| Titre : | Structural health monitoring by recursive bayesian filtering |
| Auteurs : | Yangbo Chen, Auteur ; Feng, Maria Q., Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Journal of engineering mechanics (Vol. 135 N° 4, Avril 2009) |
| Article en page(s) : | pp. 231-242 |
| Note générale : | Mécanique appliquée |
| Langues : | Anglais |
| Tags : | Bayesian analysis Filters Monitoring Assessments Vibration Identification Structural analysis. |
| Résumé : | 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. |
| DEWEY : | 620.1 |
| ISSN : | 0733-9399 |
| En ligne : | http://ascelibrary.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=JENMDT000135000004000231000001&idtype=cvips&gifs=Yes&ref=no |
