Model selection in finite element model updating using the Bayesian evidence statistic

Mthembu, L.; Marwala, Tshilidzi; Friswell, Michael I.; Adhikari, Sondipon

doi:10.1016/j.ymssp.2011.04.001

Cited by 69 publications

(28 citation statements)

References 21 publications

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“…This is a very popular approach (see e.g. [148][149][150]), but still requires the selection of a suitable probabilistic model for the remaining model parameter error. A second alternative approach consists of applying Bayesian model class selection to determine the most suitable probabilistic model class to represent the prediction error based on the information at hand.…”

Section: The Likelihood Functionmentioning

confidence: 99%

Dealing with uncertainty in model updating for damage assessment: A review

Simoen

Roeck

Lombaert

2015

Mechanical Systems and Signal Processing

383

217

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In structural engineering, model updating is often used for non-destructive damage assessment: by calibrating stiffness parameters of finite element models based on experimentally obtained (modal) data, structural damage can be identified, quantified and located. However, the model updating problem is an inverse problem prone to ill-posedness and ill-conditioning. This means the problem is extremely sensitive to small errors, which may potentially detract from the method's robustness and reliability. As many errors or uncertainties are present in model updating, both regarding the measurements as well as the employed numerical model, it is important to take these uncertainties suitably into account. This paper aims to provide an overview of the available approaches to this end, where two methods are treated in detail: a non-probabilistic fuzzy approach and a probabilistic Bayesian approach. These methods are both elaborated for the specific case of vibration-based finite element model updating for damage assessment purposes.

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Section: The Likelihood Functionmentioning

confidence: 99%

Dealing with uncertainty in model updating for damage assessment: A review

Simoen

Roeck

Lombaert

2015

Mechanical Systems and Signal Processing

383

217

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“…different finite element idealizations of the same structure. Muto and Beck [27] showed that the log evidence could be expressed as two terms, the first being a data-fitting term and the second a parsimony term (also known as the information gain measure [28]) that aims to choose the simplest model class.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data

et al. 2017

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Deterministic model updating is now a mature technology widely applied to large-scale industrial structures. It is concerned with the calibration of the parameters of a single model based on one set of test data. It is, of course, well known that different analysts produce different finite element models, make different physicsbased assumptions, and parameterize their models differently. Also, tests carried out on the same structure, by different operatives, at different times, under different ambient conditions produce different results. There is no unique model and no unique data. Therefore, model updating needs to take account of modeling and test-data variability. Much emphasis is now placed on what has become known as stochastic model updating where data are available from multiple nominally identical test structures. In this paper two currently prominent stochastic model updating techniques (sensitivity-based updating and Bayesian model updating) are described and applied to the DLR AIRMOD structure.

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“…Subsequent research has illustrated the effectiveness of this approach in the field of robust structural health assessment (Katafygiotis, et al, 1998). More recently, a wide range of research tasks utilizing this approach have been explored, such as prognosis of fatigue crack growth, model selection, etc (Zarate, et al, 2012;Mthembu, et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Piezoelectric admittance-based damage identification by Bayesian inference with pre-screening

Qi¹,

Liang²,

Tang³

2016

SPIE Proceedings

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The piezoelectric impedance-based damage identification, which traditionally uses the inverse optimization with sensitivity matrix, can in theory identify the damage location and damage severity at the same time. However, the inverse problem is underdetermined in most cases, since the number of unknowns (i.e., possible damage locations and severities) is far more than that of useful measurements. Recently, a new damage identification strategy has been proposed, which is based on Bayesian inference framework. This strategy necessitates employing forward analysis-based instead of inverse-based identification procedures. As the Bayesian inference is sampling-based algorithm, it requires repeated evaluation (FEA analysis) in the parameter space. Since in most cases, one has no prior information on the damage location and severity, the Bayesian inference should be carried out in a large parameter space that will need extremely high computing cost. In this research, a robust and efficient damage identification procedure by employing piezoelectric impedance is developed by combining the sensitivity matrix analysis and the Bayesian inference framework. The new procedure includes two steps. In the first step, a group of possible damage locations and corresponding severity are predicted based on sensitivity matrix analysis, which however does not require solving the inverse of the sensitivity matrix. The prediction from the first step is then used as the sample space. Subsequently, Bayesian inference is carried out to further determine the damage location and severity. Numerical simulations are carried out to demonstrate the damage identification performance.

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Model selection in finite element model updating using the Bayesian evidence statistic

Cited by 69 publications

References 21 publications

Dealing with uncertainty in model updating for damage assessment: A review

Dealing with uncertainty in model updating for damage assessment: A review

Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data

Piezoelectric admittance-based damage identification by Bayesian inference with pre-screening

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