TBM shield mud cake prediction model based on machine learning - Zhang, Qi; Xu, Peng; Zhang, Jing; Yang, Zhao; Li, Yu; Kong, Xintong; Yuan, Xiao (Frontiers in Earth Science, 2025)
IntroductionDuring tunnel boring machine (TBM) shield tunneling in clayey strata, the excavated soil consolidates on the cutter head or cutting tools, forming mud cakes that significantly impact the efficiency of shield tunneling.MethodsTo predict mud cakes during shield tunneling, four distinct supervised machine learning models, including logistic regression, support vector machine, random forest, and BP neural network were employed. The optimal predictive model for mud cake formation was determined by assessing the precision, recall, and F1 scores of the models. Further analysis of feature dependencies and shapley additive explanations (SHAP) is conducted to pinpoint the critical risk factors associated with mud cake formation.ResultsThe results indicate that among the four supervised machine learning models, the random forest model exhibited the best performance in predicting mud cake formation during shield tunneling, with an F1 score as high as 0.9934. Feature dependencies and SHAP information showed that the shield tunneling chamber temperature and average excavation speed had the most significant impact on mud cake formation, serving as crucial factors in determining mud cake formation. The rear earth pressure of the screw conveyor and the cutterhead penetration depth followed, constituting important elements in mud cake formation. The introduction of the interpretable method SHAP for analyzing the relationships between various factors extends beyond simple linear relationships, allowing for the examination of nonlinear patterns among factors.