TY  - EJOU
AU  - Choudhary, Sandeerah 
AU  - Abbas, Qaisar 
AU  - Akram, Tallha 
AU  - Qureshi, Irshad 
AU  - Aldajani, Mutlaq B. 
AU  - Salahuddin, Hammad 

TI  - Predicting Concrete Strength Using Data Augmentation Coupled with Multiple Optimizers in Feedforward Neural Networks
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 145
IS  - 2
SN  - 1526-1506

AB  - 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 R<sup>2</sup> 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.
KW  - Feedforward neural networks; recycled aggregates; compressive strength prediction; optimization techniques; data augmentation; grid search

DO  - 10.32604/cmes.2025.072200