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Damage Diagnosis Algorithms Using Statistical Pattern Recognition for Civil Structures Subjected to Earthquakes

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Damage Diagnosis Algorithms Using Statistical Pattern Recognition for Civil Structures Subjected to Earthquakes Department of Civil and Environmental Engineering Stanford University Damage Diagnosis Algorithms using Statistical Pattern Recognition for Civil Structures Subjected to Earthquakes By Hae Young Noh and Anne S. Kiremidjian Report No. 180 June 2013 The John A. Blume Earthquake Engineering Center was established to promote research and education in earthquake engineering. Through its activities our understanding of earthquakes and their effects on mankind’s facilities and structures is improving. The Center conducts research, provides instruction, publishes reports and articles, conducts seminar and conferences, and provides financial support for students. The Center is named for Dr. John A. Blume, a well-known consulting engineer and Stanford alumnus. Address: The John A. Blume Earthquake Engineering Center Department of Civil and Environmental Engineering Stanford University Stanford CA 94305-4020 (650) 723-4150 (650) 725-9755 (fax) [email protected] http://blume.stanford.edu ©2013 The John A. Blume Earthquake Engineering Center Abstract In order to prevent catastrophic failure and reduce maintenance costs, the demands for the automated monitoring of the performance and safety of civil structures have increased significantly in the past few decades. In particular, there has been extensive research in the development of wireless structural health monitoring systems, which enable dense installation of sensors on structural systems with low installation and maintenance costs. The main challenge of these wireless sensing units is to reduce the amount of data that need to be transmitted wirelessly because the wireless data transmission is the major source of power consumption. This dissertation introduces various damage diagnosis algorithms that use statistical pattern recognition methods at sensor level. Therefore, these algorithms do not require massive transmission of data, and thus are particularly beneficial for use in wireless sensing units. Although damage diagnosis algorithms for structural health monitoring have existed for several decades, statistical pattern recognition techniques have been applied in this field only in the past decade. This approach is receiving increasing recognition for its computational efficiency, which is required when embedding such algorithms in wireless sensing units. These algorithms can use either stationary ambient vibration responses before and after the damage or non- stationary strong motion responses such as earthquake responses. In the first part of this dissertation, three algorithms are introduced for damage diagnosis using ambient vibration responses. Each vibration response is modeled as a time-series with distinct parameters, which are closely related to the structural parameters. Damage iv diagnosis is performed by classifying the combinations of these parameters into damage states using three statistical pattern recognition methods. The algorithms are validated using the experimental data obtained from the benchmark structure of the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan, and the results show that these algorithms can detect damage while more improvement is necessary for damage localization. The second part of the dissertation introduces a wavelet-based damage diagnosis algorithm that uses non-stationary strong motion responses. Wavelet energies of each response are extracted from various frequencies at different instances, and three damage sensitive features are defined on the basis of the extracted wavelet energies. These features are probabilistically mapped to damage states using fragility functions. The framework to develop these wavelet damage sensitive feature-based fragility functions is also discussed. The efficiency and robustness of the damage sensitive features are validated using the two sets of experimental data: 30% scaled reinforced concrete bridge column tests in Reno, Nevada, and 1:8 scale model of a four-story steel special moment- resisting frame tests at the State University of New York at Buffalo. The performance of the fragility functions to classify damage is validated using the numerically simulated data obtained from the analytical model of the four-story steel special moment-resisting frame. The results show that the wavelet-based features are closely related to structural damage and the fragility functions can efficiently classify the damage state from the features. The last part of the dissertation discusses a data compression method using a sparse representation algorithm. This method constructs a set of bases to represent each structural response as their weighted sum. By creating an over-complete set of bases, the responses can be represented using a few number of bases (i.e., sparse representation). This method can reduce the amount of data to transmit and save the power consumption of the wireless sensing units. This method enables the entire transmission of response data to a server computer, and more sophisticated analysis of the data can be performed v in global level. The method is validated using the white noise experimental data collected from the four-story steel special moment-resisting frame tests at the State University of New York at Buffalo, and significant compression ratio is achieved for upper floors while maintain the information. vi Acknowledgments This research was supported primarily by the Samsung Scholarship and the John A. Blume Research Fellowship. Additional funding was provided by the National Science Foundation – Civil, Mechanical, and Manufacturing Innovation Grant No. 0800932, and the National Science Foundation – George E. Brown, Jr. Network for Earthquake Engineering Simulation Research Grant No. 15BBK16379. The support provided by these organizations is greatly appreciated. This report was originally published as the Ph.D. dissertation of the first author. The authors would like to thank Kincho H. Law, Jack W. Baker, Ram Rajagopal, Helmut Krawinkler, and Gregory G. Deierlein for their insightful comments and constructive feedback on the manuscript. The authors would also like to acknowledge Professor C-H. Loh (National Taiwan University) and the National Center for Research on Earthquake Engineering (NCREE) for providing the data collected from the Taiwanese benchmark experiment conducted at the NCEE, Taipei, Taiwan; Dr. Hoon Choi (URS Corp.), Professor M.“Saiid” Saiidi (University of Nevada, Reno) and Dr. Paul Somerville (URS Corp.) for providing the data collected from the reinforced concrete bridge column experiment conducted at the University of Nevada, Reno; and Professor Dimitrios G. Lignos (Mcgill University) and Professor Krawinkler (Stanford University) for providing the data collected from the four-story steel special moment-resisting frame experiment conducted at the State University of New York, Buffalo. vii viii Table of Contents Abstract iv Acknowledgments vii List of Tables xii List of Figures xv 1 Introduction 1 1.1 Motivation .........................................................................................................1 1.2 Objectives..........................................................................................................4 1.3 Overview ...........................................................................................................5 2 Time-Series Based Damage Diagnosis Algorithm Using Ambient Vibration Data 7 2.1 Introduction .......................................................................................................8 2.2 Description of Damage Diagnosis Algorithms ...............................................10 2.2.1 Data Conditioning .................................................................................12 2.2.2 Time-Series Modeling of Vibration Data ..............................................14 2.2.3 Damage Diagnosis Algorithms ..............................................................19 2.2.3.1 Algorithm 1: AR Model with Hypothesis Tests ......................19 2.2.3.2 Algorithm 2: AR Model with Gaussian Mixture Models ........21 2.2.3.3 Algorithm 3: AR Model with Information Criteria .................24 2.3 Application of the Damage Diagnosis Algorithms to Experimental Data Using the Taiwanese Benchmark Structure ....................................................28 ix 2.3.1 Description of Experiment ....................................................................28 2.3.2 Results and Discussion ..........................................................................29 2.3.2.1 Algorithm 1: AR Model with Hypothesis Tests ......................29 2.3.2.2 Algorithm 2: AR Model with Gaussian Mixture Models ........41 2.3.2.3 Algorithm 3: AR Model with Information Criteria .................46 2.4 Conclusions .....................................................................................................50 3 Wavelet-Based Damage Sensitive Features for Structural Damage Diagnosis Using Strong Motion Data 54 3.1 Introduction .....................................................................................................55 3.2 Development of Wavelet-Based Damage Sensitive Features .........................58 3.2.1 Wavelet Transformation and Wavelet Energies ....................................58 3.2.2 Definition of Damage Sensitive Features ..............................................66 3.2.2.1 DSF1 ........................................................................................66 3.2.2.2 DSF2 ........................................................................................67
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