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

Research output: Contribution to journal › Article › Research › peer review

Authors

  • Edoardo Patelli
  • Yves Govers
  • Matteo Broggi
  • Herbert Martins Gomes
  • Michael Link
  • John E. Mottershead

Research Organisations

External Research Organisations

  • University of Liverpool
  • German Aerospace Center (DLR)
  • Universidade Federal do Rio Grande do Sul
  • University of Kassel
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Details

Original languageEnglish
Pages (from-to)905-925
Number of pages21
JournalArchive of applied mechanics
Volume87
Issue number5
Early online date23 Feb 2017
Publication statusPublished - 1 May 2017

Abstract

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 physics-based 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.

Keywords

    Bayesian, Covariance, Deterministic, Model updating, Stochastic

ASJC Scopus subject areas

Cite this

Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data. / Patelli, Edoardo; Govers, Yves; Broggi, Matteo et al.
In: Archive of applied mechanics, Vol. 87, No. 5, 01.05.2017, p. 905-925.

Research output: Contribution to journal › Article › Research › peer review

Patelli, E, Govers, Y, Broggi, M, Gomes, HM, Link, M & Mottershead, JE 2017, 'Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data', Archive of applied mechanics, vol. 87, no. 5, pp. 905-925. https://doi.org/10.1007/s00419-017-1233-1
Patelli, E., Govers, Y., Broggi, M., Gomes, H. M., Link, M., & Mottershead, J. E. (2017). Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data. Archive of applied mechanics, 87(5), 905-925. https://doi.org/10.1007/s00419-017-1233-1
Patelli E, Govers Y, Broggi M, Gomes HM, Link M, Mottershead JE. Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data. Archive of applied mechanics. 2017 May 1;87(5):905-925. Epub 2017 Feb 23. doi: 10.1007/s00419-017-1233-1
Patelli, Edoardo ; Govers, Yves ; Broggi, Matteo et al. / Sensitivity or Bayesian model updating : a comparison of techniques using the DLR AIRMOD test data. In: Archive of applied mechanics. 2017 ; Vol. 87, No. 5. pp. 905-925.
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