Nonlinear Model Updating Using Recursive and Batch Bayesian Methods

Mingming Song*, Rodrigo Astroza, Hamed Ebrahimian, Babak Moaveni, Costas Papadimitriou

*Autor correspondiente de este trabajo

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

Resumen

This paper studies the performance of recursive and batch Bayesian methods for nonlinear model updating. Unscented Kalman filter (UKF) is selected to represent the recursive Bayesian method, and two UKF approaches are investigated and compared, i.e., non-adaptive UKF and adaptive UKF. The proposed new adaptive filter, forgetting factor adaptive UKF, estimates the model parameters and measurement noise covariance in an online manner. The forgetting factor adaptive UKF is based on the principle of matching the covariance of residuals to its theoretical values by updating the measurement noise covariance. The performance of non-adaptive UKF, adaptive UKF and batch Bayesian method are investigated when applied to a numerical nonlinear 3-story 3-bay steel frame structure for parameter estimation of material properties. Different types of modeling errors are considered in the 21 updating models to study the effects of modeling errors on model updating. It is found that adaptive UKF approach provides the most accurate parameter estimations, while batch Bayesian approach gives the smallest errors on response predictions. © 2020, The Society for Experimental Mechanics, Inc.
Idioma originalInglés
Título de la publicación alojadaModel Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics, 2020
EditoresZhu Mao
EditorialSpringer
Páginas279-286
Número de páginas8
ISBN (versión impresa)9783030487782
DOI
EstadoPublicada - 2020
Evento38th IMAC, A Conference and Exposition on Structural Dynamics, 2020 - Houston, Estados Unidos
Duración: 10 feb. 202013 feb. 2020

Serie de la publicación

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

Conferencia

Conferencia38th IMAC, A Conference and Exposition on Structural Dynamics, 2020
País/TerritorioEstados Unidos
CiudadHouston
Período10/02/2013/02/20

Nota bibliográfica

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

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