Predicting Pore-Water Pressure in Front of a TBM Using a Deep Learning Approach
Publication: International Journal of Geomechanics
Volume 21, Issue 8
Abstract
When tunneling with a tunnel boring machine (TBM) in permeable soil, excess pore-water pressure are inevitably generated in the soil surrounding the TBM. Because excess pore-water pressure reduce the effective face support pressure, accurately predicting their magnitude is important for determining the required effective face support pressure. In this study, a long–short-term memory (LSTM)-based deep learning model is employed to predict variations in pore-water pressure generated by TBM tunneling using time-series data derived from field monitoring data and TBM data collected during construction of the Green Hart Tunnel (GHT) in the Netherlands. Four obtainable input variables are selected to quantify pore-water pressure at two monitoring points that have different distances (8.3 and 107 m) along the transverse axis. Three accuracy metrics are introduced to evaluate the performance of two prediction tasks, with input variables' importance on the output ranked according to their corresponding sensitivity values. It demonstrates that the proposed LSTM-based deep learning model can accurately predict the pore-water pressure ahead of the TBM in drilling–standstill cycles, which can further serve as a tool for TBM operators to use in assessing real-time tunnel face stability.
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Acknowledgments
The authors acknowledge the data from HSL-South Organization. The authors acknowledge the financial support from the Science and Technology Development Fund of Macau SAR (Grant No. 0026/2020/AFJ), the National Natural Science Foundation of China (Grant No. 52022001), the Ministry of Science and Technology of the People's Republic of China (Grant No. 2019YFB1600700), and the Guangdong Provincial Department of Science and Technology (Grant No. 2019B111106001). The research work was also partially funded by the Center for Ocean Research in Hong Kong and Macau, which is a joint research center for ocean research between QNLM and HKUST.
Notation
The following symbols are used in this paper:
- b
- bias;
- Ct
- memory matrix at time t;
- Ct−1
- memory matrix at time t;
- e
- 2.718281828;
- ft
- forget gate vector at time t;
- ht
- hidden output vector at time t;
- ht−1
- hidden output vector at time t−1;
- it
- input gate vector at time t;
- M
- measured pore-water pressure;
- mean value of measured pore-water pressure;
- ot
- output gate vector at time t;
- P
- predicted pore-water pressure;
- mean value of predicted pore-water pressure;
- t
- time step;
- U
- weight matrix between xt and ht;
- V
- weight matrix between ht and ot;
- W
- weight matrix between ht and ht−1;
- WC
- weight matrix in memory block;
- Wf
- weight matrix in the forget gate;
- Wi
- weight matrix in the input gate;
- Wo
- weight matrix in the output gate;
- x1
- advance velocity;
- x2
- distance along the tunnel axis;
- x3
- slurry pressure;
- x4
- drilling or standstill duration; and
- xt
- input vector at time t.
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Published In
International Journal of Geomechanics
Volume 21 • Issue 8 • August 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 17, 2020
Accepted: Feb 9, 2021
Published online: May 24, 2021
Published in print: Aug 1, 2021
Discussion open until: Oct 24, 2021
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