A critical review of automated extraction of rock mass parameters using 3D point cloud data

Jiayao Chen, Qian Fang, Dingli Zhang, Hong-wei Huang
{"title":"A critical review of automated extraction of rock mass parameters using 3D point cloud data","authors":"Jiayao Chen, Qian Fang, Dingli Zhang, Hong-wei Huang","doi":"10.1093/iti/liad005","DOIUrl":null,"url":null,"abstract":"\n In this paper, a critical review is conducted to understand the current research status of the quantification technology for obtaining three-dimensional (3D) point cloud information of rock mass and extracting structural key information, which is a major challenge and problem facing rock engineering. The timely and accurate acquisition of rock mass data and fine characterization of rock mass parameters can avoid unnecessary personnel injury and property damage. Firstly, the methods of point cloud information acquisition and structural information extraction are systematically summarized and classified. Then, various existing methods are analysed for their advantages and disadvantages. Based on this analysis, the future development direction of relevant technologies is proposed to improve the level of acquisition of 3D information of rock mass and the level of extraction of key information of rock mass. The results indicate that rock mass point cloud information acquisition technology can be classified into two types: laser point cloud acquisition and image reconstruction based on Structure from Motion (SfM) algorithm. Rock mass structural information can be classified into rock mass structural planes and their attitudes, rock mass traces and their geometric parameters, and other rock mass parameters, including structural plane roughness, spacing, and block characteristics, etc. Different acquisition technologies and feature extraction methods have their own advantages, disadvantages, and applicable ranges. Therefore, a comprehensive selection of various evaluation methods should be made based on specific engineering characteristics and existing data situations in practice.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Transportation Infrastructure","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/iti/liad005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, a critical review is conducted to understand the current research status of the quantification technology for obtaining three-dimensional (3D) point cloud information of rock mass and extracting structural key information, which is a major challenge and problem facing rock engineering. The timely and accurate acquisition of rock mass data and fine characterization of rock mass parameters can avoid unnecessary personnel injury and property damage. Firstly, the methods of point cloud information acquisition and structural information extraction are systematically summarized and classified. Then, various existing methods are analysed for their advantages and disadvantages. Based on this analysis, the future development direction of relevant technologies is proposed to improve the level of acquisition of 3D information of rock mass and the level of extraction of key information of rock mass. The results indicate that rock mass point cloud information acquisition technology can be classified into two types: laser point cloud acquisition and image reconstruction based on Structure from Motion (SfM) algorithm. Rock mass structural information can be classified into rock mass structural planes and their attitudes, rock mass traces and their geometric parameters, and other rock mass parameters, including structural plane roughness, spacing, and block characteristics, etc. Different acquisition technologies and feature extraction methods have their own advantages, disadvantages, and applicable ranges. Therefore, a comprehensive selection of various evaluation methods should be made based on specific engineering characteristics and existing data situations in practice.
基于三维点云数据的岩体参数自动提取技术综述
本文综述了岩体三维点云信息获取与结构关键信息提取的量化技术的研究现状,这是岩石工程面临的重大挑战和难题。及时准确地获取岩体数据,对岩体参数进行精细表征,可以避免不必要的人员伤害和财产损失。首先对点云信息获取方法和结构信息提取方法进行了系统总结和分类。然后,分析了现有的各种方法的优缺点。在此基础上,提出了提高岩体三维信息获取水平和岩体关键信息提取水平等相关技术的未来发展方向。结果表明,岩体点云信息获取技术可分为激光点云获取和基于SfM (Structure from Motion)算法的图像重建两种。岩体结构信息可分为岩体结构面及其姿态、岩体轨迹及其几何参数以及其他岩体参数,包括结构面粗糙度、间距、块体特征等。不同的采集技术和特征提取方法各有优缺点和适用范围。因此,应结合具体工程特点和实践中已有的数据情况,对各种评价方法进行综合选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信