Predicting Compressive Strength of Sustainable Self-Consolidating Concrete Using Random Forest

This paper demonstrates the application of Random Forest (RF) algorithm for prediction of compressive strength of sustainable self-consolidating concrete (SCC) in which significant amount of cement was replaced with minerals such as fly ash, ground granulated blast furnace slag (GGBS), and silica fume. SCC improves the quality of the finished concrete product and is considered an environmentally friendly alternative to conventional concrete. RF proved capable of predicting compressive strength with high accuracy. The ability of RF algorithm to predict compressive strength established confidence on the experimental data itself which can be used for further studies on properties of self-consolidating concrete. The high level of accuracy in predicting essential engineering properties of concrete through RF algorithms offers important opportunities to enhance quality in ready mix production industry.