首页|基于实测点云数据的隧道分割及形变检测方法

基于实测点云数据的隧道分割及形变检测方法

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随着现代测绘技术的进步,点云数据成为了隧道健康监测中不可或缺的工具.针对西气东输这类的大型能源输送隧道,提出了一种基于实测点云数据的隧道分割与形变检测方法.首先,通过结合几何和密度的分割策略,准确地从原始点云数据中提取隧道结构.其次,采用时间序列和特征检测策略,实现了对隧道微小形变的准确识别.此外,引入机器学习方法进一步提高了形变检测的准确性和自动化程度.实验结果表明:此方法在实际工程应用中具有很高的准确性和稳定性,为隧道的安全运营提供了有力的技术支撑.
Segmentation and Deformation Detection Methods of Tunnels Based on Measured Point Cloud Data
With the advancement of modern surveying and mapping technology,point cloud data has become an indispensable tool in the health monitoring of tunnels.A segmentation and deformation detection method of tun-nels based on measured point cloud data is proposed for large-scale energy transmission tunnels such as the west-east gas pipeline.Firstly,by a segmentation strategy which combines with geometry and density,tunnel structures are accurately extracted from original point cloud data.Secondly,time series and feature detection strategies are used to achieve the accurate recognition of the slight deformation of tunnels.In addition,the machine learning method is introduced to further improve the accuracy and automation degree of deformation detection.Experimental results show that this method has high accuracy and stability in practical engineering applications,providing strong techni-cal support for the safe operation of tunnels.

Point cloud dataTunnel health monitoringDeformation detectionMachine learningWest-east gas transmission

安新、赵晓涛、胡琼

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中国安能集团第三工程局有限公司成都分公司 四川成都 611136

点云数据 隧道健康监测 形变检测 机器学习 西气东输

2024

科技资讯
北京国际科技服务中心 北京合作创新国际科技服务中心

科技资讯

影响因子:0.51
ISSN:1672-3791
年,卷(期):2024.22(5)
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