This paper presents an innovative method to quantify damage based on surface cracks of reinforced concrete shear walls (RCSWs). The key idea is to use artificial intelligence and convert crack patterns to graphs. In this method, the mathematics of graph theory is used to extract information (graph‐based features) from crack patterns and use them for crack quantification. The proposed graph features are used in linear regression and leave‐one‐out cross‐validation to predict the mechanical features calculated for each RCSW: Park and Ang damage index and the dissipated energy. Among the three general stages of damage, which are safe, questionable, and not safe, this paper focuses on quantifying the second stage. To validate the approach, crack images of three RCSWs are used. The walls had different aspect ratios (0.54, 0.94, and 2.00) and were subject to quasi‐static cyclic loading. Regression results demonstrate low root mean squared errors and high coefficients of determination (R2 scores above 0.845). This proves the ability of the proposed graph‐based method in quantifying damage based on surface crack patterns.

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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.

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