Damaged detection in structures using artificial neural networks and genetic algorithms
- Author
- Ngoc Lan Nguyen (UGent) , Ngoc Hoa Tran (UGent) , Tran Hieu Nguyen (UGent) , Binh Nguyen-Duc, Dang Nguyen-Le-Minh, Thanh Bui-Tien and Magd Abdel Wahab (UGent)
- Organization
- Abstract
- Recently, Structural Health Monitoring (SHM) has emerged to be one of the most effective tools for the diagnosis of damages in structures. Early identification and localization of damage not only help to reduce the maintenance cost but also extend the life cycle of the structures. In this paper, a novel approach using Artificial Neural Networks (ANNs) combined with Genetic Algorithms (GA) is proposed to increase the capacity of damage detection in SHM system. ANNs can make use of different algorithms such as recognition algorithms and regression algorithms to classify, detect, localize and evaluate the severity of the damage. Meanwhile, GA can be applied to identify training parameters as well as to solve the local minima problems from ANNs. To demonstrate the method, an analysis of a bridge is performed. Finite Element (FE) model of the bridge is created using measured vibration data and it is employed as training data for the combined ANN-GA method in the model updating process. The updated model will then be used as a baseline model for damage identification. The result shows that the proposed ANN-GA algorithm provides a high level of accuracy and efficiency in detecting damage in the considered structure.
Downloads
-
(...).pdf
- full text (Published version)
- |
- UGent only
- |
- |
- 318.72 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GVQAQMCV8B9BP89EP56DGCM0
- MLA
- Nguyen, Ngoc Lan, et al. “Damaged Detection in Structures Using Artificial Neural Networks and Genetic Algorithms.” Proceedings of the 3rd International Conference on Sustainability in Civil Engineering (ICSCE 2020), edited by Thanh Bui-Tien et al., vol. 145, Springer, 2021, pp. 33–38, doi:10.1007/978-981-16-0053-1_4.
- APA
- Nguyen, N. L., Tran, N. H., Nguyen, T. H., Nguyen-Duc, B., Nguyen-Le-Minh, D., Bui-Tien, T., & Abdel Wahab, M. (2021). Damaged detection in structures using artificial neural networks and genetic algorithms. In T. Bui-Tien, L. Nguyen Ngoc, & G. De Roeck (Eds.), Proceedings of the 3rd International Conference on Sustainability in Civil Engineering (ICSCE 2020) (Vol. 145, pp. 33–38). https://doi.org/10.1007/978-981-16-0053-1_4
- Chicago author-date
- Nguyen, Ngoc Lan, Ngoc Hoa Tran, Tran Hieu Nguyen, Binh Nguyen-Duc, Dang Nguyen-Le-Minh, Thanh Bui-Tien, and Magd Abdel Wahab. 2021. “Damaged Detection in Structures Using Artificial Neural Networks and Genetic Algorithms.” In Proceedings of the 3rd International Conference on Sustainability in Civil Engineering (ICSCE 2020), edited by Thanh Bui-Tien, Long Nguyen Ngoc, and Guido De Roeck, 145:33–38. Springer. https://doi.org/10.1007/978-981-16-0053-1_4.
- Chicago author-date (all authors)
- Nguyen, Ngoc Lan, Ngoc Hoa Tran, Tran Hieu Nguyen, Binh Nguyen-Duc, Dang Nguyen-Le-Minh, Thanh Bui-Tien, and Magd Abdel Wahab. 2021. “Damaged Detection in Structures Using Artificial Neural Networks and Genetic Algorithms.” In Proceedings of the 3rd International Conference on Sustainability in Civil Engineering (ICSCE 2020), ed by. Thanh Bui-Tien, Long Nguyen Ngoc, and Guido De Roeck, 145:33–38. Springer. doi:10.1007/978-981-16-0053-1_4.
- Vancouver
- 1.Nguyen NL, Tran NH, Nguyen TH, Nguyen-Duc B, Nguyen-Le-Minh D, Bui-Tien T, et al. Damaged detection in structures using artificial neural networks and genetic algorithms. In: Bui-Tien T, Nguyen Ngoc L, De Roeck G, editors. Proceedings of the 3rd International Conference on Sustainability in Civil Engineering (ICSCE 2020). Springer; 2021. p. 33–8.
- IEEE
- [1]N. L. Nguyen et al., “Damaged detection in structures using artificial neural networks and genetic algorithms,” in Proceedings of the 3rd International Conference on Sustainability in Civil Engineering (ICSCE 2020), Hanoi, Vietnam, 2021, vol. 145, pp. 33–38.
@inproceedings{01GVQAQMCV8B9BP89EP56DGCM0,
abstract = {{Recently, Structural Health Monitoring (SHM) has emerged to be one of the most effective tools for the diagnosis of damages in structures. Early identification and localization of damage not only help to reduce the maintenance cost but also extend the life cycle of the structures. In this paper, a novel approach using Artificial Neural Networks (ANNs) combined with Genetic Algorithms (GA) is proposed to increase the capacity of damage detection in SHM system. ANNs can make use of different algorithms such as recognition algorithms and regression algorithms to classify, detect, localize and evaluate the severity of the damage. Meanwhile, GA can be applied to identify training parameters as well as to solve the local minima problems from ANNs. To demonstrate the method, an analysis of a bridge is performed. Finite Element (FE) model of the bridge is created using measured vibration data and it is employed as training data for the combined ANN-GA method in the model updating process. The updated model will then be used as a baseline model for damage identification. The result shows that the proposed ANN-GA algorithm provides a high level of accuracy and efficiency in detecting damage in the considered structure.}},
author = {{Nguyen, Ngoc Lan and Tran, Ngoc Hoa and Nguyen, Tran Hieu and Nguyen-Duc, Binh and Nguyen-Le-Minh, Dang and Bui-Tien, Thanh and Abdel Wahab, Magd}},
booktitle = {{Proceedings of the 3rd International Conference on Sustainability in Civil Engineering (ICSCE 2020)}},
editor = {{Bui-Tien, Thanh and Nguyen Ngoc, Long and De Roeck, Guido}},
isbn = {{9789811600524}},
issn = {{2366-2557}},
language = {{eng}},
location = {{Hanoi, Vietnam}},
pages = {{33--38}},
publisher = {{Springer}},
title = {{Damaged detection in structures using artificial neural networks and genetic algorithms}},
url = {{http://doi.org/10.1007/978-981-16-0053-1_4}},
volume = {{145}},
year = {{2021}},
}
- Altmetric
- View in Altmetric