The study of effect of carbon nanotubes on the compressive strength of cement-based materials based on machine learning

Abstract

There are many factors that affect the compressive strength of carbon nanotubes/ cementious composites. However, there is a lack of comprehensive research on the effect of various properties of CNTs on the compressive strength of cement-based materials. In this paper, machine learning was used to predict the compressive strength of single system and multi-system of cement-based materials mixed with CNTs, and the optimal parameters of CNTs properties were also determined. Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF) and Extreme Gradient Boosting (XGB) were used to predict the compressive strength of the cement paste, mortar and concrete(single system) respectively. The results showed that the R2 of prediction of RF and XGB models was greater than 0.9 for all three systems, and the accuracy of XGB was higher. The data of the three systems were combined into a new dataset(multi-system), RF and XGB were used for its prediction. The results indicated that the same prediction errors were achieved by RF and XGB, and the R2 was 0.93. SHAP model was utilized to explain the prediction results. The analysis of single system showed that the content and diameter of CNTs had obvious effects for all the three systems, and the influence degree of CNTs in concrete was less than that of paste and mortar. Some optimal parameters of CNTs were determined by the multi-system SHAP results: the optimal values of the length and diameter of CNTs were 20 μm and 25 nm, and the content should be within 0.1 %.