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

Goller, B. and Beck, J. L. and Schuëller, G. I. (2012) Evidence-Based Identification of Weighting Factors in Bayesian Model Updating Using Modal Data. Journal of Engineering Mechanics, 138 (5). pp. 430-440. ISSN 0733-9399. doi:10.1061/(ASCE)EM.1943-7889.0000351. https://resolver.caltech.edu/CaltechAUTHORS:20120711-101751066

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1061/(ASCE)EM.1943-7889.0000351DOIArticle
http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EM.1943-7889.0000351PublisherArticle
Additional Information:© 2012 American Society of Civil Engineers. This manuscript was submitted on August 27, 2010; approved on November 8, 2011; published online on November 10, 2011. Discussion period open until October 1, 2012; separate discussions must be submitted for individual papers. This research was partially supported by the European Space Agency (ESA) under Project No. P8440-018-011, which is gratefully acknowledged by the authors. The first author is a recipient of a DOC-fForte-fellowship of the Austrian Academy of Science at the Institute of Engineering Mechanics (University of Innsbruck) and spent the Fall Term of 2008 as a Visiting Student Researcher at the California Institute of Technology.
Funders:
Funding AgencyGrant Number
European Space Agency (ESA)P8440-018-011
Austrian Academy of ScienceUNSPECIFIED
Subject Keywords:Structural models; Markov process; Bayesian analysis; Simulation
Issue or Number:5
DOI:10.1061/(ASCE)EM.1943-7889.0000351
Record Number:CaltechAUTHORS:20120711-101751066
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20120711-101751066
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:32350
Collection:CaltechAUTHORS
Deposited By: Jason Perez
Deposited On:11 Jul 2012 18:42
Last Modified:09 Nov 2021 21:26

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