Deformation monitoring of roadway surrounding rock is the key link to ensure the safety production of the coal mine. The traditional monitoring method can only obtain the displacement information of discrete measuring points, and it is difficult to fully reflect the spatial distribution characteristics and evolution law of surrounding rock deformation. Based on the engineering background of the extra-thick coal seam roadway in the Yushupo Coal Mine, Shanxi Province, China, this study proposes a set of full-space deformation monitoring methods for roadway surrounding rock based on explosion-proof mobile 3D laser scanning technology. Firstly, a hierarchical denoising method based on improved statistical filtering is established. The quality of point cloud data is effectively improved by region clipping, a connectivity analysis guided by multi-dimensional geometric features and adaptive density threshold three-level processing strategy. Secondly, a hierarchical point cloud registration method combining physical anchor geometric constraints and deep learning patch guided matching is proposed to reduce the registration error to millimeter level. Finally, the deformation evaluation of surrounding rock is carried out by combining the overall deformation identification with the quantitative analysis of local section slices. The engineering application results show that the deformation of the roadway floor is the most significant during the monitoring period, the maximum deformation is 90.0 mm, and the average deformation is 46.9 mm. The maximum deformation of the roof is 35.0 mm, and the convergence of both sides is asymmetric. Compared with the total station, the results show that the maximum displacement error in each direction does not exceed 5 mm, and the standard deviation is within 1.3 mm, which meets the engineering accuracy requirements of coal mine roadway deformation monitoring. This study provides a complete technical scheme for panoramic and high-precision monitoring of surrounding rock deformation in coal mine roadway.
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Chao Gao
Dexing He
Xinqiu Fang
Applied Sciences
China University of Mining and Technology
Shanxi Coal Transportation and Sales Group (China)
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Gao et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d894ad6c1944d70ce059e7 — DOI: https://doi.org/10.3390/app16073156