Blowdown 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

CdLAI=0.05 
CdLAI=0.01 
Isabel Data 
0.2 

0.1 

0.1 

0 

0 

50 60 70 80 90 100 110 120 130 140 150 

Peak Gust Wind Speed (mph) 

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 
Peak Gust Wind Speed (mph) 
CdLAI=0.05 
CdLAI=0.01 
Isabel Data 
35 Foot Deciduous Tree 35 Foot Coniferous Tree 

1 

1 

0.9 

0.8 

0.8 

0.7 

0.7 

0.6 

0.6 

0.5 

0.5 

0.4 

0.4 

0.3 

0.3 

0.2 

CdLAI=0.05 
CdLAI=0.01 
Isabel Data 
0.2 

0.1 

0.1 

0 

0 

CdLAI=0.05 
CdLAI=0.01 
Isabel Data 
0.9 

0.8 

0.8 

0.7 

0.7 

0.6 

0.6 

0.5 

0.5 

0.4 

0.4 

0.3 

0.3 

50 60 70 80 90 100 110 120 130 140 150 0 10 20 30 40 50 60 70 80 90 100 110 
120 130 140 150 

Peak Gust Wind Speed (mph) Peak Gust Wind Speed (mph) 

Figure 12.10. Probability of Blowdown Curves with Treefall Data Collected 
Following Hurricane Isabel. 

12.5 Tree Inventory Data by County 
12.5.1 Forest Inventory Analysis (FIA) Program and Database 
The Forest Inventory Analysis (FIA) Program and Database of US Forest Service 
(USFS) 
is a nationwide tree inventory database updated on a 5-year cycle by 
states/regions. It is 
derived through a field survey and statistical analysis procedure. The 
database provides a 
spatial resolution down to the county level for the final product that is 
accessible in the 
public domain. It was initially designed for the use primarily by the lumber 
industry; 
however, it contains data such as tree count and tree diameter distribution 
per species in 
every county that are useful for the analysis described in this section. It 
is recognized as 
an authoritative source of forest/tree data on a nationwide scale. The MRLC 
uses FIA 
data to verify the accuracy of satellite imagery analysis on forest cover. 

Chapter 12. Tree Blowdown 

?
12-25 

70 Foot Coniferous Tree --Shifted 10 mph 35 Foot Coniferous Tree -- Shifted 
30 mph 

1 

1 

CdLAI=0.05 
CdLAI=0.01 
Isabel Data 
0.8 

0.9 

0.7 

Probability of Blowdown 

Probability of Blowdown 

0.6 

0.5 

0.4 

0.3 

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 
Peak Gust Wind Speed (mph) 
CdLAI=0.05 
CdLAI=0.01 
Isabel Data 
0.2 

0.2 

0.1 

0.1 

0 

0 

0 20 40 60 80 100 120 140 

Peak Gust Wind Speed (mph) 

50 Foot Coniferous Tree --Shifted 15 mph 

0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Probability of Blowdown 
CdLAI=0.05 
CdLAI=0.01 
Isabel Data 
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 

Peak Gust Wind Speed (mph) 

Figure 12.11. Shifted Probability of Blowdown Curves for Coniferous Trees 
with 
Treefall Data Collected Following Hurricane Isabel. 

12.5.2 Average Tree Density and Tree Height Distribution at County Level 
The tree blowdown methodology utilizes FIA s tree count on forest land and 
tree 
diameter distribution per species per county, downloaded from the USFS 
website. Tree 
height-diameter relationships per species were also obtained from the USFS 
Southeastern 
and Northeastern Research Stations. These relationships are derived from 
their field 
measurement database and used within each Research Station to project tree 
lumber 
volume. Examples of the mean height-diameter relationships are shown in 
Figure 12.12 
and Figure 12.13 for a few dominant species in areas modeled in the HAZUS 
hurricane 
model. Using these relationships and the diameter distributions, the tree 
height 
distributions are derived in 5-ft bins for each of the 1274 counties in the 
22 states 
considered in the HAZUS hurricane model. 

The 5-ft bin tree height distributions are further simplified into three 
groups (30-40 ft, 
40-60 ft, and over 60 ft tall) and summarized over all species for each 
county. 

For each county, the average tree density is derived by dividing the tree 
count on forest 
land by the total area of forest land of the county. The forest land area 
used was also 
downloaded from the FIA database. 

HAZUS-MH MR3 Technical Manual 

?
0.0 
20.0 
40.0 
60.0 
80.0 
100.0 
120.0 
140.0 
H (ft.) 
LOBLOLLY PINE 
SHORTLEAF PINE 
0 5 101520 2530 35 4045 

D (in.) 

Figure 12.12. Example Mean Height-Diameter Relationships (Coniferous Trees). 

0.0 
20.0 
40.0 
60.0 
80.0 
100.0 
120.0 
140.0 
H (ft.) 
RED MAPLE 
SILVER MAPLE 
0 5 101520 2530 35 4045 

D (in.) 

Figure 12.13. Example Mean Height-Diameter Relationships (Deciduous Trees). 

Chapter 12. Tree Blowdown 

?
It should be noted that the FIA field survey and resulting database includes 
only 
contiguous tree covered areas not less than 12 acres in area. For the purpose 
of the 
HAZUS model, the average tree density and tree height distribution obtained 
as described 
above are applied to all tree covered areas in a county, including smaller 
patches and 
strips of tree covered areas embedded in residential subdivisions as 
identified by the 
MRLC land cover database. 

12.6 HAZUS Tree Coverage Database 
Figure 12.14 illustrates the procedures carried out to derive a default tree 
database for 
HAZUS from the MRLC land use database and the FIA tree database. 

MRLC Tree Types & 
Percent Tree Cover in 
Census Tract (including 
Tree Covered Grids in 
Developed Areas) 
County Tree Numbers by 
Diameter and by Species in 
Forestlands from USFS 
County Tree Numbers by Height 
in Forestlands by Species Group 
Height-Diameter 
Relationships Model 
by Species Group 
from USFS 
County Forestland Tree Density 
by Height Threshold 
Summed up for All 
Species Groups; and 
Divided by County 
Forestland Area 
Census Tract Forestland Tree Density 
by Height Threshold 
(For All MRLC Forested Grids) 
Map Census Tracts 
to Counties 
Dominant Tree Type, Ave. Number of Trees 
per Acre of Land in 3 Tree Height Groups 
Figure 12.14. Data and Procedures Used to Derive the HAZUS Tree Database. 

12.6.1 MRLC National Land Cover Data 
As described in detail in Chapter 5, the MRLC (Multi-Resolution Land 
Characteristics 
consortium) land cover database, presented in 30m resolution grids, provides 
relatively 
high spatial resolution of defined land cover types. Using the MRLC land 
cover database, 
combined with data derived from the FIA database, makes it possible to 
estimate the 
number of trees and predominant tree type at higher geographical resolutions 
than 
counties, including resolution at the census tract level as required for 
HAZUS. 

HAZUS-MH MR3 Technical Manual 

?
12.6.2 Tree Density, Tree Height, and Predominant Tree Type by Census Tract 
For the HAZUS wind risk software product, a default tree database is 
developed that 
contains variables as outlined below for each census tract. 

The predominant tree type is defined in the MRLC land cover database as: 

   Coniferous  if 75% or more of the tree covered area is identified as 
 Evergreen  type in the MRLC land cover database; 
   Deciduous  if 75% or more of the tree covered area is identified as 
 Deciduous  type in the MRLC land cover database; 
   Mixed  if neither of the above two criteria holds. 
The density of trees with heights not less than 30 ft and is computed for 
each census tract 
using the following formula: 

Stems per Acre of Land = Forest Land Tree Density of County W 
Tree Covered Land Area of Tract w 
Total Land Area of Tract (12.38) 

Trees less than 30 ft tall are not included in the variable of  Forest Land 
Tree Density of 
County  or in the subsequent analysis. Trees less than 30 ft tall normally 
have trunk 
diameters less than 5 in and small crown weights. These trees are neglected 
in the debris 
volume and building damage models. 

The tree covered land area of each census tract is determined based on the 
MRLC 
national land cover database. Early versions of the HAZUS hurricane model 
considered 
all land classified in the MRLC as  Deciduous Forest ,  Coniferous Forest , 
or  Mixed 
Forest  as tree covered land area. Starting with HAZUS-MH MR2, the tree 
covered land 
area also includes all areas classified as  Woody Wetlands , 20% of land 
classified as 
 Low Intensity Residential , and 10% of land classified as  High Intensity 
Residential . 

The change in the definition of tree covered land area results in the same or 
increased 
number of trees per acre for every census tract in hurricane prone states. 
The average 
percentage increases range from 4% (West Virginia) to 164% (Florida). The 
average 
increase in Florida is so high because before the change, 587 Florida census 
tracts were 
shown having zero trees per acre. Following the change, only one Florida 
Census tract 
shows zero stems per acre   the census tract for Miami International Airport. 

The tree height distribution represents the proportions of short (30-40 ft), 
medium (40-60 
ft) and tall (>60 ft) trees. They sum-up to 100%. In general, tree heights 
rarely exceed 
100 ft. which represents the 99.9th percentile height for trees over 30 ft 
tall in the 22 
states covered by the hurricane model. On a nationwide base, the average tree 
heights 
within the three bins are 34.6, 49.4 and 69.9 ft, respectively. 

The format of the HAZUS tree coverage database is summarized in Table 12.5. A 
sample 
of the actual HAZUS tree data is shown in Figure 12.15 for Wake County, NC. A 
map of 

Chapter 12. Tree Blowdown 

?
tree density is shown in Figure 12.16. The city of Raleigh, NC is in the 
lower density area 
in the center of the county. 

Table 12.5. HAZUS Tree Data Format 

Census Tract Predominant Tree Type 
Stems per 
Acre of Land 
Tree Height Distribution, % 
30-40 ft 40-60 ft >60 ft 
xxxxxxxxxxx Coniferous, Deciduous, or Mixed 0-400 0-100 0-100 0-100 

Figure 12.15. HAZUS Tree Coverage Data for Wake County, NC. 

12.7 Debris Generated from Tree Blowdown 
Hurricanes generate considerable amounts of debris from tree blowdown. In 
most cases, 
local and state governments, with federal assistance, are responsible for 
collecting and 
disposing of this debris. Tree debris disposal totals from Hurricane Isabel 
in North 
Carolina totaled over 3.2 million cubic yards and cost communities in the 
state over $31 
million to collect and dispose of this debris. Section 12.7.1 discusses how 
HAZUS-MH 
estimates the total overall tree debris generated by hurricanes. Section 
12.7.2 presents the 
methodology for estimating the amount of tree debris to be collected as the 
result of 
hurricanes. Section 12.7.3 presents comparisons of modeled versus actual 
collected tree 
debris from several recent hurricanes including Isabel (2003), the four 
hurricanes that 
struck Florida in 2004, Wilma (2005), and Rita (2005). 

HAZUS-MH MR3 Technical Manual 

?
Figure 12.16. Map of Tree Density for Wake County, NC. 

12.7.1 Total Weight and Volume of Downed Trees 
The tree debris model combines the tree coverage database and the tree 
blowdown model 
to produce estimates of tree debris weight by census tract. The tree debris 
weight reported 
by HAZUS is the expected green weight of trees greater than 30 ft tall that 
are expected 
to fail at a given windspeed for a given density of trees. The entire weight 
is reported as 
debris, even though tree debris in unpopulated areas may not be collected. 

HAZUS-MH computes the total weight of tree debris based on the modeled 
windspeed 
and the density of trees for the census tract (or block). The tree blowdown 
functions 
presented in Section 12.4.1 are converted to debris functions by relating the 
CDLAI values 
to number of trees per acre and multiplying the probability of blowdown 
values by the 
total expected weight of trees per acre, for the given type, height, and 
density of trees. 
Figure 12.17 shows the tree debris functions implemented in HAZUS-MH. 

HAZUS-MH MR1 estimated the cubic yardage of tree debris using a multiplier of 
3.6 
cubic yards per ton based on figures reported by Escambia County, Florida 
after 
Hurricane Opal (Escambia County, 1996). Further research into appropriate 
bulking 
factors to be used to convert weight of tree debris to volume has revealed a 
wide range of 
factors used by various sources. Table 12.6 shows bulking factors developed 
from several 
different sources as a part of the development of the tree debris model for 
MR2. 

Chapter 12. Tree Blowdown 

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12-31 
HAZUS-MH MR3 Technical Manual 
70 Foot Deciduous Tree 
- 
100,000.00 
200,000.00 
300,000.00 
400,000.00 
500,000.00 
600,000.00 
0 50 100 150 200 250 
Wind Speed (mph) 
Tree Debris (#/Acre) 
DEC-70-10 
DEC-70-25 
DEC-70-50 
DEC-70-100 
DEC-70-200 
DEC-70-400 
70 Foot Coniferous Tree 
- 
50,000.00 
100,000.00 
150,000.00 
200,000.00 
250,000.00 
0 50 100 150 200 250 
Wind Speed (mph) 
Tree Debris (#/Acre) 
CON-70-10 
CON-70-25 
CON-70-50 
CON-70-100 
CON-70-200 
CON-70-400 
50 Foot Deciduous Tree 
- 
20,000.00 
40,000.00 
60,000.00 
80,000.00 
100,000.00 
120,000.00 
140,000.00 
160,000.00 
180,000.00 
0 50 100 150 200 250 
Wind Speed (mph) 
Tree Debris (#/Acre) 
DEC-50-10 
DEC-50-25 
DEC-50-50 
DEC-50-100 
DEC-50-200 
DEC-50-400 
50 Foot Coniferous Tree 
- 
10,000.00 
20,000.00 
30,000.00 
40,000.00 
50,000.00 
60,000.00 
0 50 100 150 200 250 
Wind Speed (mph) 
Tree Debris (#/Acre) 
CON-50-10 
CON-50-25 
CON-50-50 
CON-50-100 
CON-50-200 
CON-50-400 
35 Foot Deciduous Tree 
- 
5,000.00 
10,000.00 
15,000.00 
20,000.00 
25,000.00 
30,000.00 
35,000.00 
40,000.00 
45,000.00 
0 50 100 150 200 250 
Wind Speed (mph) 
Tree Debris (#/Acre) 
DEC-35-10 
DEC-35-25 
DEC-35-50 
DEC-35-100 
DEC-35-200 
DEC-35-400 
35 Foot Coniferous Tree 
- 
2,000.00 
4,000.00 
6,000.00 
8,000.00 
10,000.00 
12,000.00 
14,000.00 
0 50 100 150 200 250 
Wind Speed (mph) 
Tree Debris (#/Acre) 
CON-35-10 
CON-35-25 
CON-35-50 
CON-35-100 
CON-35-200 
CON-35-400 
Figure 12.17. Tree Debris Functions in Pounds per Acre. 
Based on the data presented in Table 12.6, it appears that the actual bulking 
factor should 
be based on a number of different types of debris   from leaves and loose 
brush to large 
limbs and stumps. It is also likely that the appropriate bulking factor will 
be a function of 
the strength of the hurricane. The majority of tree debris from weaker storms 
will be the 
result of failed limbs within the crown and will not include very many entire 
trees or 
stumps. On the other hand, stronger storms will cause more complete tree 
uprooting and 
devastating crown damage and result in denser tree debris. 
A bulking factor of 4 is more appropriate for chipped or compacted tree 
debris, while a 
factor of 10 represents bulkier, uncompacted unclipped debris. For the base 
estimation of 
tree debris volume, the bulking factor in HAZUS-MH is now taken as 10 CY/ton 
to 
represent uncompacted and unchipped debris. Users performing level 2 and 3 
analyses 
are encouraged to develop their own local bulking factors based on the 
relationship 
between tree debris volume and weight from earlier or current hurricanes 
affecting their 
areas, and/or modifying the bulking factor to reflect their local debris 
chipping/compacting procedures. 
?
Table 12.6. Tree Debris Related Bulking Factors from Various Sources 

Source Description 
Bulking Factor 
(CY/ton) 
FEMA 9580.1 Woody Debris 4 
SC Dept of Health and Environmental Control Wood Chips 4 
MS Dept of Environmental Quality Uncompacted Limbs and Leaves 12 
Large Limbs and Stumps 1.85 
Mixed Yard Trimmings 18.5 
Wood Chips 4 
CA Integrated Waste Management Board Prunings, Dry 54 
Prunings, Wet 43 
Prunings, Shredded 3.8 
Leaves, Dry 5.8 
Leaves 24 
Solid Waste Authority of Palm Beach County, FL Vegetative Debris 5.5 
Alachua County, FL Public Works Department Hurricane Debris from Frances and 
Jeanne 10.5 
Uncompacted Yard Waste 8 
City of Honolulu, HI Refuse Department 
Compacted Yard Waste 4.6 
Wood Chips 4 
Loose Brush 10 

12.7.2 Tree Debris Collection Model 
The methodology for estimating tree debris collection quantities is based on 
building 
density, length of roads, and census block shapes. This empirical method is 
based on the 
concept that trees downed in close proximity to streets, highways, or 
buildings make up 
the great majority of trees brought to the curb for collection and disposal. 

12.7.2.1 Tree Debris Collection Model Description 
The underlying premise of the tree debris collection model is that trees that 
fall in close 
proximity to highways, streets, and buildings are going to be collected and 
brought to the 
curb for collection. As such, an area reduction factor was developed based on 
a 
predetermined collection area around each of the streets and buildings within 
a study 
region. 

The model applies a reduction factor to the overall estimate of downed trees 
currently 
produced as described in Section 12.7.1. The tree debris collection model is 
expressed in 
Equation 12.39. 

n 
Dc =SDi *ACi (12.39) 
i=1 ATi 

where: 

Dc = Tree debris collected 

Di = Total tree debris predicted by HAZUS-MH for census block i 

ACi = Collection area calculated for census block i 

Chapter 12. Tree Blowdown 

?
ATi = Total area of census block i 

n = Number of census blocks in the study region 

The model was developed by running HAZUS-MH at the census block level. The 
census 
block level was chosen because the boundaries of census blocks are most often 
represented by highways, streets, and roads with relatively few streets 
contained inside. A 
method for aggregating census block factors to the census tract level for 
study regions 
defined at the census tract level is discussed in Section 12.7.2. 

The collection area (ACi) for each census block is first calculated based on 
the density of 
buildings within the census block (buildings per acre). As a baseline, it is 
assumed that 
downed trees will be picked up for an area of a one acre per building in the 
census block, 
with a maximum collection area set at the area of the census block. The 
result is that all 
tree debris is assumed to be collected in census blocks when the building 
density is equal 
to or greater than one building per acre. 

For census blocks with building densities lower than one building per acre, 
the model 
also determines an alternative collection area along the census block 
perimeter and any 
roads within the census block. The depth of this perimeter collection area 
varies based on 
the building density of the census block because areas with little or no 
development will 
only have enough trees cleared to keep the roads safe for travel. The depth 
of the 
perimeter collection area varies from 25 to 200 feet linearly as building 
density varies 
from 0 to 1 building per acre. 

25 feet was chosen as the minimum depth for the perimeter collection area to 
account for 
trees that fall into roadways and rights of way in undeveloped areas. 200 
feet was chosen 
as the upper bound to be consistent with building-based collection area 
described above 
because a 200 foot by 200 foot square is approximately one acre in area. 

The perimeter collection area is calculated in the following manner: 

  The perimeter and area of each census block are calculated. 
  The ratio of the square root of the area to the perimeter is then used to 
approximate the aspect ratio of each census block. This ratio is 0.25 for 
square 
blocks and approaches 0.0 for long slender blocks. 
  From the aspect ratio and total area of each block, the width and length of 
an 
equivalent rectangle are determined. 
  Perimeter collection areas are calculated for each block using the 
approximate 
length and width with the perimeter collection area depth, as shown in 
Figure 12.18. 
  For census blocks that contain roads within their boundaries, an interior 
road 
collection area is calculated using the length of the interior roads and the 
depth of the perimeter collection area determined above. This area is added 
to 
the perimeter collection area. 
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Census 
Block 
Boundary 
Equivalent 
Rectangle 

with same 
area and 
perimeter as 
original 
census block 

Perimeter 

Perimeter 
Collection 
Area 
Collection 
Area Depth 

(25 to 200 ft 
based on 
building 
density) 

Downed Trees 
Collected 
in this Area 

Figure 12.18. Calculation of Perimeter Collection Area for a Census Block 
with No 
Interior Roads. 

  The perimeter collection area is then compared to the building based 
collection area (number of buildings times one acre). The larger of the two 
areas is retained as the total collection area (ACi), provided that the area 
is less 
than the overall census block area. 
The presence and length of interior roads present inside each census block 
was 
determined by overlaying ESRI Streetmap USA data on the HAZUS census blocks. 

Figure 12.19 shows the relationship between equivalent census block width and 
building 
density for each census block analyzed in the Hampton Roads region of 
Virginia. The 
equivalent census block width is defined as the smaller dimension of the 
equivalent 
rectangular area calculated for each census block. The different series in 
the graph 
represent different ranges of the calculated tree debris collection factors 
(ACi/ATi). The 
collection factors are: 

  Less than 0.5 for census blocks that are substantially wider than four 
times the 
perimeter collection area depth and/or that have less than 0.5 buildings per 
acre, 
  Equal to 1.0 for census blocks with widths less than or equal to twice the 
perimeter collection area depth and/or with more than one building per acre, 
and 
  Between 0.5 and 1.0 for census blocks that have widths between two and four 
times the perimeter collection area depth and/or building densities between 
0.5 and 1.0 buildings per acre. 
Thus, in Figure 12.19, we see that only wide census blocks with low building 
densities 
are assigned collection factors less than 0.5. 

Chapter 12. Tree Blowdown 

?
12-35 
HAZUS-MH MR3 Technical Manual 
1 
10 
100 
1000 
10000 
100000 
0 2 4 6 8 10 12 14 16 18 20 
Buildings per Acre 
Equivalent Census Block Width (feet) 
Factor < 0.5 
0.5 < Factor < 1 
Factor = 1 
Figure 12.19. Equivalent Census Block Width versus Buildings per Acre for 
Hampton Roads Region of Virginia. 
The resulting factors are then applied to the HAZUS-MH results for tree 
debris quantities 
for each census block. 
12.7.2.2 Implementing into HAZUS-MH 
Factors for all Census Blocks are pre-calculated for inclusion in HAZUS-MH. 
These 
reduction values are stored in the  Tree Parameters  table in the  Tree 
Debris Collection 
Factor  column, as shown in Figure 12.20. Like the other tree parameter data, 
this field 
could also be modified by the user if more detailed information is available 
for individual 
areas at the local level. 
HAZUS-MH multiplies both the weight and volume of tree debris estimates in 
the debris 
analysis results table by the collection factors and presents two columns of 
output, as 
indicated in Figure 12.21. 
Requiring that this analysis be completed at the census block level greatly 
increases the 
time it takes to run HAZUS. In order to accommodate analyses run at the 
census tract 
level, the factors developed for individual census blocks are aggregated to 
the census 
tract level using the following equation: 
S
= S
= 
= 
n 
i 
n 
i 
CBi 
CBi CBi 
CT 
A
F A F 
1 
1 
* (12.2) 
?
Figure 12.20. Addition of Tree Collection Factor to Tree Parameter Table in 
HAZUS-MH 

Figure 12.21. HAZUS-MH Debris Results Table 

Chapter 12. Tree Blowdown 

?
where: 

FCT = Tree Debris Collection Factor for a given Census Tract 

ACBi = Area of Census Block i 

FCBi = Tree Debris Collection Factor for Census Block i (ACi/ATi) 

ACT = Area of corresponding census tract 

n = Number of Census Blocks in the current Census Tract 

If HAZUS-MH is run using uniform tree characteristics within each census 
tract (as is the 
case with the default data provided in HAZUS-MH), identical estimates of tree 
debris 
generation and collection will be produced regardless of whether the analysis 
is run at the 
census tract or census block level. However, if the tree parameter data is 
changed such 
that the data vary by census block within a census tract, then results will 
differ and 
HAZUS-MH must be run at the census block level for the desired results. This 
issue is 
discussed further in the next section. 

12.7.2.3 Other Considerations for HAZUS-MH 
While the collection factors can be aggregated to the census tract level as 
discussed in the 
previous section, the tree debris generation and collection results are 
sensitive to whether 
tree parameters are tabulated at the block or the tract level. 

In HAZUS-MH, the tree parameter data are assumed to be the same for every 
census 
block within a given census tract. This situation can introduce substantial 
biases in the 
tree debris generation and collection estimates for rural counties where 
census tracts are 
very large geographically and there may be only one or two census tracts for 
an entire 
county. One such area is Camden County, NC. 

Tree fall frequencies surveyed in Camden County following Hurricane Isabel in 
2003 
demonstrated that tree coverage was scattered throughout the county with 
large areas of 
open land and smaller areas of clustered trees. Since Camden County has only 
one census 
tract, HAZUS-MH considers the county to have uniform tree coverage. 

In order to investigate further, the MRLC (Multi-Resolution Land 
Characteristics) land 
use, land cover data was revisited for a rural, four-county study region in 
North Carolina. 
Tree density statistics were re-calculated at the census block level for 
Camden, Chowan, 
Gates and Perquimans counties. The revised tree parameters were input into 
HAZUS and 
the Hurricane Isabel scenario was re-run. Table 12.7 summarizes the results 
of this reanalysis 
for these four counties. 

The large reductions in debris generated and collected indicate that the tree 
debris 
estimation is sensitive to the tree parameter data that is input to the tree 
blowdown and 
debris collection models. This indicates that when running level 2 or 3 
analyses with 
refined local data, it is desirable to re-classify the land use, land cover 
data by census 
block when using HAZUS to predict tree debris generation and collection 
quantities. This 
is especially true for rural counties with large census tracts. 

HAZUS-MH MR3 Technical Manual 

?
Table 12.7. Comparison of Modeled Tree Debris Generation and Collection 
Weight 
with Tree Parameters Compiled at the Census Tract and Census Block Levels. 

County Name 
Tree Debris Generated (tons) Tree Debris Collected (tons) 
Standard Tree 
Parameters 
Revised Tree 
Parameters 
Percent 
Reduction 
Standard Tree 
Parameters 
Revised Tree 
Parameters 
Percent 
Reduction 
Camden 
Chowan 
Gates 
Perquimans 
338,711 
311,757 
1,130,284 
540,251 
199,911 
264,208 
889,938 
314,369 
41.0% 
15.3% 
21.3% 
41.8% 
9,453 
17,539 
36,609 
23,386 
6,199 
11,276 
27,493 
12,631 
34.4% 
35.7% 
24.9% 
46.0% 

12.7.3 Comparison of Modeled and Reported Tree Debris 
This section describes the methodology and presents a comparison of a 
prototype 
implementation of the methodology with tree debris statistics received from 
the North 
Carolina (NCDEM, 2004) and Virginia (Roarty, 2004) Departments of Emergency 
Management following Hurricane Isabel in 2003 (NC Department of Emergency 
Management, 2004; and Roarty, 2004). 

12.7.3.1 Example Tree Collection Data   Hurricane Isabel, North Carolina 
Tree debris collection data were obtained from the North Carolina Department 
of 
Emergency Management to assess how the methodology performs on an actual 
storm. 
Data were available for all counties in North Carolina that filed for federal 
assistance for 
debris cleanup. The data for the counties analyzed in this study are 
presented in 
Table 12.8. 

These data were compiled by the North Carolina Department of Emergency 
Management 
using tree debris quantities from project worksheets completed by local 
governments 
following Hurricane Isabel. The numbers represent a combination of actual and 
estimated 
tree debris quantities by county based on their availability on the project 
worksheets. It is 
also important to note that there is a separate line noted for  State 
Agencies . The debris 
quantity reported for this entry is likely to result from additional debris 
collected from 
several of the affected counties studied. Because of this, the tree debris 
volumes reported 
by county are likely to be slightly lower than actual. However, this volume 
is less than 
2% of the total tree debris volume reported. 

HAZUS-MH was run using a Hurricane Isabel scenario for the 20 North Carolina 
counties listed in Table 12.8. Figure 12.22 compares modeled tree debris 
weight (total 
and collected) to the actual weight of tree debris reported collected by 
county in North 
Carolina for Hurricane Isabel (reported weights calculated from reported 
cubic yards with 
an assumed bulking factor of 10 cubic yards per ton). The series labeled  All 
Downed 
Trees  represents the total tree debris estimated by HAZUS before applying 
the 
collection factors. 

The county names along the abscissa are ordered by increasing average census 
block 
building density. In other words, the most rural counties appear on the left 
and least rural 

Chapter 12. Tree Blowdown 

?
Table 12.8. North Carolina Tree Debris Collection Data for Hurricane Isabel 
(2003) 

County 
Tree Debris 
Volume (CY) 
Collection/Disposal 
Cost ($) 
Unit Cost 
($/Cubic Yard) 
Beaufort 436,323 10,781,089 24.71 
Bertie 82,222 932,359 11.34 
Camden 706 28,765 40.74 
Carteret 135,434 802,920 5.93 
Chowan 476,768 3,704,690 7.77 
Craven 52,729 453,677 8.60 
Currituck 60,400 402,098 6.66 
Dare 220,113 1,973,850 8.97 
Gates 4,094* 45,688 11.16 
Hertford 58,194 400,399 6.88 
Hyde 48,503 384,263 7.92 
Jones 3,414 44,924 13.16 
Martin 69,951 283,732 4.06 
Northampton 37,747 228,001 6.04 
Onslow 8,881 260,441 29.33 
Pamlico 4,697 159,095 33.87 
Pasquotank 773,216 3,039,044 3.93 
Perquimans 33,600 239,320 7.12 
Tyrrell 3,700 43,424 11.74 
Washington 152,404 361,538 2.37 
State Agencies 53,121 974,307 18.34 
Region Total 2,716,217 25,543,624 9.40 

* Volume estimated from cost of removal 
(most urban/suburban) appear on the right. There appears to be a trend of 
greater over 
prediction of tree debris for the more rural counties. It is not possible to 
determine 
whether this trend is due to an overestimate of the number trees blown down, 
an 
overestimate of the collection factors in rural areas, an underreporting of 
tree debris 
collection in some rural counties, or some combination of the three. 

The overall estimate of tree debris collected for the 20 North Carolina 
counties 
considered is about 41 percent lower than the actual totals collected by the 
Department of 
Emergency Management. 

12.7.3.2 Example Tree Collection Data   Hurricane Isabel, Virginia 
Data from the Virginia Department of Emergency Management were available for 
the 16 
counties and communities that make up the Hampton Roads area. These data were 
compiled by the Hampton Roads Planning District for the Virginia Department 
of 
Emergency Management. The debris data for Virginia are shown in Table 12.9. 

The debris data received from the Virginia Department of Emergency Management 
included the following caveats: 

  Debris quantities from military facilities in the region are not included. 
HAZUS-MH MR3 Technical Manual 

?
100 
1,000 
10,000 
100,000 
1,000,000 
Weight of Tree Debris (tons) 
County 

Collected 

All Downed Trees Reported Collected 

Figure 12.22. Comparison of Modeled Total and Collected Tree Debris Weight 
with 
Collection Totals Reported by NC DEM by County in North Carolina for 
Hurricane Isabel. 

  The data shown above is for all debris, not just trees/vegetative debris. 
However, conversations with Virginia DEM and the Hampton Roads Planning 
District confirmed that over 95% of the debris reported was from vegetative 
sources. 
  Approximately 25% of the debris was reported by the Hampton Roads District 
of the Virginia Department of Transportation (VDOT). This debris was 
collected from the study area, but the proportion belonging to each county or 
community is unknown and is not included in the analysis. 
  VDOT is responsible for collection of vegetative debris from federal 
highways in the entire Hampton Roads region, as well as for the collection of 
debris along state roads in the counties not labeled  (city) . Debris 
collection 
from state roads in counties labeled  (city)  is the responsibility of the 
city 
government. 
The modeled tree debris weight was compared directly to the data provided by 
the 
Virginia DEM without modifications for the caveats listed above, assuming a 
bulking 
factor of 10 cubic yards per ton. The counties appear in order of increasing 
average 
census block building density. 

Figure 12.23 shows that for twelve of the sixteen counties, the total weight 
of tree debris 
generated by all downed trees is less than that reported by the Virginia 
Department of 
Emergency Management, indicating either that the default tree densities 
and/or the 
calculated tree blowdown rates are too low in these counties or that there 
are other 
sources of debris that are not being modeled in HAZUS. 

Chapter 12. Tree Blowdown 

?
Table 12.9. Virginia Debris Collection Data for Hurricane Isabel (2003). 

County Debris Volume (CY) 
Chesapeake (city) 915,101 
Franklin (city) 119,000 
Gloucester 190,000 
Hampton (city) 749,503 
Isle of Wight 152,953 
James City 411,848 
Newport News (city) 577,045 
Norfolk (city) 1,014,000 
Poquoson (city) 175,795 
Portsmouth (city) 430,000 
Southampton 185,200 
Suffolk (city) 400,000 
Surry 59,514 
Virginia Beach (city) 922,000 
Williamsburg (city) 79,000 
York 602,830 
VDOT HR District 2,365,860 
Total Debris: 9,349,649 

100 
1,000 
10,000 
100,000 
1,000,000 
Weight of Tree Debris (tons) 
County 

Collected 

All Downed Trees Reported Collected 

Figure 12.23. Ratio of Modeled Tree Debris to Actual Debris Collected by 
County 
in Virginia for Hurricane Isabel. 

Aggregating the data over all 16 Hampton Roads communities, we find that the 
debris 
collection model underestimates the total debris collected (excluding the 
VDOT 
contribution) by about 90%. However, given that the tree blowdown model is 
apparently 
under predicting the quantity of downed trees and the various limitations of 
the raw data 
set discussed above, it is difficult to conclude that the Virginia example 
indicates any 

HAZUS-MH MR3 Technical Manual 

?
serious flaws in the model. The model appears to capture the proper trends 
and produce 
reasonable estimates of tree debris collection rates in rural, suburban and 
urban areas. 
Additional validation and refinement of the model is recommended as similar 
data sets 
become available for future hurricanes of varying intensities and geographic 
locations. 

It is important to note that, in general, the Virginia Counties considered 
have 
substantially higher building densities (areas are more urban/suburban) than 
the counties 
analyzed in North Carolina. Table 12.10 lists the building densities for each 
of the 36 
counties investigated with the average modeled tree debris collection rates. 
Building 
densities are calculated as the total number of buildings in the HAZUS 
general building 
stock model divided by the land area of the county or region. Likewise, 
average modeled 
tree debris collection rates are determined by dividing the total collected 
tree debris 
volume by the total blown down tree volume modeled by HAZUS for the county 
and 
region levels. 

Table 12.10. Building Density and Average Tree Debris Collection Rate by 
County 
for North Carolina and Virginia. 

North Carolina Virginia 
County 
Buildings 
per Acre 
Average Tree 
Collection Rate (%) County 
Buildings 
per Acre 
Average Tree 
Collection Rate (%) 
Beaufort 0.039 4.93 Chesapeake (city) 0.308 17.88 
Bertie 0.019 3.24 Franklin (city) 0.502 37.11 
Camden 0.019 3.00 Gloucester 0.099 10.91 
Carteret 0.113 8.33 Hampton (city) 1.325 64.92 
Chowan 0.053 5.95 Isle of Wight 0.058 6.45 
Craven 0.076 7.80 James City 0.220 19.99 
Currituck 0.067 6.18 Newport News (city) 1.132 57.63 
Dare 0.106 7.77 Norfolk (city) 1.717 69.96 
Gates 0.020 3.23 Poquoson (city) 0.446 33.96 
Hertford 0.039 4.92 Portsmouth (city) 1.451 66.52 
Hyde 0.008 3.00 Southampton 0.017 3.79 
Jones 0.015 3.00 Suffolk (city) 0.085 7.43 
Martin 0.034 4.01 Surry 0.017 3.74 
Northampton 0.028 4.32 Virginia Beach (city) 0.865 54.10 
Onslow 0.103 9.52 Williamsburg (city) 0.496 39.54 
Pamlico 0.031 5.41 York 0.306 22.12 
Pasquotank 0.084 7.17 
Perquimans 0.034 4.79 
Tyrrell 0.008 2.00 
Washington 0.025 3.98 
20 NC Counties 0.047 4.68 16 VA Counties 0.231 12.87 

For the Virginia counties considered, the tree debris collection model 
estimates 12.9% of 
the total tree debris being collected versus only about 4.7% for the much 
more rural 
counties of North Carolina. Note that although the average building density 
exceeds 1.0 
buildings per acre for several Virginia counties, the average tress 
collection ratios are still 
less than 100% because some of the census blocks in those counties have 
building 
densities lower than 1.0 buildings per acre. 

The trend of decreased over-prediction of tree debris as building density 
increases may 
also be due in part to different disposal means used in rural versus urban 
areas. In rural 

Chapter 12. Tree Blowdown 

?
areas, it is common to see residents and farmers burning tree debris from 
their property 
immediately following the storm, or chopping and storing the wood for use to 
heat their 
homes for the winter. This behavior reduces the amount of debris that will 
actually be 
brought to the curb for collection by local and state governments. 

By contrast, residents of urban and suburban areas tend to not only bring the 
tree debris 
that falls to the curb, but actually create additional debris by removing any 
part of the 
broken trees that remained standing. These residents may also take advantage 
of the 
opportunity to dispose of other vegetative debris stored on their property. 
This behavior 
may lead to increased tree debris generated and collected in urban and 
suburban areas. 

A potential example of this trend was discovered in the data received from 
the North 
Carolina Department of Emergency Management. One largely urban and suburban 
county reported over 65,000 cubic yards of vegetative debris at a removal 
cost of over 
$1.5M while HAZUS-MH did not predict any downed trees for this county because 
the 
windspeeds in Hurricane Isabel did not exceed 50 mph (3 second gust). 

Another observation that may lead to underestimating tree blowdown in 
urban/suburban 
areas is that the tree database and tree blowdown model only consider trees 
greater than 
30 feet tall. The percentage of tree weight or volume coming from trees less 
than 30 feet 
tall is likely to be larger in urban/suburban areas than rural areas. 

12.7.3.3 Comparison to Other Collection Data 
In addition to the county data compared for Hurricane Isabel in NC and VA, 
tree debris 
estimates were also available from the following storms. Table 12.11 presents 
a list of 
areas for which estimates of tree debris collected are available for the 
corresponding 
storms. 

Figure 12.24 presents a comparison of modeled tree debris collected to actual 
quantities 
reported for the hurricanes and locations mentioned above. This comparison 
considers the 
weight of tree debris in tons, however, most hurricane tree debris reports 
are volume 
(cubic yards). The vertical line for each storm-location combination 
represents a range of 
weights based on bulking factors ranging from 4 to 10 cubic yards per ton. 
The small 
horizontal line represents either the actual tonnage reported (if no vertical 
line is present) 
or tonnage estimated assuming a bulking factor of 6 cubic yards per ton. 

Table 12.11. Locations of Collected Tree Debris Estimates by Hurricane 
Name and Year 

Storm   Year Locations with Data 
Rita   2005 State of Texas 
Wilma   2005 Palm Beach, Broward, & Miami-Dade Counties, FL 
Charley + Frances + Jeanne + Ivan   2004 State of Florida, Alachua & Orange 
Counties, FL 
Isabel   2003 States of North Carolina & Virginia 
Erin + Opal   1995 Escambia County, FL 

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1,000 
10,000 
100,000 
1,000,000 
10,000,000 
100,000,000 
Tons of Tree Debris 
1,000 
10,000 
100,000 
1,000,000 
10,000,000 
100,000,000 
Rita-TX Wilma-Wilma-Wilma-2004-FL 2004-2004-Isabel-Isabel-ErinOpa 
PB Broward Miami-Alachua Orange NC VA_HR l-Escam 
Dade 

Storm-Location 

Figure 12.24. Comparison of Modeled Tree Debris Collected to Reported Amounts 
for Various Locations and Various Hurricanes. 

12.8 Tree Blowdown Damage to Buildings 
12.8.1 Overview 
The building damage-to-loss model estimates expected loss as function of wind 
speed for 
2 tree types, 3 tree height groups, 6 tree densities, 4 building geometries, 
and 2 wall 
construction types. These result in 288 normalized loss curves for tree 
blowdown damage 
to buildings, for building loss and contents loss respectively. The tree 
blowdown loss 
curves are combined with the fast-running normalized loss curves presented in 
Chapter 7 
to form loss curves in each census tract that model wind, missile, and tree 
damage effects. 

A Monte Carlo simulation approach is employed to derive the blowdown loss 
functions. 
A total of 10,000 simulations are performed to derive each function, which is 
taken to be 
the mean of the 10,000 simulated losses. Figure 12.25 illustrates the process 
for one 
simulation. The following sections describe the elements not presented in the 
previous 
sections. 

12.8.2 Tree Drop Tests 
The severity of tree damage to buildings is dependent on the tree impact 
energy and the 
structure s impact resistance. In an effort to investigate tree impact damage 
on residential 
structures, the Wind Load Test Facility at Clemson University conducted tree 
drop tests 
on modeled partial house structures. To simulate the tree trunk, the Clemson 
tests used 
two steel pipes of different weights, namely 450 lb and 950 lb, both at a 
length of 20 ft. 
The pipes were released from standing position on a rig about 18 ft from the 
modeled 
structure and free-fell to the modeled house structure. The lighter pipe hits 
the eave with 
impact energy of 3600 lb-ft, and the larger pipe hits with an impact energy 
of 7600 lb-ft. 

Chapter 12. Tree Blowdown 

?
Damage by One Tree 
Economic Loss due to n Tree Hits 
L(n) = f [ L(i), n ] 
Tree Type, Heights, 
& Number of Trees 
per Acre of Land 
Prob. of Tree 
Blowdown 
vs. Wind Speed 
Damage by n Trees 
M, Number of 
Trees within 
Striking Distance 
Building Type, Height & 
Plan Dimensions 
Yes 
No 
N, Number of 
Blown-down Trees 
within Striking 
Distance 
If i=N 
i=1 
i=i+1 
up to N 
Figure 12.25. Simulation Scheme for Tree Blow-Down Damage to Building. 

Video recordings were taken during the fall and impact, and still pictures 
were also taken 
of the damage after impact. Figure 12.26 to Figure 12.29 show examples of the 
impact 
damage recorded from the tests on several modeled structures with different 
impact 
resistance capacities. 

12.8.3 Relationship between Damage Severity and Impact Energy 
A quantitative relationship between physical damage state and the tree impact 
energy is 
essential to estimate tree blowdown damage, and ensuing economic losses, to 
buildings. 
The Clemson University tree drop test data aided in developing this 
relationship. The 
assumptions involved in establishing the damage state severity model include: 

A. A tree falls solely under the action of gravity. The actions of blowing 
wind 
and the remaining resistance from root-soil interaction (for uprooting) or 
the 
remaining resistance from wood fibers at breakpoint (for above ground 
breakage) are neglected. 
B. A tree trunk impact is required to cause damage. 
C. The tree trunk does not bounce after it hits the building. 
D. If it exceeds the impact resistance of the structure, an impacting tree 
trunk cuts 
into the building until all of its kinetic energy dissipates into the 
structure. 
E. A tree hit does not cause the complete collapse of a building. 
Different building components will present different resistances to a tree 
trunk as it cuts 
through the structure. The resistance associated with a specific component of 
the 
building, such as the roof deck, top plate or bond-beam, wall sheathing, or 
an elevated 

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Figure 12.26. Small Pipe Impacting Wall Without Plywood Sheathing, 3600 lb-ft 
at 
Impact, Breaking Top Plates and Half-Way Cutting Into Wall. 

Figure 12.27. Small Pipe Impacting Wall with Plywood Sheathing, 3600 lb-ft at
Impact, Breaking Top Plates and 1/4 Cutting Into Wall. 

Chapter 12. Tree Blowdown 

?
Figure 12.28. Large Pipe Impacting Roof and Wall Without Plywood Sheathing, 
7600 lb-ft at Impact, Breaking Roof, Top Plates and Entire Wall with Apparent 
Residual Energy Hitting Ground. 

Figure 12.29. Large Pipe Impacting Roof and Wall With Plywood Sheathing, 7600 
lb-ft at Impact, Breaking Roof, Top Plates and 1/8 Wall. 

HAZUS-MH MR3 Technical Manual 

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floor, is assumed to be constant. Based on the limited number of tree drop 
damage states 
recorded by Clemson University and additional engineering inferences, an 
extended 
number of damage states are defined for the subsequent estimation of direct 
economic 
losses, in relation to impact energy, as shown in Table 12.12. Tree heights 
that will 
potentially produce the indicated impact energy and corresponding damage 
states are also 
presented for given stand-off distance, breaking point, and fall azimuth, 
etc. using pine 
trees as an example. 

Table 12.12. Damage States in Relation to Impact Energy 

Damage State # 1 2 3 4 5 6 7 8 
1-Story 
Wood 
Impact Energy 
(lb-ft)1 250 2000 5600 6400 8800 
Example Tree 
Height 2(ft) 30.0 46.9 57.5 59.0 62.6 
Damage State 
Description 
Surface 
damage 
Roof 
deck 
crack 
Top-
plates 
rupture 
< Cut 
into 
wall 
Cut 
through 
wall 
2-Story 
Wood 
Impact Energy 
(lb-ft) 250 2000 5600 6400 8800 14400 15200 17600 
Example Tree 
Height (ft) 32.0 50.2 61.3 62.8 66.6 72.5 73.3 75.2 
Damage State 
Description 
Surface 
damage 
Roof 
deck 
crack 
Top-
plates 
rupture 
< Cut 
into 
upper 
wall 
Cut 
through 
upper 
wall 
Floor-
plates 
rupture 
< Cut 
into 
lower 
wall 
Cut 
through 
lower 
wall 
1-Story 
Masonry 
Impact Energy 
(lb-ft) 250 2000 11000 13000 19000 
Example Tree 
Height (ft) 30.0 46.9 65.2 67.2 72.0 
Damage State Surface 
damage 
Roof 
deck 
crack 
Bond-
beam 
rupture 
< Cut 
into 
wall 
Cut 
through 
wall 
2-Story 
Masonry 
Impact Energy 
(lb-ft) 250 2000 11000 13000 19000 30000 32000 38000 
Example Tree 
Height (ft) 32.0 50.2 69.3 71.4 76.2 82.1 83.0 85.3 
Damage State 
Description 
Surface 
damage 
Roof 
deck 
crack 
Bond-
beam 
rupture 
< Cut 
into 
upper 
wall 
Cut 
through 
upper 
wall 
Floor-
plates 
rupture 
< Cut 
into 
lower 
wall 
Cut 
through 
lower 
wall 

Notes: 1. Impact Energy is defined as the energy derived from the normal 
component of impact velocity with respect to the eave line. 

2. Assume a pine tree with stand-off distance of 30ft, breaking at ground 
level and hitting eave perpendicularly. 
12.9 Estimation of Direct Economic Loss from Physical Damage States 
Cost Estimation Assumptions and Data. The cost estimates were prepared with 
data 
from RSMeans 2002 Repair and Remodeling Cost Data and the Means CostWorks 
2002 
software. The cost estimates have prices adjusted locally for Miami, which is 
consistent 
with the costing data of the original loss curves. 

In preparing these cost estimates, the following assumptions about the 
construction were 
made: 

  Roof covering is shingles 
Chapter 12. Tree Blowdown 

?
  =  plywood roof deck 
  Wood truss roof structure 
  The average room size is 200 SF 
  There are 7 rooms on average in a one-story building, and 14 rooms in a 
two-
story building 
  There is 1 bathroom in a one-story building, and 2 in a two-story building 
  Contents value per square foot are 50% of the building value 
  Value of building is 80 dollars per SF. 
The general approach consisted of estimating the size of the opening that was 
created for 
each of the damage states, then estimating the extent of damage to various 
components of 
the building such as roof covering, roof structure, walls, flooring, 
contents, and electrical, 
etc. The extent of the area damaged accounted for the replacement of  units  
of a 
component   for example, an even number of 4x8 sheets of plywood on the roof, 
etc. 
Additional Assumptions for One-Story. The cost estimates for Damage State 5 
(roof and 
wall) includes 50% of a 3 x5  typical window assuming that there is a 50% 
chance that a 
window will be involved in the damaged area. The assumption is that the 
amount of 
glazing on a home is approximately 20% of the wall area, which translates to 
about a 
50% chance that any vertical slice will involve a window. For damage state 4 
(roof and 
1/4 wall) the area of window was reduced further still to reflect the 
likelihood that the 
damage is only to the wall above the window, and repair to the window may be 
less 
likely. For masonry homes, it was assumed that the windows would not be 
affected for 
this damage state. 

One seventh of the cost of a set of kitchen appliances and cabinets was 
included to reflect 
the fact that 1 in 7 rooms is a kitchen which is likely involved in the 
damaged area. 
Similarly for plumbing, 1/7 of a package of plumbing cost is included (which 
is 
dominated by bathroom fixtures), based on the assumption that 1 in 7 rooms is 
a 
bathroom. 

The extent of required wall repair in damage state 4 for masonry walls is 
assumed to be 
less than for wood frame walls, because the CMU units are smaller, and 
therefore the 
area to be repaired can be more localized. 

Additional Assumptions for Two-Story. Damage state 8 includes one full window 
in the 
cost estimate. In the same manner as the one-story building, it is assumed 
that there is a 
50% chance that a window will be damaged on each story, or the equivalent of 
one full 
window. 

Table 12.13 and Table 12.14 list the assumed damage areas used in the one-
story and 
two-story cost estimates, respectively. Content damage is estimated with the 
areas of 
Additional Assumptions for One-Story. The cost estimates for Damage State 5 
(roof and 
wall) includes 50% of a 3 x5  typical window assuming that there is a 50% 
chance that a 
window will be involved in the damaged area. The assumption is that the 
amount of 

HAZUS-MH MR3 Technical Manual 

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glazing on a home is approximately 20% of the wall area, which translates to 
about a 
50% chance that any vertical slice will involve a window. For damage state 4 
(roof and 
1/4 wall) the area of window was reduced further still to reflect the 
likelihood that the 
damage is only to the wall above the window, and repair to the window may be 
less 
likely. For masonry homes, it was assumed that the windows would not be 
affected for 
this damage state. 

One seventh of the cost of a set of kitchen appliances and cabinets was 
included to reflect 
the fact that 1 in 7 rooms is a kitchen which is likely involved in the 
damaged area. 
Similarly for plumbing, 1/7 of a package of plumbing cost is included (which 
is 
dominated by bathroom fixtures), based on the assumption that 1 in 7 rooms is 
a 
bathroom. 

The extent of required wall repair in damage state 4 for masonry walls is 
assumed to be 
less than for wood frame walls, because the CMU units are smaller, and 
therefore the 
area to be repaired can be more localized. 

Additional Assumptions for Two-Story. Damage state 8 includes one full window 
in the 
cost estimate. In the same manner as the one-story building, it is assumed 
that there is a 
50% chance that a window will be damaged on each story, or the equivalent of 
one full 
window. 

Table 12.13 and Table 12.14 and a simple cost per square foot valued at 50% 
of the 
assumed building value (i.e., $40/ft2). 

Additional Assumptions for One-Story. The cost estimates for Damage State 5 
(roof and 
wall) includes 50% of a 3 x5  typical window assuming that there is a 50% 
chance that a 
window will be involved in the damaged area. The assumption is that the 
amount of 
glazing on a home is approximately 20% of the wall area, which translates to 
about a 
50% chance that any vertical slice will involve a window. For damage state 4 
(roof and 
1/4 wall) the area of window was reduced further still to reflect the 
likelihood that the 
damage is only to the wall above the window, and repair to the window may be 
less 
likely. For masonry homes, it was assumed that the windows would not be 
affected for 
this damage state. 

One seventh of the cost of a set of kitchen appliances and cabinets was 
included to reflect 
the fact that 1 in 7 rooms is a kitchen which is likely involved in the 
damaged area. 
Similarly for plumbing, 1/7 of a package of plumbing cost is included (which 
is 
dominated by bathroom fixtures), based on the assumption that 1 in 7 rooms is 
a 
bathroom. 

The extent of required wall repair in damage state 4 for masonry walls is 
assumed to be 
less than for wood frame walls, because the CMU units are smaller, and 
therefore the 
area to be repaired can be more localized. 

Additional Assumptions for Two-Story. Damage state 8 includes one full window 
in the 
cost estimate. In the same manner as the one-story building, it is assumed 
that there is a 

Chapter 12. Tree Blowdown 

?
50% chance that a window will be damaged on each story, or the equivalent of 
one full 
window. 

Table 12.13. Assumed Damage Areas Used in One-Story Cost Estimates 

Damage State # 
1 3 4 5 
Surface Damage Roof Only Roof and 1/4 Wall Roof and Wall 
Component 
Window (sf) 0 0 1.75 or 0 7 
Roof Structure (SF) 0 200 200 200 
Roof Covering(SF) 100 210 210 210 
Walls(SF) 32 32 32 or 10 64 
Electrical (l.f) 0 0 10 50 
Flooring(SF) 0 75 200 200 
Floor Structure(SF) 0 0 0 0 
Contents(SF) 0 100 150 150 
Partitions(SF wall) 0 200 320 400 
Plumbing(each) 0 0.07 0.11 0.14 
Heating(SF area) 0 100 160 200 
Kitchen and Appliances(each) 0 0.07 0.12 0.14 
Assumed Opening Size 
Roof (SF) 0 16 40 48 
Wall (SF) 0 0 12 48 

Table 12.14. Assumed Damage Areas Used in Two-Story Cost Estimates 

Damage State # 
1 3 4 5 
Surface Damage Roof Only Roof and 1/4 Wall Roof and Wall 
Component 
Window (sf) 0 0 3 or 0 14 
Roof Structure (SF) 0 200 200 200 
Roof Covering(SF) 100 210 210 210 
Walls(SF) 64 64 32 or 10 128 
Electrical (l.f) 0 0 30 100 
Flooring(SF) 0 300 300 400 
Floor Structure(SF) 0 0 0 64 
Contents(SF) 0 150 200 300 
Partitions(SF wall) 0 210 420 600 
Plumbing(each) 0 0.05 0.1 0.28 
Heating(SF area) 0 105 210 300 
Kitchen and Appliances(each) 0 0.025 0.05 0.14 
Assumed Opening Size 
Roof (SF) 0 16 40 48 
Wall (SF) 0 0 12 96 

HAZUS-MH MR3 Technical Manual 

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For kitchens and plumbing (bathrooms) the likelihood of impacting 1 of 7 
rooms on any 
floor was accounted for in the estimate by costing in 1/7 of the cost of a 
complete set of 
kitchen appliances/cabinets or plumbing fixtures. 

The area of flooring affected in each damage state accounts for the dripping 
of water 
from one story to another. 

The completed cost estimates are presented in Table 12.15. The estimates are 
also 
illustrated in Figure 12.30 to Figure 12.32 for building, contents, and 
combined costs as 
functions of impact energy. For damage states whose costs were not estimated 
item-byitem, 
overall costs were estimated based on incremental costs by comparing to the 
costs 
estimated item-by-item. Maximum potential losses resulting from one tree hit 
are also 
estimated to be approximately $10,000 of structure and $8,000 of contents for 
one-story, 
and $17,000 and $13,000 for two-story, all assumed to be reached at impact 
energy of 
100,000 lb-ft. 

Table 12.15. Estimated Building Repair Costs by Damage State 

Damage State # 1 2 3 4 5 6 7 8 
12-Story 
Wood 
Impact Energy (lb-ft)1 250 2000 5600 6400 8800 
Damage State Surface 
damage 
Roof 
deck 
crack 
Top-
plates 
rupture 
< Cut 
into 
wall 
Cut 
through 
wall 
Building Cost ($) 125 800 3789 5752 6212 
Contents Cost ($) 0 200 4000 6000 7000 
Combined Cost ($) 125 1000 7789 11752 13212 
2-Story 
Wood 
Impact Energy (lb-ft) 250 2000 5600 6400 8800 14400 15200 17600 
Damage State Surface 
damage 
Roof 
deck 
crack 
Top-
plates 
rupture 
< Cut 
into 
upper 
wall 
Cut 
through 
upper 
wall 
Floor-
plates 
rupture 
< Cut 
into 
lower 
wall 
Cut 
through 
lower 
wall 
Building Cost ($) 162 1000 3937 5923 7000 7800 9600 10283 
Contents Cost ($) 0 200 5500 8000 9000 10200 11400 12000 
Combined Cost ($) 162 1200 9437 13923 16000 18000 21000 22283 
12-Story 
Masonry 
Impact Energy (lb-ft) 250 2000 11000 13000 19000 
Damage State Surface 
damage 
Roof 
deck 
crack 
Bond-
beam 
rupture 
< Cut 
into 
wall 
Cut 
through 
wall 
Building Cost ($) 101 600 3765 5539 6692 
Contents Cost ($) 0 200 4000 6000 7000 
Combined Cost ($) 101 800 7765 11539 13692 
2-Story 
Masonry 
Impact Energy (lb-ft) 250 2000 11000 13000 19000 30000 32000 38000 
Damage State Surface 
damage 
Roof 
deck 
crack 
Bond-
beam 
rupture 
< Cut 
into 
upper 
wall 
Cut 
through 
upper 
wall 
Floor-
plates 
rupture 
< Cut 
into 
lower 
wall 
Cut 
through 
lower 
wall 
Building Cost ($) 114 700 3888 5708 7000 7800 9600 11243 
Contents Cost ($) 0 200 5500 8000 9000 10200 11400 12000 
Combined Cost ($) 114 900 9388 13708 16000 18000 21000 23243 

Chapter 12. Tree Blowdown 

?
Building Loss due to One Tree Hit 

14000 
12000 

10000 

8000 

6000 

0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 
Impact Energy (lb-ft) 
$ Loss 
1-story Wood 
1-story Masonry 
2-story Wood 
2-story Masonry 
4000 

2000 

0 

Figure 12.30. Building Repair Cost Estimates as Functions of Impact Energy. 

Contents Loss due to One Tree Hit 

14000 

12000 

10000 

8000 

0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 
Impact Energy (lb-ft) 
$ Loss 
1-story Wood 
1-story Masonry 
2-story Wood 
2-story Masonry 
6000 

4000 

2000 

0 

Figure 12.31. Contents Repair Cost Estimates as Functions of Impact Energy. 

HAZUS-MH MR3 Technical Manual 

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Total Loss due to One Tree Hit 

0 
5000 
10000 
15000 
20000 
25000 
$ Loss 
1-story Wood 
1-story Masonry 
2-story Wood 
2-story Masonry 
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 

Impact Energy (lb-ft) 

Figure 12.32. Total Repair Cost Estimates as Functions of Impact Energy. 

12.10 Mean Loss as a Function of Wind Speed 
12.10.1 Losses Due to Tree Blowdown 
Using the methodology illustrated in Figure 12.25 and the assumptions and 
results 
discussed in the previous sections, mean building and contents losses are 
derived as 
functions of peak gust speed based on 10,000 simulations for each of the 288 
cases 
summarized in Table 12.16. In the simulations, trees are assumed to be 
distributed 
uniformly random over areas not occupied by the building, with a 10 ft 
clearance from 
the building perimeter. The fall azimuth is also assumed to uniformly random. 

Table 12.16. Parameter Matrix for the 288 Cases Studied 

Parameter Wall Type 
Dimension 
(Value $) Tree Type Tree Height Tree Density 
Number of Values 2 4 2 3 6 
Wood 50x24x 9 (96k) Evergreen 312-40ft 10 
60x30x 9 (144k) 412-60ft 25 
Values 
Masonry 
40x30x17 (192k) 
Deciduous 
=60ft 
50 
50x30x17 (240k) 
100 
200 
(all hip roofs) 400 

Chapter 12. Tree Blowdown 

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For multiple impacts, each impact is assumed to damage a previously undamaged 
portion 
of the structure. 

Examples of building and contents loss functions are presented in Figure 
12.33 through 
Figure 12.37, each of which demonstrates the dependence of loss functions on 
one of the 
parameters listed in Table 12.16. Figure 12.38 compares the building and 
content losses 
for one specific combination of input parameters. 

12.10.2 Loss Function for a Specific Building Type in Given Census Tract 
The basic normalized loss functions and the tree inventory data are used in 
HAZUS as 
follows to derive a normalized loss function for a specific building type in 
a specific 
census tract: 

Input: Census tract tree inventory data: dominant tree type, tree density, 
and 
tree height distribution 

For Evergreen: Census tract loss function = height group proportion weighted 
average 
of loss functions for the 3 height groups, for the census tract tree type 
and density, and for the specific building mapped 

For Deciduous: Census tract loss function = height group proportion weighted 
average 
of loss functions for the 3 height groups, for the census tract tree type 
and density, and for the specific building mapped 

For Mixed: Census tract loss function = sum of height group proportion 
weighted 
averages of loss functions for the 3 height groups for the two base tree 
types divided by 2, for the census tract tree density, and for the specific 
building mapped 

12.10.3 Combining Tree Blowdown Normalized Loss Functions with the Basic 
Fast-Running Building and Contents Loss Functions 
The following simplified method is used to combine the tree blowdown 
normalized loss 
with the basic fast-running loss functions described in Section 7.7 for 
single-family 
residential building and content losses: 

Total Loss = Basic Loss Ratio + Tree Damage Loss Ratio   
(Basic Loss Ratio * Tree Damage Loss Ratio) (12.39) 

The only assumption associated with deriving this equation is that the damage 
areas on a 
structure resulting from the two damage mechanisms are mutually independent. 

HAZUS-MH MR3 Technical Manual 

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Building parameters: 50.x 24.x 9 Wood; Tree parameters: Evergreen =&>60ft 

Content Loss / Content Value Building Loss / Building Value 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 
0 
50 70 90 110 130 150 170 190 210 230 250 
Peak Gust (mph) 
Density=10. 
Density=25. 
Density=50. 
Density=100. 
Density=200. 
Density=400. 

Building parameters: 50.x 24.x 9 Wood; Tree parameters: Evergreen =&>60ft 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0 

50 70 90 110 130 150 170 190 210 230 
Peak Gust (mph) 
Density=10. 
Density=25. 
Density=50. 
Density=100. 
Density=200. 
Density=400. 
Figure 12.33. Dependence on Tree Density of Building (Upper) and Contents 
(Lower) Loss Functions. 

Chapter 12. Tree Blowdown 

?
 Building parameters: 50.x 24.x 9 Wood; Tree parameters: Evergreen 
Density=100. 

50 70 90 110 130 150 170 190 210 230 250 

Peak Gust (mph) 

 Building parameters: 50.x 24.x 9 Wood; Tree parameters: Evergreen 
Density=100. 

50 70 90 110 130 150 170 190 210 230 250 

Peak Gust (mph) 

0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1Building Loss / Building Value 
30-40ft 
40-60ft 
=&>60ft
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Content Loss / Content Value 
30-40ft 
40-60ft 
=&>60ft 
Figure 12.34. Dependence on Tree Height of Building (Upper) and Contents 
(Lower) Loss Functions. 

HAZUS-MH MR3 Technical Manual 

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 Building parameters: 50.x 24.x 9 Wood; Tree parameters: =&>60ft Density=100. 

50 70 90 110 130 150 170 190 210 230 250 

Peak Gust (mph) 

 Building parameters: 50.x 24.x 9 Wood; Tree parameters: =&>60ft Density=100. 

50 70 90 110 130 150 170 190 210 230 250 

Peak Gust (mph) 

0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1Building Loss / Building Value 
Evergreen 
Deciduous
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Content Loss / Content Value 
Evergreen 
Deciduous 
Figure 12.35. Dependence on Tree Type of Building (Upper) and Contents 
(Lower) 
Loss Functions. 

Chapter 12. Tree Blowdown 

?
Woodframe Building; Tree parameters: Evergreen =&>60ft Density=100. 

Content Loss / Content Value Building Loss / Building Value 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 
0 
Building parameters: 
50.x 24.x 9 
Building parameters: 
60.x 30.x 9 
Building parameters: 
40.x 30.x17. 
Building parameters: 
50.x 30.x17. 

50 70 90 110 130 150 170 190 210 230 250 
Peak Gust (mph) 
Woodframe Building; Tree parameters: Evergreen =&>60ft Density=100. 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 
0 
Building parameters: 
50.x 24.x 9 
Building parameters: 
60.x 30.x 9 
Building parameters: 
40.x 30.x17. 
Building parameters: 
50.x 30.x17. 

50 70 90 110 130 150 170 190 210 230 

Peak Gust (mph) 

Figure 12.36. Dependence on Building Dimensions (ft) of Building (Upper) and 
Contents (Lower) Loss Functions. 

HAZUS-MH MR3 Technical Manual 

?
 Building parameters: 50.x 24.x 9; Tree parameters: Evergreen =&>60ft 
Density=100. 

50 70 90 110 130 150 170 190 210 230 250 

Peak Gust (mph) 

 Building parameters: 50.x 24.x 9; Tree parameters: Evergreen =&>60ft 
Density=100. 

50 70 90 110 130 150 170 190 210 230 250 

Peak Gust (mph) 

0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1Building Loss / Building Value 
Wood 
Masonry
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Content Loss / Content Value 
Wood 
Masonry 
Figure 12.37. Dependence on Wall Type of Building (Upper) and Contents 
(Lower) 
Loss Functions. 

Chapter 12. Tree Blowdown 

?
 Building parameters: 50.x 24.x 9 Wood; Tree parameters: Evergreen =&>60ft 
Density=100. 

0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Loss / Value 
Building 
Contents 
50 70 90 110 130 150 170 190 210 230 250 

Peak Gust (mph) 

Figure 12.38. Building and Contents Loss Comparison. 

HAZUS-MH MR3 Technical Manual 

?
12-62

Chapter 12. Tree Blowdown 

?
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Chapter 13. References 

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p.13. 
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Chapter 13. References 

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Frank, R.A., C.E. Murphy, L.A. Twisdale, and T.A. Reinhold (1987). Data 
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Frank, R.A., M.D. Smith, L.A. Twisdale, and R.G. Pearson (1989). Mechanical 
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Chapter 13. References 

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Chapter 13. References