设置中隔墙的盾构隧道收敛变形移动测量点云数据融合方法

王嘉烨; 刘新根; 刘学增; 庞高兆

doi:10.3973/j.issn.2096-4498.2025.06.015

隧道建设 >

2025 , Vol. 45 >Issue 6: 1202 - 1209

DOI: https://doi.org/10.3973/j.issn.2096-4498.2025.06.015

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设置中隔墙的盾构隧道收敛变形移动测量点云数据融合方法

王嘉烨 ,
刘新根 ,
刘学增 ,
庞高兆

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（1. 上海市隧道工程轨道交通设计研究院，上海 200235； 2. 上海同岩土木工程科技股份有限公司，上海 200092； 3. 上海地下基础设施安全检测与养护装备工程技术研究中心，上海 200092； 4. 同济大学土木工程学院，上海 200092； 5. 同济大学土木信息技术教育部工程研究中心，上海 200092）

王嘉烨（1989—），男，上海人，2014年毕业于悉尼科技大学，地下结构工程专业，硕士，高级工程师，现从事隧道结构设计工作。E-mail: wang.jiaye@stedi.com.cn。*通信作者：刘新根， E-mail： 47089472@qq.com。

网络出版日期: 2025-06-20

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Mobile Measurement and Point Cloud Data Fusion Method for Convergent Deformation of Shield Tunnels With Middle Partition Walls

WANG Jiaye ,
LIU Xingen ,
LIU Xuezeng ,
PANG Gaozhao

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(1. Shanghai Tunnel Engineering & Rail Transit Design and Research Institute, Shanghai 200235, China; 2. Shanghai Tongyan Civil Engineering Technology Co., Ltd., Shanghai 200092, China; 3. Shanghai Engineering Research Center of Detecting Equipment for Underground Infrastructure, Shanghai 200092, China; 4. College of Civil Engineering, Tongji University, Shanghai 200092, China; 5. Engineering Research Center of Civil-informatics, the Ministry of Education, Tongji University, Shanghai 200092, China)

Online published: 2025-06-20

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摘要

为解决设置中隔墙的单洞双线大直径盾构隧道左、右线激光扫描点云数据配准难、收敛变形移动测量精度低的技术难题，构建基于盾构隧道管片环缝自动识别的点云数据定位算法，提出基于隧道结构特征点和距离最小偏差的盾构隧道管片环整体-局部动态迭代点云自动配准方法，实现隧道左、右线对应管片整环点云数据精准融合。对融合后的管片整环点云数据进行除噪、距离最小二乘法椭圆拟合处理，可获得设有中隔墙的盾构隧道整体水平收敛变形值。集成点云数据融合算法，研制搭载三维激光扫描仪的轨道交通隧道结构检测车TDV-SD及配套数据处理软件，并进行工程试点应用与人工对比验证。结果表明：时速为20 km时，隧道水平收敛变形检测车移动测量与人工测量绝对值最大偏差为2.0 mm，与外直径比值为0.16‰，二者测量结果具有较好的一致性，移动测量精度可基本满足大直径盾构隧道管片收敛变形影响评价指标的行业要求，验证了左、右线管片点云数据融合算法的可行性。

关键词： 单洞双线盾构隧道; 大直径盾构隧道; 中隔墙; 移动测量; 收敛变形; 数据配准融合

本文引用格式

王嘉烨 , 刘新根 , 刘学增 , 庞高兆 . 设置中隔墙的盾构隧道收敛变形移动测量点云数据融合方法[J]. 隧道建设, 2025 , 45(6) : 1202 -1209 . DOI: 10.3973/j.issn.2096-4498.2025.06.015

Abstract

Large-diameter single-tube double-track shield tunnels with middle partition walls face significant challenges such as difficult left- and right-line laser scanning point cloud data registration and low convergent deformation measurement accuracy. To address these challenges, a point-cloud data positioning algorithm that automatically identifies the circumferential joints of the shield tunnel segments is constructed. In addition, a full-local dynamic iterative point-cloud automatic registration method for shield tunnel segment rings is proposed. This method minimizes the deviation of tunnel structure feature points and distances, thus achieving precise fusion of the full-loop point cloud data of the corresponding left and right line segments of the tunnel. The overall horizontal convergent deformation of the shield tunnel with middle partition walls is determined by applying noise reduction and distance least squares elliptic fitting to the fused full-loop point cloud data of the segments. Integrating a point cloud data fusion algorithm facilitates the development of a rail transit tunnel structure inspection vehicle TDD-SD equipped with a three-dimensional laser scanner and supporting data processing software. Finally, engineering pilot applications and manual comparison verification are performed. The results demonstrate that at a measurement speed of 20 km/h, the maximum absolute deviation between the mobile measurement of the tunnel horizontal convergent deformation detection vehicle and the manual measurement is 2.0 mm. This represents a ratio of 0.16‰ to the outer diameter of the tunnel, demonstrating good consistency between the two measuring methods. The mobile measurement accuracy meets the industry standards for evaluating the impact of segment convergent deformation in large-diameter shield tunnels. The feasibility of the proposed point-cloud data fusion algorithm for the left and right line segments is also validated.

Key words： single-tube double-track shield tunnel; large-diameter shield tunnel; middle partition wall; mobile measurement; convergent deformation; data registration and fusion

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