中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Development of deep-underground engineering structures via 2D and 3D RQD prediction using non-invasive CSAMT

文献类型:期刊论文

作者Hasan, Muhammad2,3,4,5; Su, Lijun3,4,5; Cui, Peng1,3,4,5; Shang, Yanjun2,3
刊名SCIENTIFIC REPORTS
出版日期2025-01-09
卷号15期号:1页码:18
关键词Rock quality designation (RQD) Geotechnical engineering Rock mass quality Controlled-source audio-frequency magnetotellurics (CSAMT) Engineering structure
ISSN号2045-2322
DOI10.1038/s41598-025-85626-7
英文摘要

The stability criterion based on the characterization of rock masses can be used to advance deep underground engineering projects. A key geomechanical criterion in geotechnical engineering is rock quality designation (RQD), which assesses risk for engineering design success criteria. Time, cost, and credibility constraints make it difficult to accurately estimate RQD. Point-scale data makes engineering design less precise and confusing, while traditional drilling for RQD estimation are expensive and time-consuming. An innovative geophysical approach to 2D and 3D RQD estimation is presented in this study. It provides easier, faster, and cheaper access to geomechanical volumetric data. So far, no other work has used non-invasive CSAMT to estimate RQD over 1 km depth in a highly diverse rock setting. The suggested approach provides a more precise and thorough evaluation of the rock's integrity for the effective installation of the neutrino detector 700 m below ground. The results are significant because they help us make sense of complicated geological situations, estimate the likelihood of early collapse, and build deep underground structures safely, steadily, and affordably. Our approach leads to more objective indices, helps in the development of more accurate geotechnical structures, and reduces inconsistencies between appropriate geomechanical models and sparse data.

WOS关键词ELECTRICAL-RESISTIVITY ; GEOPHYSICAL METHODS ; WAVE VELOCITY ; ROCK ; INDEX ; EXPLORATION ; PARAMETERS ; STRENGTH ; TAIWAN ; REGION
资助项目The National Natural Science Foundation of China's Research Fund for International Young Scientists (RFIS-I) ; National Natural Science Foundation of China ; State Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences ; International Science and Technology Cooperation Program of Shanghai Cooperation Organization, Science and Technology Department, Xinjiang, China
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001395002400043
出版者NATURE PORTFOLIO
资助机构The National Natural Science Foundation of China's Research Fund for International Young Scientists (RFIS-I) ; National Natural Science Foundation of China ; State Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences ; International Science and Technology Cooperation Program of Shanghai Cooperation Organization, Science and Technology Department, Xinjiang, China
源URL[http://ir.imde.ac.cn/handle/131551/58669]  
专题成都山地灾害与环境研究所_山地灾害与地表过程重点实验室
通讯作者Hasan, Muhammad; Su, Lijun
作者单位1.Inst Geog Sci & Nat Resources Res CAS, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, State Key Lab Lithospher & Environm Coevolut, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.CAS HEC, Joint Res Ctr Earth Sci, Islamabad, Pakistan
5.Chinese Acad Sci, Inst Mt Hazards & Environm, State Key Lab Mt Hazards & Engn Resilience, Chengdu 610299, Peoples R China
推荐引用方式
GB/T 7714
Hasan, Muhammad,Su, Lijun,Cui, Peng,et al. Development of deep-underground engineering structures via 2D and 3D RQD prediction using non-invasive CSAMT[J]. SCIENTIFIC REPORTS,2025,15(1):18.
APA Hasan, Muhammad,Su, Lijun,Cui, Peng,&Shang, Yanjun.(2025).Development of deep-underground engineering structures via 2D and 3D RQD prediction using non-invasive CSAMT.SCIENTIFIC REPORTS,15(1),18.
MLA Hasan, Muhammad,et al."Development of deep-underground engineering structures via 2D and 3D RQD prediction using non-invasive CSAMT".SCIENTIFIC REPORTS 15.1(2025):18.

入库方式: OAI收割

来源:成都山地灾害与环境研究所

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。