Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models

Abstract

The utilization of recycled brick aggregate concrete (RBC) is an area of active research, in which further investigation is needed to develop accurate models for predicting the behavior of RBC to ensure its safe and sustainable use in construction. This study presents an effective way to determine RBC's compressive strength (CS) based on an appropriate design of ensemble machine learning (ML) models, namely Gradient Boosting, Light Gradient boosting, AdaBoost, Extreme Gradient Boosting, Stacking, and Voting. A database covering 393 test results is compiled from the relevant literature for training and testing the models. In addition, 10-fold cross-validation and random splitting of data are used to ensure the reliability of predictions, as well as prevent overfitting. The findings reveal that the Stacking model has the greatest predictive ability, with a coefficient of determination of 0.95, root mean square error of 2.74 MPa, mean absolute error of 2.09 MPa, mean absolute percentage error of 0.10 on the testing dataset. In addition, Feature importance and Partial dependence plots analysis are utilized to investigate the impact of each RBC component on its CS. In RBC’s mixed-component design, the outcomes of this research, along with a developed Graphical User Interface (GUI) for the CS, might be of great use to material engineers.