Volume 17 Issue 2
Apr.  2022
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WEI Y W, ZHONG Q, WANG D Y. Ultimate strength prediction of I-core sandwich plate based on BP neural network[J]. Chinese Journal of Ship Research, 2022, 17(2): 125–134 doi: 10.19693/j.issn.1673-3185.02335
Citation: WEI Y W, ZHONG Q, WANG D Y. Ultimate strength prediction of I-core sandwich plate based on BP neural network[J]. Chinese Journal of Ship Research, 2022, 17(2): 125–134 doi: 10.19693/j.issn.1673-3185.02335

Ultimate strength prediction of I-core sandwich plate based on BP neural network

doi: 10.19693/j.issn.1673-3185.02335
  • Received Date: 2021-03-30
  • Rev Recd Date: 2021-05-25
  • Available Online: 2022-04-06
  • Publish Date: 2022-04-20
    © 2022 The Authors. Published by Editorial Office of Chinese Journal of Ship Research. Creative Commons License
    This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  •   Objectives   In view of the incomplete evaluation of the ultimate strength of I-core sandwich panels in the past, a BP artificial neural network method is proposed to quantitatively determine the influence of relevant parameters on the ultimate strength of I-core sandwich panels.   Methods  First, the ultimate strength of I-core sandwich panels under axial compression are investigated using the nonlinear finite element method. Second, a BP neural network is constructed to predict the ultimate strength of I-core sandwich panels with different plate slenderness ratios between longitudinal webs, plate slenderness ratios of webs and column slenderness ratio of one longitudinal web. Finally, a formula for predicting the ultimate strength of I-core sandwich panels using the artificial neural network weight and bias method is proposed.   Results  The mean square error MSE and correlation coefficient R of ultimate strength prediction using the BP neural network method are 0.001 2 and 0.981 8 respectively. The proposed neural network model has good prediction accuracy, and the maximum error is less than 10%.   Conclusions  This study can provide references for the application of I-core sandwich panels in hull structures.
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