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| Multi-Output Gaussian process surrogate model for structural reliability optimization |
| Received:December 25, 2018 Revised:July 01, 2019 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20181225001 |
| KeyWord:reliability surrogate model Multi-Output Gaussian Process learning function optimization |
| Author | Institution |
| 赵维涛 |
沈阳航空航天大学 航空宇航学院, 沈阳 |
| 刘照琳 |
沈阳航空航天大学 航空宇航学院, 沈阳 |
| 祁武超 |
沈阳航空航天大学 航空宇航学院, 沈阳 |
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| Abstract: |
| The calculation cost of Reliability-Based Design Optimization is relatively expensive for structures with multiple failure modes.Therefore,this paper uses a Multi-Output Gaussian Process (MOGP) surrogate model to reduce the calculation cost.In this study,first of all,the Bucher's method is used to generate initial samples,and then uniform training samples and a learning function are both used to build the MOGP surrogate model.The learning function can obtain satisfactory training samples in a large range,which can ensure that the MOGP surrogate model has better global accuracy,so that there is no need for MOGP surrogate model to be rebuilt in the whole optimization process.The MOGP surrogate model can consider the correlation of each failure mode by using the covariance matrix,thus it has a good prediction for the multi-input and multi-output system.Numerical examples show that the proposed method has satisfactory results and high calculation efficiency,especially when the numbers of design variables and failure modes are large. |
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