测绘通报 ›› 2021, Vol. 0 ›› Issue (8): 93-96,110.doi: 10.13474/j.cnki.11-2246.2021.0248

• 轨道交通前沿测绘技术研究与应用 • 上一篇    下一篇

轨道交通盾构隧道病害空间分布特征和自相关性研究

徐鹏宇1,2, 唐超1,2, 王丽3   

  1. 1. 北京城建勘测设计研究院有限责任公司, 北京 100101;
    2. 轨道交通岩土工程深基坑北京市重点实验室, 北京 100101;
    3. 中国地质大学(北京), 北京 100083
  • 收稿日期:2021-06-14 修回日期:2021-06-24 出版日期:2021-08-25 发布日期:2021-08-30
  • 作者简介:徐鹏宇(1991-),女,硕士,工程师,研究方向为摄影测量及遥感。E-mail:837574652@qq.com

Study on space distribution and correlation of shield tunnel diseases in urban rail transit

XU Pengyu1,2, TANG Chao1,2, WANG Li3   

  1. 1. Beijing Urban Construction Exploration & Surveying Design Research Institute Co., Ltd., Beijing 100101, China;
    2. Beijing Key Laboratory of Deep Foundation Pit Geotechnical Engineering of Rail Transit, Beijing 100101, China;
    3. China University of Geosciences, Beijing 100083, China
  • Received:2021-06-14 Revised:2021-06-24 Online:2021-08-25 Published:2021-08-30

摘要: 我国轨道交通已经进入新建与维护并重期。随着地铁隧道服役年限的增加,其病害种类不断增加,且各类病害呈逐渐加重的趋势。以三维激光测量隧道逐环监测数据为基础,研究隧道区间的病害空间分布特征和自相关性,全面掌握地铁隧道运营状态和病害发展趋势,是进行隧道病害防治的前提。本文利用某地地铁上行线隧道逐环三维激光测量数据,分析了错台、掉块、裂缝和渗漏水等病害在线路走向的空间分布特征,分析了病害与周边地质环境的关系。利用灰色关联分析算法研究了病害之间的相关性,为掌握隧道病害的发展趋势和认识病害之间的耦合关系建立了定量化的分析手段。本文为掌握隧道病害空间分布规律,预测隧道病害的发生、治理提供了有效依据,可以为地铁安全运营提供有力保障。

关键词: 三维激光, 隧道病害, 灰色关联分析, 病害空间分布, 病害自相关性

Abstract: The urban rail transit in China has entered a period of equal emphasis on construction and maintenance. With the increase of the service life of subway tunnel, the variety of tunnel diseases has been increased, and it shows a trend of gradual aggravation. Based on three-dimensional laser ring-by-ring monitoring, the spatial distribution characteristics and autocorrelation of the damage in the tunnel are studied. It is the premise of tunnel disease prevention and control to master the operating state and disease development trend of subway tunnel. Ring-by-ring 3D laser measurement data of a subway tunnel are used to analyze the spatial distribution characteristics of the disease such as dislocation, falling blocks, cracks, lining split along the route. Also, this paper analyzes the relationship between the disease and the surrounding geological environment. The self-correlation between diseases is studied by using gray correlation analysis algorithm which provides a quantitative analysis method for understanding the development trend of tunnel diseases and the coupling relationship between the diseases. The research in this paper provides an effective basis for understanding the spatial distribution of tunnel diseases, predicting the occurrence and treatment of tunnel diseases, and providing a strong guarantee for the safe operation of the subway.

Key words: 3D laser, tunnel diseases, gray correlation analysis, space distribution of the tunnel disease, self-correlation of disease

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