A wind hazard warning system for safe and efficient operation of high-speed trains

Hongye Gou, Xuanying Chen, Yi Bao

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Operation safety and efficiency of high-speed trains are prone to strong winds. This study proposes a wind hazard warning system to predict wind speed and provide warning for safe and efficient operation of trains, and shows the implementation to the Lanzhou-Xinjiang High-Speed Railway. The system is composed of monitoring, remote and central processing, and execution modules. The system uses field monitoring data to predict wind speed and analyze responses of trains. Wind speed is predicted through integrating three models based on artificial neural networks and the predictions are incorporated into operation control for high speed trains under wind load. The results show that the proposed predictive model is capable of predicting wind speed, and the system is capable of providing reasonable warning for high-speed trains. It is anticipated that the system will improve the operation and management of high-speed trains for high safety and efficiency.

Original languageEnglish
Article number103952
JournalAutomation in Construction
Volume132
DOIs
StatePublished - Dec 2021

Keywords

  • Ensembled empirical mode decomposition (EEMD)
  • Extreme wind
  • High-speed trains
  • Machine learning
  • Predictive model
  • Wind speed

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