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Identifying elastoplastic parameters with Bayes' theorem considering output error, input error and model uncertainty

Rappel, H., Beex, L.A.A., Noels, L. and Bordas, S.P.A. 2019. Identifying elastoplastic parameters with Bayes' theorem considering output error, input error and model uncertainty. Probabilistic Engineering Mechanics 55 , pp. 28-41. 10.1016/j.probengmech.2018.08.004

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0266-8920
Date of First Compliant Deposit: 30 April 2019
Date of Acceptance: 30 August 2018
Last Modified: 17 Sep 2019 02:05
URI: http://orca.cf.ac.uk/id/eprint/121585

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