Smart Structures and Systems

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Damage localization and quantification of a truss bridge using PCA and convolutional neural network

  • Jiajia, Hao (School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Xinqun, Zhu (School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Yang, Yu (School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney) ;
  • Chunwei, Zhang (Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology) ;
  • Jianchun, Li (School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney)
  • Received : 2022.03.28
  • Accepted : 2022.09.02
  • Published : 2022.12.25
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Abstract

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

Keywords

Acknowledgement

This research is financially supported by the Ministry of Science and Technology of China (Grant No. 2019YFE0112400).

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