Machine learning assisted prediction of the mechanical properties of carbon nanotube‐incorporated concrete

Abstract

The incorporation of carbon nanotubes (CNTs) in concrete can improve the physical, mechanical, and durability properties. However, the interaction of CNTs with concrete and their effect on the mechanical properties remains a challenging issue. Also, the determination of mechanical properties through experimental testing is time‐consuming, laborious, and uneconomical. This study focuses on the development of machine learning (ML) models for the prediction of the mechanical properties of concrete. A comprehensive data set of 758 CNT‐modified concrete specimens was established for the compressive strength (CS), split tensile strength (STS), flexural strength (FS), and modulus of elasticity (MOE) values from the experimental studies in the literature. Afterward, the predictive models were developed using multilinear regression (MLR), support vector machine (SVM), ensemble methods (EN), regression tree (RT), and Gaussian process regression (GPR). It was found that among ML models, the GPR model predicted the CS, STS, and FS at the highest efficiency with the coefficient of determination (R2) of 0.83, 0.78, and 0.93, respectively while the performance of the SVM model was superior for predicting MOE with an R2 value of 0.91. The mean absolute error (MAE) of the GPR model for CS, STS, FS, and MOE were 2.92, 0.26, 0.35, and 1.31, respectively which were also lesser than other models. The training time of different models demonstrated that the GPR model has also a lower training time (~3 s) as compared to other models which indicates it has a high accuracy‐to‐time cost ratio. Further, the most influential parameters on CS were age, cement, water–cement ratio, and carbon nanotubes. The one‐way partial dependence analysis showed a direct correlation for age and cement but an inverse correlation for the water–cement ratio and fine aggregate. The graphical user interface provides the implication of the developed models for practical applications.