A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Partitioned genetic algorithm strategy for optimal sensor placement based on structure features of a high‐piled wharf
Health monitoring, detection, and safety assessment of high‐piled wharf structures are key problems to be solved urgently in the field of maritime transport engineering. An optimal method for placing accelerometers for monitoring a high‐piled wharf structure is presented in this paper. In this method, a partitioned genetic algorithm strategy is proposed based on the frequency and vibration modes of the high‐piled wharf obtained by a modal analysis using the finite element method. The modal assurance criterion matrix is used as an evaluation index of sensor placement results. Subsequently, the sensor placement scheme obtained by the proposed method is applied to a reduced‐scale model of a high‐piled wharf to validate the method. The results demonstrate that the proposed sensor optimal placement method reduces the number of accelerometers and improves the calculation efficiency by ensuring relatively complete information on the high‐piled wharf structure.
Partitioned genetic algorithm strategy for optimal sensor placement based on structure features of a high‐piled wharf
Health monitoring, detection, and safety assessment of high‐piled wharf structures are key problems to be solved urgently in the field of maritime transport engineering. An optimal method for placing accelerometers for monitoring a high‐piled wharf structure is presented in this paper. In this method, a partitioned genetic algorithm strategy is proposed based on the frequency and vibration modes of the high‐piled wharf obtained by a modal analysis using the finite element method. The modal assurance criterion matrix is used as an evaluation index of sensor placement results. Subsequently, the sensor placement scheme obtained by the proposed method is applied to a reduced‐scale model of a high‐piled wharf to validate the method. The results demonstrate that the proposed sensor optimal placement method reduces the number of accelerometers and improves the calculation efficiency by ensuring relatively complete information on the high‐piled wharf structure.
Partitioned genetic algorithm strategy for optimal sensor placement based on structure features of a high‐piled wharf
Su, Jing‐Bo (author) / Luan, Shao‐Lun (author) / Zhang, Li‐Min (author) / Zhu, Rui‐Hu (author) / Qin, Wang‐Gen (author)
2019-01-01
12 pages
Article (Journal)
Electronic Resource
English
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