Real-Time Prediction of TBM Response Parameters Based on Temporal Convolutional Network
Publication: Journal of Computing in Civil Engineering
Volume 39, Issue 1
Abstract
Establishing an accurate predictive model for response parameters is the foundation of control parameter optimization for tunnel boring machines (TBMs). However, existing research mostly focuses on mean values during stable stages, and lacks real-time prediction throughout the entire process, failing to meet the demand for fine-tuned parameter recommendations. This paper proposes the weight matrix method for feature selection, which provides specific numerical values and rankings of each feature’s contribution. A deep learning model based on temporal convolutional network (TCN) is proposed to achieve real-time prediction of cutterhead torque () and total thrust (), which is compared with the gated recurrent unit (GRU) and long short-term memory (LSTM). The proposed method was validated on the Yinchao project, and the results demonstrated that (1) the weight matrix method outperforms the Pearson coefficient method in terms of model accuracy, and (2) the TCN model performs better than GRU and LSTM. The method proposed in this paper achieves high precision in predicting and , and holds promise as a core algorithm for automatic control in TBM and providing crucial support for TBM’s advancement into the era of autonomous driving.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgements.
Acknowledgments
This work was supported by the National Key R&D Program of China (2022YFE0200400). The authors express their gratitude for the data provided by the second TBM Excavation Parameter Data Sharing and Machine Learning Competition in 2023.
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Published In
Journal of Computing in Civil Engineering
Volume 39 • Issue 1 • January 2025
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jan 4, 2024
Accepted: Jun 4, 2024
Published online: Oct 10, 2024
Published in print: Jan 1, 2025
Discussion open until: Mar 10, 2025
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