This research provides a spatio-temporal approach to perform real-time forecasting for the tunnel boring machine (TBM) operating parameters. By extracting the real-time TBM operational data from the data acquisition system, a Long Short-Term Memory (LSTM) based deep learning model is trained for accurate prediction. A global sensitivity analysis (GSA) by adopting the Sobol method is performed for the model to quantify the contribution of input variables. The developed methodology can be a useful tool for TBM performance improvement and it enhances the state of knowledge on underground excavation. The result from the case study indicates that: (1) The proposed spatio-temporal method provides reliable real-time forecasting with mean absolute error (MAE) and root mean squared error (RMSE) of 1.261 mm and 1.955 mm, respectively, and (2) GSA results indicate that TBM's thrust and CHD torque are the 2 most influential spatial factors, while the historical data of penetration rate is critical for accurate forecasting. Further studies could focus on backward optimization to improve TBM's performance based on the prediction.

该研究提供了一种时空方法来对隧道掘进机 (TBM) 运行参数进行实时预测。通过从数据采集系统中提取实时 TBM 运行数据，训练基于长短期记忆 (LSTM) 的深度学习模型以进行准确预测。采用Sobol方法对模型进行全局敏感性分析（GSA），量化输入变量的贡献。开发的方法可以成为提高 TBM 性能的有用工具，它提高了地下开挖的知识水平。案例研究的结果表明：（1）所提出的时空方法提供了可靠的实时预测，平均绝对误差（MAE）和均方根误差（RMSE）分别为 1.261 mm 和 1.955 mm，(2) GSA 结果表明，TBM 的推力和 CHD 扭矩是影响最大的 2 个空间因素，而穿透率的历史数据对于准确预测至关重要。进一步的研究可以集中在向后优化，以根据预测提高 TBM 的性能。