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Title: Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: a deep learning approach
Authors: Fu, Xianlei
Zhang, Limao
Keywords: Engineering::Civil engineering
Issue Date: 2021
Source: Fu, X. & Zhang, L. (2021). Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: a deep learning approach. Automation in Construction, 132, 103937-.
Project: 04MNP000279C120
Journal: Automation in Construction
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.
ISSN: 0926-5805
DOI: 10.1016/j.autcon.2021.103937
Schools: School of Civil and Environmental Engineering 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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