Options
Using statistical analysis of an acceleration-based bridge weigh-in-motion system for damage detection
Date Issued
2020-01-17
Date Available
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.
Sponsorship
Science Foundation Ireland
Other Sponsorship
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
Language
English
Status of Item
Peer reviewed
ISSN
2076-3417
This item is made available under a Creative Commons License
File(s)
Owning collection
Scopus© citations
21
Acquisition Date
Mar 18, 2024
Mar 18, 2024
Views
183
Last Month
1
1
Acquisition Date
Mar 18, 2024
Mar 18, 2024
Downloads
50
Last Month
3
3
Acquisition Date
Mar 18, 2024
Mar 18, 2024