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ADDITIONAL READING

Ahmed, N. (1983). Management Science Department, Ball State University, Mun- cie, USA. An analytical decision model for resource allocation in highway maintenance management. Transportation Research Part A, General, 17(2), 133-138. doi:10.1016/0191-2607(83)90067-5

Andersen, T. M., & Rasmussen, B. S. (1999). Effort, Taxation and Unemployment. Economics Letters, 62(1), 97-103. doi:10.1016/S0165-1765(98)00216-X

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, 227(1), 56-73. doi:10.1177/0954409712452235

Campos, J. (2009). Development in the application of ICT in condition monitoring and maintenance. Computers in Industry, 60(1), 1-20. doi:10.1016/j.comp- ind.2008.09.007

Chang, H., Liu, R. & Wang, W. (2010). Multistage linear prediction model of track quality index. Traffic and Transportation Studies: 1183-1192.

Chrismer, S. M. (1994). Mechanics-based model to predict ballast-related maintenance timing and costs. PhD. University of Massachusetts Amherst.

Currie, G., & Burke, M. (2013). Light Rail in Australia-Performance and Prospects. The 36tb Australasian Transport Research Forum, 2-4 October, Brisbane, Australia.

Demharter, K. (1982). Setzungsverhalten des gleisrostes unter vertikaler Lastein- wirkung. Minchen, Deutschland: der Technischen Univeritat Munchen.

Esveld, C. (2001). Modern railway track, The Netherlands. MRT Productions, Zaltbommel.

Heckl, M. A., & Abrahams, I. D. (2000). Curve squeal of train wheels, part 1: Mathematical model for its generation. Journal of Sound and Vibration, 229(3), 669-693. doi:10.1006/jsvi.1999.2510

Hokstad, P., Langseth, H., Lindqvist, B. H., & Vatn, J. (2005). Failure modeling and maintenance optimization for a railway line. International Journal of Performance Engineering, 1, 51-64.

Hummitszch, R. (2005). Calculation schemes for MDZ and “modified standard deviation ”. Technical University of Graz.

Iwnicki, S. D., Grassie, S., & Kik, W. (2000). Track settlement prediction using computer simulation tools. Vehicle System Dynamics, 33, 37-46.

Larsson, D. (2004). A study of the track degradation process related to changes in railway traffic. PhD. Lulea University of Technology.

Lyngby, N., Hokstad, P., & Vatn, J. (2008). RAMS Management of Railway Tracks. In K. Misra (Ed.), Handbook of Performability Engineering. Springer London. doi:10.1007/978-1-84800-131-2_68

Mazzuchi, T. A., Van Noortwijk, J. M., & Kallen, M. J. (2008). Maintenance optimization. Encyclopedia of Statistics in Quality and Reliability. John Wiley & Sons, Ltd.

Meier-hirmer, C., Riboulet, G., Sourget, F., & Roussignol, M. (2009). Maintenance optimization for a system with a gamma deterioration process and intervention delay: Application to track maintenance. Proceedings of the Institution of Mechanical Engineers O. Journal of Risk and Reliability, 223, 189-198.

Pham, H., & Wang, H. (1996). Imperfect maintenance. European Journal of Operational Research, 94(3), 425-438. doi:10.1016/S0377-2217(96)00099-9

Pham, H., & Wang, H. (1999). Some maintenance models and availability with imperfect maintenance in production systems. Analysis of Operations Research, 91, 305-318. doi:10.1023/A:1018910109348

Podoffllini, L., Zio, E., & Vatn, J. (2006). Risk-informed optimisation of railway tracks inspection and maintenance procedures. Reliability Engineering & System Safety, 91(1), 20-35. doi:10.1016/j.ress.2004.11.009

Sato, Y. (1995). Japanese studies on deterioration of ballasted track. Vehicle System Dynamics, 24(sup1), 197-208. doi:10.1080/00423119508969625

Shyr, F., & Ben-akiva, M. (1996). Modeling rail fatigue behavior with multiple hazards. Journal of Infrastructure Systems, 2(2), 73-82. doi:10.1061/(ASCE)1076- 0342(1996)2:2(73)

Veit, P. (2007). Track quality-Luxury or necessity? RTR special: Maintenance & renewal, Railway Technical Review: 8-12.

Zhang, T., Andrews, J., & Wang, R. (2013). Optimal scheduling oftrack maintenance on a railway network. Quality and Reliability Engineering International, 29(2), 285-297. doi:10.1002/qre.1381

Zhang, Y. J., Murray, M., & Ferreira, L. (2000). Modelling rail track performance: An integrated approach. Proceedings of the Institution of Civil Engineers: Transport, 141, 187-194.

Zio, E., Marella, M., & Podofillini, L. (2007). Importance measures-based prioritization for improving the performance of multi-state systems: Application to the railway industry. Reliability Engineering & System Safety, 92(10), 1303-1314. doi:10.1016/j.ress.2006.07.010

KEY TERMS AND DEFINITIONS

Artificial Neural Networks: Artificial Neural Networks are a group of models inspired by biological neural networks and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown.

Gauge: Track gauge is the spacing of the rails on a railway track and is measured between the inner faces of the load-bearing rails.

Prediction: Prediction is a statement about an uncertain event and it is often based on experience or knowledge.

Rail Degradation: Degradation is the wearing down of rail.

Track: The track on a railway or railroad is the structure consisting of the rails, fasteners, railroad sleepers and ballast (or slab track), as well as the underlying subgrade.

Tram: A tram is a rail vehicle which runs on tracks along public urban streets, and also sometimes on a segregated right of way.

Twist: Track twist is used to describe cant gradient which may be expressed in percentage of cant change per unit of length.

 
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