基于BLSTM-AM模型的TBM稳定段掘进参数预测

doi:10.13722/j.cnki.jrme.2019.1158

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Abstract The ascending branch of tunnel boring machine(TBM) tunnelling data provides rich information for real-time rock mass condition perception and the prediction of boring performance parameter. This paper proposed a bidirectional long short-term memory network combined with attention mechanism to predict the performance parameters in the stable phase of TBM tunneling. In our model，time series data of four main parameters are taken as the main input to extract the rock-machine interaction relationship，and the advance speed and RPM given in the stable phase are taken as auxiliary inputs to consider the human control behavior，and the output of the model is the predicted values of thrust and torque. Different from the traditional prediction model，the proposed model does not require geological parameters，and establishes the mapping relationship between control parameters and performance parameters by automatically learning the characteristics of the ascending branch data. In the process of model establishment，some data preprocessing techniques are used to correct the outlier data，filter noise and normalize data，etc.，and a method for segmenting the ascending and stable branch based on the torque-time curve is proposed. Relying on the Jilin Yinsong water supply tunnel project，the effectiveness and accuracy of the model are verified. The results show that the overall prediction effect of the proposed model is good，which can assist the intelligent construction of TBM with similar geological conditions.

Key words： underground engineering hard rock TBM boring parameters prediction bidirectional long short-term memory(BLSTM) attention mechanism intelligent construction

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	Articles by authors
	ZHOU Xiaoxiong
	GONG Qiuming
	YIN Lijun
	XU Hongyi
	BAN Chao

Cite this article:

ZHOU Xiaoxiong,GONG Qiuming,YIN Lijun, et al. Predicting boring parameters of TBM stable stage based on BLSTM networks combined with attention mechanism[J]. , 2020, 39(S2): 3505-3515.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2019.1158 OR https://rockmech.whrsm.ac.cn/EN/Y2020/V39/IS2/3505

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