Bridge Inspection Using Augmented Reality and Artificial Intelligence

Details

• Creators:
  Gabbard, Joe ; Sarlo, Rodrigo
• Corporate Creators:
  Virginia Tech
• Corporate Contributors:
  New Mexico. Department of Transportation ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Artificial Intelligence Augmented Reality Bridge Inspection Bridge Management Systems Computer Vision Data Collection Inspection Safety User-Centered Design
• Publication/ Report Number:
  CO4616
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; New Mexico
• Edition:
  Final Report
• Corporate Publisher:
  New Mexico. Department of Transportation
• Abstract:
  Bridge inspections are critical for maintaining infrastructure safety and reliability, requiring inspectors to conduct thorough evaluations of structural components and document their conditions. However, traditional inspection workflows rely heavily on manual measurement and data entry, which introduces inefficiencies, inconsistencies, and transcription errors. The adoption of the Specification for the National Bridge Inventory (SNBI) has increased the complexity of these assessments, further highlighting the need for modernized inspection methods. This research explores the application of Augmented Reality (AR) and Computer Vision (CV) in bridge inspections through the development of an AR-based head-mounted display system. By overlaying digital annotations, enabling hands-free interaction, and integrating automated measurement tools, the proposed system supports enhanced data collection, visualization, and more efficient data conversion in the office. Through iterative design reviews, development and field testing with professional bridge inspectors, we built InpsectAR (an AR/CV App for bridge inspection) and evaluated the usability and practicality of various inspection techniques. Key findings from real-world testing indicate that AR improves spatial awareness by anchoring annotations and data directly to defects of interest on the bridge structure. The integration of CV for crack quantification has value for facilitating measurements at a distance, but its usability as a fully automated tool is low at this time. The team determined that the best way to ensure reliability in assessments was a hybrid approach where CV is guided by the inspector, with flexibility to bypass it altogether if preferred.
• Format:
  PDF
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:318d6b56e2bb10d7d9ee2d185f5dd372dd24363ffe2f51e90b64b0fefefaffd98140178016073a6633e133b911c04e48706b2d052bd1b0082da62e59efd2686c
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/92349/dot_92349_DS1.pdf
• File Type:
  [PDF - 37.79 MB ]