The authors present a
tunneling attitude prediction method, based on the long short-term memory(LSTM) algorithm, to enhance tunneling safety and
efficiency for large-diameter slurry balance shields.
Shield tunneling parameters are analyzed using the Pearson correlation
coefficient to identify the primary factors that affect the tunneling attitude.
These factors are then used to establish a prediction dataset. An LSTM neural
network model is developed for shield attitude prediction, with the Adam
algorithm applied to optimize the LSTM′s performance.
The key prediction parameters for shield attitude include horizontal deviation
of the shield head, vertical deviation of the shield head, horizontal deviation
of the shield tail, vertical deviation of the shield tail, pitch angle, and
roll angle. The identified primary factors that influence the shield attitude
are the shield operational parameters and geological conditions, with hydraulic
cylinder pressure and the average compressive/shear strength of the strata showing
the most significant effect. The optimized Adam-LSTM
neutral network demonstrates a superior shield-attitude
prediction performance, achieving a mean square error of <0.1 and an average
prediction error of <5%, with more than 80% of the output results.
ZENG Yi
,
WU Jiamin
,
BIAN Yuewei
,
TANG Jiayou
,
YAN Tao
,
SHEN Shuilong
. Tunneling Attitude
Prediction for Large-Diameter
Slurry Balance Shields Using Long Short-Term Memory[J]. Tunnel Construction, 2024
, 44(11)
: 2139
-2148
.
DOI: 10.3973/j.issn.2096-4498.2024.11.004
