Concrete 2.0: Harnessing Machine Learning For Strength Prediction

This study predicts the capability of recycled plastic granules as a sustainable opportunity to conventional coarse mixture in concrete by using neural network. The goal is to increase a study gadget learning model capable of accurately predicting the compressive strength of concrete containing numerous chances of recycled plastic aggregate. This learning is helpful to overcome the required time period for knowing concrete strength by traditional method. This research contributes imparting a reliable tool for predicting the compressive strength of concrete with plastic combination. It optimize the combination layout for numerous programs, and inspect the effect of various kinds of plastic waste on concrete.