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.

Key words: reliability, Monte Carlo sampling, neural network, stiffened panel, ultimate limit strength, ship structure