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

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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
Artificial Neural Network (ANN), damage identification, local minima, Particle Swarm Optimization (PSO), training parameters

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MLA
Nguyen-Ngoc, L., et al. “Damage Detection in Structures Using Particle Swarm Optimization Combined with Artificial Neural Network.” SMART STRUCTURES AND SYSTEMS, vol. 28, no. 1, 2021, pp. 1–12, doi:10.12989/sss.2021.28.1.001.
APA
Nguyen-Ngoc, L., Tran, N. H., Bui-Tien, T., Mai-Duc, A., Abdel Wahab, M., X. Nguyen, H., & De Roeck, G. (2021). Damage detection in structures using Particle Swarm Optimization combined with Artificial Neural Network. SMART STRUCTURES AND SYSTEMS, 28(1), 1–12. https://doi.org/10.12989/sss.2021.28.1.001
Chicago author-date
Nguyen-Ngoc, L., Ngoc Hoa Tran, T. Bui-Tien, A. Mai-Duc, Magd Abdel Wahab, Huan X. Nguyen, and G. De Roeck. 2021. “Damage Detection in Structures Using Particle Swarm Optimization Combined with Artificial Neural Network.” SMART STRUCTURES AND SYSTEMS 28 (1): 1–12. https://doi.org/10.12989/sss.2021.28.1.001.
Chicago author-date (all authors)
Nguyen-Ngoc, L., Ngoc Hoa Tran, T. Bui-Tien, A. Mai-Duc, Magd Abdel Wahab, Huan X. Nguyen, and G. De Roeck. 2021. “Damage Detection in Structures Using Particle Swarm Optimization Combined with Artificial Neural Network.” SMART STRUCTURES AND SYSTEMS 28 (1): 1–12. doi:10.12989/sss.2021.28.1.001.
Vancouver
1.
Nguyen-Ngoc L, Tran NH, Bui-Tien T, Mai-Duc A, Abdel Wahab M, X. Nguyen H, et al. Damage detection in structures using Particle Swarm Optimization combined with Artificial Neural Network. SMART STRUCTURES AND SYSTEMS. 2021;28(1):1–12.
IEEE
[1]
L. Nguyen-Ngoc et al., “Damage detection in structures using Particle Swarm Optimization combined with Artificial Neural Network,” SMART STRUCTURES AND SYSTEMS, vol. 28, no. 1, pp. 1–12, 2021.
@article{8716381,
  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.}},
  author       = {{Nguyen-Ngoc, L. and Tran, Ngoc Hoa and Bui-Tien, T. and Mai-Duc, A. and Abdel Wahab, Magd and X. Nguyen, Huan and De Roeck, G.}},
  issn         = {{1738-1584}},
  journal      = {{SMART STRUCTURES AND SYSTEMS}},
  keywords     = {{Artificial Neural Network (ANN),damage identification,local minima,Particle Swarm Optimization (PSO),training parameters}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{1--12}},
  title        = {{Damage detection in structures using Particle Swarm Optimization combined with Artificial Neural Network}},
  url          = {{http://dx.doi.org/10.12989/sss.2021.28.1.001}},
  volume       = {{28}},
  year         = {{2021}},
}

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