Computers and Concrete

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Machine learning for structural stability: Predicting dynamics responses using physics-informed neural networks

  • Li, Zhonghong (School of Architectural Engineering and Art Design, Chongqing Chemical Industry Vocational College) ;
  • Yan, Gongxing (School of Intelligent Construction, Luzhou Vocational and Technical College)
  • Received : 2022.03.31
  • Accepted : 2022.05.26
  • Published : 2022.06.25
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Abstract

This article deals with the vibrational response of a nanobeam made of bi-directional FG materials which is modeled via nonlocal strain gradient theory along with HSDT. Also, the nanobeam is placed on a Winkler-Pasternak foundation and is under axial mechanical loading. By using the variational energy method, the formulation and end conditions are obtained. Then, DSC-IM, as the numerical solution procedure is employed to extract the results. The material properties of the nanobeam are FG which varies in two directions with in exponential manner. The results from DDN are verified by using other papers. Lastly, a thorough parametric investigation is presented to investigated the effect of different parameters.

Keywords

Acknowledgement

This work was supported by Scientific research youth project of Chongqing Education Commission (NO.: KJQN202004502), and Sichuan Province Luzhou city of science and technology planning project (2021-jyj-100).

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