## 7 Conclusions  
This paper proposes a Bayesian optimization-based temporal attention-LSTM model (BOTA-LSTM) to predict the shield attitude and total thrust. The model achieves multi-ring predictions of shield attitude and total thrust by using a temporal attention mechanism. The optimal predictive model is determined through Bayesian hyperparameter optimization. Based on the multi-ring prediction results, the shield attitude is adjusted to a reasonable range through an intelligent correction method. The following are the main conclusions of this study:  
1. The temporal attention mechanism effectively improves the predictive performance of multiple rings. Specifically, the BOTA-LSTM model has the highest accuracy for the first ring, with a gradual decrease over the subsequent four rings and a more rapid decline thereafter.
2. The proposed multi-ring intelligent correction method can integrate the multi-ring shield attitude and total thrust data with geological survey reports to control the shield attitude and total thrust within a reasonable range. Compared to a method that solely predicts and corrects for the next ring, the proposed multi-ring prediction and correction method provides the opportunity for further adjustments, if the initial correction falls short of expectations. Considering the control of total thrust can effectively avoid the risk of tunnel collapse caused by excessive thrust during shield tunneling.
3. Compared to manual tuning, which is limited by personal experience and prone to getting stuck in local optima, Bayesian hyperparameter optimization can efficiently achieve the optimal predictive model in a shorter timeframe. It enhances the predictive accuracy of the model regarding various tasks, such as HDSH (from 0.84 to 0.94) and total thrust (from 0.76 to 0.83).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Shield Construction** & & **Ring 413** & & & **Ring 415** & \\
**Parameter** & & & & & & \\ \hline A\# oil pressure (bar) & 84 & \(\rightarrow\) & 70 & 59 & \(\rightarrow\) & 50 \\ B\# oil pressure (bar) & 123 & \(\rightarrow\) & 100 & 119 & \(\rightarrow\) & 100 \\ C\# oil pressure (bar) & 123 & \(\rightarrow\) & 123 & 118 & \(\rightarrow\) & 118 \\ D\# oil pressure (bar) & 108 & \(\rightarrow\) & 120 & 119 & \(\rightarrow\) & 119 \\ E\# oil pressure (bar) & 70 & \(\rightarrow\) & 90 & 78 & \(\rightarrow\) & 40 \\ F\# oil pressure (bar) & 38 & \(\rightarrow\) & 38 & 46 & \(\rightarrow\) & 46 \\ Advance rate (mm/min) & 11.57 & \(\rightarrow\) & 11.57 & 13.2 & \(\rightarrow\) & 13.2 \\ Total thrust (kN) & 60,984 & \(\rightarrow\) & 53,944 & 58,126 & \(\rightarrow\) & 48,886 \\ Torque (kN-m) & 4000 & \(\rightarrow\) & 3800 & 2846 & \(\rightarrow\) & 2600 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of shield construction parameters before and after deviation correction.  
The LSTM model, instead of the GRU model, is selected as the benchmark model for developing the multi-ring prediction model (i.e., BOTA-LSTM model) because LSTM outperforms GRU in terms of predictive accuracy and hyperparameter tunning efficiency.  
Conceptualization, S.Z.; methodology, S.Z. and M.Y.; software, S.Z.; validation, S.C. and M.S.; resources, S.C. and M.S.; data curation, S.C. and M.S.; writing--original draft preparation, S.Z.; writing--review and editing, S.C. and M.S.; visualization, M.Y.; supervision, S.C. and M.S.; project administration, M.S. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the Zhejiang Provincial Natural Science Foundation (Grant No. LY24D020001).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Author [PERSON] was employed by the company Zhejiang University of Technology Engineering Design Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.