Existing shield attitude
prediction models suffer from overfitting and low prediction accuracy. Thus, a
combined prediction model for shield attitude based on fusion attention
mechanism is proposed. A feature extraction network using a selective kernel network
attention mechanism is introduced to enhance the extraction of relevant time-domain features and suppress redundant information. This approach
eliminates the limitations of fixed-size convolution
kernels and adaptively forms a feature map with time-domain
attention. To capture long-term information and feature
patterns, two sets of implicit output results are obtained through a
bidirectional long- and short-term
memory network and a gated recurrent unit. A multihead attention mechanism is
then used to capture the dependence between the implicit features output by the
combined model and the shield attitude output, further improving the model′s ability to capture critical implicit features. Furthermore, to
address the issue of insufficient continuity and accuracy in geological survey
drilling data, which complicates its application in machine learning model
training, secondary features based on artificial prior knowledge are introduced
to improve the model′s perception of stratum
information. Finally, the model′s performance is
evaluated using a shield example from the Guangzhou metro line 12, specifically
between the Guangzhou station and the Higher Education Mega Center North
station. The model′s performance is tested under
different parameter structures and verified through comparative experiments.
Furthermore, the influence of features on the prediction results is assessed
through interpretable experiments. The experimental results demonstrate that
the proposed model outperforms other prediction models, showing improved
accuracy and addressing the issues of overfitting and low prediction accuracy
with long time-series, high-dimension
feature data in traditional models.
LIU Zhe
,
XU Chao
,
XIONG Dongdong
. Combined Prediction of
Shield Attitude Based on Fusion Attention Mechanism[J]. Tunnel Construction, 2025
, 45(1)
: 139
-150
.
DOI: 10.3973/j.issn.2096-4498.2025.01.011
