London Underground (LUL) is one of the largest metro networks in the world and carried nearly 1.5 billion passengers in 2015. This increasing passenger demand leads to higher axle loads and shorter headways in the railway operations. However, this has a detrimental impact on the damage generated at the wheel-rail interface. In spite of the advances in rolling stock and track engineering, new developments in material manufacturing methods and rail inspection technology, cracking in rails still remains a major concern for infrastructure managers in terms of safety and maintenance costs. In this study, field data from two metro lines on the LUL network was analysed to identify the distribution and severity of the different damage types. Detailed vehicle dynamics route simulations were conducted for the lines and the calculated wheel-rail forces were investigated to assess the applicability current models for the prediction of rail damage on metro lines. These models include the Whole Life Rail Model (WLRM), previously developed for Great Britain (GB) main line tracks, and Shakedown theory. The influence of key factors such as curve radius, different friction conditions, track irregularities and wheel-rail profiles on the wheel-rail contact interface have been evaluated and compared with outputs from simulations on mainline routes. The study found that the contact patch energy (Tγ) and the interaction between wear and RCF in rails were highly influenced by the characteristics of metro tracks. It was also shown that both the Tγ and Shakedown methods can provide successful prediction of damage susceptibility of rails. However, in order to increase the accuracy of damage predictions and to ascertain the severity of different damage types, the duty conditions which are observed by the rail and the changes in contact conditions resulting from the successive vehicle passes should be considered in the modelling.
London Underground is facing the challenge of increasing timetables against spending cuts across renewals and maintenance in all assets. In order to meet this challenge, it is reviewing all maintenance practices to make sure that they are appropriate for the current asset conditions. Management of the wheel–rail interface is critical to maximising the life of wheels and rails through preventative maintenance regimes that ensure all activities offer value for money and safe operation. Detailed monitoring of the asset condition using novel non-destructive techniques has allowed the identification of the problems which currently occur at the wheel–rail interface on the London Underground network. These problems are discussed in this paper along with some of the solutions proposed to manage them. Site observations from a range of rail rolling contact fatigue monitoring sites have also been compared to the outputs from vehicle dynamic simulations. These outputs were post-processed using a circle plotting technique, which illustrates the location, direction and severity of the forces, and the Whole Life Rail Model to predict the susceptibility to rail damage for two rail steel grades. The outputs from these comparisons have helped to illustrate the wheel–rail contact conditions and forces which are driving the observed damage and potential future enhancements to improve the accuracy of the models for predicting the observed rolling contact fatigue damage.
Infrastructure managers (IMs) endeavour to eliminate rail defects at an early stage since they impact on safety and quality of operation and increase system costs. London Underground (LUL) uses several non-destructive testing (NDT) techniques in rail inspection to detect the emerging defects and monitor the growth of previously recorded defects. This task mainly aims to prioritise maintenance and renewal activities and record their completion. However, when the high traffic demand and limited maintenance periods are considered, these requirements bring additional pressures to the maintenance team. To optimise maintenance planning, sufficient and reliable field data along with accurate damage prediction are required. Recent developments in NDT technology has seen the introduction of devices to measure crack depth which is a key parameter in the assessment of crack severity and rail life. Therefore, contrary to previous research which mainly utilised observations of rail surface condition, the use of new NDT techniques can support the development and validation of new rail damage models which will help to improve maintenance planning and move to condition-based maintenance strategy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.