摘要
岩体结构面的产状和位置信息是分析高陡边坡稳定性及确定支护形式的重要依据.为解决传统接触式勘测手段存在的风险高、效率低等问题,提出了基于无人机贴近摄影和聚类算法的高陡边坡结构面自动识别方法.首先采用M210-RTK无人机获取高分辨率数字图像,并利用运动恢复结构算法(Structure From Motion,SFM)生成细节丰富的高陡边坡三维模型和三维点云;再通过K近邻算法(K-Nearest Neighbor,Knn)及PCA主成分分析法,筛选出共面点云集合并确定了结构面的边界范围;最后采用最小二乘法拟合出共面点云的最佳平面方程,以平面方程的法向量方向确定结构面的产状参数.验证实验表明:基于无人机贴近摄影所建立的三维模型精度优于2 cm,倾向和倾角的误差分别小于3°和2°.本方法应用于长沙丁字镇某高陡边坡的结构面识别,成功识别出其优势结构面,并就其对边坡稳定性的影响进行了分析,可为边坡评价和治理提供重要依据.
To analyze the stability and determine the support form of high steep slopes,it is important to con⁃firm the occurrence and location information of rock discontinuities.In order to solve high risk and low efficiency ex⁃isting in the traditional survey method,this paper proposed the new survey method to semi-automatically identify the discontinuities in slopes based on nap-of-the-object photogrammetry and a clustering algorithm.Firstly,M210-RTK was used to obtain high resolution digital images;the structure from motion algorithm was used to generate the de⁃tailed 3D models and 3D point clouds.Then,through the K-Nearest neighbor and principal component analysis algo⁃rithm,the set of coplanar point clouds was selected to determine the boundary range of discontinuities.Finally,the best planar equation of coplanar point cloud is fitted based on the least-squares method,and the normal vector direc⁃tion of the planar equation is the basis for calculating the occurrence parameters.Validation experiments show that the accuracy of the digital surface model is better than 2cm in any direction,and the error in dip and dip direction are better than 3°and 2°,respectively.This method identified rock preferred discontinuities in the abandoned mining area of Changsha,and the influence of preferred discontinuities on slope stability was analyzed,providing the vital foundation for the slope evaluation and treatment.
作者
陈昌富
何旷宇
余加勇
毛凤山
薛现凯
李锋
CHEN Changfu;HE Kuangyu;YU Jiayong;MAO Fengshan;XUE Xiankai;LI Feng(Key Laboratory of Building Safety and Energy Efficiency of Ministry of Education(Hunan University),Changsha 410082,China;College of Civil Engineering,Hunan University,Changsha 410082,China)
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2022年第1期145-154,共10页
Journal of Hunan University:Natural Sciences
基金
国家重点研发计划资助项目(2016YFC0800207)
长沙市科技计划资助项目(kq1907110)。
关键词
无人机
贴近摄影
高陡边坡
结构面
产状识别
unmanned aerial vehicle(UAV)
nap-of-the-object photogrammetry
high steep slope
disconti⁃nuities
occurrence estimation
