The maintenance and renewal of ballasted track can be optimized in terms of time and cost if a proper statistical model of track deterioration is derived from previous maintenance history and measurement data. In this regard, quite a few models with simplified assumptions on the parameters have been suggested for the deterioration of ballasted track. Meanwhile, data driven models such as the artificial neural network (ANN) and support vector regression (SVR), which are basic ingredients of machine learning (ML) technology, were introduced in this study to better represent the deterioration phenomena of track segments so that the results can be directly plugged into the optimization schemes. For this purpose, the influential parameters of track deterioration have been selected based on the maintenance history, and two ML models have been studied to find the best combination of input parameters. Through numerical experiments, it was found that at least 2 years of maintenance data were needed in our case to obtain a stable prediction of track deterioration.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Prediction of Track Deterioration Using Maintenance Data and Machine Learning Schemes


    Contributors:
    Lee, Jun S. (author) / Hwang, Sung Ho (author) / Choi, Il Yoon (author) / Kim, In Kyum (author)


    Publication date :

    2018-06-20




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown



    Track maintenance machine

    MUELLER MICHAEL | European Patent Office | 2020

    Free access

    Modelling rail track deterioration and maintenance: current practices and future needs

    Ferreira, Luis / Murray, Martin H. | Taylor & Francis Verlag | 1997


    TRACK MAINTENANCE MACHINE

    MUELLER MICHAEL | European Patent Office | 2018

    Free access

    The Track Deterioration Model

    Tzanakakis, Konstantinos | Springer Verlag | 2013