Performance Evaluation of RCA-Based Sustainable Concrete Using Artificial Neural Networks

Paper ID: EIJTEM_2026_13_2_224-237

Author's Name: Arjun Reddy Madduri, Maheswararao R, Kalyani Gurram, Venugopal P and Jayakrishna J

Volume: 13

Issue: 2

Year: 2026

Page No: 224-237

Abstract:

The usage of recycled coarse aggregate (RCA) in concrete is growing in order to satisfy consumer demand for environmentally friendly building materials. However, RCA can degrade the mechanical qualities of concrete because of the old mortar and increased porosity it contains. The strength and microstructure of the finished concrete were improved by the addition of silica fume as a mineral additive to address these problems. In order to evaluate fresh and hardened properties, including as workability using slump cone tests and mechanical attributes like compressive, split tensile, and flexural strengths, the study entailed mixing concrete with different proportions of RCA and silica fume. Higher RCA content decreased mechanical performance due to weaker interfacial zones and increased water absorption, according to the results. For example, a mix containing 60% RCA exhibited a decrease in compressive strength from 38.6 MPa in the control mix to 30.2 MPa. On the other hand, adding silica fume produced notable benefits; the ideal mixture (20% RCA and 10% silica fume) produced improved results in other characteristics as well as a compressive strength of 42.5 MPa. In order to forecast concrete qualities based on mix ratios, water-cement ratios, and other variables, an Artificial Neural Network (ANN) model was also created using MATLAB. This model achieved a high prediction accuracy with a regression value of R = 0.984. By comparing predictions with experimental findings, the model's dependability was validated, and the results showed little differences. The study backs the idea that ANN models offer a practical way to estimate concrete parameters, thereby reducing costs and resources in infrastructure projects, and that RCA combined with silica fume can produce effective sustainable concrete.

Keywords: Artificial Neural Network; Compressive Strength; Recycled Coarse Aggregate; Silica Fume.

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