Efficient excavation and closed construction
environment of double-shield TBM relies on more accurate
and real-time intelligent surrounding rock
classification methods. To achieve this, the strong correlation between the two
indices of geology and excavation parameters is analyzed based on advanced
HRNet high-resolution neural networks. Furthermore, an
accurate dual-mode intelligent classification model of
surrounding rock with high-resolution is built based on
the fusion of these two indices. As a result, real-time
surrounding rock classification can be conducted. Main findings are as follows:
(1) The advance speed and cutterhead torque are strongly correlated with rock
hardness. The linear characteristics of these two parameters distinguish
different rock hardness. (2) The cutter rotation angle and gripper pitch angle
are strongly correlated with rock integrity, and different rock integrity can
be distinguished by these two parameters. (3) A dual-mode
classification of surrounding rocks established by bonding and fully connecting
two single-mode HRNet high-resolution
neural networks fuses discrete numerical geological modal data with continuous
numerical tunneling modal data, thereby more accurately reflecting the
surrounding rock classification from multiple aspects such as surrounding rock
stability and rock-machine interaction. The application
accuracy reaches 95%. (4) The dual-mode model
outperforms single-mode model on testing set, with an
Macro F1 Score of 0.985.
LONG Haitao
,
ZHANG Shishu
,
MA Chunchi
,
LI Tianbin
,
LIU Zhuangyuan
,
LIU Yang
. Precise Dual-Mode Classification of
Surrounding Rocks of Tunnels Bored by Double-Shield TBM Based on High Resolution Neural Networks[J]. Tunnel Construction, 2025
, 45(S1)
: 168
-181
.
DOI: 10.3973/j.issn.2096-4498.2025.S1.018
