Article (Scientific journals)
Bayesian inference to identify parameters in viscoelasticity
Rappel, Hussein; Beex, L. A. A.; Bordas, S. P. A.
2018In Mechanics of Time-Dependent Materials, p. 1-38
Peer reviewed
 

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Keywords :
Bayesian inference; Bayes’ theorem; Parameter identification; Statistical identification; Viscoelasticity; Bayesian networks; Identification (control systems); Inference engines; Strain rate; Uncertainty analysis; Constant strain rate tests; Identified parameter; Input parameter; Relaxation test; Standard linear solid models; Parameter estimation
Abstract :
[en] This contribution discusses Bayesian inference (BI) as an approach to identify parameters in viscoelasticity. The aims are: (i) to show that the prior has a substantial influence for viscoelasticity, (ii) to show that this influence decreases for an increasing number of measurements and (iii) to show how different types of experiments influence the identified parameters and their uncertainties. The standard linear solid model is the material description of interest and a relaxation test, a constant strain-rate test and a creep test are the tensile experiments focused on. The experimental data are artificially created, allowing us to make a one-to-one comparison between the input parameters and the identified parameter values. Besides dealing with the aforementioned issues, we believe that this contribution forms a comprehensible start for those interested in applying BI in viscoelasticity. © 2017 Springer Science+Business Media B.V.
Disciplines :
Mechanical engineering
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Rappel, Hussein ;  Université de Liège - ULiège > Form. doct. sc. ingé. & techno. (aéro. & mécan. - Paysage)
Beex, L. A. A.;  Faculty of Science, Technology and Communication, University of Luxembourg, Maison du Nombre, 6, Avenue de la Fonte, Esch-sur-Alzette, Luxembourg
Bordas, S. P. A.;  Faculty of Science, Technology and Communication, University of Luxembourg, Maison du Nombre, 6, Avenue de la Fonte, Esch-sur-Alzette, Luxembourg, School of Engineering, Cardiff University, Queens Buildings, The Parade, Cardiff, Wales, United Kingdom, Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, 35 Stirling Highway, Crawley/Perth, WA, Australia
Language :
English
Title :
Bayesian inference to identify parameters in viscoelasticity
Publication date :
2018
Journal title :
Mechanics of Time-Dependent Materials
ISSN :
1385-2000
eISSN :
1573-2738
Publisher :
Springer Netherlands
Pages :
1-38
Peer reviewed :
Peer reviewed
Available on ORBi :
since 06 May 2018

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