A PREDICTIVE MODEL FOR STRUCTURAL TORNADO DAMAGE TO RESIDENTIAL STRUCTURES USING HOUSING DATA FROM THE 2011 JOPLIN, MO TORNADO By ALYSSA C EGNEW A THESIS PRESENTED TO THE UNDERGRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR SUMMA CUM LAUDE IN ENGINEERING UNIVERSITY OF FLORIDA 2016 © 2016 Alyssa C Egnew 2 To my Lord and Savior, Jesus Christ, in whom I can do all things, and to my parents who keep me sane in the meantime. 3 ACKNOWLEDGMENTS I would like to thank Dr. David O. Prevatt for the opportunity to work under his supervision with the UF Wind Hazard Damage Assessment Group, and David Roueche for his indispensable guidance through this research project. I would like to thank Dr. Kurtis Gurley and Dr. Curtis Taylor for agreeing to be on the panel for the thesis defense. I would also like to thank all of those who helped augment the database, specifically Malcolm Ammons, Austin Bouchard, and Jeandona Doreste. And a final thank you to the UF Center for Undergraduate Research, which funded this investigation through the University Scholars Program. 4 TABLE OF CONTENTS page ACKNOWLEDGMENTS ............................................................................................................. 4 LIST OF TABLES ......................................................................................................................... 6 LIST OF FIGURES ....................................................................................................................... 7 LIST OF ABBREVIATIONS ........................................................................................................ 8 ABSTRACT ................................................................................................................................... 9 INTRODUCTION ....................................................................................................................... 10 BACKGROUND ......................................................................................................................... 14 METHODOLOGY ...................................................................................................................... 16 RESULTS .................................................................................................................................... 20 CONCLUSION ............................................................................................................................ 35 FUTURE WORK ......................................................................................................................... 36 REFERENCES ............................................................................................................................ 37 APPENDIX A: Metadata on the 488 Homes Used in this Study .............................................................. 40 B: MATLAB R2015 Script Used to Generate Graphs of Parameters .................................. 49 C: MATLAB R2015 Script Used to Generate Multinomial Logistic Regression with Random Input ....................................................................................................................... 51 D: MATLAB R2015 Script Used to Generate Multinomial Logistic Regression with Significant Parameters Only ................................................................................................. 52 5 LIST OF TABLES Table page 1 EF-Scale Wind Speed Ranges .............................................................................................. 15 2 Degree of Damage Description ............................................................................................ 16 3 Building Code History of Joplin, Missouri ........................................................................... 26 4 Y-Intercepts and Coefficients from the Proportional Odds Model ...................................... 31 5 Testing the Proportional Odds Likelihood of Damage Output ............................................. 32 6 Comparison of Findings to Literature ................................................................................... 33 6 LIST OF FIGURES Figure page 1 Screenshot of the Beacon GIS Site ....................................................................................... 17 2 Example of Geo-Tagged Survey Imagery ............................................................................ 18 3 Screenshot of Google Street View® imagery ....................................................................... 18 4 Square Footage Histogram ................................................................................................... 21 5 Appraised Value Histogram ................................................................................................. 22 6 Year Built Histogram ............................................................................................................ 22 7 Number of Homes with One Story Versus Two Stories ....................................................... 23 8 Distance from Tornado Centerline Histogram ..................................................................... 23 9 Degree of Damage Bar Graph .............................................................................................. 24 10 Comparison of Year Built Between the Sample Set and the Entire Population ................... 25 11 Percentage of Homes Built Under Each New Building Code Adoption .............................. 27 12 Linear Regression of Degree of Damage and Number of Stories ........................................ 28 13 Linear Regression of DOD and Wind Speed ........................................................................ 28 14 Linear Regression of DOD and Appraised Value ................................................................ 29 15 Linear Regression of DOD and Square Footage .................................................................. 29 16 Linear Regression of DOD and Year Built ........................................................................... 30 7 LIST OF ABBREVIATIONS EF-Rating Enhanced Fujita scale rating of a tornado’s severity; used when estimating wind speeds based on damage to a particular Damage Indicator DOD-rating Degree of Damage rating for a particular structure type relating its level of structural damage to a wind speed range 8 Abstract of Thesis Presented to the Undergraduate School of the University of Florida in Partial Fulfillment of the Requirements for Summa Cum Laude in Engineering A PREDICTIVE MODEL FOR STRUCTURAL TORNADO DAMAGE TO RESIDENTIAL STRUCTURES USING HOUSING DATA FROM THE 2011 JOPLIN, MO TORNADO By Alyssa C Egnew March 2016 Chair: David O. Prevatt Major: Civil Engineering On May 22, 2011, an EF-5 tornado struck Joplin, Missouri, leaving 158 fatalities and over 1000 injuries in its aftermath (National Weather Service, 2011, para. 1-2). The damaged residential structures were surveyed by a reconnaissance team under Dr. David O. Prevatt just days later. This investigation used the extracted empirical data from the tornado path to find trends in the damage for future applications. In order to use the collected information, the database of damaged homes first needed to be augmented, one by one, to contain street addresses and parcel IDs that could be cross referenced with the Jasper County Tax Assessor’s database of house characteristics. In this way, the year built, appraised value, square footage, and number of stories for each home was obtained for a final sample size of 488 homes. The maximum wind speed encountered by each home was assigned using each home’s distance from the tornado centerline (from the geo-tagged imagery taken by the reconnaissance team) and the near-surface wind model of the Joplin tornado developed by Lombardo, Roueche, and Prevatt (2015). To see how each of these parameters influences the expected level of damage in a home, a multinomial logistic regression (“proportional odds model”) with an ordinal response was developed for this dataset. A multiple linear regression could not have been used because of the non-continuous categories of degree of damage (as described by McDonald, Mehta, and Mani, 2004). Hence, a proportional odds model best fit the data type and allowed for the varying wind speed with distance from the tornado centerline to be more accurately accounted for. The output of this model is the odds of the expected level of damage being less than or equal to a reference damage level, as compared to greater than that reference level. According to the model, the number of stories in a home was rejected as being a significant predictor of damage level. However, an increase in year built, wind speed, and square footage all result in greater odds of having a higher state of damage. On the other hand, an increase in appraised value results in lower odds of having a higher state of damage. The most influential parameter on damage was the one with the opposite trend, appraised value. These results can be further investigated regarding the underlying correlations to other parameters; perhaps the appraised value of the house is significant because of better maintenance, which allows it to perform better in a tornado than a house with decay. This proportional odds model, when coupled with other related studies, would be useful for city officials to forecast weak residential areas in their cities. 9 Introduction Tornadoes are one of the most potent natural disasters experienced in the United States. From 1993 to 2012, tornadoes accounted for