基于围岩参数的TBM掘进性能预测及应用研究OA北大核心CSTPCD
Prediction model of TBM tunneling performance based on surrounding rock parameters and application
传统的TBM掘进预测模型侧重于使用单一因素预测TBM掘进性能,预测结果往往与实际施工存在较大差异.以新疆某输水隧洞为背景,提出一种可考虑多种因素且符合工程实际的TBM掘进预测模型.以贯入度指数FPI作为TBM围岩可掘性的评价指标,分析了现场实测所得的岩体完整性指数Kv、岩体单轴抗压强度UCS与FPI的相关性.基于相关性分析,以Kv和UCS为模型的输入参数,FPI为因变量,建立了多元回归模型.使用现场实测所得的刀盘转速RPM、掘进速度v建立与FPI的拟合关系式,由此可以预测实际施工工程中的刀盘转速和掘进速度.在FPI预测模型的基础上,利用K-means聚类方法划分TBM的可掘性等级,并根据可掘性等级预估合理的掘进参数.研究结果表明,所建立的TBM可掘性模型可较好反映TBM施工的真实状态,可为类似地层条件的TBM施工工期预测和成本控制等问题提供参考.
Traditional prediction model of TBM tunneling focuses on using a single factor to predict the TBM tunneling perform-ance,and the predicted results are often quite different from the actual construction.In this paper,based on a tunnel project in Xinjiang,we proposed a TBM tunneling prediction model which can consider many factors and conform to the actual engineering.Firstly,the penetration index FPI was taken as an evaluation index of TBM's excavability,and the correlation between the in-situ measured rock mass integrity index Kv,uniaxial compressive strength UCS and FPI was analyzed.Based on the correlation analy-sis,a multiple regression model was established with Kv and UCS as input parameters and FPI as dependent variable.Then,a fit-ting relation between FPI and cutter speed RPM and driving speed measured on site was established,by which the cutter speed and driving speed in actual construction projects can be predicted.Finally,on the basis of FPI prediction model,K-means cluste-ring method was used to classify the TBM's tunneling grade,and reasonable tunneling parameters were estimated according to the tunneling grade.The results show that the TBM tunneling model established in this paper can well reflect the real state of TBM construction,and can provide a reference for project schedule prediction and cost control in the TBM construction process with similar formation conditions.
宗大超;赵祎睿;赵永金
中铁建大桥工程局集团第三工程有限公司,天津 300308西安建筑科技大学土木工程学院,陕西 西安 710055
水利科学
TBM贯入度指数可掘性分级多元回归
TBMpenetration indexexcavability classificationmultiple regression
《人民长江》 2024 (008)
161-165,173 / 6
陕西省创新能力支撑计划-创新团队项目(2020TD-005)
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