Construction and Building Materials, Год журнала: 2025, Номер 482, С. 141721 - 141721
Опубликована: Май 9, 2025
Язык: Английский
Construction and Building Materials, Год журнала: 2025, Номер 482, С. 141721 - 141721
Опубликована: Май 9, 2025
Язык: Английский
Engineering Failure Analysis, Год журнала: 2025, Номер unknown, С. 109332 - 109332
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Frontiers in Earth Science, Год журнала: 2025, Номер 13
Опубликована: Март 3, 2025
Introduction During tunnel boring machine (TBM) shield tunneling in clayey strata, the excavated soil consolidates on cutter head or cutting tools, forming mud cakes that significantly impact efficiency of tunneling. Methods To predict during tunneling, four distinct supervised learning models, including logistic regression, support vector machine, random forest, and BP neural network were employed. The optimal predictive model for cake formation was determined by assessing precision, recall, F1 scores models. Further analysis feature dependencies shapley additive explanations (SHAP) is conducted to pinpoint critical risk factors associated with formation. Results results indicate among forest exhibited best performance predicting an score as high 0.9934. Feature SHAP information showed chamber temperature average excavation speed had most significant formation, serving crucial determining rear earth pressure screw conveyor cutterhead penetration depth followed, constituting important elements introduction interpretable method analyzing relationships between various extends beyond simple linear relationships, allowing examination nonlinear patterns factors.
Язык: Английский
Процитировано
0Tunnelling and Underground Space Technology, Год журнала: 2025, Номер 161, С. 106541 - 106541
Опубликована: Март 5, 2025
Язык: Английский
Процитировано
0Construction and Building Materials, Год журнала: 2025, Номер 482, С. 141721 - 141721
Опубликована: Май 9, 2025
Язык: Английский
Процитировано
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