摘要
目前,隧道围岩分级方法有评价围岩稳定性分级方法和评价围岩可挖性分级方法2类,分别对应钻爆法施工围岩分级方法和TBM法施工围岩分级方法。据此,围绕现阶段2类方法研究进展予以分别阐述。首先,针对钻爆法施工围岩分级方法,介绍其发展现状和常用分级方法适用条件,并提出3条后续发展建议:1)重视工程因素对围岩分级劣化的定量表征;2)积极探索围岩亚级精细化分级方法;3)推进围岩分级方法集成化体系构建。然后,针对服务于围岩可挖性的TBM法施工围岩分级,梳理基于围岩可挖性及TBM施工适应性的常见围岩分级方法,并着重关注考虑关键地质因素、TBM掘进参数和渣料特征的TBM综合分级方法。其中,可挖性分级建议以TBM净掘进速度、岩体可挖性和现场贯入度指数等参数作为评价指标,而适应性分级则建议依据卡机风险、TBM利用率或施工速度进行评价。最后,介绍“人工智能+”在隧道工程围岩分级方法的应用动态,探索隧道围岩分级集成算法模型、智能化图像处理技术、云计算、智能决策等新方法及新技术在隧道工程围岩分级方法领域的最新应用动态,并提出“人工智能+”在隧道工程围岩分级领域的未来发展方向。
Currently,two classification methods exist for evaluating the stability and excavatability of tunnel surrounding rock.These methods correspond to the surrounding rock classification method by drilling and blasting method and that by TBM method,respectively.In this study,these surrounding rock classification methods are discussed.First,the development status and application conditions of the surrounding rock classification method by drilling and blasting method are reviewed,and suggestions for its future development are provided.These suggestions include paying attention to the quantitative characterization of construction factors on the grading deterioration characteristics of surrounding rock,actively exploring subgrade classification methods of surrounding rock,and establishing an integrated system of surrounding rock classification methods.Next,the common surrounding rock classification methods are summarized used with the TBM method and a comprehensive surrounding rock classification method based on geological factors,TBM tunneling parameters,and slag characteristics is proposed.For excavatability classification,it is suggested to use TBM net tunneling speed,rock mass drivability,and on-site penetration index as evaluation indexes.For adaptability classification,it is suggested to use machine jam risk,TBM utilization rate,or construction speed.Finally,the latest developments in"artificial intelligence+"classification methods for surrounding rock in tunneling are analyzed.This analysis focuses mainly on the application dynamics and development direction of new methods and technologies such as artificial intelligence algorithms,intelligent image processing,cloud computing,and intelligent decision making.The research aims to provide a reference for the construction and intelligent application of the surrounding rock classification system in tunnel engineering.
作者
申艳军
吕游
李曙光
张津源
马文
张蕾
许振浩
黄勇
SHEN Yanjun;LYU You;LI Shuguang;ZHANG Jinyuan;MA Wen;ZHANG Lei;XU Zhenhao;HUANG Yong(College of Geology and Environment,Xi′an University of Science and Technology,Xi′an 710054,Shaanxi,China;School of Geological Engineering and Geomatics,Chang′an University,Xi′an 710064,Shaanxi,China;China Railway 20th Bureau Group Co.,Ltd.,Xi′an 710016,Shaanxi,China;School of Qilu Transportation,Shandong University,Jinan 250061,Shandong,China;China Railway First Survey and Design Institute Group Ltd.,Xi′an 710043,Shaanxi,China)
出处
《隧道建设（中英文）》
CSCD
北大核心
2023年第4期563-582,共20页
Tunnel Construction
基金
中国铁建股份有限公司2021科技研发项目(2021-B10)
中铁二十局集团有限公司2020科技研发项目(YF2000SD01A)。
关键词
隧道工程
围岩分级
钻爆法
TBM法
人工智能+
tunnel engineering
surrounding rock classification
drilling and blasting method
TBM method
artificial intelligence+
