摘要
在全断面隧道掘进机(tunnel boring machine,TBM)掘进过程中,滚刀破岩效率受复杂地质条件及机械参数的显著影响,针对不同地质工况下滚刀破岩效率的智能预测对保障TBM高效破岩具有重要意义。提出基于支持向量机(support vector machine,SVM)与智能优化算法混合的TBM节理岩体滚刀破岩效率预测方法,分别使用粒子群优化算法(particle swarm optimization,PSO),飞蛾扑火优化算法(moth-flame optimization,MFO)和黑池鸢优化算法(black-winged kite algorithm,BKA)优化确定SVM的惩罚因子和核函数参数。预测模型输入参数包括围压、节理间距、节理倾角、岩石强度、贯入度、刀盘推力和刀盘滚动力,输出参数为比能。通过全尺寸线性切割试验和离散元数值模拟构建样本数据集,用于模型的训练和测试。结果表明:BKA-SVM模型在稳定性、计算效率和预测精度方面显著优于PSO-SVM和MFO-SVM模型,适用于TBM节理岩体滚刀破岩效率预测。对预测出的数据进行重要性分析显示,岩石强度和贯入度为最重要的影响因素,滚刀破岩最优贯入度为3 mm且岩石强度越大,比能以及比能变化趋势越大。研究成果可为TBM掘进过程中的破岩效率预测和优化提供指导,具有提高效率、降低能耗的潜在应用价值。
The rock-breaking efficiency of disc cutters is significantly influenced by complex geological conditions and mechanical parameters in tunnel excavation using tunnel boring machines(TBM).The accurate prediction of this efficiency under varying geological conditions is imperative for TBM.A prediction method was proposed for the rock-breaking efficiency of TBM disc cutters in jointed rock masses through the integration of a support vector machine(SVM)with intelligent optimization algorithms.Specifically,particle swarm optimization(PSO),moth-flame optimization(MFO),and black-winged kite algorithm(BKA)were utilized to optimize the penalty factor and kernel function parameters of the SVM.Seven parameters,including confining pressure,joint spacing,joint dip angle,rock strength,penetration depth,cutterhead thrust,and rolling force were selected as inputs and specific energy was defined as output for the prediction model.A dataset was constructed based on full-scale linear cutting tests and discrete element numerical simulations for model training and testing.The results show that the BKA-SVM model demonstrates superior performance in terms of stability,computational efficiency,and prediction accuracy when compared to the PSO-SVM and MFO-SVM models.Furthermore,the sensitivity analysis of the predicted data reveals that rock strength and penetration depth are the most influential factors,with an optimal penetration depth for rock breaking identified as 3 mm.Additionally,a positive correlation has been observed between rock strength and specific energy,with a more pronounced increasing trend.The research results provide valuable insights for the optimization of rock-breaking efficiency during TBM excavation with potential applications for enhancing operational efficiency and reducing energy consumption.
作者
范瑛
陶威龙
刘滨
刘学伟
黄兴
刘朋飞
FAN Ying;TAO Wei-long;LIU Bin;LIU Xue-wei;HUANG Xing;LIU Peng-fei(School of Civil Engineering,Architecture and Environment,Hubei University of Technology,Wuhan 430068,China;State Key Laboratory of Geomechanics and Geotechnical Engineering Safety,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences,Wuhan 430071,China;CCCC Second Harbor Engineering Co.,Ltd.,Wuhan 430040,China)
出处
《科学技术与工程》
北大核心
2026年第8期3506-3522,共17页
Science Technology and Engineering
基金
湖北省重点研发计划(2023BCB121,2024DJC002)
武汉市知识创新专项(2023020201010079)。
关键词
滚刀破岩效率
节理岩石
支持向量机(SVM)
智能优化算法
比能
参数寻优
rock-breaking efficiency of disc cutter
jointed rock mass
support vector machine(SVM)
intelligent optimization algorithm
specific energy
parameter optimization
