631
Views
26
CrossRef citations to date
0
Altmetric
Articles

Packing theory-based framework for evaluating resilient modulus of unbound granular materials

, , &
Pages 689-697 | Received 31 Mar 2012, Accepted 17 Oct 2013, Published online: 29 Nov 2013
 

Abstract

Enhancing the quality of granular layers is fundamental to optimise the structural performance of the pavements. The objective of this study is to investigate whether previously developed packing theory-based aggregate parameters can evaluate the resilient modulus of unbound granular materials. In this study, 19 differently graded unbound granular materials from two countries (USA and Sweden) were evaluated. This study validated both porosity of primary structure (PS) and contact points per particle (coordination number) as key parameters for evaluating the resilient modulus of unbound granular materials. This study showed that decreasing the PS porosity – higher coordination number – calculated based on the proposed gradation model, yields higher resilient modulus. Good correlation was observed between the proposed packing parameters and resilient modulus of several types of aggregates. The packing theory-based framework successfully recognised granular materials that exhibited poor performance in terms of resilient modulus.

Acknowledgements

The authors would like to extend their sincere gratitude to the Swedish Road Administration (Trafikverket) for providing the financial support for the project.

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 225.00 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.