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Prediction of track geometry degradation using artificial neural network: a case study
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0003-3269-5815
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-7083-4009
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-3266-2434
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
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2022 (English)In: International Journal of Rail transportation, ISSN 2324-8378, E-ISSN 2324-8386, Vol. 10, no 1, p. 24-43Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Taylor & Francis, 2022. Vol. 10, no 1, p. 24-43
Keywords [en]
Artificial neural network, prediction, degradation, track geometry, garson's algorithm
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-82710DOI: 10.1080/23248378.2021.1875065ISI: 000613707500001Scopus ID: 2-s2.0-85099801065OAI: oai:DiVA.org:ltu-82710DiVA, id: diva2:1523887
Funder
Swedish Transport AdministrationLuleå Railway Research Centre (JVTC)
Note

Validerad;2022;Nivå 2;2022-02-21 (hanlid);

Finansiär: Bana Väg För Framtiden (BVFF)

Available from: 2021-01-29 Created: 2021-01-29 Last updated: 2022-04-27Bibliographically approved
In thesis
1. Data-driven models for railway track geometry maintenance
Open this publication in new window or tab >>Data-driven models for railway track geometry maintenance
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Railways are currently experiencing higher demands on safety, infrastructure performance, network capacity and service quality, etc. As a result, a high level of track availability, service quality and infrastructure robustness against unexpected events is required, at reduced cost, to meet the demands. The track geometry is one of the critical parameters influencing the performance of the infrastructure and the capacity and quality of service of the railway network. The track geometry continuously degrades and deviates from the designed level over time and under the influence of various factors. A poor track geometry negatively affects the ride quality and passenger comfort and increases the risk of train derailment. To ensure a safe and reliable track for the trains, the track geometry should be kept in an acceptable condition. This necessitates the development of an applicable and effective tamping regime, as the main maintenance action for track geometry rejuvenation. An effective tamping regime enables enhancement of the availability and safety performance of the track, to control the track degradation and to restore the damaged track to an operational state, at the lowest possible cost. 

The purpose of the research conducted for this thesis was to develop data-driven methodologies and optimization approaches for the development of a plan for predictive railway track geometry maintenance. First, a study was carried out to investigate the application of artificial neural networks to estimation of the track geometry degradation rate in spatial space by considering the operational and constructional covariates. In addition, the relative importance of the observed covariates for the track geometry degradation was also explored. The results showed that the maintenance history, the degradation value after tamping and the frequency of the trains passing along the track were the most important covariates affecting the track geometry degradation rate. Second, a case study was performed on a heavy haul railway line to analyse the isolated twist and longitudinal level defects. A data-driven model was developed to predict the occurrence of track geometry defects. A machine learning technique, namely the RUSBoost algorithm, was used to classify the track sections into healthy and unhealthy track sections. In order to capture information about the shape of the defects, first- and second-order derivatives of the track irregularities were used. The observed anomalies in the conducted case study in the pattern of the track geometry degradation were also explored. It was found that applying a combination of the kurtosis and the standard deviation to represent the track quality is beneficial for identifying the anomalies in the trend of geometry defects. Third, a simulation-based framework was developed with the aim of allocating an effective maintenance limit by assessing the effect of different maintenance limits on the total maintenance cost. The results showed that it is not one value only, but a range of values for the maintenance limits which can be selected to minimize the total maintenance costs. However, by considering the safety aspects of track geometry maintenance, it is suggested that one should select the lower bound of the range of cost-effective limits for maintenance planning. 

Finally, an optimization model was developed to schedule track tamping activities with the aim of minimizing both the track geometry maintenance costs and the number of unplanned maintenance actions. The proposed model enables infrastructure managers to examine the effect of different scenarios for the control and management of isolated defects on the track geometry condition and the maintenance cost. The results obtained in the present research are highly relevant for specific industrial challenges and are expected to enhance the capability of infrastructure managers to make effective and efficient decisions when developing their planning and scheduling of the tamping regime. The results have been verified through interaction with experienced practitioners working for a major railway infrastructure manager and railway maintenance subcontractors.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2021
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Data-driven models, degradation, maintenance, tamping, track geometry, planning and scheduling, railway infrastructure
National Category
Infrastructure Engineering Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-84266 (URN)978-91-7790-854-8 (ISBN)978-91-7790-855-5 (ISBN)
Public defence
2021-06-18, F1031, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2021-05-17 Created: 2021-05-14 Last updated: 2022-10-31Bibliographically approved

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Khajehei, HamidAhmadi, AlirezaSoleimanmeigouni, ImanHaddadzade, Mohammad

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