Prediction and parametric modeling of compressive strength of waste marble dust concrete through machine learning and experimental analysis

The production of waste marble is the ultimate concern of the construction industry's recent success in satisfying the huge need for marble stones. This study predicts the compressive strength of waste marble powder concrete using machine learning methods XG Boost, AdaBoost, Cat-Boost, Gradient-Boosting, Light Gradient-Boosting, and decision tree. For this purpose, a comprehensive dataset was developed from previously published literature, incorporating input features such as cement, fine and coarse aggregates, superplasticizers, silica fume, marble dust, and water, with compressive strength (CS) as the output parameter. Based on the outcomes, XG Boost model outperforms other models to predict CS with R² values of 0.999 and 0.915 on the train and test stage, respectively. The decision tree model also shows consistent performance with R² > 0.85 for both train and testing phases. In addition, experimental assessment was also conducted to verify the outcomes of machine learning (ML) modeling and quantify the CS of concrete specimens having marble dust as binder replacement (5-20% by weight). In addition, feature importance analysis highlights that superplasticizers, cement, and silica fume were the most weighted factors that affected CS. According to partial dependency plot (PDP) analysis, combined data on concrete strength versus cement concentration shows that the strength of the material increases from 37.5 MPa to 57.5 MPa when the cement concentration increases from 300 kg/m³ to 500 kg/m³. Furthermore, microstructural evaluation revealed a dense and well-compacted matrix in concrete containing waste marble powder, with improved bonding between the binder and aggregates.