Identifying elastoplastic parameters with Bayes' theorem considering output error, input error and model uncertainty. (January 2019)

• Record Type: Journal Article
• Authors: Rappel, H.; Beex, L.A.A.; Noels, L.; Bordas, S.P.A.
• Is Part Of: Probabilistic engineering mechanics. Volume 55(2019)
• Page Start: 28
• Page End: 41
• Publication Date: 2019-01
• Subjects: Bayesian inference -- Bayes' theorem -- Stochastic identification -- Parameter identification -- Elastoplasticity -- Input error -- Output error -- Model uncertainty; Engineering -- Statistical methods -- Periodicals; Mechanics, Applied -- Statistical methods -- Periodicals; Probabilities -- Periodicals; Ingénierie -- Méthodes statistiques -- Périodiques; Mécanique appliquée -- Méthodes statistiques -- Périodiques; Probabilités -- Périodiques; 620.100727
• DOI: 10.1016/j.probengmech.2018.08.004
• Languages: English
• ISSNs: 0266-8920

Abstract

Abstract: We discuss Bayesian inference for the identification of elastoplastic material parameters. In addition to errors in the stress measurements, which are commonly considered, we furthermore consider errors in the strain measurements. Since a difference between the model and the experimental data may still be present if the data is not contaminated by noise, we also incorporate the possible error of the model itself. The three formulations to describe model uncertainty in this contribution are: (1) a random variable which is taken from a normal distribution with constant parameters, (2) a random variable which is taken from a normal distribution with an input-dependent mean, and (3) a Gaussian random process with a stationary covariance function. Our results show that incorporating model uncertainty often, but not always, improves the results. If the error in the strain is considered as well, the results improve even more.