Mixed Reality-Assisted Element Level Inspection and Documentation

Assessing the condition of Virginia Department of Transportation's (VDOT’s) inventory of 21,000 bridges and culverts requires substantial time and resources. Conventional assessment primarily involves periodic visual and tactile inspections conducted at “arm’s length” to quantify deficiencies that may require follow-up actions. However, there are emerging technologies that could potentially enhance the quality and efficiency of the bridge safety inspection, specifically, augmented and virtual reality (AR/VR) and artificial intelligence (AI). AR can provide historical data in a holographic image overlaid on top of the current image of a physical object, as well as access to element and defect codes prescribed in the AASHTO Manual for Bridge Evaluation. VR can enable remote inspections by recreating a visual, 3D model of a physical bridge in a virtual environment that can be remotely evaluated by engineers in an office setting. AI can provide automatic deterioration detection, measurement, and labeling of such deterioration as concrete cracks, delaminations, and spalls, and steel corrosion, in a way that is similar to human visual perception. A system combining these technologies could automatically quantify defects and bridge element data for the NBIS inventory and generate the requisite inspection reports. Furthermore, this type of platform could accelerate the condition assessment process by aiding in measuring how much deterioration or damage has occurred since the last inspection, as well as provide a temporal, geo-located illustration of the changes. Such an evolution map could shed light on the mechanisms causing the damage and help to determine the required maintenance action. Additional benefits of the prototype will be the reduction of the impact of lane closures on the traveling public, enhanced collaboration amongst the inspection teams within and outside of VDOT, and enhanced training for future inspectors.

Language

• English

Project

• Status: Active
• Funding: $385022
• Contract Numbers:

  120109

• Sponsor Organizations:

  Virginia Transportation Research Council

  530 Edgemont Road
  Charlottesville, VA  United States  22903
• Managing Organizations:

  Virginia Transportation Research Council

  530 Edgemont Road
  Charlottesville, VA  United States  22903
• Project Managers:

  Kassner, Bernie

• Performing Organizations:

  Virginia Polytechnic Institute and State University, Blacksburg

  208 Patton Hall
  Blacksburg, VA  United States  24061
• Principal Investigators:

  Sarlo, Rodrigo

  Gabbard, Joseph L

• Start Date: 20210901
• Expected Completion Date: 20240531
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Bridges; Culverts; Inspection; Virtual reality
• Identifier Terms: Virginia Department of Transportation
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; Safety and Human Factors;

Filing Info

• Accession Number: 01779801
• Record Type: Research project
• Source Agency: Virginia Department of Transportation
• Contract Numbers: 120109
• Files: RIP, STATEDOT
• Created Date: Aug 25 2021 1:34PM