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基于现场数据的TBM掘进速率研究
引用本文:罗华,陈祖煜,龚国芳,赵宇,荆留杰,王超.基于现场数据的TBM掘进速率研究[J].浙江大学学报(自然科学版 ),2018,52(8):1566-1574.
作者姓名:罗华  陈祖煜  龚国芳  赵宇  荆留杰  王超
作者单位:1. 浙江大学 岩土工程研究所, 浙江 杭州 310058; 2. 浙江大学超重力研究中心, 浙江 杭州 310058; 3. 中国水利水电科学研究院岩土工程研究所, 北京 100048; 4. 浙江大学 流体动力与机电系统国家重点实验室, 浙江 杭州 310027; 5. 中国中铁工程装备集团有限公司, 河南 郑州 450016
基金项目:国家“973”重点基础研究发展计划资助项目(2015CB058103);国家重点研发计划资助项目(2016YFC0401801)
摘    要:为了研究TBM掘进速率在不同地质条件下的变化规律,基于吉林引松供水隧道工程开敞式TBM现场掘进数据,将TBM刀盘破岩过程分为3个阶段:挤压阶段、起裂阶段和破碎阶段,并对破碎阶段应用统计回归方法,分析在不同岩石饱和单轴抗压强度、完整性系数的条件下,TBM刀盘贯入度与刀盘推力、刀盘扭矩的关系.研究表明,在特定施工条件下,刀盘贯入度随刀盘推力增大呈幂函数曲线增长,随刀盘扭矩增大呈线性关系增长,增长率与岩石饱和单轴抗压强度、完整性系数密切相关.进一步建立对于不同强度、完整性岩石的掘进机掘进速率模型,进行实际工程施工预测,预测结果的平均相对误差都低于16%,表明模型预测精度较高,可以为实际工程施工中操作参数的优化和不良地质条件的捕捉提供帮助.


Advance rate of TBM based on field boring data
LUO Hua,CHEN Zu-yu,GONG Guo-fang,ZHAO Yu,JING Liu-jie,WANG Chao.Advance rate of TBM based on field boring data[J].Journal of Zhejiang University(Engineering Science),2018,52(8):1566-1574.
Authors:LUO Hua  CHEN Zu-yu  GONG Guo-fang  ZHAO Yu  JING Liu-jie  WANG Chao
Abstract:In order to explore the variation of advance rate of tunnel boring machine (TBM) under different geological conditions, the variations of cutterhead penetration rate with cutterhead thrust and cutterhead torque in different rock saturated uniaxial compressive strengths and rock mass integrity coefficients were analyzed. The machine boring data were obtained in an unshield TBM construction section of Jilin Songhua River water diversion project. The process of the cutterhead breaking rock was divided into three phases, namely squeezing phase, cracking phase and fragmentation phase, and the data in fragmentation phase was analyzed by statistical regressions. Given a specified construction condition, the penetration rate increased exponentially with the cutterhead thrust and linearly withcutterhead torque. TBM penetration rate models for different uniaxial compressive strengths and rock mass integrity coefficients were established to predict the construction parameters in the practical engineering construction, the average relative errors of the predicted construction parameters were all within 16%. The models can optimize the construction parameters and capture abnormal geological conditions during construction due to high prediction accuracy.
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