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Volume 13 | Issue 5 | Year 2026 | Article Id. IJCE-V13I5P110 | DOI : https://doi.org/10.14445/23488352/IJCE-V13I5P110

Predictive Modelling of High-Performance Recycled Aggregate Concrete using Artificial Neural Networks

Olutosin Peter Akintunde, Jacques Snyman, Chris Ackerman, Williams K. Kupolati

Received Revised Accepted Published
06 Oct 2025 04 Nov 2025 20 Mar 2026 29 May 2026

Citation :

Olutosin Peter Akintunde, Jacques Snyman, Chris Ackerman, Williams K. Kupolati, "Predictive Modelling of High-Performance Recycled Aggregate Concrete using Artificial Neural Networks," International Journal of Civil Engineering, vol. 13, no. 5, pp. 124-148, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I5P110

Abstract

The product of so many years of research that has now resulted in the incorporation of fibre reinforcement and other cementitious components to put Recycled Aggregate Concrete (RAC) to work is High-Performance Recycled Aggregate Concrete (HPRAC). Nevertheless, predicting the strength is complicated due to nonlinear relationships between the quantity of Recycled Aggregate, dosage of Calcined Kaolin Clay (CKC), the addition of Alkali-Resistant Glass Fibre (ARGF), and the age of curing. This paper creates an Artificial Neural Network (ANN) model using MATLAB®, which uses an experimental dataset of huge size (≈2,400 data points) and 8 input parameters, trained to predict Compressive, Flexural, and Splitting Tensile Strengths. The accuracy of the prediction and generalization is validated by the ANN with Coefficients of Determination (R²) of more than 0.97 and Low Root Mean Square Error (RMSE < 2.0 MPa) of all strength properties. Sensitivity analysis shows that the most important factor is curing age, followed by RCA content, CKC dosage, and ARGF addition. These findings indicate that through offering data-based decisions to implement recycled materials in high-performance uses, ANN-based modelling can significantly decrease experimental load, improve mix design to attain structural reliability, and promote sustainable concrete practices.

Keywords

Artificial Neural Network (ANN), High-Performance Recycled Aggregate Concrete (HPRAC), Mix Design Optimization, Strength Prediction, Sustainable Concrete.

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