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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (8): 1523-1532    DOI: 10.3785/j.issn.1008-973X.2022.08.006
    
Advance rate optimization of earth pressure balance shield based on PSO-SVR
Yuan QIN1(),Hong-gan YU2,Jian-feng TAO2,*(),Hao SUN2,Cheng-liang LIU2
1. Shanghai Tunnel Engineering Co. Ltd, Shanghai 200232, China
2. School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China
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Abstract  

A data-driven utilization factor prediction model was established and the advance rate was further optimized, in view of the lack of research on the utilization factor prediction of tunnel boring machine (TBM). The effects of geological type, driver operation and loads on TBM utilization factor were studied on the basis of on-site data collected from a metro tunnel project in Singapore. A utilization factor prediction method based on support vector regression (SVR) was proposed, and the operational parameters optimization was further carried out with the goal of maximizing the advance rate. SVR was used to build the mapping model between the utilization factor of tunneling cycle and the geological type, loads and operational parameters. Then, optimization equations were established with the maximum advance rate as the objective and the geological type, loads and operational parameters as the constraints. Finally, particle swarm optimization (PSO) was applied to optimize the equations and find the optimal operational parameters under specific geological type. Results showed that R2 of SVR model on the validation set and the test set were 0.729 and 0.625 respectively, which were better than that of multiple linear regression, decision tree, k-nearest neighbors, random forest, AdaBoost and XGBoost models. Moreover, PSO can accurately find out the optimal operational parameters.

Key words： earth pressure balance shield      advance rate optimization      utilization factor prediction      PSO-SVR      operational parameter     
Received: 27 August 2021      Published: 30 August 2022
CLC:  TH 69  
  TU 94  
Fund:  教育部-中国移动联合基金建设项目(MCM20180703)；国家重点研发计划课题资助项目(2018YFB1702503)
Corresponding Authors: Jian-feng TAO     E-mail: qinyuan@stecmc.com;jftao@sjtu.edu.cn
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Yuan QIN
Hong-gan YU
Jian-feng TAO
Hao SUN
Cheng-liang LIU
Cite this article:

Yuan QIN,Hong-gan YU,Jian-feng TAO,Hao SUN,Cheng-liang LIU. Advance rate optimization of earth pressure balance shield based on PSO-SVR. Journal of ZheJiang University (Engineering Science), 2022, 56(8): 1523-1532.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.08.006     OR     https://www.zjujournals.com/eng/Y2022/V56/I8/1523


基于PSO-SVR的土压平衡盾构施工进度优化

针对隧道掘进机(TBM)利用率预测研究匮乏的问题，建立数据驱动的利用率预测模型并进一步对施工进度展开优化. 结合新加坡某地铁隧道项目数据，研究地质类型、司机操作与载荷对TBM利用率的影响，提出基于支持向量回归(SVR)的利用率预测方法，并以施工进度最大为目标展开操作参数优化. 利用SVR建立掘进环利用率与地质类型、载荷、操作参数的映射模型；建立以施工进度最大为目标，以地质类型、载荷、操作参数为约束边界的优化方程；利用粒子群优化(PSO)寻找特定地质类型下最优的操作参数. 结果表明：SVR模型在验证集和测试集上的R2分别为0.729和0.625，均优于多元线性回归、决策树、k最近邻、随机森林、AdaBoost和XGBoost模型；PSO能准确地找出最优的操作参数.


关键词： 土压平衡盾构,  施工进度优化,  利用率预测,  PSO-SVR,  操作参数 
Fig.1 Illustration for support vector regression
Fig.2 Flowchart of advance rate optimization method based on PSO-SVR
Fig.3 Details of studied section
技术参数 设计值
D/mm 6720
P/kW 8×160
Tmax/(kN·m) 8322
n/ (r?min?1) 0~4
Fmax/ kN 49220
vmax/( mm?min?1) 80
Tab.1 Main technical specification of EPB
钻孔编号 上隧道 下隧道
R 地质类型 R 地质类型
1 500 句容组(V) 500 句容组(IV)
2 388 句容组(V) 388 句容组(V)
3 302 句容组(IV) 302 句容组(复合地层)
4 265 句容组(V) 265 句容组(复合地层)
5 229 句容组(V) 229 句容组(复合地层)
6 202 句容组(IV) 202 句容组(IV)
7 192 句容组(V) 192 句容组(IV)
8 137 坎宁堡卵石层 137 坎宁堡卵石层
9 123 坎宁堡卵石层 123 ?
10 119 坎宁堡卵石层 119 坎宁堡卵石层
11 117 坎宁堡卵石层 117 坎宁堡卵石层
12 98 河流黏土 98 坎宁堡卵石层
13 96 河流黏土 96 坎宁堡卵石层
14 63 海洋黏土 64 古冲积层(复合地层)
15 44 海洋黏土 44 河流黏土(复合地层)
Tab.2 Ring number and geological type of borehole
Fig.4 Thrust of NO. 90 ring of upper tunnel varying with system state
Fig.5 Distribution of key parameters of EPB in tunneling state of upper tunnel
Fig.6 Utilization factor of each ring of upper and lower tunnels
Fig.7 Statistics of utilization factor of upper and lower tunnels under different geological types
Fig.8 Influence of operational parameters on utilization factor
Fig.9 Variations of torque and thrust with penetration rate under different geological types
数据集 数据来源 输入特征 回归变量 样本量
训练集 上隧道 T、F、n、v、geo U 67
测试集 下隧道 T、F、n、v、geo U 42
Tab.3 Information of training set and test set
Fig.10 Optimization of SVR model by grid-search method
Fig.11 Performance of SVR model on validation set and test set
Fig.12 Performance of all models on test set
算法 模型关键参数 验证集 测试集
MSE R2 MSE R2
MLR ? 0.005 9 0.602 0.007 2 0.396
RR alpha=100.0 0.005 5 0.630 0.005 6 0.529
DT 'max_depth': 2, 'max_features': 8 0.004 5 0.695 0.028 3 ?1.374
KNN 'n_neighbors': 15 0.007 1 0.520 0.009 0 0.245
SVR kernel='linear',C=0.002 0.003 8 0.729 0.004 5 0.625
RF 'max_depth': 2, 'max_features': 8, 'n_estimators': 25 0.004 4 0.705 0.008 9 0.247
AdaBoost 'learning_rate': 0.015, 'loss': 'linear', 'n_estimators': 7 0.006 8 0.540 0.012 5 ?0.046
XGBoost 'max_depth': 1, 'n_estimators': 10 0.006 5 0.558 0.009 4 0.214
Tab.4 Performance of all models on validation set and test set
Fig.13 Change of objective function value during PSO optimization
Fig.14 Optimal results of PSO in different geological types
地质类型 n*/(r?min?1) v*/(mm?min?1) AR*/(mm?min?1)
句容组(V) 3.50 31.60 8.89
句容组(IV) 3.50 33.43 9.34
坎宁堡卵石层 3.50 31.81 8.78
河流黏土 1.24 30.45 7.49
海洋黏土 3.50 27.68 6.45
Tab.5 Optimization results of PSO under different geological types
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