Statistical Analysis of Vibration Modes of a Suspension Bridge Using Spatially Dense Wireless Sensor Network
Publication: Journal of Structural Engineering
Volume 135, Issue 7
Abstract
A spatially dense wireless sensor network was designed, developed and installed on a long-span suspension bridge for a deployment to record ambient acceleration. A total 174 sets of data were collected from 64 sensor nodes on the main span and south tower of the Golden Gate Bridge. Analysis of the vibration data using power spectral densities and peak picking provide approximate estimates of vibration modes with minimal computation. For more detailed analysis of the data, autoregressive with moving average models (ARMA) give parametric estimates of vibration modes for frequencies up to . Statistical analysis of the multiple realizations give the distributions of the vibration frequencies, damping ratios, and mode shapes and 95% confidence intervals. The statistical results are compared with vibration properties using the peak picking method and previous studies of the bridge using measured data and a finite-element model. Analysis of the ambient vibration data and system identification results demonstrate that high spatial and temporal sensing using the wireless sensor network give a high resolution and confidence in the identified vibration modes. The estimation errors for the identified vibration properties are generally low, with frequencies being the most accurate and damping ratios the least accurate.
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Acknowledgments
This work reflects the advice and guidance of Professor James Demmel, Professor Steven Glaser, and Professor David Culler who participated and supported the research. Dr. Sukun Kim developed the software and closely collaborated in design of hardware and deployment of the network on the Golden Gate Bridge. The writers give special thanks to the staff and management of the Golden Gate Bridge, Highway and Transportation District, in particular, Dennis Mulligan and Jerry Kao, for their close cooperation in every step of the project. Jorge Lee provided extraordinary help in the deployment, which made this project possible. Tom Oberheim helped design and develop the sensor board. This research was supported by the National Science Foundation under Grant No. NSFEIA-0122599 and by the Center for Information Technology Research in the Interest of Society (CITRIS) at the University of California, Berkeley.
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Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 135 • Issue 7 • July 2009
Pages: 863 - 872
Copyright
© 2009 ASCE.
History
Received: Feb 12, 2008
Accepted: Feb 9, 2009
Published online: Feb 25, 2009
Published in print: Jul 2009
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