Extraction of Bridge Frequencies from a Moving Test Vehicle by Stochastic Subspace Identification
Publication: Journal of Bridge Engineering
Volume 21, Issue 3
Abstract
The technique for extracting bridge frequencies from a moving vehicle was proposed only recently. However, its efficacy may be reduced by road-surface roughness. To this end, the stochastic subspace identification (SSI) is modified to deal with the present time-variant coupled noisy vehicle–bridge interaction system. First, the governing equations for the vehicle and bridge are expressed in state space, including the effects of road roughness and multiple vehicles. Then, they are discretized and transformed to a form suitable for SSI by separating the known from the unknown parameters. Using the Hankel matrix along with the orthogonal projection theorem and singular value decomposition, the observability matrix derived with the vehicle effect suppressed can be used to identify the bridge dynamic properties. It is demonstrated that (1) the proposed SSI approach is more effective for identifying bridge frequencies below 20 Hz for the cases studied, compared with conventional approaches; (2) adding a little damping to the test vehicle can help suppress the vehicle frequency using the proposed approach; and (3) the ongoing traffic is beneficial for amplifying the frequencies of bridges with rough surface.
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Acknowledgments
The research reported herein is sponsored in part by the National Science Council (ROC) through grant number NSC 102-2221-E-002-132-MY2. Also, the senior author acknowledges the sponsorship from the Chongqing Science & Technology Commission via the contract number CSTC 2015 JCY JYS 30003 and the Tongji University 973 project with grant number 2011CB013800.
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Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 21 • Issue 3 • March 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Oct 4, 2014
Accepted: Mar 13, 2015
Published online: Sep 24, 2015
Discussion open until: Feb 24, 2016
Published in print: Mar 1, 2016
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