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Volume 42 Issue 6
Dec.  2023
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Article Navigation >  CARSOLOGICA SINICA  > 2023  >  42(6): 1224-1236
LUO Yiming, CHENG Jianmei, XU Wenjie, BA Jinghui, HUANG Shengcai, DUAN Tianyu. Analysis of water inflow conditions and prediction for water inflow of deep-buried tunnels in the karst area of Southwest China: Taking Dapozi tunnel of central Yunnan Water Diversion Project as an example[J]. CARSOLOGICA SINICA, 2023, 42(6): 1224-1236. doi: 10.11932/karst20230608
Citation: LUO Yiming, CHENG Jianmei, XU Wenjie, BA Jinghui, HUANG Shengcai, DUAN Tianyu. Analysis of water inflow conditions and prediction for water inflow of deep-buried tunnels in the karst area of Southwest China: Taking Dapozi tunnel of central Yunnan Water Diversion Project as an example[J]. CARSOLOGICA SINICA, 2023, 42(6): 1224-1236. doi: 10.11932/karst20230608
shu

Analysis of water inflow conditions and prediction for water inflow of deep-buried tunnels in the karst area of Southwest China: Taking Dapozi tunnel of central Yunnan Water Diversion Project as an example

doi: 10.11932/karst20230608
  • 1.

    School of Environmental Studies, China University of Geosciences, Wuhan, Hubei 430078, China

  • 2.

    Yunnan Geological Engineering the Second Investigation Institute Co., Ltd., Kunming, Yunnan 650218, China

  • Received Date: 2022-04-30
    Available Online: 2023-12-28
    Abstract
  • The karst area in Southwest China is characterized by complex terrain and karst development, with a large area of exposed carbonate rocks and a wide distribution of karst depressions and valleys on the surface. Therefore, this area is highly subject to water inflow in tunnel construction. The Dapozi tunnel area of the central Yunnan Province is a deep-buried tunnel in the karst area, in which exist many faults and very complex hydrogeological conditions. Therefore, it is necessary to predict the tunnel inflow during construction by analyzing the water inflow conditions and identifying its sources, which can ensure the safety of tunnel construction. Currently, numerical and analytical methods are commonly used to predict water inflow. The analytical methods mainly calculate the water inflow of tunnel through empirical calculation formulas. However, most of these methods are based on some assumptions and specific boundary conditions, which may limit their applicability in complex cases. The numerical method can simulate complex geological conditions by obtaining accurate data about geological characteristic, but its accuracy is restricted by the accuracy of borehole data in numerical calculation. Therefore, the mutual supplementation of numerical and analytical methods can significantly improve the efficiency and accuracy of water inflow prediction. Besides, most of the previous studies only predicted the water inflow of tunnel in the survey and design stage. But they did not describe the actual construction progress in the model, and the accuracy of prediction results conducted separately with numerical or analytical methods were not verified by the actual water inflow data. By analyzing the stratigraphic lithology and geological structure of the strata in the study area, we preliminarily identified the construction sections with the risks of water inflow. Based on hydrogeological surveys, we collected water samples from tunnels, springs, and wells in the study area to measure the geochemical characteristics of groundwater. Furthermore, we analyzed the sources for tunnel inflow and degrees of karst development, using Piper trilinear diagrams and Gibbs diagrams. Based on the procedures above, we used analytic and numerical methods to calculate the maximum and average water inflow of each tunnel unit, and compared the predicted values with the actual ones. We have also built a numerical groundwater flow model based on FEFLOW, which couples the simulation of regional macroscopic groundwater distribution and employs a method that combines multiple time series with the comprehensive assignment of various internal boundaries to depict the dynamic construction process. The results show the study area is developed with three main faults in. Although the permeability of these faults is weak, the gullies formed by these faults will gather atmospheric precipitation and surface water, increasing water inflow risk during tunnel construction. The TDS of groundwater in the area gradually increases from south to north, indicating that groundwater flows from the south to the north of the study area. The δD and δ18O isotope results prove that the water inflow in tunnel is from the low altitude; the distance of the groundwater runoff path is medium; atmospheric precipitation is the primary supply source. Gibbs diagram shows that the ions in the groundwater are mainly from rock weathering, suggesting that there may be karst fissures in carbonate rocks due to water-rock interaction within the area. In addition, the analytical method can efficiently calculate the water inflow of tunnel in the preliminary survey and design stage of the project. However, this method cannot dynamically predict the change in the water inflow. In essence, it is an analytical formula derived from the theory on the stable movement of the phreatic aquifer to the complete well. Therefore, the groundwater table dramatically affects the result, and the prediction accuracy in the section of the high groundwater table of analytical method is lower than that of the numerical method. The numerical method focuses on the high-accuracy model of engineering scale in the karst system, which is controlled by the macroscopic groundwater distribution. It pays attention to the description of the dynamic process and the conditions of the project area. Specifically, it describes the dynamic construction process by multi-time series. The working conditions of the excavating and lining are described by "GAP" in FEFLOW, which can accurately predict the change of water inflow in construction. Therefore, based on the actual hydrogeological conditions, the analytical-numerical method can significantly improve the efficiency and accuracy of water inflow prediction. The methods and models used in this paper are significant for preventing and controlling water inflow disasters in high-risk tunnels.

     

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    • References(22)
  • [1]
    袁广祥, 李建勇, 黄志全, 王朋姣. 隧道工程勘察阶段涌水预测的问题探讨[J]. 地质与勘探, 2015, 51(5):993-998.

    YUAN Guangxiang, LI Jianyong, HUANG Zhiquan, WANG Pengjiao. On prediction of water inflow into tunnels during engineering surveys[J]. Geology and Exploration, 2015, 51(5):993-998.
    [2]
    张世旭. 贵州麻江贤昌隧道岩溶发育特征与涌水量预测分析[D]. 贵阳: 贵州大学, 2019.

    ZHANG Shixu. Karst development characteristics and water inflow prediction of Xianchang tunnel in Majiang, Guizhou Province[D]. Guiyang: Guizhou University, 2019
    [3]
    常威, 谭家华, 黄琨, 程烯, 黄镇, 万军伟. 地下水多元示踪试验在岩溶隧道水害预测中的应用:以张吉怀高铁兰花隧道为例[J]. 中国岩溶, 2020, 39(3):400-408.

    CHANG Wei, TAN Jiahua, HUANG Kun, CHENG Xi, HUANG Zhen, WAN Junwei. Application of groundwater multi-element tracing tests to water hazard prediction of karst tunnels: An example of the Lanhua tunnel on the Zhangjiajie–Jishou–Huaihua high-speed railway[J]. Carsologica Sinica, 2020, 39(3):400-408.
    [4]
    成建梅, 罗伟, 徐子东, 胡学夫. 火山岩体围岩隧道断层带涌水量计算方法综合研究:以青云山隧道为例[J]. 地质科技情报, 2015, 34(6):193-199.

    CHENG Jianmei, LUO Wei, XU Zidong, HU Xuefu. Calculation method for water inflow of typical fault zone in volcanic rock tunnel: Case study of Qingyunshan tunnel[J]. Geological Science and Technology Information, 2015, 34(6):193-199.
    [5]
    刘广英. 燕山隧道涌水量预测方法分析与对比[J]. 铁道建筑, 2021, 61(7):67-70.

    LIU Guangying. Analysis and comparison of water inflow prediction methods for Yanshan tunnel[J]. Railway Engineering, 2021, 61(7):67-70.
    [6]
    王健华, 李术才, 李利平, 许振浩. 隧道岩溶管道型突涌水动态演化特征及涌水量综合预测[J]. 岩土工程学报, 2018, 40(10):1880-1888.

    WANG Jianhua, LI Shucai, LI Liping, XU Zhenhao. Dynamic evolution characteristics and prediction of water inflow of karst piping-type water inrush of tunnels[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(10):1880-1888.
    [7]
    谯乾峰. 滇东南特长隧洞涌突水预测研究: 以滇中引水工程大坡子隧洞为例[D]. 成都: 成都理工大学, 2014.

    QIAO Qianfeng. Research on extra-long tunnel water bursting prediction of southeast Yunnan: Taking an example of Dapozi tunnel from water diversion project in Yunnan[D]. Chengdu: Chengdu University of Technology, 2014
    [8]
    武亚遵, 潘春芳, 林云, 曹飞龙, 王子杰. 典型华北型煤矿区主要充水含水层水文地球化学特征及控制因素[J]. 地质科技情报, 2018, 37(5):191-199.

    WU Yazun, PAN Chunfang, LIN Yun, CAO Feilong, WANG Zijie. Hydrogeochemical characteristics and controlling factors of main water filled aquifers in the typical North China coalfield[J]. Geological Science and Technology Information, 2018, 37(5):191-199.
    [9]
    Xu Chenghua, Yu Dandan, Luo Zujiang. Recharge sources and genetic model of geothermal water in Tangquan, Nanjing, China[J]. Sustainability, 2021, 13(8):4449. doi: 10.3390/su13084449
    [10]
    罗明明, 黄荷, 尹德超, 周宏, 陈植华. 基于水化学和氢氧同位素的峡口隧道涌水来源识别[J]. 水文地质工程地质, 2015, 42(1):7-13.

    LUO Mingming, HUANG He, YIN Dechao, ZHOU Hong, CHEN Zhihua. Source identification of water inrush in the Xiakou tunnel based on hydrochemistry and hydrogen-oxygen isotopes[J]. Hydrogeology & Engineering Geology, 2015, 42(1):7-13.
    [11]
    范威, 王川, 金晓文, 胡德勇, 陈植华. 吉莲高速公路钟家山隧道涌突水条件分析[J]. 水文地质工程地质, 2015, 42(2):38-43.

    FAN Wei, WANG Chuan, JIN Xiaowen, HU Deyong, CHEN Zhihua. Water inrush condition analysis of the Zhongjiashan tunnel in the Jilian highway[J]. Hydrogeology & Engineering Geology, 2015, 42(2):38-43.
    [12]
    常威. 复杂岩溶水系统识别及其在隧道涌水量预测的应用研究: 以张吉怀高铁大青山隧道为例[D]. 武汉: 中国地质大学, 2021.

    CHANG Wei. Study on the identification of complex karst water system and its application in tunnel water disaster prediction: A case study of Zhangjiajie-Jishou-Huaihua high-speed railway Daqing mountain tunnel[D]. Wuhan: China University of Geosciences, 2021
    [13]
    张彦龙, 田卿燕, 张建同. 广东地区某公路岩溶隧道水害分析及其数值模拟研究[J]. 中国岩溶, 2018, 37(2):307-313.

    ZHANG Yanlong, TIAN Qingyan, ZHANG Jiantong. Water disaster analysis and numerical simulation of a karst tunnel in a highway of Guangdong Province[J]. Carsologica Sinica, 2018, 37(2):307-313.
    [14]
    江思珉, 王耀明, 栗现文, 周念清. 深长隧道涌水量预测的三维数值模拟研究[J]. 现代隧道技术, 2018, 55(2):78-83.

    JIANG Simin, WANG Yaoming, LI Xianwen, ZHOU Nianqing. 3D numerical simulation for predicting water inflow volume in deep and long tunnels[J]. Modern Tunnelling Technology, 2018, 55(2):78-83.
    [15]
    赵贵清, 陈舟, 周云, 陈长生, 张科正. 深埋隧洞涌水量预测数值模型研究进展[J]. 工程勘察, 2017, 45(4):27-34.

    ZHAO Guiqing, CHEN Zhou, ZHOU Yun, CHEN Changsheng, ZHANG Kezheng. Review of numerical study on groundwater inflow forecasting in deep-buried tunnel construction[J]. Geotechnical Investigation & Surveying, 2017, 45(4):27-34.
    [16]
    徐中平, 周训, 崔相飞, 拓明明, 王昕昀, 张颖. 岩溶区地下水数值模拟研究进展[J]. 中国岩溶, 2018, 37(4):475-483.

    XU Zhongping, ZHOU Xun, CUI Xiangfei, TUO Mingming, WANG Xinyun, ZHANG Ying. Research advances of numerical simulation of groundwater in karst areas[J]. Carsologica Sinica, 2018, 37(4):475-483.
    [17]
    梁腾飞, 成建梅, 张乃俨, 史文, 开萨尔江艾合买提. 基于数值模拟的汾河二库对晋祠泉岩溶水系统渗漏补给作用研究[J]. 中国岩溶, 2020, 39(2):147-153.

    LIANG Tengfei, CHENG Jianmei, ZHANG Naiyan, SHI Wen, KAISAERJIANG Aihemaiti. Numerical study on surface water leakage replenishment of the Fenhe 2nd reservoir into the Jinci spring system[J]. Carsologica Sinica, 2020, 39(2):147-153.
    [18]
    ZHOU Jiaqing, LIU Hongbin, LI Changdong, HE Xianglan, TANG Huiming, ZHAO Xianjin. A semi-empirical model for water inflow into a tunnel in fractured-rock aquifers considering non-Darcian flow[J]. Journal of Hydrology, 2021, 597:126149. doi: 10.1016/j.jhydrol.2021.126149
    [19]
    成建梅, 罗一鸣. 岩溶多重介质地下水模拟技术及应用进展[J]. 地质科技通报, 2022, 41(5):220-229.

    CHENG Jianmei, LUO Yiming. Overview of groundwater modeling technology and its application in karst areas with multiple-void media[J]. Bulletin of Geological Science and Technology, 2022, 41(5):220-229.
    [20]
    J H Feth, R J Gibbs. Mechanisms controlling world water chemistry: Evaporation–crystallization process[J]. Science, 1971, 172(3985):870-872. doi: 10.1126/science.172.3985.870
    [21]
    王梅, 许模. 滇池盆地东侧黑龙潭与白龙潭水力关系研究[J]. 地下水, 2018, 40(2):15-17. doi: 10.3969/j.issn.1004-1184.2018.02.005

    WANG Mei, XU Mo. On the hydraulic connection between Heilongtan and Bailongtan in the eastern part of Dianchi basin[J]. Ground Water, 2018, 40(2):15-17. doi: 10.3969/j.issn.1004-1184.2018.02.005
    [22]
    王玉蕊. 基于解析法的岩溶隧道涌水量计算改进探析[D]. 成都: 成都理工大学, 2015.

    WANG Yurui. The improvement of analytsical method for karst tunnel inrush water calculation[D]. Chengdu: Chengdu University of Technology, 2015
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