摘要
基于引汉济渭、高黎贡山隧道、合肥轨道交通、深圳春风隧道、洛阳轨道交通等典型的土压、泥水、TBM盾构施工项目,为解决提取数据时遇到无关状态数据较多、价值密度低、某些维度数据缺失、提取易失败、提取数据速度慢、数据量大等有效性差和效率低的问题,在数据挖掘源头采用多条施工线路的数据试验和无关状态分离、容错性判断等方法进行数据提取和判断,形成基于盾构施工的有效数据提取方法,解决盾构施工大数据分析时提取易失败、无效数据多、提取过程缓慢问题,为多线路并发的数据分析和实时为施工现场提供智能决策奠定基础。
Based on several typical earth pressure, mud water, TBM shield construction projects, such as Hanjiang-to-Weihe River Diversion project, Gaoligongshan Tunnel project, Hefei rail transit project, Shenzhen Chunfeng tunnel project, Luoyang rail transit project, etc, in order to solve the problems of poor data validity and inefficiency, such as more irrelevant status data, low value density, missing data in certain dimensions, easy extraction failure, slow data extraction speed, and large data volume encountered when extracting data, at the source of data mining, the data extraction and judgment methods of multiple construction routes, separation of irrelevant states, and fault tolerance judgment are used to extract and judge data, forming an effective data extraction method based on shield construction. It solves the problems of easy extraction failure, many invalid data, and slow extraction process during shield construction big data analysis, which lays the foundation for multi-line concurrent data analysis and real-time intelligent decision-making for the construction site.
作者
李叔敖
江南
褚长海
周振建
张合沛
任颖莹
LI Shuao;JIANG Nan;CHU Changhai;ZHOU Zhenjian;ZHANG Hepei;REN Yingying(State Key Laboratory of Shield Machine and Boring Technology,Zhengzhou,Henan 450001,China;China Railway Tunnel Group Co.,Ltd.,Guangzhou,Guangdong 511458,China)
出处
《施工技术（中英文）》
CAS
2022年第8期91-96,共6页
Construction Technology
基金
国家重点研发计划(2018YFB1701404)
中铁隧道局集团科技创新计划:基于深度强化学习的盾构姿态实时智能控制技术研究(隧研合2018-40)
基于人工智能与大数据分析的盾构主动参数优化及被动参数预警(隧研合2018-39)
基于大数据技术的盾构TBM智能化选型与掘进技术研究(2020-重大-04)。
关键词
隧道
盾构
大数据
试验
数据价值密度
tunnels
shields
big data
experiment
data value density
