Article(id=1314907935248056342, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907832038810534, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2026.02.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756137600000, receivedDateStr=2025-08-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791428081571, onlineDateStr=2026-10-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791428081571, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791428081571, creator=13701087609, updateTime=1791428081571, updator=13701087609, issue=Issue{id=1314907832038810534, tenantId=1146029695717560320, journalId=1235980550691926019, year='2026', volume='46', issue='2', pageStart='1', pageEnd='214', issueExtLink='null', onlineDate='null', pubDate='1774972800000', pubDateStr='2026-04-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791428056964, creator='13701087609', updateTime=1791430468655, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314917947500486818, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907832038810534, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314917947500486819, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907832038810534, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=60, endPage=65, ext={EN=ArticleExt(id=1314907935487131671, articleId=1314907935248056342, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Identification of Unstable Rock Mass in High and Steep Slopes Based on Point Cloud 3D Reconstruction, columnId=1236276106018484431, journalTitle=Mining and Metallurgical Engineering, columnName=MINING, runingTitle=null, highlight=null, articleAbstract=

Due to discontinuousness and incompleteness of point cloud data of potential unstable rock mass in high and steep slopes acquired from scanning equipment, there exist large errors in the extracted curvature feature of slopes and boundaries between unstable and stable rock mass are also blurred. To address those problems, a method for identifying unstable rock mass in high and steep slopes based on point cloud 3D reconstruction is proposed. Kriging interpolation is used in processing sparse or unevenly distributed point cloud data. More continuous and complete point cloud data can be obtained by increasing the density of data points in consideration of spatial correlation, so as to accurately reflect the real geometrical configuration of the slope. The normal vectors and curvatures of the point cloud data are estimated by adopting local surface fitting and k-neighborhood search algorithm, so as to extract the geometric features of slope and reveal the slope morphology and potential unstable areas. The inconsistency index is used to analyze the changes in the geometric features of point cloud data, and a threshold is set to screen edge points, thereby the region with unstable rock mass can be accurately delineated after the boundary between unstable and stable rock mass is clearly defined. The point cloud data within the boundary of the unstable rock mass are obtained by incremental Voronoi diagram, and then a 3D model of point cloud data is constructed for the unstable rock mass, and the risk level of the unstable rock mass is also analyzed with the Slide software. This is the whole identification process for unstable rock mass in high and steep mine slopes. The practical engineering application of this identification method shows that the slope curvature can be estimated with an error less than 0.3%, and the calculated F1 scores in identification of unstable rock mass in each group of slope all remain above 0.99, indicating that such method can bring high quality identification of unstable rock mass.

, authors=Yanxu ZHAO¹, Yang ZHANG¹, Taitang GUO², Tao LI¹, authorsList=Yanxu ZHAO, Yang ZHANG, Taitang GUO, Tao LI, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314907935843647512, articleId=1314907935248056342, tenantId=1146029695717560320, journalId=1235980550691926019, language=CN, title=三维扫描点云重构下高陡边坡危岩体识别方法, columnId=1236276106219811026, journalTitle=矿冶工程杂志, columnName=采矿, runingTitle=null, highlight=null, articleAbstract=

为解决扫描设备获取的高陡边坡潜在危岩体点云数据不连续、不完整导致边坡曲率特征挖掘误差大、危岩体与稳定岩体边界模糊的问题，提出一种基于三维扫描点云重构的高陡边坡危岩体识别方法。该方法使用克里金插值法处理稀疏或分布不均的点云数据，通过考虑空间相关性增加数据点密度，得到更连续和完整的点云数据，以准确反映边坡的真实形状。采用局部表面拟合方法和k-邻域搜索算法，估算点云数据的法向量和曲率，挖掘边坡的几何特征，以揭示边坡形态和潜在不稳定区域。利用不一致性指标分析点云数据的几何特征变化，设定阈值筛选边缘点，从而清晰界定危岩体与稳定岩体的边界，准确划分危岩体范围。基于Voronoi图的增量法建立危岩体边缘轮廓内的点云数据，构建危岩体三维扫描点云数据模型，并运用Slide软件分析危岩体风险等级，完成高陡矿山边坡危岩体识别。工程应用结果表明，本文所提方法计算的边坡曲率估计误差小于0.3%，各组边坡危岩体识别的F1值一直保持在0.99以上，危岩体识别质量高。

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赵彦旭（1991-），男，河南郑州人，助理工程师，主要从事工程抗震工作。E-mail：zhaowp518@sina.com

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赵彦旭（1991-），男，河南郑州人，助理工程师，主要从事工程抗震工作。E-mail：zhaowp518@sina.com

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