# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 5 Conclusions  
This paper, based on the Shenzhen intercity railway shield tunnel project, considers the influence of composite strata on shield posture by employing the strata area weighting method. It proposes a prediction model for shield roll angle and pitch angle based on Principal Component Analysis (PCA)-Particle Swarm Optimization (PSO)-Light Gradient Boosting Machine (LGBM). The research results can provide a decision-making basis for adjusting excavation parameters in advance under different geological conditions, significantly reducing construction risks. The specific conclusions are as follows:  
1. PCA is suitable for extracting features from multi-dimensional excavation parameters of the shield, while LGBM is well-suited for predictive tasks with high-dimensional input parameters. PSO, due to its strong dynamic adaptive capability, accurately solves for the optimal hyperparameters of the LGBM model. By combining these three algorithms, precise predictions of shield roll angle and pitch angle are achieved, with an \(MAE\) of only 0.068\({}^{\circ}\) and 0.048\({}^{\circ}\), respectively, and the \(R^{2}\) reaching 0.980 and 0.985, respectively. 2. Through comparisons with different models, it is demonstrated that PSO is more suitable than Bayesian algorithms for optimizing high-dimensional hyperparameters of LGBM. Random Forest, also a decision tree model, has slightly lower prediction accuracy and slower prediction speed compared to LGBM, while Support Vector Regression and K-Nearest Neighbors have much lower prediction accuracies than LGBM. Therefore, the PCA-PSO-LGBM model proposed in this paper is the optimal prediction model for roll angle and pitch angle. 3. Using rock grade as the geological feature for each shield ring's prediction yields lower accuracy than the strata area weighting method used in this paper. This is because the classification of rock grade is not fine enough to extract more geological information. The sensitivity analysis indicates that during the adjustment of shield tunnelling parameters, priority should be given to hinged pull, hinged thrust, lower cylinder propulsion pressures, and cutterhead speed, with a particular focus on changes in the pitch angle. 4. Although this paper achieved accurate predictions of the shield machine's roll angle and pitch angle and provided the adjustment direction of the tunnelling parameters, the model has only been validated in a single engineering project and has not realized  
Figure 18: Comparison of the sensitivity of roll angle and pitch angle to various tunnelling parameters. the quantitative adjustment of the roll angle and pitch angle. This is a limitation of this paper. In follow-up studies, we will collect more shield construction data from other projects, especially the datasets from different geological environments, to optimize the architecture of the PCA-PSO-LGBM model in order to improve the robustness of the model. At the same time, we will try to embed the prediction model into the optimization algorithm and optimize the shield tunnelling parameters according to different geological parameters to achieve precise control of the shield machine's roll angle and pitch angle. Conceptualization, H.Z. and P.T. ; methodology, J.Z. ; validation, J.H. ; formal analysis, T.F., J.Z. and Y.L. ; investigation, J.H. and T.F. ; data curation, J.H. ; writing--original draft preparation, J.H. ; writing--review and editing, P.T. and J.H. ; visualization, P.T. ; supervision, [PERSON]; project administration, P.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 52178386, 52378336. Not applicable. The datasets generated and analyzed during the current study are not publicly available due to privacy but are available from the corresponding author on reasonable request.