• 学报（中文） •

基于神经网络的蒙特卡罗可靠性分析方法

1. 上海交通大学 海洋工程国家重点实验室; 高新船舶与深海开发装备协同创新中心， 上海 200240
• 通讯作者: 陈松坤（1994-），男，湖北省十堰市人，硕士生，主要研究方向为船舶与海洋工程结构物设计与制造. 通信作者：王德禹，男，教授，博士生导师，E-mail: dywang@sjtu.edu.cn.

An Improved Monte Carlo Reliability Analysis Method Based on Neural Network

CHEN Songkun,WANG Deyu

1. State Key Laboratory of Ocean Engineering; Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract: 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.