This article proposes a deep neural network, namely CrackPropNet, to measure crack propagation on asphalt concrete (AC) specimens. It offers an accurate, flexible, efficient, and low-cost solution for crack propagation measurement using images collected during cracking tests. CrackPropNet significantly differs from traditional deep learning networks, as it involves learning to locate displacement field discontinuities by matching features at various locations in the reference and deformed images. An image library representing the diversified cracking behavior of AC was developed for supervised training. CrackPropNet achieved an optimal dataset scale F-1 of 0.755 and optimal image scale F-1 of 0.781 on the testing dataset at a running speed of 26 frame-per-second. Experiments demonstrated that low to medium-level Gaussian noises had a limited impact on the measurement accuracy of CrackPropNet. Moreover, the model showed promising generalization on fundamentally different images. As a crack measurement technique, the CrackPropNet can detect complex crack patterns accurately and efficiently in AC cracking tests. It can be applied to characterize the cracking phenomenon, evaluate AC cracking potential, validate test protocols, and verify theoretical models.

翻译：本文提出了一种名为CrackPropNet的深度神经网络，用于测量沥青混凝土(AC)试件的裂缝扩展。该方法利用开裂试验过程中采集的图像，为裂缝扩展测量提供了精准、灵活、高效且低成本的解决方案。CrackPropNet与传统深度学习网络有显著区别，其核心在于通过学习匹配参考图像与变形图像不同位置的特征，来定位位移场的不连续性。本研究构建了一个反映沥青混凝土多样化开裂行为的图像库，用于监督训练。在测试数据集上，CrackPropNet的最优数据集尺度F-1得分为0.755，最优图像尺度F-1得分为0.781，运行速度为26帧/秒。实验表明，低至中等强度的高斯噪声对CrackPropNet的测量精度影响有限。此外，该模型在本质不同的图像上展现出良好的泛化能力。作为一种裂缝测量技术，CrackPropNet能够准确高效地检测沥青混凝土开裂试验中的复杂裂缝形态，可应用于表征开裂现象、评估沥青混凝土开裂潜力、验证试验方案以及校核理论模型。