Classification of Structural Models for Wind Damage
Predictions in Florida
Jean-Paul Pinellia,, Liang Zhang a, Chelakara Subramanian a, Anne Copeb, Kurt
Gurleyb, Sneh Gulatic, and S. Hamidc
a
Florida Institute of Technology, Melbourne, FL, USA
b
University of Florida, Gainesville, FL, USA
c
Florida International University, Miami, FL, USA
ABSTRACT: The need to predict hurricane-induced losses for $1.5 trillion worth of existing
structures exposed to potential hurricane devastation in the state of Florida has prompted the
Florida Department of Insurance (FDOI) to charge a group of researchers with the task of developing a public hurricane loss projection model. The development of the model and its ability to
predict the consequences of a hurricane on a given area of Florida depends on a reasonable estimate of the different types of structures in the building stock, and on an accurate representation
of the main structural types. This paper presents the results of a statistical survey of the building
population of 9 Florida counties. Based on the results of the statistical analyses, typical structural
models for residential buildings were adopted for 4 Florida regions, and their probability of occurrence evaluated. The paper describes the building models and how they are incorporated into
the Florida Public Hurricane Loss Projection Model.
KEYWORDS: Hurricane, Insurance loss, building structural classification
1 INTRODUCTION
Over half of the hurricane-related damage in the U. S. occurs in the state of Florida, which has
$1.5 trillion in existing building stock currently exposed to potential hurricane devastation. With
approximately 85 % of the rapidly increasing population situated on or near the 1200 miles of
coastline, Florida losses will continue to mount in proportion to coastal population density. It is
therefore critical for the state of Florida, and the insurance industry operating in that state, to be
able to estimate expected losses due to hurricanes and their measure of dispersion. For this reason the Florida Department of Insurance asked a group of researchers to develop a public hurricane loss projection model.
The team adopted a probabilistic component approach to the prediction of wind-induced damage and of corresponding repair/replacement costs. The approach makes use of probabilistic information on basic damage modes. This information is used to calculate probabilistic information on combined damage states. The latter consist of combinations of basic damage modes,
determined by engineering judgment, post-disaster observations, and/or analysis. The model is
described in a companion paper [1].
A key ingredient of the proposed procedure is the development of a Monte Carlo simulation
approach that relates probabilistic strength capacities of building components subjected to wind
action through a detailed aerodynamic and structural engineering analysis of specific building
models. The simulation is described in [2]. To ensure that the damage predictions based on the
simulations are accurate and reliable, it is critical to define the right structural model. For the efficiency of the model, it is also critical to limit the total number of models to be simulated to
those that are statistically significant in any given area. Therefore, the determination of the right
mix of structural types or models that characterize the building population in any given area is an
important component of the methodology. This paper presents the results of a statistical analysis
of the building population of Florida.
2 SOURCES OF INFORMATION
The authors used several sources of information for getting Florida up-to-date building structural
type information. One source is the Florida Hurricane Catastrophe Fund (FHCF) exposure database. Another, more important, source is the Florida counties property tax appraisers’ databases.
2.1 Florida Hurricane Catastrophe Fund exposure database
Insurance companies collect construction or structural information for their policies and stored it
in their portfolio files. Their concern being more focused on fire safety issues, the classification
used in portfolio files does not directly reflect the structural characteristics of the buildings but
rather their fire resistance.
For example the “1998 Florida HurriTable 1. ISO construction classes
cane Catastrophe Fund Industry Data Guide”
Frame
Joisted Masonry
contains a descriptive list of the various conNon-Combustible
struction types used to evaluate the policies.
Masonry Non-Combustible
They are referred to as the ISO construction
Modified Fire Resistive
classes. They are based on characteristics
Fire Resistive
such as combustible and non-combustible
Heavy Timber Joisted Masonry
materials. Table 1 lists the ISO construction
Superior Non-Combustible
classes. The FHCF exposure database gives
Superior Masonry Non-Combustible
the distribution of these construction classes
Masonry Veneer
Unknown
throughout the state.
2.2 Property tax appraiser’s database
Property appraiser’s database are the most comprehensive and accurate information of building
structural characteristics accessible at the present time. Although the databases’ contents and
format vary county to county, most of them contain the critical structural information to define
the most common structural types in each county. Nine counties databases were processed. They
correspond to Brevard, Hillsborough, Pinellas, Walton, Escambia, Leon, Broward, Palm Beach
and Monroe counties. It should be pointed out that each county has its own property appraiser
team, thus the database’s formats and contents vary dramatically.
3 INFORMATION GATHERED IN THE SURVEY
Tax appraisers databases contain large quantity of building information, and it was necessary to
extract those characteristics related to the vulnerability of the buildings to wind. First, all the
buildings in each county database were divided into three major categories: single family residential buildings, condominiums, and mobile homes. Under each category, the authors chose to extract information on 6 critical building characteristics for analysis and statistical distribution.
They are roof cover, roof type, exterior wall material, number of story, year built and building area. The paper covers the case of single family residential buildings (SFRB).
3.1 Roof cover
The resistance capacity of a roof system to wind uplift usually includes the capacity of roof cover, sheathing and trusses or rafters. Roof cover is exposed to weather and usually is in the form of
asphalt shingles or tiles. When wind uplift force exceeds the capacity of the roof cover materials
or their connection, the loss of roof cover will occur. As the immediate consequence, sheathing
will be exposed to strong wind, and often heavy rain. Most plywood sheathing will weaken
without the protection of the roof cover material, which renders the loss of sheathing more likely.
Loss of sheathing will then reduce severely the overall integrity of the building structures by either increasing the internal pressure or weakening the connection of roof truss system.
The roof cover types and distributions were collected and processed for all 9 counties of the
survey. For example, Table 1 shows the distribution of roof cover for single family buildings in
Broward County (Note: Broward County’s database contains a number of data with blank record.
This problem occurs to other database too, but Broward County is the most serious case).
Table 2. Broward County SFRB roof cover distribution.
Roof material
Percentage
Shingle Wood
Shingle asbestos
Shingle composite
57%
Tile Cement
Tile Barrel
34%
lack of information
9%
100%
Broward County’s database contains a number of data with blank record. This problem occurs
to other database too, but Broward County is the most serious case.
3.2 Roof type
Different roof types have different capacity to resist strong winds as shown by many post-disaster
surveys and test results. The majority of roof types for single family houses are either gable or
hip, or a combination of the two. Only two counties, Brevard and Escambia, distinguish between
hip and gable roofs in their databases. In both cases, the ratio between gable roof and hip roof is
approximately 2:1. All the other counties just record “gable/hip” in their appraisers’ database.
Since these two counties are neither in the same region, nor share same weather conditions, it is
reasonable to assume that these two counties are randomly representative of Florida in general.
Therefore the ratio of 2:1 for gable and hip was extrapolated to all counties.
3.3 Exterior wall material
Exterior wall failures are much less commonly cited in post damage reports than roofing system
failures. There are two main major types of exterior wall material: concrete block and wood
frame. The difference between these two types is more than different material properties. A criti-
cal difference is in their damage mechanism. Table 4 shows the distribution of exterior wall material distribution of single family residential buildings in Brevard County.
Table 3. Brevard County SFRB exterior wall distribution
external material type
C.B. Stucco
C.B.Plain
Wood Sheathing
Wood Frame Stucco
Vinyl/aluminum
Brick on msnv
Brick on wood
Exterior plywood
Wood Frame no shingle
Percentage
40%
31%
9%
8%
4%
3%
2%
1%
1%
3.4 Year built
The year built is important because it relates to the building code in effect when the structure was
built. The design requirements stipulated in the building code directly affect the wind resistance
of a building. For example, the building code requirements in southern Florida were more lenient before Andrew. However, because different jurisdictions ranging from city to county have
adopted different building codes at various time, tracking the evolution of the building code and
practices for each area of Florida is fairly difficult. For this reason, although year built information has been collected and analyzed, it is not being currently used in the loss projection model.
3.5 Number of stories
Obviously, two stories single family buildings have large differences from one story buildings in
terms of structural characteristic, number of openings, value etc. The majority of single family
homes are one story buildings. For example, Table 4 describes the number of stories distribution
of Pinellas County.
Table 4. Percentage of number of story distribution of Pinellas County
Number of story
Percentage
1
92%
2
8%
3.6 Building areas
Another characteristic collected from the tax appraisers’ database is building area information.
The results show that buildings with hip roofs usually have larger area than building with gable
roofs and buildings with concrete block exterior wall generally have larger area than wood frame
buildings. The percentile distribution of home areas of single family homes in Brevard County,
regardless of structural characteristics, is shown in Figure 1 (the range is in ft2, 1ft2 = 0.1 m2)
50%
45%
40%
percentage
35%
30%
25%
20%
15%
10%
5%
0~
50
0
50
0~
10
00
10
00
~1
50
0
15
00
~2
00
0
20
00
~2
50
0
25
00
~3
00
0
30
00
~3
50
0
35
00
~4
00
0
40
00
~4
50
0
45
00
~5
00
0
50
00
~5
50
0
55
00
~6
60
00
00
0
or
ab
ov
e
0%
range
Figure 1. Brevard County SFRB area range distribution.
4 DEFINITION OF MOST COMMON STRUCTURAL TYPES.
4.1 Florida Regions
The aim of the survey was to generate a manageable number of building models to cover the majority of the Florida building stock. To define building models for each county could be cumbersome and unnecessary. Instead, we divided Florida into four regions. Geography, and the statistics from the FHCF guided us in defining regions that would have a similar building mix
throughout their counties. For example, the FHCF shows that northern Florida has a preponderance of frame houses. Figure 2 shows the resulting regional division.
Figure2. Four regions with sample counties highlighted
In each region, there are at least two counties per region which are: Escambia, Walton, and
Leon in the Northern region; Brevard, Pinellas, and Hillsborough in the Central region; Palm
Beach, and Broward in the Southeast region. In addition, Monroe County fully covers the Keys
region. These counties for which we have obtained database are referred as sample counties. The
number of counties and sample counties in each region and the population associate with them
are shown in Table 5.
Table5. Population information for each area.
Region
Northern
Central
34
27
Total number of counties
3
3
Number of sample counties
2,885,55
7,690,24
Total population
9
0
2,396,66
574,463
Sample population
0
20%
30%
% of population in sample
Southeast
5
2
5,326,99
0
2,754,20
2
54%
Keys
1
1
79,58
9
79,58
9
100%
4.2 Most common types of the four regions
To define the structural types, we chose a combination of 4 characteristics: number of story (either 1 or 2), roof cover (shingle/tile), (note: at this stage, shingle and tile are combined together
for modeling because of the limited resistance capacity information), roof type (either gable or
hip) and structural material (either concrete blocks or timber). Consequently, 8 known plus one
unknown structural types were defined in each sample county. In addition, due to the variety of
the building stock in the Keys, 4 additional types were defined for the Keys (corresponding to
buildings with metal roof cover).
Based on the information contained in the databases, the authors computed the statistics for
each structural type in every sample county and then use weighted average techniques to extrapolate the results to each region. Area statistics of each type are also listed. The results are shown in
Table 6 for three regions.
Table 6: Probability of occurrence of each type for 3 regions.
Structural Type definition
Central Region
(Number of stories, exterior
STDE
Area
Type
wall, roof cover, roof type)
P
V (ft2)
2
(ft )
Type 1
Type 2
Type 3
Type 4
Type 5
Type 6
Type 7
Type 8
Total
Coverage
unknown
1story, concrete blocks,
Shingle/Tile, Gable
1story, concrete blocks,
Shingle/Tile, Hip
1story, Wood frame, Shingle/Tile
Gable
1story, Wood frame, Shingle/Tile
Hip
2 stories, 1st story: concrete
block; 2nd story: wood frame,
Shingle/Tile, Gable
2 stories, 1st story: concrete
block; 2nd story: wood frame,
Shingle/Tile, Hip
2 stories, Wood frame, Shingle/Tile, Gable
2 stories, Wood frame, Shingle/Tile, Hip
42%
2222
Northern Region
Area STD
P
(ft2)
EV(f
t2)
Southern Region
Area STD
P
(ft2)
EV(f
t2)
12%
46%
550
1702
590
2147
22%
6%
23%
12%
39%
4%
1941
913
1908
399
2022
6%
20%
2%
2%
1%
8%
3602
1915
1894
0.4
%
1%
1.4
%
3208
969
662
1170
4%
5%
2866
3215
734
1%
2766
1470
2118
1%
2.3
%
87%
86%
89%
13%
14%
11%
1%
673
The building type statistical distribution for the Keys region is also shown in Table 7
Table 7. Probability of occurrence of each type for the Keys region.
Type
Type 1
Type 2
Type 3
Type 4
Type 9
Type 10
Type 11
Type 12
2story,all type
Total Coverage
Unknown types
1story, concrete blocks, Shingle/Tile, Gable
1story, concrete blocks, Shingle/Tile, Hip
1story, Wood frame, Shingle/Tile, Gable
1story, Wood frame, Shingle/Tile, Hip
1story, Concrete blocks, Metal, Gable
1story, Concrete blocks, Metal, Hip
1story, Wood frame, Metal, Gable
1story, Wood frame, Metal, Hip
Percentage
23%
11%
12%
6%
8%
4%
7%
3%
3%
77%
23%
2
2
Area (ft )
3295
STDEV(ft )
2071
2771
1027
3295
2070
2179
1341
5 UNCERTAINTIES
The purpose of this paper focuses on the method and results for building stock information
collection. A detailed discussion of the uncertainties involved will be the focus of a follow-up
paper. Main sources of uncertainties include the lack of completeness and errors in the county
tax appraiser’s databases, process of extrapolation and interpretation of the data, and the fact that
not all county databases were available for analysis. The error estimation for the percentage coverage of each most common structural types is still underway.
6 DAMAGE ESTIMATION
As mentioned before, each of the structural types defined in the statistical survey is being modeled in a Monte Carlo simulation. The output of the Monte Carlo simulation is a damage matrix
that for each interval of wind speed gives the probability of occurrence of all the possible damage
states for that particular type of structure [1,2]. An example of a damage state would be a combination of moderate opening damage, heavy roof cover damage, moderate sheathing damage,
and light roof-to-wall connection damage.
If damage is expressed as a percentage of replacement cost of the home, the resulting damage,
in a given area, for a certain type of home m subject to a certain wind speed vj becomes:
Damage type m (vj)=

P(damage_statei|vj) x c(damagei)
(1)
damag e_ statei
Where P(damage_statei|vj) is the probability of occurrence of each damage state and c(damagei)
is the associated cost percentage for that damage state. The damages are then added over all the
possible wind speeds to yield the probable annual damage for type m, as follows:
Annual_Damage type m=

Damage type m (vj)*P(vj-v/2<vj<vj+ v/2)
(2)
windspeed j
Where P(vj-v/2<vj<vj+ v/2) is the annual probability of occurrence of wind speed vj in the
specified interval (Note: these probabilities are estimated with a wind model described in a
companion paper [3]). The process is repeated for each building type in the area under consideration, and the average annual damage for a generic home in that area will be:
Average_Annual_Damage =

Annual_Damage type m* P(typem)
(3)
typem
Where P(typem) is the probability of occurrence of the different type of buildings as listed in Tables 6 and 7.
The total estimated expected damage to buildings for a particular zone is the damage calculated by using Eq. 3 times the total number n of houses in the zone. Multiplication of this latter result by the average value of a home in that area yields the monetary damage. Alternatively, if
dealing with a portfolio where the values of each house in the portfolio is known, the average annual damage (Eq. 3) for one house can be multiplied by the sum of the values of all the houses
insured in that area. The process is repeated for each zone, and the results for each zone are added to obtain the estimated expected hurricane-induced annual damage to buildings for the entire
state.
7 CONCLUSION
This paper presented the results of a statistical survey and analysis of the building population
in Florida. Based on the results of the survey, the most common structural types are defined for
each of four regions and their probabilities of occurrence are estimated. The goal is to predefine
the structural make up of the building population and its corresponding wind vulnerability in any
given area of the State. Thanks to these results, even in the case of portfolio files with no or incomplete information regarding the structural strength of the insured properties, insured losses
due to hurricane winds will be predicted based on location. The method can be used to compute
expected annual losses as illustrated in the paper, and expected losses induced by a specified hurricane event. This is a work in progress, and the authors are in currently evaluating the errors involved in the process.
8 ACKNOWLEDGMENT
This work was done with the financial support of the Florida Department of Insurance (FDOI).
The opinions, findings, and conclusions expressed in this paper are not necessarily those of the
FDOI.
9 REFERENCES
1.
2.
3.
Simiu, E., Pinelli, J-P., Subramanian, C., Zhang, L., Cope, A., Gurley, K., Filliben, J., and Hamid, S., “Hurricane Damage Prediction Model for Residential Structures,” Proceedings, 11 thICWE, Lubbock, Texas, June
2003
Cope, A., Gurley, K., Pinelli, J.-P., and Hamid, S., “A Simulation Model For Wind Damage Predictions In
Florida,” Proceedings, 11th ICWE, Lubbock, Texas, June 2003.
Powell,M., Soukup, G., Morisseau-Leroy,N., Landsea, C., Cocke,S., Axe, L., and Gulati, S., “State of Florida
Hurricane Loss Projection Model: Atmospheric Science Component,” 11 th ICWE, Lubbock, Texas, June 2003.