## 1 Introduction  
With the continuous development of urbanization, urban traffic has become increasingly tense, and urban rail transit is the main method for alleviating traffic problems. Shield construction technology has continuously developed, and shield tunneling is characterized by high cost efficiency and low disturbance to the surrounding soil. Thus, it is widely used in the construction of urban highway tunnels [1, 2]. Owing to the lack of certain mature guiding theories and prediction methods, the attitude deviation of shields is severe, frequently exceeding the limit. This affects the safety and efficiency of shield construction,leading to problems, such as surface subsidence, shield axis deviation, and segment floating, which constitute hidden dangers for normal tunnel operations [3; 4; 5]. The machine learning method can combine various monitoring data, such as excavation, geological, and geometric parameters. In addition, it has a self-updating mechanism and dynamic prediction ability [6; 7; 8; 9]. Thus, machine learning can intelligently regulate and optimize the excavation process, solving the randomness and nonlinear problems of the excavation process [10; 11]. Owing to the concealability of shield construction and the lack of clarity regarding the attitude migration mechanism of shields, attitude control mainly depends on the experience of shield operators [12]. Thus, it is necessary to study machine learning algorithms for the large-diameter shield-posture prediction effect. Presently, the research status of machine learning in shield tunneling is focused on four challenges of shield-tunneling sites, such as the tunneling parameters, stratum category and bad geology, surface deformation, and the attitude deviation of shield tunneling [13]. The machine learning methods currently used in shield-tunnel engineering mainly include support vector machines (SVMs), neural networks, and random forests (RFs). Support vector regression (SVR) is a commonly used machine learning method in shield-tunnel construction [14; 15; 16]. To prevent the shield attitude deviation from exceeding the limit, it is important to adjust the driving path in time. By predicting the shield-tunneling attitude deviation, the relationship between the tunneling, geological, and geometric parameters and the attitude deviation parameters can be investigated, which can significantly guide the operator to adjust the attitude in time and prevent the attitude deviation of shield tunneling [6; 17; 18; 19]. [PERSON] et al. [20] considered the database of shield tunneling, geological conditions, and operation parameters. They used the wavelet threshold (WT) to eliminate irrelevant noise in time- and frequency-domain data. Adam was employed to improve the reliability of long short-term memory (LSTM) neural networks and optimize the gradient training process, and a deep learning model for the real-time prediction of the attitude trajectory in the tunneling process was proposed. [PERSON] et al. [21] extended geological data using the interpolation method and combined geological and operational data according to the position of each operational datum. They employed a data-driven RF method and proposed a dynamic load-forecasting method based on data-driven technology and in situ data. [PERSON] et al. [22] considered shield attitude, construction parameters, and stratum characteristics. They improved the multilayer perception, SVM, a gradient-enhanced regression machine learning algorithm, and a genetic algorithm based on principal component analysis (PCA) to forecast the shield posture. By analyzing the kinematics of the thrust mechanism, [PERSON] et al. [23] developed a mathematical method for calculating the target movement of the thrust cylinder. They studied the target movement of the thrust cylinder when the shield was excavated along an arbitrary curve, to predict the shield-tunneling attitude in real time. [PERSON] et al.