# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 1 Introduction
Theoretical analysis, numerical simulation, and model experiment methods have revealed the interaction mechanism between shields and surrounding strata and provided mathematical models for shield posture changes, but there are still some unknown control factors that are difficult to describe theoretically. Research based on numerical simulation analysis mainly focuses on binary relationships, ignoring the correlation between various factors. Therefore, traditional research methods struggle to precisely predict shield posture changes with highly nonlinear characteristics. Artificial intelligence methods such as machine learning and deep learning are considered new powerful tools for solving highly nonlinear problems and have been successfully applied in various construction fields. [PERSON] et al. [11] adopted a multiple linear regression model to accurately predict the influencing variables of zeolite-alkali activated sands. [PERSON] et al. [12] constructed a hybrid ensemble machine learning (HEML) model by integrating conventional machine learning (CML) models to predict the compressive strength of geopolymer concrete. The results show that the developed HEML model achieves the highest prediction accuracy by minimizing the specific limitations of CML models. [PERSON] et al. [13] compared the predictive performance of 4 standalone algorithms and 12 hybrid data-mining algorithms on water quality index. The results indicated that the standalone with a bagging-random tree model have the strongest predictive ability. From the aspect of shield attitude prediction, artificial intelligence methods are also increasingly used by scholars and have shown significant advantages. [PERSON] et al. [14] proposed a model that uses various machine learning algorithms to describe the association between shield machine posture deviation and excavation parameters, concluding that the thrust cylinder is the most significant factor affecting shield posture deviation changes. [PERSON] et al. [15] presented a shield posture prediction framework based on machine learning and deep learning algorithms and developed three anomaly detectors based on three machine learning algorithms to prevent snake-like advancement of the shield. [PERSON] et al. [16] proposed a shield posture prediction optimization model based on a combination of Bayesian optimization categorical boosting (BO-CatBoost) and a multiobjective evolutionary algorithm (MOEAD), forming a digital twin framework for shield posture. Many scholars believe that shield posture changes are time-dependent. Therefore, typical time series prediction algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have been used for shield posture prediction. [PERSON] et al. [17] developed a dual-route hybrid deep neural network framework (D-T-RC_LSTM) for shield position prediction. [PERSON] et al. [18] incorporated an attention mechanism into LSTM, giving more attention to high-weight tunnelling parameters, achieving predictions of shield machine position and posture. [PERSON] et al. [19] also proposed a shield posture fusion prediction model based on Convolutional Neural Network (CNN), GRU, and attention mechanisms. Additionally, [PERSON] et al. [20] developed a shield posture prediction mixed deep learning method considering feature temporal attention (FTA-N-GRU) combined with an attention mechanism. Furthermore, [PERSON] et al. [21] proposed a time attention-LSTM model based on Bayesian optimization (BOTA-LSTM) to predict shield posture and total thrust force, achieving multi-loop predictions of shield posture and total thrust force, and further developed an intelligent correction method for multi-loop shield posture, adjusting shield posture and total thrust force within reasonable ranges. [PERSON] et al. [22] studied a shield machine posture and position prediction framework that includes a comprehensive feature evaluation method, Ensemble Empirical Mode Decomposition (EEMD), CNN, and GRU, capturing long-distance temporal features. [PERSON] et al.