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

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 densi...

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Bibliographic Details
Published in:The international journal of pavement engineering 2023-12, Vol.24 (1)
Main Authors: Abufares, Lama, Cao, Qingqing, Al-Qadi, Imad L.
Format: Article
Language:English
Subjects:
Asphalt
Asphalt concrete (AC)
Bulk density
cold recycling
Concrete
dielectric constant
electromagnetic mixing theory
Ground penetrating radar
ground-penetrating radar (GPR)
Laboratory tests
Moisture content
Moisture effects
Nondestructive testing
Pavements
Permittivity
Thickness
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Summary: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%.
ISSN:1029-8436
1477-268X
DOI:10.1080/10298436.2023.2189720