Structural damage detection using signal-based pattern recognition

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dc.contributor.author Qiao, Long
dc.date.accessioned 2009-05-06T16:34:25Z
dc.date.available 2009-05-06T16:34:25Z
dc.date.issued 2009-05-06T16:34:25Z
dc.identifier.uri http://hdl.handle.net/2097/1385
dc.description.abstract Civil structures are susceptible to damages over their service lives due to aging, environmental loading, fatigue and excessive response. Such deterioration significantly affects the performance and safety of structure. Therefore, it is necessary to monitor the structural performance, detect and assess damages at the earliest possible stage in order to reduce the life-cycle cost of structure and improve its reliability. Over the last two decades, extensive research has been conducted on structural health monitoring and damage detection. In this study, a signal-based pattern-recognition method was applied to detect structural damages with a single or limited number of input/output signals. This method is based on the extraction of sensitive features of the structural response under a known excitation that present a unique pattern for any particular damage scenario. Frequency-based features and time-frequency-based features of the acceleration response were extracted from the measured vibration signals by Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) to form one-dimensional or two-dimensional patterns, respectively. Three pattern recognition algorithms were investigated when performing pattern-matching: (1) correlation, (2) least square distance, and (3) Cosh spectral distance. To demonstrate the validity and accuracy of the method, numerical and experimental studies were conducted on a simple small-scale three-story steel building. In addition, the efficiency of the features extracted by Wavelet Packet Transform (WPT) was examined in the experimental study. The results show that the features of the signal for different damage scenarios can be uniquely identified by these transformations. Suitable correlation algorithm can then be used to identify the most probable damage scenario. The proposed method is suitable for structural health monitoring, especially for the online monitoring applications. Meanwhile, the choice of wavelet function affects the resolution of the detection process and is discussed in the “experimental study part” of this report. en
dc.language.iso en_US en
dc.publisher Kansas State University en
dc.subject Damage detection en
dc.subject Pattern recognition en
dc.title Structural damage detection using signal-based pattern recognition en
dc.type Dissertation en
dc.description.degree Doctor of Philosophy en
dc.description.level Doctoral en
dc.description.department Department of Civil Engineering en
dc.description.advisor Asadollah Esmaeily en
dc.subject.umi Engineering, Civil (0543) en
dc.date.published 2009 en
dc.date.graduationmonth May en


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