LiDAR, Drones and BRIM for Rapid Bridge Inspection and Management

Details

• Creators:
  Turkan, Yelda ; Calvi, Paolo ; Ji, Donghoon
• Corporate Creators:
  Oregon State University ; University of Washington ; Pacific Northwest Transportation Consortium (PacTrans) (UTC)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Bridge management systems
  • Bridges
  • Data collection
  • Drones
  • Laser radar
  • Cracking
  • Automation
  • Neural networks
  • Edge detection
  • Measurement
  More +
• Publication/ Report Number:
  2021-M-OSU-3
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report, 03/16/2021 - 02/23/2023
• Corporate Publisher:
  Pacific Northwest Transportation Consortium (PacTrans) (UTC)
• Abstract:
  Cracks are one of the main defects on concrete surfaces, and they are indicators of concrete structures' state of health. Because traditional methods to identify and assess cracks rely on manual measurements, a significant number of studies have focused on identifying ways to automate this process. Accordingly, this study proposed to combine convolutional neural network (CNN)-based algorithms and traditional image morphological operations for crack detection and measurement. The proposed approach was tested on data from six images containing ten cracks of various sizes and shapes that were obtained from laboratory experiments in a controlled environment. The proposed methodology achieved an average F1 score of 0.93, with 88.17 percent accuracy in crack length measurements and 94.40 percent accuracy in width measurements. Future research will focus on fine-tuning the proposed crack detection and measurement methodology and will evaluate it with a set of images acquired from a full-scale structure such as a bridge or a building.
• Format:
  PDF
• Funding:
  69A355174110
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/67584/dot_67584_DS1.pdf
• File Type:
  [PDF - 1.72 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:98322afb656f90d69d5821c1130af4c850372d0763b37b3b182365e86f2dfce930712fb522944bbd0e98198d10f883e58d4191b3f3e886abaeb759e9b9f41c3b