Model development for moisture content and density prediction for non-dry asphalt concrete using GPR data

ABSTRACT

Ground-penetrating radar (GPR) is a non-destructive testing technique used to assess various civil structures, including pavements. It may be applied to predict asphalt concrete (AC) layer thicknesses and dry densities. Because moisture may exist in in-service AC pavement layers and hinder the density prediction, quantifying moisture content in AC would improve its layer-density prediction. In addition, quantifying moisture content of cold recycled pavements would allow monitoring of the curing process of the treatment. Hence, the proper time for opening roads to traffic and/or placing an overlay could be identified. In this study, data were collected from both field cold-recycling projects and laboratory test slabs. The combined dataset was used to correlate measured moisture content to the dielectric constant of AC mixes. The Al-Qadi–Cao–Abufares (ACA) model was derived based on the electromagnetic mixing theory. This model is a modification to the Al-Qadi–Lahouar–Leng (ALL) model; it incorporates the effect of moisture on the bulk dielectric constant to predict density of non-dry AC. The model predicts AC density with an average error of 2% and also predicts moisture content with a root mean square error of 0.5%.

KEYWORDS:

• Asphalt concrete (AC)
• ground-penetrating radar (GPR)
• dielectric constant
• moisture content
• cold recycling
• electromagnetic mixing theory

Acknowledgements

This publication is based on the results of research conducted in cooperation with Illinois Center for Transportation (ICT), Illinois Department of Transportation (IDOT), US Department of Transportation, and Federal Highway Administration. The authors would like to acknowledge assistance provided by many individuals, including John Senger, technical review panel (TRP) chair; Steve Worsfold and Ron Wagoner, TRP members; Jason Wielinski from Heritage Research Group; Greg Renshaw, Uthman M. Ali, ICT’s senior research engineers; along with Siqi Wang, Punit Singhvi, Javier García Mainieri, José Julian Rivera-Perez, Zehui Zhu, Watheq Sayeh, Egemen Okte, Yusra Alhadidi, Gafar Sulaiman, Aravind Ramakrishnan, and Mohammad Fakhreddine – all of whom are University of Illinois at Urbana–Champaign graduate students. The contents of this paper reflect the view of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of ICT or IDOT. This paper does not constitute a standard, specification, or regulation.

Disclosure statement

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