Automated measurement of cable shape in super-long span suspension bridges

被引:0
作者
Wang, Feiyu [2 ]
Ma, Zhuang [2 ,3 ]
Cheng, Yuyao [4 ]
Chen, Wang [2 ]
Zhang, Jian [1 ,2 ]
机构
[1] Southeast Univ, Jiangsu Key Lab Mech Anal Infrastruct & Adv Equipm, Nanjing 210096, Peoples R China
[2] Southeast Univ, Sch Civil Engn, Nanjing 210096, Peoples R China
[3] CCCCSHEC Fourth Engn Co Ltd, Nanjing 210096, Peoples R China
[4] Jiangsu Univ, Dept Civil Engn & Mech, Zhenjiang 212000, Peoples R China
关键词
Super-long span suspension bridges; Cable shape; SCF-bridge; net; Point clouds; STRUCTURE-FROM-MOTION; 3D RECONSTRUCTION; LASER; INFRASTRUCTURE;
D O I
10.1016/j.autcon.2024.105748
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The current measurement of the main cable shape of large-span suspension bridges relies on the total station, which is time-consuming and labor-intensive. Therefore, this paper proposes an automatic measurement method for the cable shape of suspension bridges: (1) For obtaining target during the construction process, inertial navigation and differential Global Positioning System fusion and route planning method are adopted in combination with airborne laser scanning to get fine point clouds. (2) Addressing the challenge of large-scale point clouds segmentation, SCF-Bridge-Net is proposed based on Spatial Contextual Features Net (SCF-Net) and suspension bridges point clouds simplification method, enabling spatial positioning of the cable clamp and rapid automated calculation of geometric information. The proposed method is successfully applied to the Xianxin Road Bridge in China. The results show that the average error of the main cable shape is 1.1 cm, and the angle error of the cable clamp is approximately 0.21 degrees, validating the efficiency and reliability.
引用
收藏
页数:14
相关论文
共 54 条
  • [21] Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools
    Jiang, San
    Jiang, Cheng
    Jiang, Wanshou
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 167 : 230 - 251
  • [22] Real-time crack assessment using deep neural networks with wall-climbing unmanned aerial system
    Jiang, Shang
    Zhang, Jian
    [J]. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2020, 35 (06) : 549 - 564
  • [23] A visual inspection and diagnosis system for bridge rivets based on a convolutional neural network
    Jiang, Tengjiao
    Froseth, Gunnstein T.
    Ronnquist, Anders
    Kong, Xuan
    Deng, Lu
    [J]. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2024, 39 (24) : 3786 - 3804
  • [24] Low-High Orthoimage Pairs-Based 3D Reconstruction for Elevation Determination Using Drone
    Jiang, Yuhan
    Bai, Yong
    [J]. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2021, 147 (09)
  • [25] Automated construction progress measurement using a 4D building information model and 3D data
    Kim, Changmin
    Son, Hyojoo
    Kim, Changwan
    [J]. AUTOMATION IN CONSTRUCTION, 2013, 31 : 75 - 82
  • [26] Laefer Debra F., 2009, Structural Engineering International, V19, P184, DOI 10.2749/101686609788220196
  • [27] Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
    Landrieu, Loic
    Simonovsky, Martin
    [J]. 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, : 4558 - 4567
  • [28] Heritage Building Information Modeling (H-BIM) Applied to A Stone Bridge
    Leon-Robles, Carlos A.
    Reinoso-Gordo, Juan F.
    Gonzalez-Quinones, Juan J.
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 8 (03)
  • [29] New Approach for Low-Cost TLS Target Measurement
    Lichti, D. D.
    Glennie, C. L.
    Jahraus, A.
    Hartzell, P.
    [J]. JOURNAL OF SURVEYING ENGINEERING, 2019, 145 (03)
  • [30] Image-Based Technologies for Constructing As-Is Building Information Models for Existing Buildings
    Lu, Qiuchen
    Lee, Sanghoon
    [J]. JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2017, 31 (04)