Abstract
For implicit limit state equations in most engineering reliability analysis, a new method is presented on the basis of artificial neural network (ANN), where the training samples are appropriately selected. Due to the powerful tool of function approximation, ANN is employed to obtain the relationship of the input parameters and the output parameters in the implicit limit state equation. The reliability analysis for the implicit limit state is then transformed to that for the explicit limit state, and the computational efforts are greatly decreased. Comparing with available reliability analysis based on ANN, the presented method has a different strategy on selection of the training samples, in which the implicit state equation can be more appropriately approximated. The precision of the presented method is higher than that of the available method, and this advantage is illustrated by examples.
Original language | English |
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Pages (from-to) | 699-702 |
Number of pages | 4 |
Journal | Jixie Qiangdu/Journal of Mechanical Strength |
Volume | 28 |
Issue number | 5 |
State | Published - Oct 2006 |
Keywords
- Artificial neural network
- Failure probability
- Implicit limit state equation
- Reliability