JRMGE / Vol 14 / Issue 3

Article

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

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

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


2022, 14(3): 798-812. doi:10.1016/j.jrmge.2021.11.008


Received: 2021-05-20 / Revised: 2021-11-16 / Accepted: 2021-11-21 / Available online: 2022-03-04

2022, 14(3): 798-812.

doi:10.1016/j.jrmge.2021.11.008


Received: 2021-05-20

Revised: 2021-11-16

Accepted: 2021-11-21

Available online: 2022-03-04


Abstract:

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 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 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 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 (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 generalization capacity of the model during the excavation process.

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Keywords: 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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Xing Huang, Quantai Zhang, Quansheng Liu, Xuewei Liu, Bin Liu, Junjie Wang, Xin Yin, 2022. A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm. J. Rock Mech. Geotech. Eng. 14 (3), 798-812.

Author(s) Information

Xing Huang

Dr. Xing Huang obtained his PhD in Institute of Rock and Soil Mechanics, Chinese Academy of Sciences in 2014. He is an associate professor and master supervisor working at Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. His research interests include: (1) intelligent TBM/ shield tunneling in complex strata, (2) prediction and control of large deformation disaster in soft surrounding rock, and (3) intelligent perception, recognition and decision control methods in coal mining process. He has published more than 40 journal papers, and has won a second prize of National Science and Technology Progress Award (China), one special prize, one first prize and two second prizes of Provincial Science and Technology Progress Award.