Based on the BP neural network model of machine learning method, the corresponding random input parameters are generated by Monte Carlo method, and the prediction of TBM driving speed is studied. In this study, the machine learning method is applied to the prediction of TBM penetration rate, and the established empirical model has higher accuracy and practicability. After selecting the predictive control type of BP neural network, according to the control requirements of TBM, system composition and the characteristics of different geological tunneling, the appropriate data are selected to train the neural network, and the predictive control model of neural network for TBM with high convergence and real-time performance is established. Monte Carlo method has strong optimization and control functions in the realistic planning of many complex problems. In the process of TBM velocity prediction, the random input of parameters is realized by Monte Carlo method, which makes the prediction more accurate. BP neural network is used to predict the penetration rate of TBM. Its accuracy mainly depends on the accuracy of input parameters. The actual measured and predicted values of TBM driving speed are basically near the straight line x = y as the horizontal and vertical coordinates, and the correlation coefficient R = 0.9789. Therefore, the BP neural network combined with genetic algorithm has a high reference value for the prediction of TBM driving speed. When the TBM type is the same and the system equipment is the same, four factors, namely uniaxial compressive strength, Brazilian tensile strength, peak slope index, and distance between planes of weakness, are taken as input parameters of BP network by calculating the weight of influencing factors. In the specific operation, the genetic algorithm is used to iterate continuously to find the optimal solution of the initial weight parameters of BP neural network. In this study, this prediction method is applied to practical prediction. The feasibility of this method is verified by comparing with the final actual measurement result, which is of great practical significance to the evaluation of engineering, design scheme and cost control.

基于机器学习方法的BP神经网络模型，通过蒙特卡罗方法生成相应的随机输入参数，并对TBM驱动速度进行了研究。本研究将机器学习方法应用于TBM渗透率的预测，所建立的经验模型具有较高的准确性和实用性。在选择BP神经网络的预测控制类型后，根据TBM的控制要求，系统组成和不同地质隧道的特点，选择合适的数据对神经网络进行训练，并建立TBM神经网络的预测控制模型。建立具有高收敛性和实时性的系统。蒙特卡洛方法在许多复杂问题的实际计划中具有强大的优化和控制功能。在TBM速度预测过程中，采用蒙特卡罗方法实现参数的随机输入，使预测更加准确。BP神经网络用于预测TBM的渗透率。其精度主要取决于输入参数的精度。TBM行驶速度的实际测量值和预测值基本上在直线附近x  =  y为水平和垂直坐标，相关系数R = 0.9789。因此，结合遗传算法的BP神经网络对TBM行车速度的预测具有较高的参考价值。当TBM类型相同且系统设备相同时，通过计算权重，将单轴抗压强度，巴西抗拉强度，峰斜率指数和软弱面间距等四个因素作为BP网络的输入参数。影响因素。在具体操作中，采用遗传算法进行连续迭代，以找到BP神经网络初始权重参数的最优解。在这项研究中，这种预测方法被应用于实际预测。通过与最终的实际测量结果进行比较，验证了该方法的可行性，对工程评价具有重要的现实意义。