Advanced solution strategies for time-dependent reliability based design optimization

Yan, Shi; Lü, Zhenzhou; Huang, Zhiliang; Xu, Liyang; He, Ruyang

doi:10.1016/j.cma.2020.112916

Cited by 34 publications

(4 citation statements)

References 34 publications

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“…The case has two design variables, four parameters, and two time-dependent reliability-based constraint functions. 42 This case can be expressed in the following mathematical form (25), and the distribution informations of the variables are listed in Table 3.…”

Section: Two-bar Frame Casementioning

confidence: 99%

A response band-based method for time-dependent reliability-based robust design optimization

Zhang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part O:

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In this paper, a response band-based method for time-dependent reliability-based robust design optimization is proposed. The proposed method provides a novel alternative framework, consist of a two-step transformation stage and a solving stage, to solve the time-dependent reliability-based robust design optimization problem. The original time-dependent reliability-based robust design optimization problem is transformed into an equivalent deterministic robust design optimization problem in the transformation stage, and the equivalent problem is settled in the solving stage. In the transformation stage, the dynamic modal decomposition technique and the kriging technique are combined to overcome the problem that there is no standard for both time division and observation sampling in the commonly used transformation methods. In the solving stage, an approach for constructing the response band of the objective function is presented, which significantly reduces the computational consumption of the variation evaluation of the objective function. Five cases are employed to verify the effectiveness of the proposed method.

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Section: Two-bar Frame Casementioning

confidence: 99%

A response band-based method for time-dependent reliability-based robust design optimization

Zhang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…in [40] and [41] (among others) and is modified to suit the purposes of this work. This frame is subjected to a dynamic force F (t) which is described by a stochastic process.…”

Section: Example 1: a Two-bar Framementioning

confidence: 99%

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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“…Uncertainty in the engineering structure can be generally quantified by the probabilistic model. In the traditional reliability analysis field, the probabilistic model is considered as the most powerful technique, in which the structural inputs are treated as the random variables 1,2 with the probability density function (PDF) and the structural safety level is measured by the failure probability (FP)

{P}_f

. However, due to the limitation of the data information in practice, it is almost impossible to assume that all uncertain input variables in the structure can be characterized by probabilistic model.…”

Section: Introductionmentioning

confidence: 99%

An efficient method for estimating failure probability bounds under random‐interval mixed uncertainties by combining line sampling with adaptive Kriging

Wang

Lü

Wang

2022

Numerical Meth Engineering

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For addressing the low efficiency of structural reliability analysis under the random‐interval mixed uncertainties (RIMU), this paper establishes the line sampling method (LS) under the RIMU. The proposed LS divides the reliability analysis under RIMU into two stages. The Markov chain simulation is used to efficiently search the design point under RIMU in the first stage, then the upper and lower bounds of failure probability are estimated by LS in the second stage. To improve the computational efficiency of the proposed LS under RIMU, the Kriging model is employed to reduce the model evaluation numbers in the two stages. For efficiently searching the design point, the Kriging model is constructed and adaptively updated in the first stage to accurately recognize the Markov chain candidate state, and then it is sequentially updated by the improved U learning function in the second stage to accurately estimate the failure probability bounds. The proposed LS under RIMU with Kriging model can not only reduce the model evaluation numbers but also decrease the candidate sample pool size for constructing the Kriging model in two stages. The presented examples demonstrate the superior computational efficiency and accuracy of the proposed method by comparison with some existing methods.

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Advanced solution strategies for time-dependent reliability based design optimization

Cited by 34 publications

References 34 publications

A response band-based method for time-dependent reliability-based robust design optimization

A response band-based method for time-dependent reliability-based robust design optimization

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

An efficient method for estimating failure probability bounds under random‐interval mixed uncertainties by combining line sampling with adaptive Kriging

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