Open Access

ARTICLE

Predicting Concrete Strength Using Data Augmentation Coupled with Multiple Optimizers in Feedforward Neural Networks

Sandeerah Choudhary¹, Qaisar Abbas², Tallha Akram^3,*, Irshad Qureshi⁴, Mutlaq B. Aldajani², Hammad Salahuddin¹

1 Department of Civil Engineering, COMSATS University Islamabad, Wah Campus, Wah, 47040, Pakistan
2 College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
3 Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
4 Department of Civil Engineering, University of Engineering & Technology, Taxila, 47040, Pakistan

* Corresponding Author: Tallha Akram. Email:

(This article belongs to the Special Issue: AI and Optimization in Material and Structural Engineering: Emerging Trends and Applications)

Computer Modeling in Engineering & Sciences 2025, 145(2), 1755-1787. https://doi.org/10.32604/cmes.2025.072200

Received 21 August 2025; Accepted 16 October 2025; Issue published 26 November 2025

Abstract

The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete (RAC) as an eco-friendly alternative to conventional concrete. However, predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters. This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks (FFNN), Random Forest (RF), and XGBoost. A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module: Bayesian Regularization, Levenberg-Marquardt, and three conjugate gradient variants—Powell/Beale Restarts, Fletcher-Powell, and Polak-Ribiere. Hyperparameter tuning, dropout regularization, and early stopping were employed to enhance generalization. Comparative analysis revealed that FFNN outperformed RF and XGBoost, achieving an R² of 0.9669. To ensure interpretability, accumulated local effects (ALE) along with partial dependence plots (PDP) were utilized. This revealed trends consistent with the pre-existent domain knowledge. This allows estimation of strength from the properties of the mix without extensive lab testing, permitting designers to track the performance and sustainability trends in concrete mix designs while promoting responsible construction and demolition waste utilization.

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Keywords

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