Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste

Pervious concrete is a kind of concrete used for storm-water management due to its high porosity and permeability. However, its’ flexural strength as the most desirable mechanical properties was predicted in this study. The paper aims to demonstrate three soft-computing models, i.e., artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were applied for the prediction of flexural strength ( σ f) of pervious Concrete (PC) incorporated with calcium carbide waste (CCW) and rice husk ash (RHA) as supplementary cementation materials. The models were trained on the experimental data obtained by replacing cement content from 0 to 10