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The Application of Advanced Inventory Techniques in Urban Inventory Data Development to Earthquake Risk Modeling and Mitigation in Mid- America

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The Application of Advanced Inventory Techniques in Urban Inventory Data Development to Earthquake Risk Modeling and Mitigation in Mid- America THE APPLICATION OF ADVANCED INVENTORY TECHNIQUES IN URBAN INVENTORY DATA DEVELOPMENT TO EARTHQUAKE RISK MODELING AND MITIGATION IN MID- AMERICA A Dissertation Presented to The Academic Faculty by Subrahmanyam Muthukumar In partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in City and Regional Planning in the College of Architecture Georgia Institute of Technology December, 2008 COPYRIGHT 2008 BY SUBRAHMANYAM MUTHUKUMAR THE APPLICATION OF ADVANCED INVENTORY TECHNIQUES IN URBAN INVENTORY DATA DEVELOPMENT TO EARTHQUAKE RISK MODELING AND MITIGATION IN MID- AMERICA Approved by: Dr. Steven P. French, Advisor Dr. Patrick McCarthy College of Architecture School of Economics Georgia Institute of Technology Georgia Institute of Technology Dr. William Drummond Dr. Barry Goodno College of Architecture School of Civil and Environmental Georgia Institute of Technology Engineering Georgia Institute of Technology Dr. Jiawen Yang College of Architecture Georgia Institute of Technology Date Approved: September 4, 2008 To my wife, Suchita ACKNOWLEDGEMENTS Developing the research and then writing a dissertation is an arduous task and patently not possible without the personal assistance of numerous people. First and foremost, the author is completely beholden to his wife for her staunch and constant support throughout the entire process. There are really no words that can convey the author’s gratefulness towards his wife and daughter for their tireless sacrifice and patience during this period. The author also wishes to express his gratitude to his parents and brother for their unceasing love and encouragement throughout the course of his educational career. The author would also like to gratefully acknowledge the advice, support, encouragement and inspiration provided by Dr. Steven French throughout his tenure at Georgia Tech. Without his guidance, this dissertation would not have been possible. Thanks are also expressed to the remaining members of the author’s dissertation committee – Drs. William Drummond, Barry Goodno, Patrick McCarthy and Jiawen Yang for their time, comments and innumerable educational inputs. Special thanks go to Dr. Linda Thomas-Mobley for her invaluable assistance in the development of the research. The author also wishes to extend his special thanks to Ning Ai, Sarajoy Crewe and Liora Sahar for their sincere willingness to share their considerable knowledge and their timely assistance and friendship. The author is also grateful to all his friends, too numerous to list here, who have in one way or another encouraged him to persevere and complete the dissertation. Finally, gratitude is expressed to the Mid-America Earthquake Center – this research was supported by the Mid-America Earthquake Center under National Science Foundation Grant EEC-9701785 iv TABLE OF CONTENTS ACKNOWLEDGEMENTS ................................................................................................ iv LIST OF TABLES..............................................................................................................x LIST OF FIGURES......................................................................................................... xiv LIST OF ABBREVIATIONS............................................................................................xvii SUMMARY....................................................................................................................xviii CHAPTER 1 . INTRODUCTION .......................................................................................1 1.1. BACKGROUND FOR DISASTER MITIGATION.....................................................................3 1.2. THE NEED FOR ACCURATE URBAN INVENTORIES............................................................8 1.3. HAZARD MITIGATION IN THE PLANNING PROCESS FRAMEWORK ..................................10 1.4. EXISTING METHODS FOR URBAN INVENTORY DATA COLLECTION AND LIMITATIONS......15 1.4.1. Urban Inventory Data Sources...........................................................................16 1.4.2. Classification of the Urban Building Inventory ................................................17 1.4.3. Building Inventory Development in HAZUS MR-3............................................18 1.5. ADVANCED INVENTORY TECHNOLOGIES AND TECHNIQUES FOR DATA COLLECTION.....24 1.5.1. Remote Sensing Technologies..........................................................................24 1.5.2. Building Inventory Estimation Methods............................................................26 1.5.3.1. Knowledge-based Rules ....................................................................................27 1.5.3.2. Classification Models .........................................................................................28 1.6. THE EARTHQUAKE MODELING PROCESS REQUIREMENTS ...........................................29 1.7. DESCRIPTION OF RESEARCH ......................................................................................32 1.7.1. Research Statement............................................................................................32 1.7.2. Research Goals and Objectives ........................................................................32 1.7.3. Significance of the Research Effort ..................................................................33 1.7.4. Scope of Research..............................................................................................36 1.8. ORGANIZATION OF DISSERTATION ..............................................................................36 CHAPTER 2 . LITERATURE REVIEW...........................................................................39 2.1. PATTERN RECOGNITION AND THE POTENTIAL FOR AUTOMATION .................................40 2.2. MULTINOMIAL LOGISTIC REGRESSION FOR CLASSIFICATION .......................................43 2.3. ARTIFICIAL NEURAL NETWORK SOLUTIONS FOR CATEGORICAL DATA ANALYSIS.........45 v 2.3.1. A historical perspective on Artificial Neural Networks....................................46 2.3.2. What is an Artificial Neural Network? ...............................................................47 2.3.3. Transfer Functions..............................................................................................52 2.3.4. Applications of Artificial Neural Networks........................................................53 2.3.4.1. Function approximation......................................................................................54 2.3.4.2. Time series analysis and prediction ...................................................................54 2.3.4.3. Classification ......................................................................................................54 2.3.4.4. Data mining ........................................................................................................55 2.3.5. Why use Artificial Neural Networks? ................................................................56 2.3.6. Artificial Neural Network topologies for classification....................................57 2.3.6.1. Neural Computing for Classification...................................................................58 2.3.6.2. Discriminant Functions.......................................................................................58 2.3.7. Conceptual issues in designing and training Artificial Neural Networks......60 2.3.7.1. Error minimization search procedures ...............................................................61 2.3.7.2. Learning rate ......................................................................................................63 2.3.7.3. Learning algorithms............................................................................................64 2.3.7.4. Processing elements in the hidden layer ...........................................................66 2.3.7.5. Stop criteria ........................................................................................................66 2.3.7.6. Performance Measures......................................................................................68 2.4. SHAPE RECOGNITION BACKGROUND, TECHNIQUES AND APPLICATIONS ......................69 2.4.1. Definition of a Shape ..........................................................................................72 2.4.2. The Process of Shape Analysis.........................................................................73 2.4.2.1. Shape Acquisition ..............................................................................................74 2.4.2.2. Shape Representation .......................................................................................76 2.4.2.3. Feature Extraction or Shape Description after Shape Representation ..............78 2.4.2.4. Invariant Representations ..................................................................................78 2.4.2.5. Statistical and Mathematical Approaches ..........................................................86 2.4.2.6. Structural and Syntactic Methods ......................................................................89 2.4.2.7. Syntactic Shape Recognition .............................................................................96 2.4.2.8. Shape Recognition and Classification................................................................97 2.5. GEOMETRY MANIPULATIONS IN THE GIS ENVIRONMENT..............................................98 2.5.1. Representation of Points, Lines and Regions in GIS ......................................99
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