Prediction of Rapid Chloride Penetration Resistance of Metakaolin Based Concrete Using Multi-Expression Programming

Abstract

This study investigates the resistance of concrete to Rapid Chloride ions Penetration (RCP) as an indirect measure of the concrete’s durability. The RCP resistance of concrete is modelled in multi-expression programming approach using different input variables, such as, age of concrete, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength (CS) of concrete. The parametric investigation was carried out by varying the hyperparameters, i.e., number of subpopulations Nsub, subpopulation size Ssize, crossover probability Cprob, mutation probability Mprob, tournament size Tsize, code length Cleng, and number of generations Ngener to get an optimum model. The performance of all the 29 number of trained models were assessed by comparing mean absolute error (MAE) values. The optimum model was obtained for Nsub = 50, Ssize = 100, Cprob = 0.9, Mprob = 0.01, Tsize = 9, Cleng = 100, and Ngener = 300 with MAE of 279.17 in case of training (TR) phase, whereas 301.66 for testing (TS) phase. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R and R2 values equaling 0.96 and 0.93 (for the TR phase), and 0.92 and 0.90 (for the TS phase), respectively. Similarly, parametric and sensitivity analyses revealed that the RCP resistance is governed by the age of concrete, amount of binder, concrete CS, and aggregate quantity in the concrete mix. Among all the input variables, the RCP resistance sharply increased within the first 28 days age of the concrete specimen and similarly plummeted with increasing the quantity of fine aggregate, thus validating the model results.