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contributor authorB. Goller
contributor authorJ. L. Beck
contributor authorG. I. Schuëller
date accessioned2017-05-08T21:43:44Z
date available2017-05-08T21:43:44Z
date copyrightMay 2012
date issued2012
identifier other%28asce%29em%2E1943-7889%2E0000360.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/60822
description abstractIn Bayesian model updating, parameter identification of structural systems using modal data can be based on the formulation of the likelihood function as a product of two probability density functions, one relating to modal frequencies and one to mode-shape components. The selection of the prior distribution of the prediction-error variances relating to these two types of data has to be performed carefully so that the relative contributions are weighted to give balanced results. A methodology is proposed in this paper to select these weights by performing Bayesian updating at the model class level, where the model classes differ by having different ratios of the two prediction-error variances. The most probable model class on the basis of the modal data then gives the best choice for this variance ratio. Two illustrative examples, one using simulated data and one using experimental data, point out the effect of the different relative contributions of the modal frequencies and mode-shape components to the total amount of information extracted from the modal data.
publisherAmerican Society of Civil Engineers
titleEvidence-Based Identification of Weighting Factors in Bayesian Model Updating Using Modal Data
typeJournal Paper
journal volume138
journal issue5
journal titleJournal of Engineering Mechanics
identifier doi10.1061/(ASCE)EM.1943-7889.0000351
treeJournal of Engineering Mechanics:;2012:;Volume ( 138 ):;issue: 005
contenttypeFulltext


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