## 1 Introduction  
Shield tunneling construction methods are widely used in subway, transportation, water conservancy, and other tunnel projects for their high efficiency, safety, and convenient construction characteristics [1]. The Shield tunnel construction process is a large, complex, and dynamic system, and the direction of the shield machine is hard to control. Once the shield machine deviates from the DTA, it will cause dislocation and damage to the segments, which will further affect the quality of the tunnel [2]. At present, the control of shield machine attitude (SMA) is still in the manual control stage, and the control process can only be implemented after the SMA deviation, which has the deficiencies of untimely and inaccurate control [3, 4]. Consequently, to guarantee the accuracy of tunneling direction, the major work is to develop intelligent warning technology for assisting machine operators in correcting deviation in advance. Some researchers used theoretical and experimental methods to study the prediction and control mechanism of SMA. [PERSON] et al. [5] proposed an effective pose control system based on a target motion determination algorithm and displacement control of thrust cylinders, then tested it on the test rig of the thrust system. Other relevant works involved those by [PERSON] and [PERSON] [6], [PERSON] et al. [7], [PERSON] et al. [8], etc. These methods have been discussed concerning the mechanism but are lacking the timely guidance for the machine operators to adjust the SMA, and additionally rarely consider factors such as shield data, thus resulting in low attitude control accuracy. During the last five years, with the abundance of data and the development of AI technologies, many algorithms with good generalization performance have been widely used in shield performance prediction [9; 10; 11; 12; 13] and geological information prediction [14; 15; 16], being able to provide timely guidance for the machine operators. Inspired by these methods, some scholars have also tried to apply AI technologies for real-time warning and control of the SMA. Based on the shield data, [PERSON] et al. [17] established a hybrid trajectory deviation prediction model combining principal component analysis (PCA) and gated recurrent unit (GRU). For the SMA prediction, similar yields include the extreme gradient boosting (XGBoost) model established by [PERSON] et al. [18] and a hybrid deep learning model proposed by [PERSON] et al. [19]. These methods made significant attempts in the development of SMA prediction models, but so far as we know, there is still a lack of a universal approach. Furthermore, the substantial shield machine data make the data preprocessing work more demanding. Therefore, it is an emergent need to comprehensively evaluate various AI algorithms for predicting SMA and develop professional data preprocessing algorithms. Literature review reveals that the comprehensive comparison of multiple AI technologies has attracted the attention of researchers in various fields, such as additive manufacturing [20], medical science [21; 22], and civil engineering [13; 23]. However, there are no reports that use various algorithms to predict SMA and evaluate their predictive effectiveness comprehensively. In summary, based on the existing research work, this paper aims to establish the prediction models of SMA by using various ML and DL algorithms. The datasets are collected from five earth pressure balance (EPB) shield machines in Chengdu Metro Line 19, China. Concurrently, based on the characteristics of EPB data, we develop a data preprocessing algorithm, including three phases of data segmentation, filtering, and standardization. Then, we use EVS and RMSE indicators to evaluate the prediction effect of SMA by different algorithms and specify the optimal model. Finally, we establish a warning predictor for SMA based on the prediction results. The remainder of this paper is organized as follows. Section 2 introduces eight basic AI algorithms and SMA prediction framework.