3D Change Detection Method for Exterior Wall of LNG Storage Tank Supported by Multi-Source Spatial Data

被引:2
|
作者
Gao, Chutian [1 ]
Guo, Ming [1 ,2 ]
Wang, Guoli [1 ]
Guo, Kecai [3 ]
Zhao, Youshan [4 ,5 ]
机构
[1] Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, Beijing 102616, Peoples R China
[2] Natl Adm Surveying, Key Lab Modern Urban Surveying & Mapping, Beijing 102616, Peoples R China
[3] Beijing Shenxin Reach Technol Co Ltd, Beijing 102444, Peoples R China
[4] China Acad Bldg Res, Beijing 100013, Peoples R China
[5] CABR Testing Ctr Co Ltd, Beijing 100013, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
3D change detection; LiDAR; LNG storage tank; photogrammetry; point cloud; BUILDINGS;
D O I
10.1002/adts.202300941
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Change detection methods for measuring deformation of the LNG storage tank's exterior wall cannot meet the requirements for extensive and comprehensive deformation analysis. The method proposed in this paper uses multi-source spatial data to detect the deformation of the LNG storage tank's exterior wall. Exterior wall data collected simultaneously by terrestrial and airborne laser scanners, as well as close-range and oblique photogrammetry, are pre-processed separately and then registered to the global point clouds. The deformation state of exterior wall is represented by a thorough assessment approach that takes into account four change indices. The results suggest that the registration precision of global point clouds is 6 mm. The global ovality of the LNG storage tank is 0.053%, with a tilt change of 2.58 degrees and a deviation of 27 mm in the direction of the azimuth of 321.98 degrees, an average value of 0.07 mm for depression and protrusion deformation, and a surface flatness of 21.3 mm. In this case, the flatness is greater than the variation threshold of the standard specification, indicating that the tank may have structural uneven settlement or other deformation problems. The attraction of the proposed method resides in its capacity to efficiently and accurately obtain spatial positioning and color information of the LNG storage tank's exterior wall, thereby providing a robust foundation for precise judgments regarding the deformation state. In this study, spatial localization and visual data are integrated for the full data acquisition of the LNG storage tank's exterior wall, and a step-by-step registration method is used to produce the global point clouds. 3D deformations of the exterior wall are systematically described in different dimensions, from global to local.image
引用
收藏
页数:22
相关论文
共 50 条
  • [21] APPROACH TO CONSTRUCTING 3D VIRTUAL SCENE OF IRRIGATION AREA USING MULTI-SOURCE DATA
    Cheng, Shuai
    Dou, Mingzhu
    Wang, Jinxin
    Zhang, Shuqing
    Chen, Xiangcong
    ISPRS JOINT INTERNATIONAL GEOINFORMATION CONFERENCE 2015, 2015, II-2 (W2): : 227 - 233
  • [22] Alternative 3D modeling approaches based on complex multi-source geological data interpretation
    Mingchao L.
    Yanqing H.
    Zhengjian M.
    Wei G.
    Trans. Tianjin Univ., 1 (7-14): : 7 - 14
  • [23] Alternative 3D Modeling Approaches Based on Complex Multi-Source Geological Data Interpretation
    李明超
    韩彦青
    缪正建
    高伟
    Transactions of Tianjin University, 2014, 20 (01) : 7 - 14
  • [24] Rapidly Realizing 3D Visualisation for Urban Street Based on Multi-Source Data Integration
    Kang, Zhizhong
    Zhang, Zuxun
    Zhang, Jianqing
    Zlatanova, Sisi
    GEOMATICS SOLUTIONS FOR DISASTER MANAGEMENT, 2007, : 149 - 163
  • [25] Estimation Method of Regional Tank-Washing Wastewater Quantity Based on Multi-Source Data
    Xu, Yong
    Zhu, Kaize
    Zhong, Huiling
    SUSTAINABILITY, 2024, 16 (01)
  • [26] Key Technology Research of massive multi-source heterogeneous spatial data visualization and management system based on 3D digital earth
    Liu, Zhi-Wen
    Li, Sheng-Yang
    Yu, Hai-Jun
    Hao, Zhong-Weng
    2016 INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND ARTIFICIAL INTELLIGENCE (ISAI 2016), 2016, : 241 - 246
  • [27] Unsupervised spatial self-similarity difference-based change detection method for multi-source heterogeneous images
    Zhu, Linye
    Sun, Wenbin
    Fan, Deqin
    Xing, Huaqiao
    Liu, Xiaoqi
    PATTERN RECOGNITION, 2024, 149
  • [28] 3D multi-source model of elastic volcanic ground deformation
    Camacho, Antonio G.
    Fernandez, Jose
    Samsonov, Sergey V.
    Tiampo, Kristy F.
    Palano, Mimmo
    EARTH AND PLANETARY SCIENCE LETTERS, 2020, 547
  • [29] 3D building model generation from MLS point cloud and 3D mesh using multi-source data fusion
    Liu, Weiquan
    Zang, Yu
    Xiong, Zhangyue
    Bian, Xuesheng
    Wen, Chenglu
    Lu, Xiaolei
    Wang, Cheng
    Marcato Junior, Jose
    Goncalves, Wesley Nunes
    Li, Jonathan
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 116
  • [30] A Method for Predicting High-Resolution 3D Variations in Temperature and Salinity Fields Using Multi-Source Ocean Data
    Cao, Xiaohu
    Liu, Chang
    Zhang, Shaoqing
    Gao, Feng
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2024, 12 (08)