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Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach.

Mykola Sysyn1, Michal Przybylowicz1, Olga Nabochenko2

  • 1Institute of Railway Systems and Public Transport, Technical University of Dresden, 01069 Dresden, Germany.

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Summary
This summary is machine-generated.

Sleeper voids in ballasted tracks accelerate deterioration and cause instabilities. New methods using dynamic behavior analysis and machine learning can identify these voids for improved track maintenance and reliability.

Keywords:
ballasted track superstructuredynamic simulationmachine learningrail deflectionsleeper support conditiontrack-side and on-board measurementwavelet scattering

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Area of Science:

  • Railway Engineering
  • Geotechnical Engineering
  • Vibrational Analysis

Background:

  • Ballasted track superstructures suffer rapid geometric deterioration due to ballast settlement and sleeper voids.
  • Sleeper voids create localized instabilities, increasing dynamic loads, vibrations, and maintenance costs, impacting transport reliability.
  • Early identification of sleeper support conditions is crucial for preventing track defects like ballast breakdown and subgrade issues.

Purpose of the Study:

  • To develop and validate methods for identifying sleeper support conditions in ballasted tracks.
  • To differentiate dynamic behaviors between void zones and geometric irregularities.
  • To enable proactive maintenance planning by detecting local instabilities.

Main Methods:

  • Numerical simulation using a three-beam dynamic model to analyze superstructure and rolling stock interaction.
  • Analysis of spectral features in time-domain using scalograms and scattergrams.
  • Experimental investigation using multipoint track-side measurements of rail displacements via high-speed video and digital image correlation (DIC).
  • Application of machine learning with wavelet scattering for void identification from track-side data.

Main Results:

  • Distinct dynamic behavior features were identified for void zones versus geometric irregularities.
  • Theoretical research clarified similarities and differences between track-side and on-board measurement dynamics.
  • Machine learning methods achieved accurate void identification using track-side measurements.
  • On-board measurement application showed moderate identification results, highlighting areas for improvement.

Conclusions:

  • The developed methods effectively identify sleeper voids using dynamic analysis and machine learning.
  • Accurate void detection aids in preventing track instabilities and reducing maintenance expenses.
  • Further refinement of on-board measurement techniques is recommended for enhanced void identification.