Abstract
•Developed an ML-based methodology for modeling the compressive strength of GRAC.•Employed TGAN for data augmentation to overcome the limitation of small datasets.•Achieved less than 6 % error in predicting GRAC strength with boosting ML models.
In the quest to reduce the environmental impact of the construction sector, the adoption of sustainable and eco-friendly materials is imperative. Geopolymer recycled aggregate concrete (GRAC) emerges as a promising solution by substituting supplementary cementitious materials, including fly ash and slag cement, for ordinary Portland cement and utilizing recycled aggregates from construction and demolition waste, thus significantly lowering carbon emissions and resource consumption. Despite its potential, the widespread implementation of GRAC has been hindered by the lack of an effective mix design methodology. This study seeks to bridge this gap through a novel machine learning (ML)-based approach to accurately model the compressive strength (CS) of GRAC, a critical parameter for ensuring structural integrity and safety. By compiling a comprehensive database from existing literature and enhancing it with synthetic data generated through a tabular generative adversarial network, this research employs eight ensemble ML techniques, comprising three bagging and five boosting methods, to predict the CS of GRAC with high precision. The boosting models, notably extreme gradient boosting, light gradient boosting, gradient boosting, and categorical gradient boosting regressors, demonstrated superior performance, achieving a mean absolute percentage error of less than 6 %. This precision in prediction underscores the viability of ML in optimizing GRAC formulations for enhanced structural applications. The identification of testing age, natural fine aggregate content, and recycled aggregate ratio as pivotal factors offers valuable insights into the mix design process, facilitating more informed decisions in material selection and proportioning. Moreover, the development of a user-friendly graphical interface for CS prediction exemplifies the practical application of this research, potentially accelerating the adoption of GRAC in mainstream construction practices. By enabling the practical use of GRAC, this research contributes to the global effort to promote sustainable development within the construction industry.
