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

^*Corresponding author for this work

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

1 Scopus citations

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.

Original language	English
Title of host publication	Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019
Editors	Robert Barthorpe
Publisher	Springer New York LLC
Pages	211-221
Number of pages	11
ISBN (Print)	9783030120740
DOIs	https://doi.org/10.1007/978-3-030-12075-7_24
State	Published - 2020
Event	37th IMAC, A Conference and Exposition on Structural Dynamics, 2019 - Orlando, United States Duration: 28 Jan 2019 → 31 Jan 2019

Publication series

Name	Conference Proceedings of the Society for Experimental Mechanics Series
ISSN (Print)	2191-5644
ISSN (Electronic)	2191-5652

Conference

Conference	37th IMAC, A Conference and Exposition on Structural Dynamics, 2019
Country/Territory	United States
City	Orlando
Period	28/01/19 → 31/01/19

Bibliographical 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:
© Society for Experimental Mechanics, Inc. 2020.

Keywords

Dual filtering
Finite element model
Modeling uncertainty
Parameter estimation

Access to Document

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

Cite this

Astroza, R., Alessandri, A., & Conte, J. P. (2020). Finite element model updating accounting for modeling uncertainty. In 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).

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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",

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

language = "English",

isbn = "9783030120740",

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

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Astroza, R, Alessandri, A & Conte, JP 2020, Finite element model updating accounting for modeling uncertainty. in 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, United States, 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).

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

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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T3 - Conference Proceedings of the Society for Experimental Mechanics Series

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

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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. In 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

Abstract

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Keywords

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