Abstract: Accurate quantification and classification of muck caking remain challenging, limiting the effectiveness of early warning during earth pressure balance shield tunneling in a highly cohesive stratum. To address these challenges, a case study was conducted on a section between Guigu Street Station and West Fanrong Road Station of the Changchun Metro Line 5, and an intelligent muck caking grading and early warning framework integrating K-means clustering, random oversampling (ROS), Shapley additive explanations (SHAP), and long short-term memory (LSTM) networks is proposed. First, tunneling parameters and muck conditioning parameters highly correlated with the evolution of muck caking were screened and selected. K-means clustering was then used to automatically classify the muck caking grade, and the rationality and engineering significance of the grading results were verified. Based on the clustering results, an LSTM time-series model for predicting muck caking grades was constructed, and the ROS strategy was introduced to mitigate class imbalance problems for the samples. Finally, the effectiveness of the model prediction was evaluated using SHAP analysis. The results show the following: (1) The K-means clustering method divides muck caking into four grades, and the grading results present strong engineering interpretability. (2) Compared with the LSTM and artificial neural network models, the ROS-LSTM model achieves the highest weighted F₁ scores at all prediction time-steps and delivers more balanced performance across all categories. Specifically, the false-negative rate of misclassifying muck caking samples as normal tunneling samples is consistently below 6%, which can provide a reference for multistep early warning of muck caking in onsite construction. (3) SHAP analysis indicates that all factors contribute to the muck caking grading classification, and the decision-making behavior of the model is consistent with the engineering laws in the evolution of muck caking, further verifying the rationality of feature selection and clustering-based grading results.

Key words: earth pressure balance shield, muck caking grading, K-means clustering, long short-term memory network, imbalanced samples, explainable machine learning