Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/10/9/1141 |
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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 Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach as this method is determined as the most efficient method for time consuming finite element model updating problems among filtering and random walk approaches. The parameters identified using this Bayesian approach are compared with the true parameters in the simulation, and further, the efficiency of the identification method is discussed. The aim of this study is to observe whether the mentioned method is suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, for a real structural specimen using a limited surface displacement measurement vector gained by Digital Image Correlation (DIC) and to see how much information is indeed needed to estimate the parameters accurately even by considering the model error and whether this approach can also practically be used for health monitoring purposes before the occurrence of severe damage and collapse. |
| first_indexed | 2024-03-10T16:55:51Z |
| format | Article |
| id | doaj.art-74b39a3269a4489d93a41df0bd7e78f3 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T16:55:51Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-74b39a3269a4489d93a41df0bd7e78f32023-11-20T11:08:47ZengMDPI AGMetals2075-47012020-08-01109114110.3390/met10091141Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model ErrorEhsan 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 Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach as this method is determined as the most efficient method for time consuming finite element model updating problems among filtering and random walk approaches. The parameters identified using this Bayesian approach are compared with the true parameters in the simulation, and further, the efficiency of the identification method is discussed. The aim of this study is to observe whether the mentioned method is suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, for a real structural specimen using a limited surface displacement measurement vector gained by Digital Image Correlation (DIC) and to see how much information is indeed needed to estimate the parameters accurately even by considering the model error and whether this approach can also practically be used for health monitoring purposes before the occurrence of severe damage and collapse.https://www.mdpi.com/2075-4701/10/9/1141health monitoringviscoplastic-damage modeluncertainty quantificationBayesian parameter and damage identificationfunctional approximation |
| spellingShingle | Ehsan Adeli Bojana Rosić Hermann G. Matthies Sven Reinstädler Dieter Dinkler Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error Metals health monitoring viscoplastic-damage model uncertainty quantification Bayesian parameter and damage identification functional approximation |
| title | Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error |
| title_full | Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error |
| title_fullStr | Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error |
| title_full_unstemmed | Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error |
| title_short | Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error |
| title_sort | bayesian parameter determination of a ct test described by a viscoplastic damage model considering the model error |
| topic | health monitoring viscoplastic-damage model uncertainty quantification Bayesian parameter and damage identification functional approximation |
| url | https://www.mdpi.com/2075-4701/10/9/1141 |
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