## 5 Conclusions  
SMA is the important technical parameter of concern for machine operators during shield tunneling. In this paper, we committed to using various ML algorithms and DL algorithms to establish SMA prediction models. In parallel, we developed a data preprocessing algorithm for EPB tunneling parameters, including data segmentation, noise reduction, and standardization. These modules were used to extract effective data, eliminate interference signals, and unify the dimension of various parameters, which can provide more effective data for the training phase. Then, we used the EVS and RMSE indicators to comprehensively analyze and compare the prediction results of various algorithms on SMA and indicate the optimal model. Finally, we proposed a warning predictor for SMA based on the prediction results. The main conclusions are as follows:  
1. LSTM and GRU algorithms with EVS > 0.98 and RMSE < 1.5 were the two most accurate models for predicting SMA, because they are better at capturing characteristics of data in time sequences format. The comparative analysis indicated that the prediction performance of eight algorithms in descending order is as follows: GRU > LSTM > AdaBoost > RF > SVR > BPNN > CNN > KNN.
2. SMA warning predictor combined with ML algorithms and DL algorithms can achieve timely warning based on the tunneling information of five historical drilling cycles. The predictor will provide auxiliary support for machine operators to adjust the tunneling parameters in advance.
3. A certain scale of tunneling data set is necessary. When using relatively small data sets (dataset size < 1000), the prediction accuracy of these algorithms will be significantly reduced.  
This study comprehensively assessed the eight basic AI algorithms on SMA prediction problem and then established a warning predictor. It proved the great potential of AI technology in the dynamic prediction of SMA. However, the following work needs to be further supplemented and improved: first, use the optimization algorithm to improve the prediction accuracy of the model; second, collect more tunneling data to enhance the robustness of the model; third, combine with the kinematics theory to establish a complete SMA intelligent warning and control system.  
Conceptualization, Y.W. and B.X.; Data curation, H.X. and P.Y.; Formal analysis, H.X. and R.C.; Project administration, Y.W., L.L. and B.X.; Methodology, H.X. and R.C.; Writing--original draft, H.X.; Writing--review and editing, Y.W., P.Y., L.L. and R.C. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Key Research Project of China Institute of Water Resources and Hydropower Research Limited (Grant No. HTGE0203A03201900000, HTGE0203A2020200000), and Natural Science Foundation of Shaanxi Province (Grant No. 2019 JLZ-13, Grant No. 2019 JLP-23).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** The data sets generated and analyzed during the current study are not publicly available due to privacy but are available from the corresponding author on reasonable request.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Number of Training Sets** & **EVS** & **RMSE** \\ \hline LSTM & 500, 1000, 2000, 3000 & 0.66, 0.72, 0.95, 0.99 & 9.80, 8.40, 2.38, 1.35 \\ GRU & 500, 1000, 2000, 3000 & 0.76, 0.75, 0.97, 0.99 & 14.60, 7.03, 1.68, 1.41 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction results of VDST with different training sets.  
**Acknowledgments:** The authors are grateful for data support from the China Railway Engineering Equipment Group Corporation. All individuals included in this section have consented to the acknowledgement.  
**Conflicts of Interest:** The authors declare no conflict of interest.