# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 1 Introduction
[23] combined LSTM and GRU, proposing a multi-step intelligent prediction scheme for shield machine position and posture based on Beluga Whale Optimization (BWO)-CNN-LSTM-GRU, achieving coordinated control of multiple systems of the shield machine. Although the aforementioned artificial intelligence methods have overcome the limitations of traditional methods, most are based on recurrent neural network prediction models that process data incrementally, require high memory demands, and still need to improve in prediction accuracy [24]. Additionally, due to the complexity of shield equipment, there are various shield excavation parameters and geological parameters that also significantly impact shield tunnelling. Therefore, shield posture prediction is a task involving high-dimensional input parameters. The Principal Component Analysis [25] (PCA) method can integrate multi-source data, retaining most features of the input parameters and reducing the dimensionality of high-dimensional shield data to some extent. As a machine learning intelligent algorithm proposed by Microsoft in 2017, the Light Gradient Boosting Machine [26] (LGBM) method, due to its low memory requirements, fast computation speed, and high precision, is suitable for prediction tasks with high-dimensional input parameters. To obtain the optimal LGBM model, it is necessary to optimize its hyperparameters. Currently, optimization methods for machine learning hyperparameters include empirical tuning, random search, grid search, gradient-based optimization, etc. However, a class of algorithms inspired by phenomena or behaviors in nature possesses strong dynamic adaptive capabilities and can seek global optimal solutions across a broader problem space. These algorithms are commonly chosen for machine learning hyperparameter optimization and are known as metaheuristic algorithms. Particle Swarm Optimization [27] (PSO) is a representative of these algorithms, demonstrating superiority in solving practical problems due to its ease of implementation, high precision, and fast convergence. The roll angle and pitch angle of shield machines are two important posture parameters during the tunnelling process. A schematic diagram is shown in Figure 1. The roll angle refers to the rotation angle of a shield's cutterhead in the vertical direction. If the roll angle is too large or deviates from the predetermined range, it will lead to an imbalanced working face between the cutterhead and the tunnel wall, which can cause tunnelling faults or affect the quality of the tunnel. The pitch angle ++refers to the angle between the shield machine's axis and the horizontal plane. Improper control of the pitch angle may cause the shield machine to \"pitch down\", affecting not only the forming quality and structural safety of the tunnel but also having adverse effects on subsequent construction progress and costs. Especially when the shield is tunnelling through composite strata, due to the diverse geological structures and significant differences in the physical properties and mechanical characteristics of different strata, operators need to continuously adjust the thrust pressure of different sections of the shield to maintain a balanced state with the surrounding resistance. Therefore, this paper proposes a PCA-PSO-LGBM-based prediction model for shield roll angle and pitch angle by considering the influence of composite strata using the strata area weighting method, providing a decision-making basis for adjusting the shield's tunnelling parameters under different geological conditions. The remainder of this paper is organized as follows. Section 2 introduces the basic principles of the PCA-PSO-LGBM model. Section 3 describes the engineering background and data processing methods. Section 4 presents the prediction results and related discussions. Section 5 provides the conclusions of this paper.