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Modelling the evolution of ballasted railway track geometry by a two-level piecewise model
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.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon.
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2018 (English)In: Structure and Infrastructure Engineering, ISSN 1573-2479, E-ISSN 1744-8980, Vol. 14, no 1, p. 33-45Article in journal (Refereed) Published
Abstract [en]

Accurate prediction and efficient simulation of the evolution of track geometry condition is a prerequisite for planning effective railway track maintenance. In this regard, the degradation and tamping effect should be equipped with proper and efficient probabilistic models. The possible correlation induced by the spatial structure also needs to be taken into account when modelling the track geometry degradation. To address these issues, a two-level piecewise linear model is proposed to model the degradation path. At the first level, the degradation characteristic of each track section is modelled by a piecewise linear model with known break points at the tamping times. At the second level, Autoregressive Moving Average models are used to capture the spatial dependences between the parameters of the regression lines indexed by their locations. To illustrate the model, a comprehensive case study is presented using data from the Main Western Line in Sweden

Place, publisher, year, edition, pages
Taylor & Francis, 2018. Vol. 14, no 1, p. 33-45
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-63815DOI: 10.1080/15732479.2017.1326946ISI: 000415674800003Scopus ID: 2-s2.0-85019663898OAI: oai:DiVA.org:ltu-63815DiVA, id: diva2:1107169
Note

Validerad;2017;Nivå 2;2017-11-01 (andbra)

Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2019-08-21Bibliographically approved
In thesis
1. Predictive Models for Railway Track Geometry Degradation
Open this publication in new window or tab >>Predictive Models for Railway Track Geometry Degradation
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Prediktiva modeller för degradering av spårgeometri i järnväg
Abstract [en]

Railways are a vital and effective means of mass transportation and play a vital role in modern transportation and social development. The benefits of the railway compared to other transportation modes are a high capacity, high efficiency and low pollution, and owing to these advantages, railways are nowadays experiencing a higher demand for the transportation of passengers and goods. This is in turn imposing higher demands on the railway capacity and service quality. As a result, infrastructure managers are being driven to develop new strategies and plans to fulfil new requirements, which include a higher level of resilience against failure, a more robust and available infrastructure, and cost reduction. This can be achieved by making efficient and effective maintenance decisions by applying RAMS (reliability, availability, maintainability, and safety) analysis and LCC (life cycle cost) assessment.

A major part of the railway maintenance burden is related to track geometry maintenance. Due to the forces induced on the track by traffic, the railway degrades over time, causing deviations from the designed vertical and horizontal alignment. When the track geometry degrades to an unacceptable level, this can cause catastrophic consequences, such as derailment. Maintenance actions are used to control the degradation of the track and restore the geometry condition of the track sections to an acceptable state.

With the current advancements in the field of technologies for railway track geometry measurement, a large amount of event data and condition monitoring data is available. Such technologies, along with advances in predictive analytics, are providing the possibility of predicting the track geometry condition in support of a predictive maintenance strategy. The aim of the research conducted for this thesis has been to develop methodologies and tools for the prediction of railway track geometry degradation, in order to facilitate and enhance the capability of making effective decisions for inspection and maintenance planning. To achieve the purpose of this research, literature studies, case studies and simulations have been conducted.

Firstly, a literature review was performed to identify the existing knowledge gaps and challenges for track geometry degradation modelling and maintenance planning. Secondly, a case study was conducted to analyse the effect of tamping on the track geometry condition. By considering the track geometry condition before tamping as the predictor, a probabilistic approach was utilised to model the recovery after tamping interventions. Thirdly, a two-level piecewise linear framework was developed to model the track geometry evolution over a spatial and temporal space. This model was implemented in a comprehensive case study. Fourthly, a data-driven analytical model was developed to predict the occurrence of track geometry defects. This model enables infrastructure managers to predict the occurrence of severe isolated geometry defects. Finally, an integrated model was created to investigate the effect of different inspection intervals on the track geometry condition.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2019
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Data-driven models, Degradation, Inspection, Maintenance, Predictive analytics, Tamping, Track geometry, Railway infrastructure, RAMS
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-72811 (URN)978-91-7790-310-9 (ISBN)978-91-7790-311-6 (ISBN)
Public defence
2019-09-12, F1031, Luleå, 09:30 (English)
Opponent
Supervisors
Available from: 2019-02-07 Created: 2019-02-07 Last updated: 2019-08-21Bibliographically approved

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Soleimanmeigouni, ImanXiao, XunAhmadi, AlirezaKumar, Uday

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