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Field testing and finite element modelling of old metal railway bridges with corrosion impact analysis

Field testing and finite element modelling of old metal railway bridges with corrosion impact analysis
Field testing and finite element modelling of old metal railway bridges with corrosion impact analysis
Bridges are vital to transport infrastructure and connectivity, but old bridges pose significant maintenance and possibly even safety concerns. Accurately assessing the structural health of old bridges requires an approach that accounts for both bridge conditions and detailed simulation. This paper describes field monitoring and associated numerical investigation to assess the impact of corrosion on the structural performance of two old steel railway bridges, with an emphasis on nonlinear behaviour. Measured structural accelerations (natural frequencies of 13.9 Hz for bridge 1 and 7.2 Hz for bridge 2) and deflections during train passage are used to verify the accuracy of finite element models of each bridge in capturing modal responses and deflection behaviour. Parametric analyses across multiple corrosion scenarios for both representative bridges showed that a uniform thickness loss of around 30 % resulted in only a 10–12 % decrease in natural frequency and a 40–73 % increase in deflection (less than 1.5 mm) but led to substantial stress increases of up to 60–180 %. This demonstrates that stress is significantly more sensitive to corrosion than global stiffness or frequency. Moreover, localised corrosion produced far greater stress amplification than uniform corrosion with comparable total mass loss, confirming its potential to cause local yielding even when global deflection remains within acceptable limits. The study establishes a validated framework combining field measurements with corrosion-sensitive FE modelling to quantify the impact of both uniform and localised corrosion on old bridges. The findings highlight the need to integrate global monitoring data with detailed local inspections and modelling to improve the accuracy of bridge condition assessment and long-term service life prediction.
Corrosion, Field monitoring, Finite element model, Localised corrosion, Metallic railway bridges, Numerical modelling, Old bridges
2352-0124
Ghassemi, Saba
da19d3f0-b3f1-431a-9db7-2c6aa9ce7bfc
Zhang, Ziliang
1fca0696-ebe9-4ea3-913b-a7ae0888783b
Watson, Geoff
a7b86a0a-9a2c-44d2-99ed-a6c02b2a356d
Milne, David
6b321a45-c19a-4243-b562-517a69e5affc
Alder, Alex
8a172a06-8893-498e-be07-b3b700062a5c
Powrie, William
600c3f02-00f8-4486-ae4b-b4fc8ec77c3c
Kashani, Mohammad M.
d1074b3a-5853-4eb5-a4ef-7d741b1c025d
Ghassemi, Saba
da19d3f0-b3f1-431a-9db7-2c6aa9ce7bfc
Zhang, Ziliang
1fca0696-ebe9-4ea3-913b-a7ae0888783b
Watson, Geoff
a7b86a0a-9a2c-44d2-99ed-a6c02b2a356d
Milne, David
6b321a45-c19a-4243-b562-517a69e5affc
Alder, Alex
8a172a06-8893-498e-be07-b3b700062a5c
Powrie, William
600c3f02-00f8-4486-ae4b-b4fc8ec77c3c
Kashani, Mohammad M.
d1074b3a-5853-4eb5-a4ef-7d741b1c025d

Ghassemi, Saba, Zhang, Ziliang, Watson, Geoff, Milne, David, Alder, Alex, Powrie, William and Kashani, Mohammad M. (2025) Field testing and finite element modelling of old metal railway bridges with corrosion impact analysis. Structures, 82, [110813]. (doi:10.1016/j.istruc.2025.110813).

Record type: Article

Abstract

Bridges are vital to transport infrastructure and connectivity, but old bridges pose significant maintenance and possibly even safety concerns. Accurately assessing the structural health of old bridges requires an approach that accounts for both bridge conditions and detailed simulation. This paper describes field monitoring and associated numerical investigation to assess the impact of corrosion on the structural performance of two old steel railway bridges, with an emphasis on nonlinear behaviour. Measured structural accelerations (natural frequencies of 13.9 Hz for bridge 1 and 7.2 Hz for bridge 2) and deflections during train passage are used to verify the accuracy of finite element models of each bridge in capturing modal responses and deflection behaviour. Parametric analyses across multiple corrosion scenarios for both representative bridges showed that a uniform thickness loss of around 30 % resulted in only a 10–12 % decrease in natural frequency and a 40–73 % increase in deflection (less than 1.5 mm) but led to substantial stress increases of up to 60–180 %. This demonstrates that stress is significantly more sensitive to corrosion than global stiffness or frequency. Moreover, localised corrosion produced far greater stress amplification than uniform corrosion with comparable total mass loss, confirming its potential to cause local yielding even when global deflection remains within acceptable limits. The study establishes a validated framework combining field measurements with corrosion-sensitive FE modelling to quantify the impact of both uniform and localised corrosion on old bridges. The findings highlight the need to integrate global monitoring data with detailed local inspections and modelling to improve the accuracy of bridge condition assessment and long-term service life prediction.

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More information

Accepted/In Press date: 29 November 2025
e-pub ahead of print date: 3 December 2025
Additional Information: Publisher Copyright: © 2025 The Authors. Published by Elsevier Ltd on behalf of Institution of Structural Engineers. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/
Keywords: Corrosion, Field monitoring, Finite element model, Localised corrosion, Metallic railway bridges, Numerical modelling, Old bridges

Identifiers

Local EPrints ID: 508652
URI: http://eprints.soton.ac.uk/id/eprint/508652
ISSN: 2352-0124
PURE UUID: ba96b8f5-86da-42fc-b50e-84baee676ed8
ORCID for Geoff Watson: ORCID iD orcid.org/0000-0003-3074-5196
ORCID for David Milne: ORCID iD orcid.org/0000-0001-6702-3918
ORCID for William Powrie: ORCID iD orcid.org/0000-0002-2271-0826
ORCID for Mohammad M. Kashani: ORCID iD orcid.org/0000-0003-0008-0007

Catalogue record

Date deposited: 28 Jan 2026 18:11
Last modified: 29 Jan 2026 04:16

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Contributors

Author: Saba Ghassemi
Author: Ziliang Zhang
Author: Geoff Watson ORCID iD
Author: David Milne ORCID iD
Author: Alex Alder
Author: William Powrie ORCID iD

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