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Application of Deep Learning Algorithms in Railway Track Deterioration Modeling

Fathi Jam, Faeze | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55014 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Shafahi, Yosef
  7. Abstract:
  8. Rail track deterioration models are integral components of rail infrastructure maintenance management systems. In particular, track geometry defects are one of the leading causes of train accidents, and control, management, and modification of the track geometric conditions are among the most important tasks of railway maintenance management systems. Track geometry data such as profile, alignment, gauge, cross-level, and twist constantly change over time. Therefore, these features have the characteristics of time series data.In this study, a large database from outputs of EM120, a track recording machine, was provided for the years 2009 to 2020 and for all 19 railway zones of Iranian Railways (approximately 14,000 km of railway track and 100 GB of data).Among Deep Learning methods, CNN, LSTM, and CNN-LSTM models were used to predict track geometry degradation one by one. Long short-term memory (LSTM) has the advantage of analyzing relationships among time-series data through its memory function, while CNN models may filter out the noise of the input data and extract more valuable features that would be more useful for the final prediction model. By integrating convolutional neural network (CNN) and long short-term memory (LSTM), a CNN-LSTM model is considered to make more accurate and point-wise predictions. The models were built from the average segments of 100 and 200 meters. The forecasting results of proposed models were analyzed and compared, and the CNN-LSTM model with a segment length of 200 m and sequence length of 6 reported the best forecasting performance, achieving an R-squared value of 0.913
  9. Keywords:
  10. Maintenance Management ; Deep Learning ; Railway Track Deterioration ; Track Car Recorder (EM120) ; Railway Network

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