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Volume 43 Issue 5
Dec.  2024
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Article Navigation >  CARSOLOGICA SINICA  > 2024  >  43(5): 1156-1165
GUO Baode, ZHANG Yi, WANG Xinwen, CHEN Zhigao, DONG Zhiming, MENG Yan. Rapid assessment of water and mud inrush likelihood in karst tunnels based on decision tree model: A case in Siding tunnel of Guihe expressway[J]. CARSOLOGICA SINICA, 2024, 43(5): 1156-1165. doi: 10.11932/karst20240512
Citation: GUO Baode, ZHANG Yi, WANG Xinwen, CHEN Zhigao, DONG Zhiming, MENG Yan. Rapid assessment of water and mud inrush likelihood in karst tunnels based on decision tree model: A case in Siding tunnel of Guihe expressway[J]. CARSOLOGICA SINICA, 2024, 43(5): 1156-1165. doi: 10.11932/karst20240512
shu

Rapid assessment of water and mud inrush likelihood in karst tunnels based on decision tree model: A case in Siding tunnel of Guihe expressway

doi: 10.11932/karst20240512
  • 1.

    Huabang Construction Investment Group Co., LTD, Lanzhou, Gansu 730199, China

  • 2.

    China Railway Eryuan Engineering Group Co., Ltd., Chengdu, Sichuan 610031, China

  • 3.

    Guilin Hydrological Engineering Geological Survey Institute Co., Ltd. of Guangxi Zhuang Autonomous Region, Guilin, Guangxi 541002, China

  • 4.

    CHELBI Engineering Consultants Incorporation, Beijing 100029, China

  • 5.

    Key Laboratory of Karst Dynamics, MNR & GZAR/Innovation Center of Karst Collapse Prevention, CGS/Institute of Karst Geology, CAGS, Guilin, Guangxi 541004, China

  • Received Date: 2023-08-01
  • Accepted Date: 2024-05-13
  • Rev Recd Date: 2024-02-27
    • Available Online: 2024-12-30
    Abstract
  • With the ongoing advancement of infrastructure projects, such as highways and railways, in China, karst tunnel construction is increasingly evolving towards longer, larger, and deeper dimensions. The problem of water and mud inrush in tunnels is becoming more and more severe. Effective measures for the advance prediction of water and mud inrush in tunnels have consistently been a pressing convern for management departments and engineering construction units. This issue also remains a significant challenge in related research fields. It is essential for us to assess the likelihood of tunnel water and mud inrush when determining whether to carry out advance prediction and identifying the appropriate mileage segment for conducting the prediction. This assessment is vital for targeted interventions, minimizing construction costs, and enhancing prediction accuracy. To some extent, the assessment of the likelihood of water inrush in tunnels falls under the category of advance prediction and serves as the foundational element for conducting such prediction.The rapid assessment of the likelihood of water inrush in karst tunnels is an important basis for deciding whether to carry out advance prediction and to take measures for emergency treatment. This rapid assessment was often absent in previous work. Even though assessments for the likelihood of water inrush were conducted occasionally in several tunnels, they were primarily based on qualitative methods of geological analysis. This often resulted in discrepancies from actual conditions, leading to misjudgments. In some tunnels, significant manpower and resources were sometimes expended on predicting water inrush, advance prediction, and tunnel waterproofing design, yet no water inrush occurred. Conversely, the sections of some tunnels that were neither predicted nor adequately protected experienced severe water and mud inrush. Therefore, the accuracy of assessments regarding the likelihood of water inrush in tunnels is crucial for determining whether to undertake subsequent prediction and prevention efforts. This study explored Siding tunnel on Guihe expressway as a case study. Utilizing karst hydrogeological surveys and research, in conjunction with geophysical exploration and drilling data, the study started with the degree of karst development and the relationship between the tunnel and karst groundwater, both of which are closely associated with water and mud inrush in the tunnel. Ten representative parameters for the decision tree model were identified and quantified, including karst rates in the transverse and longitudinal sections of the tunnel, as well as the vertical zonation relationship between the tunnel and groundwater. Based on this analysis, a rapid assessment method, along with indicator parameters and a predictive model for assessing the likelihood of water and mud inrush in the karst tunnel, was developed and practically validated. The results indicate that the highest probability of water inrush in each mileage of Siding tunnel is 88.1%, suggesting a very high probability of large-scale water and mud inrush. Notably, during the actual construction process, the occurrence of water and mud inrush was observed in the segment with the highest predicted likelihood.Using a decision tree model to analyze and assess the likelihood of water and mud inrush in the tunnel represents a valuable exploratory study. The decision tree model offers the advantage of selecting the optimal solution among multiple complex options, effectively mitigating errors arising from human experience-based judgments. This approach enhances the scientific rigor geological analysis related to assessments of water and mud inrush in tunnels. According to the principles of the decision tree method, this model can quickly assess both the likelihood and magnitude of water inrush based on various critical values for water inrush sizes and the probabilities associated with different branches. Furthermore, it can determine the probability of a specific scale of water inrush based on the rapid assessment of the likelihood of water inrush. In the future, it will be essential to refine the indicator parameters of scheme branches, state branches, and probability branches on different scenarios of water and mud inrush in tunnels. This refinement will provide targeted guidance for advance predictions in similar projects.

     

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    • References(13)
  • [1]
    蒋树屏, 林志, 王少飞. 2018年中国公路隧道发展[J]. 隧道建设, 2019, 39(7):1217-1220.

    JIANG Shuping, LIN Zhi, WANG Shaofei. Development of highway tunnels in China in 2018[J]. Tunnel Construction, 2019, 39(7): 1217-1220.
    [2]
    李术才, 王康, 李利平, 周宗青, 石少帅, 柳尚. 岩溶隧道突水灾害形成机理及发展趋势[J]. 力学学报, 2017, 49(1):22-30. doi: 10.6052/0459-1879-16-345

    LI Shucai, WANG Kang, LI Liping, ZHOU Zongqing, SHI Shaoshuai, LIU Shang. Mechanical mechanism and development trend of water-inrush disasters in karst tunnels[J]. Chinese Journal of Theoretical and Applied Mechanics, 2017, 49(1): 22-30. doi: 10.6052/0459-1879-16-345
    [3]
    蒙彦, 雷明堂. 岩溶区隧道涌水研究现状及建议[J]. 中国岩溶, 2003, 22(4):287-292. doi: 10.3969/j.issn.1001-4810.2003.04.007

    MENG Yan, LEI Mingtang. The advance and suggestion for the study on discharge rate in karst tunnel gushing[J]. Carsologica Sinica, 2003, 22(4): 287-292. doi: 10.3969/j.issn.1001-4810.2003.04.007
    [4]
    Thomas Goetz. The decision tree[M]. Rodale, 2010 (ISBN10: 9781605297293).
    [5]
    伍小刚. 隧道超前地质预报物探方法选择与解译阈值研究[D]. 成都:成都理工大学, 2020.

    WU Xiaogang. Study on method selection and interpretation threshold of advanced geological forecast in tunnel[D]. Chengdu: Chengdu University of Technology, 2020.
    [6]
    韩行瑞. 岩溶隧道涌水及其专家评判系统[J]. 中国岩溶, 2004, 23(3):213-218.

    HAN Xingrui. Karst water bursting in tunnel and expert judging system[J]. Carsologica Sinica, 2004, 23(3): 213-218.
    [7]
    林永生, 邹胜章, 朱丹尼. 岩溶隧道涌水原因揭秘[J]. 中国矿业, 2020, 29(Suppl.1):582-583, 586.

    LIN Yongsheng, ZOU Shengzhang, ZHU Danni. The causes of water bursting in karst tunnel[J]. China Mining Magazine, 2020, 29(Suppl.1): 582-583, 586.
    [8]
    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. doi: 10.11779/CJGE201810015

    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. doi: 10.11779/CJGE201810015
    [9]
    杨光. 基于多源异构数据的滑坡灾害决策树预测模型研究[D]. 北京:中国地质大学(北京), 2016.

    YANG Guang. Research on decision-tree prediction model of landslide based on multi-source heterogeneous data[D]. Beijing: China University of Geosciences (Beijing), 2016.
    [10]
    洪郡伶. 整合层次分析法及决策树於设备投资决策之实证研究[D]. 北京:清华大学, 2017.

    HONG Junling. An empirical investigation of decision making on investment of equipment using analytic hierarchy process and decision tree analysis[D]. Beijing: Tsinghua University, 2017.
    [11]
    陈语. 隧道大变形灾害动态风险评估与支护决策研究[D]. 成都:成都理工大学, 2017.

    CHEN Yu. Dynamic risk assessment and risk-based support decision analysis on tunnel squeezing[D]. Chengdu: Chengdu University of Technology, 2017.
    [12]
    丁胜祥, 董增川, 张莉. 基于决策树算法的洪水预报模型[J]. 水力发电, 2011, 37(7):8-11, 33.

    DING Shengxiang, DONG Zengchuan, ZHANG Li. Flood forecasting model based on decision tree algorithm[J]. Water Power, 2011, 37(7): 8-11, 33.
    [13]
    Yongli Gao, E Calvin Alexander Jr. Sinkhole hazard assessment in Minnesota using a decision tree model[J]. Environmental Geology, 2008, 54(5): 945-956. doi: 10.1007/s00254-007-0897-1
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