A sampling-based method for high-dimensional time-variant reliability analysis

Li, Hongshuang; Wang, Tao; Yuan, Jiao-Yang; Zhang, Hang

doi:10.1016/j.ymssp.2019.02.050

Cited by 61 publications

(10 citation statements)

References 47 publications

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“…where F = m l=0 {F l : G l ≤ 0} is the failure of the series system; f (x) is the probability distribution function of the (static) random variables; and I F [•] is the indicator function, which is equal to 1 in case failure occurs at any time instant within [0, T ]. Note that the computation of this failure probability is far from trivial, as it is a series system problem [25,30]. Of all simulationbased reliability analysis methods, Monte Carlo simulation is a widely used technique which can be applied to estimate this failure probability.…”

Section: Composite Limit States Transformation Of Reliability Problem With Time-variant Behaviormentioning

confidence: 99%

“…Substituting Eq. ( 26) into (25), the proposed importance sampling density function H(Z|x) is finally expressed by:…”

Section: Importance Sampling Density Function For Zmentioning

confidence: 99%

“…Recently, Li at al. [25] proposed a Generalized subset simulation (GSS) to handle high-dimensional time-variant reliability. Similarly, Du et al [26] adopted parallel subset simulation to handle time-variant reliability with both deterioration in material properties and dynamic load.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An efficient importance sampling approach for reliability analysis of time-variant structures subject to time-dependent stochastic load

Yuan

Liu

Faes

et al. 2021

Mechanical Systems and Signal Processing

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Section: Composite Limit States Transformation Of Reliability Problem With Time-variant Behaviormentioning

confidence: 99%

“…Substituting Eq. ( 26) into (25), the proposed importance sampling density function H(Z|x) is finally expressed by:…”

Section: Importance Sampling Density Function For Zmentioning

confidence: 99%

See 1 more Smart Citation

An efficient importance sampling approach for reliability analysis of time-variant structures subject to time-dependent stochastic load

Yuan

Liu

Faes

et al. 2021

Mechanical Systems and Signal Processing

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“…Although many first-passage methods [10,12,13] aimed at accuracy and efficiency have been developed for two decades, it is generally hard to achieve the first-time out-crossing rate due to complicated mathematical characteristics. Different numerical simulation methods have been developed including Monte Carlo methods [14] and their improved versions, namely important sampling methods [15,16] and subset methods [17][18][19]. Although the numerical simulation method is accurate, it demands huge computational cost.…”

Section: Introductionmentioning

confidence: 99%

A Time-Variant Reliability Analysis Method Based on the Stochastic Process Discretization under Random and Interval Variables

Liu

Yan

et al. 2021

Symmetry

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In practical engineering, it is a cost-consuming problem to consider the time-variant reliability of both random variables and interval variables, which usually requires a lot of calculation. Therefore, a time-variant reliability analysis approach with hybrid uncertain variables is proposed in this paper. In the design period, the stochastic process is discretized into random variables. Simultaneously, the original random variables and the discrete random variables are converted into independent normal variables, and the interval variables are changed into standard variables. Then it is transformed into a hybrid reliability problem of static series system. At different times, the limited state functions are linearized at the most probable point (MPP) and at the most unfavorable point (MUP). The transformed static system reliability problem with hybrid uncertain variables can be solved effectively by introducing random variables. To solve the double-loop nested optimization in the hybrid reliability calculation, an effective iterative method is proposed. Two numerical examples and an engineering example demonstrate the validity of the present approach.

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“…[2][3][4][5] In order to overcome this weakness, the simulation-based methods can be used to obtain the accurate estimation of the TDFP for nonlinear performance, such as the importance sampling method 13 and the subset simulation method. 14 It should be noted that the computational cost of these two types of methods are large and it may be unaffordable in engineering application.…”

Section: Introductionmentioning

confidence: 99%

Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model

Yan

Lü

2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Aiming at accurately and efficiently estimating the time-dependent failure probability, a novel time-dependent reliability analysis method based on active learning Kriging model is proposed. Although active surrogate model methods have been used to estimate the time-dependent failure probability, efficiently estimating the time-dependent failure probability by a fewer computational time remains an issue because screening all the candidate samples iteratively by the active surrogate model is time-consuming. This article is intended to address this issue by establishing an optimization strategy to search the new training samples for updating the surrogate model. The optimization strategy is performed in the adaptive sampling region which is first proposed. The adaptive sampling region is adjustable by the current surrogate model in order to provide a proper candidate samples region of the input variables. The proposed method employs the optimization strategy to select the optimal sample to be the new training sample point in each iteration, and it does not need to predict the values of all the candidate samples at every time instant in each iterative step. Several examples are introduced to illustrate the accuracy and efficiency of the proposed method for estimating the time-dependent failure probability by simultaneously considering the computational cost and precision.

show abstract

A sampling-based method for high-dimensional time-variant reliability analysis

Cited by 61 publications

References 47 publications

An efficient importance sampling approach for reliability analysis of time-variant structures subject to time-dependent stochastic load

An efficient importance sampling approach for reliability analysis of time-variant structures subject to time-dependent stochastic load

A Time-Variant Reliability Analysis Method Based on the Stochastic Process Discretization under Random and Interval Variables

Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model

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