## 1 Introduction  
Shield tunneling is widely used in subway tunnel construction as an efficient and convenient method for use in urban areas [1, 2]. However, poor shield movement performance can result in ground settlements, decreased tunnel construction quality, and deviations in shield attitude. To minimize risks during the shield tunneling process, accurately predicting shield movement performance, such as shield attitude and total thrust, is extremely crucial [3]. It is important for a shield machine to proceed along the designed axis. However, due to the complex geological conditions, such as the variation of rock hardness within a tunnel section, the shield machine may be subjected to uneven forces. These uneven forces can cause the shield machine to deviate from the designed axis. Currently, the control of shield attitude mainly relies on manual operation, which can result in snake tracking and deviations due to delayed adjustments [4]. Therefore, it is necessary to develop a method that not only accurately predicts the deviations in shield attitude but also intelligently adjusts the attitude based on these predictions. Data-driven machine learning has attracted attention in geotechnical engineering due to its powerful data processing ability [5; 6; 7; 8; 9; 10]. Among various machine learning algorithms, recurrent neural networks (RNNs) are generally proven to be the most effective method for time-series problems like shield movement performance prediction [11; 12; 13; 14; 15; 16]. For the shield attitude prediction, [PERSON] et al. [17] proved the accuracy of an LSTM-based model in predicting the attitude and position of the shield machine. [PERSON] et al. [18] introduced an innovative approach for predicting the shield attitude. Their method employs a combination of a gated recursive unit (GRU) algorithm and adaptive boosting, utilizing data gathered from five earth pressure balance shield machines. [PERSON] et al. [19] used wavelet transform to reduce the data noise and employed the Adam-optimized LSTM for real-time prediction of shield attitude. [PERSON] et al. [20] developed a deep learning model that integrates the graph convolutional network and LSTM model to predict deviations in the tail section of shield machines. Although existing research has already successfully predicted shield attitudes for the next 1 to 10 s, the operation and adjustment of shield machines typically require a longer timeframe [21; 22]. Predictions made within 10 s or less may not provide operators with sufficient reaction time for effective adjustments. In addition, few existing studies consider the multi-ring shield attitude prediction, but it is crucial and useful in engineering practice because multi-ring shield attitude prediction can help operators develop a reasonable long-term plan and prevent frequent corrective interventions. [PERSON] et al. [23] proposed an LSTM-attention model to predict shield attitude. They proved that the attention mechanism can enhance the accuracy of LSTM in predicting the shield attitude of the next ring. Inspired by the method, this study constructs a temporal attention-LSTM model to achieve multi-ring shield attitude prediction. The total thrust is another key indicator that directly reflects the shield movement performance [24; 25; 26; 27; 28; 29]. [PERSON] et al. [30] used LSTM and GRU to predict six parameters of tunnel boring machines, including torque, thrust, etc. [PERSON] et al. [31; 32] developed hybrid models based on particle swarm optimization (PSO) and RNNs to predict the torque and thrust. During the shield tunneling process, the shield attitude and total thrust are interdependent, and an inappropriate total thrust can potentially result in deviations in shield attitude [33]. More seriously, neglecting control of the total thrust during the shield attitude adjustment process may lead to the collapse of soft rock ahead of the shield advancement.