(1. PowerChina Chengdu Engineering Corporation Limited,Chengdu,Sichuan 611130,China;2. School of Civil Engineering,Shandong University,Jinan,Shandong 250061,China;3. School of Engineering and Technology,China University of Geophysics(Beijing),Beijing 100083,China)
Abstract:The existing advanced geological forecast methods consider the integration of multiple source information,but still face issues such as limited participation of geological information and incomplete sources of integrated data. This study proposes a multi-source data fusion method for tunnel advanced geological forecast based on the full-process construction information. It screens and establishes a full-process indicator system,containing 7 major categories and 232 indicators,and a targeted unfavorable geological problem indicator system. Besides,A mapping conversion method of ITV-IRV(indicator test value and indicator risk value),which combine quantitative indicator segment functions,semi-qualitative indicator node interpolation,and qualitative indicator threshold classification,is proposed,as well as a data area segmentation method. Finally,Analytic Hierarchy Process and Huber Weighting method are used for weight analysis. Fuzzy Fusion Theory and so on methods are applied for data fusion and obtaining the risk probabilities of unfavorable geological problems. The results show that:(1) The full-process indicator system and unfavorable geological problem indicator system provide comprehensive advanced geological forecast indicators. (2) The ITV-IRV mapping conversion method,data area segmentation method and indicator weight analysis methods,enable data normalized across indicators and be ready for multi-source data fusion. (3) The multi-source data fusion operation,combining mathematical geological logic,Fuzzy Fusion Theory,and other fusion methods,can accurately and effectively obtain the risk probabilities of unfavorable geological problems. (4) Engineering applications demonstrate that the proposed advanced geological forecast method improves forecasting accuracy,enhances comprehensiveness,increases efficiency and effectively guides construction.
[1] 曲海锋,刘志刚,朱合华. 隧道信息化施工中综合超前地质预报技术[J]. 岩石力学与工程学报,2006,25(6):1 246–1 251.(QU Haifeng,LIU Zhigang,ZHU Hehua. Technique of synthetic geologic prediction ahead in tunnel informational construction[J]. Chinese Journal of Rock Mechanics and Engineering,2006,25(6):1 246–1 251. (in Chinese))
[2] 李术才,李树忱,张庆松,等. 岩溶裂隙水与不良地质情况超前预报研究[J]. 岩石力学与工程学报,2007,26(2):217–225.(LI Shucai,LI Shuchen,ZHANG Qingsong,et al. Forecast of karst-fractured groundwater and defective geological conditions[J]. Chinese Journal of Rock Mechanics and Engineering,2007,26(2):217–225.(in Chinese))
[3] 李天斌,孟陆波,朱 劲,等. 隧道超前地质预报综合分析方法[J]. 岩石力学与工程学报,2009,28(12):2 429–2 436.(LI Tianbin,MENG Lubo,ZHU Jin,et al. Comprehensive analysis method for advanced forecast of geology in tunnels[J]. Chinese Journal of Rock Mechanics and Engineering,2009,28(12):2 429–2 436.(in Chinese))
[4] 梁博森. 基于物探方法提高巷道掘进超前预报精度的研究[J]. 内蒙古煤炭经济,2017,(14):58,75.(LIANG Bosen. Research on improving the accuracy of advance prediction of roadway excavation based on geophysical prospecting method[J]. Inner Mongolia Coal Economy,2017,(14):58,75.(in Chinese))
[5] 刘 斌,聂利超,李术才,等. 三维电阻率空间结构约束反演成像方法[J]. 岩石力学与工程学报,2012,31(11):2 258–2 268.(LIU Bin,NIE Lichao,LI Shucai,et al. 3D electrical resistivity inversion tomography with spatial structural constraint[J]. Chinese Journal of Rock Mechanics and Engineering,2012,31(11):2 258–2 268.(in Chinese))
[6] 徐善初,陈建平,左昌群,等. 模糊综合评判法在隧道施工岩溶预报中的应用[J]. 现代隧道技术,2011,48(5):76–81.(XU Shanchu,CHEN Jianping,ZUO Changqun,et al. Application of the fuzzy comprehensive evaluation method to karst forecasting in tunnel construction[J]. Modern Tunnelling Technology,2011,48(5):76–81.(in Chinese))
[7] 李术才,李晓昭,靖洪文,等. 深长隧道突水突泥重大灾害致灾机制及预测预警与控制理论研究进展[J]. 中国基础科学,2017,(3):27–43.(LI Shucai,LI Xiaozhao,JING Hongwen,et al. Research development of catastrophe mechanism and forecast controlling theory of water inrush and mud gushing in deep long tunnel[J]. China Basic Science,2017,(3):27–43.(in Chinese))
[8] 张 平,任 松,吴 斐,等. 多源数据融合的深埋隧道岩爆预测方法[J]. 东南大学学报:自然科学版,2024,54(3):707–716.(ZHANG Ping,REN Song,WU Fei,et al. Prediction method of rockburst in deep buried tunnel based on multi-source data fusion[J]. Journal of Southeast University:Natural Science,2024,54(3):707–716.(in Chinese))
[9] 刘树才. 煤矿底板突水机理及破坏裂隙带演化动态探测技术[博士学位论文][D]. 徐州:中国矿业大学,2008.(LIU Shucai. Mechanism of water inrush from coal seam floor and continuous survey of fractured zones in coal seam floor[Ph. D. Thesis][D]. Xuzhou:China University of Mining and Technology,2008.(in Chinese))
[10] 底青云,王妙月,伍法权,等. 地球物理综合勘探技术在南水北调西线工程深埋长隧洞勘察中的应用[J]. 岩石力学与工程学报,2005,24(20):3 631–3 638.(DI Qingyun,WANG Miaoyue,WU Faquan,et al. Geophysical exploration over long deep tunnel for west route of south-to-north water transfer project[J]. Chinese Journal of Rock Mechanics and Engineering,2005,24(20):3 631–3 638.(in Chinese))
[11] XUE G Q,YAN Y J,LI X. Pseudo-seismic wavelet transformation of transient electromagnetic response in engineering geology exploration[J]. Geophysical Research Letters,2007,34(16):377–396.
[12] 杨建辉,沈 恺,周 杰,等. 穿越富水断层破碎带隧道塌方机制分析与预防[J]. 工程地质学报,2023,31(1):248–257.(YANG Jianhui,SHEN Kai,ZHOU Jie,et al. Mechanism and prevention of tunnel collapse through water-rich fault fracture zone[J]. Journal of Engineering Geology,2023,31(1):248–257.(in Chinese))
[13] 中华人民共和国国家标准编写组. GB 50287—2016水力发电工程地质勘察规范[S]. 北京:中国计划出版社,2016.(The National Standards Compilation Group of People?s Republic of China. GB 50287—2016 Code for hydropower engineering geological investigation[S]. Beijing:China Planning Publishing House,2016.(in Chinese))
[14] SAATY R W. The analytic hierarchy process-what it is and how it is used[J]. Mathematical Modelling,1987,9(3/5):161–176.
[15] ZHANG K,CHIBATI N,REVIL A,et al. Induced polarization of volcanic rocks-6:relationships with other petrophysical properties[J]. Geophysical Journal International,2023,234(3):2 375–2 393.
[16] EREMENKO A A,MULEV S N,SHTIRTS V A. Microseismic monitoring of geodynamic phenomena in rockburst-hazardous mining conditions[J]. Journal of Mining Science,2022,58(1):10–19.
[17] ZHANG K,XUE Y,XU Z,et al. Numerical study of water inflow into tunnels in stratified rock masses with a dual permeability model[J]. Environmental Earth Sciences,2021,80(7):260–271.
[18] DESIFATMA E,DJAJA IGPFS,PRATOMO P M,et al. Robust inversion of 1D magnetotelluric data using the Huber loss function[J]. Computational Geosciences,2024,28(4):629–643.
[19] 朱 珍,王旭春,袁永才,等. 基于加权平均法的岩溶隧道突涌水风险评估[J]. 公路工程,2015,40(6):51–54.(ZHU Zhen,WANG Xuchun,YUAN Yongcai,et al. Risk assessment of water inrush in karst tunnels based onweighted average method[J]. Highway Engineering,2015,40(6):51–54.(in Chinese))
[20] ZYADA Z,KAWAI Y,MATSUNO T,et al. Fuzzy sensor fusion for mine detection[C]// SCIS & ISIS 2006. [S. l.]:[s. n.],2006:349–354.
[21] 蒙友波,廖艳梅,覃 锋,等. 遥感影像融合下自然资源地类特征提取仿真[J]. 计算机仿真,2023,40(9):162–166.(MENG Youbo,LIAO Yanmei,QIN Feng,et al. Simulation of natural resource land feature extraction based on remote sensing image fusion[J]. Computer Simulation,2023,40(9):162–166.(in Chinese))
[22] SLOUGHTER J M L,RAFTERY A E,GNEITING T,et al. Probabilistic quantitative precipitation forecasting using Bayesian model averaging[J]. Monthly Weather Review,2007,135(9):3 209–3 220.