Development of a model for in-situ non-dry asphalt concrete density prediction using dielectric properties
Abufares, Lama H A
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Permalink
https://hdl.handle.net/2142/115753
Description
Title
Development of a model for in-situ non-dry asphalt concrete density prediction using dielectric properties
Author(s)
Abufares, Lama H A
Issue Date
2022-04-27
Director of Research (if dissertation) or Advisor (if thesis)
Al-Qadi, Imad L.
Department of Study
Civil & Environmental Eng
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
GPR, asphalt concrete, dielectric constant, density, moisture content, EM mixing theory
Abstract
Ground penetrating radar (GPR) is a nondestructive testing technique used on many civil structures, including pavements. It is applied to predict asphalt concrete (AC) layer thicknesses and dry densities. Detecting moisture in AC, which affects the performance of existing and recycled pavements, has been a challenge. Moisture detection would assist in identifying potential problematic spots, so remedial actions may be taken. Knowing moisture content in AC would improve AC density prediction accuracy by GPR. In addition, predicting cold recycling treatment moisture content could help in monitoring the curing process. This would guide decision makers to determine the proper time for opening treated roads to traffic and/or place an overlay. In this study, data were collected from both field cold recycling projects and indoor test slabs. The combined dataset was used to correlate measured moisture content to the dielectric constant of AC mixes and develop prediction models. Al-Qadi Cao Abufares (ACA) model is developed in this study based on the electromagnetic mixing theory. This model is a modification to the Al-Qadi Lahouar Leng (ALL) model; it incorporates moisture effect on the bulk dielectric constant and thus the AC density prediction. The introduced ACA model predicts non-dry AC density with an average error of 2% and predicts moisture content with a root mean square error (RMSE) of 0.5%.
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