Vol. 2 (2026)

Articles

Application of Artificial Neural Network to Validate and Predict the Physical Properties of Graphene Oxide-induced Rubberized Concrete

Published 2026-09-18

  • Gyanendra Kumar Chaturvedy

Gyanendra Kumar Chaturvedy
Department of Civil Engineering, Shivajirao S. Jondhle College of Engineering and Technology, Asangaon, Thane, Maharashtra, India

Abstract

The heap of exhausted rubber tires is a big threat to the sustainable environment. One of the solutions for these exhausted rubber tires is their application in concrete. Conventional concrete (CC) can be converted into rubberized concrete (RC) after the partial or complete substitution of natural aggregates (NA) by rubber aggregates (RA). In this research paper, graphene oxide (GO) is mixed with RC to analyze its effect on the physical characteristics of RC such as workability, density, and air content. Sixteen different concrete samples, having a water to cement ratio (w/c) of 0.252 and 0.3 each, were cast based on the variations of RA and GO as partial replacements of manufactured sand (MS) and cement. The experimental results were then used to model for artificial neural network (ANN) using WEKA software for validation and prediction of the physical properties of RC. The predicted outputs for each physical property had a coefficient of determination (R2) more than 0.92 for training and testing datasets of each physical property, indicating the developed model had higher goodness of fit. Sensitivity analysis was also performed on the developed model to visualize each variable's effect on RC's physical properties. The results suggest to use RA and GO in concrete, up to 15% and 0.05% respectively, without much affecting its physical properties and encourage the greener construction practices by reusing the waste rubber tires.

Keywords

Rubberized concrete, Rubber aggregate, Graphene oxide, Machine learning approach, Artificial neural network

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