Finite element model updating accounting for modeling uncertainty

Rodrigo Astroza, Andres Alessandri, Joel P. Conte

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

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 originalInglés
Título de la publicación alojadaModel Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 37th IMAC, A Conference and Exposition on Structural Dynamics 2019
EditoresRobert Barthorpe
EditorialSpringer New York LLC
Páginas211-221
Número de páginas11
ISBN (versión impresa)9783030120740
DOI
EstadoPublicada - 2020
Evento37th IMAC, A Conference and Exposition on Structural Dynamics, 2019 - Orlando, Estados Unidos
Duración: 28 ene 201931 ene 2019

Serie de la publicación

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

Conferencia

Conferencia37th IMAC, A Conference and Exposition on Structural Dynamics, 2019
País/TerritorioEstados Unidos
CiudadOrlando
Período28/01/1931/01/19

Nota bibliográfica

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

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