Updating Models and Their Uncertainties. II: Model Identifiability

Katafygiotis, Lambros S.; Beck, James L.

doi:10.1061/(asce)0733-9399(1998)124:4(463)

Cited by 360 publications

(218 citation statements)

References 4 publications

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“…For example, between θ 4 and θ 5 which are sensor harness stiffnesses at the wing-fuselage connection. Also, θ 6 , θ 7 and θ 8 all located at the VTP-HTP joint are strongly correlated. Mass and stiffness parameters are found to be negatively correlated simply because a reduction in mass has a similar effect on natural frequencies as an increase in stiffness and vice versa-this correlation is not meaningful physically.…”

Section: Results Obtained By the Sensitivity Methodsmentioning

confidence: 92%

“…Bayesian model updating, making use of the well-known Bayes' rule, came to the attention of the engineering research community mainly through the pioneering work of Beck and Katafygiotis [6,7] in the late 1990s-a detailed up-to-date exposition is given by Yuen [8]. At first, the most serious obstacle to practical engineering application was the excessive levels of computer resource needed.…”

Section: Bayesian Model Updatingmentioning

confidence: 99%

“…The stiffness parameters include the support stiffnesses (θ 1 -θ 5 ) and joint stiffnesses (θ 6 , θ 7 and θ 14 -θ 18 ). Mass parameters (θ 8 -θ 13 )are included to represent variability in the position of cable bundles, screws and glue after each reassembly of the structure.…”

Section: Experimental Example: Dlr Airmod Structurementioning

confidence: 99%

“…The statistical approach of Collins et al in 1974 [1] is the source of two streams of research on (i) sensitivity-based [2][3][4][5] and (ii) Bayesian methods [6][7][8] that have developed largely independently. The purpose of this paper is to make a comparison of these two approaches in terms of the claims made for them, the assumptions upon which the methods are based and an assessment of the performance of the techniques when applied to the same experimental data from the DLR AIRcraft MODel (AIRMOD) [9,10].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Results Obtained By the Sensitivity Methodsmentioning

confidence: 92%

Section: Bayesian Model Updatingmentioning

confidence: 99%

Section: Experimental Example: Dlr Airmod Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…The Bayesian method then allows a posterior probability distribution to be constructed from the optimal prior probability distribution and the experimental data. Many works have been published in the literature (see for instance textbooks on the Bayesian method such as [59,60,61,62] and papers devoted to the use of the Bayesian method in the context of uncertain mechanical and dynamical systems such as [12,128,129,130,131,132,133]. We will use such a Bayesian approach in Sections 4.6 and 6.…”

Section: Identification Of the Stochastic Model Of Random Variables Imentioning

confidence: 99%

Stochastic modeling of uncertainties in computational structural dynamics—Recent theoretical advances

Soize

2013

Journal of Sound and Vibration

139

106

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To cite this version:Christian Soize. Stochastic modeling of uncertainties in computational structural dynamics -Recent theoretical advances. Journal of Sound and Vibration, Elsevier, 2013, 332 (10) AbstractThis paper deals with a short overview on stochastic modeling of uncertainties. We introduce the types of uncertainties, the variability of real systems, the types of probabilistic approaches, the representations for the stochastic models of uncertainties, the construction of the stochastic models using the maximum entropy principle, the propagation of uncertainties, the methods to solve the stochastic dynamical equations, the identification of the prior and the posterior stochastic models, the robust updating of the computational models and the robust design with uncertain computational models. We present recent theoretical advances in this field concerning the parametric and the nonparametric probabilistic approaches of uncertainties in computational structural dynamics for the construction of the prior stochastic models of both the uncertainties on the computational model parameters and on the modeling uncertainties, and for their identification with experimental data. We also present the construction of the posterior stochastic model of uncertainties using the Bayesian method when experimental data are available.

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Identification of linear structural systems using earthquake-induced vibration data

Luş

Betti

Longman

1999

Earthquake Engng. Struct. Dyn.

132

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This paper addresses the issue of system identi"cation for linear structural systems using earthquake induced time histories of the structural response. The proposed methodology is based on the Eigensystem Realization Algorithm (ERA) and on the Observer/Kalman "lter IDenti"cation (OKID) approach to perform identi"cation of structural systems using general input}output data via Markov parameters. The e$ciency of the proposed technique is shown by numerical examples for the case of eight-storey building "nite element models subjected to earthquake excitation and by the analysis of the data from the dynamic response of the Vincent-Thomas cable suspension bridge (Long Beach, CA) recorded during the Whittier and the Northridge earthquakes. The e!ects of noise in the measurements and of inadequate instrumentation are investigated. It is shown that the identi"ed models show excellent agreement with the real systems in predicting the structural response time histories when subjected to earthquake-induced ground motion.

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Updating Models and Their Uncertainties. II: Model Identifiability

Cited by 360 publications

References 4 publications

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

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

Stochastic modeling of uncertainties in computational structural dynamics—Recent theoretical advances

Identification of linear structural systems using earthquake-induced vibration data

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