Large underground structures geometry evaluation based on point cloud data analysis

Abstract

As a result of underground exploitation performed by blasting technique or mechanical excavation large underground structures are created. Usually, due to the mining and geological conditions of the exploitation, obtained excavations geometry may be far from this assumed at the design stage. The geometry of the excavations is of key importance in several important stages of the technological process. Taking into account economic issues, deviations from the planned cross-section of the excavation generate additional, often considerable operating costs. If the cross-sectional area is greater than the assumed one, the ore may be impoverished, since the dimensions of mining faces are usually following the dimensions of the deposit zone. On the other hand, when the cross-sectional area is smaller than assumed, to maintain the proper operation of the mining plant, it is necessary to re-cut the excavation. It is particularly important from a mine ventilation point of view, as it generates problems in supplying adequate amounts of air to work areas, as well as transport, causing a significant problem for moving machines. Underground mine workings tend to change over time as a result of processes taking place in the rock mass aimed at establishing a new stress and strain state of rock mass after the excavation has been made. Considering the extent of underground mining structures, there is a need to develop a method that would allow the assessment of the geometry of workings, both newly created and existing ones, with the assumption of quick acquisition of measurement data, their processing, and analysis. Addressing those needs, in this paper, the authors are presenting a method based on a 3D point cloud of excavation geometry obtained thanks to measurements with the use of Terrestrial Laser Scanner/LiDAR, the procedure used to convert the point cloud into a spatial model of excavations, as well as data processing procedures used to extract informative parameters describing the geometry. Several statistical parameters are proposed for feature analysis along the tunnel length.