- Title
- Data-driven algorithm based on the scaled boundary finite element method and deep learning for the identification of multiple cracks in massive structures
- Creator
- Jiang, Shouyan; Deng, Wangtao; Ooi, Ean Tat; Sun, Liguo; Du, Chengbin
- Date
- 2024
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/201018
- Identifier
- vital:19463
- Identifier
-
https://doi.org/10.1016/j.compstruc.2023.107211
- Identifier
- ISSN:0045-7949 (ISSN)
- Abstract
- Structural defect identification is a vital aspect of structural health monitoring used to assess the safety of engineering structures. However, quantitatively determining the dimensions of structural defects is often difficult. Therefore, this study presents an innovative data-driven algorithm that combines the scaled boundary finite element method (SBFEM) and a deep learning framework based on a dilated causal convolutional neural network (CNN) to identify crack-like defects in large-scale structures. The SBFEM is used to simulate different crack-like defects. Mesh generation is significantly simplified by a simple procedure that requires only changing the scale centre at the crack tip and the positions of the nodes at the crack opening. This minimises remeshing and enables simple generation of sufficient data to train the neural network. In addition, an absorbing boundary model based on Rayleigh damping is used to avoid computing the entire model when simulating wave propagation in massive structures. To ensure that sequential data remain ordered and to obtain a large receptive field without increasing the complexity of the neural network, a dilated causal CNN is employed in the deep learning framework. Therefore, more historical information is captured, and the complex mapping relationship between the echo signal and the crack information is efficiently learnt. The proposed model can accurately identify the number, location, and depth of cracks in massive structures. Moreover, it is robust to noise, which is demonstrated via numerical examples. Therefore, the proposed algorithm provides valuable insight into the detection and diagnosis of structural defects, which can ultimately improve the safety of engineering structures. © 2023 Elsevier Ltd
- Publisher
- Elsevier Ltd
- Relation
- Computers and Structures Vol. 291, no. (2024), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2023 Elsevier Ltd
- Subject
- 40 Engineering; Boundary absorbing layer; Crack identification; Dilated causal convolutional neural network; Massive structures; Scaled boundary finite element method
- Reviewed
- Funder
- The authors gratefully acknowledge support for this research from the National Natural Science Foundation of China (Grant No. 52279130 ), the National Key R&D Program Project of China-Key Project of Intergovernmental International Scientific and Technological Innovation Cooperation (Grant No. 2018YFE0122400), and Open Research Fund of Key Laboratory of Engineering Geophysical Prospecting and Detection of Chinese Geophysical Society (CJ2021GE06).
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