Slurry pressure balance shield, a kind of tunnel boring machine, is significantly affected by the operation parameters such as advance speed and torque. In this study, a dynamic regulation model is established based on wavelet transform and bidirectional long short-term memory method (Bi-LSTM) to predict advance speed and torque. For comparison, twenty parameters of the shield machine are input to predict the tunneling behavior through the Bi-LSTM model and the LSTM model. Comparison results indicate that the proposed model has a relatively high accuracy. Moreover, parametric sensitivity analysis is made to filter the parameters by using light gradient boosting machine, so that the ranking of importance for all parameters can be gained. The proposed dynamic regulation model is validated by the Sutong gas-insulated transmission line project in China with a complex underwater-work condition, which has a guiding significance in operating and adjusting the shield machine in a dynamic process.

泥浆压力平衡盾构机是隧道掘进机的一种，其进给速度和扭矩等操作参数影响显着。本研究建立了基于小波变换和双向长短期记忆方法（Bi-LSTM）的动态调节模型来预测前进速度和扭矩。为了比较，通过Bi-LSTM模型和LSTM模型输入盾构机的20个参数来预测隧道行为。比较结果表明，所提出的模型具有较高的精度。此外，通过使用光梯度提升机对参数进行参数敏感性分析，从而得到所有参数的重要性排序。