基于扭矩贯入指标和神经网络的围岩质量预判方法  

Method of Estimating Surrounding Rock Quality Based on Torque Penetration Index and Neural Network

作　　者：吴帆 张云旆 寇甲兵 刘立鹏[2] 李鹏宇 WU Fan;ZHANG Yun-pei;KOU Jia-bing;LIU Li-peng;LI Peng-yu(Central Yunnan Water Diversion Engineering Co.,Ltd.,Kunming 650000,China;China Institute of Water Resource and Hydropower Research,Beijing 100048,China;China Railway Engineering Equipment Group Co.,Ltd.,Zhengzhou 450016,China)

机构地区：[1]云南省滇中引水工程有限公司,昆明650000 [2]中国水利水电科学研究院,北京100048 [3]中铁工程装备集团有限公司,郑州450016

出　　处：《长江科学院院报》2022年第12期33-41,共9页Journal of Changjiang River Scientific Research Institute

基　　金：云南省重点科技专项计划(202002AF080003-4)。

摘　　要：全断面岩石掘进机(TBM)的地质适应性较差,当遭遇不良地质条件或者围岩质量较差时,容易引发卡机、塌方等地质灾害,影响施工进度,威胁人员安全。基于此,首先通过TBM数据预处理,将原始数据分割为完整的掘进段,其次以掘进段为单位计算扭矩贯入指标(TPI),基于时间序列法和神经网络在掘进开始前对围岩质量进行预测,基于TPI的基尼不纯度,在掘进上升段对围岩质量进行判断。结果表明:TPI能够较好地反映围岩地质条件,基于时间序列法和神经网络能够较为准确地对TPI进行预测,通过TPI的基尼不纯度能够较好地对围岩质量进行判断。Featured with inferior geological adaptability,TBM(Full Face Rock Tunnel Boring Machine)is prone to cause geological disasters such as jamming and collapses when encountered with unfavorable geological conditions or poor surrounding rock quality,hence affecting construction progress and threatening personnel safety.Through TBM data preprocessing,the original data is first divided into complete driving segments,and the torque penetration index(TPI)is calculated.The quality of surrounding rock is then predicted before boring by using time series method and neural network,and the quality of surrounding rock is judged in the rising segment of boring based on the Gini impurity of TPI.Results demonstrate that TPI well reflects the geological conditions of surrounding rock.TPI can be accurately predicted by using time series method and neural network.The quality of surrounding rock can be well judged by the Gini impurity of TPI.

关 键 词：TBM TPI 神经网络 围岩质量 基尼不纯度 

分 类 号：TV554.2[水利工程—水利水电工程]