The incorporation of waste ground glass powder (GGP) in concrete as a partial replacement of cement offers significant environmental benefits, such as reduction in CO₂ emission from cement manufacturing and decrease in the use of colossal landfill space. However, concrete is a heterogeneous material, and the prediction of its accurate compressive strength is challenging due to the inclusion of several non-linear parameters. This study explores the utilization of different machine learning (ML) algorithms: linear regression (LR), ElasticNet regression (ENR), a K-Nearest Neighbor regressor (KNN), a decision tree regressor (DT), a random forest regressor (RF), and a support vector regressor (SVR). A total of 187 sets of pertinent mix design experimental data were collected to train and test the ML algorithms. Concrete mix components such as cement content, coarse and fine aggregates, the water–cement ratio (W/C), various GGP chemical properties, and the curing time were set as input data (X), while the compressive strength was set as the output data (Y). Hyperparameter tuning was carried out to optimize the ML models, and the results were compared with the help of the coefficient of determination (R²) and root mean square error (RMSE). Among the algorithms considered, SVR demonstrates the highest accuracy and predictive capability with an R² value of 0.95 and RMSE of 3.40 MPa. Additionally, all the models exhibit R² values greater than 0.8, suggesting that ML models provide highly accurate and cost-effective means for evaluating and optimizing the compressive strength of GGP-incorporated sustainable concrete.

The incorporation of waste ground glass powder (GGP) as a partial replacement in cement offers significant environmental benefits, such as reduction in CO2 emission from cement manufacturing and decrease in the use of colossal landfill space. However, concrete is a heterogeneous material and the prediction of its accurate compressive strength is challenging due to the inclusion of several nonlinear parameters. This paper explores the utilization of different machine learning (ML) algorithm; Linear Regression (LR), Elastic Net regression (ENR), K-nearest Neighbor Regressor (KNN), Decision Tree Regressor (DT), Random Forest Regressor (RF), and Support Vector Machine (SVM). A total of 188 sets of pertinent mix design experimental data are collected to train and test the MLalgorithms. Concrete mix components such as cement content, coarse and fine aggregates, water-cement ratio (W/C) ratio, various GGP chemical properties, and curing time are set as input data (X) while the compressive strength is set as the output data (Y). The hyperparameter tuning is carried out to optimize the ML models, and the results are compared with the help of the coefficient of determination (R2) and root-mean-square error (RMSE). Among the algorithms used, SVM demonstrates the highest accuracy and predictive capability among all models with R2 value of 0.95 and RSME of 3.40 MPa. Additionally, all the models exhibit R2 value higher than 0.8, suggesting ML models provide highly accurate and cost-effective means for evaluating and optimizing the compressive strength of GGP concrete.