小断面土石组合地质条件下TBM施工围岩可掘性分级识别
Classification and Predictive Research on Excavability of Surrounding Rock for TBM Construction in Small Section with Soil-Rock Composite Geological Condition
杨耀红 1刘德福 2张智晓 3韩兴忠 2孙小虎4
作者信息
- 1. 华北水利水电大学水利学院,郑州 450046;河南省黄河流域水资源节约集约利用重点实验室,郑州 450046
- 2. 华北水利水电大学水利学院,郑州 450046
- 3. 中州水务控股有限公司,郑州 450000
- 4. 华北水利水电大学水利学院,郑州 450046;中水北方勘测设计研究有限责任公司,天津 300000
- 折叠
摘要
围岩可掘性分级以及识别研究对隧道掘进机(TBM)高效率施工及智能化控制意义重大.依托南水北调安阳市西部调水工程TBM施工实际数据,利用掘进性能综合指标单位贯入度推力(FPI)、单位贯入度扭矩(TPI)建立了小断面土石组合地质条件下TBM施工围岩可掘性分级标准;提出了 PCA-RF模型对围岩可掘性分级进行识别,并与BP、SVR和RF模型进行了比较讨论.结果表明:①建立的小断面土石组合围岩TBM施工可掘性分级标准是适用的,克服了土石组合围岩下传统围岩分类方法的局限性;②小断面土石组合围岩TBM施工可掘性分级PCA-RF 识别模型的识别准确率达到了 98.3％,高于BP、SVR和RF模型,可以满足工程施工需要.
Abstract
The efficient construction and intelligent control of TBM heavily rely on the classification and real-time i-dentification of surrounding rock excavability.To address this,we establish a classification standard for surrounding rock excavability in TBM construction under geological conditions characterized by small sections and soil-rock com-binations based on actual data(penetration thrust and torque per unit penetration)from the Anyang Western Water Diversion Project.Moreover,we introduce the PCA-RF model for real-time identification and prediction of sur-rounding rock excavability,and then compared the results with those of BP,SVR,and RF models.Our research yields the following conclusions:1)The classification standard for surrounding rock excavability in TBM construc-tion under the geological conditions of small sections and soil-rock combinations proves to be applicable.This stand-ard resolves the limitations of traditional methods for classifying surrounding rock in soil-rock composite environ-ments.2)The PCA-RF model demonstrates an identification and prediction accuracy of 98.3％for the surrounding rock excavability in TBM construction under the geological conditions of small sections and soil-rock combinations.This accuracy surpasses that of the BP,SVR,and RF models and fulfills the demands of engineering construction.
关键词
隧道掘进机(TBM)/小断面/土石组合/可掘性分级/PCA-RF模型Key words
tunnel boring machine(TBM)/small section/soil-rock combination/classification of excavability/PCA-RF model引用本文复制引用
基金项目
国家自然科学基金重点项目(51679089)
河南省学科创新引智基地项目(GXJD004)
出版年
2024
国家科技期刊平台
NSTL
万方数据