## 6 Conclusions  
This study proposed a hybrid PCA-SVR to predict the attitude motion trajectory of a shield-tunneling machine. The model was validated using the in situ data of the shield tunnel project of the Xingye Express line in Zhuhai, China. The conclusions are as follows:  
1. An intelligent model combining principal component analysis (PCA) and support vector regression (SVR) was employed to predict the trajectory of the shield's driving attitude. PCA effectively reduced the data dimension and accompanying data noise,while the SVR model adopted the principle of structural risk minimization and statistical learning theory to predict the attitude parameters of the shield machine. The PCA-SVR model was validated using in situ data from the shield-tunnel project of the Xingye Express line in Zhuhai, China, and accurately foresaw the trajectory deviations of tunneling.
2. An engineering example confirmed that the proposed hybrid PCA-SVR model accurately predicted the attitude motion trajectory. Compared with the standalone SVR model, PCA-SVR reduced the calculation time cost and exhibited a high prediction accuracy. The proposed prediction model has the potential to guide shield operators in adjusting tunneling parameters, thereby improving the attitude motion trajectory.
3. The sensitivity analysis revealed that the propeller cylinder pressure significantly affected the attitude trajectory of the shield machine. Additionally, the correlation between the geometric and geological parameters of the shield and the output parameters was generally strong. However, the correlation between certain driving parameters and the output parameters was weak. Deviations from the desired tunneling attitude (DTA) were regulated by the thrust of the push cylinders in the corresponding positions. Future work should refine the model with more parameters and advanced machine learning, and test it at various geological sites, including those with complex rocks, to enhance its accuracy and general applicability. Additionally, research will focus on real-time prediction, with the proposal of a program embeddable in the shield computer for real-time data export and training.  
Conceptualization, H.L., D.Z. and D.S.; methodology, Y.T., S.Q. and D.S.; data curation, H.L. and H.L.; data analysis, D.Z. and Y.T.; writing--original draft preparation, Y.T.; writing--review and editing, D.S.; supervision, H.L.; project administration, D.Z.; funding acquisition, D.S. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shenzhen Science and Technology Program (Grant No. 20220808143139001), Key Research and Development Project of Guangdong Province (Grant No. 2019B111105001), and Shenzhen University 2035 Program for Excellent Research (2022B007).  
Not applicable.  
All data included in this study are available upon request by contact with the corresponding author.  
We are grateful for the guidance and support provided by our supervisor. We also appreciate the assistance received from our colleagues during this study. Thank you to the reviewers for their constructive comments.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company CCCC Tunnel Engineering 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.