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Laboratory Verification of Vehicle Contact Point Response for Bridge Condition Monitoring
Author(s)
Date Issued
2022-08-26
Date Available
2024-09-11T09:48:22Z
Abstract
Limited maintenance budgets, coupled with an ageing bridge-stock, mean that there is much appetite for efficient and inexpensive techniques for monitoring and detection of damage in bridges. Drive-by bridge monitoring techniques, which use in-vehicle sensors to monitor changes in bridge condition over time, represent a solution to this challenge. This paper presents the concept of using an inferred acceleration response at the point of contact between the vehicle tyre and the bridge surface as an enhanced method of monitoring the bridge frequency. An expression is presented, based on a quarter-car vehicle model, which allows the contact-point response to be inferred from the in-vehicle measurements. A numerical example is initially provided to demonstrate the benefits of using the contact-point response to identify the bridge frequency. The concept is then tested using a laboratory-scale vehicle-bridge interaction model and it is shown that the contact-point response can clearly estimate the bridge frequency. Two damage cases are also simulated in the laboratory, and it is shown that changes in bridge frequency can be detected, with the contact-point response being more sensitive to damage than the signals measured on the vehicle. It is observed that the detected frequency is sensitive to vehicle speed, which is an important consideration when combining the results of multiple vehicle passages. Overall, the results verify that the contact-point response can enhance drive-by bridge monitoring results.
Type of Material
Conference Publication
Web versions
Language
English
Status of Item
Peer reviewed
Journal
Holmes, N., de Paor, C., & West, R.P.
Conference Details
The 2022 Civil Engineering Research in Ireland (CERI) Conference, Dublin, Ireland, 25-26 August 2022
ISBN
978-0-9573957-5-6
This item is made available under a Creative Commons License
File(s)
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Name
CERI2022_v1.0.pdf
Size
1.38 MB
Format
Adobe PDF
Checksum (MD5)
533818006ec54899d0b05ab988f93488
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