Smart Structures and Systems

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Damage detection in structures using Particle Swarm Optimization combined with Artificial Neural Network

  • Nguyen-Ngoc, L. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications) ;
  • Tran-Ngoc, H. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications) ;
  • Bui-Tien, T. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications) ;
  • Mai-Duc, A. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications) ;
  • Abdel Wahab, M. (Institute of Research and Development, Duy Tan University) ;
  • Nguyen, Huan X. (London Digital Twin Research Centre, Faculty of Science and Technology, Middlesex University) ;
  • De Roeck, G. (Department of Civil Engineering, KU Leuven)
  • Received : 2019.11.18
  • Accepted : 2021.03.24
  • Published : 2021.07.25
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Abstract

In this paper, a novel approach to damage identification in structures using Particle Swarm Optimization (PSO) combined with Artificial neural network (ANN) is proposed. With recent substantial advances, ANN has been extensively utilized in a wide variety of fields. However, because of the application of backpropagation algorithms based on gradient descent techniques, ANN may be trapped in local minima when seeking the best solution. This may reduce the accuracy of ANN. Therefore, we propose employing an evolutionary algorithm, namely PSO to deal with the local minimum problems of ANN. PSO is employed to improve the training parameters of ANN consisting of weight and bias ratios by reducing the deviation between calculated and desired results. These training parameters are then used to train the network. Since PSO applies global search techniques to look for the best solution, it can assist the network in avoiding local minima by looking for a beneficial starting point. In order to assess the effectiveness of the proposed approach, both numerical and experimental models with different damage scenarios are employed. The results show that ANN -PSO not only significantly reduces computational time compared to PSO but also possibly identifies damages in the considered structures more accurately than ANN and PSO separately.

Keywords

Acknowledgement

The authors acknowledge the financial support of VLIRUOS TEAM Project, VN2018TEA479A103, "Damage assessment tools for Structural Health Monitoring of Vietnamese infrastructures", funded by the Flemish Government. This work is also supported by the Newton Fund Institutional Links through the U.K. Department of Business, Energy, and Industrial Strategy and managed by the British Council under Grant 429715093 and partly supported by the UK-India Education and Research Initiative (UKIERI) grant ID 'DST-UKIERI 2018-19-11'. Moreover, the author needs to acknowledge the financial supports from Ministry of Education and Training (MOET) under the project research B2018-GHA-04SP, University of Transport and Communications (UTC) under the project code T2019-CT-04TD, and Bijzonder Onderzoeksfonds (BOF) of Ghent University.

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