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Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm

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  • Xun Zhang
  • Juelong Li
  • Jianchun Xing
  • Ping Wang
  • Qiliang Yang
  • Ronghao Wang
  • Can He

Abstract

Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO) algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

Suggested Citation

  • Xun Zhang & Juelong Li & Jianchun Xing & Ping Wang & Qiliang Yang & Ronghao Wang & Can He, 2014. "Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-12, June.
    • Handle: RePEc:hin:jnlmpe:743904
    DOI: 10.1155/2014/743904
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    Cited by:

    1. Naderipour, Amirreza & Ramtin, Amir Reza & Abdullah, Aldrin & Marzbali, Massoomeh Hedayati & Nowdeh, Saber Arabi & Kamyab, Hesam, 2022. "Hybrid energy system optimization with battery storage for remote area application considering loss of energy probability and economic analysis," Energy, Elsevier, vol. 239(PD).
    2. Dash, Deepak Ranjan & Dash, P.K. & Bisoi, Ranjeeta, 2021. "Short term solar power forecasting using hybrid minimum variance expanded RVFLN and Sine-Cosine Levy Flight PSO algorithm," Renewable Energy, Elsevier, vol. 174(C), pages 513-537.

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