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An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm

Hoa Tran (UGent) , Samir Khatir (UGent) , G. De Roeck, T. Bui-Tien and Magd Abdel Wahab (UGent)
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Abstract
This paper presents a new approach for damage detection in structures by applying a flexible combination based on an artificial neural network (ANN) and cuckoo search (CS) algorithm. ANN has become one of the most powerful tools employing computational intelligence techniques to tackle complex problems in numerous fields. However, due to the application of backpropagation algorithms based on gradient descent, a major drawback of ANN is the common problem of local minima that acts as a great hindrance to the search for the best solution. To overcome this disadvantage, we propose to combine ANN with evolutionary algorithms based on global search techniques. This paper employs CS to improve ANN training parameters (weight and bias) by minimizing the difference between real and desired outputs and then using these parameters to generate the network. Two numerical models, comprising a steel beam calibrated using experimental measurements and a large-scale truss bridge, are used to assess the robustness of the proposed approach. The results demonstrate that ANN combined with CS (ANN-CS) is accurate and requires a lower computational time than ANN, and evolutionary algorithm (EA) alone in terms of structural damage localization and quantification.
Keywords
Civil and Structural Engineering, Artificial neural network (ANN), Local minima, Evolutionary algorithm (EA), Cuckoo search (CS), Particle swarm optimization (PSO), Genetic algorithm (GA), Training parameters, Damage detection, Model updating, GENETIC ALGORITHM, OPTIMIZATION, IDENTIFICATION, LOCALIZATION, PSO

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MLA
Tran, Hoa, et al. “An Efficient Artificial Neural Network for Damage Detection in Bridges and Beam-like Structures by Improving Training Parameters Using Cuckoo Search Algorithm.” ENGINEERING STRUCTURES, vol. 199, 2019.
APA
Tran, H., Khatir, S., De Roeck, G., Bui-Tien, T., & Abdel Wahab, M. (2019). An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. ENGINEERING STRUCTURES, 199.
Chicago author-date
Tran, Hoa, Samir Khatir, G. De Roeck, T. Bui-Tien, and Magd Abdel Wahab. 2019. “An Efficient Artificial Neural Network for Damage Detection in Bridges and Beam-like Structures by Improving Training Parameters Using Cuckoo Search Algorithm.” ENGINEERING STRUCTURES 199.
Chicago author-date (all authors)
Tran, Hoa, Samir Khatir, G. De Roeck, T. Bui-Tien, and Magd Abdel Wahab. 2019. “An Efficient Artificial Neural Network for Damage Detection in Bridges and Beam-like Structures by Improving Training Parameters Using Cuckoo Search Algorithm.” ENGINEERING STRUCTURES 199.
Vancouver
1.
Tran H, Khatir S, De Roeck G, Bui-Tien T, Abdel Wahab M. An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. ENGINEERING STRUCTURES. 2019;199.
IEEE
[1]
H. Tran, S. Khatir, G. De Roeck, T. Bui-Tien, and M. Abdel Wahab, “An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm,” ENGINEERING STRUCTURES, vol. 199, 2019.
@article{8628138,
  abstract     = {{This paper presents a new approach for damage detection in structures by applying a flexible combination based on an artificial neural network (ANN) and cuckoo search (CS) algorithm. ANN has become one of the most powerful tools employing computational intelligence techniques to tackle complex problems in numerous fields. However, due to the application of backpropagation algorithms based on gradient descent, a major drawback of ANN is the common problem of local minima that acts as a great hindrance to the search for the best solution. To overcome this disadvantage, we propose to combine ANN with evolutionary algorithms based on global search techniques. This paper employs CS to improve ANN training parameters (weight and bias) by minimizing the difference between real and desired outputs and then using these parameters to generate the network. Two numerical models, comprising a steel beam calibrated using experimental measurements and a large-scale truss bridge, are used to assess the robustness of the proposed approach. The results demonstrate that ANN combined with CS (ANN-CS) is accurate and requires a lower computational time than ANN, and evolutionary algorithm (EA) alone in terms of structural damage localization and quantification.}},
  articleno    = {{109637}},
  author       = {{Tran, Hoa and Khatir, Samir and De Roeck, G. and Bui-Tien, T. and Abdel Wahab, Magd}},
  issn         = {{0141-0296}},
  journal      = {{ENGINEERING STRUCTURES}},
  keywords     = {{Civil and Structural Engineering,Artificial neural network (ANN),Local minima,Evolutionary algorithm (EA),Cuckoo search (CS),Particle swarm optimization (PSO),Genetic algorithm (GA),Training parameters,Damage detection,Model updating,GENETIC ALGORITHM,OPTIMIZATION,IDENTIFICATION,LOCALIZATION,PSO}},
  language     = {{eng}},
  title        = {{An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm}},
  url          = {{http://dx.doi.org/10.1016/j.engstruct.2019.109637}},
  volume       = {{199}},
  year         = {{2019}},
}

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