Review and Evaluation of the Prediction Methods for Voids in the Mineral Aggregate in Asphalt Mixtures
Publication: Journal of Materials in Civil Engineering
Volume 35, Issue 3
Abstract
The percentage of voids in mineral aggregates (VMA) is an important consideration in the design of asphalt mixtures. Accurate and precise prediction of VMA is critical in pavement engineering. This study reviewed and evaluated current VMA prediction methods. From the perspective of treating aggregate gradation, the methods were classified into four categories: the overall gradation prediction method (OGPM), gradual gradation prediction method (GGPM), indirect parameter prediction method (IPPM), and theoretical derivation method (TDM). Fifty-four groups of published test data from previous studies were used to evaluate the prediction results of the models, and the precision and accuracy of the prediction results were compared. From the perspective of the completeness of the modeling parameters and the theoretical foundation, the reasons for the differences in prediction results with different methods were analyzed. It was concluded that the TMD has a solid theoretical basis, especially the Yu method in TMD exhibited the best prediction precision and accuracy. Some modifications to the current methods were suggested, such as incorporating other factors affecting VMA and optimizing the particle shape indicators.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This research was funded by the National Natural Science Foundation of China (Grant No. 52008365), the Fundamental Research Funds for Central Universities of Civil Aviation University of China (Project No. 3122018C018). The author would like to acknowledge their financial support.
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Journal of Materials in Civil Engineering
Volume 35 • Issue 3 • March 2023
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© 2022 American Society of Civil Engineers.
History
Received: Sep 14, 2021
Accepted: May 6, 2022
Published online: Dec 22, 2022
Published in print: Mar 1, 2023
Discussion open until: May 22, 2023
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