Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites

Abstract Traditional experimental investigation on the mechanical properties of cement composites is deprecated due to the intensive time and labor involved. Existing predictive models can hardly map the complicated relationships among mechanical attributes and behavior. This study first adopts machine learning to predict the mechanical properties of carbon nanotube (CNT)-reinforced cement composites. For this purpose, predictive models are trained on the previously published experimental data and results demonstrate that machine learning models present better generalization ability and predictive performance than the traditional response surface methodology. A sensitivity analysis indicates that the factor having the maximum influence on compressive strength is the length of CNTs, whereas that having the maximum influence on flexural strength is the curing temperature. Thus, it can be concluded that compared with the traditional experimental investigation and regression methods, machine learning can efficiently and accurately predict the mechanical properties of CNT-reinforced cement composites.