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

Abstract

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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.