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Open AccessArticle

Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

1
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
2
Applied Mechanics and Data Analysis, University of Twente, 7522 NB Enschede, The Netherlands
3
Institute of Scientific Computing, Technische Universität Braunschweig, 38106 Braunschweig, Germany
4
Institute of Structural Analysis, Technische Universität Braunschweig, 38106 Braunschweig, Germany
*
Author to whom correspondence should be addressed.
Metals 2020, 10(7), 876; https://doi.org/10.3390/met10070876
Received: 24 May 2020 / Revised: 16 June 2020 / Accepted: 24 June 2020 / Published: 1 July 2020
The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.
Keywords: viscoplastic-damage model; uncertainty quantification; Bayesian parameter and damage identification; functional approximation viscoplastic-damage model; uncertainty quantification; Bayesian parameter and damage identification; functional approximation
MDPI and ACS Style

Adeli, E.; Rosić, B.; Matthies, H.G.; Reinstädler, S.; Dinkler, D. Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage. Metals 2020, 10, 876.

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