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Efficient Kriging surrogate modeling approach for system reliability analysis

Published online by Cambridge University Press:  04 May 2017

Zhen Hu ,
Saideep Nannapaneni  and
Sankaran Mahadevan
Zhen Hu
Affiliation:
Department of Civil & Environmental Engineering, Vanderbilt University, Nashville, Tennessee, USA
Saideep Nannapaneni
Affiliation:
Department of Civil & Environmental Engineering, Vanderbilt University, Nashville, Tennessee, USA
Sankaran Mahadevan*
Affiliation:
Department of Civil & Environmental Engineering, Vanderbilt University, Nashville, Tennessee, USA
*
Reprint requests to: Sankaran Mahadevan, Department of Civil & Environmental Engineering, Vanderbilt University, Box 1831, Station B, Nashville, TN 37235, USA. E-mail: [email protected]
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Abstract

Current limit state surrogate modeling methods for system reliability analysis usually build surrogate models for failure modes individually or build composite limit states. In practical engineering applications, multiple system responses may be obtained from a single setting of inputs. In such cases, building surrogate models individually will ignore the correlation between different system responses and building composite limit states may be computationally expensive because the nonlinearity of composite limit state is usually higher than individual limit states. This paper proposes a new efficient Kriging surrogate modeling approach for system reliability analysis by constructing composite Kriging surrogates through selection of Kriging surrogates constructed individually and Kriging surrogates built based on singular value decomposition. The resulting composite surrogate model will combine the advantages of both types of Kriging surrogate models and thus reduce the number of required training points. A new stopping criterion and a new surrogate model refinement strategy are proposed to further improve the efficiency of this approach. The surrogate models are refined adaptively with high accuracy near the active failure boundary until the proposed new stopping criterion is satisfied. Three numerical examples including a series, a parallel, and a combined system are used to demonstrate the effectiveness of the proposed method.

Keywords

KrigingReliability AnalysisSingular Value DecompositionSystem
Type
Special Issue Articles
Information
AI EDAM , Volume 31 , Special Issue 2: Uncertainty Quantification for Engineering Design , May 2017 , pp. 143 - 160
Copyright
Copyright © Cambridge University Press 2017 

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