Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network

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 behaviour 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 generalisation 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 characterise the cracking phenomenon, evaluate AC cracking potential, validate test protocols, and verify theoretical models.

KEYWORDS:

• Asphalt concrete
• crack propagation
• digital image correlation
• optical flow
• deep learning

Data availability statement

Examples of the image database and pre-trained CrackPropNet are available at https://github.com/zehuiz2/CrackPropNet.

Author contributions

The authors confirm their contribution to the paper as follows: study conception and design: Zehui Zhu and Imad L. Al-Qadi; data collection: Zehui Zhu; analysis and in – terpretation of results: Zehui Zhu and Imad L. Al-Qadi; draft manuscript preparation: Zehui Zhu and Imad L. Al-Qadi. All authors reviewed the results and approved the final version of the manuscript.

Acknowledgment

The authors would like to thank Jose Julian Rivera Perez, Berangere Doll, Uthman Mohamed Ali, and Maxwell Barry for their help in preparing test specimens and collecting raw images. The contents of this report reflect the view of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Illinois Center for Transportation or the Illinois Department of Transportation.

Disclosure statement

No potential conflict of interest was reported by the author(s).