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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (4): 633-641    DOI: 10.3785/j.issn.1008-973X.2020.04.001
Mechanical Engineering，Electrical Engineering     
XGBoost based intelligent determination system design of tunnel boring machine operation parameters
Fei WANG(),Guo-fang GONG*(),Li-wen DUAN,Yong-feng QIN
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
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Abstract  

An intelligent determination method was presented for the operating parameters of hard rock tunnel boring machine (TBM) based on extreme gradient boosting (XGBoost) prediction model in order to realize the homogeneity of the tunnel constructions. The field operation index (FOI) was defined as the characteristic parameter of the surrounding rock types in order to replace field penetration index (FPI), and the XGBoost based prediction model was established to realize the accurate prediction of FOI value. The expert model was established to associate the FOI value and the specific TBM operation parameters selected by excellent drivers. Then the intelligent determination of the TBM operation parameters can be accomplished. The experiments on practical engineering data show that the operation parameter can be estimated by the proposed parameters determination system. The experimental results indicated that the mean relative error of thrust speed and cutterhead rotational speed decreased by 8.84 % and 7.97 % compared with the conventional system.

Key words： tunnel boring machine (TBM)      intelligent determination      field operation index (FOI)      extreme gradient boosting (XGBoost)      prediction     
Received: 26 March 2019      Published: 05 April 2020
CLC:  TH 137  
Corresponding Authors: Guo-fang GONG     E-mail: tropicalfei@zju.edu.cn;gfgong@zju.edu.cn
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Fei WANG
Guo-fang GONG
Li-wen DUAN
Yong-feng QIN
Cite this article:

Fei WANG,Guo-fang GONG,Li-wen DUAN,Yong-feng QIN. XGBoost based intelligent determination system design of tunnel boring machine operation parameters. Journal of ZheJiang University (Engineering Science), 2020, 54(4): 633-641.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.04.001     OR     http://www.zjujournals.com/eng/Y2020/V54/I4/633


基于XGBoost的隧道掘进机操作参数智能决策系统设计

为了实现隧道施工的同质化，提出基于极端梯度提升算法（XGBoost）预测模型的隧道掘进机（TBM）操作参数的智能决策方法. 定义场操作系数指数（FOI）作为替代传统场切深指数（FPI）的围岩级别特征参数，使用XGBoost算法建立预测模型以实现对FOI的预测，对围岩级别进行预测、判断. 通过对优秀司机在特定FOI下TBM操作参数的选择，建立专家模型实现FOI与特定TBM操作参数的关联，实现TBM操作参数的智能决策. 使用引松工程的现场数据进行对比实验，结果表明，设计的TBM操作参数的智能决策系统能够实现对优秀的TBM司机操作参数决策的复现，相比于以FPI为特征参数的传统智能决策系统，新系统的推进速度和刀盘转速两部分的平均相对误差分别下降8.84 %和7.97 %.


关键词： 隧道掘进机(TBM),  智能决策,  场操作系数指数（FOI）,  极端梯度提升算法（XGBoost）,  预测 
Fig.1 Arrangement of TBM construction in Yinsong project
Fig.2 Proportion of surrounding rock grade in project
Fig.3 Penetration distribution in training data set
Fig.4 Relationship between penetration and mileage under normal excavation condition
Fig.5 Comparison of penetration data before and after removal of initial excavation stage
Fig.6 Relationship between total thrust and penetration under different surrounding rock grades
围岩级别 a b R2
II 3.47 0.54 0.78
III 3.84 0.34 0.84
IV 4.07 0.23 0.83
V 4.11 0.13 0.79
Tab.1 Parameter list of data fitting for different surrounding rock type
Fig.7 Relationship between FPI and thrust speed
参数 数值
粒子个数 100
最大迭代次数 100
惯性权重 0.5
加速度权重 0.5
加速度权重 0.5
最小适应度差 0.001
Tab.2 Hyperparametric table of PSO algorithm
Fig.8 Relationship between total thrust force and penetration rate
Fig.9 Relationship between total thrust force and operation index
Fig.10 Flow chart of XGBoost algorithm
Fig.11 Influence of model input length on model training time
Fig.12 Influence of model input length on mean relative error and ${R^2}$
Fig.13 Variation of mean relative error with prediction length
Fig.14 FOI prediction performance
Fig.15 Operation parameters selection of driver in different field operation index
Fig.16 Comparison of man-machine decision on thrust speed
Fig.17 Comparison of man-machine decision on cutterhead rotary speed
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