357
Views
2
CrossRef citations to date
0
Altmetric
Articles

Development of framework of the predictive performance-engineered mix design procedure for asphalt mixtures

ORCID Icon, ORCID Icon, &
Pages 4190-4205 | Received 21 Aug 2020, Accepted 28 May 2021, Published online: 09 Jun 2021
 

ABSTRACT

This paper presents a new asphalt mixture design framework for predictive performance-engineered mix design (PEMD) and the theory and procedures that underlie the proposed design method. This method allows pavement engineers to determine an optimized mix design based on the predicted pavement/mixture performance for all possible combinations of a given set of component materials (i.e. aggregate and binder) in the design space. The proposed PEMD process is based on the ‘performance-volumetrics relationship’ (PVR) concept. The calibration of the PVR is based on the mixture performance predicted from FlexPAVETM, a three-dimensional finite element program that performs viscoelastic analysis under moving loads, using the material properties of the asphalt mixture in question at widely spaced volumetric conditions. Three mixtures of different nominal maximum aggregate sizes and binder types are used to demonstrate the proposed PEMD process. Finally, the predicted performance results obtained from different design approaches are compared.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Federal Highway Administration [Grant Number DTFH61-08-H-00005].

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.