船舶在航行过程中常受到动力载荷的作用，因此有必要对船体结构的动力极限强度预测方法进行研究。本文首先通过与现有试验结果对比，验证了本文有限元计算方法的正确性。随后通过数值计算，得到828组几何与材料参数不同的船体加筋板模型在不同加载速度下的动力极限强度。而后，以板柔度系数、加筋梁柱柔度系数、材料屈服强度为主要输入参数，构建3层BP神经网络，预测加筋板动力极限强度。所得神经网络均方差及相关系数分别达到0.00047与0.99。将训练的神经网络应用于实船加筋板，与有限元计算结果对比，最大误差仅6.4%，证明该BP神经网络能较好预测实船结构动力极限强度。
Ships are often subjected to dynamic loads during voyage, so it is necessary to study the prediction method of dynamic ultimate strength of ship structures. In this paper, the numerical method is first verified by comparing with the existing experimental results. Then, the dynamic ultimate strength of 828 stiffened panels with different geometric and material parameters at different loading speeds is obtained by FEM. Then, a three-layer BP neural network was constructed to predict the ultimate dynamic strength of the stiffened panels by taking the plate slenderness ratio, column slenderness ratio of stiffener and the material yield strength as the main input parameters. The mean square error and correlation coefficient of the trained neural network are 0.00047 and 0.99, respectively. The trained BP neural network is applied to real ship plates and the maximum error is 6.4%, indicates this method can predict the dynamic ultimate strength of the real ship structure.
2023,45(19): 1-8 收稿日期：2022-09-06
DOI：10.3404/j.issn.1672-7649.2023.19.001
分类号：U661.4
基金项目：国家自然科学基金资助项目（51809168，51979163）；财政部、教育部重大专项（201335）
作者简介：熊宇飞(1994-),男,博士研究生,研究方向为船体结构的屈曲与极限强度
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