Bayesian inference to identify parameters in viscoelasticity

• Published in: Mechanics of time-dependent materials Vol. 22; no. 2; pp. 221 - 258
• Main Authors: Rappel, Hussein, Beex, Lars A. A., Bordas, Stéphane P. A.
• Format: Journal Article Web Resource
• Language: English
• Published: Dordrecht Springer Netherlands 01.05.2018; Springer Nature B.V
• Subjects: Bayesian analysis; Bayesian inference; Bayesian networks; Bayes’ theorem; Characterization and Evaluation of Materials; Classical Mechanics; Constant strain rate tests; Creep tests; Engineering; Engineering, computing & technology; Identification (control systems); Identified parameter; Inference engines; Ingénierie mécanique; Ingénierie, informatique & technologie; Input parameter; Mechanical engineering; Parameter estimation; Parameter identification; Parameter uncertainty; Polymer Sciences; Relaxation test; Solid Mechanics; Standard linear solid models; Statistical identification; Statistical inference; Strain rate; Stress relaxation tests; Uncertainty analysis; Viscoelasticity; Viscoelasticity; Bayes’ theorem; Bayesian inference; Parameter identification; Statistical identification
• ISSN: 1385-2000; 1573-2738; 1573-2738
• DOI: 10.1007/s11043-017-9361-0

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.