隧道围岩分级方法研究进展及“人工智能+”应用动态

doi:10.3973/j.issn.2096-4498.2023.04.003

中国科学引文数据库（CSCD）来源期刊
中文核心期刊中文科技核心期刊
Scopus RCCSE中国核心学术期刊
美国EBSCO数据库俄罗斯《文摘杂志》
《日本科学技术振兴机构数据库（中国）》

隧道建设（中英文） ›› 2023, Vol. 43 ›› Issue (4): 563-582.DOI: 10.3973/j.issn.2096-4498.2023.04.003

隧道围岩分级方法研究进展及“人工智能+”应用动态

申艳军1, 2，吕游1，李曙光3, *，张津源4，马文1，张蕾1，许振浩4，黄勇5

(1. 西安科技大学地质与环境学院，陕西西安710054； 2. 长安大学地质工程与测绘学院，陕西西安710064； 3. 中铁二十局集团有限公司, 陕西西安710016； 4. 山东大学齐鲁交通学院，山东济南250061； 5. 中铁第一勘察设计院集团有限公司，陕西西安710043)

出版日期:2023-04-20 发布日期:2023-05-23
作者简介:申艳军(1984—)，男，河南安阳人，2012年毕业于中国地质大学（武汉），岩土工程专业，博士，教授，现从事隧道工程与地质灾害研究工作。Email： shenyj@xust.end.cn。*通信作者：李曙光， Email： lssgg2015@163.com。

Research Progress of Tunnel Surrounding Rock Classification Method and Application Trend of "Artificial Intelligence+"

SHEN Yanjun1, 2, LYU You1, LI Shuguang3, *, ZHANG Jinyuan4, MA Wen1, ZHANG Lei1, XU Zhenhao4, HUANG Yong5

(1.College of Geology and Environment,Xi′an University of Science and Technology,Xi′an 710054,Shaanxi,China; 2.School of Geological Engineering and Geomatics, Chang′an University,Xi′an 710064,Shaanxi, China;3.China Railway 20th Bureau Group Co., Ltd., Xi′an 710016,Shaanxi,China;4.School of Qilu Transportation,Shandong University,Jinan 250061,Shandong,China; 5.China Railway First Survey and Design Institute Group Ltd.,Xi′an 710043,Shaanxi,China)

Online:2023-04-20 Published:2023-05-23

摘要/Abstract

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。

关键词: 隧道工程, 围岩分级, 钻爆法, TBM法, 人工智能+

Abstract:

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.

Key words: tunnel engineering, surrounding rock classification, drilling and blasting method, TBM method, artificial intelligence+

申艳军, 吕游, 李曙光, 张津源, 马文, 张蕾, 许振浩, 黄勇. 隧道围岩分级方法研究进展及“人工智能+”应用动态[J]. 隧道建设, 2023, 43(4): 563-582.

SHEN Yanjun, LYU You, LI Shuguang, ZHANG Jinyuan, MA Wen, ZHANG Lei, XU Zhenhao, HUANG Yong. Research Progress of Tunnel Surrounding Rock Classification Method and Application Trend of "Artificial Intelligence+"[J]. Tunnel Construction, 2023, 43(4): 563-582.

[1]	王志坚, 童建军. Overview and Prospect of Researches on Intelligent Engineering Technologies for Tunnels Constructed by Drilling-and-Blasting Method（钻爆法隧道智能建造技术研究综述与展望）[J]. 隧道建设, 2023, 43(4): 529-548.
[2]	郭卫社, 洪开荣, 高攀, 李凤远, 李叔敖, 赵旭. Status Quo and Prospects of Tunnel Intelligent Construction in China（我国隧道智能建造技术发展与展望）[J]. 隧道建设, 2023, 43(4): 549-562.
[3]	郑永光, 张娜, 刘扬扬, 孙春焕, 荆留杰, 李鹏宇. 基于多模态控制策略的TBM掘进参数预测模型研究[J]. 隧道建设, 2023, 43(4): 583-591.
[4]	梁连, 方焘, 方立建, 肖炎冬. 基于PIV技术的隧道施工引起的地层变形规律试验研究[J]. 隧道建设, 2023, 43(4): 625-633.
[5]	王建辉, 高萌, 王润生, 阳凯, 黄长鑫, 张连震, 张青, 赵耀, 韩旭, 赵军云, 王安妮, 李志鹏, 翁贤杰. 极小覆岩厚度条件下地铁隧道逆台阶法开挖稳定性分析[J]. 隧道建设, 2023, 43(4): 690-697.
[6]	肖明清, 徐晨, 谢壁婷, 陈文鹏, 王克金. Research on Design Technology of Sequential Supporting for High-Speed Railway Tunnel Constructed by Drilling-and-Blasting Method（高速铁路钻爆法隧道分序支护设计技术研究）[J]. 隧道建设, 2023, 43(3): 369-382.
[7]	贾连辉, 陈帅, 贾正文, 荆留杰, 陈强, 牛孔肖. Research on Intelligent Construction Systems andKey Technologies for Drilling-and-Blasting Tunnels（钻爆法隧道智能建造体系及关键技术研究） [J]. 隧道建设, 2023, 43(3): 392-407.
[8]	杨振兴, 曾垂刚, 李凤远, 张凯, 王利明, 张敏. 基于大数据的泥水盾构排渣量测算方法优化研究[J]. 隧道建设, 2023, 43(3): 417-422.
[9]	王少飞, 陈树汪, 曾泽润, 喻佳, 赵倩, 王珊珊. 活动断裂影响区隧道高地应力软岩大变形特征分析及支护体系研究[J]. 隧道建设, 2023, 43(1): 141-150.
[10]	王立新, 王强, 钟宇健, 徐硕硕, 邱军领, 赖金星, 汪珂. 基于BP神经网络的近接施工对地铁结构影响反演分析[J]. 隧道建设, 2022, 42(S2): 102-113.
[11]	王祥, 赵祎睿, 宋战平, 赵克明, 苏婉莹. 基于掘进性能的悬臂掘进机施工围岩分级方法[J]. 隧道建设, 2022, 42(S1): 197-204.
[12]	潘红伟, 郭德赛, 宋战平, 徐甜, 张玉伟, 丁立波. 基于N-K模型的隧道施工事故多风险因素耦合分析[J]. 隧道建设, 2022, 42(9): 1537-1545.
[13]	梁铭, 彭浩, 解威威, 宋冠先, 朱孟龙, 张亚飞. 基于超前钻探及优化集成算法的隧道围岩双层质量评价[J]. 隧道建设, 2022, 42(8): 1443-1452.
[14]	马伟斌, 王志伟. Research on Prefabrication and Assembly of Railway Tunnels Excavated Using Drilling-and-Blasting and Prospects of Intelligent Construction（钻爆法铁路隧道预制装配化建造研究及智能建造展望）[J]. 隧道建设, 2022, 42(7): 1119-1134.
[15]	蒋雅君, 喻良敏, 王萃娟, 彭涛, 王虎群. 基于杨木木屑改性的隧道工程透水砂浆性能研究[J]. 隧道建设, 2022, 42(7): 1196-1206.

隧道围岩分级方法研究进展及“人工智能+”应用动态

Research Progress of Tunnel Surrounding Rock Classification Method and Application Trend of "Artificial Intelligence+"

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价