Localization and Automatic Quantification of Crack Damage in Steel Structures Using Ultrasonic Guided Wave Technology

Ultrasonic guided wave (UGW) technology is an effective method for intelligent monitoring of crack damage in thin-walled plate structures. In this paper, a finite-element (FE) model of UGW propagation in Q345 steel plate with crack damage was established and verified. A visual localization method for crack damage in steel plate based on the time reversal–probability damage imaging (TR-PDI) method is proposed. On this basis, the time–frequency domain characteristics of UGW signal are extracted using continuous wavelet transform. Two deep learning (DL) models, the two-dimensional convolutional neural network (2D-CNN) and DenseNet, are used to quantify the crack length and depth by UGW signal. The robustness and generalization performance of the automatic quantification method of crack damage based on UGW-DL algorithm were verified using three databases. The results show that the TR-PDI method can realize visual localization of crack damage in steel plate. The accuracy of two DL models in quantifying crack length and depth exceeded 92.9% and 99%, respectively, and the DenseNet model outperformed the 2D-CNN model. The fusion database constructed using the experimental results and FE data can significantly improve the robustness and generalization ability of the UGW-DL algorithm in crack diagnosis.