Chloride diffusion modeling of concrete using tree‐based forest models

Reinforced concrete structures can experience various harsh environments during their service life, among which chloride ion exposure, especially in marine environments, can cause the durability reduction and deterioration of concrete structures. Artificial intelligence (AI)‐based modeling of the non‐steady‐state apparent chloride diffusion coefficient (D_C) of concrete for a long exposure time using the experimental field results can assist in identifying the influential factors and better estimating the service life of a concrete structure. In this study, two novel extensions of ensemble AI algorithms, including genetic programming forest (GPF) and linear genetic programming forest (LGPF) algorithms, were proposed to model the D_C of concrete. The experimental field data were gathered from the literature. Different structures of the proposed ensemble methods were developed and examined, and the best‐developed model was selected for further analysis, including sensitivity analysis and parametric study. In addition, the random forest (RF) method was used as the control ensemble technique to have a comparison. The results show that the best LGPF model possesses superior performance than the best‐developed GPF and RF models. In addition, the results show that silica fume‐to‐binder ratio, exposure time, and exposure conditions have the most significant impacts on the D_C of concrete. This study contributes to the civil engineering practice by developing a new tool to model the D_C of concrete that facilitates the durability assessment of concrete structures.