Instance and semantic segmentation of point clouds of large metallic truss bridges

Several methods have been developed for the semantic segmentation of reinforced concrete bridges, however, there is a gap for truss bridges. Therefore, in this study a state-of-the-art methodology for the instance and semantic segmentation of point clouds of truss bridges for modelling purposes is presented, which, to the best of the authors' knowledge, is the first such methodology. This algorithm segments each truss element and classifies them as a chord, diagonal, vertical post, interior lateral brace, bottom lateral brace, or strut. The algorithm consists of a sequence of methods, including principal component analysis or clustering, that analyse each point and its neighbours in the point cloud. Case studies show that by adjusting only six manually measured parameters, the algorithm can automatically segment a truss bridge point cloud.

• Record URL:
  • https://doi.org/10.1016/j.autcon.2023.104865
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0926580523001255
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09265805
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Lamas, Daniel
  • Justo, Andrés
  • Soilán, Mario
  • Cabaleiro, Manuel
  • Riveiro, Belén
• Publication Date: 2023-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 104865
• Serial:
  • Automation in Construction
  • Volume: 151
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0926-5805
  • Serial URL: http://www.sciencedirect.com/science/journal/09265805

Subject/Index Terms

• TRT Terms: Data models; Laser radar; Machine vision; Truss bridges
• Subject Areas: Bridges and other structures; Data and Information Technology;

Filing Info

• Accession Number: 01885265
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 20 2023 10:09AM