A machine learning approach for assessing the compressive strength of cementitious composites reinforced by graphene derivatives

The potential reinforcement effect of graphene derivatives (GDs) on cementitious composites (CCs) has attracted significant attention. Previous studies, however, have produced varied results regarding the impact of GDs on CCs. This can be attributed to differences in the properties of GDs and the fabrication details of CCs reinforced by GDs. Experiments to explore these factors are both time-consuming and cost-ineffective. Additionally, no predictive model currently exists for assessing the influence of GDs on the compressive strength of CCs. In terms of Machine Learning (ML), most existing models focus on continuous parameters, including mixture design properties of CCs and reinforcing filler content, but ignore discontinuous parameters such as dispersion technique of GDs in CCs, curing type, and type of GDs. Compiling a unique dataset, this study tailors ML models to comprehensively explore the effect of GDs inclusion on the compressive strength of CCs, considering continuous and discontinuous parameters, including GD properties, fabrication details, and mixture design properties. The most used dispersion techniques and types of GDs were divided into different categories in this study. Moreover, the dataset included cement strength grade and fineness modules to distinguish between the effect of cement types' variety and GDs. Finally, the backwards elimination technique confirmed the necessity of such a customized dataset for trustworthy predictions. Artificial neural networks (ANN), decision trees, and support vector regressors could successfully investigate the impact of GDs' inclusion on the compressive strength of CCs, with ANN demonstrating superior prediction performance. Among the GDs properties, sensitivity analysis revealed that lateral size had the highest effect. Among the fabrication conditions, the dispersion technique had the greatest effect. Considering all investigated parameters, the water-to-cement ratio was considered the most influential, followed by lateral size and curing time. A low w/c significantly reduces the strength growth rate due to poor dispersion of GDs.

Automated summary

The potential reinforcement effect of graphene derivatives on cementitious composites has attracted significant attention. This study utilizes machine learning models to comprehensively explore the effect of graphene derivatives inclusion on the compressive strength of cementitious composites, considering various parameters. The results indicate that the artificial neural network model demonstrates superior prediction performance, and the lateral size of graphene derivatives and the dispersion technique have the highest impact.