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Optimal sensor placement for long-span cable-stayed bridge using a novel particle swarm optimization algorithm

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Abstract

In health monitoring of long-span structures, proper arrangement of sensors is a key point because of the need to acquire effective structural health information with limited testing resources. This study proposes a novel approach called dual-structure coding and mutation particle swarm optimization (DSC-MPSO) algorithm for the sensor placement. The cumulative effective modal mass participation factor is firstly derived to select the main contributions modes. A novel method combining dual-structure coding with the mutation operator is then utilized to determine the optimal sensors configurations. Finally, the feasibility of the DSC-MPSO algorithm is verified by optimizing the sensors locations for a long-span cable-stayed bridge. The effective independence method, genetic algorithm and standard particle swarm optimization algorithm are taken as contrast experiments. The simulation results show that the proposed algorithm in this paper could improve the convergence speed and precision. Accordingly, the method is effective in solving optimal sensor placement problems.

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Acknowledgments

This project was supported by the National Natural Science Foundation of China (Grant No. 61321491). We would like to express our appreciation to the anonymous reviewers and the Associate Editor for their valuable comments and suggestions.

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Authors and Affiliations

  1. College of Defense Engineering, PLA University of Science and Technology, Nanjing, China

    Xun Zhang, Jianchun Xing, Ping Wang, Qiliang Yang & Can He

  2. Research Center of Coastal Defense Engineering, Beijing, China

    Juelong Li

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  1. Juelong Li
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  2. Xun Zhang
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  3. Jianchun Xing
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  4. Ping Wang
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  5. Qiliang Yang
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  6. Can He
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Correspondence to Xun Zhang.

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Li, J., Zhang, X., Xing, J. et al. Optimal sensor placement for long-span cable-stayed bridge using a novel particle swarm optimization algorithm. J Civil Struct Health Monit 5, 677–685 (2015). https://doi.org/10.1007/s13349-015-0145-4

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  • DOI: https://doi.org/10.1007/s13349-015-0145-4

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