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  • Many important parameters influencing structural behaviour involve unacceptable uncertainties. An extensive development of efficient methods for stochastic modelling enabled reducing these uncertainties in input parameters. According to Bayes’ rule, we obtain a more accurate description of the uncertain parameter involving an expert knowledge as well as experimental data. The aim of this contribution is to demonstrate two techniques for making the identification process more efficient and less time consuming. The first technique consists in replacement of the full numerical model by its polynomial approximation in order to reduce the computational effort. The particular approximation is based on polynomial chaos expansion constructed by linear regression based on Latin Hypercube Sampling. The obtained surrogate model is then used within Markov chain Monte Carlo sampling so as to update the uncertainty in the model inputs based on the experimental data. The second technique concerns a guided choice of the most informative experimental observation. Particularly, we apply sensitivity analysis to determine the most sensitive component of the structural response to the identified parameter. The advantages of the presented approach are demonstrated on a simple illustrative example of a frame structure.
  • Many important parameters influencing structural behaviour involve unacceptable uncertainties. An extensive development of efficient methods for stochastic modelling enabled reducing these uncertainties in input parameters. According to Bayes’ rule, we obtain a more accurate description of the uncertain parameter involving an expert knowledge as well as experimental data. The aim of this contribution is to demonstrate two techniques for making the identification process more efficient and less time consuming. The first technique consists in replacement of the full numerical model by its polynomial approximation in order to reduce the computational effort. The particular approximation is based on polynomial chaos expansion constructed by linear regression based on Latin Hypercube Sampling. The obtained surrogate model is then used within Markov chain Monte Carlo sampling so as to update the uncertainty in the model inputs based on the experimental data. The second technique concerns a guided choice of the most informative experimental observation. Particularly, we apply sensitivity analysis to determine the most sensitive component of the structural response to the identified parameter. The advantages of the presented approach are demonstrated on a simple illustrative example of a frame structure. (en)
Title
  • Efficient Bayesian parameter identification
  • Efficient Bayesian parameter identification (en)
skos:prefLabel
  • Efficient Bayesian parameter identification
  • Efficient Bayesian parameter identification (en)
skos:notation
  • RIV/68407700:21110/14:00218681!RIV15-MSM-21110___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GAP105/12/1146), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 13854
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21110/14:00218681
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Bayesian identification; Markov chain Monte Carlo; Stochastic modelling; Polynomial chaos expansion; Sensitivity analysis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [BD772B154618]
http://linked.open...v/mistoKonaniAkce
  • Svratka
http://linked.open...i/riv/mistoVydani
  • Brno
http://linked.open...i/riv/nazevZdroje
  • 20 th International Conference Engineering Mechanics 2014
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Janouchová, Eliška
  • Kučerová, Anna
  • Sýkora, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 1805-8248
number of pages
http://purl.org/ne...btex#hasPublisher
  • Vysoké učení technické v Brně
https://schema.org/isbn
  • 978-80-214-4871-1
http://localhost/t...ganizacniJednotka
  • 21110
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