Nonlinear Model Updating Using Recursive and Batch Bayesian Methods

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dc.coverageDOI: 10.1007/978-3-030-47638-0_31
dc.creatorSong, Mingming
dc.creatorAstroza, Rodrigo
dc.creatorEbrahimian, Hamed
dc.creatorMoaveni, Babak
dc.creatorPapadimitriou, Costas
dc.date2020
dc.date.accessioned2025-11-18T19:41:40Z
dc.date.available2025-11-18T19:41:40Z
dc.description<p>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.</p>eng
dc.descriptionThis 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.spa
dc.identifierhttps://investigadores.uandes.cl/en/publications/fd0b0b17-4482-4f18-8200-a762554feae3
dc.identifier.urihttps://repositorio.uandes.cl/handle/uandes/51937
dc.languageeng
dc.publisherSpringer
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceMao, Zhu (Ed.), Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics, 2020, p.279-286. Springer. [ISBN 9783030487782]
dc.subjectAdaptive UKF
dc.subjectBayesian model updating
dc.subjectMeasurement noise covariance
dc.subjectModeling errors
dc.subjectUnscented Kalman filter
dc.titleNonlinear Model Updating Using Recursive and Batch Bayesian Methodseng
dc.typeConference contributioneng
dc.typeContribución a la conferenciaspa
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