A Regression-Based Machine Learning Approach for Rehabilitation of Corroded RC Culverts Strengthened with SMA

Abstract

Box culverts are critical components of transportation infrastructure, yet their load-carrying capacity is often compromised by reinforcement corrosion and geometric deterioration. This study proposes an integrated finite element (FEM) and machine learning (ML) framework to evaluate and predict the ultimate capacity of corroded reinforced concrete box culverts strengthened with shape memory alloy (SMA). Severe corrosion was simulated by reducing the slab thickness by 20 mm, and SMA was subsequently applied to the corroded culvert in different strengthening configurations to assess its rehabilitation effectiveness. Case-2 showed the best performance in increasing the capacity of the corroded culvert. A total of 314 FEM-based generated data points were used to train and test five regression models: GB, ANN, SVM, RF, and KNN. The performance of the model was evaluated in terms of R2, MAE, RMSE, and MAPE. The SHAP and Partial Dependence Plot (PDP) analysis was conducted for model interpretability and understanding the physical relevance. The analysis suggests that SMA helps in restoring and improving the carrying capacity and resistance to deformation of the corroded culvert. Among the predictive models, GB achieved the highest accuracy, followed by ANN and SVM. Interpretability analyses consistently identified culvert width as the dominant parameter, followed by depth and length, while concrete strength showed a moderate positive influence. The proposed FEM–ML–XAI framework offers a reliable and interpretable tool for assessing and rehabilitating corrosion-damaged culverts using SMA.