基于机器学习的敞开式TBM掘进参数预测模型研究  

Research on Machine Learning Based Open TBM Excavation Parameter Prediction Model

作　　者：张立龙 ZHANG Lilong(China Railway 16th Bureau Group Second Engineering Co.,Ltd.,Tianjin 300162,China)

机构地区：[1]中铁十六局集团第二工程有限公司,天津300162

出　　处：《河南科技》2025年第4期55-62,共8页Henan Science and Technology

摘　　要：【目的】为促进TBM智能、安全、高效施工,依托新疆某输水工程,现场采集TBM掘进参数与岩石参数,基于TBM稳定段的工作参数建立岩-机互馈模型。【方法】首先,确定岩体参数为岩石抗压强度、围岩等级,TBM参数为贯入度、推力、刀盘转矩、刀盘转速;其次,取每个掘进循环的稳定段的TBM工作参数的均值作为该循环TBM的参数值,进行模型训练集与验证集的划分;最后,分别通过最小二乘法、支持向量机法、单层神经网络法、双层神经网络法、随机森林法等5种方法建立岩-机互馈模型,并对各模型的预测结果进行对比分析。【结果】结果表明,采用双层神经网络建立映射关系模型的预测效果最好,可将贯入度、推力、刀盘转速、刀盘扭矩的预测偏差分别控制在0.2 mm/r、1500kN、0.5 r/min、9 kN·m以内。【结论】该方法可以提高TBM掘进效率,促进TBM设备智能化,为TBM安全高效施工提供保障。[Purposes]To facilitate the intelligent,safe,and efficient construction of TBM,this study re⁃lies on a water conveyance project in Xinjiang to collect on-site TBM excavation parameters and rock pa⁃rameters,establishing a rock-machine mutual feedback model based on the operational parameters of TBM during stable excavation segments.[Methods]Initially,the rock mass parameters are defined as rock compressive strength and surrounding rock grade,while the TBM parameters include penetration rate,thrust,cutterhead torque,and cutterhead rotation speed.Subsequently,the mean values of TBM op⁃erational parameters during the stable phase of each excavation cycle are taken as the parameter values for that cycle,and the data is divided into training and validation sets for model development.Finally,the rock-machine mutual feedback model is constructed using five methods respectively:least squares,sup⁃port vector machine,single-layer neural network,double-layer neural network,and random forest,with a comparative analysis of the predictive results of each model.[Findings]The results indicate that the double-layer neural network model exhibits the best predictive performance,capable of controlling the prediction deviations for penetration rate,thrust,cutterhead rotation speed,and cutterhead torque within 0.2 mm/r,1500 kN,0.5 r/min,and 9 kN·m units.[Conclusions]This method can enhance the excavation efficiency of TBM,promote the intelligentization of TBM equipment,and provide a guarantee for the safe and efficient construction of TBM.

关 键 词：TBM 掘进参数 神经网络 智能控制 岩机-互馈 

分 类 号：U45[建筑科学—桥梁与隧道工程]