A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics
Accurate structural damage identification calls for dense sensor networks, which are becoming more feasible as the price of electronic sensing systems reduces. To transmit and process data from all nodes of a dense network is a computationally expensive BIG DATA problem; therefore scalable algorithms are needed so that inferences about the current state of the structure can be made efficiently. In this paper, an iterative spatial compressive sensing scheme for damage existence identification and localization is proposed and investigated. At each iteration, damage existence is identified from randomly collected sparse samples and damage localization is iteratively detected via sensing–processing cycles with metaheuristic sampling distribution updating. Specifically, simulated annealing and ant colony analogy are used for guidance in future selection of sensing locations. This framework is subsequently validated by numerical and experimental implementations for gusset plate crack identification. Copyright © 2016 John Wiley & Sons, Ltd.
Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics
Accurate structural damage identification calls for dense sensor networks, which are becoming more feasible as the price of electronic sensing systems reduces. To transmit and process data from all nodes of a dense network is a computationally expensive BIG DATA problem; therefore scalable algorithms are needed so that inferences about the current state of the structure can be made efficiently. In this paper, an iterative spatial compressive sensing scheme for damage existence identification and localization is proposed and investigated. At each iteration, damage existence is identified from randomly collected sparse samples and damage localization is iteratively detected via sensing–processing cycles with metaheuristic sampling distribution updating. Specifically, simulated annealing and ant colony analogy are used for guidance in future selection of sensing locations. This framework is subsequently validated by numerical and experimental implementations for gusset plate crack identification. Copyright © 2016 John Wiley & Sons, Ltd.
Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics
Yao, Ruigen (author) / Pakzad, Shamim N. (author) / Venkitasubramaniam, Parvathinathan (author)
2017-04-01
14 pages
Article (Journal)
Electronic Resource
English
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