Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains

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Title: Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
Authors: Ren, YifeiO'Brien, Eugene J.Cantero, DanielKeenahan, Jennifer
Permanent link: http://hdl.handle.net/10197/12969
Date: 14-May-2022
Online since: 2022-07-04T15:36:25Z
Abstract: This study introduces a novel method to determine apparent profile of the track and detect railway bridge condition using sensors on in-service trains. The concept uses a type of Inverse Newmark-β integration scheme on data from a batch of trains. In a self-calibration process, an optimization algorithm is used to find vehicle dynamic properties and speed. For bridge health monitoring, the apparent profile of the bridge is first determined, i.e., the true profile plus components of ballast and bridge deflection under the moving train. The apparent profile is used, in turn, to calculate the moving reference deflection influence line, i.e., the deflection due to a moving (static) unit load. The moving reference influence line is shown to be a good indicator of bridge stiffness. This numerical approach is assessed using an elaborate finite element model operated by an independent research group. The results show that the moving reference influence line can be found accurately and that it constitutes an effective indicator of the condition of a bridge.
Funding Details: University College Dublin
Funding Details: Chinese Scholarship Council
Type of material: Journal Article
Publisher: MDPI
Journal: Applied Sciences
Volume: 12
Issue: 10
Start page: 1
End page: 24
Copyright (published version): 2022 The Authors
Keywords: Drive-byOptimisationTrack profileBridge damageApparent profileMoving referenceInfluence line
DOI: 10.3390/app12104972
Language: en
Status of Item: Peer reviewed
ISSN: 2076-3417
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by/3.0/ie/
Appears in Collections:Civil Engineering Research Collection

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