An intelligent framework for compressive strength prediction of eco-friendly SFR-RCAC: Base and stacked ensemble models combined with experimental verification

This paper adopts an approach based on base and stacked ensemble models to correctly model the compressive strength (CS), which is a key parameter to provide structural integrity and reliability of steel fiber-reinforced recycled coarse aggregate concrete (SFR-RCAC). To this end, a reliable framework is adopted that includes cleaned 440 instances with 11 input features. Additionally, the impact of the input features on the model is investigated in detail via SHapley additive explanation (SHAP) and partial dependence plots (PDPs) analyses. To facilitate practical implementation, a graphical user interface (GUI) is designed to make the estimation process user-friendly and its reliability is verified by additional experimental tests. Based on the results, all the developed models are capable of predicting the CS of SFR-RCAC with extraordinary accuracy and reliability: the 6 base ensemble models achieved an average R2 = 0.936 and RMSE = 3.55 during the testing phase, while the 35 stacked models recorded R2 = 0.942 and RMSE = 3.38, respectively. Notably, the stacked ensemble model (SM26) with the combination of Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Extra Trees Regressor (ETR) showed the best prediction performance in the test phase with the highest R2 (0.948) and the lowest RMSE (3.20) as well as the highest total score (287). Additionally, the error rate between the experimental values and the GUI predictions for the 10 designed mixes remains below +/- 8 %, verifying that the proposed GUI has high accuracy and robust generalization capability. Moreover, based on SHAP and PDP analyses, it is recommended for practical engineering applications to optimize the CS of SFR-RCAC by limiting the recycled coarse aggregate substitution ratio (Rr) to approximately 0.40, maintaining the steel fiber volume fraction (Vf) around 1.0 %, keeping the fiber factor (F) within the range of 0.6-0.8, and adjusting the water-tobinder (W/B) ratio between 0.30 and 0.40. To conclude, this research reveals the outstanding performance of the proposed models and GUI for predicting the CS value of SFR-RCAC and provides a significant contribution to the existing literature in this field. Thus, by promoting the efficient use of recycled coarse aggregates, it reduces the consumption of natural resources and allows the recycling of environmentally hazardous waste in the construction industry.