An adaptive failure boundary approximation method for reliability analysis and its applications

Song, Kunling; Zhang, Yugang; Zhuang, Xinchen; Yu, Xiaohui; Song, Bifeng

doi:10.1007/s00366-020-01011-0

Cited by 13 publications

(8 citation statements)

References 34 publications

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“…Making feasibility predictions with metamodels is a common strategy. This problem has a close relation with reliability analysis [19], where the computation complexity is mainly from the statistical estimation of f in Eq. 1.…”

Section: Metamodels For Characterizing Feasibilitymentioning

confidence: 99%

“…Unfortunately, the direct application of the F1 score (as well as the precision and recall) is costly because of the requirement of suitable test data. Thus, common AL methods [5,11,19] use a maximum number of function evaluations as the stopping criterion. However, it is not possible to estimate upfront how many function evaluation are necessary to obtain an accurate estimate of the feasible region: adopting a predefined maximum number of function evaluations can lead to an inaccurate estimation of the feasible region or to perform too many (unnecessary) expensive evaluations of the function f .…”

Section: Stopping Criterionmentioning

confidence: 99%

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Adaptive sampling with automatic stopping for feasible region identification in engineering design

Qing

Knudde

Garbuglia

et al. 2021

Engineering with Computers

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Engineering design is a complex process to find a suitable trade-off among different, and sometimes conflicting, design specifications. In reality, these requirements can be often considered as constraints of the design problem, that can be defined in terms of performance measures or geometrical characteristics of the device under study. In this paper, a new design space exploration methodology is presented for discovering feasible regions in the design space, where the term feasible region indicates the set of all design configurations satisfying all constraints of the design problem. The proposed method is based on Gaussian Process metamodels to estimate the feasible region and leverages a information-based adaptive sampling technique to sequentially refine the prediction accuracy, which is applicable for multiple constraints problems. In order to efficiently stop the adaptive sampling process, a novel framework to estimate the metamodel's prediction accuracy is proposed. The efficiency, accuracy and robustness of the proposed approach are compared with state-of-art techniques on suitable benchmark problems and practical engineering examples.

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Section: Metamodels For Characterizing Feasibilitymentioning

confidence: 99%

Section: Stopping Criterionmentioning

confidence: 99%

Adaptive sampling with automatic stopping for feasible region identification in engineering design

Qing

Knudde

Garbuglia

et al. 2021

Engineering with Computers

View full text Add to dashboard Cite

show abstract

“…The toolbox has been used in many Kriging Refs. (Kaymaz, 2005;Echard et al, 2011;Lv et al, 2015;Song et al, 2021).…”

Section: Kriging Theory and Formulationmentioning

confidence: 99%

Adaptive hyperball Kriging method for efficient reliability analysis

Yang

Prayogo

2022

AIEDAM

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Although an accurate reliability assessment is essential to build a resilient infrastructure, it usually requires time-consuming computation. To reduce the computational burden, machine learning-based surrogate models have been used extensively to predict the probability of failure for structural designs. Nevertheless, the surrogate model still needs to compute and assess a certain number of training samples to achieve sufficient prediction accuracy. This paper proposes a new surrogate method for reliability analysis called Adaptive Hyperball Kriging Reliability Analysis (AHKRA). The AHKRA method revolves around using a hyperball-based sampling region. The radius of the hyperball represents the precision of reliability analysis. It is iteratively adjusted based on the number of samples required to evaluate the probability of failure with a target coefficient of variation. AHKRA adopts samples in a hyperball instead of an n-sigma rule-based sampling region to avoid the curse of dimensionality. The application of AHKRA in ten mathematical and two practical cases verifies its accuracy, efficiency, and robustness as it outperforms previous Kriging-based methods.

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“…To deal with the problem of limited experiment resources, various surrogate models [3,4] have been proposed, aiming to construct a mathematical model that accurately mimics the behavior of the original problem with an affordable experimental design [5]. Popular surrogate techniques include the Kriging method [6][7][8][9], artificial neural network [10][11][12], polynomial chaos expansion (PCE) method [13][14][15][16] etc. In this paper, we focus on the PCE model, which has been widely used in engineering to quantify uncertainties efficiently.…”

Section: Introductionmentioning

confidence: 99%

Optimized sparse polynomial chaos expansion with entropy regularization

et al. 2022

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Sparse Polynomial Chaos Expansion (PCE) is widely used in various engineering fields to quantitatively analyse the influence of uncertainty, while alleviating the problem of dimensionality curse. However, current sparse PCE techniques focus on choosing features with the largest coefficients, which may ignore uncertainties propagated with high order features. Hence, this paper proposes the idea of selecting polynomial chaos basis based on information entropy, which aims to retain the advantages of existing sparse techniques while considering entropy change as output uncertainty. A novel entropy-based optimization method is proposed to update the state-of-the-art sparse PCE models. This work further develops an entropy-based synthetic sparse model, which has higher computational efficiency. Two benchmark functions and a computational fluid dynamics (CFD) experiment are used to compare the accuracy and efficiency between the proposed method and classical methods. The results show that entropy-based methods can better capture the features of uncertainty propagation, improving accuracy and reducing sparsity while avoiding over-fitting problems.

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An adaptive failure boundary approximation method for reliability analysis and its applications

Cited by 13 publications

References 34 publications

Adaptive sampling with automatic stopping for feasible region identification in engineering design

Adaptive sampling with automatic stopping for feasible region identification in engineering design

Adaptive hyperball Kriging method for efficient reliability analysis

Optimized sparse polynomial chaos expansion with entropy regularization

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