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Health Monitoring of Civil Infrastructures Using Wireless Sensor Networks

Kim, Sukun, Pakzad, Shamim, Culler, David, Demmel, James, Fenves, Gregory, Glaser, Steven, Turon, Martin·2007·2007 6th International Symposium on Information Processing in Sensor Networks

Abstract

A Wireless Sensor Network (WSN) for Structural Health Monitoring (SHM) is designed, implemented, deployed and tested on the 4200 ft long main span and the south tower of the Golden Gate Bridge (GGB). Ambient structural vibrations are reliably measured at a low cost and without interfering with the operation of the bridge. Requirements that SHM imposes on WSN are identified and new solutions to meet these requirements are proposed and implemented. In the GGB deployment, 64 nodes are distributed over the main span and the tower, collecting ambient vibrations synchronously at 1 kHz rate, with less than 10 mus jitter, and with an accuracy of 30 muG. The sampled data is collected reliably over a 46-hop network, with a bandwidth of 441 B/s at the 46th hop. The collected data agrees with theoretical models and previous studies of the bridge. The deployment is the largest WSN for SHM.

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Metadata

Authors
Kim, S., Pakzad, S., Culler, D., Demmel, J., Fenves, G., Glaser, S., & Turon, M.
Venue
2007 6th International Symposium on Information Processing in Sensor Networks
Publisher
IEEE
Published
2007-04
Pages
254-263
OpenAlex
W4254435174
Cited by
372
References
0
FWCI
60.05
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Health Monitoring of Civil Infrastructures Using Wireless Sensor Networks. https://app.cochat.ai/papers/10.1109/ipsn.2007.4379685
The paper
Kim, S., Pakzad, S., Culler, D., Demmel, J., Fenves, G., Glaser, S., & Turon, M. (2007). Health Monitoring of Civil Infrastructures Using Wireless Sensor Networks. 2007 6th International Symposium on Information Processing in Sensor Networks. https://doi.org/10.1109/ipsn.2007.4379685

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.