Evaluation of Factors Influencing the Compaction Characteristic of Recycled Aggregate Asphalt Mixture
Publication: Journal of Materials in Civil Engineering
Volume 35, Issue 9
Abstract
Aggregate and air void distribution determined by compaction commonly affects damage appearance and development inside asphalt mixture and is related to asphalt pavement durability and quality. The main objective of this study is to investigate the recycled aggregate (RA) effect on asphalt mixture compaction behavior under different engineering conditions. First, the aggregate fragmentation caused by compaction effort was simulated using the superpave gyratory compactor. In this regard, the influences of aggregate type and RA content were investigated. Second, the indoor experiment scheme was determined using the Taguchi method to obtain compaction data of recycled aggregate asphalt mixture (RAAM). Finally, a genetic algorithm-based backpropagation (GA-BP) artificial neural network (ANN) model using the 216 data sets of the indoor experiment was developed to predict and explore the relative contribution of engineering-conditions-related parameters to RAAM compaction difficulty. The results showed that the aggregate particles suffer fragmentation mainly in the early compaction of recycled aggregate asphalt mixture. The effect of RA on aggregate fragmentation during the compaction process is not statistically significant. The 10-14-1 GA-based BP ANN model developed in this study is an effective method in predicting the compaction energy consumption of RAAM with a correlation coefficient of 98.59% and a mean-squared error value of 0.6266. The gradation shape, NMAS, , , and and incorporated content of recycled aggregate have a considerable positive correlation with the compaction difficulty. The limitation of this study is that the compaction difficulty prediction model is developed according to indoor test data. Therefore, the model’s applicability to field pavement projects required further practical verification.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The research was funded by the National Key Research and Development Program of China (Grant No. 2019YFE0116300), the Natural Science Foundation of Jiangsu Province (Grant No. BK20221468), and the Fundamental Research Funds for the Central University (Grant No. 2242021R41163).
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Journal of Materials in Civil Engineering
Volume 35 • Issue 9 • September 2023
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© 2023 American Society of Civil Engineers.
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Received: Oct 17, 2022
Accepted: Feb 7, 2023
Published online: Jun 21, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 21, 2023
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