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。
Abstract
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
Key words
TBM /
TPI /
neural network /
surrounding rock quality /
Gini impurity
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参考文献
[1] 洪开荣, 冯欢欢. 近2年我国隧道及地下工程发展与思考(2019—2020年)[J] . 隧道建设(中英文), 2021, 41(8): 1259-1280.
[2] 杜立杰.中国TBM施工技术进展、挑战及对策[J].隧道建设,2017,37(9):1063-1075.
[3] 齐梦学.我国TBM法隧道工程技术的发展、现状及展望[J].隧道建设(中英文),2021,41(11):1964-1979.
[4] 赵纪平.浅谈TBM掘进机在软弱围岩中的施工技术[J].西部探矿工程,2004(6):96-97.
[5] 胡 军.TBM在软弱围岩施工的针对性改造技术[J].铁道建筑技术,2021(11):77-82.
[6] 孔祥政.敞开式TBM对软弱围岩的掘进方法[J].水利建设与管理,2017,37(5):12-15.
[7] 杨延栋,赵建兵,周建军,等.不同岩体质量隧道TBM施工性能评价方法[J].科学技术与工程,2022,22(19):8502-8508.
[8] 田彦朝,贺 飞,张 啸,等.长距离掘进软弱围岩的TBM刀盘扩挖技术[J].隧道建设(中英文),2019,39(5):884-889.
[9] 方义清.软弱围岩隧道施工安全风险管理分析[J].工程建设与设计,2022(4):206-209.
[10]李玉波.三维地震波法超前地质预报在引汉济渭工程TBM施工中的应用[J].水利水电技术,2017,48(8):131-136.
[11]宋 杰. 隧道施工不良地质三维地震波超前探测方法及其工程应用[D]. 济南:山东大学,2016.
[12]苏 鹏. 聚集电流激发极化法隧道超前探测正演研究[D]. 北京:中国地质大学,2019.
[13]于太彰, 李建斌, 荆留杰, 等. TBM 施工信息云计算平台的设计与实践[J]. 现代隧道技术, 2018, 55(6): 33-41,52.
[14]GUO D, LI J, JIANG S, et al. Intelligent Assistant Driving Method for Tunnel Boring Machine Based on Big Data[J]. Acta Geotechnica, 2021, 17(4): 1019-1030.
[15]朱梦琦,朱合华,王 昕,等.基于集成CART算法的TBM掘进参数与围岩等级预测[J].岩石力学与工程学报, 2020, 39(9): 1860-1871.
[16]HOU S,LIU Y,YANG Q. Real-time Prediction of Rock Mass Classification Based on TBM Operation Big Data and Stacking Technique of Ensemble Learning[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2022,14(1):123-143.
[17]WU Z, WEI R, CHU Z,et al. Real-time Rock Mass Condition Prediction with TBM Tunneling Big Data Using a Novel Rock-Machine Mutual Feedback Perception Method[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2021, 13(6): 1311-1325.
[18]ZHANG Q, LIU Z, TAN J. Prediction of Geological Conditions for a Tunnel Boring Machine Using Big Operational Data[J]. Automation in Construction, 2019, 100: 73-83.
[19]LIU Q, WANG X, HUANG X,et al. Prediction Model of Rock Mass Class Using Classification and Regression Tree Integrated Adaboost Algorithm Based on TBM Driving Data[J]. Tunnelling and Underground Space Technology, 2020, 106: 103595.
[20]CHEN Z, ZHANG Y, LI J, et al. Diagnosing Tunnel Collapse Sections Based on TBM Tunneling Big Data and Deep Learning: A Case Study on the Yinsong Project, China[J]. Tunnelling and Underground Space Technology, 2020, 108: 103700.
[21]刘诗洋, 陈祖煜, 张云旆,等. 基于卷积神经网络对TBM塌方段的反演分析 [J]. 固体力学学报, 2021, 42(3): 287-301.
[22]FENG S, CHEN Z, LUO H, et al. Tunnel Boring Machines (TBM) Performance Prediction: A Case Study Using Big Data and Deep Learning[J]. Tunnelling and Underground Space Technology, 2021, 110: 103636.
[23]HOU S, LIU Y. Early Warning of Tunnel Collapse Based on ADAM-optimised Long Short-Term Memory Network and TBM Operation Parameters[J]. Engineering Applications of Artificial Intelligence, 2022, 112: 104842.
[24]杜立杰, 齐志冲, 韩小亮, 等. 基于现场数据的TBM可掘性和掘进性能预测方法[J]. 煤炭学报,2015,40(6):1284-1289.
[25]熊 帆,基于PSO-SVR算法的TBM掘进效率预测及围岩分级研究[D]. 西安:长安大学,2016.
[26]倪锦初, 朱学贤, 凌 旋, 等. 香炉山深埋长隧洞TBM选型研究[J]. 人民长江,2022,53(1): 154-159.
[27]梁军博. 基于遗传算法的TBM操作参数优化决策模型构建[D]. 大连:大连理工大学,2020.
[28]李 杰. 基于数据驱动TBM掘进载荷预测与参数分析[D]. 大连:大连理工大学,2019.
[29]ZHU M, GUTIERREZ M, ZHU H,et al. Performance Evaluation Indicator (PEI): A New Paradigm to Evaluate The Competence of Machine Learning Classifiers in Predicting Rock Mass Conditions[J]. Advanced Engineering Informatics, 2021, 47: 101232.
[30]李蓬喜. 基于机器学习的TBM掘进参数及围岩等级预测研究[D]. 哈尔滨:哈尔滨工业大学,2019.
[31]GUO D, LI J, JIANG S, et al. Intelligent Assistant Driving Method for Tunnel Boring Machine Based on Big Data[J]. Acta Geotechnica, 2022, 17: 1019-1030.
[32]WANG S, WANG Y, LI X, et al. Big Data-based Boring Indexes and Their Application During TBM Tunneling[J]. Advances in Civil Engineering, 2021(4): 1-18.
[33]罗 华. 基于线性回归和深度置信网络的TBM性能预测研究[D]. 杭州:浙江大学,2018.
基金
云南省重点科技专项计划(202002AF080003-4)
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