A Digital Twin Framework for Bridges

Current inspection and maintenance practices for bridges involve periodic manual visual surveys which are time consuming and cumbersome due to number of bridges in need of assessment. Unprecedented capabilities have become possible in recent years thanks to strides made in sensing technologies, computer vision, and machine learning. These technologies can be integrated into a digital twin framework for better insight into a bridge’s structural integrity, maintenance needs, and potential risks. A digital twin framework involves creating a virtual model that mimics the behavior and performance of a physical bridge. Data collection about a bridge’s physical condition is the first step in developing a framework. Physics simulation and graphical environment are next needed to host relevant models of the bridge. An illustrative example involving these steps and digital twin use for scenario simulation for different mechanisms is provided. The example focuses on modeling of a railway bridge in Lyndhurst, New Jersey.

• Record URL:
  https://doi.org/10.1061/9780784485231.052
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784485231
• 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:
  • Najafi, Amirali
  • Amir, Zaid
  • Salman, Baris
  • Sanaei, Parisa
  • Lojano-Quispe, Erick
  • Maher, Ali
  • Schaefer, Richard
• 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
• Pagination: pp 433-441
• Monograph Title: Computing in Civil Engineering 2023: Visualization, Information Modeling, and Simulation

Subject/Index Terms

• TRT Terms: Bridges; Computer vision; Inspection; Machine learning; Maintenance; Remote sensing
• Geographic Terms: New Jersey
• Subject Areas: Bridges and other structures; Highways;

Filing Info

• Accession Number: 01910139
• Record Type: Publication
• ISBN: 9780784485231
• Files: TRIS, ASCE
• Created Date: Feb 27 2024 4:03PM