Face stability control of excavation with earth pressure balance machine (EPB) approach is the best available method to reduce the ground deformation and settlement of surface structures in a tunneling project in urban areas. In the present paper, several models have proposed through a statistical method, including feed-forward stepwise regression (FSR) and machine learning techniques such as support vector machine (SVM), Takagi–Sugeno fuzzy model (TS), and multilayer perceptron neural network (ANN-MLP), to provide a predictive strategy for EPB machine during the tunnel excavation. For this purpose, a monitoring dataset of machine performance parameters including advance speed, screw conveyor speed, screw conveyor torque, thrust force, and cutterhead rotation speed from Tehran Metro Line 6 Southern Extension Sector (TML6-SE) has been compiled. Then, the relation between the performance parameters and target values were investigated to analyze the available inputs and offer a new equation using the FSR. Moreover, evaluation metrics and loss functions were utilized for the evaluation of the developed models’ efficiency. The results proved the significance of the presented methods in this paper that could be used to predict the earth pressure balance operation with high efficiency.

采用土压平衡机（EPB）方法进行开挖工作面稳定性控制是城市地区隧道工程中减少地表变形和地表结构沉降的最佳可行方法。在本文中，通过统计方法提出了几种模型，包括前馈逐步回归（FSR）和机器学习技术，例如支持向量机（SVM）、Takagi-Sugeno模糊模型（TS）和多层感知器神经网络（ANN-MLP），为土压平衡机在隧道开挖过程中提供预测策略。为此，我们编制了德黑兰地铁 6 号线南延段 (TML6-SE) 机器性能参数的监测数据集，包括前进速度、螺旋输送机速度、螺旋输送机扭矩、推力和刀盘转速。然后，研究性能参数和目标值之间的关系，以分析可用的输入并使用 FSR 提供​​新的方程。此外，利用评估指标和损失函数来评估所开发模型的效率。结果证明了本文提出的方法的意义，可用于高效预测土压平衡运行。