312
Views
11
CrossRef citations to date
0
Altmetric
Original scientific papers

Long-term field evaluation and analysis of top-down cracking for Superpave projects

, , &
Pages 831-846 | Published online: 13 Aug 2013
 

Abstract

A field evaluation of cracking performance was undertaken for 10 pavement sections as part of a long-term Superpave monitoring project. It was found that top-down cracking was the major type of cracking. Also, moisture damage was identified in four of these sections. An analysis based on the energy ratio (ER) parameter showed that the mixtures that were affected by moisture generally exhibited a much faster reduction in the ER (indicating reduced fracture resistance) than those that were not. Further analysis of cracking performance was conducted using the enhanced hot mix asphalt fracture mechanics-based pavement performance model (HMA-FM-E). The HMA-FM-E is capable of predicting the entire process of top-down cracking from the onset of cracking until pavement failure, and thus provides more valuable information that may better assist material and pavement engineers to optimise their designs. The results showed that the predictions for sections not affected by moisture generally agreed well with field observations. More importantly, it was identified that the key to further improve the accuracy of the performance model is to more accurately predict changes of mixture properties affected by moisture in addition to oxidative ageing.

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 204.00 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.