article · Scientific Reports
Using waste construction powders as partial replacements for cement contributes to greener concrete production, but exposure to high temperatures can degrade concrete strength. To better understand these effects, three machine learning models were developed to forecast the compressive strength of concrete containing marble and granite waste powders after exposure to heat. The models evaluated 324 cubic specimens tested across four variables: granite powder content, marble powder content, temperature up to 800 degrees Celsius, and heating duration up to two hours. Among the techniques tested, the extreme gradient boosting model showed the highest accuracy and lowest prediction errors. Further analysis indicated that both granite and marble waste powders positively contribute to compressive strength, whereas elevated temperature causes the greatest reduction. A graphical user interface was also created to help users apply these predictions.
Replacing cement with construction waste powders supports sustainable building, but builders need to know how these materials perform during fires or extreme heat. Using machine learning to accurately forecast concrete strength under thermal stress avoids extensive, costly physical testing, helping engineers assess sustainable concrete formulations safely.
This work is directly applied, having validated models against experimental data and packaged them into a functioning graphical user interface. The primary end users are civil engineers, concrete manufacturers, and construction researchers designing sustainable concrete mixes for fire-prone environments. Because the software tool is already constructed, it appears close to industry deployment for decision support and mix design optimisation.
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The addition of powders from waste construction materials as partial cement substitute in concrete represents a significant step toward green concrete construction. High temperatures have a substantial influence on concrete strength, resulting in a reduction in mechanical properties. The prediction of the impacts of waste powders on concrete strength is an important topic in sustainable construction. Such models are needed to understand the complex interactions between waste materials’ powders and concrete strength. In this study, three machine learning approaches, extreme gradient boosting (XGBoost), random forest (RF), and M5P, were used for constructing the prediction model for the impact of elevated temperatures on the compressive strength of concrete modified by marble and granite construction waste powders as partial cement replacements in concrete. Dataset of 324 tested cubic specimens with four input variables, waste granite powder dose (GWP), waste marble powder (MWP), temperature (T), and duration (D) were chosen for developing the prediction models. The output was the concrete compressive strength (CS). MWP and GWP ranged between 0 and 9%, temperatures were ranged between 25 °C and 800 °C, and duration up to 2 h. Hyperparameters in the RF and XGB models were optimized using grid search. K-fold cross-validation and several statistical measures, including R2MAPE, RMSE, and MAE, were utilized to validate and check the accuracy of the proposed models. The developed models were evaluated against experimental data and previously established models. The XGB model demonstrated the highest R2 of 0.9989, alongside the lowest prediction errors: MAE of 0.1351 MPa, RMSE of 0.1842 MPa, and MAPE of 0.48%. The results showed that the XGB prediction model for the concrete compressive strength outperformed the other proposed models. The SHAP analysis, Individual Conditional Expectation (ICE), and Partial Dependence Plots (PDP) revealed that GWP and MWP positively influence the compressive strength, while the temperature exerts the most negative influence on predicting the compressive strength. Finally, a graphical user interface (GUI) for the compressive strength of concrete containing GWP and MWP subjected to elevated temperatures has been created, which may be of considerable assistance, guidance, and efficiency in research and construction industry contexts.
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DOI: 10.1038/s41598-025-11239-9
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