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Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network
Highlights A practical vibration-based CNN combined FE model and field-testingdata is proposed for in-service steel railway bridges. A data augmentation strategy is proposed to classify various acceleration responses related to damage scenarios. t-Distributed Stochastic Neighbour Embedding (t-SNE) and Gradient weighted Class Activation Mapping (Grad-CAM) are used for visualization of feature extraction and feature mapping. The proposed method can be used to assess the health state of structure to fulfil the regulatory, such as Australian Standard, and safety aspects of steel railway bridges. The proposed techniques can be utilized for rail infrastructure management without the need of involving specialized expertise and complex signal processing.
Abstract Railway bridges exposed to extreme environmental conditions can gradually lose their effective cross-section at critical locations and cause catastrophic failure. This paper has proposed a practical vibration-based deep learning approach for damage classification of various extents and degrees of cross section losses due to damages like corrosion in operational railway bridges using vibration-based Convolutional Neural Networks (CNN)s. Firstly, field testing of an in-service railway bridge is conducted and the modal parameters of the bridge are obtained to validate the developed Finite Element (FE) model of the bridge. In the next phase, corrosion scenarios of the main bridge members are generated as various quantities of cross section losses of these members by the validated FE following the Australian Standard AS7636. In the deep learning part, a 1D CNN aligned with novel specific data augmentation strategies is developed to classify various acceleration responses related to each damage scenario simulated by the validated FE. Furthermore, a visualization of feature extraction and feature mapping using t-Distributed Stochastic Neighbour Embedding (t-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) is illustrated. The case studies on the simulated and field data-validated FE model results applying background noises and variations, and the real field testing data suggest that the proposed method can reach a perfect damage classification close to an accuracy of 100%.
Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network
Highlights A practical vibration-based CNN combined FE model and field-testingdata is proposed for in-service steel railway bridges. A data augmentation strategy is proposed to classify various acceleration responses related to damage scenarios. t-Distributed Stochastic Neighbour Embedding (t-SNE) and Gradient weighted Class Activation Mapping (Grad-CAM) are used for visualization of feature extraction and feature mapping. The proposed method can be used to assess the health state of structure to fulfil the regulatory, such as Australian Standard, and safety aspects of steel railway bridges. The proposed techniques can be utilized for rail infrastructure management without the need of involving specialized expertise and complex signal processing.
Abstract Railway bridges exposed to extreme environmental conditions can gradually lose their effective cross-section at critical locations and cause catastrophic failure. This paper has proposed a practical vibration-based deep learning approach for damage classification of various extents and degrees of cross section losses due to damages like corrosion in operational railway bridges using vibration-based Convolutional Neural Networks (CNN)s. Firstly, field testing of an in-service railway bridge is conducted and the modal parameters of the bridge are obtained to validate the developed Finite Element (FE) model of the bridge. In the next phase, corrosion scenarios of the main bridge members are generated as various quantities of cross section losses of these members by the validated FE following the Australian Standard AS7636. In the deep learning part, a 1D CNN aligned with novel specific data augmentation strategies is developed to classify various acceleration responses related to each damage scenario simulated by the validated FE. Furthermore, a visualization of feature extraction and feature mapping using t-Distributed Stochastic Neighbour Embedding (t-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) is illustrated. The case studies on the simulated and field data-validated FE model results applying background noises and variations, and the real field testing data suggest that the proposed method can reach a perfect damage classification close to an accuracy of 100%.
Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network
Ghiasi, Alireza (author) / Moghaddam, Mahdi Kazemi (author) / Ng, Ching-Tai (author) / Sheikh, Abdul Hamid (author) / Shi, Javen Qinfeng (author)
Engineering Structures ; 264
2022-05-22
Article (Journal)
Electronic Resource
English
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