Structural Health Monitoring by Recursive Bayesian Filtering
Publication: Journal of Engineering Mechanics
Volume 135, Issue 4
Abstract
A new vision of structural health monitoring (SHM) is presented, in which the ultimate goal of SHM is not limited to damage identification, but to describe the structure by a probabilistic model, whose parameters and uncertainty are periodically updated using measured data in a recursive Bayesian filtering (RBF) approach. Such a model of a structure is essential in evaluating its current condition and predicting its future performance in a probabilistic context. RBF is conventionally implemented by the extended Kalman filter, which suffers from its intrinsic drawbacks. Recent progress on high-fidelity propagation of a probability distribution through nonlinear functions has revived RBF as a promising tool for SHM. The central difference filter, as an example of the new versions of RBF, is implemented in this study, with the adaptation of a convergence and consistency improvement technique. Two numerical examples are presented to demonstrate the superior capacity of RBF for a SHM purpose. The proposed method is also validated by large-scale shake table tests on a reinforced concrete two-span three-bent bridge specimen.
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Acknowledgments
This research is supported by Caltrans, under Contract No. 59A0311, and managed by Mr. Li-Hong Sheng.
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 135 • Issue 4 • April 2009
Pages: 231 - 242
Copyright
© 2009 ASCE.
History
Received: Mar 30, 2006
Accepted: Apr 25, 2008
Published online: Apr 1, 2009
Published in print: Apr 2009
Notes
Note. Associate Editor: Erik A. Johnson
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