Vision-Based Quantification of Stiffness Degradation in Reinforced Concrete Shear Walls Using Graph-Based Surface Crack Features and Machine Learning

The rapid and accurate assessment of structural damage in reinforced concrete shear walls (RCSWs) following seismic events remains a critical yet unresolved challenge in structural engineering. Current methods rely heavily on subjective visual interpretations, limiting their generalizability and practical deployment. This work streamlines and automates the approach of FEMA 306 and eliminates its subjectivity. In this regard, this study proposes a novel framework that quantitatively links observed surface crack patterns to structural stiffness degradation using a combination of mathematical graph representations and the walls’ design parameters. Crack images are converted into mathematical graphs, where nodes and edges represent the spatial distribution and connectivity of cracks, respectively. Afterward, graph-based features are extracted and combined with design properties such as horizontal and vertical reinforcement ratios to train supervised machine learning (ML) models. The primary innovation of this approach lies in its ability to predict the stiffness reduction factor (λk), a code-compatible damage index, without requiring displacement, drift, or force data. The framework is validated using a data set of 19 large-scale RCSWs tested under cyclic loading. The results demonstrate that the proposed method achieves reliable predictions, with the multilayer perceptron (MLP) model yielding the R2 score of 0.57 and a root mean squared error (RMSE) of 0.13 on the test set. This work offers a scalable and interpretable approach for postdamage evaluation of RCSWs using visually observable data.