Machine learning approach to predict the mechanical properties of cementitious materials containing carbon nanotubes

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dc.contributor.authorOkasha N.M.
dc.contributor.authorMirrashid M.
dc.contributor.authorNaderpour H.
dc.contributor.authorCiftcioglu A.O.
dc.contributor.authorMeddage D.P.P.
dc.contributor.authorEzami N.
dc.date.accessioned2024-07-22T08:01:01Z
dc.date.available2024-07-22T08:01:01Z
dc.date.issued2024
dc.description.abstractThis 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
dc.identifier.DOI-ID10.1016/j.dibe.2024.100494
dc.identifier.issn26661659
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11272
dc.language.isoEnglish
dc.publisherElsevier Ltd
dc.rightsAll Open Access; Gold Open Access
dc.subjectAspect ratio
dc.subjectBending strength
dc.subjectCements
dc.subjectConstruction industry
dc.subjectElastic moduli
dc.subjectForecasting
dc.subjectGlass ceramics
dc.subjectGraphic methods
dc.subjectMachine learning
dc.subjectNeural networks
dc.subjectCementitious materials
dc.subjectComposites material
dc.subjectCuring age
dc.subjectGradient boosting
dc.subjectKey variables
dc.subjectMachine learning approaches
dc.subjectMachine-learning
dc.subjectNeural-networks
dc.subjectWater to cement (binder) ratios
dc.subjectWater-to-cement ratios
dc.subjectCarbon nanotubes
dc.titleMachine learning approach to predict the mechanical properties of cementitious materials containing carbon nanotubes
dc.typeArticle

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