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<p>To explore the influence of water inflow on the safe and efficient tunneling construction of TBM in water-rich Luohe Formation sandstone. Based on the tunneling data at the main inclined shaft TBM construction site of Kekegai Coal Mine, a relationship model between tunneling speed and water inflow is established by using statistical analysis and machine learning theory; XRD and SEM tests on the microstructural components of Luohe Formation sandstone are carried out to analyze the mechanism of water inflow in argillaceous cemented sandstone affecting the tunneling speed of TBM. The Libsvm support vector machine (SVR) model is more effective than multiple regression and random forest (RF) models in evaluating nonlinear problems. When the Gaussian radial basis function (RBF) is selected as the kernel function, the prediction result R<sup>2</sup> reaches 0.974, MAE is 0.699, and MAPE is 1.89%. When the water inflow is small, the mud cake formed by water–rock interaction and the roof fall phenomenon of surrounding rock are the main factors for the large fluctuation of tunneling speed. When the water inflow reaches 45–60&#xa0;m<sup>3</sup>/h, the significant increase in tunneling speed is due to the fact that the water pressure seepage scouring greatly reduces the probability of mud cake and surrounding rock roof fall phenomena. This conclusion is verified by the engineering practice of the main inclined shaft of Kekegai Mine. The research results provide a reference for the control of water inflow during TBM tunneling under similar geological conditions.</p>

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A comprehensive investigation of the relationship between propulsion speed and water influx in coal mine TBM inclined shaft projects

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月21日

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  • Qing Yang,
  • Chuanxin Rong,
  • Mingjing Li,
  • Xiaojian Wang

摘要

To explore the influence of water inflow on the safe and efficient tunneling construction of TBM in water-rich Luohe Formation sandstone. Based on the tunneling data at the main inclined shaft TBM construction site of Kekegai Coal Mine, a relationship model between tunneling speed and water inflow is established by using statistical analysis and machine learning theory; XRD and SEM tests on the microstructural components of Luohe Formation sandstone are carried out to analyze the mechanism of water inflow in argillaceous cemented sandstone affecting the tunneling speed of TBM. The Libsvm support vector machine (SVR) model is more effective than multiple regression and random forest (RF) models in evaluating nonlinear problems. When the Gaussian radial basis function (RBF) is selected as the kernel function, the prediction result R2 reaches 0.974, MAE is 0.699, and MAPE is 1.89%. When the water inflow is small, the mud cake formed by water–rock interaction and the roof fall phenomenon of surrounding rock are the main factors for the large fluctuation of tunneling speed. When the water inflow reaches 45–60 m3/h, the significant increase in tunneling speed is due to the fact that the water pressure seepage scouring greatly reduces the probability of mud cake and surrounding rock roof fall phenomena. This conclusion is verified by the engineering practice of the main inclined shaft of Kekegai Mine. The research results provide a reference for the control of water inflow during TBM tunneling under similar geological conditions.