Railway Sector Asset Management: Annotated Bibliography and Report

This report delves into the critical aspects of asset management within the railway sector, emphasizing the importance of dependable infrastructure and efficient maintenance practices. It begins with an introduction highlighting the need for effective management of railway assets, considering factors like investment, procurement, and the technical condition of assets. The report then presents an annotated bibliography of key research papers, including studies by Andrews (2013, 2014), Nielsen et al. (2013), and Yianni et al. (2017). These papers explore various modeling approaches for track and bridge asset management, such as stochastic and Petri-net-based models, and discuss life cycle cost analysis and the challenges of maintaining aging infrastructure. The report concludes by reiterating the significance of proactive asset management for ensuring safety, reducing costs, and optimizing the lifespan of railway assets, referencing the impact of poor maintenance on the sector's financial and operational efficiency. The report provides a comprehensive overview of the current state of asset management in the railway sector, offering valuable insights for professionals and students alike.

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Asset Management in the Railway Sector
Name of the student
Name of the university
Author note

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Table of Contents
Introduction:....................................................................................................................................3
Annotated bibliography...................................................................................................................3
Conclusions......................................................................................................................................6
References........................................................................................................................................7

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Introduction:
Dependable framework and on-time prepared to-work vehicles are fundamental to
guarantee smooth tasks both in mainline and open rail transport. It is hence essential that every
one of the parties engaged in investment, acquirement and purchasing know the technical
condition and estimation of the assets being referred to. This is valid for financing, renting or
rental hand-overs, re-deals, and for circumstances where future ventures should be grouped or
guarantee claims are made. Railway managers are constantly under the pressure of needingto
check the operational costs. Along with this they are required to prioritize investment decisions,
extending the life cycles of assets and finding areas where more expenses can be curbed. The
condition in which the railway infrastructure is kept is a vital parameter to the traffic safety,
infrastructure availability, revenue and maintenance cost (Jovanovic et al., 2015).
Annotated bibliography
Andrews, J 2013, ‘A modelling approach to railway track asset management’, Proceedings
of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, Vol.
227, No. 1, pp.56-73.
The article presents data that reflects the persistent increase in size of the railway network in the
UK. The 20,000 miles of track in the UK is beginning to carry faster and heavier trains than it
used to previously, along with the sheer increase in the number of trains. The article suggests that
the main challenge is the effective management of this ageing and frequently utilized system
given that there are limited financial resources. Andrews proposes the use of modeling tools to

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research further into the alternative strategies for asset management in this sector. This article
takes on a modeling approach; firstly, by recognizing and analyzing the track geometry, with the
help of the data gathered at regular intervals by a train of measurement and implying the
conditions of the ballast. The distributions for the track geometry by time are produced by the
analysis, which are relevant to the network of railways under consideration. This helps avoid the
use of generic and inappropriate data. With this newly accumulated data, a track section is
modeled which takes in the newly formulated maintenance and renewal procedures which help
in predicting the ballast section condition overtime. This model can incorporate differet
maintenance strategies due to the use of the Petri sum formulation with a Monte Carlo solution
routine.
Andrews, J., Prescott, D. and De Rozières, F 2014, ‘A stochastic model for railway track
asset management’ Reliability Engineering & System Safety, Vol. 130, pp.76-84.
This is another paper by Andrews J mentioned paper along with 2 other researchers. Here they
have analyzed data from special trains that measure the geometry of the rails, and predict the
correction needed on tracks that have degraded to a point where they need renewal or
maintenance. Tamping is also carried out by these special trains that insert tines into the ballast
on either side of the sleepers. The ballast is packed in a manner such that the required rail
position is maintained. The advantage of this process is that it reduces the time between the
interventions required during track maintenance. The disadvantage being it also breaks the
ballast which expedites the track geometry degradation. Two kinds of maintenance responses
have been used in this model which governs the times required to improve the geometry of the
track during scheduled maintenance. They are the variables FNorm(t) and Femerg(t) , which mean

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routine track maintenance time and emergency (Priority) times. The latter being ones where
speed restrictions or closing of line rules are imposed on the network.

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Nielsen, D., Raman, D. and Chattopadhyay, G 2013, ‘Life cycle management for railway
bridge assets’, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of
Rail and Rapid Transit, Vol. 227, No. 5, pp.570-581.
This paper opines on the increasing load on the track and the bridge due to the increased bulk
material transport. Bridges are now approaching their designed loading limit and this could prove
disastrous. This paper does a life cycle cost analysis for the requirement that is maintenance of
the structural integrity of the railway bridges uses appropriate data. The paper intends to provide
a faster and far more accurate means of servicing bridges and managing and maintaining assets
in the railway industry in this sphere. The paper allows for detailed analysis of individual
elements in the bridges for a more in-depth acquisition of data which in turn allows for more
informed decisions. The advantage of using the model proposed by this paper according to the
researchers is that the newer system can implement an optimized system for life cycle cost
management of bridges in the railway sector.
Yianni, P.C., Rama, D., Neves, L.C., Andrews, J.D. and Castlo, D 2017, ‘A Petri-Net-based
modelling approach to railway bridge asset management’, Structure and Infrastructure
Engineering, Vol. 13, No. 2, pp.287-297.
In this paper the researchers have discussed about how the Markov based model has been in use
for over two decades (25 years) and how the entire maintenance and standardization of the
railway infrastructure is based on the Markov model. They have further opined that the Markov
stochastic model although widely applied has its limitations, such as the assumption that the time
intervals are discrete and the fixed count of the bridges renders the probabilistic estimations to be

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inaccurate. The paper proposes the use of the Petri-net based model for application in the
maintenance of railway infrastructure since it is being increasingly used in several fields of
engineering and business. The data was gathered from a variety of sources such as the Civil
Asset Register and Reporting System or CARRS, the SCMI or the Structure Condition
Monitoring Index and other sources. The paper uses a time based approach to calibrate the
deterioration and builds data on the basis of routine inspection of structures and each element of
the structure. They have concluded that the newer model suggests a model that inspects and
repairs elements according to the guidelines set by the industry. This model can be used to run
simulations of bridge assets beginning in any condition with inspection custom made for each
element with the incorporation of policies. The outputs can also be compared, which allows for
managers to get an estimate of when work could be required which is very useful.
Conclusions:
Asset management is imperative in this day and age where poor maintenance practices
affect the life cycle of the various assets such as tracks, bridges and so on in the railway industry.
This forces the sector to incur additional costs and in general utilizes an abundance of extra
resources both natural and financial which could otherwise be put to use elsewhere. Poor
maintenance also affects the safety of the network which should not be taken lightly (Kans et al.,
2016).

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References:
Andrews, J 2013, ‘A modelling approach to railway track asset management’, Proceedings of the
Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, Vol. 227, No. 1,
pp.56-73.
Andrews, J., Prescott, D. and De Rozières, F 2014, ‘A stochastic model for railway track asset
management’ Reliability Engineering & System Safety, Vol. 130, pp.76-84.
Jovanovic, S., Guler, H. and Coko, B 2015, ‘Track degradation analysis in the scope of railway
infrastructure maintenance management systems’, Gradevinar, Vol. 67, No. 3, pp.247-257.
Kans, M., Galar, D. and Thaduri, A 2016, ‘Maintenance 4.0 in railway transportation industry’,
In Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM
2015) (pp. 317-331).Springer, Cham.
Nielsen, D., Raman, D. and Chattopadhyay, G 2013, ‘Life cycle management for railway bridge
assets’, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and
Rapid Transit, Vol. 227, No. 5, pp.570-581.
Yianni, P.C., Rama, D., Neves, L.C., Andrews, J.D. and Castlo, D 2017, ‘A Petri-Net-based
modelling approach to railway bridge asset management’, Structure and Infrastructure
Engineering, Vol. 13, No. 2, pp.287-297.