Testing of In-Service Bridges Using Automated Ultrasonic Testing Methods

Work on this project is currently on hold. Shortly after the contract for this project was executed, the contractor’s company was bought by another company. This necessitated a new contract before work could be started. The contracting process is currently underway. The project will develop methods, equipment, and software for advanced automation of ultrasonic testing of bridge members. Work in the initial phase will involve exploring and developing mechanical and electrical equipment needed for automated testing, identifying bridge candidates, developing test procedures specific for those bridges, identifying and qualifying technicians to conduct the tests, and developing mock ups for the qualification procedures (tester and testing procedure). A scanning device specific to steel bridge element inspection will be developed. Laboratory mockups will be produced with known void locations to help develop the encoder and an accurate scanning procedure to identify, characterize, and quantify deficiencies when evaluating discontinuities in the mockups and bridge candidate members. Proprietary scanning devices will be explored to find the most suitable for the project needs or a new one will be fabricated if none of the existing devices meets the project needs. Additionally, after developing the mechanical equipment to mount and move the sensor and encoder, robotic equipment will be developed for automated sensor placement. Electronic methods for collecting and storing data will include hardware and software incorporated into the equipment. New programming routines will be developed to assist in the robotic mechanical equipment and to provide automated control of the sensor scan. Work in the second phase will focus on laboratory and field testing. The evaluation and analysis of the collected data will be used to ensure that the automated ultrasonic testing (AUT) technology is an acceptable alternative to conventional testing methods. The data will also be used to introduce automated data analysis. The raw data will be post-processed that will include compiling all data and organizing in a usable form. Several software systems able to simplify this process will be evaluated. The project team will also investigate software development specific to the needs of this project and which would use vision algorithms capable of automatically analyzing scan data from video graphics array (VGA) video output. The video output feed would be acquired directly from the AUT data acquisition system and fed into an auxiliary laptop/computer, which would capture the video data synchronized with positional information from the AUT encoder. This would provide the visual representation of the full scan in a data format compatible with most commercially available programming languages. The automation will need to make sure that there is an increase in analysis efficiency while maintaining an acceptable probability of detection.


  • English


  • Status: Active
  • Funding: $100000
  • Contract Numbers:

    Project 20-30, IDEA 191

  • Sponsor Organizations:

    National Cooperative Highway Research Program

    Transportation Research Board
    500 Fifth Street, NW
    Washington, DC  United States  20001

    American Association of State Highway and Transportation Officials (AASHTO)

    444 North Capitol Street, NW
    Washington, DC  United States  20001

    Federal Highway Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Project Managers:

    Jawed, Inam

  • Performing Organizations:

    Bridge Diagnostics, Incorporated

  • Principal Investigators:

    Boone, Shane

  • Start Date: 20170107
  • Expected Completion Date: 0
  • Actual Completion Date: 20181031
  • Source Data: RiP Project 41370

Subject/Index Terms

Filing Info

  • Accession Number: 01622194
  • Record Type: Research project
  • Source Agency: Transportation Research Board
  • Contract Numbers: Project 20-30, IDEA 191
  • Files: TRB, RiP
  • Created Date: Jan 7 2017 1:00AM