Time-variant reliability-based design optimization using sequential kriging modeling

Li, Mingyang; Bai, Guangxing; Wang, Zequn

doi:10.1007/s00158-018-1951-1

Cited by 25 publications

(8 citation statements)

References 42 publications

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“…This strategy is thus far utilized in several reliability analysis and optimization studies, either directly or as a key part of novel proposed techniques (Liu et al, 2019;Chaudhuri et al, 2019;Sadoughi et al, 2018;Wang and Ma, 2018). Li et al (2018) present an interesting application of adaptive Kriging in time-dependent structural optimization. Despite some similarities with this work, the authors perform RBDO through Stochastic Equivalent Transformation (Wang and Chen, 2016).…”

Section: Adaptive Kriging In Structural Optimizationmentioning

confidence: 99%

“…Wang and Chen (2016) presented an equivalent stochastic process transformation approach for solving general time-variant reliability problems. This approach was employed by Li et al (2018) to solve RBDO problems.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the above observations, this paper proposes a general procedure for solving timevariant risk optimization problems, based on adaptive Kriging (Jones et al, 1998;Echard et al, 2011;Schöbi et al, 2017). The proposed scheme has some similarities with Li et al (2018); however, herein it is applied for solving time-variant risk optimization problems. Moreover, equivalent stochastic process transformation is not employed herein.…”

Section: Introductionmentioning

confidence: 99%

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A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

Kroetz

Moustapha

Beck

et al. 2020

Reliability Engineering & System Safety

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Several methods have been proposed in the literature to solve reliability-based optimization problems, where failure probabilities are design constraints. However, few methods address the problem of life-cycle cost or risk optimization, where failure probabilities are part of the objective function. Moreover, few papers in the literature address time-variant reliability problems in lifecycle cost or risk optimization formulations; in particular, because most often computationally expensive Monte Carlo simulation is required. This paper proposes a numerical framework for solving general risk optimization problems involving time-variant reliability analysis. To alleviate the computational burden of Monte Carlo simulation, two adaptive coupled surrogate models are used: the first one to approximate the objective function, and the second one to approximate the quasi-static limit state function. An iterative procedure is implemented for choosing additional support points to increase the accuracy of the surrogate models. Three application problems are used to illustrate the proposed approach. Two examples involve random load and random resistance degradation processes. The third problem is related to load-path dependent failures. This subject had not yet been addressed in the context of risk-based optimization. It is shown herein that accurate solutions are obtained, with extremely limited numbers of objective function and limit state functions calls.

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Section: Adaptive Kriging In Structural Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

Kroetz

Moustapha

Beck

et al. 2020

Reliability Engineering & System Safety

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“…Nowadays, the commonly used surrogate models are response surfaces (RSs) [21,22] support vector machines (SVMs), 23,24 neural networks (NNs), 25,26 and Kriging models. [27][28][29] RSs are, generally, effective for simple systems. However, the accuracy and stability cannot be guaranteed for high dimensional and/or highly nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

High dimensional reliability analysis based on combinations of adaptive Kriging and dimension reduction technique

Xiao

Zhan

2022

Quality & Reliability Eng

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High dimensional reliability analysis is unavoidable if a structural system involving stochastic process because Karhunen–Loève (K‐L) expansion is commonly used. Reliability analysis for structural systems with computationally intensive numerical models and high dimensions is challenging. In this study, an effective high dimensional reliability analysis method is proposed based on principal components and active subspace (PCAS) and active Kriging, and is termed as PCAS‐AK. The proposed method can address two shortcomings existing in PCAS: the latter select training samples randomly and cannot deal with parallel processing. The proposed method, however, selects training samples adaptively, subsequently, the training samples that have high contribution to probability of failure are selected, which can improve computational efficiency. To reduce overall computational time and allow parallelization, K‐weighted‐means combined with learning function are used to select multiple samples at each iteration. After the dimension reduction of both input and output spaces, the samples selected by adaptive method are used to construct Kriging model in the latent low‐dimensional space. Three examples are used to demonstrate the applicability and accuracy of the proposed method. Results show that the proposed method can deal with parallel processing and is, generally, more effective than PCAS for high dimensional reliability problems.

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“…For the sake of compactness RA will be used for the remainder of the paper. Recent studies towards timevariant RA are amongst other found in [32,45,63,72,70] and towards system-level RA in [79,7]. 5 Not all existing methods, such as Haldar & Mahadevan's mean value first-order secondmoment (MVFOSM) method [28], can be straightforwardly be categorized.…”

Section: Introductionmentioning

confidence: 99%

ESLA: a new surrogate-assisted single-loop reliability-based design optimization technique

Wauters

Couckuyt

Degroote

2021

Struct Multidisc Optim

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In this paper, we address the formulation of a novel scheme for reliabilitybased design optimization, in which the design optimization problem is characterized by constraints that must be met with a certain probability. Assessment of the aforementioned is typically referred to as reliability analysis. Conventional methods rely on sampling approaches or by reformulating the problem as a two-level optimization that requires gradient or Hessian information of the constraints to obtain a trustworthy solution. However, the computational cost of such methods makes them often impractical. To overcome the aforementioned, a surrogateassisted asymptotic reliability analysis (SARA) is presented that makes use of surrogate-derived gradient and Hessian information. The sub-optimization problem is reformulated as a set of constraints using the Karush-Kuhn-Tucker conditions and fitted in an efficient global optimization-like setting through the formulation of the reliability-based expected improvement (RBEI), obtaining the novel efficient single-loop approach (ESLA). The method is tested on a series of test cases which prove the effectiveness of the novel scheme.

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Time-variant reliability-based design optimization using sequential kriging modeling

Cited by 25 publications

References 42 publications

A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems

High dimensional reliability analysis based on combinations of adaptive Kriging and dimension reduction technique

ESLA: a new surrogate-assisted single-loop reliability-based design optimization technique

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