Structural planes recognition and occurrence statistics information collection method for high rock slopes

JIANG Shuihua; YU Qi; HUANG He; CHANG Zhilu; MENG Jingjing

doi:10.13272/j.issn.1671-251x.2024060021

Volume 50 Issue 7

Jul. 2024

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Journal of Mine Automation > 2024 > 50(7): 156-164. > DOI: 10.13272/j.issn.1671-251x.2024060021

JIANG Shuihua, YU Qi, HUANG He, et al. Structural planes recognition and occurrence statistics information collection method for high rock slopes[J]. Journal of Mine Automation，2024，50（7）：156-164. DOI: 10.13272/j.issn.1671-251x.2024060021

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Structural planes recognition and occurrence statistics information collection method for high rock slopes

1.
School of Infrastructure Engineering, Nanchang University, Nanchang 330031, China
2.
China Railway Water Conservancy and Hydropower Planning and Design Group Co., Ltd., Nanchang 330029, China
3.
School of Resources and Environment, Nanchang University, Nanchang 330031, China

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Received Date: June 05, 2024
Revised Date: July 20, 2024
Available Online: August 01, 2024

Graphical Abstract

Abstract

Abstract

Accurately recognizing the structural planes of high rock slopes and obtaining occurrence information are important prerequisites for conducting slope stability analysis. Unmanned aerial vehicle photogrammetry technology provides the possibility to solve the problem of accurate surveying of high slope structural planes. But it lacks efficient and accurate image post-processing methods. The existing research has not considered the uncertainty of structural plane occurrence information features, resulting in poor accuracy and efficiency in structural plane recognition. In order to solve the above problems, taking a high slope of an open-pit mine in Nanchang City, Jiangxi Province as the research background, an integrated method for recognizing structural planes and collecting occurrence information by integrating unmanned aerial vehicle photography, post-processing algorithms, and statistical analysis is proposed. Firstly, the method obtains surface images of the slope using the Phantom 4 Pro V2.0 unmanned aerial vehicle. Secondly, the method uses Context Capture software for processing, and the high-density 3D point cloud data is obtained. Secondly, the K-nearest neighbor (KNN) algorithm is used to determine the number of nearest neighbor points to construct a set of similar points. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for clustering analysis to recognize slope structural planes, obtain structural plane occurrence information, and perform statistical feature analysis. Finally, comparative verification is conducted through on-site survey data. The results show that this method can quickly obtain complete high-density point cloud data, accurately and efficiently recognize most structural planes of high rock slopes. The recognition results are basically consistent with the actual situation of slope engineering sites. This method can obtain information on the number, occurrence, and statistical features of high slope structural planes. The probability distribution of most structural plane dip angles and inclinations fits well with the measured data, providing an important data source for the construction of high slope fracture network models and stability analysis.

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