Smart Structures and Systems

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Crack detection in concrete slabs by graph-based anomalies calculation

  • Sun, Weifang (College of Mechanical and Electrical Engineering, Wenzhou University) ;
  • Zhou, Yuqing (College of Mechanical and Electrical Engineering, Wenzhou University) ;
  • Xiang, Jiawei (College of Mechanical and Electrical Engineering, Wenzhou University) ;
  • Chen, Binqiang (School of Aerospace Engineering, Xiamen University) ;
  • Feng, Wei (College of Mechanical and Electrical Engineering, Henan University of Technology)
  • Received : 2020.11.12
  • Accepted : 2021.12.17
  • Published : 2022.03.25
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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 sub-blocks 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.

Keywords

Acknowledgement

The research described in this paper was financially supported by the Zhejiang Provincial Natural Science Foundation of China (Nos. LQ21E050003 and LQ20E050027), the Zhejiang Special Support Program for High-level Personnel Recruitment of China (No. 2018R52034), the National Natural Science Foundation of China (No. U1909217), and the Fundamental Scientific Research Project of Wenzhou (No. G20190013).

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