The impact of oxides of cementitious materials on mortar strength: A machine learning perspective

Abstract

This study uses machine learning to predict the compressive strength of cement-sand mortar incorporating supplementary cementitious materials (SCMs). The research addresses a gap in the literature by specifically examining how the oxide composition of SCMs influences mortar strength. Using a dataset of various mortar mixes, several machine learning models were tested, with the extreme gradient boosting (XGB) model emerging as the most effective, achieving a testing R2 of 0.90. The results show that the curing period is the most influential factor on compressive strength, followed by the oxide compositions of the SCMs. This work highlights the potential of machine learning for enhancing material performance predictions and supports the development of more sustainable and durable construction practices.