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UNIVERSITY of CALIFORNIA Los Angeles a Data-Driven Building Seismic Response Prediction Framework

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UNIVERSITY of CALIFORNIA Los Angeles a Data-Driven Building Seismic Response Prediction Framework UNIVERSITY OF CALIFORNIA Los Angeles A Data-driven Building Seismic Response Prediction Framework: from Simulation and Recordings to Statistical Learning A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Civil Engineering by Han Sun 2019 © Copyright by Han Sun 2019 ABSTRACT OF THE DISSERTATION A Data-driven Building Seismic Response Prediction Framework: from Simulation and Recordings to Statistical Learning by Han Sun Doctor of Philosophy in Civil Engineering University of California, Los Angeles, 2019 Professor John Wright Wallace, Chair Structural seismic resilience society has been grown rapidly in the past three decades. Extensive probabilistic techniques have been developed to address uncertainties from ground motions and building systems to reduce structural damage, economic loss and social impact of buildings subjected to seismic hazards where seismic structural responses are essential and often are retrieved through Nonlinear Response History Analysis. This process is largely limited by accuracy of model and computational effort. An alternative data-driven framework is proposed to reconstruct structure responses through machine learning techniques from limited available sources which may potentially benefit for “real-time” interpolating monitoring data to enable rapid damage assessment and reducing computational effort for regional seismic hazard assessment. It also provides statistical insight to understand uncertainties of seismic building responses from both structural and earthquake engineering perspective. ii The dissertation of Han Sun is approved. Henry J. Burton Ali Mosleh Jonathan Paul Stewart John Wright Wallace, Committee Chair University of California, Los Angeles, 2019 2019 iii To my parents … For their encouragement on science iv Table of Contents ACKNOWLEDGEMENT ................................................................................................ XVI VITA ................................................................................................................................. XVII 1. INTRODUCTION .............................................................................................................. 1 1.1 Overview ............................................................................................................................... 1 1.2 Research Significance ........................................................................................................... 3 1.3 Outline of the Thesis ............................................................................................................. 4 2. A CRITICAL EXAMINATION OF THE VIABILITY OF USING ML TO ADDRESS STRUCTURAL ENGINEERING ............................................................................................... 6 2.1 Introduction ........................................................................................................................... 6 2.2 Brief Introduction of Machine Learning ............................................................................... 8 2.2.1 General Formulation of ML Setting .............................................................................. 8 2.2.2 Feature Engineering ..................................................................................................... 10 2.2.3 Model Training and Performance Metrics ................................................................... 12 2.3 Motivation of Using Machine Learning in Structural Engineering Field ........................... 12 2.4 Examples of Machine Learning Applications in structural engineering ............................ 16 2.4.1 Predicting Structural Nonlinear Responses and Damage ............................................ 16 2.4.2 Experimental Data Interpretation and Empirical Fitting ............................................. 17 2.4.3 Information Extraction from Visual Media ................................................................. 18 2.4.4 Pattern Recognition for Structural Health Monitoring ................................................ 19 2.5 Discussion in Applying Machine Learning to Structural Engineering ............................... 20 2.5.1 Data Source .................................................................................................................. 21 2.5.2 Model Interpretability .................................................................................................. 22 2.5.3 Model Extrapolation Ability ........................................................................................ 23 2.6 Summary ............................................................................................................................. 23 3. INTER-BUILDING INTERPOLATION OF PEAK SEISMIC RESPONSE USING SPATIALLY CORRELATED DEMAND PARAMETERS .................................................. 26 3.1 Introduction ......................................................................................................................... 26 v 3.2 Measuring Structural Response Correlation for A Portfolio of Buildings Subjected A Scenario Earthquake ................................................................................................................. 30 3.2.1 Scenario Earthquakes and Ground Motions ................................................................ 30 3.2.2 Structural Models, Response Simulations and Engineering Demand Parameters ....... 32 3.3 Spatial Correlation of Peak EDPs from Structural Response Simulations ......................... 35 3.4 Modeling Spatial Correlation of EDPs Using Semi-variograms ........................................ 44 3.5 Kriging Model for Interpolating Peak Structural Responses .............................................. 53 3.6 Summary ............................................................................................................................. 64 4. RECONSTRUCTING SEISMIC RESPONSE DEMANDS ACROSS MULTIPLE TALL BUILDINGS USING KERNEL-BASED MACHINE LEARNING METHODS ..... 68 4.1 Introduction ......................................................................................................................... 68 4.2 Scenario Earthquake and Ground Motions ......................................................................... 71 4.3 Description of Buildings and Structural Modeling ............................................................. 72 4.3.1 Building Descriptions .................................................................................................. 72 4.3.2 Structural Modeling ..................................................................................................... 75 4.4 Nonlinear Strucutral Response Simulation ......................................................................... 76 4.5 Description of Machine Learning Models .......................................................................... 81 4.5.1 Statistical Model Design .............................................................................................. 81 4.5.2 Ordinary Least Squares ................................................................................................ 83 4.5.3 Kernel Ridge Regression ............................................................................................. 84 4.5.4 Kernel Support Vector Regression .............................................................................. 87 4.5.5 Model Parameter Tuning ............................................................................................. 89 4.6 Application and Performance Evaluation of Machine Learning Models ........................... 90 4.6.1 Evaluating Relative Importance of Features (Predictors) ............................................ 90 4.6.2 Model Evaluation ......................................................................................................... 91 4.6.3 Baseline Scenario Dataset ............................................................................................ 92 4.6.4 Effect of Within-Building Sensor Location on Model Performance ........................... 99 4.6.5 Effect of Response Demand Level of Model Performance ....................................... 101 4.7 Summary ........................................................................................................................... 103 5. A GENERALIZED CROSS-BUILDING ENGINEERING DEMAND PARAMETER RECONSTRUCTION MODEL .............................................................................................. 106 5.1 Introduction ....................................................................................................................... 106 vi 5.2 Description of Data ........................................................................................................... 111 5.2.1 Data Source ................................................................................................................ 111 5.2.2 Transfer recordings to EDP ....................................................................................... 114 5.3 Model Formation and Determination of Coefficients ....................................................... 119 5.3.1 Mix Effect Model ....................................................................................................... 119 5.3.2 Meta Data Retrieval ................................................................................................... 121 5.3.3 2-stage Regression ....................................................................................................
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