Abstract
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.
Abstract (translated)
本文提出了一种深度学习网络,即CrackPropNet,用于测量 asphalt concrete (AC) 样本中的裂缝传播。它提供了一种准确、灵活、高效、低成本的解决方案,通过在裂缝测试中收集的图像进行裂缝传播测量。CrackPropNet 与传统深度学习网络存在较大差异,因为它涉及通过学习在参考和变形图像中不同位置的特征来定位位移场离散性。为了监督训练,我们开发了表示 AC 样本多样化裂缝行为的图像库。CrackPropNet 在测试数据集上实现了最佳数据集尺度 F-1 的 0.755 和最佳图像尺度 F-1 的 0.781,以每秒 26 帧的速度运行。实验表明,低到中等程度的Gaussian噪声对 CrackPropNet 的测量精度具有有限的影响。此外,模型在 fundamentally 不同的图像上表现出良好的泛化能力。作为裂缝测量技术,CrackPropNet 在 AC 裂缝测试中准确高效地检测复杂的裂缝模式。它可以用于描述裂缝现象、评估 AC 裂缝潜在性、验证测试协议并验证理论模型。
URL
https://arxiv.org/abs/2303.05957
