Finite element model updating accounting for modeling uncertainty

Rodrigo Astroza; Andres Alessandri; Joel P. Conte

doi:10.1007/978-3-030-12075-7_24

Finite element model updating accounting for modeling uncertainty

Rodrigo Astroza^*, Andres Alessandri, Joel P. Conte

^*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

A novel approach to deal with modeling uncertainty when updating mechanics-based finite element (FE) models is presented. In this method, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to seismic base excitation is employed to illustrate and validate the proposed methodology. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature.

Idioma original	Inglés
Título de la publicación alojada	Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019
Editores	Robert Barthorpe
Editorial	Springer New York LLC
Páginas	211-221
Número de páginas	11
ISBN (versión impresa)	9783030120740
DOI	https://doi.org/10.1007/978-3-030-12075-7_24
Estado	Publicada - 2020
Evento	37th IMAC, A Conference and Exposition on Structural Dynamics, 2019 - Orlando, Estados Unidos Duración: 28 ene. 2019 → 31 ene. 2019

Serie de la publicación

Nombre	Conference Proceedings of the Society for Experimental Mechanics Series
ISSN (versión impresa)	2191-5644
ISSN (versión digital)	2191-5652

Conferencia

Conferencia	37th IMAC, A Conference and Exposition on Structural Dynamics, 2019
País/Territorio	Estados Unidos
Ciudad	Orlando
Período	28/01/19 → 31/01/19

Nota bibliográfica

Publisher Copyright:
© Society for Experimental Mechanics, Inc. 2020.

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10.1007/978-3-030-12075-7_24

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Astroza, R., Alessandri, A., & Conte, J. P. (2020). Finite element model updating accounting for modeling uncertainty. En R. Barthorpe (Ed.), Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019 (pp. 211-221). (Conference Proceedings of the Society for Experimental Mechanics Series). Springer New York LLC. https://doi.org/10.1007/978-3-030-12075-7_24

Astroza, Rodrigo ; Alessandri, Andres ; Conte, Joel P. / Finite element model updating accounting for modeling uncertainty. Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019. editor / Robert Barthorpe. Springer New York LLC, 2020. pp. 211-221 (Conference Proceedings of the Society for Experimental Mechanics Series).

@inproceedings{7219153c9b93411390ae18a4bccf2b8d,

title = "Finite element model updating accounting for modeling uncertainty",

abstract = "A novel approach to deal with modeling uncertainty when updating mechanics-based finite element (FE) models is presented. In this method, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to seismic base excitation is employed to illustrate and validate the proposed methodology. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature.",

keywords = "Dual filtering, Finite element model, Modeling uncertainty, Parameter estimation",

author = "Rodrigo Astroza and Andres Alessandri and Conte, {Joel P.}",

note = "Funding Information: R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), through FONDECYT research grant No. 11160009. Funding Information: Acknowledgements R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), through FONDECYT research grant No. 11160009. Publisher Copyright: {\textcopyright} Society for Experimental Mechanics, Inc. 2020.; 37th IMAC, A Conference and Exposition on Structural Dynamics, 2019 ; Conference date: 28-01-2019 Through 31-01-2019",

year = "2020",

doi = "10.1007/978-3-030-12075-7_24",

language = "English",

isbn = "9783030120740",

series = "Conference Proceedings of the Society for Experimental Mechanics Series",

publisher = "Springer New York LLC",

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booktitle = "Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019",

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Astroza, R, Alessandri, A & Conte, JP 2020, Finite element model updating accounting for modeling uncertainty. En R Barthorpe (ed.), Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019. Conference Proceedings of the Society for Experimental Mechanics Series, Springer New York LLC, pp. 211-221, 37th IMAC, A Conference and Exposition on Structural Dynamics, 2019, Orlando, Estados Unidos, 28/01/19. https://doi.org/10.1007/978-3-030-12075-7_24

Finite element model updating accounting for modeling uncertainty. / Astroza, Rodrigo; Alessandri, Andres; Conte, Joel P.
Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019. ed. / Robert Barthorpe. Springer New York LLC, 2020. p. 211-221 (Conference Proceedings of the Society for Experimental Mechanics Series).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Finite element model updating accounting for modeling uncertainty

AU - Astroza, Rodrigo

AU - Alessandri, Andres

AU - Conte, Joel P.

N1 - Funding Information: R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), through FONDECYT research grant No. 11160009. Funding Information: Acknowledgements R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), through FONDECYT research grant No. 11160009. Publisher Copyright: © Society for Experimental Mechanics, Inc. 2020.

PY - 2020

Y1 - 2020

N2 - A novel approach to deal with modeling uncertainty when updating mechanics-based finite element (FE) models is presented. In this method, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to seismic base excitation is employed to illustrate and validate the proposed methodology. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature.

AB - A novel approach to deal with modeling uncertainty when updating mechanics-based finite element (FE) models is presented. In this method, a dual adaptive filtering approach is adopted, where the Unscented Kalman filter (UKF) is used to estimate the unknown parameters of the nonlinear FE model and a linear Kalman filter (KF) is employed to estimate the diagonal terms of the covariance matrix of the simulation error vector based on a covariance-matching technique. Numerically simulated response data of a two-dimensional three-story three-bay steel frame structure with eight unknown material model parameters subjected to seismic base excitation is employed to illustrate and validate the proposed methodology. The results of the validation studies show that the proposed approach significantly outperforms the parameter-only estimation approach widely investigated and used in the literature.

KW - Dual filtering

KW - Finite element model

KW - Modeling uncertainty

KW - Parameter estimation

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M3 - Conference contribution

AN - SCOPUS:85067343289

SN - 9783030120740

T3 - Conference Proceedings of the Society for Experimental Mechanics Series

SP - 211

EP - 221

BT - Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019

A2 - Barthorpe, Robert

PB - Springer New York LLC

T2 - 37th IMAC, A Conference and Exposition on Structural Dynamics, 2019

Y2 - 28 January 2019 through 31 January 2019

ER -

Astroza R, Alessandri A, Conte JP. Finite element model updating accounting for modeling uncertainty. En Barthorpe R, editor, Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019. Springer New York LLC. 2020. p. 211-221. (Conference Proceedings of the Society for Experimental Mechanics Series). doi: 10.1007/978-3-030-12075-7_24

Finite element model updating accounting for modeling uncertainty

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