Predicting Rapid Chloride Permeability of Self-Consolidating Concrete: A Comparative Study on Statistical and Neural Network Models

This paper is intended to compare robustness of linear and nonlinear regressions, and neural network prediction models in estimating rapid chloride permeability of self-consolidating concretes based on their mixture proportions. Several models were developed by varying number of independent variables and samples (mixtures) allotted to training and testing. The results of this study showed the superior performance of neural network models in comparison with the prediction models obtained by linear and nonlinear regressions, particularly when testing evaluations were chosen from the boundaries of mixture proportions. Within the linear and nonlinear prediction models, power relationships produced the most consistent performance