Bayesian Model Updating Using Modal Data Based on Dynamic Condensation
Publication: Journal of Engineering Mechanics
Volume 146, Issue 2
Abstract
This paper introduces a methodology for Bayesian model updating of a linear dynamic system using the modal data that consists of the posterior statistics of the modal properties, identified from dynamic test data using a Bayesian modal identification method. To avoid direct mode matching or solving the eigenvalue problem, Eigen system equation is used to establish the relationship between modal data and the structural model parameters. The dynamic condensation technique is proposed to reduce the full system model to a smaller model with the degrees of freedom (DOFs) in the reduced model corresponding to the observed DOFs. This eliminates the need for selecting the observed DOFs of the full system mode shape. The proposed methodology is computationally efficient because neither iteration nor numerical optimization is required to obtain the reduced model. The performance and effectiveness of the proposed methodology was demonstrated by means of two simulated examples. The transitional Markov chain Monte Carlo (TMCMC) method is used to obtain samples distributed according to the posterior distribution.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
The author gratefully acknowledges the New Faculty Seed Grant from the Indian Institute of Technology Delhi.
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 146 • Issue 2 • February 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Oct 30, 2018
Accepted: Jun 20, 2019
Published online: Dec 3, 2019
Published in print: Feb 1, 2020
Discussion open until: May 3, 2020
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