Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)-gated recurrent unit (GRU) neural network  

作　　者：Ke Man Liwen Wu Xiaoli Liu Zhifei Song Kena Li Nawnit Kumar 

机构地区：[1]College of Civil Engineering,North China University of Technology,Beijing,China [2]Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,Shenzhen University,Shenzhen,China [3]State Key Laboratory of Hydroscience and Engineering,Tsinghua University,Beijing,China

出　　处：《Deep Underground Science and Engineering》2024年第4期413-425,共13页深地科学（英文）

基　　金：State Key Laboratory of Hydroscience and Hydraulic Engineering of Tsinghua University,Grant/Award Number:2019-KY-03;Key Technology of Intelligent Construction of Urban Underground Space of North China University of Technology,Grant/Award Number:110051360022XN108-19;Research Start-up Fund Project of North China University of Technology,Grant/Award Number:110051360002;Yujie Project of North China University of Technology,Grant/Award Number:216051360020XN199/006;National Natural Science Foundation of China,Grant/Award Numbers:51522903,51774184;National Key R&D Program of China,Grant/Award Numbers:2018YFC1504801,2018YFC1504902。

摘　　要：Due to the complexity of underground engineering geology,the tunnel boring machine(TBM)usually shows poor adaptability to the surrounding rock mass,leading to machine jamming and geological hazards.For the TBM project of Lanzhou Water Source Construction,this study proposed a neural network called PCA-GRU,which combines principal component analysis(PCA)with gated recurrent unit(GRU)to improve the accuracy of predicting rock mass classification in TBM tunneling.The input variables from the PCA dimension reduction of nine parameters in the sample data set were utilized for establishing the PCA-GRU model.Subsequently,in order to speed up the response time of surrounding rock mass classification predictions,the PCA-GRU model was optimized.Finally,the prediction results obtained by the PCA-GRU model were compared with those of four other models and further examined using random sampling analysis.As indicated by the results,the PCA-GRU model can predict the rock mass classification in TBM tunneling rapidly,requiring about 20 s to run.It performs better than the previous four models in predicting the rock mass classification,with accuracy A,macro precision MP,and macro recall MR being 0.9667,0.963,and 0.9763,respectively.In Class II,III,and IV rock mass prediction,the PCA-GRU model demonstrates better precision P and recall R owing to the dimension reduction technique.The random sampling analysis indicates that the PCA-GRU model shows stronger generalization,making it more appropriate in situations where the distribution of various rock mass classes and lithologies change in percentage.

关 键 词：gated recurrent unit(GRU) prediction of rock mass classification principal component analysis(PCA) TBM tunneling 

分 类 号：U455.31[建筑科学—桥梁与隧道工程]