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

被引:3
|
作者
Okasha, Nader M. [1 ]
Mirrashid, Masoomeh [2 ]
Naderpour, Hosein [3 ]
Ciftcioglu, Aybike Ozyuksel [4 ]
Meddage, D. P. P. [5 ]
Ezami, Nima [6 ,7 ]
机构
[1] Abu Dhabi Univ, Coll Engn, Dept Civil Engn, POB 1790, Al Ain, U Arab Emirates
[2] Abu Dhabi Univ, Coll Engn, Abu Dhabi, U Arab Emirates
[3] Semnan Univ, Fac Civil Engn, Semnan, Iran
[4] Celal Bayar Univ, Fac Engn, Dept Civil Engn, Manisa, Turkiye
[5] Univ New South Wales, Fac Civil & Environm Engn, Kensington, Australia
[6] Univ Toronto, Dept Civil & Mineral Engn, Toronto, ON M5S 1A4, Canada
[7] GEI Consultants Inc, Markham, ON L3R 4M8, Canada
来源
DEVELOPMENTS IN THE BUILT ENVIRONMENT | 2024年 / 19卷
关键词
Carbon nanotubes; Composite materials; Computational intelligence; Elastic modulus; Flexural strength; COMPOSITES; STRENGTH; ENERGY;
D O I
10.1016/j.dibe.2024.100494
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
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页数:18
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