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Deep neural network and evolved optimization algorithm for damage assessment in a truss bridge

Ngoc Lan Nguyen (UGent) , Quyet Nguyen-Huu, Guido De Roeck, Thanh Bui-Tien and Magd Abdel Wahab (UGent)
(2024) MATHEMATICS. 12(15).
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Abstract
In Structural Health Monitoring (SHM) of bridges, accurately assessing damage is critical to maintaining the safety and integrity of a structure. One of the primary challenges in damage assessment is the precise localization and quantification of defects, which is essential for making timely maintenance decisions and reducing the risk of structural failures. This paper introduces a novel damage detection method for SHM of a truss bridge by coupling a Deep Neural Network (DNN) model with an evolved Artificial Rabbit Optimization (EVARO) algorithm. The integration of DNN with the stochastic search capability of the EVARO algorithm helps to avoid local minima, thereby ensuring more accurate and reliable results. Additionally, the optimization algorithm's effectiveness is further enhanced by incorporating evolving predator features and the Cauchy motion search mechanism. The proposed method is first validated using various data benchmark problems, demonstrating its effectiveness compared to other well-known algorithms. Secondly, a case study involving the Chuong Duong truss bridge under different simulated damage scenarios further confirms the superiority of the proposed method in both localizing and quantifying damages.
Keywords
damage assessment, truss bridge, machine learning, optimization algorithm, STRUCTURAL DAMAGE, MODAL DATA, IDENTIFICATION

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Citation

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MLA
Nguyen, Ngoc Lan, et al. “Deep Neural Network and Evolved Optimization Algorithm for Damage Assessment in a Truss Bridge.” MATHEMATICS, vol. 12, no. 15, 2024, doi:10.3390/math12152300.
APA
Nguyen, N. L., Nguyen-Huu, Q., De Roeck, G., Bui-Tien, T., & Abdel Wahab, M. (2024). Deep neural network and evolved optimization algorithm for damage assessment in a truss bridge. MATHEMATICS, 12(15). https://doi.org/10.3390/math12152300
Chicago author-date
Nguyen, Ngoc Lan, Quyet Nguyen-Huu, Guido De Roeck, Thanh Bui-Tien, and Magd Abdel Wahab. 2024. “Deep Neural Network and Evolved Optimization Algorithm for Damage Assessment in a Truss Bridge.” MATHEMATICS 12 (15). https://doi.org/10.3390/math12152300.
Chicago author-date (all authors)
Nguyen, Ngoc Lan, Quyet Nguyen-Huu, Guido De Roeck, Thanh Bui-Tien, and Magd Abdel Wahab. 2024. “Deep Neural Network and Evolved Optimization Algorithm for Damage Assessment in a Truss Bridge.” MATHEMATICS 12 (15). doi:10.3390/math12152300.
Vancouver
1.
Nguyen NL, Nguyen-Huu Q, De Roeck G, Bui-Tien T, Abdel Wahab M. Deep neural network and evolved optimization algorithm for damage assessment in a truss bridge. MATHEMATICS. 2024;12(15).
IEEE
[1]
N. L. Nguyen, Q. Nguyen-Huu, G. De Roeck, T. Bui-Tien, and M. Abdel Wahab, “Deep neural network and evolved optimization algorithm for damage assessment in a truss bridge,” MATHEMATICS, vol. 12, no. 15, 2024.
@article{01J3QA5C053D0EH51EHA6YTM7B,
  abstract     = {{In Structural Health Monitoring (SHM) of bridges, accurately assessing damage is critical to maintaining the safety and integrity of a structure. One of the primary challenges in damage assessment is the precise localization and quantification of defects, which is essential for making timely maintenance decisions and reducing the risk of structural failures. This paper introduces a novel damage detection method for SHM of a truss bridge by coupling a Deep Neural Network (DNN) model with an evolved Artificial Rabbit Optimization (EVARO) algorithm. The integration of DNN with the stochastic search capability of the EVARO algorithm helps to avoid local minima, thereby ensuring more accurate and reliable results. Additionally, the optimization algorithm's effectiveness is further enhanced by incorporating evolving predator features and the Cauchy motion search mechanism. The proposed method is first validated using various data benchmark problems, demonstrating its effectiveness compared to other well-known algorithms. Secondly, a case study involving the Chuong Duong truss bridge under different simulated damage scenarios further confirms the superiority of the proposed method in both localizing and quantifying damages.}},
  articleno    = {{2300}},
  author       = {{Nguyen, Ngoc Lan and Nguyen-Huu, Quyet and De Roeck, Guido and Bui-Tien, Thanh and Abdel Wahab, Magd}},
  issn         = {{2227-7390}},
  journal      = {{MATHEMATICS}},
  keywords     = {{damage assessment,truss bridge,machine learning,optimization algorithm,STRUCTURAL DAMAGE,MODAL DATA,IDENTIFICATION}},
  language     = {{eng}},
  number       = {{15}},
  pages        = {{25}},
  title        = {{Deep neural network and evolved optimization algorithm for damage assessment in a truss bridge}},
  url          = {{http://doi.org/10.3390/math12152300}},
  volume       = {{12}},
  year         = {{2024}},
}
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