Journal Published Online: 14 March 2025
Volume 53, Issue 2

Simple Analytical Procedure to Estimate Optimum Asphalt Content

CODEN: JTEVAB

Abstract

Asphalt concrete mix design is an iterative process that involves material selection, testing, and evaluation to meet the design criteria. Traditional mix design procedures require many samples to establish the aggregate structure and optimum asphalt content. For a trial aggregate blend, the Marshall and Superpave mix design methods require at least 17 and 12 samples, respectively, for determination of the optimum asphalt content portion of the mix design. This article proposes a new methodology for estimating the optimum asphalt content of a mix. The proposed procedure is based on the same equations that are currently used in the traditional asphalt mix design procedures but requires fewer samples (4 or 5). It is based on the premise that although both compaction level and aggregate structure control the optimum asphalt content and volumetrics of a mix, in practice, only the latter is used by designers to meet mix design requirements, including the compaction level. The procedure is based on the estimation of the asphalt content required to achieve a bulk specific gravity of a mix that meets simultaneously the selected design air voids and voids in mineral aggregate. To validate the procedure, a mix design was developed in the lab and two additional datasets with existing designs were compiled from producers and public sources and analyzed. The validation process with the lab sample demonstrated the effectiveness of the procedure by eliminating the first selected aggregate blend and estimated asphalt content based on only five samples. The validation with existing data sources also showed excellent agreement.

Author Information

Rayapeddi Kumar, Jayanth Kumar
Jas. W. Glover, Ltd., Honolulu, HI, USA
Archilla, Adrian Ricardo
Department of Civil, Environmental, and Construction Engineering, University of Hawaii at Manoa, Honolulu, HI, USA
Pages: 16
Price: $25.00
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Details
Stock #: JTE20240242
ISSN: 0090-3973
DOI: 10.1520/JTE20240242