随机点蚀损伤钢板的极限强度预测

ULTIMATE STRENGTH PREDICTION OF STEEL PLATE WITH RANDOM PITTING CORROSION DAMAGE

该文通过构建随机点蚀损伤钢板的数值模型,开展200个模型的非线性有限元分析,研究随机点蚀损伤下,不同长宽比、长细比和腐蚀强度的板极限强度退化规律,揭示上述三个参数对其极限强度的影响;借助人工神经网路的强非线性拟合能力,将三个影响因素作为预测网络的输入参数,将相应的有限元结果作为网络的输出结果,构建一个三层的BP神经网络模型,预测随机点蚀板的极限强度;且利用已有文献的大量数据验证BP模型的预测精度。相同的腐蚀强度下,同一结构尺寸(长宽比和长细比)板的极限强度会由于点蚀的随机性产生明显的变异;板的长宽比和长细比均对板的极限强度有不同程度的影响,且极限强度的退化受结构尺寸和腐蚀强度的联合影响;所构建的人工神经网络模型具有良好的预测精度,其预测误差不超过10%,可用于量化随机点蚀板的极限强度。

This paper established numerical models of steel plates with random pitting damage, and performed nonlinear finite element （FE） analyses on 200 models of the pitted plates. The factors affecting the ultimate strength such as slenderness ratio, aspect ratio and corrosion volume ratio, were studied aiming at exploring their effect on the reduction of structural strength. A BP neural network with three layer structures was constructed and then used to predict the ultimate strength of the pitted plates, taking advantage of its excellent ability to handle the nonlinear problem. The input layer of the BP model contained the three influential parameters mentioned above, while the output layer comprised the FE results. The accuracy of the established BP model was validated against the numerous open data from the literature. Significant variation of ultimate strength arises in the randomly pitted plated structures with the same dimensional parameters （slenderness ratio and aspect ratio） under the same degree of pitting degradation, due to the randomness of the pitting corrosion. The strength reduction is affected by random pitting corrosion, as well as structural size. The constructed BP model has good accuracy to predict the ultimate strength of randomly pitted plates with a relative error no more than 10%.