# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 1 Introduction  
Shields are widely used in tunnel construction because of their safety, high efficiency, and environmental protection. The tunnelling performance of the shield is a key concern in project management, which not only reflects the progress of the project but also reflects whether the control of the shield is reasonable and whether the efficiency of the equipment is fully utilized. Therefore, it is important to accurately predict the changes in the tunnelling performance of the shield and analyze the factors affecting the tunnelling performance to guide engineers and technicians in adjusting the tunnelling parameters and improving the efficiency and safety of the project. At present, the perspectives for evaluating the performance of shield tunnelling can be divided into two categories. One is to focus on the shield tunnelling efficiency, using micro indicators such as thrust, penetration, propulsion speed, and other measures [1]; the second is to judge from the perspective of shield construction energy consumption and progress, construction power consumption, average daily propulsion, and other macro indicators [2]. Since the macro indicators have too many influencing factors, the general evaluation of tunnelling performance is based on micro indicators, of which penetration is the most commonly used shield tunnelling performance evaluation index [3]. The penetration comprehensively reflects the state of propulsion and cutting during the advancement of the shield, which embodies the ability of the cutter plate of the shield to penetrate the ground in the process of boring. In order to predict the performance of shield tunnelling, scholars have carried out a great deal of research, of which 'penetration' is the most commonly used evaluation index. Early studies mainly include experimental methods based on experience [4; 5], theoretical techniques rooted in soil mechanics [6; 7], and experimental techniques utilizing tools such as finite element analysis [8; 9]. However, these methods still have some limitations. Firstly, empirical methods rely on regression analysis to establish the relationship between soil or rock parameters and shield tunnelling parameters. Still, this method is highly dependent on physical assumptions under specific geological conditions and lacks universality. Secondly, in traditional theoretical approaches (e.g., Colorado School of Mines' prediction model, CSM) [6], the prediction results often have a large deviation due to the lack of simulation ability regarding the interaction between shields and soils. Finally, although the experimental method based on finite element analysis can simulate the shield tunnelling process more accurately, a large amount of computation and the fact that the results are only applicable to a specific stratum, which requires the establishment of multiple models according to different geological conditions, lead to poor model generality and robustness. The models used by researchers in the initial studies are mostly non-time series regression models, such as support vector regression (SVR) [10], back-propagation neural networks (BPNNs) [11; 12], and Extreme Gradient Boosting (XGBoost) [13; 14]. These studies verified the adaptability of data-driven methods in shield performance prediction, but it is difficult for the above models to capture the information contained in the feature changes over time. Also, because shield construction data are time-dependent, ignoring the temporal relationship between the data will affect the reasonableness and accuracy of TBM performance prediction [15]. Recurrent neural network (RNN)-based long short-term memory neural networks (LSTMs) and gated recurrent unit networks (GRUs), which are deep learning algorithms that have a significant advantage in processing time series data and can effectively record the long-term and short-term dependencies of the data, capture patterns in the sequences, and handle long series data well [16], have become the focus of some researchers. [PERSON] et al. [17] and [PERSON] et al.