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A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm

引用

Journal of Rock Mechanics and Geotechnical Engineering 2022年第3期14卷 798-812页

作者： 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 MechanicsChinese Academy of SciencesWuhan430071China Key Laboratory of Geotechnical and Structural Engineering Safety of Hubei Province School of Civil EngineeringWuhan UniversityWuhan430072China The 2nd Engineering Company of China Railway 12th Bureau Group Taiyuan030032China

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 ***,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 ***,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 ***,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 ***,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is *** stopping and checkpoint algorithms are integrated to optimize the training ***,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling *** 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 ***,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM *** 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

关键词： Tunnel boring machine(TBM) Real-time cutter-head torque prediction Bidirectional long short-term memory (BLSTM) Bayesian optimization multi-algorithm fusion optimization Incremental learning

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