Multi-output machine learning techniques to predict strength characteristics of nano graphene oxide reinforced cement composites

Abstract

The use of nano graphene oxide (GO) in cement composites has shown tremendous potential for improving strength and performance characteristics. The impact of the addition of GO in concrete remains uncertain due to the interaction of the mix ingredients with the graphene oxide. To examine the influence of multiple coupling parameters on forecasting the mechanical properties using traditional experimental methods are cumbersome. In this study, Machine Learning (ML) approaches are used to investigate the intricate relationship between the multiple influencing parameters on the mechanical properties of GO reinforced cement composites. A comprehensive collection of 260 datasets related to GO, with 10 input parameters, was collected to train and test the machine learning models. Different Machine Learning techniques were applied to predict the multi-output parameters simultaneously. The SHapley Additive exPlanations approach identified the most influential parameters of the composite strength characteristics. The results revealed that the XGBoost model delivered highly accurate predictions, with lower RMSE, MSE, and MAE values, and a higher R² value of 0.9 compared to other ML models. Multi-Output Machine Learning Techniques have proven to be a quick and cost-effective solution, an alternative to time-consuming traditional tests.