Damage diagnosis algorithms using statistical pattern recognition for civil structures subjected to earthquakes [electronic resource]

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 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 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.

Advisor: Kiremidjian Anne S. (Anne Setian) primary advisor. Thesis advisor
Baker Jack W. advisor. Thesis advisor
Law K. H. (Kincho H.) advisor. Thesis advisor