Renewal management framework for urban rail transit assets
Abstract
Decision-making surrounding asset renewal is essential for the efficient use of renewal resources and safe operation of urban rail transit. In this study, major problems in the current management of urban rail industries in countries with the same problems as those in China were analysed, and in response, a renewal management framework based on service life estimation was proposed to provide adequate decision-making support for urban rail transit assets. In this framework, the cumulative failure frequency of an asset is used to indicate its health condition, and considering the uncertainties and heterogeneities in the deterioration process of assets, a Poisson–Weibull process model-based methodology was developed to estimate the service and residual lives of each asset, which are then employed in analysing its renewal demand and renewal period. Finally, the model is validated through an empirical study of rail renewal in the Beijing Metro. Our evaluation demonstrates that the proposed framework can estimate each asset’s service life accurately and can be used by asset management personnel to establish reasonable renewal plans and provide decision-making support for a scientifically informed resource allocation, thus mitigating major problems in current management practices.
Keyword : urban rail transit, China, asset renewal, Poisson–Weibull process, service life, residual life
How to Cite
Bai, W., Liu, R., An, R., Wang, F., & Sun, Q. (2019). Renewal management framework for urban rail transit assets. Transport, 34(1), 9-18. https://doi.org/10.3846/transport.2019.6722
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Rashedi, R.; Hegazy, T. 2016. Holistic analysis of infrastructure deterioration and rehabilitation using system dynamics, Journal of Infrastructure Systems 22(1): 04015016. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000273
Rashedi, R.; Hegazy, T. 2015. Capital renewal optimisation for large-scale infrastructure networks: genetic algorithms versus advanced mathematical tools, Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance 11(3): 253–262. https://doi.org/10.1080/15732479.2013.866968
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The Railway Ministry of People’s Republic of China. 2010. Rules of Railway Track Maintenance. 1st edition. Beijing: China Railway Publishing House (in Chinese).
Zakeri, J.-A.; Shahriari, S. 2012. Developing a deterioration probabilistic model for rail wear, International Journal of Traffic and Transportation Engineering 1(2): 13–18.
Zambon, I.; Vidovic, A.; Strauss, A.; Matos, J.; Amado, J. 2017. Comparison of stochastic prediction models based on visual inspections of bridge decks, Journal of Civil Engineering and Management 23(5): 553–561. https://doi.org/10.3846/13923730.2017.1323795
Zhang, J. X.; Han, B. M. 2011. Research on management modes of Beijing urban rail transit asset replacement and renovation, International Conference on Information, Services and Management Engineering, 26–28 December 2011, Beijing, China, 2029–2031.
Aoki, K.; Yamamoto, K.; Kobayashi, K. 2007. Optimal inspection and replacement policy using stochastic method for deterioration prediction, in The 11th World Conference on Transportation Research, 24–28 June 2007, Berkeley CA, US, 1–13.
Bai, L.; Liu, R.; Sun, Q.; Wang F.; Xu, P. 2015. Markov-based model for the prediction of railway track irregularities, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 229(2): 150–159. https://doi.org/10.1177/0954409713503460
Baik, H.-S.; Jeong, H. S.; Abraham, D. M. 2006. Estimating transition probabilities in Markov chain-based deterioration models for management of wastewater systems, Journal of Water Resources Planning and Management 132(1): 15–24. https://doi.org/10.1061/(ASCE)0733-9496(2006)132:1(15)
Barone, G.; Frangopol, D. M. 2014. Life-cycle maintenance of deteriorating structures by multi-objective optimization involving reliability, risk, availability, hazard and cost, Structural Safety 48: 40–50. https://doi.org/10.1016/j.strusafe.2014.02.002
Caetano, L. F.; Teixeira, P. F. 2013. Availability approach to optimizing railway track renewal operations, Journal of Transportation Engineering 139(9): 941–948. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000575
Caetano, L. F.; Teixeira, P. F. 2016. Predictive maintenance model for ballast tamping, Journal of Transportation Engineering 142(4): 04016006. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000825
Cao, J. H.; Cheng, K. Z. 2012. Introduction to Reliability Mathematics. Beijing: Higher Education Press. (in Chinese).
Chen, L.; Ng, J. 2007. Asset management for urban rail transit, Urban Rapid Rail Transit 20(4): 21–24 (in Chinese). https://doi.org/10.3969/j.issn.1672-6073.2007.04.007
CAM. 2017. Annual statistics and analysis report of urban rail transit. Report. China Association of Metros (CAM). (in Chinese).
DB 11/995:2013. Code for Design of Urban Rail Transit. Chinese Standard (in Chinese).
DB 11/T718:2016. Code for Maintenance of Urban Rail Transit Facility. Chinese Standard (in Chinese).
Durango-Cohen, P. L.; Madanat, S. M. 2002. Optimal maintenance and repair policies in infrastructure management under uncertain facility deterioration rates: an adaptive control approach, Transportation Research Part A: Policy and Practice 36(9): 763–778. https://doi.org/10.1016/S0965-8564(01)00038-6
Durango-Cohen, P. L.; Madanat, S. M. 2008. Optimization of inspection and maintenance decisions for infrastructure facilities under performance model uncertainty: a quasi-Bayes approach, Transportation Research Part A: Policy and Practice 42(8): 1074–1085. https://doi.org/10.1016/j.tra.2008.03.004
GB 50157:2013. Code for Design of Metro. Chinese Standard (in Chinese).
GB 50490:2009. Technical Code of Urban Rail Transit. Chinese Standard (in Chinese).
Han, D.; Kaito, K.; Kobayashi, K. 2014. Application of Bayesian estimation method with Markov hazard model to improve deterioration forecasts for infrastructure asset management, KSCE Journal of Civil Engineering 18(7): 2107–2119. https://doi.org/10.1007/s12205-012-0070-6
Hegazy, T.; Rashedi, R. 2013. Large-scale asset renewal optimization using genetic algorithms plus segmentation, Journal of Computing in Civil Engineering 27(4): 419–426. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000249
IEC 62278:2002. Railway Applications. Specification and Demonstration of Reliability, Availability, Maintainability and Safety (RAMS).
ISO 2394:2015. General Principles on Reliability for Structures.
Kobayashi, K.; Kaito, K.; Lethanh, N. 2010. Deterioration fore casting model with multistage weibull hazard functions, Journal of Infrastructure Systems 16(4): 282–291. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000033
Kobayashi, K.; Kaito, K.; Lethanh, N. 2012. A statistical deterioration forecasting method using hidden Markov model for infrastructure management, Transportation Research Part B: Methodological 46(4): 544–561. https://doi.org/10.1016/j.trb.2011.11.008
Li, Y.-L.; Wang, Y. 2012. Problems and recommendations of Beijing metro asset management, Journal of Transportation Systems Engineering and Information Technology 12(2): 194–197. (in Chinese). https://doi.org/10.16097/j.cnki.1009-6744.2012.02.008
Liu, Z. L.; Bai, Y.; Wu, Y. L. 2011. Decision-making method on rail transit infrastructure renovation project, International Conference on Management Science and Engineering, Advances in Artificial Intelligence, 1 January 2011, Chengdu, China, 351–356.
Mishalani, R. G.; Gong, L. 2009. Optimal infrastructure condition sampling over space and time for maintenance decision-making under uncertainty, Transportation Research Part B: Methodological 43(3): 311–324. https://doi.org/10.1016/j.trb.2008.07.003
Mishalani, R. G.; Madanat, S. M. 2002. Computation of infrastructure transition probabilities using stochastic duration models, Journal of Infrastructure Systems 4(8): 139–148. https://doi.org/10.1061/(ASCE)1076-0342(2002)8:4(139)
QB(J)/BDY(A)XL 003:2009. Beijing Metro Track Maintenance Rules (in Chinese).
Rashedi, R.; Hegazy, T. 2016. Holistic analysis of infrastructure deterioration and rehabilitation using system dynamics, Journal of Infrastructure Systems 22(1): 04015016. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000273
Rashedi, R.; Hegazy, T. 2015. Capital renewal optimisation for large-scale infrastructure networks: genetic algorithms versus advanced mathematical tools, Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance 11(3): 253–262. https://doi.org/10.1080/15732479.2013.866968
Scarf, P.; Dwight, R.; McCusker, A.; Chan, A. 2007. Asset replacement for an urban railway using a modified two-cycle replacement model, Journal of the Operational Research Society 58(9): 1123–1137. https://doi.org/10.1057/palgrave.jors.2602288
Scarf, P. A.; Hartman, J. C. 2008. Replacement of capital equipment, in K. A. H. Kobbacy, D. N. P. Murthy (Eds.). Complex System Maintenance Handbook, 287–319. https://doi.org/10.1007/978-1-84800-011-7_12
The Railway Ministry of People’s Republic of China. 2010. Rules of Railway Track Maintenance. 1st edition. Beijing: China Railway Publishing House (in Chinese).
Zakeri, J.-A.; Shahriari, S. 2012. Developing a deterioration probabilistic model for rail wear, International Journal of Traffic and Transportation Engineering 1(2): 13–18.
Zambon, I.; Vidovic, A.; Strauss, A.; Matos, J.; Amado, J. 2017. Comparison of stochastic prediction models based on visual inspections of bridge decks, Journal of Civil Engineering and Management 23(5): 553–561. https://doi.org/10.3846/13923730.2017.1323795
Zhang, J. X.; Han, B. M. 2011. Research on management modes of Beijing urban rail transit asset replacement and renovation, International Conference on Information, Services and Management Engineering, 26–28 December 2011, Beijing, China, 2029–2031.