Evaluating the Properties of Pervious Concrete Pavement Towards Sustainable Environment Using Machine Learning Method

Abstract

Permeable concrete pavements (PCP) are a part of the innovative concepts of our present infrastructure which are currently being implemented. These pavements step towards the green and sustainable environment, conserving the groundwater by recharging. PCP operates on the principle of Rain & Drain concept, which eliminates the first flush of stormwater onto the pavements by creating - high water permeability through the existing interconnected large pore structure. Due to these phenomena even after offering the good permeability, it suffers many unresolved issues related to physical, mechanical and structural properties. A lot of recent studies are carried out enhancing PCP by mixing additives, Fibers, SCM’s etc. into it. In this paper, directly attempted into the mix design, based on targeted density to which the mix has to be obtained. This density-based mix design improves the compressive strength and could satisfy the permeability of the concrete by obtaining porosity. Mix by modification and blending of aggregates helps in achieving the strength, plays a vital role. This leads to our current step in our research to attain the better properties, by selecting the aggregate sizes and ratios among the materials of the mix. For generating the Machine Learning models, R-Studio platform is used to input the data sets with varying dependent and independent variables.

Keywords: Density based mix design, Compressive strength, Permeability and Porosity, Machine Learning model.