Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data

doi:10.1007/s11709-022-0823-3

Front. Struct. Civ. Eng.

2022, Vol. 16

Issue (4) : 401-413 https://doi.org/10.1007/s11709-022-0823-3

RESEARCH ARTICLE

Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data

Zaobao LIU^1,²(

), Yongchen WANG¹, Long LI¹, Xingli FANG³, Junze WANG¹

¹. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Institute of Deep Engineering and Intelligent Technology, Northeastern University, Shenyang 110819, China
². State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China
³. Software College, Northeastern University, Shenyang 110819, China

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Abstract

Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

Keywords hard rock tunnel tunnel bore machine advance rate prediction temporal convolutional networks soft computing construction big data

Corresponding Author(s): Zaobao LIU

Just Accepted Date: 12 April 2022 Online First Date: 27 June 2022 Issue Date: 09 August 2022

Cite this article:

Zaobao LIU,Yongchen WANG,Long LI, et al. Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data[J]. Front. Struct. Civ. Eng., 2022, 16(4): 401-413.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0823-3
https://academic.hep.com.cn/fsce/EN/Y2022/V16/I4/401

Fig.1 Causal convolution structure.

Fig.2 Residual block structure.

Fig.3 One-dimensional dilated temporal convolution with two blocks.

Fig.4 Flow chart of TCN.

Fig.5 Flow chart of advance rate prediction model.

Fig.6 Variation trend of some parameters in complete driving cycle.

Fig.7 Variation trend of some parameters in typical driving cycle.

Fig.8 Correlation analysis between characteristic parameters and advance rate: (a) total thrust; (b) penetration rate; (c) cutterhead rotation and (d) cutterhead torque.

Tab.1 Hyper-parameters of the advance rate prediction model

Fig.9 Comparison of measured value and TCN predicted value of advance rate.

Tab.2 Performance of TCN-based model on different data sets

Tab.3 Models prediction results of advance rate on different datasets based on R²

Fig.10 Comparison of the predicted and measured advance rate based on RNNs: (a) RNN model and (b) LSTM model.

Fig.11 Comparison of absolute errors of all models.

Fig.12 Partial comparison between the measured and the predicted value of advance rate: (a) early period and (b) stable period.

Tab.4 Comparison of performance evaluation indexes of prediction models

Fig.13 Optimal cycles of evaluation indexes of each model.

Fig.14 Fitting of predicted and measured advance rate: (a) RNN model; (b) LSTM model; (c) TCN model.

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