People have always been concerned about the velocity
and safety of shield tunneling, which have a close relationship with stratum
conditions and operation parameters. However, it is difficult to predict the
shield performance using traditional methods due to the complicated shieldground
interaction, impeding the development of the rapid tunneling technology. In
this paper, a new prediction method for shield advancing speed based on the
AttentionResNetLong Shortterm Memory(LSTM) hybrid model is proposed to address this problem.
Compared with traditional neural networks, such as LSTM and GRU, the proposed
model has introduced the Attention mechanism, which can update the weight
matrix adaptively according to various stratum conditions during longdistance shield tunneling. As a result, the model can modify the
weight matrix itself when confronted with different tasks. This ability
effectively increases the prediction accuracy, which has been validated through
realtime prediction of shield advancing speed in the
eastern route of the ChinaRussia gas pipeline project.
The method can analyze the correlations between input and output parameters
during shield tunneling. Moreover, it has good adaptability, which is of great
significance in selecting the operation parameters for the velocity and safety
of shield tunneling.
GAO Kun
,
YU Sihao
,
XU Weiqing
,
ZHANG Zixin
. Novel Prediction Method for Shield Advancing Speed
Based on AttentionResNetLong
ShortTerm Memory Hybrid Neural Network[J]. Tunnel Construction, 2023
, 43(4)
: 592
-601
.
DOI: 10.3973/j.issn.2096-4498.2023.04.005
