A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Obtaining a trustworthy approach to forecast the chloride penetration into self‐compacting concrete via rapid test may lead to frugality in cost, time, and energy to provide a durable mix design. Different single and hybrid regression methods are developed to predict the results of rapid chloride penetration tests in the present study. Cement content, fly ash, and silica fume replacement percent with cement, temperature and fine and coarse aggregates are considered as input variables. All predicted values using expanded models have a good agreement with experimentally measured results. Evaluating the accuracy and precision of single and hybrid optimized models by five statistical performance criteria (R2, root mean square error, mean absolute error, mean absolute percentage error, and performance index) illustrates that the hybrid support vector regression with optimization algorithm is a high‐accurate promising model for predicting the results of a rapid chloride penetration test.