Artificial Intelligence-Driven Models for Predicting Chloride Diffusion in Concrete: A Comparative Regression Analysis

Reinforced concrete buildings may encounter several challenging conditions throughout their lifespan, including exposure to chloride ions. This exposure, particularly in coastal areas, may lead to a decrease in durability and degradation of the concrete structures. Utilizing experimental field findings, the application of artificial intelligence (AI) might create models to accurately predict the nonsteady state evident concrete’s chloride diffusion coefficient (Dc) over a prolonged duration. This approach can help detect the factors that have a significant impact and improve the estimation of the durability of a concrete construction. Present research showcases the use of Support Vector Regression (SVR) in forecasting the Dc of concrete under different exposure situation. The prediction models were improved by utilizing the Jellyfish search optimization (JSO), and Henry gas solubility optimization (HGSO) and were trained on a dataset including 216 data points. The findings demonstrate that the (hybrid SVR with JSO), and SHGSO (hybrid SVR with HGSO) models have significant promise in properly forecasting the Dc of concrete under different exposure situation, while maintaining suitable values of the R2. A comprehensive index includes various kinds of metrics depicts that the SJSO value of objective function (OFU) was roughly 50% smaller at 0.3769 compared to the SHGSO at 0.7284. The variance percentage between two developed models for these metrics presents that there is at least a 35% variance between the two models, where in some cases the variance gets a 95% reduction, which presents the capability and reliability of the SJSO in the prediction purposes.