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| Seuring, Stefan |
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| Nor Azizi, S. |
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| Pato, Margarida Vaz |
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| Kölker, Katrin |
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| Huber, Oliver |
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| Király, Tamás |
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| Spengler, Thomas Stefan |
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| Al-Ammar, Essam A. |
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| Dargahi, Fatemeh |
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| Mota, Rui |
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| Mazalan, Nurul Aliah Amirah |
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| Macharis, Cathy | Brussels |
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| Arunasari, Yova Tri |
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| Nunez, Alfredo | Delft |
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| Bouhorma, Mohammed |
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| Bonato, Matteo |
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| Fitriani, Ira |
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| Autor Correspondente Coelho, Sílvia. |
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| Pond, Stephen |
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| Okwara, Ukoha Kalu |
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| Toufigh, Vahid |
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| Campisi, Tiziana | Enna |
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| Ermolieva, Tatiana |
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| Sánchez-Cambronero, Santos |
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| Agzamov, Akhror |
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Malekjafarian, Abdollah
University College Dublin
in Cooperation with on an Cooperation-Score of 37%
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Publications (13/13 displayed)
- 2024Predicting the duration of motorway incidents using machine learningcitations
- 2023Methodological Framework for Assessing and Strengthening the Resistance of Railway Critical Infrastructure Elements
- 2022Indirect Monitoring of Frequencies of a Multiple Span Bridge Using Data Collected from an Instrumented Train: A Field Case Studycitations
- 2021Measuring traffic load on Forth Road Suspension Bridge using Weigh-InMotion and image data
- 2020Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridgecitations
- 2019Estimation of traffic load effects on Forth Road Bridge using camera measurements
- 2019Evaluation of the extreme traffic load effects on the Forth Road Bridge using image analysis of traffic datacitations
- 2018Estimation of traffic load effects on Forth Road Bridge using camera measurements
- 2017Damage detection using curvatures obtained from vehicle measurementscitations
- 2017On the use of a passing vehicle for the estimation of bridge mode shapescitations
- 2017On the use of drive-by measurement for indirect bridge monitoring
- 2017Application of empirical mode decomposition to drive-by bridge damage detectioncitations
- 2016A mode shape-based damage detection approach using laser measurement from a vehicle crossing a simply supported bridgecitations
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article
Damage detection using curvatures obtained from vehicle measurements
Abstract
This paper describes a new procedure for bridge damage identification through drive-by monitoring. Instantaneous curvature (IC) is presented as a means to determine a local loss of stiffness in a bridge through measurements collected from a passing instrumented vehicle. Moving reference curvature (MRC) is compared with IC as a damage detection tool. It is assumed that absolute displacements on the bridge can be measured by the vehicle. The bridge is represented by a finite element (FE) model. A Half-car model is used to represent the passing vehicle. Damage is represented as a local loss of stiffness in different parts of the bridge. 1% random noise and no noise environments are considered to evaluate the effectiveness of the method. A generic road surface profile is also assumed. Numerical simulations show that the local damage can be detected using IC if the deflection responses can be measured with sufficient accuracy. Damage quantification can be obtained from MRC. ; European Commission Horizon 2020 ; TRUSS-ITN
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