Structural Engineering and Mechanics

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Stochastic vibration analysis of functionally graded beams using artificial neural networks

  • Trinh, Minh-Chien (Division of Mechanical System Engineering, Jeonbuk National University) ;
  • Jun, Hyungmin (Division of Mechanical System Engineering, Jeonbuk National University)
  • Received : 2021.01.09
  • Accepted : 2021.03.16
  • Published : 2021.06.10
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Abstract

Inevitable source-uncertainties in geometry configuration, boundary condition, and material properties may deviate the structural dynamics from its expected responses. This paper aims to examine the influence of these uncertainties on the vibration of functionally graded beams. Finite element procedures are presented for Timoshenko beams and utilized to generate reliable datasets. A prerequisite to the uncertainty quantification of the beam vibration using Monte Carlo simulation is generating large datasets, that require executing the numerical procedure many times leading to high computational cost. Utilizing artificial neural networks to model beam vibration can be a good approach. Initially, the optimal network for each beam configuration can be determined based on numerical performance and probabilistic criteria. Instead of executing thousands of times of the finite element procedure in stochastic analysis, these optimal networks serve as good alternatives to which the convergence of the Monte Carlo simulation, and the sensitivity and probabilistic vibration characteristics of each beam exposed to randomness are investigated. The simple procedure presented here is efficient to quantify the uncertainty of different stochastic behaviors of composite structures.

Keywords

Acknowledgement

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2020R1I1A3073577) and the Korea Institute of Planning and Evaluation for Technology in Food, the Agriculture and Forestry(IPET) and the Korea Smart Farm R&D Foundation through Smart Farm Innovation Technology Development Program funded by the Ministry of Agriculture, Food and Rural Affairs(MAFRA), the Ministry of Science and ICT(MSIT) and the Rural Development Administration(RDA) (421016041HD030).

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