Parameter selection and stochastic model updating using perturbation methods with parameter weighting matrix assignment

Husain, Nurulakmar Abu; Khodaparast, Hamed Haddad; Ouyang, Huajiang

doi:10.1016/j.ymssp.2012.04.001

Cited by 48 publications

(27 citation statements)

References 33 publications

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“…However, high computational 2 Shock and Vibration costs due to a large amount of samples required for a satisfactory estimation greatly restrain the applications of Bayesian updating approaches. As a result, surrogate models such as the Gaussian process model with the perturbation approaches and sensitivity analysis approaches have been employed in stochastic model updating to improve the efficiency [13][14][15][16]. Though, the surrogate model approaches own the superiority of computational efficiency over Monte Carlo (MC) based methods.…”

Section: Introductionmentioning

confidence: 99%

A Copula‐Based and Monte Carlo Sampling Approach for Structural Dynamics Model Updating with Interval Uncertainty

Chen

Shen

Liu

2018

Shock and Vibration

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As the uncertainty is widely existent in the engineering structure, it is necessary to study the finite element (FE) modeling and updating in consideration of the uncertainty. A FE model updating approach in structural dynamics with interval uncertain parameters is proposed in this work. Firstly, the mathematical relationship between the updating parameters and the output interesting qualities is created based on the copula approach and the vast samples of inputs and outputs are obtained by the Monte Carlo (MC) sampling technology according to the copula model. Secondly, the samples of updating parameters are rechosen by combining the copula model and the experiment intervals of the interesting qualities. Next, 95% confidence intervals of updating parameters are calculated by the nonparameter kernel density estimation (KDE) approach, which is regarded as the intervals of updating parameters. Lastly, the proposed approach is validated in a two degree-of-freedom mass-spring system, simple plates, and the transport mirror system. The updating results evidently demonstrate the feasibility and reliability of this approach.

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Section: Introductionmentioning

confidence: 99%

A Copula‐Based and Monte Carlo Sampling Approach for Structural Dynamics Model Updating with Interval Uncertainty

Chen

Shen

Liu

2018

Shock and Vibration

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“…Model updating technique has also been performed on miscellaneous types of structures [33]. For instance, Abu Husain has demonstrated model updating technique on welded flat plate and hat-shape structure [34], which from the initial correlation exhibits percentage of errors of below 5%. However, after carrying model updating procedure, the level of discrepancies is reduced to below 3%.…”

Section: Introductionmentioning

confidence: 99%

“…However, after carrying model updating procedure, the level of discrepancies is reduced to below 3%. Aside from this, there are many investigations of model updating procedures on other structures such as complex aerospace structures, bridges, and others [16,18,22,34,35]. Model updating is already a frequent field of study.…”

Section: Introductionmentioning

confidence: 99%

Dynamics properties of a Go-kart chassis structure and its prediction improvement using model updating approach

Abdullah

Sani

Husain

et al. 2017

Int. J. Automot. Mech. Eng.

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Model updating is concerned about the correction of finite element models by processing the record of dynamic response from test structures in order to have an accurate model for any simulated analysis. Finite element model updating had emerged years ago as an important subject in structural dynamics. It has been used frequently and has been successfully applied to many fields especially in detecting the dynamic stiffness of a structure. The purpose of this study is to perform model updating of a go-kart chassis structure in order to reduce the percentage of error between the experimental modal analysis (EMA) and finite element analysis (FEA). Modal properties (natural frequency, mode shapes, and damping ratio) of the go-kart chassis structure were determined using both EMA and FEA. Correlation of the modal parameters gathered in FEA and EMA was carried out before optimizing the data from finite element. By adjusting the selective parameters, incongruities between those two analyses are generally reduced. The sensitivity of selected parameters is also obtained. The significant reduction in percentage of error before and after model updating procedure was carried out in this study clearly shows that model updating technique is a reliable method in reducing the discrepancies between EMA and FEA. Therefore, in cases of high discrepancies between analytical and actual test data, model updating can be considered as an option in order to obtain better correlation between those two sets of data.

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“…Considering the measurement errors as random quantities, many statistical approaches have been developed to update the parameters of structures based on the measured data. These approaches include the stochastic perturbation methods [3,[12][13][14][15][16], the Monte Carlo simulation methods [17,18], the Bayesian updating methods [19][20][21][22][23][24][25][26] and so on. For example, Jacquelin et al [12] proposed a random matrix approach to derive the closed-form expressions for the mean matrix and the covariance matrix of the updated stiffness matrix by the perturbation technique.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Jacquelin et al [12] proposed a random matrix approach to derive the closed-form expressions for the mean matrix and the covariance matrix of the updated stiffness matrix by the perturbation technique. Husain et al [13] considered the statistical properties of experimental data and updating parameters as random variables, and used the perturbation method to update the parameters. Combined with the sensitive method, Hua et al use a Monte Carlo simulation method to solve the updating parameters [18].…”

Section: Introductionmentioning

confidence: 99%

A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load

Huang

et al. 2017

Applied Sciences

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Parameter selection and stochastic model updating using perturbation methods with parameter weighting matrix assignment

Cited by 48 publications

References 33 publications

A Copula‐Based and Monte Carlo Sampling Approach for Structural Dynamics Model Updating with Interval Uncertainty

A Copula‐Based and Monte Carlo Sampling Approach for Structural Dynamics Model Updating with Interval Uncertainty

Dynamics properties of a Go-kart chassis structure and its prediction improvement using model updating approach

A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load

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