Prediction on Dynamic Yield Stress and Plastic Viscosity of Recycled Coarse Aggregate Concrete Using Machine Learning Algorithms

Recycled coarse aggregates (RCA) offer an alternative to natural coarse aggregates in concrete production, reducing natural aggregate extraction and landfill burdens and potentially lowering embodied energy and CO2 emissions. This study leverages machine learning algorithms to predict the dynamic yield stress (DYS) and plastic viscosity (PV) of RCA concrete (RCAC). A database of 380 RCAC mixtures, incorporating 11 input features, was analyzed using six machine learning models: Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM). The model performance was compared, followed by sensitivity analyses to identify critical factors influencing DYS and PV. For DYS, the DT model demonstrated the highest predictive performance (testing R2/RMSE/MAE = 0.95/18.25/13.99; others: 0.90–0.93/12.14–26.10/15.40–19.50) due to its robustness on smaller datasets. The XGBoost model led for PV (testing R2/RMSE/MAE = 0.93/7.06/4.58; others: 0.82–0.89/8.69–11.20/6.06–7.51) owing to its sequential residual minimization that captures nonlinear interactions. Sensitivity analyses revealed that polycarboxylate superplasticizer content and water-to-binder ratio significantly influence DYS, while cement content and saturated-surface-dried water absorption of RCA (i.e., measured with open pores filled and the aggregate surface dry) dominate PV. The time-dependent role in affecting PV was also highlighted. By optimizing and comparing different machine learning algorithms, this study advances predictive methodologies for the rheological properties of RCAC, addressing the underexplored use of machine learning for RCAC rheology (DYS and PV) and the limitations of traditional empirical rheology methods, thereby promoting the efficient use of recycled materials in sustainable concrete design.