Indirect Prediction Of Graphene Nanoplatelets-reinforced Cementitious Composites Compressive Strength By Using Machine Learning Approaches

This article discusses the use of graphene nanoplatelets (GrNs) in cementitious composites and the development of machine learning models to predict their compressive strength. Four ML methods were employed and the CatBoost model showed exceptional prediction efficiency. The study also highlights the importance of GrN thickness in determining compressive strength.