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Neural network-based prediction of the long-term time-dependent mechanical behavior of laminated composite plates with arbitrary hygrothermal effects

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Abstract

Recurrent neural network (RNN)-based accelerated prediction was achieved for the long-term time-dependent behavior of viscoelastic composite laminated Mindlin plates subjected to arbitrary mechanical and hygrothermal loading. Time-integrated constitutive stress-strain relation was simplified via Laplace transform to a linear system to reduce the computational storage. A fast converging smooth finite element method named cell-based smoothed discrete shear gap was employed to enhance the data generation procedure for straining RNNs with a sparse mesh. This technique is applicable under varying hygrothermal conditions for real engineering structure problems with fluctuating temperature and moisture. Hence, accurate RNN-based long-term deformation prediction for laminated structures was realized using the history of environmental temperature and moisture condition.

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Acknowledgments

This work was supported by the National Research Foundation (NRF) of Korea funded by the Korean government (MSIP) (Grant No. 2012R1A3A2048841).

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Author notes

  1. These authors have equally contributed to this work.

Authors and Affiliations

  1. Division of Construction Computation, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Sy-Ngoc Nguyen

  2. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Sy-Ngoc Nguyen

  3. Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, 151-742, Korea

    Chien Truong-Quoc, Sunyoung Im & Maenghyo Cho

  4. Department of Mechanical Design Engineering, Kumoh National Institute of Technology, Gumi, 39177, Korea

    Jang-woo Han

Authors
  1. Sy-Ngoc Nguyen
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  2. Chien Truong-Quoc
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  3. Jang-woo Han
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  4. Sunyoung Im
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  5. Maenghyo Cho
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Corresponding authors

Correspondence to Sy-Ngoc Nguyen or Maenghyo Cho.

Additional information

Sy-Ngoc Nguyen is a Researcher of the Institute of Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam. He received his Ph.D. in Mechanical Engineering from Seoul National University. His research interests include solid mechanics, composite materials, plate and shell structures, finite element analysis, and viscoelasticity.

Chien Truong-Quoc is a Ph.D. student of the Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea. He received his Master in Mechanical Engineering from Hanoi University of Science and Technology. His research interests include solid mechanics, dynamic analysis, and neural network computation.

Jang-woo Han is an Assistant Professor of the Department of Mechanical Design Engineering, Kumoh National Institute of Technology, Gumi, Korea. He received his Ph.D. in Mechanical Engineering from Seoul National University. His research interests include solid mechanics, viscoelasticity, and composite laminated plate and shell structures.

Sunyoung Im is a Ph.D. student of the Department of Mechanical and Aeropace Engineering, Seoul National University, Seoul, Korea. She received her Master in Mechanical Engineering from Seoul National University. Her research interests include neural network constitutive modeling, nonlinear structure analysis, and system reduction computation.

Maenghyo Cho is a Professor of the Department of Mechanical and Aeropace Engineering, Seoul National University, Seoul, Korea. He received his Ph.D. in Mechanical Engineering from the University of Washington. His research interests include continuum mechanics, multiscale simulation multiphysics analysis, and composite materials.

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Nguyen, SN., Truong-Quoc, C., Han, Jw. et al. Neural network-based prediction of the long-term time-dependent mechanical behavior of laminated composite plates with arbitrary hygrothermal effects. J Mech Sci Technol 35, 4643–4654 (2021). https://doi.org/10.1007/s12206-021-0932-2

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  • DOI: https://doi.org/10.1007/s12206-021-0932-2

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