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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Chunwei Zhang is a Chair Distinguished Professor at Shenyang University of Technology. He is the Founding Director of the Multidisciplinary Center for Infrastructure Engineering (MCIE) at Shenyang University of Technology, and of the Structural Vibration Control (SVC) Group at Qingdao University of Technology, China. His research achievement and worldwide impact have been highly recognized by the international academia society, as evidenced by the continuous inclusions into the prestigious rankings, such as the Clarivate Highly Cited Researcher, Elsevier Most Cited Chinese Researcher, and Stanford World Top 2% Scientists, among others. Apart from publications, his inventions have been implemented in the engineering practice as evidenced by the active control system for the Canton Tower structure. He is also a commended author of CRC Press published books and proceedings. Asma A. Mousavi is a 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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