This article presents a novel deep learning-based damage evaluation approach by using speckled images. A deep convolutional neural network (DCNN) for predicting the stress intensity factor (SIF) at the crack tip is designed. Based on the proposed DCNN, the SIF can be automatically predicted through computational vision. The data bank consisting of a reference speckled image and lots of deformed speckled images is prepared by a camera and an MTS testing machine. Experiments were performed to verify the method, and the achieved results are quite remarkable with larger than 96% of predicted SIF values falling within 5% of true SIF values when sufficient training images are available. The results also confirm that the appropriate subset size of images within the field of view is 400 × 400 pixel resolutions.

本文通过使用斑点图像提出了一种新颖的基于深度学习的损伤评估方法。设计了用于预测裂纹尖端应力强度因子（SIF）的深度卷积神经网络（DCNN）。基于提出的DCNN，可以通过计算视觉自动预测SIF。由参考斑点图像和大量变形斑点图像组成的数据库是由照相机和MTS测试机准备的。进行了实验以验证该方法，并且当有足够的训练图像可用时，超过96％的预计SIF值落在真实SIF值的5％之内，所取得的结果非常出色。结果还证实，视场内图像的适当子集大小为400×400像素分辨率。