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Related Experiment Video

Updated: Mar 1, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

A Big Data Analysis Approach for Rail Failure Risk Assessment.

Ali Jamshidi1, Shahrzad Faghih-Roohi2, Siamak Hajizadeh1

  • 1Section of Railway Engineering, Delft University of Technology, Delft, The Netherlands.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|June 1, 2017
PubMed
Summary

This study introduces an image processing method to detect rail squats, a defect that can cause failures. The approach models rail failure risk based on squat size and growth, enhancing railway safety.

Keywords:
Big data analysisrail failure riskrail surface defects

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Last Updated: Mar 1, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Area of Science:

  • Civil Engineering
  • Mechanical Engineering
  • Materials Science

Background:

  • Railway infrastructure integrity is crucial for safe and efficient transportation.
  • Rail failures, often caused by surface defects like squats, lead to significant delays and safety concerns.
  • Automated monitoring systems are needed to manage vast amounts of inspection data.

Purpose of the Study:

  • To develop and assess an image processing approach for detecting and analyzing rail squats.
  • To model the risk of rail failure based on squat characteristics, specifically visual length and crack growth.
  • To evaluate the proposed method's practicality and efficiency on a real-world railway network.

Main Methods:

  • Utilizing video camera data for automatic detection of rail squats.
  • Applying image processing techniques to measure squat visual length.
  • Developing a failure risk model incorporating squat growth and crack propagation scenarios.
  • Conducting severity and crack growth analyses under simulated rail traffic loads.

Main Results:

  • Successfully detected severe squats prone to rail breaks using image processing.
  • Quantified rail failure risk based on measured squat dimensions and estimated growth.
  • Demonstrated the approach's effectiveness on a busy Dutch railway track with varying squat severities.
  • Validated the practicality and efficiency of the automated squat analysis method.

Conclusions:

  • The proposed image processing method offers an efficient way to assess rail failure risk from squats.
  • Automated analysis of rail surface defects like squats is feasible and beneficial for railway safety.
  • The developed risk model provides valuable insights into defect progression and potential failures.