## 6 Conclusions  
This study developed a TBM cutterhead rotation speed prediction model based on dimensional analysis, integrating physical knowledge with data analysis to address the traditional limitations in interpreting parameter relationships. The key findings are as follows:  
1. The TBM cutterhead rotation speed prediction model, established using dimensional analysis and elastic net regression, achieved accurate predictions after comprehensive data preprocessing. Validation on an independent test set demonstrated the proposed model's strong predictive ability and broad applicability. 2. By employing numerical filtering, boxplot analysis, and low-pass filtering techniques, the noise and interference from outliers in the excavation data were significantly reduced. This improved the model's stability and prediction accuracy, providing crucial theoretical support for advancing intelligent tunneling with shield TBMs. This study developed a tunnel boring machine (TBM) cutterhead rotation speed prediction model based on dimensional analysis and elastic net regression. By integrating physical knowledge with data analytics, it addresses the limitations of traditional methods in explaining parameter relationships. The main conclusions are as follows:  
1. This study successfully developed a tunnel boring machine (TBM) cutterhead rotational speed prediction model based on dimensional analysis and elastic net regression. The validation results demonstrated a high degree of consistency between the predicted and actual values, with the majority of prediction errors within 15%, and 90% of the errors below 10%. These results indicate that the model can effectively and accurately predict the cutterhead rotational speed. 2. Through data preprocessing techniques such as numerical filtering, boxplot analysis, and low-pass filtering, significant reductions in noise and outliers in the excavation data were achieved. This further enhanced the model's stability and prediction accuracy, providing reliable support for data-driven modeling in complex operating conditions. 3. By integrating dimensional analysis with elastic net regression optimization, the study effectively balanced the L1 and L2 regularization terms, ensuring both the physical interpretability of the model and its adaptability. This provides a theoretical foundation for capturing the complex relationships between the TBM cutterhead rotational speed and input parameters. 4. This research offers theoretical support for the intelligent tunneling of TBMs. Future work could focus on improving data quality, expanding the range of model parameters, and combining machine learning techniques for predictive maintenance, further enhancing the operational efficiency and stability of TBM operations. In summary, this study successfully constructed a TBM cutterhead rotational speed prediction model based on dimensional analysis and elastic net regression, validating its high accuracy and broad applicability under various working conditions. By optimizing data processing methods and parameter selection, the model not only improved the stability and prediction accuracy of TBM operations but also laid the foundation for the further development of intelligent tunneling technology. Future research should aim to further enhance data quality, expand the model's applicability, and incorporate intelligent maintenance technologies to improve the efficiency and safety of TBM operations. J.L. : writing--original draft preparation, data curation; F.L. : investigation, data curation; K.W. : writing--original draft preparation, methodology; C.Z. : writing--review and editing, conceptualization, project administration. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.