This paper presents a sequential Kriging optimization approach (SKO) for time-variant reliability-based design optimization (tRBDO) with the consideration of stochastic processes. To handle the extremely high dimensionality associated with time-variant uncertainties, stochastic processes are transformed to random parameters through the equivalent stochastic transformation, leading to equivalent time-independent reliability models that are capable of capturing system failures over time. To alleviate computational burden, Kriging-based surrogate modeling is utilized to predict the response of engineered systems. It is further integrated with Monte Carlo simulation (MCS) to approximate the probability of failure. To reduce the epistemic uncertainty due to the lack of data, a maximum confidence enhancement method (MCE) is employed to iteratively identify important points for updating surrogate models. Sensitivities of reliability with respect to design variables are estimated using the first-order score function in the proposed tRBDO framework. Two case studies are introduced to demonstrate the efficiency and accuracy of the proposed approach.

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