766
Views
45
CrossRef citations to date
0
Altmetric
Original Articles

Discrete element modelling of railway ballast under triaxial conditions

&
Pages 257-270 | Received 24 Aug 2008, Published online: 22 Dec 2008
 

Abstract

Railway ballast is a granular material with a complex stress–strain relationship under monotonic loading. The discrete element method has been widely used to investigate the mechanical behaviour for the behaviour of granular materials. In this paper, discrete element modelling has been used to capture the essential mechanical features of railway ballast. The effects of particle shape and interparticle friction have been studied. Parallel bonds have also been introduced in the simulations to simulate the interlocking of small-scale asperities, so that the correct stress–strain relationship for ballast can be modelled. Asperity breakage has been modelled by using small balls bonded at the edges of the main body of a particle. A range of confining pressures have been applied to the assembly of particles. The simulations demonstrate the validity of numerical modelling of railway ballast. In particular, the unique contribution of this paper is to show that the monotonic shearing behaviour of railway ballast can be correctly modelled under a range of confining pressures, providing micromechanical insight into the behaviour.

Acknowledgements

The authors would like to thank Rail Research UK for funding this work.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 203.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.