LiDAR, Drones and BrIM for Rapid Bridge Inspection and Management

The mobility of people and goods is highly dependent on the health of a nation’s transportation system. However, the U.S. infrastructure has been repeatedly rated “poor”, while the budget available to either repair or replace these structures is limited. Timely inspection and effective maintenance and management of bridges is crucial to avoid issues that may have a negative impact on public mobility. However, current bridge inspection practice inhibits the collection and analysis of data pertaining to the status of bridges in an efficient and timely manner. This problem is further exacerbated by the large number of U.S. bridges in need of attention and by the limited number of inspectors available that can conduct assessment activities. To overcome the challenges associated with the current bridge inspection and management approaches, a number of researchers have implemented a variety of technologies including unmanned aerial vehicles, a.k.a. drones, and lidar for as-is rapid and accurate bridge data collection. However, processing this data is not fully automated and persists as an extremely time consuming and challenging task. Bridge Information Models (BrIM) is another technology that has been investigated in the context of bridge inspections and management. BrIM is an object-oriented database that enables storing all bridge data, including 2D drawings and 3D models, inspection notes, images and maintenance information. Recent research efforts have focused on implementing BrIM for bridge structural condition assessment, and concluded that it is a suitable concept and technology that can be used to improve current bridge inspection and management processes. In this project, the research team proposes a novel data collection and analysis method that enables rapid collection of 3D geometrical information from existing bridges in the form of 3D dense point clouds using drones and lidar, and convert them into BrIM in an automated and efficient manner. 3D point clouds will be automatically segmented and classified into different structural components using artificial intelligence (AI) algorithms. In the final step, the labeled point clouds will be automatically converted to BrIM. In summary, the team will develop a framework to improve the current bridge inspection and management practice significantly in terms of efficiency and safety, thus improve public mobility.

Language

  • English

Project

  • Status: Active
  • Funding: $360000
  • Contract Numbers:

    69A355174110

  • Sponsor Organizations:

    Pacific Northwest Transportation Consortium

    University of Washington
    More Hall Room 112
    Seattle, WA  United States  98195-2700

    Office of the Assistant Secretary for Research and Technology

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

    Oregon State University, Corvallis

    Department of Civil Engineering
    202 Apperson Hall
    Corvallis, OR  United States  97331-2302
  • Project Managers:

    Turkan, Yelda

  • Performing Organizations:

    Oregon State University, Corvallis

    Department of Civil Engineering
    202 Apperson Hall
    Corvallis, OR  United States  97331-2302

    University of Washington, Seattle

    Civil and Environmental Engineering Department
    201 More Hall, Box 352700
    Seattle, WA  United States  98195-2700
  • Principal Investigators:

    Calvi, Paolo

    Turkan, Yelda

  • Start Date: 20210316
  • Expected Completion Date: 20220315
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01784893
  • Record Type: Research project
  • Source Agency: Pacific Northwest Transportation Consortium
  • Contract Numbers: 69A355174110
  • Files: UTC, RIP
  • Created Date: Oct 15 2021 2:35PM