This presents a comprehensive review of the applications in time, frequency, and time-frequency domains of signal processing techniques for damage perception, localization, and quantification in various structural systems. It covers a range of experimental techniques and introduces ANNs for automated damage identification.
This presents a comprehensive review of the applications in time, frequency, and time-frequency domains of signal processing techniques for damage perception, localization, and quantification in various structural systems. It covers a range of experimental techniques and introduces ANNs for automated damage identification.
Chunwei Zhang is a Distinguished Professor and Du Xing Endowed Scholar at South China University of Technology. He is the Founding Director of the Multidisciplinary Center for Infrastructure Engineering (MCIE) at Shenyang University of Technology, and the Founding Director of the Structural Vibration Control (SVC) Group at Qingdao University of Technology, China. His research achievements and worldwide impact have been recognized by the international academic community and his inventions implemented in practice, such as in the active control system for the Canton tower. He is also a commended author of several CRC Press books and proceedings. Asma A. Mousavi is a postdoctoral researcher at South China University of Technology.
Inhaltsangabe
1. Introduction. 2. Methodology and Approaches. 3. Implementation of the Proposed Approaches in Structural Damage Detection and Quantification. 4. Implementation of the Proposed Approaches in Structural Damage Localization. 5. Experimental Verification of the Proposed Artificial Neural Network Aided Approaches in Structural Damage Identification. 6. Conclusions.
1. Introduction. 2. Methodology and Approaches. 3. Implementation of the Proposed Approaches in Structural Damage Detection and Quantification. 4. Implementation of the Proposed Approaches in Structural Damage Localization. 5. Experimental Verification of the Proposed Artificial Neural Network Aided Approaches in Structural Damage Identification. 6. Conclusions.
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