Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented...Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.展开更多
The breakage and bending of ducts result in a difficulty to cope with ventilation issues in bidirectional excavation tunnels with a long inclined shaft using a single ventilation method based on ducts.To discuss the h...The breakage and bending of ducts result in a difficulty to cope with ventilation issues in bidirectional excavation tunnels with a long inclined shaft using a single ventilation method based on ducts.To discuss the hybrid ventilation system applied in bidirectional excavation tunnels with a long inclined shaft,this study has established a full-scale computational fluid dynamics model based on field tests,the Poly-Hexcore method,and the sliding mesh technique.The distribution of wind speed,temperature field,and CO in the tunnel are taken as indices to compare the ventilation efficiency of three ventilation systems(duct,duct-ventilation shaft,duct–ventilated shaft-axial fan).The results show that the hybrid ventilation scheme based on duct-ventilation shaft–axial fan performs the best among the three ventilation systems.Compared to the duct,the wind speed and cooling rate in the tunnel are enhanced by 7.5%–30.6%and 14.1%–17.7%,respectively,for the duct-vent shaft-axial fan condition,and the volume fractions of CO are reduced by 26.9%–73.9%.This contributes to the effective design of combined ventilation for bidirectional excavation tunnels with an inclined shaft,ultimately improving the air quality within the tunnel.展开更多
基金financially supported by the National Natural Science Foundation of China (Grant Nos. 52074258, 41941018, and U21A20153)
文摘Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process.
基金Project(N2022G031)supported by the Science and Technology Research and Development Program Project of China RailwayProjects(2022-Key-23,2021-Special-01A)supported by the Science and Technology Research and Development Program Project of China Railway Group LimitedProject(52308419)supported by the National Natural Science Foundation of China。
文摘The breakage and bending of ducts result in a difficulty to cope with ventilation issues in bidirectional excavation tunnels with a long inclined shaft using a single ventilation method based on ducts.To discuss the hybrid ventilation system applied in bidirectional excavation tunnels with a long inclined shaft,this study has established a full-scale computational fluid dynamics model based on field tests,the Poly-Hexcore method,and the sliding mesh technique.The distribution of wind speed,temperature field,and CO in the tunnel are taken as indices to compare the ventilation efficiency of three ventilation systems(duct,duct-ventilation shaft,duct–ventilated shaft-axial fan).The results show that the hybrid ventilation scheme based on duct-ventilation shaft–axial fan performs the best among the three ventilation systems.Compared to the duct,the wind speed and cooling rate in the tunnel are enhanced by 7.5%–30.6%and 14.1%–17.7%,respectively,for the duct-vent shaft-axial fan condition,and the volume fractions of CO are reduced by 26.9%–73.9%.This contributes to the effective design of combined ventilation for bidirectional excavation tunnels with an inclined shaft,ultimately improving the air quality within the tunnel.
