典型文献
Machine learning-based automatic control of tunneling posture of shield machine
文献摘要：
For a tunnel driven by a shield machine,the posture of the driving machine is essential to the con-struction quality and environmental impact.However,the machine posture is controlled by the expe-rienced driver of shield machine by setting hundreds of tunneling parameters empirically.Machine learning(ML)algorithm is an alternative method that can let the computer to learn from the driver's operation and try to model the relationship between parameters automatically.Thus,in this paper,three ML algorithms,i.e.multi-layer perception(MLP),support vector machine(SVM)and gradient boosting regression(GBR),are improved by genetic algorithm(GA)and principal component analysis(PCA)to predict the tunneling posture of the shield machine.A set of the parameters for shield tunneling is extracted from the construction site of a Shanghai metro.In total,53,785 pairwise data points are collected for about 373 d and the ratio between training set,validation set and test set is 3:1:1.Each pairwise data point includes 83 types of parameters covering the shield posture,construction parame-ters,and soil stratum properties at the same time.The test results show that the averaged R2 of MLP,SVM and GBR based models are 0.942,0.935 and 0.6,respectively.Then the automatic control for the posture of shield tunnel is illustrated with an application example of the proposed models.The proposed method is proved to be helpful in controlling the construction quality with optimized construction parameters.
文献关键词：
中图分类号：
作者姓名：
Hongwei Huang;Jiaqi Chang;Dongming Zhang;Jie Zhang;Huiming Wu;Gang Li
作者机构：
Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education,Tongji University,Shanghai,China;Department of Geotechnical Engineering,Tongji University,Shanghai,China;Shanghai Tunnel Engineering Co.,Ltd.,Shanghai,China
文献出处：
引用格式：
[1]Hongwei Huang;Jiaqi Chang;Dongming Zhang;Jie Zhang;Huiming Wu;Gang Li-.Machine learning-based automatic control of tunneling posture of shield machine)[J].岩石力学与岩土工程学报（英文版）,2022(04):1153-1164
A类：
B类：
Machine,learning,tunneling,posture,shield,machine,For,driven,by,driving,essential,quality,environmental,impact,However,controlled,expe,rienced,driver,setting,hundreds,parameters,empirically,alternative,method,that,can,let,computer,from,operation,try,relationship,between,automatically,Thus,this,paper,three,algorithms,multi,layer,perception,MLP,support,vector,gradient,boosting,regression,GBR,are,improved,genetic,GA,principal,component,analysis,predict,extracted,construction,site,Shanghai,metro,In,total,pairwise,data,points,collected,about,training,validation,test,Each,includes,types,covering,soil,stratum,properties,same,results,show,averaged,models,respectively,Then,illustrated,application,example,proposed,helpful,controlling,optimized
AB值：
0.459818
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