•  
  •  
 

Abstract

In order to solve the problem of realizing the autonomous deviation correction of tunnel boring machines (TBMs ), a TBM deviation correction control method that integrated the random forest (RF) algorithm with the genetic algorithm (GA) was proposed based on actual engineering data.The method combined a prediction model with an optimization model,using target deviation values as input to invert and output the required TBM deviation correction parameter values,thereby further improving the automation level of TBM deviation correction.By comparing it with the actual data,the feasibility of the model was verified.The results show that the RF algorithm-based prediction model achieves an R2 value of 0.875,an EMSE of 6.287,and an EMAE of 2.03,demonstrating that the accuracy control can meet the requirements of the construction specification.The RF-GA-based TBM deviation correction model can control deviations within 7 mm in real time,and the average accuracy of the output TBM tunneling parameters compared with actual data is above 90%.In the comparative experiment,the variation pattern of TBM parameters output by the control model is consistent with the actual pattern.This paper provides a new approach for achieving TBM attitude control and studying the variation pattern of TBM parameters in practical engineering.

Publication Date

8-15-2025

DOI

10.14048/j.issn.1671-2579.2025.04.022

First Page

174

Last Page

183

Submission Date

August 2025

Reference

[1]陈丹,刘喆,刘建友,等.铁路盾构隧道智能建造技术现状与展望 [J].隧道建设 (中英文 ),2021,41(6):923-932.CHEN Dan,LIU Zhe,LIU Jianyou,et al.State-of-art and prospects for intelligent construction technology for railway shield tunneling [J].Tunnel Construction,2021,41(6):923-932.
[2]HU M,WU B J,ZHOU W B,et al.Self-driving shield:Intelligent systems,methodologies,and practice [J].Automation in Construction,2022,139:104326.
[3]夏汉庸,尹和军,徐教煌,等.基于机器学习的多施工参数盾构施工姿态预测 [J].测绘通报,2021 (1):157-160,164.XIA Hanyong,YIN Hejun,XU Jiaohuang,et al.Multi-construction parameter shield construction attitude prediction based on machine learning [J].Bulletin of Surveying and Mapping,2021 (1):157-160,164.
[4]李增良.基于深度学习的盾构竖向姿态组合预测 [J].隧道建设 (中英文 ),2021,41(5):758-763.LI Zengliang.Combined prediction model for shield vertical posture based on deep learning [J].Tunnel Construction,2021,41(5):758-763.
[5]胡长明,侯雅君,李靓,等.盾构竖向姿态的组合预测模型研究 [J].铁道科学与工程学报,2021,18(6):1521 -1528.HU Changming,HOU Yajun,LI Liang,et al.Research on the combined prediction model of shield vertical attitude[J].Journal of Railway Science and Engineering,2021,18(6):1521 -1528.
[6]ZHOU C,XU H C,DING L Y,et al.Dynamic prediction for attitude and position in shield tunneling:A deep learning method [J].Automation in Construction,2019,105:102840.
[7]ZHANG N,ZHANG N,ZHENG Q,et al.Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network [J].Acta Geotechnica,2022,17(4):1167 -1182.
[8]WANG P,KONG X G,GUO Z K,et al.Prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling [J].IEEE Access,2019,7:163487 -163501.
[9]陈仁朋,邹聂,吴怀娜,等.盾构掘进地表沉降机器学习预测与控制研究综述 [J].华中科技大学学报 (自然科学版 ),2022,50(8):56-65.CHEN Renpeng,ZOU Nie,WU Huaina,et al.Review of prediction and control for surface settlement caused by shield tunneling based on machine learning [J].Journal of Huazhong University of Science and Technology (Natural Science Edition ),2022,50(8):56-65.
[10]汲红旗,宋中华,刘维正,等.泥质砂岩地层盾构掘进施工扰动效应与地表纵向变形预测方法 [J].中外公路,2022,42(1):28-33.JI Hongqi,SONG Zhonghua,LIU Weizheng,et al.Research on shiled tunneling-incuded disturbance and prediction method of surface longitudinal deformation in muddy sandstone strata [J].Journal of China & Foreign Highway,2022,42(1):28-33.
[11]EDELMANN D,MÓRI T F,SZÉKELY G J.On relationships between the Pearson and the distance correlation coefficients [J].Statistics & Probability Letters,2021,169:108960.
[12]徐乔,孟凡利,余绍淮.基于面向对象随机森林分类模型的滑坡遥感解译 [J].中外公路,2019,39(3):30-34.XU Qiao,MENG Fanli,YU Shaohuai.Atellite image landslide interpretation using random forest algorithm based on object-oriented method [J].Journal of China & Foreign Highway,2019,39(3):30-34.[13]翁剑成,孙宇星,孔宁,等.基于多源数据的公交专用道效能评价方法与影响模型 [J].中国公路学报,2022,35(4):267-276.WENG Jiancheng,SUN Yuxing,KONG Ning,et al.Evaluation method and influence model of bus lane performance based on multi-source data [J].China Journal of Highway and Transport,2022,35(4):267-276.
[14]张军辉,范海山,张石平,等.考虑层间接触状态的路面动力响应解析解及参数反演 [J].中国公路学报,2021,34(5):11-23.ZHANG Junhui,FAN Haishan,ZHANG Shiping,et al.Analytical solution for the dynamic responses and parameter inversion of pavement structures considering the condition of interlayer contact [J].China Journal of Highway and Transport,2021,34(5):11-23.
[15]冯阳飞.基于遗传算法的道路平面线形组合设计参数优化[J].中外公路,2020,40(6):1-6.FENG Yangfei.Discussion on optimal design parameter of road curve based on GA [J].Journal of China & Foreign Highway,2020,40(6):1-6.
[16]夏子恒,贺岩松,张志飞,等.基于多目标参考信号优选的车 辆 路 噪 主 动 控 制 [J].中 国 公 路 学 报,2023,36(2):229-239.XIA Ziheng,HE Yansong,ZHANG Zhifei,et al.Road noise cancellation based on multi-objective optimization of reference signals [J].China Journal of Highway and Transport,2023,36(2):229-239.
[17]刘建国.深圳地铁盾构隧道施工技术与经验 [J].隧道建设,2012,32(1):72-87.LIU Jianguo.Overview of shield tunneling in construction of Shenzhen metro [J].Tunnel Construction,2012,32(1):72-87.
[18]吴惠明,常佳奇,李刚,等.基于支持向量机的盾构掘进姿态预测与施工参数优化方法 [J].隧道建设 (中英文 ),2021,41(增刊 1):11-18.WU Huiming,CHANG Jiaqi,LI Gang,et al.Prediction of shield tunneling posture and optimization of construction parameters based on support vector machine [J].Tunnel Construction,2021,41(sup 1):11-18.
[19]王林涛.盾构掘进姿态控制关键技术研究 [D].杭州:浙江大学,2014.WANG Lintao.Research on key technologies of attitude control in shield tunneling [D].Hangzhou:Zhejiang University,2014.
[20]李宏安.砂‒粘复合地层土压平衡盾构隧道开挖面稳定性研究 [D].北京：中国地质大学 （北京）,2013.LI Hongan.Face stability analysis of EPB shield-driving tunnel in sandy-clay mixed ground [D].Beijing:China University of Geosciences,2013.

Download

Share

COinS