卫钰汶, 仲强, 王德禹. 基于BP神经网络的I型金属夹芯板极限强度预测[J]. 中国舰船研究, 2022, 17(2): 125–134. doi: 10.19693/j.issn.1673-3185.02335
引用本文: 卫钰汶, 仲强, 王德禹. 基于BP神经网络的I型金属夹芯板极限强度预测[J]. 中国舰船研究, 2022, 17(2): 125–134. doi: 10.19693/j.issn.1673-3185.02335
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

基于BP神经网络的I型金属夹芯板极限强度预测

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

  • 摘要:
      目的  针对过去对I型金属夹芯板的极限强度评估不完善的问题,提出一种采用 BP人工神经网络的方法来定量确定各相关参数对I型金属夹芯板极限强度的影响。
      方法  首先,采用非线性有限元法研究I型金属夹芯板在面内轴向压缩载荷条件下的极限强度;然后,构造BP神经网络以对不同面板柔度系数βp、腹板柔度系数βw和梁柱柔度系数λ下I型金属夹芯板的极限强度进行预测;最后,提出采用人工神经网络权值和偏置法预测I型金属夹芯板极限强度的公式。
      结果  针对所计算的算例尺寸,显示采用BP神经网络方法的极限强度预测的均方差MSE和相关系数R分别为0.001 2和0.981 8,所构建的神经网络模型具有较好的预测精度,最大误差不超过10%。
      结论  所得结论可为I型金属夹芯板在船体结构中的应用提供参考。

     

    Abstract:
      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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