Machine Learning Based Reactivity Prediction of Fly Ash Type F Produced from South Korea

Abstract

Fly ash (FA) is the most commonly used supplementary cementitious material in the world. However, the reactivity of FA varies substantially. In this study, new machine learning (ML) model has been developed to efficiently predict the amorphous content in FA type F. Compared to the existing ML model using types F and C of FA from different countries, this study more focused on the improved prediction of FA type F only produced from South Korea. It was found that the contents of CaO and SiO2 impact high in predicting the amount of aluminosilicate glass. However, the contribution of Al2O3 and Fe2O3 are ranked differently. The improved model algorithm was proposed as a combination of three ensemble techniques of bagging, boosting, and stacking. As a result of the test, the final model shows of 0.80 in predicting the amount of aluminosilicate glass in FA type F.