Predict the Mechanical Properties of Cementitious Materials Containing Carbon Nanotubes Using Machine Learning Algorithms
Keywords:
Carbon nanotubes, Composite materials, Computational intelligence, Elastic modulus, Flexural strength.Abstract
In this work, the use of machine learning algorithms to predict the mechanical properties of cementitious materials boosted with carbon nanotubes (CNTs) is investigated. The main goal is to estimate the flexural strength and elastic modulus of these novel composite materials, which have the potential to have a big influence on the building industry. The water-to-cement ratio, sand-to-cement ratio, curing age, CNT aspect ratio, CNT content, surfactant-to-CNT ratio, and sonication duration were among the seven crucial factors that were investigated. Support vector regression, histogram gradient boosting, and artificial neural networks were among the prediction techniques used. The neural network model was also used to develop an easy-to-use formula. Each model's performance was evaluated, and the results showed that the neural network was the best at predicting the elastic modulus and the histogram gradient boosting model was the best at doing so for flexural strength. These findings demonstrate how well the methods used may predict the characteristics of cementitious materials boosted by carbon nanotubes. Furthermore, the formulas that are extracted from the neural network provide important information on how input parameters and mechanical qualities relate to one another.
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