Automated Detection of Characterization of Cracks Using Structure-From-Motion Based Photogrammetry: A Feasibility Study

In infrastructure such as pavement, bridges and tunnels, crack widths and patterns on surfaces are two of the most important signs used to estimate durability. Conventional techniques suffer from challenges such as tediousness, subjectivity, and high cost. A new measurement technique that overcomes these challenges while measuring crack displacement with high accuracy and low cost in aging structures is needed. The research will develop a Structure-from-Motion Based photogrammetry technique for measuring crack widths and patterns using videos taken by commercially available low cost digital cameras. Software will be developed to analyze the videos by combining deep-learning techniques and modern close-range photogrammetry. 3D models of the pavement and bridge structures with high accuracy will be constructed using the videos and will be compared and validated using the results generated from high accuracy LiDAR system. Post-processing algorithms will be developed to automatically calculate the real lengths as well as the real width and depth of a crack at any arbitrary locations. This method for 3D crack mapping will provide us a high accuracy, low cost, and easy-to-operate tool for pavement and bridge management.

• Record URL:
  https://tridurle.wsu.edu/automated-detection-of-characterization-of-cracks/

Language

• English

Project

• Status: Active
• Funding: $
• Contract Numbers:

  ORSO 135461

• Sponsor Organizations:

  Transportation Infrastructure Durability & Life Extension

  Washington State University
  Civil & Environmental Engineering
  Pullman, Washington  United States  99164

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Transportation Infrastructure Durability & Life Extension

  Washington State University
  Civil & Environmental Engineering
  Pullman, Washington  United States  99164
• Project Managers:

  Kline, Robin

• Performing Organizations:

  Missouri University of Science & Technology, Rolla

  Department of Engineering
  202 University Center
  Rolla, MO    65409
• Principal Investigators:

  Zhang, Xiong

• Start Date: 20200501
• Expected Completion Date: 20220930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Algorithms; Feasibility analysis; Flaw detection; Laser radar; Machine learning; Pavement cracking; Pavement maintenance; Photogrammetry; Video
• Subject Areas: Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 01754237
• Record Type: Research project
• Source Agency: National Center for Transportation Infrastructure Durability and Life-Extension
• Contract Numbers: ORSO 135461
• Files: UTC, RIP
• Created Date: Oct 5 2020 5:01PM