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Smart Structures and Systems Volume 16, Number 2, August 2015 , pages 243-262 DOI: https://doi.org/10.12989/sss.2015.16.2.243 |
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Optimal sensor placement under uncertainties using a nondirective movement glowworm swarm optimization algorithm |
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Guang-Dong Zhou, Ting-Hua Yi, Huan Zhang and Hong-Nan Li
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| Abstract | ||
| Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency. | ||
| Key Words | ||
| structural health monitoring; optimal sensor placement; glowworm swarm optimization algorithm; information entropy; multi-objective optimization | ||
| Address | ||
| Guang-Dong Zhou, Hong-Nan Li: College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China Ting-Hua Yi: School of Civil Engineering, Dalian University of Technology, Dalian 116024, China Huan Zhang: State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China | ||