Mathematical modeling to predict the compressive strength of eco- friendly pervious concrete modified with waste glass powder

Abstract Due to the climatic change and increase the flood rick in many countries, the usage of pervious concrete has been increased as a solution of the water collecting in the underground, since its usage will be in the low loaded area the usage of waste materials to obtain eco-friendly pervious concrete is one of the challenges to the researchers. This article deals with the proposing mathematical model (Linear regression, non-linear regression and artificial neural network) to predict the compressive strength of pervious concrete modified with waste glass powder as partial replacement of cement. Based on the obtained result artificial neural network (ANN) provide higher accuracy and efficiency compare to linear regression (LR) and nonlinear regression model (NLR) since its scatter index value (SI) value lower than 0.1 and its coefficient of determination value (R2) higher than LR by 22% and 17% compare to NLR.