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    Evidence-Based Identification of Weighting Factors in Bayesian Model Updating Using Modal Data

    Source: Journal of Engineering Mechanics:;2012:;Volume ( 138 ):;issue: 005
    Author:
    B. Goller
    ,
    J. L. Beck
    ,
    G. I. Schuëller
    DOI: 10.1061/(ASCE)EM.1943-7889.0000351
    Publisher: American Society of Civil Engineers
    Abstract: In 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.
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      Evidence-Based Identification of Weighting Factors in Bayesian Model Updating Using Modal Data

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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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    DSpace software copyright © 2002-2015  DuraSpace
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