Intelligent Deformation Monitoring for Lifting Space Frames Based on Point Cloud Data

JIN Qinming; CHENG Guozhong; LI Dongsheng; WANG Cong; CHEN Shasha; WANG Ruirong; BI Jinggang

doi:10.13204/j.gyjzG21061811

Volume 52 Issue 12

Dec. 2022

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2022 > 52(12): 209-215

Yao Jian, Tang Tingting, Chen Baixin. COMPARISON AND STUDY ON CALCULATION METHODS OF COLD-FORMED THIN-WALLED LIPPED CHANNEL MEMBERS' CAPACITY OF DISTORTIONAL BUCKLING[J]. INDUSTRIAL CONSTRUCTION, 2013, 43(6): 120-123. doi: 10.13204/j.gyjz201306024

Citation:

JIN Qinming, CHENG Guozhong, LI Dongsheng, WANG Cong, CHEN Shasha, WANG Ruirong, BI Jinggang. Intelligent Deformation Monitoring for Lifting Space Frames Based on Point Cloud Data[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(12): 209-215. doi: 10.13204/j.gyjzG21061811

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Intelligent Deformation Monitoring for Lifting Space Frames Based on Point Cloud Data

doi: 10.13204/j.gyjzG21061811

1. China State Railway Investment and Construction Group Co., Ltd., Beijing 102601, China;
2. School of Civil Engineering, Chongqing University, Chongqing 400045, China;
3. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China

Received Date: 2021-06-18
Available Online: 2023-03-22

Abstract

Abstract

Deformation monitoring is one of the most essential means of ensuring construction safety for lifting space frames. Current traditional methods only enable deformation monitoring at local points, but not for the whole structure. Three-dimensional (3D) laser scanning technology can capture accurate point clouds of as-built structures through full-coverage scanning, thus providing a innovative solution to the above issue. To this end, the research on intelligent deformation monitoring for lifting space frames based on point cloud data, including point cloud data preprocessing, non-rigid matching of point clouds and lifting deformation visualization, was carried out based on a practical engineering project, namely Luzhou Railway Station. Based on clustering algorithms, random sample consensus, graph structural methods and orthogonal procrustes analysis, a non-rigid matching algorithm integrating intelligent sphere positioning, coarse sphere matching, non-rigid sphere matching was proposed for the non-rigid matching of point clouds of space frames before and after lifting. The results showed that the proposed intelligent deformation monitoring approach based on point cloud data was efficient, comprehensive and practical.
- space frame,
- deformation monitoring,
- 3D laser scanning,
- point cloud data,
- non-ridge matching

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References

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