Machine learning approach to predict the mechanical properties of cementitious materials containing carbon nanotubes
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Date
2024
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Abstract
This research explores the use of machine learning to predict the mechanical properties of cementitious materials enhanced with carbon nanotubes (CNTs). Specifically, the study focuses on estimating the elastic modulus and flexural strength of these novel composite materials, with the potential to significantly impact the construction industry. Seven key variables were analyzed including water-to-cement ratio, sand-to-cement ratio, curing age, CNT aspect ratio, CNT content, surfactant-to-CNT ratio, and sonication time. Artificial neural network, support vector regression, and histogram gradient boosting, were used to predict these mechanical properties. Furthermore, a user-friendly formula was extracted from the neural network model. Each model performance was evaluated, revealing the neural network to be the most effective for predicting the elastic modulus. However, the histogram gradient boosting model outperformed all others in predicting flexural strength. These findings highlight the effectiveness of the employed techniques, in accurately predicting the properties of CNT-enhanced cementitious materials. Additionally, extracting formulas from the neural network provides valuable insights into the interplay between input parameters and mechanical properties. © 2024 The Authors
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Keywords
Aspect ratio , Bending strength , Cements , Construction industry , Elastic moduli , Forecasting , Glass ceramics , Graphic methods , Machine learning , Neural networks , Cementitious materials , Composites material , Curing age , Gradient boosting , Key variables , Machine learning approaches , Machine-learning , Neural-networks , Water to cement (binder) ratios , Water-to-cement ratios , Carbon nanotubes