A Kriging Model Based Finite Element Model Updating Method for Damage Detection

Yang, Xiuming; Guo, Xinglin; Ouyang, Huajiang; Li, Dongsheng

doi:10.3390/app7101039

Cited by 25 publications

(13 citation statements)

References 41 publications

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“…In kriging, the approximated values are seen as a stochastic process, which parameters are estimated based on the known output values for given input parameters. An example of construction of kriging‐based surrogate model for FEM analysis is published in Yang et al …”

Section: Predicting Settlements During Tunnel Excavation Via Bayesianmentioning

confidence: 99%

Applying Bayesian approach to predict deformations during tunnel construction

Janda

Šejnoha

2018

Num Anal Meth Geomechanics

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In this paper a fully probabilistic approach based on the Bayesian statistical method is presented to predict ground settlements in both transverse and longitudinal directions during gradual excavation of a tunnel. To that end, the convergence confinement method is adopted to give estimates of ground deformation numerically. Together with in situ measurements of the evolution of vertical deflections at selected points along the tunnel line, it allows for the construction of a likelihood function and consequently in the framework of Bayesian inference to provide posterior improved knowledge of model parameters entering the numerical analysis. In this regard, the Bayesian updating is first exploited in the material identification step and next used to yield predictions of ground settlement in sections along the tunnel line ahead of the tunnel face. This methodology thus makes it possible to improve original designs by utilizing an increasing number of data (measurements) collected in the course of tunnel construction.

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Section: Predicting Settlements During Tunnel Excavation Via Bayesianmentioning

confidence: 99%

Applying Bayesian approach to predict deformations during tunnel construction

Janda

Šejnoha

2018

Num Anal Meth Geomechanics

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“…Equations (5) and (6) show that the only parameter is the correlation parameters (θ) in Ψ. By substituting Equations (5) and (6) into Equation 4, the concentrated log-likelihood function is obtained as…”

Section: The Kriging Modelmentioning

confidence: 99%

“…A surrogate model can play a valuable role in addressing these computational issues [2][3][4][5]. The surrogate model is constructed by fitting the input-output relations of a high-fidelity model, thus constructing a fast approximation of the high-fidelity model.…”

Section: Introductionmentioning

confidence: 99%

“…For the SS method, infill criteria have been developed to update the surrogate model by learning information about the response surface. The SS method can involve single-update-infill sampling [5,[12][13][14] and multiple-update-infill sampling [10,15]. Single-update-infill sampling consists in the selection of only a single infill sample at each infill stage, whereas the multiple-update-infill sampling method consists in the selection of multiple infill samples to accelerate the improvement of global surrogate modeling.…”

Section: Introductionmentioning

confidence: 99%

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The Multiple-Update-Infill Sampling Method Using Minimum Energy Design for Sequential Surrogate Modeling

et al. 2018

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Computer experiments are widely used to evaluate the performance and reliability of engineering systems with the lowest possible time and cost. Sometimes, a high-fidelity model is required to ensure predictive accuracy; this becomes computationally intensive when many computational analyses are required (for example, inverse analysis or uncertainty analysis). In this context, a surrogate model can play a valuable role in addressing computational issues. Surrogate models are fast approximations of high-fidelity models. One efficient way for surrogate modeling is the sequential sampling (SS) method. The SS method sequentially adds samples to refine the surrogate model. This paper proposes a multiple-update-infill sampling method using a minimum energy design to improve the global quality of the surrogate model. The minimum energy design was recently developed for global optimization to find multiple optima. The proposed method was evaluated with other multiple-update-infill sampling methods in terms of convergence, accuracy, sampling efficiency, and computational cost.

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“…The result shows that this method can effectively be used for identifying the crack parameters. Yang et al 15 presented a new surrogate model based on model updating method using frequency response function for damage identification. The proposed method has good accuracy and robustness, which has been verified by a numerical simulation of a cantilever and experimental test data of a laboratory three-story structure.…”

Section: Introductionmentioning

confidence: 99%

An improved two-step method based on Kriging model for beam structures

Xie

Jiang

Xiang

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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An improved two-step method for multiple cracks detection in beam structures is presented in this paper. First, the crack locations are identified by the mode shape curvatures. Second, samples of varying crack depths at the crack locations and corresponding natural frequencies are used to construct the initial Kriging model in place of the crack detection database. Finally, a particle swarm optimization algorithm is employed to detect the crack depths and update the Kriging model by an optimal point adding process. The numerical simulation and experimental verification of a cantilever beam with two cracks demonstrate that the proposed method is effective. The detection results indicate that the Kriging model can effectively reduce computation time with a similar accuracy. The detection results also indicate that the more natural frequencies used, the higher accuracy of crack depths detected.

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A Kriging Model Based Finite Element Model Updating Method for Damage Detection

Cited by 25 publications

References 41 publications

Applying Bayesian approach to predict deformations during tunnel construction

Applying Bayesian approach to predict deformations during tunnel construction

The Multiple-Update-Infill Sampling Method Using Minimum Energy Design for Sequential Surrogate Modeling

An improved two-step method based on Kriging model for beam structures

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