An adaptive divergence-based method for structural reliability analysis via multiple Kriging models

Meta-models or surrogate models are convenient tools for reliability assessment of problems with time-consuming numerical models. Recently, an adaptive method called AK-MCS has been widely used for reliability analysis by combining Mont-Carlo simulation method and Kriging surrogate model. The AK-MCS method usually uses constant regression as a Kriging trend. However, other regression trends may have better performance for some problems. So, a method is proposed by combining multiple Kriging meta-models with various trends. The proposed method is based on the maximum entropy of predictions to select training samples. Using multiple Kriging models can reduce the sensitivity to the regression trend. So, the propped method can have better performance for different problems. The proposed method is applied to some examples to show its efficiency.

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“…So, the presented method in Ref. [21] is employed in the current study to use the results of multiple Kriging models. U(x) in Eq.…”

Section: Entropy-based Proposed Methodsmentioning

confidence: 99%

“…So, the presented method in Ref. [21] is employed to filter out inaccurate Kriging models. According to the stopping criterion in Eq.…”

Section: Entropy-based Proposed Methodsmentioning

confidence: 99%

An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models

Vahedi

Ghasemi

Miri

2019

Period. Polytech. Civil Eng.

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“…Kriging model holds good approximate performance in prediction, and thus is widely applied in performance analysis [20], [24], probabilistic failure analysis [25], [26], sensitivity assessment [27], [28], and design optimization [29], [30], for structure system. However, Kriging model is inefficient for dynamic structural probabilistic analysis, because multiple models are required modeling for dynamic operational process and greatly enlarge computational complexity and consumptions.…”

Section: A Extremum Kriging Modelmentioning

confidence: 99%

“…, θ m ] represents correlation parameter vector to be estimated by R. The hyperparameters θ are called as distance weights or length scales, which is typically obtained by solving MLE with gradient descent optimizer. In engineering, Gaussian correlation function is generally regarded as the SCF to find the optimal parameters in Kriging model, because Gaussian function can enhance the Kriging modelling of high dimensional problem in computational efficiency and accuracy by effectively reducing dimensions [25], [32]. The Gaussian correlation function R(•) in Eq.…”

Section: A Extremum Kriging Modelmentioning

confidence: 99%

Decomposed-Coordinated Framework With Enhanced Extremum Kriging for Multicomponent Dynamic Probabilistic Failure Analyses

Feng

Fei

et al. 2019

IEEE Access

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For multicomponent structures enduring dynamic workloads coming from multi-physical fields, safety assessment is significant to guarantee the normal operation of entire structure system. In this paper, an enhanced extremum Kriging-based decomposed coordinated framework (E2K-DCF) is proposed to improve the dynamic probabilistic failure analyses of multicomponent structures. In this method, extremum Kriging model (EKM) is developed by introducing Kriging model into extremum response surface method (ERSM) to process the transient response problem and shorten computational burden in dynamic probabilistic failure analyses. Multiple population genetic algorithm (MPGA) is employed to solve maximum likelihood equation (MLE) and find the optimal hyperparameter θ in the EKM, which is promising to enhance approximate accuracy; decomposed-coordinated (DC) strategy is used to handle the coordinated relationship of multiple analytical objectives. To validate the proposed E2K-DCF, the probabilistic failure analysis of turbine blisk radial deformation is conducted by comparing with different methods within time domain [0 s, 215 s], considering fluid-thermal-structural interaction. It is revealed that the failure probability of blisk radial deformation is only 0.0022 when the allowable value is 2.5702 × 10 −3 m acquired from real world practice. Compared to the other approaches, this E2K-DCF has obvious advantages in fitting time and accuracy as well as simulation efficiency and accuracy. The results illustrate that the E2K-DCF is effective and applicable in dynamic probabilistic failure analysis. The efforts of this paper provide a novel viewpoint for the transient reliability evaluation of multicomponent structures, which is likely to enrich mechanical reliability theory. INDEX TERMS E2K-DCF, multicomponent structure, multiple population genetic algorithm, probabilistic failure, turbine blisk.

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“…Unlike other surrogate models, Kriging model can not only give the prevaluation of unknown function but also get the error estimate of the prevaluation. Only a small number of samples can accurately describe the relationship between the inputs and outputs of the structure, which is widely used in the field of evaluation of machine tools [13], structural reliability analysis [14], and model updating [15]. In the field of FEMU, Zhang and Guo [16] applied Kriging theory to FEM confirmation to predict the response of the structure.…”

Section: Introductionmentioning

confidence: 99%

Model Updating Method Based on Kriging Model for Structural Dynamics

Yin

Dong

et al. 2019

Shock and Vibration

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Model updating in structural dynamics has attracted much attention in recent decades. And high computational cost is frequently encountered during model updating. Surrogate model has attracted considerable attention for saving computational cost in finite element model updating (FEMU). In this study, a model updating method using frequency response function (FRF) based on Kriging model is proposed. The optimal excitation point is selected by using modal participation criterion. Initial sample points are chosen via design of experiment (DOE), and Kriging model is built using the corresponding acceleration frequency response functions. Then, Kriging model is improved via new sample points using mean square error (MSE) criterion and is used to replace the finite element model to participate in optimization. Cuckoo algorithm is used to obtain the updating parameters, where the objective function with the minimum frequency response deviation is constructed. And the proposed method is applied to a plane truss model FEMU, and the results are compared with those by the second-order response surface model (RSM) and the radial basis function model (RBF). The analysis results showed that the proposed method has good accuracy and high computational efficiency; errors of updating parameters are less than 0.2%; damage identification is with high precision. After updating, the curves of real and imaginary parts of acceleration FRF are in good agreement with the real ones.

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An adaptive divergence-based method for structural reliability analysis via multiple Kriging models

Cited by 30 publications

References 28 publications

An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models

An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models

Decomposed-Coordinated Framework With Enhanced Extremum Kriging for Multicomponent Dynamic Probabilistic Failure Analyses

Model Updating Method Based on Kriging Model for Structural Dynamics

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