Predicting the compressive strength of concrete incorporating waste powders exposed to elevated temperatures utilizing machine learning.

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 R