Accelerated Earth-Rockfill Dam Compaction by Collaborative Operation of Unmanned Roller Fleet
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 7
Abstract
The use of unmanned rollers for the compaction of the earth-rockfill dams is of great significance. However, collaborative control of multiple unmanned rollers at the construction site, enabling each unmanned roller to operate on the optimal rolling path with optimal rolling parameters to improve compaction efficiency, is an unaddressed challenge. There are three technical barriers, i.e., how to plan the optimal rolling path, how to determine the optimal rolling parameters, and how to control the unmanned roller fleet (URF) based on the optimal rolling plan. This study aims to solve these technical barriers. First, a Stripe-genetic algorithm (GA) path planning method that can balance the task load of multiple unmanned rollers is proposed. Second, a spatial global rolling parameter decision (SGRPD) method is proposed to maximize the stripe compaction efficiency. Finally, a URF-based intelligent compaction system composed of the control module, communication module, remote server, and cloud platform integrated with Stripe-GA and SGRPD is proposed. Field experiments conducted on the construction site of a 295-m high earth-rockfill dam have verified the effectiveness and advantages of the proposed system. The results demonstrated that the proposed system could operate multiple unmanned rollers to achieve higher compaction quality and efficiency by compaction with optimal rolling path and parameters. Compared with the monitoring-based manual compaction method, its compaction quality and efficiency are improved by 2.57% and 24.18%, respectively. The proposed system has the potential to be applied to earthwork constructions, such as roads and airports.
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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 including the maps for path planning, the compaction parameter optimization results, the path planning results, and codes.
Acknowledgments
This work was supported by the Yalong River Joint Funds of the National Natural Science Foundation of China (Grant No. U1965207), the National Natural Science Foundation of China (Grant No. 51779169), and the Tianjin Artificial Intelligence Project of China (Grant No. 2020YJSZXB05).
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Journal of Construction Engineering and Management
Volume 148 • Issue 7 • July 2022
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© 2022 American Society of Civil Engineers.
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Received: Sep 9, 2021
Accepted: Jan 9, 2022
Published online: Apr 20, 2022
Published in print: Jul 1, 2022
Discussion open until: Sep 20, 2022
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