基于RMLS点云的盾构隧道管片接缝多测度交互识别指标

doi:10.11947/j.AGCS.2026.20250546

测绘学报 ›› 2026, Vol. 55 ›› Issue (6): 1101-1115.doi: 10.11947/j.AGCS.2026.20250546

• 大型工程基础设施测绘与地下空间智能感知 • 上一篇

基于RMLS点云的盾构隧道管片接缝多测度交互识别指标

有泽¹(), 王丽英¹(), 宇翼巍¹, 秦志伟², 谢春喜³, 耿一末¹, 李鑫奥¹

^1.辽宁工程技术大学测绘与地理科学学院，辽宁　阜新　123000
^2.辽宁省自然资源卫星应用技术中心，辽宁　沈阳　110031
^3.中国铁路设计集团有限公司测绘地理信息院，天津　300251

收稿日期:2025-12-29 修回日期:2026-06-05 发布日期:2026-07-28
通讯作者: 王丽英 E-mail:youze1997@163.com;wangliyinglntu@163.com
作者简介:有泽（1997—），男，博士生，研究方向为LiDAR点云智能处理与应用。E-mail：youze1997@163.com
基金资助:
国家自然科学基金(42571520);辽宁省自然基金联合基金(20240315)

A segment joint multi-measure interaction index for shield tunnel recognition in RMLS point clouds

Ze YOU¹(), Liying WANG¹(), Yiwei YU¹, Zhiwei QIN², Chunxi XIE³, Yimo GENG¹, Xinao LI¹

^1.School of Geomatics, Liaoning Technical University, Fuxin 123000, China
^2.Natural Resources Satellite Application Technology Center of Liaoning Province, Shenyang 110031, China
^3.Institute of Surveying, Mapping and Geographic Information, China Railway Design Corporation, Tianjin 300251, China

Received:2025-12-29 Revised:2026-06-05 Published:2026-07-28
Contact: Liying WANG E-mail:youze1997@163.com;wangliyinglntu@163.com
About author:YOU Ze (1997—), male, PhD candidate, majors in intelligent processing and applications of LiDAR point clouds. E-mail: youze1997@163.com
Supported by:
The National Natural Science Foundation of China(42571520);Joint Fund Project of Liaoning Provincial Natural Science Foundation(20240315)

摘要/Abstract

摘要：

管片接缝是影响盾构隧道结构安全的关键薄弱部位，其精准识别对于隧道健康监测至关重要。轨道式移动激光扫描技术（RMLS）为接缝识别提供了高质量的数据基础。然而，隧道场景中存在的噪声、遮挡及点云密度不均现象，使得现有方法在精度与稳健性方面存在明显不足。为此，本文提出了基于多测度交互识别（SJMI）指标的管片接缝识别方法。首先，采用横截面椭圆拟合法剔除明显的非衬砌点；然后，设计融合双侧回落楔形测度及反射强度残差测度的SJMI指标，量化表征管片接缝的特征差异；最后，基于构建的SJMI指标，依次实现管片环缝与纵缝的高精度识别。实测RMLS点云验证表明，SJMI指标有效增强了真实接缝识别能力并抑制了噪声与虚警；环缝的IoU、召回率和精确度分别达到90.21%、90.32%和99.84%，纵缝对应指标为94.52%、95.89%和98.23%，较现有最优方法，环缝IoU提升2.25个百分点、纵缝IoU提升7.96个百分点，精度优势显著。同时，SJMI方法在复杂环境下保持了较强的稳健性，可为盾构隧道的数字化运维、接缝异常检测及结构健康评估提供可靠技术支撑。

关键词: 盾构隧道, 管片接缝, 轨道式移动激光扫描, 点云, 多测度交互, 聚类

Abstract:

Segment joints are critical weak points affecting the structural safety of shield tunnels, and their accurate recognition is essential for structural health monitoring. Rail-borne mobile laser scanning (RMLS) provides a high-quality data foundation for the recognition of segment joints. However, noise, occlusions, and uneven point cloud density in tunnels reduce the accuracy and robustness of existing methods. To address these limitations, this study proposes a shield tunnel segment joint recognition method based on the segment joint multi-measure interaction (SJMI) index. The method first removes clearly non-lining points using a cross-sectional ellipse fitting approach. Next, a SJMI is constructed by combining a bilateral wedge-fall geometric measure with an intensity residual measure, quantitatively capturing the distinctive characteristics of segment joints. Finally, the constructed SJMI index is used to sequentially recognize circumferential and longitudinal joints with high accuracy. Experiments on real RMLS point cloud data demonstrate that the proposed SJMI-based method effectively captures segment joint features, improves the recognition of actual joints, and suppresses noise and false positives. Quantitative evaluation shows that for circumferential joints, the IoU, recall, and precision reach 90.21%, 90.32%, and 99.84%, respectively, while for longitudinal joints, the corresponding metrics are 94.52%, 95.89%, and 98.23%. Compared with the current best-performing method, the IoU of circumferential and longitudinal joints increased by 2.25 percentage points and 7.96 percentage points, respectively, clearly demonstrating the significant accuracy advantage of the proposed method. In addition, the SJMI-based approach maintains strong robustness in complex tunnel environments and provides a reliable technical basis for the digital operation and maintenance of shield tunnels, segment joint anomaly detection, and structural health assessment.

Key words: shield tunnel, segment joint, rail-borne mobile laser scanning, point cloud, multi-measure interaction, clustering

中图分类号:

P237

有泽, 王丽英, 宇翼巍, 秦志伟, 谢春喜, 耿一末, 李鑫奥. 基于RMLS点云的盾构隧道管片接缝多测度交互识别指标[J]. 测绘学报, 2026, 55(6): 1101-1115.

Ze YOU, Liying WANG, Yiwei YU, Zhiwei QIN, Chunxi XIE, Yimo GENG, Xinao LI. A segment joint multi-measure interaction index for shield tunnel recognition in RMLS point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(6): 1101-1115.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20250546

http://xb.chinasmp.com/CN/Y2026/V55/I6/1101

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算法	环缝IoU	纵缝IoU
SJMI	90.21	94.52
PointNet	70.88	64.47
Cylinder3D	77.52	44.57
MinkUNet	79.31	59.88
基于单一测度的局部极值方法^[25]	87.96	86.56
GL-Net	79.69	71.09

算法	训练/计算测度时间/h	测试/识别接缝时间/min	总时间/h
SJMI	1.20	6.8	1.31
PointNet	2.46	8.6	2.60
Cylinder3D	4.05	10.6	4.23
MinkUNet	2.65	1.2	2.67
基于单一测度的局部极值方法^[25]	1.68	12.2	1.88
GL-Net	3.07	12.0	3.27

基于RMLS点云的盾构隧道管片接缝多测度交互识别指标

A segment joint multi-measure interaction index for shield tunnel recognition in RMLS point clouds

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接缝类型	IoU	召回率	精确度
环缝	90.21	90.32	99.84
纵缝	94.52	95.89	98.23