The authors conduct a case
analysis of a large-diameter
slurry shield tunnel crossing the Yangtze river in Wuhan, China. The Bayesian
optimization algorithm (BO) is employed to optimize key hyperparameters in the
prediction model for tunnel face pressure, which is based on graph
convolutional networks (GCN) and long shortterm memory
(LSTM) neural networks. Furthermore, the Shapley additive explanations (SHAP)
method is applied to globally interpret the prediction model and calculate the
Shapley value of each input parameter for the prediction target, enhancing the
model′s interpretability and transparency. The research
findings are as follows: (1) The proposed BOGCN-LSTM method demonstrates high accuracy across all historical time
steps, achieving an average goodness of fit (R2), root
mean square error (ERMSE), mean absolute error (EMAE), and mean absolute percentage error (EMAPE)of 0.943, 0.245, 0.173, and 1.183%, respectively. (2) Among
historical time steps t-1 to t-10, the metrics at time step t-3—R2 of
0.953, ERMSE of 0.233, EMAEof
0.159, and EMAPE of 1.151%—show the best overall predictive performance, with a computational
running rate of 1.7 times per second. (3) The global interpretation results
using the SHAP method indicate that the air cushion chamber pressure, inlet and
outlet slurry pressures, and cutterhead squeezing pressure difference
significantly influence the research objectives, offering valuable decision-making insights for controlling tunnel face pressure in large-diameter slurry shield operations. The BO-GCN-LSTM deep learning model effectively predicts tunnel face pressure,
assisting shield tunneling operators in making informed parameter adjustments.
HAN Dong
,
ZHANG Mingshu
,
TAO Zanxu
,
LEI Yu
,
WU Xianguo
. Prediction of Tunnel Face
Pressure of Large-Diameter Slurry Shield Tunnel Based on Hybrid Deep Learning Method[J]. Tunnel Construction, 2024
, 44(11)
: 2181
-2189
.
DOI: 10.3973/j.issn.2096-4498.2024.11.008
