Analyzing and Predicting Permeability Coefficient of Roller-Compacted Concrete (RCC)

The permeability of roller-compacted concrete (RCC) substantially affects its functionality and safety. This study investigates the effect of mix design parameters on the performance of RCC. For this purpose, approximately 500 laboratory specimens were prepared and tested. A formula and an artificial neural network (ANN) were proposed to predict the permeability coefficient of RCC by considering the main parameters, which were then verified independently using new specimens. Furthermore, the experimental data were analyzed by the Taguchi method and analysis of variance (ANOVA) to evaluate the level of parameter contribution. Based on the results, the permeability coefficient was highly dependent on the mix design and strength of the RCC specimens. The ANN model can predict the permeability coefficient of RCC more accurately than the proposed formula. The statistical analyses revealed that the water-to-cement ratio had the highest effect on the permeability coefficient and the mechanical properties. The findings of this investigation indicated valuable information regarding cost and time savings as well as eliminated laboratory trial and error in designing RCC structures.