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 cutter-head 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 algo-rithm 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 BLSTM-based 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 per-formance 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(R2)and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for real-time cutter-head torque prediction and can effectively improve the prediction accuracy and general-ization capacity of the model during the excavation process.

Xing Huang;Quantai Zhang;Quansheng Liu;Xuewei Liu;Bin Liu;Junjie Wang;Xin Yin

State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan,430071,China;Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province,School of Civil Engineering,Wuhan University,Wuhan,430072,China;The 2nd Engineering Company of China Railway 12th Bureau Group,Taiyuan,030032,China

岩石力学与岩土工程学报（英文版）

[1]Xing Huang;Quantai Zhang;Quansheng Liu;Xuewei Liu;Bin Liu;Junjie Wang;Xin Yin-.A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm)[J].岩石力学与岩土工程学报（英文版）,2022(03):798-812

Songhua

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