摘要
依托引绰济辽工程(YC)和引松供水工程(YS),从理论分析、统计检验等方面系统回顾并总结基于知识驱动方法提取的现场贯入指标FPI和扭矩贯入指标TPI在TBM智能施工机器学习中的应用。从智能预测围岩分类和掘进参数两方面出发,将基于特征参数的预测结果与通过数据驱动获得的结果进行比较。研究结果表明:通过知识驱动获取的参数FPI和TPI可以降低数据维度和噪音,提高预测效率。在围岩分类智能预测方面,知识驱动和数据驱动方法均表现出较好的精度水平;在掘进参数预测方面,知识驱动的预测精度远高于数据驱动。作者认为单独使用FPI和TPI或者将其与数据驱动参数结合,可以丰富TBM领域机器学习的输入参数,获得较好的预测成果。
This paper presents a comprehensive review on the application of tunneling feature parameters FPI and TPI within the context of the Yinchuo Jiliao Project(YC)and Yinsong Water Supply Project(YS).These parameters are extracted using knowledge-driven methods in TBM(Tunnel Boring Machine)intelligent construction,leveraging theoretical analysis,statistical testing,and clustering techniques.The research encompasses intelligent predictions for both rock classifications and tunneling parameters.The findings of this study indicate that utilizing FPI and TPI obtained through knowledge-based approaches can lead to reduced data dimensions and noise,consequently enhancing prediction efficiency.In terms of intelligent prediction of surrounding rock classifications,both knowledge-based and data-based methods have demonstrated commendable accuracy levels.However,concerning the prediction of tunneling parameters,knowledge-based approaches exhibit significantly higher prediction accuracy than the data-driven methods.Therefore,the study suggests that the independent utilization of FPI and TPI or in conjunction with data-based parameters can all be used for the machine learning predictions within the TBM field.
作者
陈祖煜
范立涛
张云旆
肖浩汉
王琳
Chen Zuyu;Fan Litao;Zhang Yunpei;Xiao Haohan;Wang Lin(State Key Laboratory of Eco-Hydraulics in Northwest Arid Region,Xi'an University of Technology,Xi'an 710048,China;State Key Laboratory of Basin Water Cycle Simulation and Regulation,China Institute of Water Resources and Hydropower Research,Bejing 100048,China)
出处
《土木工程学报》
EI
CSCD
北大核心
2024年第6期1-12,共12页
China Civil Engineering Journal
基金
TBM,盾构施工人工智能辅助决策系统关键技术研究(2021JLM-53)
中国水利水电科学研究院基本科研业务费专项项目(GE0145B012021)
国家自然科学基金面上项目(52179121)
流域水循环模拟与调控国家重点实验室自主研究课题(SKL2022ZD05)。
关键词
TPI
FPI
知识驱动
数据驱动
围岩分类
掘进参数预测
TPl
FPI
knowledge-based learning
data-based learning
classification of surrounding rock
prediction of tunneling parameters
