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Railway Track Bed Research: Estimating the Limiting Critical Velocity of a High Speed Train

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  • Full or part time
    Prof M Forde
    Dr A Giannopoulos
  • Application Deadline
    No more applications being accepted
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The world-wide demand for construction of high-speed railways is growing. China leads the world followed by Japan. In the UK we are adding capacity to our railway network with the construction of High Speed Two – HS2. In addition railways worldwide (Network Rail, UK, SNCF, France + Amtrak, USA and so on..) are upgrading existing Classic Railways on soil embankments to operate at higher speeds by using new higher speed rolling stock.

The gap in knowledge is: “How to actually estimate the limiting Critical Velocity of a High Speed Train on the soil embankment on site?”

This project will involve working at the cutting edge of new ideas and comprise:
• A literature review of ground profiles & classical soil mechanics data and the role of site investigations
• Understanding geophysics and CPT In-situ testing techniques on site
• Understanding the relationship between Rayleigh waves and compression & shear waves
• Undertaking field work on the full-scale railway track at Kings Buildings + the heritage railway site in Fife.
• Using the spectral-analysis-of-surface-waves (SASW) method to estimate the Rayleigh wave velocity on site and then link this train critical velocity.

The project will also involve liaising with industry.

The project will involve liaising (via Skype) with a distinguished former Visiting Professor - Carlton Ho, PhD, PE, University of Massachusetts at Amherst, MA USA. We have another distinguished Visiting Professor - Xuecheng Bian, Zhejiang University, Hangzhou, China. Day-to-day liaison will be with Prof Mike Forde, Dr Robert De Bold (Research Associate) & Zihao Yan (current research student).

How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 91.80

Research output data provided by the Research Excellence Framework (REF)

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