Online automatic identification of the modal parameters of a long span arch bridge

• Type: Article in International Scientific Journal
• Date: 2009

Authors

magalhaes, f (Author) FEUP; cunha, a (Author) FEUP; caetano, e (Author) FEUP

Journal

Title: Mechanical Systems and Signal Processing

Vol. 23

Pages: 316-329

ISSN: 0888-3270

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 148 Citations

Scopus - 289 Citations

Other information

Authenticus ID: P-003-NKB

DOI: 10.1016/j.ymssp.2008.05.003

Abstract (EN): The "Infante D. Henrique" bridge is a concrete arch bridge, with a span of 280m that crosses the Douro River, linking the cities of Porto and Gaia located in the North of Portugal. This structure is being monitored by a recently installed dynamic monitoring system that comprises 12 acceleration channels. This paper describes the bridge structure, its dynamic parameters identified with a previously developed ambient vibration test, the installed monitoring equipment and the software that continuously processes the data received from the bridge through an Internet connection. Special emphasis is given to the algorithms that have been developed and implemented to perform the online automatic identification of the structure modal parameters from its measured responses during normal operation. The proposed methodology uses the covariance driven stochastic subspace identification method (SSI-COV), which is then complemented by a new algorithm developed for the automatic analysis of stabilization diagrams. This new tool, based on a hierarchical clustering algorithm, proved to be very efficient on the identification of the bridge first 12 modes. The results achieved during 2 months of observation, which involved the analysis of more than 2500 datasets, are presented in detail. It is demonstrated that with the combination of high-quality equipment and powerful identification algorithms, it is possible to estimate, in an automatic manner, accurate modal parameters for several modes. These can then be used as inputs for damage detection algorithms.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14