Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/10/7/876 |
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| author | Ehsan Adeli Bojana Rosić Hermann G. Matthies Sven Reinstädler Dieter Dinkler |
| author_facet | Ehsan Adeli Bojana Rosić Hermann G. Matthies Sven Reinstädler Dieter Dinkler |
| author_sort | Ehsan Adeli |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:44:37Z |
| format | Article |
| id | doaj.art-87b3bb89c5da483680e7678c48efa73c |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T18:44:37Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-87b3bb89c5da483680e7678c48efa73c2023-11-20T05:34:22ZengMDPI AGMetals2075-47012020-07-0110787610.3390/met10070876Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with DamageEhsan Adeli0Bojana Rosić1Hermann G. Matthies2Sven Reinstädler3Dieter Dinkler4Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USAApplied Mechanics and Data Analysis, University of Twente, 7522 NB Enschede, The NetherlandsInstitute of Scientific Computing, Technische Universität Braunschweig, 38106 Braunschweig, GermanyInstitute of Structural Analysis, Technische Universität Braunschweig, 38106 Braunschweig, GermanyInstitute of Structural Analysis, Technische Universität Braunschweig, 38106 Braunschweig, GermanyThe 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.https://www.mdpi.com/2075-4701/10/7/876viscoplastic-damage modeluncertainty quantificationBayesian parameter and damage identificationfunctional approximation |
| spellingShingle | Ehsan Adeli Bojana Rosić Hermann G. Matthies Sven Reinstädler Dieter Dinkler Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage Metals viscoplastic-damage model uncertainty quantification Bayesian parameter and damage identification functional approximation |
| title | Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage |
| title_full | Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage |
| title_fullStr | Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage |
| title_full_unstemmed | Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage |
| title_short | Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage |
| title_sort | comparison of bayesian methods on parameter identification for a viscoplastic model with damage |
| topic | viscoplastic-damage model uncertainty quantification Bayesian parameter and damage identification functional approximation |
| url | https://www.mdpi.com/2075-4701/10/7/876 |
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