Moisture Content Prediction Model of Asphalt Mixtures Based on Dielectric Properties
Publication: Journal of Materials in Civil Engineering
Volume 34, Issue 4
Abstract
Moisture damage is one of the important factors affecting the service performance of asphalt pavement. Therefore, the use of nondestructive testing methods to detect the moisture content of asphalt pavement and provide a safety warning has important engineering significance. In order to realize the nondestructive detection of the moisture content of asphalt pavements, through theoretical derivation, this study established four kinds of prediction models of moisture content of asphalt mixtures based on four kinds of composite dielectric models. Four types of asphalt mixtures were prepared, and the relative dielectric constants of asphalt mixtures before and after immersion were measured. After comparing the accuracy, the Rayleigh model was found to have strong applicability for any type of asphalt mixture and could be used as a basic theoretical model for the prediction of asphalt mixture moisture content. In addition, this study obtained the characterization of the warning value of the relative dielectric constant based on the moisture content and set the lowest and highest warning value of the relative dielectric constant of the asphalt mixture as the criterion for judging the moisture content of the asphalt pavement, which has important practical significance in ensuring the service performance of the asphalt pavement and the driving safety of vehicles.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
The authors acknowledge the financial support of the 973 Program of the Ministry of Science and Technology of China (Project No. 2015CB060100) and the Key Research and Development Project of the Science and Technology Department of Hubei Province of China (Project No. 2020BCA085). Special thanks to the 1,000-Youth Elite Program of China for the start-up funds used to purchase laboratory equipment crucial to this research.
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© 2022 American Society of Civil Engineers.
History
Received: Apr 16, 2021
Accepted: Aug 23, 2021
Published online: Jan 20, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 20, 2022
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