Intelligent Prediction of Tunneling Parameters for Large Diameter Slurry Shield

doi:10.11988/ckyyb.20231316

Abstract

Abstract:

An intelligent prediction model for shield tunneling parameters accounting for geological conditions is presented by employing a geological data processing technique which integrates in-situ stress extraction and tunneling section stratum information coding. The method encompasses data acquisition, preprocessing and decomposition, and model construction, training and testing, as well as result evaluation and analysis. The model is applied to predict the shield parameters for the large-diameter slurry shield tunnel project of the Luyuan North Street section on the Beijing-Harbin Expressway within the Beijing East Sixth Ring Road reconstruction project. Findings reveal that accounting for geological conditions enhances the prediction accuracy of shield thrust and cutter-head torque by 38.53% and 44.86%, respectively. This improvement secures the construction safety of subsequent shield tunneling operations. The research outcomes can serve as a reference for future similar projects.

Key words: shield tunnel, boring parameters, intelligent prediction, geological code, shield thrust, cutter head torque

CLC Number:

U455

LI Jian, TAO Bo-wen, CAI Qi, YAO Jian-qiang, WANG Gan. Intelligent Prediction of Tunneling Parameters for Large Diameter Slurry Shield[J]. Journal of Changjiang River Scientific Research Institute, 2025, 42(3): 148-155.

Figures/Tables 12

Fig.1 Geological profile

Fig.2 Identification of abnormal values by box and line diagram

Table 1 Parameters of shield machine data acquisition

参数名称	单位	符号	参数名称	单位	符号
NO_iA液压力	MPa	PA_i	送泥流量	m³/min	F_in
NO_iB液压力	MPa	PB_i	送泥密度	g/cc	D_in
NO_iA液累计	L	CVA_i	排泥流量	m³/min	F_out
NO_iB液累计	L	CVB_i	排泥密度	m³/min	D_out

Fig.3 Data before and after denoising

Fig.4 Geological information encoding method

Table 2 Data parameters of shield machine

参数名称	单位	符号	参数名称	单位	符号
刀盘转速	rpm	N	切口水圧	bar	P
刀盘扭矩	kN·m	T	气仓压力	bar	C
推进速度	mm/min	V	仓内泥水增加量	g/min	R
盾构推力	kN	F

Fig.5 Bi-LSTM structure

Fig.6 Passive response parameter prediction

Fig.7 Model training process

Fig.8 Comparison of prediction accuracy

Fig.9 Prediction results of shield thrust with and without considering geological conditions

Fig.10 Prediction results of cutterhead torque with and without considering geological conditions

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