A graph-based method for quantifying crack patterns on reinforced concrete shear walls

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.