DEVELOPMENT AND VALIDATION OF A TORNADO RISK...

DEVELOPMENT AND VALIDATION OF A TORNADO RISK ASSESSMENT TOOL FOR

RESIDENTIAL STRUCTURES

By

ARPIT ANIL BHUSAR

A THESIS PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2017

Khalil Gibran said that parents are like a bow, and children like arrows.

The more the bow bends and stretches, the farther the arrow flies.

I fly, not because I am special, but because my parents stretched for me.

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ACKNOWLEDGMENTS

First and foremost, I would like to express my heartfelt gratitude to my advisor, Dr.

David O. Prevatt, for his guidance and constant support to complete this thesis. His patience and

understanding throughout my time as his student are greatly appreciated. I would not have been

able to achieve so much without his trust and encouragement. I would like to thank Dr. Kurtis

Gurley for serving on my master’s thesis committee. I would like to extend my sincerest thanks

to Dr. David B. Roueche for his guidance and help.

I would like to thank my mom and dad for their endless support, trust, and

encouragement. I would not have made it this far without their love and care.

I am indebted to my colleagues and friends who supported and helped me in the process

of writing my thesis, especially: Aravind Viswanathan, Mitali Talele, Anshul Shah, Allan

Gutierrez, Anlun Chen, Siddhesh Rahate, Anant Jain, Sambhav Jain, Mohit Israni, Aayush Shah

and Aditya Verma.

Additionally I would like to thank the Risk Prediction Initiative (RPI) Bermuda Institute

of Ocean Sciences (BIOS) for supporting my work and providing me with the opportunity to

work on the project. The conclusions of this thesis do not necessarily represent those of the

sponsors.

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TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...............................................................................................................4

LIST OF TABLES ...........................................................................................................................7

LIST OF FIGURES .........................................................................................................................8

LIST OF ABBREVIATIONS ........................................................................................................10

ABSTRACT ...................................................................................................................................12

CHAPTER

1 INTRODUCTION ..................................................................................................................14

Problem Statement ..................................................................................................................20 Research Objectives ................................................................................................................21

Organization of Thesis ............................................................................................................21

2 ENGINEERING BASED TORNADO DAMAGE ASSESSMENT (ETDA) TOOL ...........22

Background .............................................................................................................................22

Framework of the ETDA tool .................................................................................................23

Tornado Wind Field Module ...........................................................................................24 Wind Load Module ..........................................................................................................25 Wind Borne Debris Module ............................................................................................29

Structural Resistance Module ..........................................................................................30 Structural Model of Residential Building ...............................................................................32

Output of the ETDA Simulations ...........................................................................................33

3 DEVELOPMENT OF DATASETS FOR VALIDATION OF THE ENGINEERING

BASED TORNADO DAMAGE ASSESSMENT (ETDA) TOOL........................................36

Post Disaster Damage Surveys ...............................................................................................36 Background ......................................................................................................................36 22nd May 2011 Joplin, MO Tornado Survey ...................................................................38

26th December 2015 Garland / Rowlett, TX Tornado Survey .........................................40 Overview ..................................................................................................................40 Post disaster damage survey methodology ...............................................................40

Selection of Houses for Validation .........................................................................................43 Joplin ...............................................................................................................................43 Garland / Rowlett ............................................................................................................44

Observed Damage Ratios .......................................................................................................45 Damage Estimation Methodology ...................................................................................45

ETDA Estimated Damage Ratios ...........................................................................................48

6

Wind Field Model ............................................................................................................48

Location and Orientation of the House ...........................................................................50 Optimization of the Performance of the ETDA tool using High Performance

Computing....................................................................................................................50

4 VALIDATION OF THE ENGINEERING BASED TORNADO DAMAGE

ASSESSMENT (ETDA) TOOL .............................................................................................52

Background .............................................................................................................................52 Methods for Assessing the Agreement between Observed and Predicted Damage Ratios ....52

Comparison of Observed and Predicted Damage to a Single House ..............................53

Damage Progression Plots ...............................................................................................54 Binned Damage Progression Plots ..................................................................................55

Receiver Operating Characteristics (ROC) Curve ..........................................................55 Results.....................................................................................................................................57

Comparison of Observed and Predicted Damage to a Single House ..............................57 Damage Progression Plots ...............................................................................................59

Binned Damage Progression Plots ..................................................................................62 Receiver Operating Characteristics (ROC) Curve ..........................................................64

5 DEVELOPMENT OF VULNERABILITY COMPONENT FOR THE ENGINEERING

BASED TORNADO DAMAGE ASSESSMENT (ETDA) TOOL........................................68

Background .............................................................................................................................68

Insured Loss Data ...................................................................................................................69

Relationship between Insured Loss Ratios and ETDA Predicted Damage Ratios .................70 Replacement Cost Ratio .........................................................................................................71 Estimation of Interior Damage ...............................................................................................72

Loss Ratio Estimation Methodology ......................................................................................74 Results.....................................................................................................................................77

6 CONCLUSIONS ....................................................................................................................79

Future Scope ...........................................................................................................................80

Applications ............................................................................................................................81

LIST OF REFERENCES ...............................................................................................................82

BIOGRAPHICAL SKETCH .........................................................................................................87

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LIST OF TABLES

Table page

1-1 Summary of fatalities, insured losses and NSF funding by major hazard type .................15

2-1 Summary of structural capacities in prototype model .......................................................31

3-1 Recommended DOD ratings for wind speed ranges (McDonald and Mehta 2006) ..........38

3-2 Recommended EF – scale wind speed ranges (McDonald and Mehta 2006)....................38

3-3 Description of the symbols used in Figure 3-1 ..................................................................40

3-4 Observed damage ratios for 5821 Winell Drive, Garland, TX ..........................................47

3-5 Comparison of the 2015 Garland, TX tornado with 2011 Joplin, MO tornado .................50

4-1 Classification of the AUC score in the traditional academic point system (Tape) ............57

4-2 Summary of ETDA performance relative to the observed damage trends. .......................64

4-3 AUC for ROC curves from the Joplin and Garland/Rowlett tornadoes. ...........................65

5-1 Structural repair cost ratios (Cope 2004) ...........................................................................72

5-2 Non-structural repair cost ratios (Cope 2004) ...................................................................72

5-3 Interior damage equations; y=interior loss ratio; x = exterior (Pinelli et al. 2005) ...........73

5-4 Estimated interior damage ratios from ETDA estimated damage ratio .............................75

5-5 MEP damage ratios ............................................................................................................76

5-6 Empirical loss ratio for 5834 Macgregor Drive, Garland, TX ...........................................76

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LIST OF FIGURES

Figure page

1-1 The average annual occurrence of tornadoes in the US between 1991-2010 ....................14

1-2 Conceptual tornado damage swath based on current performance and after the

implementation of the dual-objective design that reduces lower wind-speed damage ......17

1-3 A conceptual model of the expanding bull’s-eye effect with a sample tornado

scenario ..............................................................................................................................18

2-1 Wind vector and isotachs representing a wind velocity field of 2011 Joplin, MO

tornado ...............................................................................................................................24

2-2 Tangential velocity profile and pressure drop distribution ................................................29

2-3 Isometric rendering of the 117 m2 prototype house used in the ETDA tool .....................32

2-4 Schematic of the tornado path in relation to the location and orientation of an

individual building .............................................................................................................33

2-5 Flowchart of the engineering based tornado damage assessment tool ..............................34

3-1 Interactive map of 22nd May 2011 Joplin, MO tornado damage survey ..........................39

3-2 Map showing location of houses surveyed in one of the five transects .............................41

3-3 Damage assessment photo captures on 7514, Atlantic Drive Rowlett, TX. Based on

observed damage DOD 6 and EF-2 ratings were assigned. ...............................................42

3-4 Location of houses documented prior capturing post-disaster damage data. ....................43

3-5 Map showing locations of the 82 houses, color coded based on the assigned DOD

ratings, used to the validate ETDA tool results relative to the 22nd May 2011 Joplin,

MO tornado path. ...............................................................................................................44

3-6 Locations of all surveyed houses by region. Colors depict the decade in which the

house was built. Numbers inside each parcel give the assigned damage rating. ...............45

3-7 Damage state for the eight building envelope components ...............................................46

3-8 Photographs showing all the four elevations of 5821 Winell Drive, Garland, TX ............47

3-9 Comparison of damage ratios for 8 components estimated independently by two

students ..............................................................................................................................48

4-1 House#1 - Spider plot comparing the ETDA estimated damage ratios with

empirically-determined results...........................................................................................58

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4-2 House #2 - Spider plot comparing the ETDA estimated damage ratios with


4-3 House #3 - Spider plot comparing the ETDA estimated damage ratios with


4-4 Variation of roof sheathing damage ratios with distance from the Garland/Rowlett

tornado center.....................................................................................................................60

4-5 Spatial variation of ETDA predicted damage ratios with visually observed damage

ratios for the Joplin tornado for 82 houses.........................................................................60

4-6 Spatial variation of ETDA predicted damage ratios with visually observed damage

ratios for the Garland/Rowlett tornado for 712 houses. .....................................................61

4-7 Average observed and predicted damage ratios (DR) for the Joplin tornado. Bin size

is 100 m. The red dashed line indicates the radius to maximum wind speeds. .................62

4-8 Average observed and predicted damage ratios (DR) for the Garland/Rowlett

tornado. Bin size is 25 m. The red dashed line indicates the radius to maximum wind

speeds. ................................................................................................................................63

4-9 ROC curves from the Joplin tornado. The limit states chosen for each component are

provided in the title of each subplot. AUC is given in each subplot. ................................64

4-10 ROC curves from the Joplin tornado. The limit states chosen for each component are

provided in the title of each subplot. AUC is given in each subplot. ................................65

4-11 Variation of the AUC score for the eight components with damage state for 82

houses from Joplin dataset .................................................................................................66

4-12 Variation of the AUC score for the eight components with damage state for 712

houses from Garland/Rowlett dataset ................................................................................67

5-1 Variation of the loss ratios for 291 houses with Degree of Damage (DOD) ratings .........70

5-2 Variation of loss ratio with ETDA estimated damage ratio binned at 10% intervals ........71

5-3 Variation of loss ratio with ETDA estimated damage ratios .............................................73

5-4 5834 Macgregor Drive, Garland, TX assigned a DOD 6 rating and had insured loss

ratio of 137.35%.................................................................................................................75

5-5 Variation of insured loss ratios and estimated loss ratios. The solid red line indicates

ideal prediction and the scatter points represent the insured and the estimated loss

ratio for a house. ................................................................................................................77

5-6 Spatial variation of insured loss ratios, estimated loss ratios using observed damage

ratios and estimated loss ratios using ETDA estimated damage ratios .............................78

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LIST OF ABBREVIATIONS

ASCE American Society of Civil Engineers

AUC Area Under the Receiver Operating Characteristics Curve

BIOS Bermuda Institute of Ocean Sciences

C&C Components and Cladding

DAD Database – Assisted Design Methodology

DAT Damage Assessment Toolkit

DOD Degree of Damage

DR Damage Ratio

EF Enhanced Fujita

ETDA Engineering based Tornado Damage Assessment

FEA Finite Element Analysis

FEMA Federal Emergency Management Agency

FPHLM Florida Public Hurricane Loss Projection Model

FPR False Positive Rate

GIS Geographic Information System

GPS Global Positioning System

HAZUS-MH Hazards United States – Multi-Hazard

ISU Iowa State University

MATLAB Matrix Laboratory

MC Monte Carlo

MEP Mechanical Electrical Plumbing

MWFRS Main Wind Force Resisting System

NIST National Institute of Science and Technology

NOAA National Ocean and Atmospheric Administration

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NWS National Weather Service

RMW Radius to Maximum Winds

ROC Receiver Operating Characteristics

RPI Risk Prediction Initiative

SLURM Simple Linux Utility for Resource Management

TPR True Positive Rate

TPU Tokyo Polytechnic University

WHDAG Wind Hazard Damage Assessment Group

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Abstract of Thesis Presented to the Graduate School

of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Master of Science

DEVELOPMENT AND VALIDATION OF A TORNADO RISK ASSESSMENT TOOL FOR

RESIDENTIAL STRUCTURES

By

Arpit Anil Bhusar

May 2017

Chair: David O. Prevatt

Major: Civil Engineering

Between 1995 and 2014, $154.9 billion in insured catastrophe losses in the United States

was caused by tornadoes. 80-85% of the damaged structures are residential structures.

Communicating the tornado damage risk to residential structures with the community and

decision makers is imperative. There is a need for the development of a validated engineering-

based damage assessment model to provide an alternative to the empirical models.

The researchers at the University of Florida proposed an Engineering-based Tornado

Damage Assessment (ETDA) tool which estimates the damage to eight building envelope

components using a numerical model of a tornado, and a probabilistic model of the structural

capacity of residential buildings within a Monte Carlo simulation framework. The objective of

the study is to develop and validate the ETDA tool capable of estimating structural damage and

economic losses to a portfolio of structures.

The tool is validated using the post-disaster damage data from 22nd May 2011 Joplin, MO

tornado and the 26th December 2015 Garland/Rowlett, TX tornado. Visually observed damage

ratios for 794 houses from the aforementioned datasets are compared with the ETDA predicted

damage ratios spatially. The tool is validated using the Receiver Operating Characteristics

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(ROC) curve method with an overall Area Under the Curve (AUC) score of higher than 0.8,

suggesting excellent predictive powers.

Insured loss ratio data for 291 houses from the Garland/Rowlett tornado, provided by a

general insurer in the Garland/Rowlett region, is used to establish relationship between the

structural damage ratios and the insured loss ratios. Vulnerability component using the structural

and non-structural damage ratios with the replacement cost ratios is developed to predict tornado

induced loss ratios. Reasonable agreement between the predicted loss ratios and the actual

insured loss ratios is observed.

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CHAPTER 1

INTRODUCTION

The AMS (2013) defines a tornado as “A rotating column of air, in contact with the

surface, pendant from a cumuliform cloud, and often visible as a funnel cloud and/or circulating

debris/dust at the ground”. Tornadoes are observed all over the world, but the majority occur in

the United States. The area known as Tornado Alley, in the south-central United States,

experiences the highest number of tornadoes. Tornado distribution in the U.S. is illustrated in

Figure 1-1 (NOAA). The United States experiences more than 1,000 tornadoes every year.

Figure 1-1. The average annual occurrence of tornadoes in the US between 1991-2010 (NOAA

2016)

Table 1-1 shows the summary of fatalities (NOAA 2015; USGS 2015), insured losses

(PCS 2015) and NSF hazard funding by major hazard type between 1995 and 2015. The NSF

hazard funding data is extracted based on keyword search of NSF awards database between 1964

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and 2016. The fatalities and insured losses both can be reduced significantly as the data for

Earthquakes shows.

Table 1-1. Summary of fatalities, insured losses and NSF funding by major hazard type

Description Earthquakes Hurricanes Tornadoes

Fatalities 3 1251 1674

Insured losses ($US billions) 0.5 161.2 154.9

NSF hazard funding ($ US billion) 2.93 0.72 0.22

Wind damage related to tornadoes amounted to 37% of inflation-adjusted US catastrophe

losses between 1994 and 2013 (PCS 2014), totaling $155 billion. These losses will only

continue to grow in the future as populations in tornado-prone regions increase, creating larger

and more widespread targets for the approximately 1,000 tornadoes that strike the United States

each year. The majority of the structures damaged in these tornadoes are predominantly

residential structures. This begs the question that are the houses constructed so a week or the

tornadoes too strong to resist?

The real reason for the damage to residential structures is that current design standards

like ASCE 7 – 10 do not take into account the tornado load acting on the houses. The probability

of a tornado striking a specific house is extremely small and hence tornado loading is neglected

(ASCE 2010). But considering a tornado strike on a community-scale, instead of a single house,

this risk escalates and the billions of tornado-induced losses attest to that (PCS 2014). The

residents and authorities cannot neglect tornado-resilient design and then when they strike,

justify the damage caused by saying tornadoes are extreme to be dealt with.

Some believe, there is a false perception among communities that tornado-resilient

structures cannot be built economically and nothing can be done to prevent these losses.

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Communities underestimate the tornado damage risk which acts as a deterrent for their

preparedness and response leading to unexpected economic loss, injuries, and fatalities.

For tornado-resilient communities, all the houses need not be designed to resist an EF-5

strength tornado or 200 mph (McDonald and Mehta 2006). 85-90% of the buildings in the United

States are residential and low-rise commercial buildings (Prevatt et al. 2012). Grieser and

Terenzi (2016) used a bias corrected tornado intensity distribution to model tornadoes as moving

Rankine vortices and estimated the area fraction of a tornado footprint. Vulnerability function of

European buildings were defined and it was found that 10% of the tornadoes contribute to 99%

of the damage. The majority of this damage can be significantly reduced if the minimum design

speeds are established for tornado-prone regions.

Building practices in tornado-prone states are similar. According to ASCE 7 – 10 (2010),

for Building Risk Category II, the design wind speeds are 115 miles/hour. The city of Moore,

OK, which experienced 3 deadly tornadoes in a decade increased the minimum design wind

speeds from 90mph to 135 mph (Moore 2014). To address another myth, this increase in the

design standards resulted in an increase of $1 per square feet and has a benefit to cost ratio of 3

(Simmons et al. 2015). Research has concluded that tornado-resilient communities can be built

economically.

False perceptions need to be dispelled and communities at risk should be made more

aware of mitigating steps available to minimize the impact of tornadoes. A future with false

notions and perceptions is unsustainable, yet the attention of engineering communities to

assessing the risk of tornado damage to vulnerable communities has to this point been limited.

This is in part due to the limited knowledge and understanding of tornadoes as a phenomenon

and the structure tornado interaction.

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The damage from Hurricane Andrew in 1992 served as a wake-up call to communities in

Florida and it stimulated the development of hurricane resistant construction. In a similar way,

the 22nd May 2011 Joplin, MO tornado that caused 162 fatalities and approximately $2 billion in

economic losses (Prevatt et al. 2012) caused many to ask whether a better approach for tornado-

resilient construction is required.

The scientific community noted the severity of the situation and significant strides in

tornado understanding were made. van de Lindt et al. (2012) proposed a dual objective based

tornado design philosophy for residential buildings can reduce damage and save lives by

focusing on separate tornado intensity levels. Figure 1-2 shows (on the left) a hypothetical

tornado damage swath path and the performance of current residential buildings and (on the

right) the improved swath as a result of the implementation of the dual-objective design proposed

by van de Lindt et al. (2012) achieved by applying all three philosophies; namely, component,

system, and alternative.

Figure 1-2. Conceptual tornado damage swath based on current performance and after the

implementation of the dual-objective design that reduces lower wind-speed damage

(van de Lindt et al. 2012)

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Lombardo et al. (2015) estimated near surface wind speeds for the 22nd mat 2011 Joplin

MO tornado using tree fall pattern data. These wind speeds are used in the following research.

Simmons et al. (2013) found that in the year 2011 alone, tornadoes were responsible for

550 fatalities and economic losses of approximately $28 billion. Folger (2011) found that,

despite the improved forecasting and warning systems, the number of tornado events with

economic losses of over $1billion caused by a single tornado event is increasing. Improved

tornado forecasting techniques alone can minimize fatalities and injuries, but since the existing

infrastructure cannot be evacuated or strengthened at moment’s notice, it cannot help to reduce

the tornado damage to existing infrastructure.

Ashley et al. (2013) defined expanding bull's eye effect, shown in Figure 1-3 as, humans

and their possessions—of geophysical hazards are enlarging as populations grow and spread. It is

not solely the population magnitude that is important in creating disaster potential, it is how the

population and built environment are distributed across the landscape that defines how the

fundamental components of risk and vulnerability are realized in a disaster.

Figure 1-3. A conceptual model of the expanding bull’s-eye effect with a sample tornado

scenario (Ashley et al. 2013)

As the population and economy continue to grow, the area of the communities expand,

increasing the vulnerable area in case of a tornado to strike. While climate change may amplify

the risk of certain hazards, the root cause of escalating disasters is not necessarily event

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frequency, or risk, related. Rather, Ashley et al. (2013) confirms that the upward trend in

disasters is predicated on increasing exposure and vulnerability of populations.

This results an increase in the potential losses resulting from tornadoes to residential

structures. Thus, the insurance industry and its regulators need an accurate and engineering based

approach of assessing tornado risk. The outputs from such a tool can be used in generating loss

estimates that can be used as input in the determination of the insurance premiums. Additionally,

the Government officials and policy-makers need a validated damage prediction tool to quantify

economic loss to assist in identifying high-risk zone; implementing necessary preventative

measures and mitigation strategies; and preparing for response and recovery to multiple hazards.

Multiple engineering-based models exist for hurricanes and flood, notably the Florida

Public Hurricane Loss Model (Gurley et al. 2005) sponsored by the Florida Office of Insurance

Regulation, and HAZUS-MH Hurricane sponsored by the Federal Emergency Management

Administration (FEMA) (Vickery et al. 2006a; Vickery et al. 2006b). But none such engineering

based model exists for predicting tornado risk.

For the most part, the prediction of tornado damage has been based on empirical models

using prior damage from previous tornadoes. The accuracy of the empirical models in use today

depends largely upon the quality and quantity of the recorded data as well as the appropriateness

of applying those empirical models from one region (location) to another. Additionally, these

models are proprietary and are not in public domain. Further, it is uncertain whether such models

can be relied upon to predict losses in geographic areas where historical loss data does not exist.

There is a need for the development of a validated engineering-based damage assessment models

to provide an alternative to the empirical models that is applicable to any region of the US, and

for a full range of potential tornado characteristics (e.g., size, intensity, duration).

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Peng et al. (2016) proposed an Engineering-Based Tornado Damage Assessment (ETDA)

tool, based in many ways upon proven, existing approaches from the Florida Public Hurricane

Loss Model (Gurley et al. 2005). The tool can predict tornado-induced damage to eight building

envelope components and simulate tornado-induced damage to a community. It uses four

modules namely, the tornado wind field module, wind load module, wind-borne debris module

and structural resistance module in a Monte Carlo simulation framework to predict percentage

damage.

In the initial development, the Engineering based Tornado Damage Assessment (ETDA)

tool was only validated against a limited dataset of four damaged houses observed in the 2011

Joplin, MO tornado. More robust validation is needed before the ETDA can be reliably used as a

standard to which other proprietary models can be compared.

Problem Statement

People generally believe that tornado-resilient communities cannot be built or not

financially feasible. False perceptions need to be dispelled and communities at risk should be

made more aware of mitigating steps available to minimize the impact of tornadoes. A future

with false notions and perceptions is unsustainable. More importantly, there lacks a validated

tool which can accurately assess the tornado damage risk. Such a tool can be used to spread

awareness about tornado damage and communicate the risk to the authorities and the residents.

Damage prediction models for estimating future tornado damages are critical for

communicating risks of tornado damage to the public, for demonstrating the benefits of

strengthened construction to better resist tornadoes, and for setting public policy on wind

resistance through building codes and design standards.

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Research Objectives

The goal of this research is to develop and validate an engineering-based tornado damage

assessment model to both the public and private sectors, to be used in research, outreach or

education capacities. The tool will estimate damage to a portfolio of single family residential

structures which can be used to assess the tornado damage risk to communities. The validated

tool will ultimately result in more tornado-resilient communities.

The second specific goal of this research is to assess trends between structural damage

ratios and insured loss ratios and validate it with the insurance claims data obtained for a

portfolio of insured houses. The developed vulnerability component can be appended to the

existing ETDA tool and used to communicate tornado risk to communities.

Organization of Thesis

The remainder of this thesis is organized as follows. Chapter 2 describes the ETDA tool.

Chapter 3 describes the two datasets, observed and predicted used to validate the ETDA tool.

Chapter 4 presents comparisons of the ETDA damage predictions with the actual damage

observations using graphs known as spider plots. Also, Chapter 4 describes a more robust

approach to validating the ETDA tool against the damage observations using a method known as

Receiver Operating Characteristic Curve Method (Gehl et al. 2013). Chapter 5 presents an

analytical model for estimating economic losses from the observed structural damage and

compares its output to the actual losses paid out by the insurance provider. Chapter 6 summarizes

the major findings and conclusions from the project, including the limitations of the model and

critical areas needing further research.

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CHAPTER 2

ENGINEERING BASED TORNADO DAMAGE ASSESSMENT (ETDA) TOOL

Engineering Based Tornado Damage Assessment (ETDA) model is a mathematical tool

that predicts the tornado induced damage to residential structures. The numerical model is

applicable to building portfolios and tornadoes of any intensity. Once appropriate physical model

of buildings are selected, it will predict the damages caused to single family residential

structures. The ETDA model is based on probabilistic modelling of the structural capacity of

residential buildings and the tornado. It utilizes a Monte Carlo simulation framework that

includes four modules, namely, the tornado wind field module, the wind load module, the

windborne debris module and the structural resistance module. Expected damage is predicted for

eight common building envelope components. The individual modules of the ETDA tool are

described below, along with the overall flow of the simulation process. A complete description

of the model is provided in Peng et al. (2016).

Background

The goal of ETDA tool is to quantify the risk or probability of damage for given extreme

wind event at a location. During the past decades, owing to the strides in the computational

methodologies and wind field understanding resulting in the development of advanced numerical

prediction tools. Monte Carlo simulation method is used to factor in the uncertainty in the

estimation of factors like the structural capacities, wind speeds, and the pressure coefficients.

Multiple wind hazard vulnerability models are developed to quantify the physical damage of

structures during extreme wind loading events. The HAZUS-MH hurricane model (Vickery et al.

2006a; Vickery et al. 2006b) is a wind-induced loss projection model, contracted by FEMA,

produces loss estimates used by government authorities when planning for hurricane risk

mitigation, emergency preparedness, response, and recovery. The HAZUS-MH model, based on

23

scientific and engineering principles, contains national databases for the built environment and

specific properties of a given region and uses experimental data to produce realistic loss

estimates for that region.

The Florida Public Hurricane Loss Projection model (FPHLPM) is a probabilistic

hurricane damage prediction model capable of predicting damage to residential structures (Cope

2004; Gurley et al. 2005; Pinelli et al. 2011). The model consists of actuarial, computer science,

engineering, financial and meteorological components.

Strader et al. (2016) simulates and predicts the probability of a tornado occurring in the

selected area, expressed in % using the Tornado Impact Monte Carlo (TorMC) model using the

parameters namely, study area, the number of simulation years, raster cost surface, frequency,

intensity, location of tornado, etc. Using the available historical tornado information database,

the TorMC randomly samples with replacement a year between 1954-2015 and generates the

number of tornadoes, tornado lengths, azimuths, tornado widths, tornado placement that were

observed in the selected year. And hence a number of Housing Units being affected are

predicted. The authors have predicted the probability of HUs being affected by significant

tornadoes throughout the state of Oklahoma.

Framework of the ETDA tool

The Engineering based Tornado Damage Assessment (ETDA) tool is a probabilistic tool

the predicts the damage to single family light framed wooden structures using a prototype house

model. The individual damage prediction modules namely, the tornado wind field module,

tornado wind load module, wind borne debris module and the structural resistance module are

combined in a Monte Carlo simulation framework to capture the uncertainties in the tornado

wind loading and building response. The individual modules of the ETDA tool are described

24

below, along with the overall flow of the simulation process. A complete description of the

model is provided in Peng et al. (2016)

Tornado Wind Field Module

This module simulates the instantaneous wind field of a tornado vortex, based upon a

Rankine vortex model, at each increment of time as the vortex translates over a user defined

path. Wind velocity time histories, defined as the vector sum of the tangential, radial and

translational velocities, are generated for every point within the tornado path and every time

increment to provide the wind speed and direction. The user is able to modify the maximum

speed of the tornado, the width of the tornado and the relationships between tornado velocity

components (e.g., the ratio of tangential to radial flow) to simulate a full range of realistic

tornado scenarios.

Figure 2-1. Wind vector and isotachs representing a wind velocity field of 2011 Joplin, MO

tornado (Peng et al. 2016)

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Figure 2-1 shows a plan of the wind field of the 2011 Joplin tornado translating along the

X-axis from left to right. The tangential and radial velocity are added to mathematically recreate

the wind field for a stationary tornado. The forward motion is then added to the wind field to

simulate a translating tornado, as shown in Figure 2-1.

Wind Load Module

Straight line winds generated in extreme wind events like hurricanes are well defined, but

limited knowledge is available about rotating winds generated during tornados. The increasing

frequency and losses due to tornado events underscores the need for better understanding of the

development and decay of tornadoes and ultimately the resulting wind effects on buildings. Since

the tornado forecasting is not as accurate as hurricanes, pre disaster data collection deployment is

not frequently possible. Hence most of our knowledge about tornadoes loads from either

laboratory testing field investigations or numerical simulations.

Current building codes and design standards provide the framework for the estimation of

expected wind load calculations. For example, the ASCE 7 -10 (2010) estimates design wind

pressures for Component and Cladding (C&C) on low-rise structures as shown in Eq. 1-1 and

Eq. 1-2:

p = q × h [GCp − GCpi] (1-1)

qh = 0.00256 × Kh × Kzt × Kd × V2 (1-2)

where qh represents the velocity pressure at the mean roof height (h), and GCp & GCpi are

the external and internal pressure coefficient respectively. In equation estimation of velocity

pressure, Kh is exposure factor for height and terrain; Kzt is topographic effect factor, which takes

into account the acceleration of airflow over mountainous terrain; Kd is a wind directionality

factor; and V is the design 3-sec gust wind speed estimated at 10m height from ground level for

exposure category C (Peng et al. 2015). From these aforementioned equations, the suction

26

pressure at any location on the structure is a function of design wind speed and pressures

coefficients. The existing research with respect to wind loads on buildings is divided into two

parts: quantification of ground-level winds and wind-induced loads on structures (Peng 2015).

Haan et al.(2008) discuss the design, construction, and performance of the large tornado

simulator at ISU. Haan et al. (2009) evaluated the effect of tornado wind loads on a single story

gable roof structure using the laboratory tornado simulator. ASCE 7-05 provisions were

compared with the estimated tornado loads to understand the difference between the effect of

straight-line winds and vortex-like winds on structures. The peak values of external uplift force

coefficients for tornadoes was found to be larger in magnitude by a factor of 1.8-3.2 than the

prescribed values in ASCE 7-05.

Building on the work of Haan et al. (2009), Kikitsu et al. (2010) estimated the effects of

vortex-like winds on gable-roofed structures using the same tornado simulator. Their research

proposed a fundamental load estimation model, for the total tornado-induced wind loads. The

model considered the effect of atmospheric pressure change and aerodynamic wind pressure

developed on the structure. This model also established a relationship between the leakage ratio

of building with internal pressures. The mathematical model showed good agreement with the

experimental data.

Hu et al. (2011) performed an experimental study to quantify the characteristics of the

tornado-like vortex and to improve the understanding of the flow-structure interactions for a low

rise gable roof structure. It was found that tornado line winds generate at least 3 times higher

wind loads than straight line winds.

Case et al. (2014) used the same tornado simulator at ISU to study the effect of low-rise

building geometry on tornado-induced loads using multiple test models. The study found that

27

peak tornado-induced wind loads vary with plan area, aspect ratio, eave height, roof slope, and

other geometric parameters.

Sabareesh et al. (2013a; 2012; 2013b) performed various experimental investigations at

the Tokyo Polytechnic University (TPU) on the effect of winds on a cubic building exposed to

tornado-like flow using a ward-type tornado-like-flow simulator.

Thampi et al. (2011) predicted the progressive damage to a single story gable roofed light

framed wooden residential structure using the Finite Element Analysis (FEA) as an EF5 tornado

passed through the building. Internal and external pressure coefficients based on the lab tests at

ISU for tornado-induced loading were used in the study. The validity of the proposed

methodology was demonstrated by comparing the observed and predicted damage from the

numerical analysis. Uplift connectors designed for resisting 90 mph straight-line wind as per

building code barely resist 90 mph tornado wind in a sealed building.

Roueche et al. (2013) used Database-Assisted Design Methodology (DAD) to estimate

structural reactions within a light-framed wooden gable structure subjected to a tornado. The

results from this study demonstrated that the design loads provided in the ASCE 7-10 are

inadequate to resist the tornado-induced wind loading. When compared with the ASCE defined

values, the peak vertical tornado loads were found to be 28% and 60% higher.

Roueche et al. (2016) accounted for the static pressure drop with the tornado vortex

adjusted the tornado load models to account for it. Hence a consistent comparison between

straight line and tornado loads was made that is more useful for structural design. It was found

that peak enveloped pressures observed during the translation of a tornado with a given intensity

directly over a structure trend slightly higher than those observed for straight line winds of the

same intensity.

28

Peng et al. (2016) simulates the external wind loads at each structure (defined by its

minimum distance from the tornado center), using the tornado wind velocity time history, and

based upon the total tornado pressure following Simiu and Scanlan (1996), defined as the sum of

the dynamic and static components as given below in Eq. 1-3:

𝑃𝑇 =1

2𝜌𝑉ℎ

2 × [𝐺𝐶𝑝 − 𝐺𝐶𝑝𝑖] + 𝑃(𝑟)

(1-3)

where 𝑃(𝑟) is the static pressure drop associated with the rotational airflow of the vortex,

𝑉h is the 3-sec gust wind velocity at mean roof height, 𝜌 is the air density and 𝐺Cp and 𝐺Cpi are

external and internal pressure coefficients. The pressure drop component, 𝑃(𝑟), is also based

upon the model of Simiu and Scanlan (1996), but is reduced to 10% when the ratio of the area of

openings to the total surface area of the building is greater than 0.13%, following results from

(Kikitsu et al. 2010). The external pressure coefficient (𝐺Cp) values for the roof and walls of the

building are extracted from the online database of Wind Engineering Information Center for

straight line wind pressures provided by the at Tokyo Polytechnic University (TPU). Internal

pressure coefficients, 𝐺𝐶pi, are determined using the internal pressure model proposed by

Holmes et al. (2006), which is dependent upon the number of openings in the building, and the

location of dominant openings, but limited to the range of +/- 0.55.

Figure 2-2 shows the variation of normalized tangential velocity with the distance of

building away from the tornado center line. In the left graph, the Y axis denotes the ratio of

tangential velocity and the maximum tangential velocity (Vmax), while the X axis represents the

ratio of the distance of the building with away from the tornado center line to the radius to

maximum wind speeds (Rmax)(Peng 2015).

Figure 2-2 (left) shows the variation of normalized pressure drop profile with the distance

of building away from the tornado center line. The ETDA generated pressure distribution profile

29

is compared with the Manchester tornado data, the blue solid line, and the experimental results,

red circles.

Figure 2-2. Tangential velocity profile and pressure drop distribution (Peng et al. 2016)

The highest wind speeds are observed at the radius to maximum wind speeds, at distance

Rmax away from tornado center line. The red circles are the experimental data points from the

Iowa State University (ISU) tornado simulator, and the solid black curve are the analytical

results obtained from the Rankine Vortex model. The experimental and analytical results show

good agreement, suggesting that the Rankine vortex model is able to replicate the wind field of a

tornado (Peng 2015).

Wind Borne Debris Module

The ETDA tool uses the wind borne debris module defined by Mendez (2009). This

module simulates effects of linear and planar debris carried in the wind, such as 2 by 4 lumber

and plywood sheathing, respectively. The windborne debris module is designed to respond to

0 1 2 3 40

0.2

0.4

0.6

0.8

1

1.2

R/Rmax

V/V

max

Tangential Velocity Profile

Present

ISU Simulator (Vane 5)

-4 -2 0 2 4

-1

-0.8

-0.6

-0.4

-0.2

0

R/Rmax

P/P

min

Pressure Drop Profile

Present

ISU Tornado Simulator (Vane 5)

Manchester Tornado

30

the progressive damage occurring to the structure during the passage of the tornado. For

example, if a roof sheathing panel fails, it becomes windborne debris in the next time-step

iteration of the ETDA model and is included in the model with potential for creating openings in

the building envelope.

Structural Resistance Module

At each time-step iteration, the loads on components of the structure are compared to the

structural capacity of the component, and if loads are greater than capacity, that component is

assumed failed and is removed. The components of a structure that are assessed in this model

include Roof cover (e.g., shingles), roof sheathing, roof-to-wall connections, wall sheathing, wall

cover, windows, entry doors and garage doors. Probabilistic capacity distributions are taken from

previous studies, including Cope (2004), Datin et al. (2010), Gurley et al. (2005) and

Shanmugam et al. (2009). Details of the structural capacities used in this studies are provided in

the following section. Henderson et al. (2013) investigated the failure of roof sheathing made

using oriented strand board (OSB) and plywood panels under fluctuating wind loads. It was

observed that fasteners with the incremental failure mechanism were able to more effectively

distribute the load. The proposed ETDA tool by Peng et al. (2016) used the roof to wall

connection capacity of 92psf as observed by Henderson et al. (2013). This capacity was

estimated for 8d twisted nails. It was found that in local construction practices, smooth 6d nails

and hence the roof sheathing and wall sheathing capacity was updated to 74.3 psf (Datin et al.

2010) for the current ETDA tool.

Shanmugam et al. (2009) tested the uplift capacities of 100 roof-to-wall toenail

connections and 34 plank sheathing units from field and laboratory tests. This study proposed the

use of the lognormal distribution to model uplift capacities for both two and three nail

31

connections. The proposed distributions and parameters in the study can be used to evaluate the

component level reliability of toenail connections and roof sheathing.

The structural capacity of the various sub-systems within the vertical load path of the

house was developed from experimental testing over the previous two decades and published in

the literature. The base resistance values used in the current version of the ETDA are

summarized in Table 2-1.

The structural capacities in the model can be easily adjusted to better represent local

construction practices if warranted. The base values shown in Table 2-1 were chosen to represent

typical non-engineered structures in tornado-prone regions of the US without wind-resistant

building codes.

Table 2-1. Summary of structural capacities in prototype model

Building component Mean / kPa

(psf) COV

Distribution

type Data source

Windows 2.4 (50) 0.2 Normal (Gurley et al. 2005)

Entry Doors 3.6 (75) 0.2 Normal (Gurley et al. 2005)

Garage Doors 2.5 (52) 0.2 Normal (Shen 2013)

Roof Cover

(Asphalt shingle) 2.9 (60) 0.2 Normal (Gurley et al. 2005)

Roof & Wall Sheathing Panel

(8d 6”/12”) 3.56 (74.3) 0.15 Normal (Datin et al. 2010)

Wall Cover

(Vinyl siding) 3.2 (66) 0.2 Normal (Gurley et al. 2005)

Roof-to-wall Connection

(Three 16d Toe Nails SP)

1.97 kN

(442 lb) 0.38 Normal

(Shanmugam et al.

2009)

Note that three components specified (windows, entry doors, and garage doors) are part

of the fenestration and ingress/egress systems for the building and they are not strictly part of the

vertical load chain. However, they are included here because their failure during a wind event

32

will significantly increase the internal building pressure and therefore increase the likelihood of

failure of the vertical load components.

Structural Model of Residential Building

Structurally, these houses consist of several components and structural elements that

define their performance under gravity, wind and other loading conditions and contribute to the

repair or replacement cost. These include the following:

1. Roof covering

2. Roof sheathing

3. Roof-to-wall connections

4. Wall sheathing

5. Wall covering

6. Entry doors

7. Windows

8. Garage doors

Figure 2-3. Isometric rendering of the 117 m2 prototype house used in the ETDA tool (Peng et

al. 2016)

The ETDA tool utilizes a single prototype structure in the damage simulations. While this

is somewhat limited given the heterogeneous distribution of buildings, it was decided that a

single prototype house model be developed to evaluate the ETDA tool. The structure is a single-

33

story, gable roof, wood-frame, a slab-on-grade structure measuring 32 ft wide and 40 ft long, as

shown in Figure 2-3 (Peng et al. 2016). The prototype structure represents the typical house

observed in Joplin, MO after the 2011 tornado.

Output of the ETDA Simulations

The ETDA tool uses the output from each of these four modules in an iterative, Monte

Carlo simulation procedure. The center of tornado’s vortex is moved incrementally along the

damage path, the wind velocity updated and the structural loads and resistances are checked at

each iteration to determine if the failure of any component has occurred. The path of the tornado

extends a total distance of 8 times the radius of maximum wind speeds of the tornado, with the

tornado path closest to the modeled structure at the midpoint of the path. The tornado path is

further subdivided into 41 equal time steps of size 0.2 times the radius to maximum wind-speeds.

The progression is illustrated schematically in Figure 2-4 (Peng et al. 2016).

Figure 2-4. Schematic of the tornado path in relation to the location and orientation of an

individual building. (Peng et al. 2016)

The ETDA tool estimates the progressive damage at each time step. Because of the

stochastic nature of the analysis, 5,000 simulations of the tornado translating past the house are

performed for each house scenario, and the output represents the average, or expected, damage

ratio for each component. Here damage ratio is defined as the ratio of the number of damaged

34

component members to the total number of component members (e.g., 3 windows damaged out

of 8 total windows). Figure 2-5 shows the flow chart of the framework for the ETDA tool.

Figure 2-5. Flowchart of the engineering based tornado damage assessment tool (Peng et al.

2016)

The modules are interdependent, meaning that if for example, the windborne debris

module determines a window has broken on the windward wall, the internal pressures and

atmospheric pressure component of the wind load module respond accordingly. At the end of

35

5,000 simulations, the ETDA tool generates 5,000 damage ratios for each of the 41-time steps for

each of the eight components.

The ETDA tool predicts the tornado-induced damage to each of the eight components at

each of the 41-time steps for 5,000 Monte Carlo simulations. This is the damage to the house at

when the tornado is at that time step. Sum of the damage along 41-time steps gives the total

damage to the house due to the tornado loads in that simulation. The mean of such 5,000 damage

ratios is the ETDA predicted damage ratio to a single component of a single house.

36

CHAPTER 3

DEVELOPMENT OF DATASETS FOR VALIDATION OF THE ENGINEERING BASED

TORNADO DAMAGE ASSESSMENT (ETDA) TOOL

Validation is the most important step for any mathematical model. This process ensures

that the results predicted by the model are in conformity actual observed data. The ETDA tool is

validated by comparing the ETDA predicted damage rations with the observed damage ratios

estimated from the data collected from post-disaster damage assessment surveys. The following

section discusses some previous post-disaster damage assessment surveys, the method in the

selection of suitable houses for validation and in developing the empirical damage ratio dataset

from the post-disaster data. Additionally, this section describes the methodology followed for

generating the inputs for the ETDA tool namely, the tornado wind field module and the location

and orientation of the houses. These parameters are used for developing the predicted dataset

using the ETDA tool.

Post Disaster Damage Surveys

Post-disaster damage assessment surveys provide useful data to evaluate performance of

structural components during disasters and learn the behavior of the wind hazard. This

information improves the understanding of building failure mechanisms, wind field models and

the performance of existing building code provisions. The effectiveness of existing design and

construction practices can be evaluated from the forensic data. Also, the data can be used for

mitigation of damage, urban planning and development and validation of risk predicting tools.

Background

The United States experienced devastating tornadoes in the spring of 2011. In particular,

an EF4 tornado (wind speeds between 175 and 200 mph) cut through Tuscaloosa, Alabama, on

April 27 2011 and an EF5 (the wind speeds greater than 200 mph) tornado struck Joplin,

Missouri, on May 22 2011, damaging 7,500 houses and causing 162 fatalities. Prevatt et al.

37

(2012) and Roueche and Prevatt (2013) summarized the observed structural damage patterns and

their possible causes from post-disaster damage surveys. Since tornado-resiliant design

considerations were not included in the local building codes, to which the structures were built,

many of the affected structures in these areas collapsed. Furthermore, the structural collapse of

the surveyed buildings often initiated with the failure of the roof system, which was very likely

to trigger the collapse of perimeter walls due to the loss of lateral bracing provided by the roof.

Buildings further away from the center experience damage patterns that are similar to structures

subjected to straight-line hurricane force winds (Prevatt et al. 2012).

Lyu et al. (2016) describe the preliminary investigation and analysis of the 23 June 2016

Jiangsu Province, China tornado. This tornado along with rainstorm and hailstorm had claimed

99 lives and caused more than 3800 flats to collapse as well as damaged 48 high-voltage circuits.

The paper concluded that the local builders need to be educated about the importance of the load

transfer path and highlighted the need for the development of wind resistance code in China.

For tornado events, wind speeds are estimated using post-disaster damage assessment.

The Fujita-scale, developed by Fujita (1971) is the most widely used method for rating tornadoes

throughout the world. However, Phan and Simiu (1998) pointed out that the F-scale fails to take

the construction quality and variability into account, and hence lacks a definitive correlation

between post-disaster observed damage and tornado wind speeds. McDonald and Mehta (2006)

developed the Degree of Damage (DOD) rating to more efficiently and accurately quantify the

relationship between various damage states and the tornado wind speeds. Based on these

estimated wind speeds, which takes into account the quality of construction, Enhanced Fujita

(EF) Scale was developed by the researchers at Texas Tech University (McDonald and Mehta

2006) for a rating of tornado intensity.

38

Table 3-1. Recommended DOD ratings for wind speed ranges (McDonald and Mehta 2006)

DOD Damage description Exp LB UB

1 Threshold of visible damage 65 53 80

2 Loss of roof covering material (<20%), gutters and or awning;

loss of vinyl or metal siding

79 63 97

3 Broken glass in doors and windows 96 79 114

4 Uplift of roof deck and loss of significant roof covering

material (>20%); collapse of chimney; garage doors collapse

inward or outward; failure of porch or carport

97 81 116

5 Entire house shifts off foundation 121 103 141

6 Large sections of roof structure removed; most walls remain

standing

122 104 142

7 Exterior walls collapsed 132 113 153

8 Most walls collapsed in bottom floor, except small interior

rooms

152 127 178

9 All walls collapsed 170 142 198

10 Destruction of engineered and or well-constructed residence:

slab swept clean

200 165 220

Table 3-2. Recommended EF – scale wind speed ranges (McDonald and Mehta 2006)

EF classes 3-second gust speed, mph

EF0 65-85

EF1 86-110

EF2 111-135

EF3 136-165

EF4 166-200

EF5 >200

Two separate datasets collected in the aftermath of 22nd May 2011 Joplin, MO, and 26th

December 2015 are used for validating the ETDA tool. Each dataset contains geo-tagged

photographs of the tornado damage sustained by residential structures from which the damage

levels to each structure, the orientation of each structure, and location with respect to the tornado

centerline, have been determined. The following sections describe the datasets in more detail.

22nd May 2011 Joplin, MO Tornado Survey

On 22nd May 2011 a strong tornado stuck the city of Joplin with wind speeds close to 89

m/s (200 mph), affecting more than 8,000 houses and causing an estimated $2.2 billion in

39

insured losses (Simmons et al. 2013). Prevatt et al. (2012) collected information, consisting of

hand-written notes and geo-tagged photographs, of 1,349 individual structures, each of which

was rated in accordance with the procedure of the Enhanced Fujita (EF) Scale (McDonald and

Mehta 2006). Of the 1,349 houses for which damage was documented by photographs, 707 were

documented by members of the damage assessment team while on the ground visually inspecting

the damage and therefore multiple photographs are available for each structure for the most part.

For the remaining 642 houses, the damage photographs were taken by a camera mounted to a car

and so typically only one to two photographs are available for each house.

An interactive map of the 22 May 2011 Joplin, MO tornado is publicly available at

http://esridev.caps.ua.edu/JoplinTornado/. The map provides a visualization of the overall

damage caused by the tornado. A screenshot of the map is provided in Figure 3-1. The

description of the symbols used in the figure is shown in Table 3-3. The interactive components

are annotated in the screenshot and described below:

Figure 3-1. Interactive map of 22nd May 2011 Joplin, MO tornado damage survey

(http://esridev.caps.ua.edu/JoplinTornado/)

http://esridev.caps.ua.edu/JoplinTornado/

http://esridev.caps.ua.edu/JoplinTornado/

40

Table 3-3. Description of the symbols used in Figure 3-1

Symbol Description

A Legend providing the colors associated with each EF Scale rating and the outline of

the tornado path.

B Pop-up annotation showing the photo represented by a colored dot, triggered by the

user clicking on one of the colored dots.

C Options for the following:

Find Address – search for an address within the mapped region

Measure – calculate distance or areas by drawing lines or polygons on the map

Legend – toggle for the Legend display

D The drop-down menu for selecting from available layers, including damage path, EF

Scale contours, aerial damage imagery, case studies, and survey locations.

E Toggles for the baseline map, including pre-tornado aerial imagery, topographic or

street maps.

Following the Joplin tornado and damage assessment, the data collected by the team has

been used to propose a new dual-objective design paradigm for tornadoes (Prevatt et al. 2012;

van de Lindt et al. 2012), and compare with a wind field model developed from tree-fall patterns

(Lombardo et al. 2015).

26th December 2015 Garland / Rowlett, TX Tornado Survey

Overview

At 6:45 pm on 26 December 2015 a tornado, with maximum wind speeds estimated at

80.4 m/s (NWS 2016), touched down and traveled 16 miles in an NNE direction, causing

extensive damage to approximately 1,200 houses and causing 13 fatalities in the towns of

Garland, and Rowlett, Texas. Following up on it on January 7th and 8th 2016, performed a post-

disaster damage assessment. A total of 5,615 photographs were taken during the assessment,

documenting the damage to 712 individual houses. Gutierrez et al. (2016) summarized the

impacts in a preliminary damage report.

Post disaster damage survey methodology

The objective of WHDAG’s assessment was threefold: 1) to evaluate the impact that

house characteristics (year built, area, wall material, etc.) had on the final damage state; 2) to

41

establish a relationship between physical damage levels and economic losses in tornadoes (in

collaboration with a local insurance provider); and 3) to provide a detailed database of physical

damages to structures which can be used for validating the ETDA tool.

Figure 3-2. Map showing location of houses surveyed in one of the five transects

The team created maps (shown in Figure 3-2 ) of the survey regions identifying each

parcel to be surveyed along with its year of construction and street address for ease of

identification in the field.

42

Four photographs were taken of each damaged houses, capturing the front, side and back

elevations. A representative surveyed house is shown in Figure 3-4. This ensured complete

damage observation for the house during analysis, so that the visually estimated damage ratios

could be quantified as accurately as possible.

Figure 3-3. Damage assessment photo captures on 7514, Atlantic Drive Rowlett, TX. Based on

observed damage DOD 6 and EF-2 ratings were assigned.

Photos were linked to a specific houses in two ways. The first was by geo-tagging the

photos as they were taken using GPS functionality of the team’s cameras. The second was by

taking a picture of the map, with a pointer identifying the parcel being assessed in the subsequent

photographs, shown in Figure 3-3. This improved data collection methodology significantly

improved the quality of post-tornado damage data from previous damage assessments.

43

Figure 3-4. Location of houses documented prior capturing post-disaster damage data.

Selection of Houses for Validation

Joplin

While the entire Joplin tornado database contains 1,349 structures, not all have the

complete damage documented with sufficient detail to be useful in validating the ETDA tool.

The best houses for validation are a single story, wood frame, gable roof houses for which

complete damage can be defined using a combination of available damage photographs from the

ground survey. Since the ETDA tool does not currently have the ability to predict wall failures,

the best houses for validation also should not be mostly destroyed, as it is nearly impossible to

make estimates of structural damage to cladding elements in this situation (e.g., estimating %

shingles removed when the roof is completely destroyed).

An algorithm was developed in MATLAB (Mathworks, 2016) to rank each house in the

database by the number of photographs taken nearby. Suitable houses were then selected based

upon the number and quality of photographs available of the overall exterior damage. The most

suitable houses had photographs showing all four sides of the house. From the initial dataset of

1,349 houses, 82 houses were identified as having a sufficient number of quality photographs for

estimating the complete damage to the houses. Figure 3-5 shows the location of the selected

houses with respect tornado center line (Kuligowski et al. 2013). The selected houses are color

coded based on the assigned degree of damage rating.

44

Figure 3-5. Map showing locations of the 82 houses, color coded based on the assigned DOD

ratings, used to the validate ETDA tool results relative to the 22nd May 2011

Joplin, MO tornado path.

Garland / Rowlett

The full dataset from the Garland/Rowlett tornado included 718 individual houses of

various shapes and sizes, including both one- and two-story houses, and hip and gable houses.

The prototype house used by the ETDA tool is a single-story, gable roof, but it is expected that

even for houses that differ from this prototype, the ETDA tool can still reasonably predict overall

damage patterns. Upon reviewing all the photographs, 712 houses were deemed suitable for

inclusion in the validation dataset based upon the number of photographs covering all four

elevations.

Figure 3-6 show the location of the selected 712 houses with respect to the tornado center

line. The selected houses are divided in the five regions surveyed in the aftermath of the tornado.

The houses are color coded based on the year of construction of the house. The numbers inside

each parcel represent the assigned DOD rating. Once the damage to each surveyed structure was

estimated, the locations with highest observed damage was joined to determine the tornado

center line. The red dashed line shows the tornado center line for the Garland/Rowlett, TX

tornado.

45

Figure 3-6. Locations of all surveyed houses by region. Colors depict the decade in which the

house was built. Numbers inside each parcel give the assigned damage rating.

Observed Damage Ratios

The ETDA tool predicts damage ratio for each of the eight building component for each

of the house. For the model to be validated, it needs to be compared with same parameters. To

validate the ETDA tool, visually observed damage ratios are estimated for the eight components

for all the houses in the validation dataset. The following section describes the damage ratio

estimation methodology followed.

Damage Estimation Methodology

In order to validate the model, it is important to obtain the damage ratios (DR) for each

house of that was surveyed. The damage ratio is defined as the ratio of the quantity or area of a

given component that was damaged to the total quantity or area of the component. For example,

46

if a house has eight windows and six of them are damaged, then the damage ratio for windows is

defined as 6/8 or 75%. The eight building components are the following:

Entry Doors

Garage Doors

Roof to Wall Connections

Roof Cover

Roof Sheathing

Wall Cover

Wall Sheathing

Windows

All the suitable houses from the surveyed database are assigned with a visually observed

damage ratios for each of the eight components. Figure 3-7 shows how the damage to each of the

eight components was identified.

Figure 3-7. Damage state for the eight building envelope components

Damage to components like entry doors, garage doors and windows is binary. Either they

are damaged or they are not, and hence the damage can be quantified without to quantify. But for

the rest of the components, it is not as easily quantifiable. Hence two students estimated the

47

observed damage ratios (shown in Table 3-4) independently using the all four elevations

captured during the post-disaster damage assessment survey of the house shown in

Figure 3-8. Subjectivity in the visual estimation of damage ratios adds uncertainty to the

estimates that can be considerable for some houses.

Figure 3-8. Photographs showing all the four elevations of 5821 Winell Drive, Garland, TX

Table 3-4. Observed damage ratios for 5821 Winell Drive, Garland, TX

Component Student 1 Student 2

Entry Door 0 0

Garage Door 100 100

Roof to Wall Connection 20 10

Roof Covering 40 20

Roof Sheathing 25 15

Wall Covering 30 25

Wall Sheathing 25 20

Windows 43 33

48

This subjectivity in the estimation of damage ratio has led to sometimes significant

disagreements between the damage ratios estimated by the two individuals. Figure 3-9 shows the

variation in the observed damage ratios by two students for 712 houses.

Figure 3-9. Comparison of damage ratios for 8 components estimated independently by two

students

The observed damage ratios estimated by two individuals are compared and the housing

data is arranged with an increasing disagreement between the two observed values. A reasonable

threshold of 15% disagreement between the observed damage ratios for each of the eight

components is implemented. For the houses which having more than 15% of the disagreement,

two graduate students together reevaluated the damage ratio for these houses. Hence the

subjectivity in damage ratio estimation methodology was addressed in an aforementioned way.

ETDA Estimated Damage Ratios

Wind Field Model

The ETDA tool is a mathematical damage prediction tool. For the validation process, it is

critical to use a numerical tornado vortex model such that the predicted wind speeds at a given

49

location match reasonably well with estimates of the actual wind speeds. Measured wind speeds

in the Joplin, MO tornado are not available, but Lombardo et al. (2015) did fit a tornado wind

field model to free-fall patterns, which provides an estimate of the key parameters needed to

simulate the tornado wind-field in the ETDA tool. Based on the Lombardo et al. (2015) study,

the vortex is given a radius to a maximum wind speed of 843 ft and a forward (translational)

velocity of 30 mph, with a maximum tangential wind velocity of 145 mph. The centerline path of

the tornado was established in the Kuligowski et al. (2013).

Unlike the Joplin tornado, there were no well-defined tree-fall patterns or any other

common indicator in the Garland/Rowlett tornado for estimating the size of the tornado vortex,

the ratio of tangential to radial wind speeds, and other factors. The best estimate of the tornado

wind field was obtained by scaling the parameters of the ETDA tool used to represent the Joplin,

MO tornado down to the size of the Garland/Rowlett tornado. This was accomplished by the

ratio of total damage width in Joplin compared to that in the Garland/Rowlett tornado. The

justification for the chosen wind field parameters for the Garland/Rowlett tornado is given in

more detail below.

Translational Speed = length of tornado path

Tornado duration=

13 miles

17 minutes= 46 mph

Maximum width of damage of Joplin tornado (WJoplin) = 5280 ft

Maximum width of damage of Garland tornado (WGarland) = 1650 ft

Tornado inner core radius for Joplin (RJoplin) = 843 ft

Tornado inner core radius for Garland (RJoplin) = 264 ft

𝑅Garland = (WGarland

WJoplin) ∗ RJoplin

RGarland = 264 m

50

For the maximum tangential wind speeds of 107 mph, the maximum horizontal wind

speeds were about 150mph. Thus, while the peak wind speed of the tornado was estimated at 170

mph by the National Weather Service (NWS 2016), the average peak wind speed at the center of

the tornado along the length of the path is expected to be ~150 mph.

Table 3-5. Comparison of the 2015 Garland, TX tornado with 2011 Joplin, MO tornado

Characteristic Joplin Garland

Tornado Core Radius (ft) 843 264

Tornado Maximum Width (m) 3940 1310

Translational Speed (m/s) 13.3 20.6

Maximum Tangential Velocity (m/s) 64.7 47.8

Maximum Wind Speed (m/s) 88.9 73.6

Location and Orientation of the House

The ETDA tool estimates the wind loads on each house depending on its distance and

orientation with respect to the tornado center line. House locations based on the GPS coordinates

from the captured photographs and the addresses were used to identify the house locations. An

algorithm was used in ArcGIS to find the perpendicular distance between the tornado centerline

and the each house. Based on the location of the house and the tornado path, the orientation of

the house with respect to the tornado center line is determined by visual inspection. Once the

parameters of the wind-field model have been set, the wind loading is determined at every house

using the location of the house relative to the tornado path and the orientation of the house.

Optimization of the Performance of the ETDA tool using High Performance Computing

Running the ETDA tool on a regular desktop computer1 requires approximately 0.7 hours

to perform 5,000 simulations of a given tornado translating past a building model and causing

damage. At this rate, it would require approximately 560 hours to estimate the damage ratios for

1 RAM:7GB; Processor: Intel Core 2 Duo E8500 @ 3.16GHz

51

a larger dataset of 800 houses. This makes alterations and iterations to the model difficult. Hence

the ETDA tool was deployed on HiPerGator 2.0, the high-performance computer at the

University of Florida. HiPerGator 2.0 has 30,000 processor cores, 120 TeraBytes of RAM, 1

PetaByte of disk storage, and capability of performing 1,100 trillion floating-point operations per

second.

SLURM (Simple Linux Utility for Resource Management) is a software package for

submitting, scheduling, and monitoring jobs on large compute clusters, like HiPerGator 2.0. The

input dataset (i.e., houses to be simulated) was subdivided into multiple subsets, and the analysis

of each subset was deployed to a separate core of HiPerGator. This reduced the run time for the

ETDA tool by a factor of 32 at best. But this method is still somewhat cumbersome in that it

requires the datasets to be fragmented and then recombined manually to make use of the

additional processing cores.

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CHAPTER 4

VALIDATION OF THE ENGINEERING BASED TORNADO DAMAGE ASSESSMENT

(ETDA) TOOL

Background

Validation is the most important step for any mathematical model. This process ensures

that the results predicted by the model are in conformity actual observed data. In a probabilistic

setting, a perfect match between the predicted and observed data is seldom possible. Datasets are

generally checked for statistical validation, wherein the statistical validation methods are

employed to conclude a statistical agreement between the observed and predicted data.

Conventional parametric and non-parametric statistical significance tests (e.g., t-tests,

Wilcoxon, etc.) are not well-suited to such a comparison for individual houses because they are

intended to evaluate distributions of sample sets, not individual data. Damage ratios for all the

houses could be grouped together and compared to the mean of the ETDA prediction for each

house, but the houses experienced a large range of wind speeds and damage levels that limit the

usefulness of such a comparison.

Receiver Operating Characteristics (ROC) Curve is a diagnostics tool for evaluating the

accuracy of a test to correctly identify a specific outcome (Fawcett 2006). ROC was developed

during World War II as a way to evaluate the accuracy of radar to correctly identify enemy war

planes from “noise”, such as birds. Gehl et al. (2013) used Area under Curve (AUC) to

demonstrate that using multiple intensity measures (IM) leads to better prediction of the damage

state of the building than a single IM.

Methods for Assessing the Agreement between Observed and Predicted Damage Ratios

The accuracy of the ETDA tool at predicting tornado-induced structural damage can be

assessed in several ways. Four methods will be presented in this research work, progressing from

the most granular to the most general, and summarized as follows:

53

Spider Plots. This is a graphical method for comparing the average ETDA predicted

damage ratios to the actual observed damage ratios for a single house. All eight

components are shown on the same plot for ease of comparison.

Damage Progression Plots. These plots present the average ETDA predicted damage

ratios to the actual observed damage ratios as a function of the distance of each house

from the centerline of the tornado. Separate subplots show the relationship between

damage ratio and distance from the tornado for each of the eight components. Each point

represents either the predicted or observed damage ratio for a single component of a

single house.

Binned Damage Progression Plots. These plots are similar to the Damage Progression

Plots but the damage ratios are split into equal bins by distance from the tornado center.

Thus each point represents the average observed or average predicted damage ratio for a

single component over a given bin.

Receiver Operating Characteristic (ROC) Curves. ROC curves are commonly used as a

model evaluation tool and in this particular case show how accurate the ETDA tool is at

predicting whether a given damage level occurs or not. Rather than simply using the

average predicted damage ratio given by the ETDA tool for a given house, it incorporates

the uncertainty in that prediction by considering the probability that the actual damage

ratio is observed based on the ETDA tool.

Each of these methods has their own benefits and limitations, and there is no one right

way of assessing the accuracy of the ETDA tool. Combined, the methods are a reasonably robust

way of demonstrating and confirming the accuracy and limitations of the ETDA tool. Results are

provided for each in the next section.

Comparison of Observed and Predicted Damage to a Single House

The simplest way to visualize the accuracy of the ETDA tool is by directly comparing the

observed damage ratios, from the field survey, with the damage ratios predicted by the ETDA

tool. A convenient graphical representation of the comparison is the spider plot or radar plot.

Caution should be exercised in reading too much into these single comparisons, however. The

ETDA predictions represent the average, or expected, damage ratio to each of the eight

components obtained from thousands of simulations. There can be many possible damage

scenarios in the simulations, illustrating the uncertainty in the wind loads, structural capacities,

54

and more. The observed damage ratios essentially represent one possibility of the simulations,

therefore it is not unsurprising if comparisons of individual houses demonstrate differences

between predicted and observed damage ratios.

Damage Progression Plots

The spider plot graphical representation visualizes the relative damage that occurred

among the eight components within a single structure, and this is useful for some purposes.

However, the spider plot representation shows nothing of the effect of variability in wind loading

on damage ratios. In a translating tornado, wind speeds are typically highest within the vortex

itself. The edge of the vortex is commonly termed the radius to maximum winds, or RMW, and

wind speeds decrease away from the RMW. Therefore, damage should also decrease with

increasing distance from the tornado center beyond the RMW.

Tornado wind loading is directly related to the wind speed, which decays exponentially

outwards from the radius of maximum wind speeds. In order to highlight this variation, the

progression of damage ratios away from the tornado center line is required. The damage

progression plots demonstrate this effect for each of the eight structural components separately.

The vertical axis on each plot represents the observed or expected ETDA damage ratio. The

horizontal axis represents the distance from the center of the tornado, as determined using the

minimum perpendicular distance between the GPS location of each house and the estimated

center of the tornado path. Each point represents a single house. Expected damage ratio from the

ETDA tool and observed damage ratio estimated through visual assessment of the damage to the

house from the available photographs are graphed for each of the eight components. The red line

indicates the estimated RMW.

55

Binned Damage Progression Plots

The spider plots and damage progression plots discussed above are useful but are limited

in that they do not necessarily compare the same damage ratio. To address this and compare

averaged observed and predicted damage ratios, we bin the observations by the distance from the

tornado center. For the Joplin tornado, which was much wider, 100 m bins are used. For the

Garland/Rowlett tornado, 25 m bins are used. Damage ratios for all houses within that bin were

averaged to obtain a single value. Thus for a bin centered at a distance of 50 m in the Joplin

tornado, all houses between 0 and 100 m from the center of the tornado are estimated and the

average damage ratio for each component is calculated. From these plots, it is possible to pull out

some general trends regarding the performance of the ETDA tool.

Receiver Operating Characteristics (ROC) Curve

Receiver Operating Characteristics (ROC) curve is a diagnostics tool for evaluating the

accuracy of a test to correctly identify a specific outcome. ROC was developed during World

War II as a way to evaluate the accuracy of radar to correctly identify enemy war planes from

“noise”, such as birds. ROC can assess the accuracy of a probabilistic model like the ETDA tool

for given a set of observed outcomes (e.g., damage to 712 individual houses).

ROC method works by classifying the observed and predicted damage levels into binary

outcomes (failure or no failure) using limit states. For example, if a damage ratio of 25% is

chosen for roof sheathing, any houses that experienced a sheathing damage ratio of 25% or

greater would be classified as “Failure”, and any houses with less than 25% sheathing damage as

“No Failure”. Next, the probability of exceeding the limit state is evaluated for each house by

dividing the number of ETDA predictions for a given component, and house with damage

greater than the limit state by the total number of damage predictions (i.e., 5000). For example, if

1,500 of the 5,000 ETDA simulations for a given house predict more than 25% sheathing

56

damage, the probability of exceeding the limit state for that house would be 1,500

5000= 0.3 or 30%.

Then a critical threshold is defined such that any probability greater or equal to the threshold is

classified as “Failure”, and any probability less than the threshold is classified as “No Failure”.

For a given critical threshold, each site (i.e., surveyed house) can be classified as follows for

each structural building component:

True Positive – ETDA predicts Failure and Failure are observed.

True Negative – ETDA predicts No Failure and No Failure is observed.

False Positive – ETDA predicts Failure but No Failure is observed.

False Negative – ETDA predicts No Failure but Failure occurs.

The result of this classification is that for each critical threshold, and for each building

component, there are 712 sites for Garland/Rowlett and 82 sites for Joplin that are classified as

one of the above classifications. The True Positive Rate (TPR) can then be defined as the ratio of

the True Positives to the sum of the True Positives and False Positives. The False Positive Rate

(FPR) can be defined as the ratio of the False Positives to the sum of the True Positives and False

Positives. TPR and FPR are both functions of the critical threshold.

ROC graphically compares the ratios of true positive versus false positive predictions for

various critical thresholds, which results in a curve plotted on axes bounded from 0 to 1. The

area under the curve (AUC) indicates the accuracy of the test, with 1 indicating a perfect

predictor and 0.5 indicating no predictive capabilities. The desirable benefits of ROC analysis

are that

1. Each data point can be treated individually (i.e., no need to subjectively bin the data);

2. The outcome is a single score (AUC) for each of the eight building components; and

3. The score is not significantly influenced by the choice of limit state (e.g., 25% roof

sheathing loss vs 50% roof sheathing loss (Hajian-Tilaki 2013).

57

However, it was found that the above statement is true only if the damage state is varied

within the range of either the observed damage ratio or the predicted damage ratio. Hence, as

long as the damage state is in that range, the AUC score is fairly constant. When the damage

state was selected higher than any of the predicted or observed value, all the observations were a

false negative, leading to an extremely low AUC Score (~0.5). Table 4-1 shows the

interpretation of the AUC score in the traditional academic point system (Tape).

Additionally the AUC scores are dependent on the on the proportion of the true positives

and false positives and hence is insensitive to the variation in the selected damage state.

Table 4-1. Classification of the AUC score in the traditional academic point system (Tape)

AUC Score Rating

0.90-1.0 Excellent (A)

0.80-0.90 Good (B)

0.70-0.80 Fair (C)

0.60-0.70 Poor (D)

0.50-0.60 Fail (F)

Results

Comparison of Observed and Predicted Damage to a Single House

Figure 4-1, Figure 4-2 and Figure 4-3 compare the visually observed and ETDA

predicted damage ratios for a single house. The spider plot shows reasonable agreement between

the ETDA estimated and observed damage ratio for each of the eight components. Over

prediction or under prediction of damage ratio for each of the component can be visualized from

the following results. For House#1, the ETDA tool over predicted the damage to roof cover,

windows and entry door. It under predicted the damage to the garage door, roof to wall

connections, roof sheathing, wall sheathing and wall cover.

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Figure 4-1. House#1 - Spider plot comparing the ETDA estimated damage ratios with

empirically-determined results

Figure 4-2. House #2 - Spider plot comparing the ETDA estimated damage ratios with


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Figure 4-3. House #3 - Spider plot comparing the ETDA estimated damage ratios with


Damage Progression Plots

Figure 4-4 shows the variation in roof sheathing damage ratios with distance from the

Garland/Rowlett tornado center. The observed damage ratios and predicted damage ratios are

represented using black circles and blue circles respectively. The Radius to Maximum Wind

Speeds (RMW) is shown using the red dashed line. There are many factors that can contribute to

this uncertainty. For example, the tornado could increase or decrease in intensity along the path.

60

Figure 4-4. Variation of roof sheathing damage ratios with distance from the Garland/Rowlett

tornado center.

. Figure 4-5 and Figure 4-6 show the progression of damage for each of the eight

components in the Joplin tornado and the Garland/Rowlett tornado respectively.

Figure 4-5. Spatial variation of ETDA predicted damage ratios with visually observed damage

ratios for the Joplin tornado for 82 houses.

61

There are many factors that can contribute to this uncertainty. For example, the tornado

could increase or decrease in intensity along the path. A particular house could be shielded by

another and experience reduced wind speeds. One house could be older than another and have

weaker roof sheathing capacities. All of these and more are possible for explaining the variability

in observed damage ratios.

The expected damage ratios from the ETDA tool are much smoother, with an obvious,

progressive increase in damage within the tornado RMW. These values are less scattered than

the observed damage ratios in part because they represent averaged values over 5,000

simulations.

Figure 4-6. Spatial variation of ETDA predicted damage ratios with visually observed damage

ratios for the Garland/Rowlett tornado for 712 houses.

62

Thus some of the inherent uncertainties, such as the component capacity, are eliminated

and not visible in this plot. Other uncertainties, such as shielding, or variations in tornado

intensity along the path, are not included in the model at all. Thus, differences should be

expected between the observed and ETDA predicted damage ratios because the former is a single

observation while the latter is an averaged value.

Binned Damage Progression Plots

Figure 4-7 and Figure 4-8 show the binned damage progression plots for Joplin and

garland/Rowlett dataset respectively. Overall, the ETDA tool produces reasonable trends in

damage progression relative to the observed damage.

Figure 4-7. Average observed and predicted damage ratios (DR) for the Joplin tornado. Bin

size is 100 m. The red dashed line indicates the radius to maximum wind speeds.

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The damage ratios are binned at 100m and 25m intervals for Joplin and Garland/Rowlett

tornado datasets respectively. In the Joplin tornado, the ETDA tool tended to under-predict both

the rate at which the damage progressed as well as the damage ratios within the RMW. In the

Garland/Rowlett tornado, the ETDA tool tended to over-predict the rate at which damaged

openings occurred. Roof-to-wall connection damage was under-predicted. Other components

showed reasonable agreement between predicted and observed damage.

Figure 4-8. Average observed and predicted damage ratios (DR) for the Garland/Rowlett

tornado. Bin size is 25 m. The red dashed line indicates the radius to maximum

wind speeds.

Table 4-2, which highlights for each tornado whether the ETDA tool tends to over-

predict, under-predict, both over- and under-predict, or match reasonably well the observed

damage trends for each building component.

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Table 4-2. Summary of ETDA performance relative to the observed damage trends.

Component Joplin, MO tornado Garland/Rowlett, TX tornado

Entry Door Matches Over-predicts

Garage Door Matches Mixed

Windows Under-predicts Under-predicts

Roof-to-Wall Connections Under-predicts Mixed

Roof Cover Under-predicts Matches

Roof Sheathing Under-predicts Matches

Wall Cover Under-predicts Matches

Wall Sheathing Under-predicts Over-predicts

Receiver Operating Characteristics (ROC) Curve

ROC curves for the Joplin tornado are shown in Figure 4-9, and for the Garland/Rowlett

tornado in Figure 4-10. The AUC ranges from 0.83 to 0.99 and were generally better for Joplin

than for the Garland/Rowlett tornado. Overall the high AUC scores indicate the ETDA tool is

able to accurately predict whether a given site is likely to experience a given damage level or not.

Figure 4-9. ROC curves from the Joplin tornado. The limit states chosen for each component

are provided in the title of each subplot. AUC is given in each subplot.

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Figure 4-10. ROC curves from the Joplin tornado. The limit states chosen for each component

are provided in the title of each subplot. AUC is given in each subplot.

Table 4-3. AUC for ROC curves from the Joplin and Garland/Rowlett tornadoes.

Component Joplin, MO Garland/Rowlett

Entry Door 0.86 0.86

Garage Door 0.92 0.82

Windows 0.92 0.86

Roof-to-Wall Connections 0.99 0.83

Roof Cover 0.91 0.88

Roof Sheathing 0.99 0.82

Wall Cover 0.88 0.84

Wall Sheathing 0.89 0.86

Table 4-3 shows the comparison of the AUC score for each component from Joplin and

Garland/Rowlett dataset. The Area Under the ROC Curve (AUC) score of higher than 0.9 and

0.8 suggests excellent and predictive abilities of the ETDA tool respectively. The AUC score is

slightly lower for the Garland/Rowlett tornado dataset. This can be because of the use of a scaled

down tornado wind field model, different construction practices and types of the houses.

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The outcome of the Receiver Operating Characteristics (ROC) Curve method is

independent of the selected damage state. The damage state was varied from 1% to 100% for

each of the eight components and the AUC scores collected. Figure 4-11 and Figure 4-12 shows

the variation of the AUC scores with the damage states. It can be seen that the AUC scores are

fairly constant and independent of the selected damage state.

Figure 4-11. Variation of the AUC score for the eight components with damage state for 82

houses from Joplin dataset

Figure 4-12 shows the variation of the AUC score with the damage state for

Garland/Rowlett tornado dataset. It can be seen that there is sudden decline in the AUC score

with damage states exceeding 80%. Since the ROC curve and the AUC Scores depend on the

67

ratio of true positives and false negatives, they are not significantly affected by the variation in

the damage state.

Figure 4-12. Variation of the AUC score for the eight components with damage state for 712

houses from Garland/Rowlett dataset

68

CHAPTER 5

DEVELOPMENT OF VULNERABILITY COMPONENT FOR THE ENGINEERING BASED

TORNADO DAMAGE ASSESSMENT (ETDA) TOOL

Structural damage ratios are helpful to the engineering community to educate the

communities about the potential tornado damage scenario; to prepare a case for more resilient

design codes. Engineering damage ratios convey the message, but economic loss ratios can help

the people relate the tornado event with the financial outgo. Relationship between structural

damage ratios and financial losses increases the utility of the Engineering based Tornado

Damage Assessment (ETDA) tool.

Background

Hamid et al. (2010) describes the loss prediction methodology used in the FPHLM. They

have developed empirical relationship between the structural damage ratios, predicted by the

FPHLM and the economic loss ratios. Pinelli et al. (2005) studied the post disaster damage from

the 2004 hurricane season and developed empirical relationships between the exterior damage

ratios and interior damage ratios. Gurley et al. (2005) defined the damage to Mechanical

Electrical and Plumbing components as a constant product of interior damage ratio.

Cope (2004) estimated the replacement cost ratio for the building components for a single

story gable roof single family structure. Johnson (2015) and Weeks (2014) performed similar

studies and proposed cost weights for various external and internal building components.

Pinelli et al. (2009) performed benefit cost analysis on cost effectiveness of hurricane

mitigation measures for residential buildings using the Florida Public Hurricane Loss Prediction

Model (FPHLM). A combination of different mitigation measures was applied as a retrofit to an

existing house. They observed that all the mitigation techniques improved the performance of the

building but a few selected mitigation measures were economically viable.

69

Empirical equations provided in Pinelli et al. (2005) and described in the Table 5-3, are

used to develop a relationship between exterior and interior damage ratios. The maximum

interior damage ratio from these empirical equations in are used in the calculation of Mechanical,

Electrical and Plumbing (MEP) damage ratios. Sum of damage ratio and cost weight ratio is used

in the estimation of empirical loss ratio.

Insured Loss Data

The insurance company provided insured loss ratios for the 291 houses from the 26th

December, 2015 Garland / Rowlett, TX tornado incident. The insured loss ratio is defined as the

cost of repair to the initial cost of construction. This actuarial data is used in establishing

relationship between the empirically estimated loss ratio and the actual insured loss ratio. DOD

ratings are a measure of quantifying the tornado induced damage to a house on a scale of 0 to 10

(DOD 0 to DOD 10). This reduces the effect of individual house characteristic and show the

variation of loss ratios with damage state.

Figure 5-1 shows the variation of loss ratios with DOD ratings. As expected, the DOD

rating follows a cumulative distribution function. There is drastic increase in loss with the

increase in damage state. There is significant variability in the loss ratio associated with DOD 4.

The damage state 4 (DOD 4) is defined as "Uplift of roof deck and loss of significant roof

covering material (>20%); collapse of chimney; garage doors collapse inward or outward; failure

of porch or carport". Roof covering loss of 20% is a major contributor to this variability. DOD 4

does not distinguish between roof covering (shingles) and roof sheathing damage. Hence houses

with DOD 4 rating having only shingle damage have a very low loss ratio and a house with DOD

rating 4 with sheathing damage can have loss ratio up to 100%.

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Figure 5-1. Variation of the loss ratios for 291 houses with Degree of Damage (DOD) ratings

Relationship between Insured Loss Ratios and ETDA Predicted Damage Ratios

The ultimate objective for developing the ETDA tool is to predict insured loss ratios to

the houses in a what-if tornado scenario, quantifying the risk associated with a certain tornado

outbreak. Figure 5-2. shows the variation of the insured loss ratios for 291 houses obtained from

the local insurance company, with the ETDA estimated damage ratios. The loss ratios and the

ETDA predicted damage ratios are binned at 10% intervals.

The variation of the ETDA estimated damage ratios for all the eight components with the

actual insured loss ratio follow a similar trend. As expected, even at low roof to wall

connections, roof sheathing, wall cover and wall sheathing damage ratios, the loss ratios are very

high. At the same time, high estimated damage ratios for entry doors, garage doors, roof covers

and windows suggest a higher loss ratio. This suggests that higher loss ratio is observed even at

lower damage levels to these components.

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Figure 5-2. Variation of loss ratio with ETDA estimated damage ratio binned at 10% intervals

Replacement Cost Ratio

Replacement cost ratios help in establishing a link between the predicted structural

damage ratios and the corresponding economic losses. The replacement cost ratios can be

defined as the cost of replacing a damaged component of a house with respect to the cost of

reconstructing a the house (Cope 2004). Since the replacement cost ratios include the additional

costs of removal, repair and remodeling, the sum of the replacement cost ratios for all the

components of a house is generally greater than 100%. The relationship between exterior damage

and interior damage is generally independent of the type of loading. It is primarily based on

72

water damage and not the wind damage itself. Hence it can be assumed to be same as provided in

Pinelli et al. (2005).

The replacement cost ratios for a single family house are estimated in Cope (2004).

Table 5-1 and Table 5-2 show the structural and non-structural repair cost ratios (Cope 2004).

These replacement cost ratios are used in the estimation of empirical loss ratios.

Table 5-1. Structural repair cost ratios (Cope 2004)

Components % Weight

Roof Sheathing 5%

Roof Cover 7%

Trusses 9%

Exterior Walls 22%

Windows 4%

Shutters 2%

Exterior Doors 1%

Garage 1%

Total 51%

Table 5-2. Non-structural repair cost ratios (Cope 2004)

Components % Weight

Plumbing 10%

Mechanical 7%

Electrical 7%

Miscellaneous 35%

Total 59%

Estimation of Interior Damage

Once the damage to building envelope components are calculated using Monte Carlo

simulation, the internal, mechanical, electrical and plumbing, and contents damages to the

building are then estimated from the external damage to the building. There is no explicit means

to compute interior and utilities damage ratio. Damage to the interior and utilities occurs when

the building envelope is breached, allowing wind and rain to enter. Damage to roof sheathing,

roof cover, walls, windows and doors causes the highest interior damage.

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Pinelli et al. (2005) derived interior damage equations as functions of each of the sux

external building envelope components. These empirical equations are primarily developed on

the basis of experience and engineering judgment. These equations are validated using the data

from 2004 hurricane season.

Table 5-3. Interior damage equations; y=interior loss ratio; x = exterior (Pinelli et al. 2005)

Modeled Component Interior Equations

Roof Sheathing y = 1.29 * x

Roof Cover y = 0.62*x2-0.2*x

Wall and Wall Sheathing y = 7.91*x3-8.70*x2+4.21*x

Windows y = 0.39*x3+0.31*x2

Doors y = 0.26*x

Figure 5-3. Variation of loss ratio with ETDA estimated damage ratios

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The interior damage ratio estimation equations are applied to each of the six building

envelope components, one at a time. The maximum interior damage ratio predicted by these

empirical equations is the total interior damage ratio for each house. The maximum interior

damage ratio is used to avoid the duplication in counting of the interior damage ratios (Hamid et

al. 2010).

Gurley et al. (2005) defined the empirical relationship between interior damage ratios and

utilities damage ratios. Utilities damage is estimated as a fixed percent of interior damage ratio

and a fixed damage coefficient is defined for each MEP utility. These coefficients are based on

engineering judgment. Mechanical damage, electrical damage and plumbing damage ratios are

assumed as 0.4, 0.5 and 0.35 times the interior damage respectively.

Once the damage to all the components is estimated, the product of % damage to each

component and the cost weight of the component is added to give the empirical damage ratio to a

house. Hamid et al. (2010) defined the economic loss resulting from the damage to a structural

component is expressed as a percentage of the house’s value. The loss ratio for each component

can be obtained as a product of the percentage damage to the component with the replacement

cost ratio for the component. The value of the house can be estimated as a product of area and

per square feet cost of an average house in that region. For example, if 60% of the wall sheathing

is damaged, and for this particular house type the replacement ratio of wall sheathing is 5%, the

value of the house lost as a result of the damaged wall sheathing would be 0.60 x 0.05 = 3.0%. If

the value of this house were $120,000, the cost to replace 60% of the wall sheathing would be

$120,000 x 0.030 = $3,600.

Loss Ratio Estimation Methodology

Empirical loss estimation methodology, discussed in section 5.4 is demonstrated using

the data for one of the houses provided by the local insurance company. Observed damage ratios

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are used to estimate the interior damage ratios associated with each of the six building envelope

components as provided in Pinelli et al. (2005), one at a time. Table 5-4 shows the estimated

interior damage ratios from the ETDA estimated damage ratios. The total interior damage is the

maximum interior damage value produced by these equations.

Figure 5-4. 5834 Macgregor Drive, Garland, TX was assigned a DOD 6 rating and had insured

loss ratio of 137.35%

Table 5-4. Estimated interior damage ratios from ETDA estimated damage ratio

Component Observed Damage Ratio Interior Damage Ratio

Roof Sheathing 90% 116.1%

Roof Cover 90% 32.22%

Wall Cover 70% 139.71%

Wall Sheathing 70% 139.71%

Windows 100% 70%

Entry Doors 100% 26%

Maximum 139.71%

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Table 5-4 shows that the highest interior damage ratio of 139.71% as predicted by the

damage to the wall cover and wall sheathing. Using this interior damage ratio, MEP damage

ratios are estimated as shows in Table 5-5.

Table 5-5. MEP damage ratios

Component Relation Damage Ratio

Mechanical 0.4 * Max Interior Damage Ratio 58.8%

Electrical 0.5 * Max Interior Damage Ratio 73.5%

Plumbing 0.35 * Max Interior Damage Ratio 51.45%

The estimated damage ratios are multiplied with the respective cost weight ratio to

estimate the component wise loss ratio. Total loss ratio is the sum of all these component wise

loss ratios, as shown in Eq. 5-1 . Table 5-6 shows the component wise loss ratio for each

component.

𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝐿𝑜𝑠𝑠 𝑅𝑎𝑡𝑖𝑜 = ∑ 𝐷𝑎𝑚𝑎𝑔𝑒 𝑡𝑜 𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 × 𝑅𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡 𝐶𝑜𝑠𝑡 𝑅𝑎𝑡𝑖𝑜

(5-1)

Table 5-6. Empirical loss ratio for 5834 Macgregor Drive, Garland, TX

Component Damage ratio Replacement

cost ratio

Loss ratio

(damage ratio x replacement cost ratio)

Entry Door 100% 1% 1%

Garage Door 100% 1% 1%

Roof to Wall

Connections 60% 9% 6%

Roof Cover 90% 7% 6%

Roof Sheathing 90% 5% 5%

Wall Cover 70% 0% 0%

Wall Sheathing 70% 22% 16%

Windows 100% 4% 4%

Interior 140% 36% 50%

Mechanical 59% 7% 4%

Electrical 74% 7% 5%

Plumbing 51% 10% 5%

Sum Total

110% 103%

77

Results

The empirical loss estimation methodology demonstrated in section 5.5 is used to

estimate the empirical loss ratio for the dataset of 291 houses. The empirical loss ratios are

estimated using the visually observed damage ratios. The empirical loss ratios are graphed

against the actual insured loss ratios provided by the local insurance company as shown in Figure

5-5. The empirical loss ratios are in overall agreement with the actual insured loss ratios and

show an expected variation. The loss projection model underestimates the insured loss ratio.

Also the high variability for the 100% loss ratio mark reflects the effect of interior damage ratio

on the overall damage ratio for the structure.

Figure 5-5. Variation of insured loss ratios and estimated loss ratios. The solid red line

indicates ideal prediction and the scatter points represent the insured and the

estimated loss ratio for a house.

78

Empirical loss ratios are estimated using visually observed and ETDA estimated damage

ratios. The comparison of empirical loss ratios and actual insured loss ratios shown in Figure 5-5

does not take into account the individual variability of the houses. To overcome that, the loss

ratios are binned at 25m intervals. Figure 5-6 shows the spatial variation of the loss ratios with

the distance away from the tornado center line. The plot between actual insured loss ratio and

loss ratio estimated from observed damage ratio seem "parallel" suggesting a proportional

multiplicative factor is required. The bump in interval 212.5m is due to houses in that region

having 40% loss ratio. These are the houses with observed DOD rating of 4. The spatial variation

of the Loss Ratios from ETDA Estimated DRs show a steeper fall for the houses outside the

RMW, mimicking the variation of ETDA estimated damage ratio for most components

Figure 5-6. Spatial variation of insured loss ratios, estimated loss ratios using observed

damage ratios and estimated loss ratios using ETDA estimated damage ratios

79

CHAPTER 6

CONCLUSIONS

It is a commonly-held belief that tornado-resilient communities are not economically

feasible because tornadoes themselves are such powerful natural phenomena. Recent research

now suggests this is not the case, and that despite creating extremely high winds near the center,

the tornado intensity reduces with distance away from the center to levels permitting feasible

design. Nevertheless, post-damage assessment studies that have documented wide-spread,

catastrophic damage appear to support the contention of uniformly extreme forces in tornadoes.

Within the past six years, new studies have proposed why such damage occurs and they provide

potential solutions. The residential infrastructure that is affected by tornadoes is designed to such

low structural capacity that it makes the near complete destruction of communities almost

inevitable by any but the least powerful tornado. This research has demonstrated the use of a

mathematical tool that can predict tornado-induced damage to a community.

The ETDA output is validated using damage observations from 82 houses inspected

following the 22nd May 2011 Joplin, MO tornado. Greater differences between predicted and

observed damage ratios were observed when the distance of the house from tornado center line

was less than the radius of maximum winds. This suggests that the ETDA tool is accurate at

predicting damage from straight line winds, but may need more refinement to accurately predict

damage within the tornado core. The AUC scores were found to be in the range of 0.86 to 0.99

suggesting the tool to have excellent damage prediction capabilities.

The ETDA tool, developed for the 22nd May Joplin, MO tornado was scaled down and

applied to the Garland / Rowlett tornado. 712 houses surveyed after the 26 December 2015,

Garland, TX tornado were used to validate the ability of the ETDA tool to replicate any tornado.

The AUC scores were slightly lower than for the Joplin dataset in the range of 0.83 to 0.89,

80

suggesting the ETDA tool can predict the damage to other tornado locations and scenarios.

Hence the ETDA tool can be used to estimate tornado induced damages in any part of the United

States.

The ETDA tool is a validated tornado-risk assessment tool capable of predicting

structural damage and resulting economic losses to residential structures. Within a reasonable

uncertainty the overall patterns and magnitudes of damage were predicted.

Insured loss ratio were found to mimic the decay in tornado wind speeds away from the

tornado centerline. Trends between the insured loss ratios and the structural damages is

successfully established using the claims data provided by the insurance company. The

vulnerability component for the ETDA tool successfully predicts the spatial trends in loss ratio of

residential structures under tornado-induced wind loads.

Future Scope

Even though the ETDA tool is validated and can accurately predict the tornado induced

damages and losses, there is scope for improvement. As of now, the ETDA tool is limited by the

use of single prototype house model. Multiple house models with variation in plan aspect ratio

and roof type can be included in the ETDA tool to improve its ability to replicate the structures

in a community.

In its current form, the ETDA tool does not have the ability to use the parallel computing

capabilities and reduce the computation time. The tool can be updated to run on parallel

processors.

Additionally, empirical damage ratios and a deterministic loss ratio estimation model is

proposed in this research. A probabilistic vulnerability component can be developed to take into

account the uncertainty in the loss prediction component.

81

Applications

The validated ETDA tool enables insurance companies and other risk-analysis companies

to check their proprietary models against an engineering-based model. And it also enables

scenario-based analysis for educating communities on the potential impacts they face from

tornadoes.

The validated ETDA tool unlocks immense potential in tornado risk assessment and

quantification of potential savings from improved construction practices. The tool can further be

used to estimate the potential risk for different type of construction practices. Additionally, it can

be used to quantify the reduction in damage risk from tornado with tornado resilient retrofitting.

Further, this data can be used to estimate the reduction in predicted risk with the increased cost

of retrofit.

82

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BIOGRAPHICAL SKETCH

Mr. Arpit A. Bhusar was raised in Amravati, a city located in the Maharashtra state in

central India. He received his bachelor’s degree in Civil Engineering from the Government

College of Engineering, Amravati (GCOE Amravati) in August 2015. After graduating, he

immediately joined the Department of Civil and Coastal Engineering at the University of Florida

as a Master’s student under the guidance of Dr. David O. Prevatt. He received his master’s

degree in civil engineering with concentration in structural engineering from the University of

Florida in the May of 2017.

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