Time-variant reliability-based design optimization using sequential kriging modeling
Li, Mingyang Bai, Guangxing Wang, Zequn
Li, Mingyang
Bai, Guangxing
Wang, Zequn
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2018-03-21
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Article
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Time-variant reliability analysis,Design optimization,Stochastic processes,Simulation-based,Kriging surrogate model
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Citation
Li, Mingyang; Bai, Guangxing; Wang, Zequn. 2018. Time-variant reliability-based design optimization using sequential kriging modeling. Structural and Multidisciplinary Optimization, vol. 58:no. 3:pp 1051-1065
Abstract
This paper presents a sequential Kriging modeling approach (SKM) for time-variant reliability-based design optimization (tRBDO) involving stochastic processes. To handle the temporal uncertainty, time-variant limit state functions are transformed into time-independent domain by converting the stochastic processes and time parameter to random variables. Kriging surrogate models are then built and enhanced by a design-driven adaptive sampling scheme to accurately identify potential instantaneous failure events. By generating random realizations of stochastic processes, the time-variant probability of failure is evaluated by the surrogate models in Monte Carlo simulation (MCS). In tRBDO, the first-order score function is employed to estimate the sensitivity of time-variant reliability with respect to design variables. Three case studies are introduced to demonstrate the efficiency and accuracy of the proposed approach.
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Springer Berlin Heidelberg
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Structural and Multidisciplinary Optimization;v.58:no.3
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1615-147X