Adaptive approaches in metamodel-based reliability analysis: A review

Teixeira, Rui; Nogal, María; O’Connor, Alan

doi:10.1016/j.strusafe.2020.102019

Cited by 156 publications

(33 citation statements)

References 149 publications

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“…Active learning Kriging (AK) model combining with sampling method has been investigated by many researchers and different methods have been proposed, such as AK-MCS, 38 AK-IS, 39 AK-LS 40,41 and other advanced methods. [42][43][44][45][46] Motivated by those methods, AK model is embedded in ARBIS-based method (named as AK-ARBIS) to estimate the reliability with interval distribution parameters in this section.…”

Section: Bounds Estimation For Failure Probability With Ak-arbismentioning

confidence: 99%

A novel method for reliability analysis with interval parameters based on active learning Kriging and adaptive radial‐based importance sampling

Wang

Zhou

et al. 2022

Numerical Meth Engineering

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For the reliability analysis of complex engineering structures, the estimation of the bounds of failure probability with interval distribution parameters is an important task when the perfect information of random variables is unavailable and the corresponding probability distributions are imprecise. The present work proposes an active learning Kriging‐based method combining with adaptive radial based importance sampling to compute the bounds of failure probability. For computing the bounds, the classical double‐loop optimization model is always investigated in the standard normal space. To decouple the computation, the inner‐loop optimization is addressed with the monotonicity of the commonly used probability distributions. When suffering the high dimensional problem, the dimension reduction method is introduced in monotonic analysis. While for the outer‐loop optimization, the normal space is decomposed with spheres, then the proposed method with an adaptive updated procedure is given. With this method, the bounds of failure probability can be estimated efficiently, especial for the rare event. Numerical examples are investigated to validate the rationality and superiority of the proposed method. Finally, the proposed method is applied to the reliability analysis of turbine blade and aeronautical hydraulic pipeline system with interval distribution parameters.

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Section: Bounds Estimation For Failure Probability With Ak-arbismentioning

confidence: 99%

A novel method for reliability analysis with interval parameters based on active learning Kriging and adaptive radial‐based importance sampling

Wang

Zhou

et al. 2022

Numerical Meth Engineering

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“…In the last few decades, adaptive schemes 23–25 have been conceived to improve the metamodel efficiency in terms of number of points used to obtain the desired accuracy. Following this approach, the training set is not sampled a priori but instead continuously updated during the metamodel construction adding new points where needed.…”

Section: Introductionmentioning

confidence: 99%

“…19 In the development of noise impact management and prediction tools, efficient metamodels can provide an estimate of the effect of unconventional concepts and procedures at a reasonable computational cost. [20][21][22] In the last few decades, adaptive schemes [23][24][25] have been conceived to improve the metamodel efficiency in terms of number of points used to obtain the desired accuracy. Following this approach, the training set is not sampled a priori but instead continuously updated during the metamodel construction adding new points where needed.…”

Section: Introductionmentioning

confidence: 99%

Adaptive RBF with hyperparameter optimisation for aeroacoustic applications

Burghignoli

Rossetti

Centracchio

et al. 2022

International Journal of Aeroacoustics

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The present work reports an investigation on the use of adaptive metamodels based on radial basis functions (RBFs) for aeroacoustic applications of highly innovative configurations. The relevance of the topic lies on the paramount importance of metamodelling techniques within the design optimisation process of disruptive aircraft layouts. Indeed, the air traffic growth, consequently the hard environmental constraints imposed by regulations, will make a technological breakthrough, an imperative need within few years. As a consequence, the engineering community is paying particular attention to the development of innovative techniques for the design of unconventional configurations. For this class of applications, the designer cannot successfully rely on historical data or low-fidelity models, and the expensive direct simulations remain the only valuable design strategy. In this regard, it can be demonstrated that the use of surrogate models, i.e., metamodels, significantly reduces the computing costs, especially in view of a robust approach to the optimised design. In order to further improve the efficiency of metamodel-based techniques, dynamic approaches based on hyperparameter optimisation and adaptive sampling procedures have been recently developed. The case study presented here pertains the exploiting of dynamic RBF-based metamodels for noise shielding applications. The analysis of the metamodel performances and its convergence properties shows how the final number of direct simulations is significantly reduced by the hyperparameter optimisation algorithm, still strongly depending on the choice of the RBF kernel function.

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“…The commonly used surrogate model methods include Kriging model [Xue et al 2017], Polynomial Chaos Expansion (PCE) [Marelli and Sudret 2018], Artificial Neural Networks (ANN) [Papadopoulos et al 2012], and Response surface method (RSM) [Jiang et al 2015] et al Among them, the Kriging model usually has good performance in approximating local characteristics. Based on this characteristic, scholars proposed many adaptive Kriging methods [Teixeira et al 2020;Wang and Shafieezadeh 2019;Xiao N C et al 2019] for structural reliability analysis. However, the construction of Kriging model is relatively complex, and it is very time-consuming to construct Kriging model in the case of large samples.…”

Section: Introductionmentioning

confidence: 99%

“…RSM usually has three main forms: using polynomial basis functions, radial basis functions, and spline basis functions [Teixeira et al 2020]. Due to the compromise between practicability and efficiency, polynomial basis RSM is one of the most popular metamodeling technique for reliability [Guimarães et al 2018], and many scholars have studied and developed it.…”

Section: Introductionmentioning

confidence: 99%

Reliability Evaluation Based on Multiple Response Surfaces Method Considering Construction Uncertainties of Cable Tension for a Hybrid Roof Structure

Jiang

Zhou

Wang

et al. 2021

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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For large-span hybrid roof structures, the construction uncertainties of cable tension usually have significant influences on its mechanical performance and should be considered in reliability evaluation. An effective approach to quantify uncertainties of cable tensions and to evaluate structural reliability is proposed to carry out the studies by combining the finite element simulation with the multiple response surfaces method. Taking a hybrid roof structure with cables and steel trusses as an example, the main procedures on this issue are illustrated. Firstly, a finite element model is established for the hybrid roof structure considering construction deviations, such as the deviations of cable force between the design values and the real measured values.The ultimate bearing capacity of the structure is calculated for models with and without deviations, and the effects of construction deviations on structural bearing capacity are analyzed. Then, an uncertainty model of cable tension for structural reliability

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Adaptive approaches in metamodel-based reliability analysis: A review

Cited by 156 publications

References 149 publications

A novel method for reliability analysis with interval parameters based on active learning Kriging and adaptive radial‐based importance sampling

A novel method for reliability analysis with interval parameters based on active learning Kriging and adaptive radial‐based importance sampling

Adaptive RBF with hyperparameter optimisation for aeroacoustic applications

Reliability Evaluation Based on Multiple Response Surfaces Method Considering Construction Uncertainties of Cable Tension for a Hybrid Roof Structure

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