Shield tunnelling trajectory deviation will cause segment dislocation ground subsidence and other hazards. A deep learning model for real-time prediction of trajectory deviation during shield tunneling is proposed. The model combines a Temporal Convolutional Network (TCN) and Gated Gate Unit (GRU) and introduces an attention mechanism. First, wavelet transform denoising (WT) is used to remove the noise of the data collected during the shield excavation process, the local features of the input time series are extracted by the TCN algorithm, and then the GRU algorithm is used to extract the long-term dependence features of the time series data, Finally, the attention mechanism is introduced to further enhance the model's attention to important information in the input data. Taking the third section of the Fuzhou Binhai Express Line as an example, the hybrid model was verified and compared with other three deep learning models with better effects. The results show that the prediction accuracy of the model for the movement trajectory of the shield is higher than that of other models, and this prediction framework provides a promising solution for the real-time prediction of the movement trajectory of the shield during the excavation of the shield.
Wang Yuchao
,
Xie Xiongyao
,
Huang Changfu
,
Xiao Zhonglin
. Real Time Prediction of Shield Tunneling Trajectory Based on Hybrid Deep Learning Algorithm[J]. Chinese Journal of Underground Space and Engineering, 2024
, 20(S1)
: 59
-69
.
DOI: 10.20174/j.JUSE.2024.S1.08
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