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
-
摘要: 隧洞涌水预测是隧洞安全施工的重要保证，而分析涌水条件、识别涌水来源是涌水量预测的前提。本文以滇中引水工程大坡子隧洞为研究对象，通过分析研究区地层岩性、地质构造，结合地下水水化学特征、同位素结果，厘清了研究区地下水的水文地质条件，确定了隧洞涌水补给来源；在此基础上，利用解析法和数值法对隧洞最大单位涌水量和正常单位涌水量进行计算，并将预测涌水量与实测涌水量进行了对比。其中，基于FEFLOW构建的地下水流数值模型，耦合模拟区域宏观流场的同时，采用多时间序列与各类内边界综合赋值的方法刻画动态施工过程。结果表明：解析法可以在工程初勘设计阶段高效计算隧洞涌水量，但不能动态预测隧洞涌水变化且在地下水位较高区段预测精确度不如数值法；数值法能够精细刻画含水系统结构、参数分布以及隧洞施工工况，能较精确预报施工涌水量变化；故采用解析−数值方法可以显著提高涌水量预测效率和精度。本文采用的方法和模型对于大风险隧洞突涌水灾害防控具有重要意义。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. -
图 1 大坡子隧洞涌水条件三维示意图
① 基岩裂隙水 ② 岩溶水 ③ 大坡子隧洞 ④ 表层裂隙 ⑤ 浅层裂隙 ⑥ 岩溶槽谷 ⑦ 断层
Figure 1. 3D schematic diagram of water inflow conditions of Dapozi tunnel
① Bedrock fissure water ② Karst water ③ Dapozi tunnel ④ Surface fracture ⑤ Shallow fracture ⑥ Karst valley ⑦ Fault
图 2 研究区水文地质图及采样点分布图
Figure 2. Hydrogeological map and distribution of sampling points of the study area
图 3 研究区不同点位水样Piper三线图
Figure 3. Piper diagram of water samples at different locations in the study area
图 4 研究区不同点位水样Gibbs图
Figure 4. Gibbs diagram of water samples at different locations in the study area
图 5 研究区水样δD-δ18O比值图
Figure 5. δD-δ18O ratio diagram of water samples in the study area
图 8 不同方法预测平均单位涌水量与实测值对比图
Figure 8. Comparison between predicted average water inflow and measured value by different methods
图 9 不同方法预测最大单位涌水量与实测值对图
Figure 9. Comparison between predicted maximum water inflow and measured value by different methods
图 10 数值法预测1号支洞上下游涌水与实测值对比图
Figure 10. Comparison between numerical method for predicting upstream and downstream water inflow of No. 1 tunnel and actual observation value
图 11 数值法预测涌水量与施工时间动态变化图
Figure 11. Dynamic variation diagram of water inflow predicted by numerical method and construction time
表 1 研究区地下水水化学组分特征
Table 1. Characteristics of hydrochemical components of groundwater in the study area
编号 水样性质 位置 pH ${\rm{HCO}}_3^{-}$ Cl− ${\rm{SO}}_4^{2-}$ Ca2+ Mg2+ K++Na+ TDS
mg·L−1舒卡列夫
分类mg·L−1 Q001 泉水 补给区 8.55 108.58 0.89 18.49 26.24 7.39 7.09 93.6 HCO3-Ca∙Mg J001 井水 径流区 7.28 80.52 0.35 15.31 17.76 7.68 3.27 79.9 HCO3-Ca∙Mg Q002 泉水 8.31 61.00 0.20 20.03 12.72 7.29 4.84 63.5 HCO3-Ca∙Mg J002 井水 8.02 451.40 5.32 45.00 78.56 38.93 35.51 307 HCO3-Ca∙Mg Q003 泉水 排泄区 7.25 399.55 3.90 23.49 77.44 33.83 12.38 284.0 HCO3-Ca∙Mg Q004 泉水 7.01 502.03 5.32 68.25 100.00 42.77 33.42 360.0 HCO3-Ca∙Mg Q005 泉水 7.00 305.00 3.55 25.20 85.84 12.93 6.90 289.0 HCO3-Ca S001 涌水 8.09 294.02 1.95 25.02 47.12 27.46 20.30 198.4 HCO3-Ca∙Mg 
下载: 导出CSV
表 2 地下水补给高程计算结果
Table 2. Calculation results of groundwater recharge elevation
样品来源 编号 δDV-SMOW /‰ δ18OV-SMOW /‰ 推测补给高程/m 牛白村饮用水点 J001 −77.09 −10.95 1 996.15 下他腊村井点 J002 −79.83 −10.81 1 942.31 鱼渣珠泉点 Q001 −76.87 −10.96 2 000.00 泉80 Q002 −76.49 −11.08 2 046.15 泉635 Q003 −78.63 −10.80 1 938.46 泉637 Q004 −83.64 −11.40 2 169.23 泉630 Q005 −72.19 −9.55 1 457.69 大坡子隧洞1号支洞 S001 −74.30 −10.04 1 646.15
下载: 导出CSV
表 3 解析法预测的隧洞各区段单位涌水量值
Table 3. Calculations of water inflow by different analytical formula
位置 起始里程 终点里程 穿越地层 K
m·d−1隧洞
长度
/mqmax/m3·(d·m)−1 qs/m3·(d·m)−1 大岛洋志 铁路经验 科斯加可夫 铁路经验 1#支洞上游 HH83+500 HH84+772 P2β 0.08 1 702.96 53.86 71.93 32.77 25.06 1#支洞下游 HH84+772 HH85+777 T2g 0.12 1 142.82 47.73 68.16 28.50 23.69 HH85+777 HH86+532 T1y 0.10 351.39 12.65 10.35 20.85 29.75 HH86+532 HH87+500 T2g 0.12 506.23 21.14 30.21 12.62 10.49 2#支洞上游 HH87+500 HH88+315 T2g 0.12 1 531.51 53.33 76.14 29.88 26.44 HH88+315 HH89+525 T2g 0.12 1 116.49 42.27 55.23 23.51 19.19 2#支洞下游 HH89+525 HH90+240 T2g 0.12 589.27 22.30 29.15 12.42 10.12
下载: 导出CSV
表 4 模型边界划分依据
Table 4. Classification of model boundary
边界名称 边界特性 北部边界 发育有F51-2断层，透水性弱，因此可以概化为隔水边界 南部边界 位于畔山－大田山岩溶水系统分界线处，作为地下水分水岭，可将其概化为隔水边界 东部边界 发育有FVI-35断层，其上盘主要地层为T1f、P2β等非可溶岩地层，透水性弱，因此概化为隔水边界 西部边界 主要地形以山地为主，高程明显高于两侧，因此将山峰高程最高点的连线作为地表分水岭，故将其概化为隔水边界 东北部边界 地形为研究区高程最低点，故可将F51-2和FVI-35延伸作为研究区的最低排泄点，故将其概化为流量交换边界
下载: 导出CSV
表 5 研究区水文参数地质参数取值
Table 5. Values of hydrological and geological parameters in the study area
地层、构造代号 Kxx/m·d−1 Kyy/m·d−1 Kzz/m·d−1 有效孔隙度 us N、E 0.10 0.10 0.10 0.12 0.0001 T2g 0.90 0.90 0.95 0.20 0.0005 T1y 0.60 0.60 0.60 0.20 0.0003 T1f、T1x 0.15 0.10 0.10 0.12 0.0001 P2β 0.15 0.10 0.10 0.12 0.0001 P1y 0.90 0.90 0.95 0.20 0.0005 断层F42-1、F42-2、F42-3 0.10 0.10 0.70 0.20 0.0005
下载: 导出CSV
表 6 数值法预测的隧洞各区段涌水量值
Table 6. Calculation of water inflow by numerical model
位置 起始里程 终点里程 穿越地层 隧洞长度
/mqs
/m3·(d∙m)−1qmax
/m3·(d∙m)−11#支洞上游 HH83+500 HH84+772 P2β 1 702.96 1.82 5.17 1#支洞下游 HH84+772 HH85+777 T2g 1 142.82 3.33 6.83 HH85+777 HH86+532 T1y 351.39 24.45 26.27 HH86+532 HH87+500 T2g 506.23 9.70 12.87 2#支洞上游 HH87+500 HH88+315 T2g 1 531.51 5.14 16.67 HH88+315 HH89+525 T2g 1 116.49 11.76 16.25 2#支洞下游 HH89+525 HH90+240 T2g 589.27 17.61 42.32
下载: 导出CSV
-
[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.1004-1184.2018.02.005WANG 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 -
点击查看大图
计量
- 文章访问数: 287
- HTML浏览量: 85
- PDF下载量: 63
- 被引次数: 0
