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  5. Experimental Testing of a Moving Force Identification Bridge Weigh-in-Motion Algorithm
 
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Experimental Testing of a Moving Force Identification Bridge Weigh-in-Motion Algorithm

Author(s)
Rowley, C.  
O'Brien, Eugene J.  
González, Arturo  
et al.  
Uri
http://hdl.handle.net/10197/4853
Date Issued
2008-11-18
Date Available
2013-11-08T09:03:44Z
Abstract
Bridge weigh-in-motion systems are based on the measurement of strain on a bridge and the use of the measurements to estimate the static weights of passing traffic loads. Traditionally, commercial systems employ a static algorithm and use the bridge influence line to infer static axle weights. This paper describes the experimental testing of an algorithm based on moving force identification theory. In this approach the bridge is dynamically modeled using the finite element method and an eigenvalue reduction technique is employed to reduce the dimension of the system. The inverse problem of finding the applied forces from measured responses is then formulated as a least squares problem with Tikhonov regularization. The optimal regularization parameter is solved using the L-curve method. Finally, the static axle loads, impact factors and truck frequencies are obtained from a complete time history of the identified moving forces.
Type of Material
Journal Article
Publisher
Springer-Verlag
Journal
Experimental Mechanics
Volume
49
Issue
5
Start Page
743
End Page
746
Copyright (Published Version)
2008 Springer-Verlag
Subjects

Bridge

Weigh-in-motion

Force identification

Regularization

Dynamic programming

Traffic loads

DOI
10.1007/s11340-008-9188-3
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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J39 - rep vers.pdf

Size

466.49 KB

Format

Adobe PDF

Checksum (MD5)

45c611e2480255d4a8ad22882b989cb6

Owning collection
Civil Engineering Research Collection
Mapped collections
Earth Institute Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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