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Title: Rail track geometry degradation and maintenance decision making
Author: Audley, Matthew
ISNI:       0000 0004 5362 3623
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2014
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Maintaining track on the UK railway network accounts for a large proportion of the total operating costs. During the year 2012/ 2013 Network Rail spent £391million on the maintenance of track. In order to maintain and operate the railway to a high standard at a lower cost, an asset management strategy is needed. This study uses a model to predict the effects of different asset management strategies. An optimisation routine is then used to obtain a number of optimum strategies based on maintenance cost and track quality. The first part of this study utilises data on the UK rail network track geometry, including records of maintenance performed, to analyse the effects of corrective maintenance on track geometry degradation . The Weibull distribution is used to analyse the distributions of times for the track geometry to degrade to specified states following maintenance. The quality of the track geometry is investigated after corrective maintenance has been performed. This study has shown that such maintenance becomes less effective the more times it is carried out and there is a point at which a renewal becomes the most cost effective solution. The second part focuses on a Petri net modelling approach to rail track asset management. Two Petri nets have been developed- a single track section model and a multi track section model. These are used to predict the track condition over time accounting for a specified asset management strategy. The results of the analysis show that Petri net models are an accurate technique for modelling maintenance strategies. In the final part of this study a multi objective genetic algorithm was used to maximise track quality while minimising the life cycle cost. This has been accomplished by determining the optimum geometry measurement interval, number of maintenance machines on the network and maintenance and renewal criteria. Such a predictive approach would be used by rail operators to support the setting of the maintenance strategy.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available