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:文章提议建立一个深层神经网络,即CrackPropNet,以测量沥青混凝土(AC)标本的裂变传播情况,为利用在裂变试验中收集的图像测量裂变传播情况提供了准确、灵活、高效和低成本的解决方案。CrackPropNet与传统的深层学习网络有很大不同,因为它涉及通过在参考和变形图像中不同地点的相匹配特征来学习定位迁移场的不连续性。为监督培训开发了一个代表AC多种裂变行为的图像库。CrackPropNet实现了F-1的最佳数据集比例为0.755和F-1的最佳数据集比例为0.781,运行速度为每秒26个框架。实验显示,低到中等水平的高山噪音对CrackPropNet的测量准确性影响有限。此外,模型展示了对根本不同图像的有希望的概括性。作为裂变测量技术,CrackPropNet可以在AC裂变试验中准确和高效地检测复杂的裂变形模式。它可以用于确定裂变形现象的特征,评估AC断裂变形现象,评估AC的潜能、验证测试和理论模型。</s>