Prediction of Compressive Strength of Green Concrete with Admixtures Using Neural Networks
Priyanka Singh, Partha Khaskil
Abstract
Concrete is manufactured by mixing cement, water, fine aggregates and coarse aggregates in certain proportions to obtain a desired strength. In addition, fly ash, super plasticizers, retarders are added to enhance any desired property depending upon the function of use of concrete in structures. Compressive strength of concrete is dependent upon several parameters most likely to be water-cement ratio, cement strength, quality of concrete material, and quality control during production of concrete. In this work, we present a neural network model for prediction of compressive strength of concrete. Different sets of data based upon several concrete design mixes were taken and were fed to the model. The model is then such trained for prediction, which are being influenced by several input attributes and were jotted down a linear regression analysis.