Intelligent diagnosis of fatigue cracks in OSDs based on UGW time-frequency characteristics

Fatigue cracking is one of the most typical and prevalent diseases of orthotropic steel decks (OSDs), and fatigue crack monitoring is significant for the safety and durability of bridge structures. Ultrasonic guided wave (UGW) technology is available for large-area damage monitoring of thin-walled structures, but it is still challenging to accurately assess and localize fatigue cracks with uncertainty in complex OSD structures. This study proposes an intelligent diagnostic framework synergistically integrating residual neural networks (ResNet) with UGW time-frequency representations to achieve unified multi-task fatigue crack assessment in OSDs, encompassing classification, quantification and localization. First, the UGW signals are preprocessed and converted to time-frequency images by continuous wavelet transform (CWT). Second, the performance of different neural networks is investigated in the diagnostic tasks, which include crack stage, crack size, cracking mode and crack location. Finally, the fatigue crack in OSDs is comprehensively reconstructed according to the predicted information from multiple monitoring paths. The results show that ResNet has great performance, with classification accuracies of 89.9 % for crack stage and 78.0 % for cracking mode; and regression R-squares of 0.974 for crack size and 0.841 for crack location. After reconstruction with multipath information, the synthesis accuracies of crack stage and cracking mode are 92.7 % and 95.2 %, and the prediction errors of crack length and crack location are within 2.5 mm and 15 mm, respectively. The proposed intelligent diagnostic method can realize the comprehensive diagnosis of classification, quantification and localization of fatigue cracks in OSDs. • Proposed a ResNet-based UGW diagnostic method for classifying, quantifying and localizing fatigue cracks in OSDs. • Investigated the performance of different neural networks in diagnostic tasks, and hyperparameter tuning of the model. • Enhanced CWT images using the results of completed predictions to improve the accuracy of uncompleted diagnostic tasks. • The fatigue crack in OSDs was reconstructed and visualized using the predicted information from multiple monitoring paths.