Intelligent Evaluation Method of Tunnel Rock Mass Quality Based on Machine Vision Algorithm
The site area of No.1 cross-hole of Luluo Tunnel is colluvial rubble soil of Quaternary Holocene series,with dense joints,broken rocks and poor geological conditions.In view of this condition,based on the constructed 3D point cloud model of rock mass structure surface,the statistical analysis method of structural plan occurrence,number of joint groups,roughness and block size is studied,and the application of intelligent characterization of rock mass structure surface in 3D indexes is further improved.The classification results of tunnel surrounding rock are quickly obtained based on the six major structure surface characteristics of rock mass based on machine vision statistics.The results show that the quality of the surrounding rock of the selected tunnel transverse tunnel excavation face is average,and the whole is in level Ⅲ,which is consistent with the geological exploration results,which verifies the reliability of the rock mass quality assessment method based on machine vision algorithm.
tunnelssurrounding rocksquality evaluationmachine vision algorithm3D point cloud modelindicator characteristics
万方数据
维普
