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Smart Structures and Systems Volume 29, Number 3, March 2022 , pages 421-431 DOI: https://doi.org/10.12989/sss.2022.29.3.421 |
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Crack detection in concrete slabs by graph-based anomalies calculation |
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Weifang Sun, Yuqing Zhou, Jiawei Xiang, Binqiang Chen and Wei Feng
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| Abstract | ||
| Concrete slab cracks monitoring of modern high-speed railway is important for safety and reliability of train operation, to prevent catastrophic failure, and to reduce maintenance costs. This paper proposes a curvature filtering improved crack detection method in concrete slabs of high-speed railway via graph-based anomalies calculation. Firstly, large curvature information contained in the images is extracted for the crack identification based on an improved curvature filtering method. Secondly, a graph-based model is developed for the image sub-blocks anomalies calculation where the baseline of the subblocks is acquired by crack-free samples. Once the anomaly is large than the acquired baseline, the sub-block is considered as crack-contained block. The experimental results indicate that the proposed method performs better than convolutional neural network method even under different curvature structures and illumination conditions. This work therefore provides a useful tool for concrete slabs crack detection and is broadly applicable to variety of infrastructure systems. | ||
| Key Words | ||
| anomalies evaluation; concrete slabs; crack detection; high-speed railway | ||
| Address | ||
| (1) Weifang Sun, Yuqing Zhou, Jiawei Xiang: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China; (2) Binqiang Chen: School of Aerospace Engineering, Xiamen University, Xiamen 361005, China; (3) Wei Feng: College of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China. | ||