Development of Forecasting Model for Prediction of Compressive Strength of Foamed Concrete using Density with W/C ratio and S/C ratio by the Application of ANN

Abstract

Abstract This Artificial neural network study presents the prediction model for a cellular foamed concrete. Foamed Concrete is a cementitious material that should consist of a minimum of 20% of foam, which is mechanically entrained using the mechanical generator of foam. Foamed Concrete possesses a cellular microstructure. By which they become a highly air-entrained system having unusual physical and mechanical properties. It is the perfect mixture of cement, water, sand (fine aggregate), and perforated foam. Published information related to the prediction of foamed concrete is limited, and rational guidelines to evaluate the compressive strength of the concrete are not widely available. This study aims to encourage the strength of foamed concrete economically and predict the strength in the compressive form of concrete. A dataset of 153 instances having an input parameter proportion of Density, W/C ratio, & S/C ratio have been taken to predict compressive strength to elevate and expand the precision and accuracy of the foamed concrete. The data has been trained with the help of ANN, in which we conduct a network analysis to forecast the compound’s performance and stability. The deficiency of strength of foamed concrete is to be sorted out with the help of ANN, and the prominent and reliable equation for the compression power is generated. ANN helps to optimize the compressive strength at the time of physical casting of the concrete.