Heuristic Optimization for Digital Twin Modeling of Existing Bridges from Point Cloud Data by Parametric Prototype Models

Digital twins (DTs) can support the operation and maintenance process of bridges by providing a digital model representing the actual asset in reality. The underlying semantic-geometric model of bridges can be created from point cloud data (PCD), obtained by laser scanning or photogrammetry. The bridge PCD, however, needs to be processed and abstracted to a parametric model to handle geometric updates. Today, this process is conducted manually which in turn increases the geometric modeling costs. This paper aims to automate semantic segmentation and parametric modeling as essential steps in the geometric modeling of bridges. The point cloud of bridges is semantically segmented first through a deep-learning model. The value of parameters is then extracted by a heuristic optimization algorithm. Finally, the model of the entire bridge is created. The results of the paper show that the geometric modeling process of bridges can be automated to a large extent through computational methods.

• Record URL:
  • https://doi.org/10.1061/9780784485224.041
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784485224
• Supplemental Notes:
  • © 2024 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Mafipour, M Saeed
  • 0000-0002-2076-8653
  • Vilgertshofer, Simon
  • Borrmann, André
• Conference:
  • ASCE International Conference on Computing in Civil Engineering 2023
  • Location: Corvallis Oregon, United States
  • Date: 2023-6-25 to 2023-6-28
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 334-342
• Monograph Title: Computing in Civil Engineering 2023: Data, Sensing, and Analytics

Subject/Index Terms

• TRT Terms: Bridges; Computer models; Data segmentation; Digital twins; Optimization; Parametric analysis
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways;

Filing Info

• Accession Number: 01913577
• Record Type: Publication
• ISBN: 9780784485224
• Files: TRIS, ASCE
• Created Date: Apr 1 2024 9:18AM