• Title/Summary/Keyword: bi-directional FG concrete nanobeam

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

  • Li, Zhonghong;Yan, Gongxing
    • Computers and Concrete
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    • v.29 no.6
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    • pp.419-432
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    • 2022
  • 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.

Intelligent modeling to investigate the stability of a two-dimensional functionally graded porosity-dependent nanobeam

  • Zhou, Jinxuan;Moradi, Zohre;Safa, Maryam;Khadimallah, Mohamed Amine
    • Computers and Concrete
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    • v.30 no.2
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    • pp.85-97
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    • 2022
  • Using a combination of nonlocal Eringen as well as classical beam theories, this research explores the thermal buckling of a bidirectional functionally graded nanobeam. The formulations of the presented problem are acquired by means on conserved energy as well as nonlocal theory. The results are obtained via generalized differential quadrature method (GDQM). The mechanical properties of the generated material vary in both axial and lateral directions, two-dimensional functionally graded material (2D-FGM). In nanostructures, porosity gaps are seen as a flaw. Finally, the information gained is used to the creation of small-scale sensors, providing an outstanding overview of nanostructure production history.