Abstract: 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.

Key words: prediction of shield posture, selective kernel network, feature attention, combined model, multihead attention