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隧道建设(中英文) ›› 2022, Vol. 42 ›› Issue (S2): 234-241.DOI: 10.3973/j.issn.2096-4498.2022.S2.029

• 研究与探索 • 上一篇    下一篇

盾构实时监测数据分析与挖掘

袁梦1, 张晓冰2, 胡振中2, *   

  1. (1. 广州地铁建设管理有限公司, 广东 广州 510000 2. 清华大学深圳国际研究生院, 广东 深圳 518071)
  • 出版日期:2022-12-30 发布日期:2023-03-24
  • 作者简介:袁梦(1990—),男,江西赣州人,2015年毕业于华南理工大学,桥梁与隧道工程专业,硕士,工程师,现从事轨道交通建设工程管理。Email: yuanmeng@gzmtr.com。*通信作者: 胡振中, Email: huzhenzhong@tsinghua.edu.cn。

Analysis and Mining of RealTime Monitoring Data from Shield Machines

YUAN Meng1, ZHANG Xiaobing2, HU Zhenzhong2 ,*   

  1. (1. Guangzhou Metro Construction Management Co., Ltd., Guangzhou 510000, Guangdong, China; 2. Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, Guangdong, China)

  • Online:2022-12-30 Published:2023-03-24

摘要: 为充分利用隧道施工时盾构记录的大量监测数据,首先,采用数据分析与挖掘技术对盾构监测数据进行预处理; 其次,借助关联规则分析方法以及改进的Apriori算法,对盾构施工时10余项参数之间的关系进行分析与挖掘,并对典型关联结果进行解释,验证人们已掌握的规律、修正人们的误解以及发现潜在未知的规律;最后,基于CART分类算法对盾构所处地层进行实时预测,并构建三维地质模型对地层预测结果进行解释和应用。结果表明,分类模型能更真实反映地层信息,且可作为地质勘测结果的有效补充。广州地铁某标段的工程实践验证了对盾构监测数据进行分析和挖掘的可行性与合理性。

关键词: 盾构施工, 监测数据, 数据挖掘, 关联规则, Apriori算法, 分类算法, CART算法, 土层分类 

Abstract: In order to make full use of a large number of monitoring data recorded by shield machine during tunnel construction, data analysis and mining techniques are adopted to preprocess the monitoring data. Then, with the help of association rule analysis method and improved Apriori algorithm, the relationship among more than ten parameters in shield construction is analyzed and mined, and the typical association results are explained, which verify the mastered laws, correct misunderstanding and discover the potential unknown laws. Finally, based on classification and regression tree algorithm, the realtime prediction of the stratum where the shield machine is located is made, and a threedimensional geological model is built to interpret and apply the prediction results. The results show that the classification model can reflect the stratum information more truly and can be used as an effective supplement to the geological survey results. The feasibility and rationality of analyzing and mining the monitoring data of shield machine is verified by Guangzhou metro section.

Key words: shield construction, monitoring data, data mining, association rules, Apriori algorithm, classification algorithm, classification and regression tree algorithm, soil classification