A deep learning-based approach for crack damage detection using strain field

This paper proposes a novel approach that utilizes a deep convolutional neural network (DCNN) for crack damage detection in thin plates. The DCNN model converts the detection task into a regression task and identifies crack tips through the strain field. Numerical simulations and experiments under quasi-static tensile were conducted to demonstrate the proposed method. The results indicate that this method exhibits high accuracy in crack damage detection. Furthermore, the study explores the use of active learning to address the challenge of data scarcity and extends the application of the DCNN model to similar tasks by transfer learning. This study provides some new perspectives for the field of damage detection.