The durability of the reinforced cement concrete (RCC) structures exposed to open areas, i.e., bridges and marine structures, is affected by the chloride ions. The fly ash, silica fume, and ground granulated blast slag materials are used to reduce the chloride ions penetration. Still, the laboratory procedure for determining the chloride permeability is time-consuming and lengthy. This investigation introduces an optimal performance model to assess the chloride permeability of fly ash concrete. For that purpose, a database of chloride permeability results of 288 concrete specimens has been compiled from the literature. This research employs genetic and particle swarmoptimized relevance vector machine (RVM) models. Moreover, these RVM models have been configured by single and dual kernels. In addition, the extreme gradient-boosting (XGBoost) model has been developed and compared with RVM models. The performance comparison reveals that the RVM1 model has predicted chloride permeability with a performance index of 1.95, root mean square error of 286.8311C, a correlation coefficient of 0.9923, and the variance accounted for of 98.42 in the testing phase, close to the ideal values, followed by XGBoost model. The variance inflation factor (VIF) revealed that the binder and water-to-binder ratio features have considerable multicollinearity. This research also demonstrates that the database and structural multicollinearity highly influence the prediction capabilities of the RVM4 models. The score analysis, regression error characteristics curve, accuracy matrix, computational cost, and reliability analysis confirm that the RVM1 model is an optimal performance model in predicting the chloride permeability of fly ash concrete. This investigation will help concrete designers and engineers to determine the chloride permeability without performing laboratory procedures in mega construction projects.
