Comparative Performance Analysis of Machine Learning Models for Compressive Strength Prediction in Concrete Mix Design

Recycled aggregate concrete (RAC) is a sustainable alternative to conventional concrete, reducing environmental hazards and conserving resources. Accurate compressive strength (CS) prediction is critical for its broader acceptance. This study uses machine learning (ML) models (elastic net regression, KNN, ANN, SVR, RF, XGBoost, CatBoost, symbolic regression, stacking) trained on 1030 conventional concrete mixtures from UCI to support RAC’s CS prediction. The best model achieved R2 = 0.92; performance order: CatBoost > XGBoost > RF > SVR > ANN > symbolic regression > KNN > elastic net regression. Stacking improved RMSE by 6% over CatBoost. During the testing, sensitivity analysis revealed that CS exhibits pronounced sensitivity to the cement (C) content and testing age (TA). This aligns with existing experimental research. External validation, which is often neglected by prediction model research, was performed, from which a high-quality evaluating model was used for generalizability and reliability, enhancing the heterogenicity of its usefulness. Lastly, a user-friendly graphical interface was developed that allows users to input custom parameters to obtain sustainable RAC mixtures. This study offers insights into optimizing concrete mix designs for RAC, improving its performance and sustainability. It also advances the knowledge of cementitious materials, aligning with industrial and environmental objectives.