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A segmental evaluation model for determining residual rail service life based on a discrete-state conditional probabilistic method

Author

Listed:
  • Wenfei Bai
  • Quanxin Sun
  • Futian Wang
  • Rengkui Liu
  • Ru An

Abstract

Because steel rail is one of the most fundamental components of railway operations, the accurate estimation of residual rail service life is of great significance in ensuring the safe operation of railways. In addition, maintenance expenses must be minimized in a manner that allows limited railroad resources to be optimally allotted. In this study, the typical types of continuous rail segments on a rail line are classified into non-sharply curved rail segments and sharply curved rail segments. Using these classifications, a model for estimating the residual service lives of rail segments using a discrete-state conditional probability method is proposed based on an analysis of rail deterioration characteristics. The model considers several heterogeneous factors to determine their influence on the deterioration process and is shown to be capable of estimating the residual service lives of rail segments. Finally, the model is validated through a case study of the Beijing Metro, using inspection records of rail defects in conjunction with heterogeneous factor data to predict the service life of the rail, which is then compared with its actual service life. The model is found to show good agreement with the rail inspection and maintenance records of the Beijing Metro, indicating its appropriateness for use by railroad management in allocating future rail maintenance resources.

Suggested Citation

  • Wenfei Bai & Quanxin Sun & Futian Wang & Rengkui Liu & Ru An, 2019. "A segmental evaluation model for determining residual rail service life based on a discrete-state conditional probabilistic method," Journal of Risk and Reliability, , vol. 233(2), pages 211-225, April.
    • Handle: RePEc:sae:risrel:v:233:y:2019:i:2:p:211-225
    DOI: 10.1177/1748006X18768916
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    References listed on IDEAS

    as
    1. Durango-Cohen, Pablo L. & Madanat, Samer 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, Elsevier, vol. 42(8), pages 1074-1085, October.
    2. Kobayashi, Kiyoshi & Kaito, Kiyoyuki & Lethanh, Nam, 2012. "A statistical deterioration forecasting method using hidden Markov model for infrastructure management," Transportation Research Part B: Methodological, Elsevier, vol. 46(4), pages 544-561.
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