Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network

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%.