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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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Computer Vision and Augmented Reality for Human-Centered Fatigue Crack Inspection.

Rushil Mojidra1, Jian Li1,2, Ali Mohammadkhorasani3

  • 1Department of Civil, Environmental and Architectural Engineering, The University of Kansas, Lawrence, KS 66045, USA.

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|June 19, 2024
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Summary

This study introduces a new human-centered bridge inspection method using computer vision and augmented reality (AR) for faster, more accurate fatigue crack detection. The system analyzes structural motion via AR headset video, improving bridge safety and maintenance efficiency.

Keywords:
HoloLens2augmented realitybridge inspectioncomputer visionfatigue crackshuman-centered inspection

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Area of Science:

  • Structural Engineering
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Many US bridges exceed their design life, increasing vulnerability to fatigue cracks.
  • Current visual inspection methods are slow, labor-intensive, and error-prone.
  • Advanced technologies are needed to improve bridge structural health monitoring.

Purpose of the Study:

  • To develop a novel human-centered bridge inspection methodology.
  • To enhance the efficiency and accuracy of fatigue crack detection.
  • To integrate computer vision and augmented reality (AR) for real-time analysis.

Main Methods:

  • A computer vision algorithm analyzes structural surface motion from AR headset video.
  • Feature point tracking and distance variation measurement detect crack opening/closing patterns.
  • An AR environment visualizes crack detection results as holograms and supports human-in-the-loop decision-making.

Main Results:

  • The method enables near-real-time, computationally efficient fatigue crack detection.
  • Elimination of camera motion compensation enhances reliability.
  • Integrated AR visualization aids inspectors in making informed field decisions.

Conclusions:

  • The proposed methodology significantly improves fatigue crack detection efficiency and accuracy.
  • The human-centered approach with AR enhances visualization and human-machine collaboration.
  • The system demonstrates efficacy for real-world bridge inspection applications.