Tunnel?boring?machine (TBM) tunneling parameter prediction was conducted based on the Yinsong project in Jilin. A TBM tunneling data segmentation method was proposed to extract features from rising phase and stable phase. Thrust, cutter head speed, advance rate, torque, cutter head speed setting, advance rate setting, penetration rate, field penetration index (FPI) and torque penetration index (TPI) in the first 30 s of rising phase were extracted as the input of the prediction models. The locally linear embedding (LLE) was used to reduce the dimensions of the characteristic data of rising phase. A prediction model for TBM construction control parameters (propulsion speed, cutter head speed) and load parameters (total propulsion force, cutter head torque) was established based on the support vector regression (SVR). The impact on the prediction effect of whether to combine the FPI and TPI indexes of the previous tunneling cycle was analyzed and compared. Results show that favorable prediction effects for propulsion speed, cutter head speed, total propulsion force and cutter head torque were obtained based on the proposed model. The mean absolute percentage errors on the test set were all below 15%. The proposed method can provide guidance for TBM site operation due to the high prediction accuracy.
Jian-bin LI,Ying-ying WU,Peng-yu LI,Xiao-feng ZHENG,Jian-an XU,Xiang-yu JU. TBM tunneling parameters prediction based on Locally Linear Embedding and Support Vector Regression. Journal of ZheJiang University (Engineering Science), 2021, 55(8): 1426-1435.
Fig.1Structure of tunneling parameter prediction model
Fig.2Geographical plan of TBM section 3
Fig.3Proportion of rock strength and rock mass
参数
数值
开挖直径/mm
7 930
滚刀数量
17 inch 8 把，19 inch 48 把
刀盘总推力/kN
23260
刀盘驱动功率/kW
3500
刀盘扭矩/(kN·m)
8410
刀盘脱困扭矩/(kN·m)
12615
最大刀盘转速/(r·min?1)
7.6
Tab.1TBM Specification
Fig.4Geological section map of TBM section 3
Fig.5TBM tunneling cycle segment description
Fig.6Flow chart of tunneling cycle segmentation algorithm
Fig.7Flow chart of thrust and torque prediction model
指标类别
参数
MAE
RMSE
R2
MAPE/%
含上一循环FPI、TPI
F
793.60
1065.97
0.90
6.72
T
174.58
242.05
0.89
8.97
不含上一循环FPI、TPI
F
880.44
1199.78
0.88
7.54
T
257.59
367.71
0.74
14.93
Tab.2Impact of FPI and TPI on thrust and torque prediction results
Fig.8Flow chart of advance rate and cutter head speed prediction model
指标类别
参数
MAE
RMSE
R2
MAPE/%
含上一循环FPI、TPI
v
6.52
9.49
0.50
13.08
n
0.24
0.38
0.82
4.03
不含上一循环FPI、TPI
v
7.87
11.18
0.30
16.04
n
0.32
0.47
0.72
5.46
Tab.3Impact of FPI and TPI on prediction results of advance rate and cutter head speed
Fig.9Prediction results of thrust and torque
Fig.10Prediction results of advance rate and cutter head speed
Fig.11Advance rate and cutter head speed per minute in one TBM tunneling cycle
Fig.12Thrust prediction per minute in one TBM tunneling cycle
Fig.13Torque prediction per minute in one TBM tunneling cycle
预测参数
数据集
MAE
MAPE%
R2
RMSE
总推进力
训练集
698.01
5.76
0.91
970.19
测试集
810.65
6.63
0.89
1086.04
刀盘扭矩
训练集
149.65
7.14
0.90
214.17
测试集
161.26
7.53
0.89
223.99
推进速度
训练集
5.81
11.29
0.62
8.35
测试集
6.71
13.43
0.52
9.46
刀盘转速
训练集
0.21
3.65
0.84
0.36
测试集
0.24
4.04
0.81
0.39
Tab.4Prediction results of tunneling parameter prediction model
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