A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis

Nowadays, recycled aggregate concrete (RAC) has been most extensively applied in the construction industry as a sustainable resource to decrease carbon dioxide emissions and construction waste. Predicting the compressive strength (CS) of RAC is crucial to understanding the behavior and performance of this environment-friendly (EF) concrete. This paper developed the models for forecasting the CS of RAC materials using hybrid machine learning (ML) models and ML with hyperparameter tuning techniques. The RAC experimental datasets were collected from the research literature, where the datasets were utilized for the 70% training and 30% testing phases of the models. This study used some renowned AI models such as XGBoost (Extreme Gradient-Boosting), GBM (Gradient Boosting Machine), RF (Random Forest), and the hybrid GBM–XGBoost model. The ensemble GBM–XGBoost algorithm showed the highest level of accuracy for CS prediction, with \({R}^{2}=\) $R^2=$ 0.982 for the training stage and \({R}^{2}=\) $R^2=$ 0.793 for the testing stage. The evaluation of the statistical indicators of AI algorithms revealed that the ensemble GBM–XBR had a more accurate prediction. The SHapley Additive exPlainations (SHAP) analysis showed that the effective water–cement ratio (We/C), nominal maximum RCA size, and replacement ratio positively correlated with the CS of RAC, which were the most significant parameters. The partial dependence plots (PDP) study displayed the optimal quantity of each parameter, which could help in mix design to achieve a targeted CS. Furthermore, the output of both the SHAP and PDP analyses could assist researchers and the industry in determining the quality of raw ingredients when preparing RAC.