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An importance learning method for non-probabilistic reliability analysis and optimization
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DOI：10.1007/s00158-018-2128-7.png)
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With the time-consuming computations incurred by nested double-loop strategy and multiple performance functions, the enhancement of computational efficiency for the non-probabilistic reliability estimation and optimization is a challenging problem in the assessment of structural safety. In this study, a novel importance learning method (ILM) is proposed on the basis of active learning technique using Kriging metamodel, which builds the Kriging model accurately and efficiently by considering the influence of the most concerned point. To further accelerate the convergence rate of non-probabilistic reliability analysis, a new stopping criterion is constructed to ensure accuracy of the Kriging model. For solving the non-probabilistic reliability-based design optimization (NRBDO) problems with multiple non-probabilistic constraints, a new active learning function is further developed based upon the ILM for dealing with this problem efficiently. The proposed ILM is verified by two non-probabilistic reliability estimation examples and three NRBDO examples. Comparing with the existing active learning methods, the optimal results calculated by the proposed ILM show high performance in terms of efficiency and accuracy.
Keyword:
Non-probabilistic reliability
Non-probabilistic reliability-based design optimization
Convex model
Importance learning method
Kriging model
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Efficient optimization of reliability-constrained structural design problems including interval uncertainty包括区间不确定性的可靠性约束结构设计问题的有效优化
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