Experimental assessment and hybrid machine learning prediction and optimization of compressive strength of steel fiber reinforced demolition waste concrete

With the rapid growth of artificial intelligence, machine learning has emerged as a useful tool in the construction industry for optimizing materials and predicting concrete properties. This study explores the use of steel fiber and recycled demolition waste (DW) to improve concrete’s mechanical properties. The main objective is to predict the compressive strength (CS) of DW-modified fiber-reinforced concrete (FRC) by assessing the effects of cement, RHA, fiber content, natural and demolished waste aggregates, water, and superplasticizer dosages. Seven mix designs with varying fiber and DW levels were experimentally tested. Machine learning methods, including adaptive boosting (ADB), extreme gradient boosting (XGB), random forest (RF), and stacking models (XGB-ADB, XGB-RF), were applied to analyze these variables’ impact on CS. A dataset of 405 points was compiled from literature via a systematic review. The hybrid XGB-RF and XGB models showed the best performance with R2 values of 0.849 and 0.845, respectively. SHAP analysis identified cement, water, and superplasticizer as key factors affecting CS. Experimental validation supported the modeling results and the development of a graphical user interface. The novelty lies in integrating hybrid ML, explainable analysis, and experimental validation to predict the CS of the modified concrete and support mix design through a user-friendly GUI.