The operating parameters of a tunnel boring machine (TBM) reflect its geological conditions and working status and are accordingly critical data for ensuring safe and efficient tunnel construction. The accurate prediction of the advance rate, rotation speed, thrust, and torque indicators based on the operating parameters can guide the control and application of a TBM. In this study, we analyzed the relationships between the TBM operating parameters and daily collected TBM data. We used the smoothing method and outlier detection to process this data, and determined the stable values of four different TBM indicators in the ascending phase of a complete TBM operational segment. Then, we evaluated the application of five different statistical and ensemble machine learning methods (Bayesian ridge regression (BR), nearest neighbors regression, random forests, gradient tree boosting (GTB), and support vector machine) and two different deep neural networks (a convolutional neural network (CNN) and long short-term memory network (LSTM)) to establish prediction models. The GTB method provided the best prediction accuracy and the BR method provided the least calculation time of the five different statistical and ensemble machine learning methods evaluated. The LSTM method provided a higher prediction accuracy than the CNN model. The ensemble machine learning methods were found to be the most accurate for the relatively limited data sets used in this study, suggesting that sufficient data must be present before the advantages of deep neural networks can be truly realized. The successful application of statistical, ensemble, and deep neural network machine learning methods to predict TBM indicators in this study suggests the promise of machine learning in this application.

隧道掘进机（TBM）的运行参数反映了其地质条件和工作状态，因此是确保安全有效建造隧道的关键数据。基于操作参数对前进速度，转速，推力和扭矩指示器的准确预测可以指导TBM的控制和应用。在这项研究中，我们分析了TBM操作参数和每日收集的TBM数据之间的关系。我们使用平滑方法和离群值检测来处理此数据，并在整个TBM运营段的上升阶段确定四个不同TBM指标的稳定值。然后，我们评估了五种不同的统计和整体机器学习方法（贝叶斯岭回归（BR），最近邻回归，随机森林，梯度树增强（GTB）和支持向量机）和两个不同的深度神经网络（卷积神经网络（CNN）和长短期记忆网络（LSTM））来建立预测模型。在评估的五种不同的统计和整体机器学习方法中，GTB方法提供了最佳的预测准确性，而BR方法则提供了最少的计算时间。与CNN模型相比，LSTM方法提供了更高的预测准确性。对于本研究中使用的相对有限的数据集，发现集成机器学习方法是最准确的，这表明在真正实现深度神经网络的优势之前必须存在足够的数据。成功应用统计，合奏，