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Multiaxial sensor placement optimization in structural health monitoring using distributed wolf algorithm
Author(s) -
Yi TingHua,
Li HongNan,
Wang ChuanWei
Publication year - 2016
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.1806
Subject(s) - structural health monitoring , benchmark (surveying) , computer science , coding (social sciences) , convergence (economics) , key (lock) , algorithm , mathematical optimization , modal , distributed computing , engineering , mathematics , statistics , chemistry , computer security , geodesy , economic growth , polymer chemistry , economics , geography , structural engineering
Summary Optimal sensor placement technique plays a key role in the design of an effective structural health monitoring system. Recent advances in sensing technologies have also promoted using multiaxial sensors to perform efficiently and economically monitoring for civil engineering structures. However, the available evaluation criteria for the optimal sensor placement can only guarantee that the optimization is conducted in a single structural direction but not in multi‐dimension space, which may result in the non‐optimal placement of multiaxial sensors. To tackle this issue thoroughly, a new multiaxial optimal criterion termed as the triaxial modal assurance criterion is developed by taking account into three translational degrees of freedom as a single unit in the Fisher information matrix. Afterwards, a novel distributed wolf algorithm is proposed to improve the optimization performance in identifying the best sensor locations. The dual‐structure coding method is improved and adopted to represent the solution. The shuffling strategy is proposed to enhance the searching capability and convergence performance. The attacking process is also modified to prevent the algorithm from being trapped in a local minimum. The effectiveness of the proposed scheme is investigated by the benchmark structure developed by the University of Central Florida, USA. The results clearly demonstrate that the proposed distributed wolf algorithm outperforms the existing algorithm in its global optimization capability. Copyright © 2015 John Wiley & Sons, Ltd.