To more accurately estimate and control the magnitude of the shield tail clearance,a hybrid deep learning model with the integration of an online physics-informed deep neural network(online PDNN)and non-dominated sort...To more accurately estimate and control the magnitude of the shield tail clearance,a hybrid deep learning model with the integration of an online physics-informed deep neural network(online PDNN)and non-dominated sorting genetic algorithm-II(NSGA-II)is developed.The online PDNN has evolved from a deep learning framework constrained by the underlying physical mechanism of shield tail clearance measurements.The algorithm is used to forecast the shield tail clearance in tunnel boring machines(TBMs).The NSGA-II is employed to conduct the multi-objective optimization(MOO)process for shield tail clearance.The proposed method is validated in a tunnel case in China.Experimental results reveal that:(1)In comparison with some state-of-the-art algorithms,the online PDNN model demonstrates superior capability in predicting shield tail clearance above,upper-left,and upper-right,with R^(2)scores of 0.93,0.90,and 0.90,respectively;(2)The MOO achieves a comprehensive optimal solution,with the overall improvement percentage of shield tail clearance reaching 30.87%and a hypervolume of 32 under the 20%constraint condition,which surpasses the average performance of other MOO frameworks by 23 and 5.48%,respectively.The novelty of this research lies in coupling the constructed physical constraints and the online update mechanism into a causal analysis-oriented data-driven model,which not only enhances the model’s performance and interpretability but also realizes the control for the shield tail clearance by the integration of NSGA-II.展开更多
The use of shield method in tunnel construction is limited by the engineering conditions of highwater pressure.This is mainly due to the uncertainty of the pressure-bearing capacity of the sealing chambers of the shie...The use of shield method in tunnel construction is limited by the engineering conditions of highwater pressure.This is mainly due to the uncertainty of the pressure-bearing capacity of the sealing chambers of the shield tail under different grades and conditions when subjected to different external water pressures.Therefore,it is crucial to determine the pressure-bearing capacity of the sealing chambers.However,there is a lack of studies on the calculating method of the pressure-bearing capacity,which requires more theoretical investigation.To explore the common patterns of multi-grade sealing-related parameters and quantify the pressure-bearing capacity of the sealing chambers,a breakdown and leakage model of the shield tail is proposed,targeting the basic sealing unit of the system.Based on non-Newtonian fluid dynamics and fractal theory of porous media,the model is used to calculate the breakdown pressure and grease seepage rate corresponding to tunneling and shutdown states.In addition,a hydraulic breakdown device of the sealing unit of the static shield tail is built to investigate the relationship between the shield tail clearance and the shield tail brush porous media area,which helps to verify the theoretical model.Finally,the analysis of sealing chamber geometry parameters,grease rheological parameters,and an environmental parameter using the proposed theoretical model shows that the pressure-bearing capacity of the shield tail can be improved by increasing the shield tail clearance and grease yield stress.It also shows that the length of the sealing chamber and the plastic viscosity of the grease do not have a significant effect on the breakdown pressure of the shield tail.The model proposed in this paper will provide ideas for the calculation of the pressure-bearing capacity of multi-grade sealing chambers in the future.展开更多
基金supported in part by the National Natural Science Foundation of China(Grand No.72271101)the Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources,China(No.QTKS0034W25018).
文摘To more accurately estimate and control the magnitude of the shield tail clearance,a hybrid deep learning model with the integration of an online physics-informed deep neural network(online PDNN)and non-dominated sorting genetic algorithm-II(NSGA-II)is developed.The online PDNN has evolved from a deep learning framework constrained by the underlying physical mechanism of shield tail clearance measurements.The algorithm is used to forecast the shield tail clearance in tunnel boring machines(TBMs).The NSGA-II is employed to conduct the multi-objective optimization(MOO)process for shield tail clearance.The proposed method is validated in a tunnel case in China.Experimental results reveal that:(1)In comparison with some state-of-the-art algorithms,the online PDNN model demonstrates superior capability in predicting shield tail clearance above,upper-left,and upper-right,with R^(2)scores of 0.93,0.90,and 0.90,respectively;(2)The MOO achieves a comprehensive optimal solution,with the overall improvement percentage of shield tail clearance reaching 30.87%and a hypervolume of 32 under the 20%constraint condition,which surpasses the average performance of other MOO frameworks by 23 and 5.48%,respectively.The novelty of this research lies in coupling the constructed physical constraints and the online update mechanism into a causal analysis-oriented data-driven model,which not only enhances the model’s performance and interpretability but also realizes the control for the shield tail clearance by the integration of NSGA-II.
基金supported by the National Natural Science Foundation of China(Grant No.52378387)the Beijing Nova Program(Grant No.20220484037)China Railway 14th Bureau Group Corporation Limited Science and Technology R&D Program Subjects(No.913700001630559891202201).
文摘The use of shield method in tunnel construction is limited by the engineering conditions of highwater pressure.This is mainly due to the uncertainty of the pressure-bearing capacity of the sealing chambers of the shield tail under different grades and conditions when subjected to different external water pressures.Therefore,it is crucial to determine the pressure-bearing capacity of the sealing chambers.However,there is a lack of studies on the calculating method of the pressure-bearing capacity,which requires more theoretical investigation.To explore the common patterns of multi-grade sealing-related parameters and quantify the pressure-bearing capacity of the sealing chambers,a breakdown and leakage model of the shield tail is proposed,targeting the basic sealing unit of the system.Based on non-Newtonian fluid dynamics and fractal theory of porous media,the model is used to calculate the breakdown pressure and grease seepage rate corresponding to tunneling and shutdown states.In addition,a hydraulic breakdown device of the sealing unit of the static shield tail is built to investigate the relationship between the shield tail clearance and the shield tail brush porous media area,which helps to verify the theoretical model.Finally,the analysis of sealing chamber geometry parameters,grease rheological parameters,and an environmental parameter using the proposed theoretical model shows that the pressure-bearing capacity of the shield tail can be improved by increasing the shield tail clearance and grease yield stress.It also shows that the length of the sealing chamber and the plastic viscosity of the grease do not have a significant effect on the breakdown pressure of the shield tail.The model proposed in this paper will provide ideas for the calculation of the pressure-bearing capacity of multi-grade sealing chambers in the future.
