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Structural health monitoring by recursive Bayesian filtering
- Source :
- Journal of Engineering Mechanics. April, 2009, Vol. 135 Issue 4, p231, 12 p.
- Publication Year :
- 2009
-
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. CE Database subject headings: Bayesian analysis; Filters; Monitoring; Assessments; Vibration; Identification; Structural analysis.
Details
- Language :
- English
- ISSN :
- 07339399
- Volume :
- 135
- Issue :
- 4
- Database :
- Gale General OneFile
- Journal :
- Journal of Engineering Mechanics
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.197417896