IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v234y2023ics0951832023000959.html
   My bibliography  Save this article

Service reliability assessment of ballastless track in high speed railway via improved response surface method

Author

Listed:
  • Li, Zai-Wei
  • Zhou, Yun-Lai
  • Liu, Xiao-Zhou
  • Abdel Wahab, Magd

Abstract

The spectral feature of the track irregularity has a great impact on the running stability of high-speed trains. This study assesses the service reliability of the high-speed rail (HSR) ballastless track structure considering the effect of wavelength distribution characteristics of the track irregularities. The serviceability limit state (SLS) function of the ballastless track is established with respect to the derailment coefficient and wheel-unloading rate. To overcome the problem of high computational cost in the iteration of reliability assessment for the vehicle-track system, this study proposes an improved response surface method (RSM) and the explicit solution of the SLS function of the ballastless track can be realized. Compared with the traditional RSM, the improved RSM can adaptively adjust the variable interpolation coefficient to increase the iterative convergence speed. To obtain the vertical and lateral wheel-rail forces, a 3-D vehicle-track coupling model is established based on multibody dynamics and the finite element method (FEM). To analyze the wavelength effect of the track irregularities, a binary wavelet-based inversion algorithm is proposed to generate time series of both the vertical and alignment irregularities from the track irregularity spectrum (TIS). By comparing it with the MCS method, it is found that by using the improved RSM method, the convergence condition of the reliability index can be satisfied only by tens of iterations and the total computation time is 1.92 × 104 shorter than using MCS. Finally, the effect of different wavelengths of the track irregularity on the service reliability of track structure is discussed. The results show that the wavelength of 32 to 64 m is the main unfavorable wavelength range affecting the track service reliability under the normal operation condition of HSR.

Suggested Citation

  • Li, Zai-Wei & Zhou, Yun-Lai & Liu, Xiao-Zhou & Abdel Wahab, Magd, 2023. "Service reliability assessment of ballastless track in high speed railway via improved response surface method," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
  • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000959
    DOI: 10.1016/j.ress.2023.109180
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023000959
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109180?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    2. Alban, Andres & Darji, Hardik A. & Imamura, Atsuki & Nakayama, Marvin K., 2017. "Efficient Monte Carlo methods for estimating failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 376-394.
    3. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2018. "Time-variant fatigue reliability assessment of welded joints based on the PHI2 and response surface methods," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 120-130.
    4. Sedghi, Mahdieh & Kauppila, Osmo & Bergquist, Bjarne & Vanhatalo, Erik & Kulahci, Murat, 2021. "A taxonomy of railway track maintenance planning and scheduling: A review and research trends," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Zhang, Z. & Jiang, C. & Wang, G.G. & Han, X., 2015. "First and second order approximate reliability analysis methods using evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 40-49.
    6. Macchi, Marco & Garetti, Marco & Centrone, Domenico & Fumagalli, Luca & Piero Pavirani, Gian, 2012. "Maintenance management of railway infrastructures based on reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 71-83.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Xiaoxi & Pan, Yongjun & Zhou, Junxiao & Li, Zhixiong & Liao, Tianjun & Li, Jie, 2024. "Forward and reverse design of adhesive in batteries via dynamics and machine learning algorithms for enhanced mechanical safety," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    2. Miao, Xingyuan & Zhao, Hong, 2023. "Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Li, Haoqian & Wang, Yong & Zeng, Jing & Li, Fansong & Yang, Zhenhuan & Mei, Guiming & Ye, Yunguang, 2024. "Virtual point tracking method for online detection of relative wheel-rail displacement of railway vehicles," Reliability Engineering and System Safety, Elsevier, vol. 246(C).

    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. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    2. Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Keshtegar, Behrooz & Chakraborty, Subrata, 2018. "An efficient-robust structural reliability method by adaptive finite-step length based on Armijo line search," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 195-206.
    4. Jiang, Chen & Yan, Yifang & Wang, Dapeng & Qiu, Haobo & Gao, Liang, 2021. "Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    5. Yaqun, Qi & Ping, Jin & Ruizhi, Li & Sheng, Zhang & Guobiao, Cai, 2020. "Dynamic reliability analysis for the reusable thrust chamber: A multi-failure modes investigation based on coupled thermal-structural analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Yuan, Kai & Xiao, Ning-Cong & Wang, Zhonglai & Shang, Kun, 2020. "System reliability analysis by combining structure function and active learning kriging model," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. Li, Guosheng & Ma, Shuaichao & Zhang, Dequan & Yang, Leping & Zhang, Weihua & Wu, Zeping, 2024. "An efficient sequential anisotropic RBF reliability analysis method with fast cross-validation and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    8. Xiongxiong You & Mengya Zhang & Diyin Tang & Zhanwen Niu, 2022. "An active learning method combining adaptive kriging and weighted penalty for structural reliability analysis," Journal of Risk and Reliability, , vol. 236(1), pages 160-172, February.
    9. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    10. Zhou, Changcong & Zhang, Hanlin & Valdebenito, Marcos A. & Zhao, Haodong, 2022. "A general hierarchical ensemble-learning framework for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Teixeira, Rui & Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan, 2021. "Reliability analysis using a multi-metamodel complement-basis approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    12. Zhang, Jian & Gong, Weijie & Yue, Xinxin & Shi, Maolin & Chen, Lei, 2022. "Efficient reliability analysis using prediction-oriented active sparse polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    13. Zhang, Yang & Xu, Jun & Beer, Michael, 2023. "A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    14. Wang, Dapeng & Qiu, Haobo & Gao, Liang & Jiang, Chen, 2021. "A single-loop Kriging coupled with subset simulation for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Cui, Da & Wang, Guoqiang & Lu, Yanpeng & Sun, Kangkang, 2020. "Reliability design and optimization of the planetary gear by a GA based on the DEM and Kriging model," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    16. Krupenev, Dmitry & Boyarkin, Denis & Iakubovskii, Dmitrii, 2020. "Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    17. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    18. Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    19. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2020. "Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    20. Wang, Fan & Li, Heng, 2018. "System reliability under prescribed marginals and correlations: Are we correct about the effect of correlations?," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 94-104.

    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:eee:reensy:v:234:y:2023:i:c:s0951832023000959. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.