## 1 Introduction  
The demand for urbanization and the scarcity of spatial resources continuously drives the development of underground spaces. The shield tunneling method has been widely applied as the mainstream technology for underground space development. However, the shield tunneling process is influenced by various factors, such as external construction environment and equipment performance, leading to various abnormal scenarios that might occur. Common abnormal scenarios include sudden geological change [1], mudcake events [2], cutter wear [3], face instability [4], and excessive ground settlement [5]. These not only affect the safety of tunnel construction but also increase project costs. Therefore, our research objective is to develop an anomaly detection model that can detect and identify multiple types of abnormal scenarios in shield tunneling construction, providing effective support for risk monitoring and management in the shield tunneling construction process. The existing analysis of anomalies in the shield tunneling process mainly focuses on the following three issues: selecting monitoring parameters for anomaly detection, designing anomaly detection models, and identifying anomaly scenarios. The selection of monitoring parameters for anomaly detection can affect a model's performance. Existing research on anomaly detection in shield tunneling often selectssuitable detection parameters according to the characteristics of abnormal scenarios. One method for monitoring parameter selection is based on empirical research. For example, [PERSON] et al. [6] used cover depth, advance rate, earth pressure, moisture content, soil elastic modulus, and a standard penetration test value to detect excessive ground settlement during shield tunneling. Similarly, [PERSON] et al. [7] summarized advance speed, total thrust, torque, type of conditioning agent, and seven other parameters associated with mudcake events based on more than 30 literature cases and used them as inputs for the mudcake event detection model. Another method for monitoring parameter selection is using feature selection techniques in machine learning. [PERSON] et al. [8] used the Shapley additive explanations (SHAP) method to obtain input features of the excessive ground settlement detection model: torque, vertical deviation, pitching angle, groundwater level, and jack pressure. However, these studies require individual parameter selection for each abnormal scenario, and the selected monitoring parameters are usually specific. Even for the same abnormal scenario, the monitoring parameters summarized by different studies are also different (references [6,8]). As a result, these studies are limited in the types of abnormal scenarios they can detect, and they might not be able to detect some unknown anomalies. Therefore, there is a need to find monitoring parameters that are more universally applicable to detecting various abnormal scenarios in the shield tunneling process. In terms of anomaly detection model design, traditional anomaly detection research usually relies on supervised models, which need labeled data. [PERSON] et al. [9] proposed a random forest-based classification method that needs labeled operational data as input and predicts the abnormal states in the shield tunneling process. However, anomalies in the shield tunneling process are diverse and constitute a minority of the entire dataset, making it challenging to acquire labeled anomaly samples to train the models. Existing research focuses more on unsupervised methods, which detect anomalies according to whether parameter features change. [PERSON] et al. [10] and [PERSON] et al. [11] proposed anomaly detection models that can learn features of construction parameters during a normal shield tunneling process. They suggested that when anomalies occur, the features of the construction parameters will change. This will lead to significant differences between expected features and actual features, which can then be used to determine the occurrence of anomalies.