Bayesian parameter identification in plasticity

Ehsan Adeli; Bojana Rosić; Hermann G. Matthies; Sven Reinstädler

Bayesian parameter identification in plasticity

Ehsan Adeli, Bojana Rosić, Hermann G. Matthies, Sven Reinstädler

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

4 Citations (Scopus)

25 Downloads (Pure)

Abstract

To evaluate the cyclic behaviour under different loading conditions using the kinematic and isotropic hardening theory of steel a Chaboche visco-plastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behaviour as experimental data. Then the model parameters are identified by applying a variaty of Bayes’s theorem. Identified parameters are compared with the true parameters in the simulation and the efficiency of the identification method is discussed.

Original language	English
Title of host publication	Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017
Editors	Eugenio Onate, Djordje Peric, D. Roger J. Owen, Michele Chiumenti
Place of Publication	978-84-946909-6-9
Publisher	CIMNE
Pages	247-255
Number of pages	9
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017 - Barcelona, Spain Duration: 5 Sep 2017 → 7 Sep 2017 Conference number: 14 http://congress.cimne.com/complas2017/frontal/Series.asp

Conference

Conference	14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017
Abbreviated title	COMPLAS 2017
Country/Territory	Spain
City	Barcelona
Period	5/09/17 → 7/09/17
Internet address	http://congress.cimne.com/complas2017/frontal/Series.asp

Keywords

Polynomial chaos
Probabilistic inverse approach
Uncertainty quantification
Viscoplastic model

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Adeli2017bayesianFinal published version, 528 KB

http://hdl.handle.net/2117/180669

Cite this

@inproceedings{494dc9f9a3ca4faaa13c5edca6e17b34,

title = "Bayesian parameter identification in plasticity",

abstract = "To evaluate the cyclic behaviour under different loading conditions using the kinematic and isotropic hardening theory of steel a Chaboche visco-plastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behaviour as experimental data. Then the model parameters are identified by applying a variaty of Bayes{\textquoteright}s theorem. Identified parameters are compared with the true parameters in the simulation and the efficiency of the identification method is discussed.",

keywords = "Polynomial chaos, Probabilistic inverse approach, Uncertainty quantification, Viscoplastic model",

author = "Ehsan Adeli and Bojana Rosi{\'c} and Matthies, {Hermann G.} and Sven Reinst{\"a}dler",

year = "2017",

month = jan,

day = "1",

language = "English",

pages = "247--255",

editor = "Eugenio Onate and Djordje Peric and Owen, {D. Roger J.} and Michele Chiumenti",

booktitle = "Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017",

publisher = "CIMNE",

note = "14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017, COMPLAS 2017 ; Conference date: 05-09-2017 Through 07-09-2017",

url = "http://congress.cimne.com/complas2017/frontal/Series.asp",

}

Adeli, E, Rosić, B, Matthies, HG & Reinstädler, S 2017, Bayesian parameter identification in plasticity. in E Onate, D Peric, DRJ Owen & M Chiumenti (eds), Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017. CIMNE, 978-84-946909-6-9, pp. 247-255, 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017, Barcelona, Spain, 5/09/17. <http://hdl.handle.net/2117/180669>

Bayesian parameter identification in plasticity. / Adeli, Ehsan; Rosić, Bojana; Matthies, Hermann G. et al.

Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017. ed. / Eugenio Onate; Djordje Peric; D. Roger J. Owen; Michele Chiumenti. 978-84-946909-6-9 : CIMNE, 2017. p. 247-255.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Bayesian parameter identification in plasticity

AU - Adeli, Ehsan

AU - Rosić, Bojana

AU - Matthies, Hermann G.

AU - Reinstädler, Sven

N1 - Conference code: 14

PY - 2017/1/1

Y1 - 2017/1/1

N2 - To evaluate the cyclic behaviour under different loading conditions using the kinematic and isotropic hardening theory of steel a Chaboche visco-plastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behaviour as experimental data. Then the model parameters are identified by applying a variaty of Bayes’s theorem. Identified parameters are compared with the true parameters in the simulation and the efficiency of the identification method is discussed.

AB - To evaluate the cyclic behaviour under different loading conditions using the kinematic and isotropic hardening theory of steel a Chaboche visco-plastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behaviour as experimental data. Then the model parameters are identified by applying a variaty of Bayes’s theorem. Identified parameters are compared with the true parameters in the simulation and the efficiency of the identification method is discussed.

KW - Polynomial chaos

KW - Probabilistic inverse approach

KW - Uncertainty quantification

KW - Viscoplastic model

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M3 - Conference contribution

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SP - 247

EP - 255

BT - Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017

A2 - Onate, Eugenio

A2 - Peric, Djordje

A2 - Owen, D. Roger J.

A2 - Chiumenti, Michele

PB - CIMNE

CY - 978-84-946909-6-9

T2 - 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017

Y2 - 5 September 2017 through 7 September 2017

ER -

Bayesian parameter identification in plasticity

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Keywords

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