Advances in nano research

  • 제17권5호
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  • Pages.445-454
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  • 2024
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  • 2287-237X(pISSN)
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  • 2287-2388(eISSN)
테크노프레스 (Techno-Press)
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On application of machine learning techniques for predicting the bending and buckling behavior of FGM nanobeams

  • Aman Garg (State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology) ;
  • Mohamed-Ouejdi Belarbi (Laboratoire de Recherche en Genie Civil, LRGC, Universite de Biskra) ;
  • Li Li (State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology) ;
  • Abdelouahed Tounsi (Department of Civil and Environmental Engineering, King Fahd University of Petroleum &Minerals)
  • 투고 : 2022.03.14
  • 심사 : 2024.10.15
  • 발행 : 2024.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

The present article aims to carry out a comparative study between various machine learning based algorithms, which can predict the bending and buckling behavior of functionally graded (FG) nanobeams accurately. The algorithm has been developed in the framework of two regression machine learning models namely, Gaussian Process Regression (GPR), and Random Forest (RF). Geometric and material properties are taken as the variables including length-to-thickness ratio, power-law index, and nonlocal parameter. For having random non-biased input dataset, the Sobol sequence has been used. Using these values, maximum deflections and critical buckling loads are obtained. These values along with the corresponding input variables, surrogate models were formulated. It has been observed that the GPR model is able to predict the behavior of FG nanobeams more accurately as compared to the behavior predicted by RF surrogate model even for an unseen dataset.

키워드

과제정보

This work is partially supported by the Innovation-Driven Development Special Fund Project of Guangxi (Grant No. Guike AA23062040), the Key Research and Development Program of Guangxi (Grant No. Guike AB23026106), the National Natural Science Foundation of China (Grant No. 52175095), the Science and Technology Planning Project of Liuzhou (Grant Nos. 2022AAA0102, and 2022AAA0104), and the Young Top-notch Talent Cultivation Program of Hubei Province of China.

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