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Machine learning-based damage detection of RC wall using graph features of crack patterns

Concrete crack quantification is one of the challenges that has been investigated. In this article, a computer vision method is used to detect and quantify the cracks on a concrete surface. After processing the crack images, cracks are modeled as graphs for feature extraction. To study the proposed method, concrete surface crack images from a reinforced concrete shell under quasi-linear load at each load step are used. Having the graph and mechanical features, a PCA analysis is performed to study the dependency of the features. Using GPC as graph principal components and MPC as mechanical principal components, a linear Pearson correlation analysis is performed on the GPC and MPC data, results of which demonstrates more than 75% consistency. Finding the graph features in inherent relationship with the mechanical features, the paper continues with a machine learning study between the two features. Due to the low in-hand data, two different machine learning algorithms are used for the verification purpose. Results of the linear regression model and leave-one-out model showed a very close accuracy with 1% and 2% error, respectively. All findings attest the novel idea of presenting graph features. Graph features can be interpreted to use as a representative for mechanical features. Moreover, this method provides the opportunity of studying crack from a mathematical and fundamental viewpoint.

DOI: 10.32548/RS.2022.003


1. Chang PC, Flatau A, Liu SC. Review Paper: Health Monitoring of Civil Infrastructure: Http://DxDoiOrg/101177/1475921703036169 2016; 2(3): 257–267. DOI: 10.1177/1475921703036169.

2. Jahanshahi MR, Masri SF. Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures. Automation in Construction 2012; 22: 567–576. DOI: 10.1016/J.AUTCON.2011.11.018.

3. Shi J, Tomasi C. Good features to track. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1994: 593–600. DOI: 10.1109/CVPR.1994.323794.

4. Pearson K. VII. Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London 1895; 58(347–352): 240–242.

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