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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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