An Improved Monte Carlo Reliability Analysis Method Based on Neural Network

被引：0
|
作者：
Chen S. [1 ]
Wang D. [1 ]
机构：
[1] State Key Laboratory of Ocean Engineering, Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, Shanghai Jiao Tong University, Shanghai
来源：
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2018年 / 52卷 / 06期
关键词：
Monte Carlo sampling; Neural network; Reliability; Ship structure; Stiffened panel; Ultimate limit strength;
D O I：
10.16183/j.cnki.jsjtu.2018.06.009
中图分类号：
学科分类号：
摘要：
Monte Carlo (MC) is a very accurate method in the structure reliability calculation, however, its application is limited due to a large number of computation when it comes to complex engineering structures. It is time-consuming even in a single analysis. To reduce the calculation, the neural network approach is adopted to construct the BP-MC method. The back propagation (BP) neural network is built through design of experiments (DOE), then the weighting factors and the distance to failure surface are used as filters to pick up the design points out of the MC points. Those picked points are prone to cause the structure failure, and transferred into the training set to update the BP model. The filter-update process continues until the convergence of the BP, and then reliability index is calculated with the BP model on the MC points. The efficiency and usability are elucidated with a mathematic model and a stiffened panel model at the end of this paper. © 2018, Shanghai Jiao Tong University Press. All right reserved.
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页码：687 / 692
页数：5
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