Traditional machine
learning models often fall short in accurately predicting shield tunneling
posture because of performance limitations when increasing network depth. To
address this issue, a shield posture prediction method that leverages a deep
residual long short-term
memory(LSTM) model is proposed. This method integrates residual connections
into the LSTM framework to address network degradation and enhance the model′s ability to learn long-term dependencies in
shield tunneling time-series data. Additionally, a
Bayesian optimization algorithm is employed to fine-tune
hyperparameters and optimize the shield posture prediction model. Validation
conducted in a realworld shield tunneling project in
Zhejiang demonstrates that the deep residual LSTM model outperforms
conventional LSTM and artificial neural network models. Taking the shield tail
horizontal deviation prediction as an example, the deep residual LSTM model has
a determination coefficient (R2) of 0.90 and a mean absolute error(MAE)
of 0.76 mm. In comparison, the LSTM model yields an R2 of 0.64 and MAE of 1.08 mm, whereas the
artificial neural network model shows an R2 of 0.68 and MAE of 1.93 mm.
Furthermore, compared to the LSTM model, the deep residual LSTM model can
effectively utilize more network layers (from 5 to 8 layers), demonstrating the
significant role of residual connections in preventing network degradation and
improving feature learning.
ZHOU Kangmin
,
CHENG Kang
,
ZENG Shaoxiang
,
DING Zhi
,
YU Song
,
FENG Zhiguo
. Prediction of Shield
Attitude Using Deep Residual Long Short-Term Memory Model[J]. Tunnel Construction, 2024
, 44(8)
: 1643
-1651
.
DOI: 10.3973/j.issn.2096-4498.2024.08.011
