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Partitioned genetic algorithm strategy for optimal sensor placement based on structure features of a high‐piled wharf
Author(s) -
Su JingBo,
Luan ShaoLun,
Zhang LiMin,
Zhu RuiHu,
Qin WangGen
Publication year - 2019
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2289
Subject(s) - wharf , modal , finite element method , engineering , genetic algorithm , accelerometer , structural engineering , vibration , structural health monitoring , modal analysis , computer science , algorithm , acoustics , materials science , physics , machine learning , polymer chemistry , operating system
Summary 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.