A LiDAR-Based Backfill Monitoring System

A backfill system in underground mines supports the walls and roofs of mined-out areas and improves the structural integrity of mines. However, there has been a significant gap in the visualization and monitoring of the backfill progress. To better observe the process of the paste backfill material...

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Bibliographic Details
Published in:Applied sciences 2024-12, Vol.14 (24), p.12073
Main Authors: Xu, Xingliang, Huang, Pengli, He, Zhengxiang, Zhao, Ziyu, Bi, Lin
Format: Article
Language:English
Subjects:
Algorithms
backfill progress
backfill simulation
backfill visualization
Cement hydration
Coal mining
Design
Digitization
International economic relations
Lasers
LiDAR sensor
Mineral industry
Mines
Mines and mineral resources
Mining industry
Monitoring systems
Optical radar
paste backfill
point cloud processing
Remote sensing
Sensors
Tunnels
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Summary:A backfill system in underground mines supports the walls and roofs of mined-out areas and improves the structural integrity of mines. However, there has been a significant gap in the visualization and monitoring of the backfill progress. To better observe the process of the paste backfill material filling the tunnels, a LiDAR-based backfill monitoring system is proposed. As long as the rising top surface of the backfill material enters the LiDAR range, the proposed system can compute the plane coefficient of this surface. The intersection boundary of the tunnel and the backfill material can be obtained by substituting the plane coefficient into the space where the initial tunnel is located. A surface point generation and slurry point determination algorithm are proposed to obtain the point cloud of the backfill body based on the intersection boundary. After Poisson surface reconstruction and volume computation, the point cloud model is reconstructed into a 3D mesh, and the backfill progress is digitized as the ratio of the backfill body volume to the initial tunnel volume. The volumes of the meshes are compared with the results computed by two other algorithms; the error is less than 1%. The time to compute a set of data increases with the amount of data, ranging from 8 to 20 s, which is sufficient to update a set of data with a tiny increase in progress. As the digitized results update, the visualization progress is transmitted to the mining control center, allowing unexpected problems inside the tunnel to be monitored and addressed based on the messages provided by the proposed system.
ISSN:2076-3417
2076-3417
DOI:10.3390/app142412073