Artificial intelligence-aided semi-automatic joint trace detection from textured three-dimensional models of rock mass  

作　　者：Seyedahmad Mehrishal Jineon Kim Yulong Shao Jae Joon Song 

机构地区：[1]Department of Energy Systems Engineering(SNU Division of Integrated Graduate Education for Next-Generation Energy),Seoul National University,Seoul,08826,South Korea [2]Department of Engineering,University of Mohaghegh Ardabili,Ardabil,5619911367,Iran [3]Department of Energy Systems Engineering,Seoul National University,Seoul,08826,South Korea

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2025年第4期1973-1985,共13页岩石力学与岩土工程学报(英文)

基　　金：supported by grants from the Human Resources Development program (Grant No.20204010600250);the Training Program of CCUS for the Green Growth (Grant No.20214000000500)by the Korea Institute of Energy Technology Evaluation and Planning (KETEP);funded by the Ministry of Trade,Industry,and Energy of the Korean Government (MOTIE).

摘　　要：It is of great importance to obtain precise trace data,as traces are frequently the sole visible and measurable parameter in most outcrops.The manual recognition and detection of traces on high-resolution three-dimensional(3D)models are relatively straightforward but time-consuming.One potential solution to enhance this process is to use machine learning algorithms to detect the 3D traces.In this study,a unique pixel-wise texture mapper algorithm generates a dense point cloud representation of an outcrop with the precise resolution of the original textured 3D model.A virtual digital image rendering was then employed to capture virtual images of selected regions.This technique helps to overcome limitations caused by the surface morphology of the rock mass,such as restricted access,lighting conditions,and shading effects.After AI-powered trace detection on two-dimensional(2D)images,a 3D data structuring technique was applied to the selected trace pixels.In the 3D data structuring,the trace data were structured through 2D thinning,3D reprojection,clustering,segmentation,and segment linking.Finally,the linked segments were exported as 3D polylines,with each polyline in the output corresponding to a trace.The efficacy of the proposed method was assessed using a 3D model of a real-world case study,which was used to compare the results of artificial intelligence(AI)-aided and human intelligence trace detection.Rosette diagrams,which visualize the distribution of trace orientations,confirmed the high similarity between the automatically and manually generated trace maps.In conclusion,the proposed semi-automatic method was easy to use,fast,and accurate in detecting the dominant jointing system of the rock mass.

关 键 词：Automatic trace detection Digital joint mapping Rock discontinuities characterization Three-dimensional(3D)trace network 

分 类 号：TU45[建筑科学—岩土工程]