岩土力学 ›› 2022, Vol. 43 ›› Issue (1): 195-204.doi: 10.16285/j.rsm.2021.1201

• 岩土工程研究 • 上一篇    下一篇

隧道开挖地表沉降动态预测及影响因素分析

江帅1, 2，朱勇2, 3，栗青1，周辉2, 3，涂洪亮2, 3，杨凡杰2, 3   

  1. 1. 沈阳工业大学 建筑与土木工程学院，辽宁 沈阳 110870；2. 中国科学院武汉岩土力学研究所 岩土力学与工程国家重点试验室， 湖北 武汉 430071；3. 中国科学院大学，北京 100049
  • 收稿日期:2021-08-03 修回日期:2021-09-10 出版日期:2022-01-10 发布日期:2022-01-07
  • 通讯作者: 周辉，男，1972年生，博士，研究员，博士生导师，主要从事岩石力学试验、理论、数值分析与工程安全性分析方面的研究。 E-mail: hzhou@whrsm.ac.cn E-mail:17854114851@163.com
  • 作者简介:江帅，男，1995年生，硕士研究生，主要从事隧道及地下空间稳定性分析方面的研究。
  • 基金资助:
    国家重点研发计划项目（No. 2019YFC0605104，No. 2019YFC0605103）；国家自然科学基金专项项目（No. 41941018）；中国科学院科技服务网络计划（STS计划）项目（No.KFJ-STS-QYZD-174）

Dynamic prediction and influence factors analysis of ground surface settlement during tunnel excavation

JIANG Shuai1, 2, ZHU Yong2, 3, LI Qing1, ZHOU Hui2, 3, TU Hong-liang2, 3, YANG Fan-jie2, 3   

  1. 1. School of Architecture and Civil Engineering, Shenyang University of Technology, Shenyang , Liaoning 110870, China; 2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei 430071, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2021-08-03 Revised:2021-09-10 Online:2022-01-10 Published:2022-01-07
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (2019YFC0605104, 2019YFC0605103), the National Natural Science Foundation of China (41941018) and the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-STS-QYZD-174).

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摘要： 城市地铁隧道施工不可避免地对围岩产生扰动引起地表沉降，动态预测隧道开挖引起的地表沉降是确保地表建构筑物与隧道施工安全的重要基础。针对隧道施工过程中地表沉降难以准确动态预测的问题，在定义纵向开挖度系数? 的基础上，建立横向地表沉降动态预测模型。该模型能够描述同一监测位置沉降曲线随掌子面推进而不断变化的规律，实现施工现场沉降动态预测。结果表明：在特定约束条件下，该模型能够退化为Peck模型以及随机介质理论预测模型；通过现场施工验证了该动态预测模型的准确性与适用性；基于纵向开挖度系数? 将隧道沿纵向分为强烈、中度和轻度共3个影响段，较好地反映了开挖掌子面在不同位置对同一监测截面的影响程度；通过分析建筑物和隔离桩对地表沉降曲线的影响可知，建筑物与其附近地层呈现出协同变形、共同承载的特征，在隧道一侧添加地质钻隔离桩对该侧地表沉降值的减小程度可达71.9%。研究成果对滇中引水工程现场施工与类似工程具有一定指导和借鉴意义。

关键词: 沉降预测模型, 动态预测, 纵向开挖度系数, 随机介质理论, 影响分段, 现场监测

Abstract: The construction of urban subway tunnel inevitably produces disturbance to surrounding rock and causes ground surface settlement. Dynamic prediction of ground surface settlement caused by tunnel excavation is an important method to ensure the safety of above-ground buildings and tunnel construction. In view of the difficulty of accurate dynamic prediction of ground surface settlement during tunnel construction, based on the definition of longitudinal excavation coefficient ? , a dynamic prediction model of lateral ground surface settlement is established. The model can accurately describe the variation of the settlement of the same monitoring location with the advancement of the tunnel face, and then realize the dynamic prediction of the ground surface settlement at the construction site. The results show that under certain constraints, this model can be degenerated into Peck model and stochastic medium theory prediction model. The accuracy and applicability of the dynamic prediction model are verified by on-site construction. The tunnel can be divided into three affected segments longitudinally (i.e., intense influence, moderate influence, and mild influence) based on the obtained ? , which well reflected the influence degree of the excavated tunnel face on the same monitoring section at different positions. Through the analysis of the influence of the buildings and isolation piles on the ground surface settlement curve, it can be found that the building and its adjacent ground surface present the characteristics of cooperative deformation and joint bearing. Moreover, installing geological drill isolation piles on the side of the tunnel can reduce the ground surface settlement of that side up to 71.9%. The research results have a certain guiding and reference significance for the on-site construction of the Central Yunnan Water Diversion Project and similar projects.

Key words: settlement prediction model, dynamic prediction, longitudinal excavation coefficient, stochastic medium theory, affected segmentation, on-site monitoring

中图分类号: TU 452
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