Railroad infrastructure 4.0: Development and application of an automatic ballast support condition assessment system

Yu Qian, Marcus S. Dersch, Zhengboyang Gao, J. Riley Edwards

Research output: Contribution to journalArticle

Abstract

Industry 4.0, or the fourth industrial revolution, is the current trend of automation and data exchange in manufacturing technologies. Even though Industry 4.0 emerged as a manufacturing initiative, the idea of bridging digital and physical systems can be expanded beyond the manufacturing industry. “Railroad Infrastructure 4.0” is thus proposed in this study to revolutionize the maintenance operations of the railroad industry. In North America, most primary freight and passenger rail corridors are constructed using ballasted track. One of the primary maintenance activities is to ensure the ballast is performing adequately. Being able to monitor the ballast condition and conduct tamping operations (e.g. maintenance activities) at optimal intervals can increase the safety and efficiency of railroad operations. Previously, techniques such as ground penetrating radar (GPR) and Matrix Based Tactile Surface Sensors (MBTSS) have been used to assess the condition of ballast, but these investigative tools lack the capability of automatically and continuously monitoring the track system. Researchers at the University of Illinois at Urbana-Champaign and the University of South Carolina have developed a non-intrusive method as a key component of Railroad Infrastructure 4.0 to continuously quantify ballast pressure distribution (i.e. ballast condition) under the sleeper (also known as a “crosstie”). This method innovatively uses the bending moment profile across the concrete sleeper, and the approximated rail seat loads as inputs, to back-calculate the ballast support condition through the use of an optimization algorithm. The ballast support condition assessment system that was developed has been validated for accuracy from extensive laboratory experimentation. The laboratory validated system was deployed in the field on a Class I heavy axle load (HAL) freight railroad in the United States to continuously monitor the ballast pressure distribution beneath concrete sleepers in real-time under revenue service operating conditions. The evaluation of ballast pressure distributions between adjacent sleepers as well as tonnage accumulated are also included. To better quantify the variation of ballast pressure beneath the sleepers, the Ballast Pressure Index (BPI) is also proposed. The information presented in this paper demonstrates the concept and potential of Railroad Infrastructure 4.0 as a future framework for railroad maintenance planning and management.

Original languageEnglish (US)
Pages (from-to)19-34
Number of pages16
JournalTransportation Geotechnics
Volume19
DOIs
StatePublished - Jun 2019

Keywords

  • Automation
  • Ballast
  • Big data
  • Concrete sleeper
  • Cyber-physical systems
  • Field application
  • Industry 4.0
  • Laboratory experimentation
  • Optimization algorithm
  • Railway track structure
  • Real-time monitoring

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation
  • Geotechnical Engineering and Engineering Geology

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