Prediction of track geometry degradation using artificial neural network: a case study Article Swipe
Hamid Khajehei
,
Alireza Ahmadi
,
Iman Soleimanmeigouni
,
Mohammad Haddadzade
,
Arne Nissen
,
Mohammad Javad Latifi Jebelli
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/23248378.2021.1875065
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/23248378.2021.1875065
The aim of this study has been to predict the track geometry degradation rate using artificial neural network. Tack geometry measurements, asset information, and maintenance history for five line sections from the Swedish railway network were collected, processed, and prepared to develop the ANN model. The information of track was taken into account and different features of track sections were considered as model input variables. In addition, Garson method was applied to explore the relative importance of the variables affecting geometry degradation rate. By analysing the performance of the model, we found out that the ANN has an acceptable capability in explaining the variability of degradation rates in different locations of the track. In addition, it is found that the maintenance history, the degradation level after tamping, and the frequency of trains passing along the track have the strongest contributions among the considered set of features in prediction of degradation rate.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/23248378.2021.1875065
- https://www.tandfonline.com/doi/pdf/10.1080/23248378.2021.1875065?needAccess=true
- OA Status
- bronze
- Cited By
- 62
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3121426511
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3121426511Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/23248378.2021.1875065Digital Object Identifier
- Title
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Prediction of track geometry degradation using artificial neural network: a case studyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-01-25Full publication date if available
- Authors
-
Hamid Khajehei, Alireza Ahmadi, Iman Soleimanmeigouni, Mohammad Haddadzade, Arne Nissen, Mohammad Javad Latifi JebelliList of authors in order
- Landing page
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https://doi.org/10.1080/23248378.2021.1875065Publisher landing page
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https://www.tandfonline.com/doi/pdf/10.1080/23248378.2021.1875065?needAccess=trueDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.tandfonline.com/doi/pdf/10.1080/23248378.2021.1875065?needAccess=trueDirect OA link when available
- Concepts
-
Artificial neural network, Track (disk drive), Degradation (telecommunications), Train, Track geometry, Computer science, Line (geometry), Simulation, Engineering, Geometry, Artificial intelligence, Mathematics, Geography, Telecommunications, Operating system, CartographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
62Total citation count in OpenAlex
- Citations by year (recent)
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2025: 16, 2024: 15, 2023: 16, 2022: 13, 2021: 2Per-year citation counts (last 5 years)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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