Bayesian evidence for finite element model updating

Linda Mthembu, Tshilidzi Marwala, Michael I. Friswell, Sondipon Adhikari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


This paper considers the problem of model selection within the context of finite element model updating. Given that a number of FEM updating models, with different updating parameters, can be designed, this paper proposes using the Bayesian evidence statistic to assess the probability of each updating model. This makes it possible then to evaluate the need for alternative updating parameters in the updating of the initial FE model. The model evidences are compared using the Bayes factor, which is the ratio of evidences. The Jeffrey's scale is used to determine the differences in the models. The Bayesian evidence is calculated by integrating the likelihood of the data given the model and its parameters over the a priori model parameter space using the new nested sampling algorithm. The nested algorithm samples this likelihood distribution by using a hard likelihood-value constraint on the sampling region while providing the posterior samples of the updating model parameters as a by-product. This method is used to calculate the evidence of a number of plausible finite element models.

Original languageEnglish
Title of host publicationIMAC-XXVII
Subtitle of host publicationConference and Exposition on Structural Dynamics - Model Verification and Validation
Publication statusPublished - 2009
Externally publishedYes
Event27th Conference and Exposition on Structural Dynamics 2009, IMAC XXVII - Orlando, FL, United States
Duration: 9 Feb 200912 Feb 2009

Publication series

NameConference Proceedings of the Society for Experimental Mechanics Series
ISSN (Print)2191-5644
ISSN (Electronic)2191-5652


Conference27th Conference and Exposition on Structural Dynamics 2009, IMAC XXVII
Country/TerritoryUnited States
CityOrlando, FL

ASJC Scopus subject areas

  • General Engineering
  • Computational Mechanics
  • Mechanical Engineering


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