IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v233y2019i2p211-225.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X18768916
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X18768916?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.
    2. Qiao, Yu & Saeed, Tariq Usman & Chen, Sikai & Nateghi, Roshanak & Labi, Samuel, 2018. "Acquiring insights into infrastructure repair policy using discrete choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 491-508.
    3. Daeseok Han & Jin-Hyuk Lee & Ki-Tae Park, 2022. "Deterioration Models for Bridge Pavement Materials for a Life Cycle Cost Analysis," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    4. Li, Sirui & Liu, Ying & Wang, Pengfei & Liu, Peng & Meng, Jun, 2020. "A novel approach for predicting urban pavement damage based on facility information: A case study of Beijing, China," Transport Policy, Elsevier, vol. 91(C), pages 26-37.
    5. Mizutani, Daijiro & Nakazato, Yuto & Ikushima, Rie & Satsukawa, Koki & Kawasaki, Yosuke & Kuwahara, Masao, 2024. "Optimal intervention policy of emergency storage batteries for expressway transportation systems considering deterioration risk during lead time of replacement," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Sevcíková, Hana & Raftery, Adrian E. & Waddell, Paul A., 2011. "Uncertain benefits: Application of Bayesian melding to the Alaskan Way Viaduct in Seattle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 540-553, July.
    7. Yingnan Yang & Hongming Xie, 2021. "Determination of Optimal MR&R Strategy and Inspection Intervals to Support Infrastructure Maintenance Decision Making," Sustainability, MDPI, vol. 13(5), pages 1-10, March.
    8. Assaf, A. George & Gillen, David & Tsionas, Efthymios G., 2014. "Understanding relative efficiency among airports: A general dynamic model for distinguishing technical and allocative efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 18-34.
    9. Kobayashi, K. & Kaito, K. & Lethanh, N., 2014. "A competing Markov model for cracking prediction on civil structures," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 345-362.
    10. Tsionas, Efthymios & Assaf, A. George & Gillen, David & Mattila, Anna S., 2017. "Modeling technical and service efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 113-125.
    11. Zhe Sun & Tiantian Chen & Xiaolin Meng & Yan Bao & Liangliang Hu & Ruirui Zhao, 2023. "A Critical Review for Trustworthy and Explainable Structural Health Monitoring and Risk Prognosis of Bridges with Human-In-The-Loop," Sustainability, MDPI, vol. 15(8), pages 1-28, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:233:y:2019:i:2:p:211-225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.