Using statistical analysis of an acceleration-based bridge weigh-in-motion system for damage detection

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Title: Using statistical analysis of an acceleration-based bridge weigh-in-motion system for damage detection
Authors: O'Brien, Eugene J.Khan, Muhammad ArslanMcCrum, DanielZnidaric, Ales
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Date: 17-Jan-2020
Online since: 2022-06-03T12:06:19Z
Abstract: This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles-data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.
Funding Details: Science Foundation Ireland
Funding Details: National Science Foundation (USA)
Invest Northern Ireland
Type of material: Journal Article
Publisher: MDPI
Journal: Applied Sciences
Volume: 10
Issue: 2
Start page: 1
End page: 20
Copyright (published version): 2020 the Authors
Keywords: Bridge health monitoringBWIMStructure dynamicsDamage detectionSHMVehicle-bridge interaction
DOI: 10.3390/app10020663
Language: en
Status of Item: Peer reviewed
ISSN: 2076-3417
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Appears in Collections:Civil Engineering Research Collection

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