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| Bayesian reliability analysis for structures based on gaussian process classification |
| Received:July 31, 2011 Revised:January 15, 2012 |
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| DOI：10.7511/jslx20126003 |
| KeyWord:bayesian reliability incomplete information surrogate models model uncertainty gaussian process classification |
| Author | Institution |
| 曹鸿钧 |
西安电子科技大学 电子设备结构教育部重点实验室,西安 |
| 朱玉强 |
西安电子科技大学 电子设备结构教育部重点实验室,西安 |
| 张功 |
西安电子科技大学 电子设备结构教育部重点实验室,西安 |
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| Abstract: |
| Bayesian reliability method is one of the efficient approaches for reliability analysis for structures with incomplete probability information.The computational cost of the Bayesian reliability estimation is often prohibitive for real applications.It is necessary to use surrogate models to replace actual models in order to reduce the computational burden.Commonly used surrogate modeling approaches,which construct approximation models for response functions rather than limit state surfaces,are indirect and not easy to take model uncertainties into account.Furthermore,these methods are difficult to be used for problems exhibiting discontinuous responses and disjoint failure domains.In order to handle these difficulties,this paper presents a method to identify the limit state surface by using Gaussian process classification.The variances of distribution parameters of failure probability due to the model uncertainty are derived.An adaptive sampling criterion for updating the surrogate model is proposed.An example is presented to demonstrate the efficiency and effectiveness of the proposed method. |