Application of kriging method to structural reliability problems

Kaymaz, İrfan

doi:10.1016/j.strusafe.2004.09.001

Cited by 601 publications

(247 citation statements)

References 18 publications

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“…Other methods which have gained popularity among researchers over the last few decades include artificial neural network [3], polynomial chaos [4], support vector machine [5] and the Kriging metamodel [6].…”

Section: Introductionmentioning

confidence: 99%

Inverse Response Surface Method in Reliability-Based Design

Lehký¹,

Šomodíková²

2017

Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series.

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The paper introduces an inverse response surface method utilized when performing reliabilitybased design optimization of time-consuming problems. Proposed procedure is based on a coupling of the adaptive response surface method and the artificial neural network-based inverse reliability method. The validity and accuracy of the method is tested using examples with explicit nonlinear limit state functions. Obtained results as well as important aspects of the method are discussed. KeywordsResponse surface, artificial neural network, inverse reliability analysis, reliability-based design, failure probability, reliability index.

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Section: Introductionmentioning

confidence: 99%

Inverse Response Surface Method in Reliability-Based Design

Lehký¹,

Šomodíková²

2017

Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series.

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“…Due to its characteristics, kriging method has also been used to construct metamodels or surrogate models in various scientific areas. To illustrate this fact in the structures reliability field, we can refer the work developed by Kaymaz [3]. In his paper it is investigated the use of the kriging method for structural reliability problems by comparing it with the response surface method based on least square regression.…”

Section: Introductionmentioning

confidence: 99%

Analysis of sandwich beam structures using kriging based higher order models

Loja

Barbosa

Soares

2015

Composite Structures

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a b s t r a c tFunctionally graded composite materials can provide continuously varying properties, which distribution can vary according to a specific location within the composite. More frequently, functionally graded materials consider a through thickness variation law, which can be more or less smoother, possessing however an important characteristic which is the continuous properties variation profiles, which eliminate the abrupt stresses discontinuities found on laminated composites. This study aims to analyze the transient dynamic behavior of sandwich structures, having a metallic core and functionally graded outer layers. To this purpose, the properties of the particulate composite metal-ceramic outer layers, are estimated using Mori-Tanaka scheme and the dynamic analyses considers first order and higher order shear deformation theories implemented though kriging finite element method. The transient dynamic response of these structures is carried out through Bossak-Newmark method. The illustrative cases presented in this work, consider the influence of the shape functions interpolation domain, the properties through-thickness distribution, the influence of considering different materials, aspect ratios and boundary conditions.

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“…In the closely related field of structural reliability analysis, metamodeling techniques have been widely employed to accelerate the computation of failure probability [15,16,17,18,19,20]. Compared to other metamodels, Kriging metamodel is particularly useful for reliability analysis due to the direct availability of error estimation that allows one to deploy active learning strategy [21,22,23] (i.e., Kriging with adaptive sampling).…”

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confidence: 99%

Global sensitivity analysis via multi-fidelity polynomial chaos expansion

Palar

Zuhal

Shimoyama

et al. 2018

Reliability Engineering & System Safety

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The presence of uncertainties are inevitable in engineering design and analysis, where failure in understanding their effects might lead to the structural or functional failure of the systems. The role of global sensitivity analysis in this aspect is to quantify and rank the effects of input random variables and their combinations to the variance of the random output. In problems where the use of expensive computer simulations are required, metamodels are widely used to speed up the process of global sensitivity analysis. In this paper, a multi-fidelity framework for global sensitivity analysis using polynomial chaos expansion (PCE) is presented. The goal is to accelerate the computation of Sobol sensitivity indices when the deterministic simulation is expensive and simulations with multiple levels of fidelity are available. This is especially useful in cases where a partial differential equation solver computer code is utilized to solve engineering problems. The multi-fidelity PCE is constructed by combining the lowfidelity and correction PCE. Following this step, the Sobol indices are computed using this combined PCE. The PCE coefficients for both low-fidelity and correction PCE are computed with spectral projection technique and sparse grid integration. In order to demonstrate the capability of the proposed method for sensitivity analysis, several simulations are conducted. On the aerodynamic example, the multi-fidelity approach is able to obtain an accurate value of Sobol indices with 36.66% computational cost compared to the standard single-fidelity PCE for a nearly similar accuracy.

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Application of kriging method to structural reliability problems

Cited by 601 publications

References 18 publications

Inverse Response Surface Method in Reliability-Based Design

Inverse Response Surface Method in Reliability-Based Design

Analysis of sandwich beam structures using kriging based higher order models

Global sensitivity analysis via multi-fidelity polynomial chaos expansion

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