616
Views
17
CrossRef citations to date
0
Altmetric
Articles

Optimisation of hot mix asphalt performance based on aggregate selection

Pages 924-940 | Received 30 Dec 2014, Accepted 09 Apr 2015, Published online: 26 Jun 2015
 

Abstract

Researchers over the last four decades have identified and demonstrated the effects of aggregate morphological properties (particularly shape, size distribution, angularity and texture) on the mechanical properties of hot mix asphalt (HMA). Rare studies, however, have clearly established the relationships between the aggregate properties and pavement performance. Therefore, they have not provided methods to optimise aggregate properties at the design stage to improve that performance. This study focuses on understanding the effects of aggregate gradation and type on moisture damage resistance of HMA and on pavement performance as indicated by stiffness and rutting. Results show that basalt aggregate achieves higher moisture susceptibility resistance and stiffness than limestone aggregate. Coarser gradation has the highest permanent deformation, while open gradation 2C provides the lowest moisture damage resistance. Furthermore, dense gradation 4C provides the lowest rutting and the highest stripping resistance. It is indicated that suitable selection of aggregate type and gradation can improve pavement performance and reduce the moisture damage problem of HMA.

Disclosure statement

No potential conflict of interest was reported by the authors.

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.