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 R 2 values of 0.999 and 0.915 on the train and test stage, respectively. The decision tree model also shows consistent performance with R 2 > 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/m 3 to 500 kg/m 3. Furthermore, microstructural evaluation revealed a dense and well-compacted matrix in concrete containing waste marble powder, with improved bonding between the binder and aggregates.

Fly ash foam composite concrete (FFC) is a sustainable, lightweight alternative to traditional concrete. However, accurately predicting its compressive strength (CS) through conventional laboratory methods is challenging due to its non‐linear behavior induced by the addition of foam content and fly ash. Also, the laboratory determination of CS is time‐consuming and resource‐intensive. Since there are very few techniques available in the literature to precisely predict CS of FFC based on its mixture composition, this study aims to overcome these limitations by developing accurate prediction models for CS of FFC using machine learning techniques such as AdaBoost, gradient boosting regressor (GBR), and multi expression programming (MEP). The mixture composition data of FFC to be used for this purpose was collected from already published literature having inputs such as water‐to‐cement ratio, foam content, and concrete age. The machine learning models were trained and tested on the training and testing subsets of this data. The model performance was evaluated using error metrics, external validation, and k‐fold cross‐validation. Among the models, AdaBoost demonstrated the highest prediction accuracy with an R ² value of 0.977, while MEP offered the unique advantage of producing an interpretable mathematical equation. Further analysis using SHAP, individual conditional expectation, and partial dependence plots, etc., highlighted that foam content, fly ash, and w/c are key factors to determine CS of FFC. To facilitate practical application, a user‐friendly graphical interface (GUI) was developed, enabling engineers and practitioners to estimate FFC's CS efficiently and accurately using the best‐performing model from this study. The findings from this study will help engineers to accurately determine CS of FFC, and the insights from explanatory analysis will aid in mixture composition optimization.

Blast furnace slag (BFS) concrete offers significant environmental and durability advantages over ordinary portland cement (OPC) concrete, including reduced CO₂ emissions, enhanced long-term strength, and stronger resistance to chemical attacks. However, refining its mix design using conventional experimental methods is time-consuming and costly. This study addresses this challenge by developing advanced machine learning (ML) models to predict the compressive strength of BFS-incorporated concrete. A large dataset of 675 samples featuring cement, BFS, fly ash, aggregates, water, superplasticizer (SP), and curing age was assembled. Six ML models—AdaBoost, Decision Tree, Gradient Boosting Regressor, K-Nearest Neighbors, LightGBM, and XGBoost were evaluated. Comprehensive hyperparameter tuning via grid search and cross-validation optimized model performance and mitigated overfitting. Predictive accuracy was assessed using R², RMSE, MAE, and MAPE metrics. Model interpretability was enhanced through SHAP analysis and partial dependence plots (PDP), revealing curing age, SP, and cement as dominant features influencing compressive strength. Results demonstrated that LightGBM (test R² = 0.946, RMSE = 4.41 MPa) and XGBoost (test R² = 0.943, RMSE = 4.52 MPa) exhibited almost comparable predictive performance; however, LightGBM achieved the highest overall accuracy, reflected in its slightly higher test R² and lower RMSE, which declares LightGBM the best model for predicting CS of BFS-concrete. PDP analysis revealed that the optimal BFS replacement was observed between 30 and 40% range. This ML framework eliminates resource-intensive experimentation, accelerating sustainable concrete design with industrial byproducts.