Track geometry degradation and maintenance modelling: A review

Soleimanmeigouni, Iman; Ahmadi, Alireza; Kumar, Uday

doi:10.1177/0954409716657849

Cited by 173Railway Engineering and DynamicsInfrastructure Maintenance and MonitoringTransportation Systems and Safety

Track geometry degradation and maintenance modelling: A review

Soleimanmeigouni, Iman, Ahmadi, Alireza, Kumar, Uday·2018·Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit

DOI 10.1177/0954409716657849

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Abstract

Increased demand for railway transportation is creating a need for higher train speeds and axle loads. These, in turn, increase the likelihood of track degradation and failures. Modelling the degradation behaviour of track geometry and development of applicable and effective maintenance strategies has become a challenging concern for railway infrastructure managers. During the last three decades, a number of track geometry degradation and maintenance modelling approaches have been developed to predict and improve the railway track geometry condition. In this paper, existing track geometry measures are identified and discussed. Available models for track geometry degradation are reviewed and classified. Tamping recovery models are also reviewed and discussed to identify the issues and challenges of different available methodologies and models. Existing track geometry maintenance models are reviewed and critical observations on each contribution are provided. The most important track maintenance scheduling models are identified and discussed. Finally, the paper provides directions for further research.

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ML can help improve operation of low-carbon modes, for example by reducing rail operations and maintenance costs and predicting track degradation.
“ML can also help to improve the operation of low-carbon modes, for example, by reducing the operations and maintenance costs of rail [383] and predicting track degradation [746].”
From Tackling climate change with machine learning (2023) · cites this paper as [746]

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Authors: Soleimanmeigouni, I., Ahmadi, A., & Kumar, U.
Affiliations: Luleå University of Technology (SE)
Corresponding author: Soleimanmeigouni, Iman
ORCID: Soleimanmeigouni, Iman
Kumar, Uday
Venue: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Publisher: SAGE Publications
Published: 2016-07-14
Volume: 232
Issue: 1
Pages: 73-102
ISSN: 0954-4097 (ISSN-L)
2041-3017
DOI: 10.1177/0954409716657849
OpenAlex: W2468016995
Cited by: 173
References: 88
Citations by year: 2026: 18 · 2025: 17 · 2024: 15 · 2023: 25 · 2022: 24 · 2021: 22 · 2020: 20 · 2019: 17 · 2018: 9 · 2017: 5 · 2016: 1
FWCI: 9.47
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit (publishedVersion)
Topics: Physical Sciences › Engineering › Mechanical Engineering › Railway Engineering and Dynamics
Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Transportation Systems and Safety
Keywords: track geometry degradation, railway track condition, track maintenance scheduling, axle load, train speed

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Track geometry degradation and maintenance modelling: A review

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