Abstract
Chloride-induced corrosion is a major threat to the durability of reinforced concrete (RC) structures. This is especially critical in marine tidal zones, where surface chloride concentration (Cs) plays a key role in predicting chloride ingress using Fick's second law. However, traditional assessment methods are time-consuming and impractical, necessitating advanced predictive models. This study developed a deep learning-based framework utilizing a convolutional neural network (CNN) trained on 284 samples with 11 critical features related to material composition and environmental conditions. The CNN's performance was benchmarked against four machine learning (ML) models: stepwise linear regression (SLR), support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF). Results demonstrated CNN's superiority, achieving a coefficient of determination (R(2)) = 0.849 and a lower root mean square error (RMSE) = 0.18%, outperforming conventional models. Shapley additive explanation (SHAP) analysis revealed exposure time, water content, and fine aggregate as the most critical factors influencing Cs predictions. The findings highlighted the importance of material composition and environmental exposure in optimizing concrete mix designs to mitigate chloride ingress in tidal zones. This research can enhance durability assessment, proactive maintenance strategies, and service life estimation of RC structures in harsh marine environments. Furthermore, it can contribute to the sustainable development goals (SDGs) by promoting resilient infrastructure, sustainable construction practices, and improved climate adaptation strategies. By integrating deep learning in durability assessments, this study can provide a scalable, efficient solution for optimizing maintenance planning and reducing premature failures in coastal RC structures, ultimately extending their service life.