• Title, Summary, Keyword: TQI(Track Quality Index)

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Introduction of Track Quality Index(TQI) Methods using Track Induction Data (궤도검측데이터를 활용한 궤도품질지수 산출 방법론 고찰)

  • Kim, Nam-Hong;Lee, Syeung-Yeol;Won, Yong-Hoan;Kim, Kwan-Hyung;Lee, Sung-Uk
    • Proceedings of the KSR Conference
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    • pp.66-72
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    • 2009
  • In order to forecast the progress of the track irregularity, we should observe the long-term track quality and divide a track into some separated divisions which have homogeneous property. For this, we define the division of track which has homogeneous property as a 'Segment' and manage the 'TQI(Track Quality Index)' using track induction data based on each segment. In this study, we introduce some methods of estimating track quality and figure out the TQIs of sample section using new FRA TQI method. In addition, we conducted a basic study of the forecasting model for the progress of track irregularity by analyzing track maintenance data.

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Evaluation of Applicability of Apparent Track Stiffness Measured by Light-Weight Deflectometer as a Ballasted Track Condition Index (소형동평판재하시험기로 측정한 궤도 겉보기 강성의 자갈궤도 상태평가 지표로서의 적용성 고찰)

  • Choi, Yeong-Tae;Hwang, Sung Ho;Jang, Seung Yup;Park, Bongsik;Shim, Gwang Seop
    • Journal of the Korean Geoenvironmental Society
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    • v.19 no.2
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    • pp.37-44
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    • 2018
  • Ballasted track has been widely used due to its flexibility. However, the plastic deformation of ballasted track causes the evolution of track geometrical errors, and hence it requires continuous maintenance; increase in number of trains, weight, and speed expedites maintenance frequency and cost as well. Ballast stiffness is well-known as an indicator of design and maintenance. In this regard, this paper aims to suggest the method to measure ballast track stiffness using light-weight deflectometer (LWD) and thus verify its applicability as a maintenance measure. Preliminary field tests determined simple field testing protocol to measure track stiffness. The apparent ballast stiffness by LWD shows good corelation with TQI (Track Quality Index) and maintenance length. That is, as average of apparent stiffness increase, TQI and tamping length decrease exponentially. Therefore, apparent stiffness can be used as an index for ballast condition assessment.

Prediction of Track Quality Index (TQI) Using Vehicle Acceleration Data based on Machine Learning (차량가속도데이터를 이용한 머신러닝 기반의 궤도품질지수(TQI) 예측)

  • Choi, Chanyong;Kim, Hunki;Kim, Young Cheul;Kim, Sang-su
    • Journal of the Korean Geosynthetics Society
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    • v.19 no.1
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    • pp.45-53
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    • 2020
  • There is an increasing tendency to try to make predictive analysis using measurement data based on machine learning techniques in the railway industries. In this paper, it was predicted that Track quality index (TQI) using vehicle acceleration data based on the machine learning method. The XGB (XGBoost) was the most accurate with 85% in the all data sets. Unlike the SVM model with a single algorithm, the RF and XGB model with a ensemble system were considered to be good at the prediction performance. In the case of the Surface TQI, it is shown that the acceleration of the z axis is highly related to the vertical direction and is in good agreement with the previous studies. Therefore, it is appropriate to apply the model with the ensemble algorithm to predict the track quality index using the vehicle vibration acceleration data because the accuracy may vary depending on the applied model in the machine learning methods.

A Bayesian Regression Model to Estimate the Deterioration Rate of Track Irregularities (궤도틀림 진전율 추정을 위한 베이지안 회귀분석 모형 연구)

  • Park, Bum Hwan
    • Journal of the Korean Society for Railway
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    • v.19 no.4
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    • pp.547-554
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    • 2016
  • This study considered how to estimate the deterioration rate of the track quality index, which represents track geometric irregularity. Most existing studies have used a simple linear regression and regarded the slope of the regression equation as the progress rate. In this paper, we present a Bayesian approach to estimate the track irregularity progress. This Bayesian approach has many advantages, among which the biggest is that it can formally include the prior distribution of parameters which can be derived from historic data or from expert experiences; then, the rate can be expressed as a probability distribution. We investigated the possibility of applying the Bayesian method to the estimation of the deterioration rate by comparing our bayesian approach to the conventional linear regression approach.