A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: A deep learning approach
Abstract 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.
Highlights A spatio-temporal prediction approach to estimate TBM performance is proposed. Long short-term memory based deep learning model is used to perform real-time forecasting. Global sensitivity analysis is performed to quantify the contribution of variables. A realistic tunnel case in Singapore is used to demonstrate the applicability and effectiveness. It can achieve a high accuracy with an MAE of 1.261 mm and an RMSE of 1.955 mm.
Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: A deep learning approach
Abstract 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.
Highlights A spatio-temporal prediction approach to estimate TBM performance is proposed. Long short-term memory based deep learning model is used to perform real-time forecasting. Global sensitivity analysis is performed to quantify the contribution of variables. A realistic tunnel case in Singapore is used to demonstrate the applicability and effectiveness. It can achieve a high accuracy with an MAE of 1.261 mm and an RMSE of 1.955 mm.
Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: A deep learning approach
Fu, Xianlei (author) / Zhang, Limao (author)
2021-08-31
Article (Journal)
Electronic Resource
English
| DOAJ | 2025
| Elsevier | 2025