Prediction of compressive strength of alkali-activated construction demolition waste geopolymers using ensemble machine learning

This paper mainly employed the random forest (RF), gradient boosting (GB) and extreme gradient boosting (XGB) to predict the compressive strength of alkali-activated construction demolition waste geopolymers (CDWG). The performances of three ensemble machine learning (ML) models were evaluated and the effects of eight different input features on the compressive strength of CDWG were deeply analyzed. The results confirm the applicability of RF, GB and XGB algorithms in aspect of strength prediction for the CDWG with the high predictive accuracy (R2 > 0.9). Among them, the performances of GB and XGB models are better than RF model. The liquid to solid ratio (L/S) has a negative correlation with the compressive strength of CDWG, while the pretreatment temperature, heat treatment time and curing age have a positive correlation with the compressive strength of CDWG. The obvious enhancement of compressive strength of CDWG mainly occurs in the early age. Decreasing L/S and raising pretreatment temperature have a significant positive gain on the compressive strength of CDWG. In the preparation process of CDWG, it is suggested that the L/S and %Na2O of Na2SiO3-based alkaline activators are controlled at about 0.3 and 7 % respectively, and appropriately increasing pretreatment temperature and prolonging heat treatment time.