Updating computational models in the frequency domain based on measured data: a survey

Natke, H. G.

doi:10.1016/0266-8920(88)90005-7

Cited by 93 publications

(28 citation statements)

References 20 publications

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“…Bayesian analysis has also been used extensively for structural identification, i.e. the task of identifying dynamic properties of structural systems based on vibration measurements (Natke 1988;Beck and Katafygiotis 1998).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Updating with Structural Reliability Methods

Straub¹,

Papaioannou²

2015

J. Eng. Mech.

247

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Bayesian updating is a powerful method to learn and calibrate models with data and observations. Because of the difficulties involved in computing the high-dimensional integrals necessary for Bayesian updating, Markov Chain Monte Carlo (MCMC) sampling methods have been developed and successfully applied for this task. The disadvantage of MCMC methods is the difficulty of ensuring the stationarity of the Markov chain. We present an alternative to MCMC that is particularly effective for updating mechanical and other computational models, termed BUS: Bayesian Updating with Structural reliability methods. With BUS, structural reliability methods are applied to compute the posterior distribution of uncertain model parameters and model outputs in general. We propose an algorithm for the implementation of BUS, which can be interpreted as an enhancement of the classical rejection sampling algorithm for Bayesian updating. This algorithm is based on the subset simulation and its efficiency is not dependent on the number of random variables in the model. The method is demonstrated by application to parameter identification in a dynamic system, Bayesian updating of the material parameters of a structural system, and Bayesian updating of a random-field-based FE model of a geotechnical site.

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Section: Introductionmentioning

confidence: 99%

Bayesian Updating with Structural Reliability Methods

Straub¹,

Papaioannou²

2015

J. Eng. Mech.

247

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“…Perhaps the simplest approach is to parameterise substructure mass and sti!ness matrices with a scalar multiplier as was advocated by Natke [9]. Although this method can be applied to a known erroneous substructure, it is unlikely to produce an updated model that is improved physically because it is unable to separate the error, that might for instance be present in a dimension across a structural member, from a multiplier that a!ects every term in the substructure sti!ness (or mass) matrix.…”

Section: Parameters For Model Updatingmentioning

confidence: 99%

“…We remarked previously however that the sensitivity of the predictions to di!erent parameters can show large di!erences. Only if the updating equations are linear in the parameters, such as in the substructure parameters method [9], can normalisation of the parameters be carried out. Otherwise, it is the usual practice to non-dimensionalise the change in each parameter by dividing by its initial value.…”

Section: Introductionmentioning

confidence: 99%

Selection and Updating of Parameters for an Aluminium Space-Frame Model

Mottershead

Mares

Friswell

et al. 2000

Mechanical Systems and Signal Processing

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“…Several review articles of finite element model updating reveal a wealth of updating algorithms but success seems to remain case dependent and applicability is bounded by the skill of the analyst in choosing a correct updating procedure ( [1][2][3][4][5][6][7][8][9]). This paper suggests structural reanalysis method in which the correlated finite element model is evaluated using FRFs (Frequency Response Functions) of the structure.…”

Section: Introductionmentioning

confidence: 99%

Structural Reanalysis of Dynamic Systems Using Model Updating Method

Han¹

2011

ENG

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Model updating methodologies are invariably successful when used on noise-free simulated data, but tend to be unpredictable when presented with real experimental data that are unavoidably corrupted with uncorrelated noise content. In this paper, reanalysis using frequency response functions for correlating and updating dynamic systems is presented. A transformation matrix is obtained from the relationship between the complex and the normal frequency response functions of a structure. The transformation matrix is employed to calculate the modified damping matrix of the system. The modified mass and stiffness matrices are identified from the normal frequency response functions by using the least squares method. A numerical example is employed to illustrate the applicability of the proposed method. The result indicates that the present method is effective.

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Updating computational models in the frequency domain based on measured data: a survey

Cited by 93 publications

References 20 publications

Bayesian Updating with Structural Reliability Methods

Bayesian Updating with Structural Reliability Methods

Selection and Updating of Parameters for an Aluminium Space-Frame Model

Structural Reanalysis of Dynamic Systems Using Model Updating Method

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