## 5 Conclusions  
This paper proposes the AD_SI model to realize anomaly detection and identification for the shield tunneling process. This model has two main phases: detecting various anomalies and identifying the specific abnormal scenarios. In this method, energy consumption data are innovatively proposed as a monitoring parameter for anomaly detection. By constructing an anomaly detection model based on energy consumption, we aim to detect various anomalies during the shield tunneling process, thereby enhancing the model's applicability. In addition, we further identify the scenarios of the anomalies based on the relationship among different parameters and thus provide a more straightforward explanation for the detected anomalies.  
The model was applied to a subway tunnel construction project in Nanjing, and the following conclusions were obtained:  
1. The energy consumption data during the shield tunneling process can serve as a monitoring parameter for anomaly detection. The AD_SI model we propose can detect various anomalies in time based on the energy consumption data. Furthermore, the AD_SI model performs better than models that are based on other commonly used monitoring parameters.
2. The representation and identification of shield tunneling scenarios can be realized based on the correlation among construction parameters. After detecting prolonged anomalies, the AD_SI model uses the verification based on the correlation of parameters to accurately identify the shield tunneling scenario, thereby realizing anomaly  
Figure 11: The performance of each anomaly detection model on abnormal event 3.  
identification. This provides a further explanation for abnormal energy consumption behavior.
3. Compared to all the comparative models, the AD_SI model exhibits higher sensitivity, enabling it to detect various anomalies in time. Its warning time is significantly earlier than the engineers' discovery time, which can provide effective support for risk monitoring and disposal in the shield tunneling process.  
However, the AD_SI model still has certain limitations. Firstly, we only considered the linear relationships among the parameters when realizing anomaly identification. Future research could take into consideration the non-linear relationships among the parameters. In addition, we only validated the detection and identification performance of the AD_SI model on the geological transition and mudcake events. Future research could explore applying the model to the detection of other shield tunneling abnormal scenarios, thus further enhancing the model's practicality.  
Conceptualization, M.H. and H.W.; methodology, F.Z. and M.H.; formal analysis, F.Z. and M.H.; investigation, M.H. and F.Z.; resources, H.W.; data curation, F.Z. and H.W.; writing--original draft preparation, F.Z. and M.H.; writing--review and editing, M.H. and H.W.; visualization, M.H. and F.Z.; supervision, M.H. and H.W.; project administration, M.H. and H.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement The data in the case study are not publicly available due to the confidentiality requirement of the project. The authors would like to express their sincere appreciation to the editor and the anonymous reviewers for their invaluable comments and constructive suggestions on earlier versions of this manuscript. The authors would like to thank [PERSON] for providing help.  
Author [PERSON] was employed by the company Shanghai Tunnel Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.