loading page

Automatic Damage Detection of Bridge Finger-Type Expansion Joints Based on Video Object Detection and Sound Recorded on Vehicle
  • +1
  • Takanori Imai ,
  • Di SU ,
  • Tsukasa Mizutani ,
  • Kai Xue
Takanori Imai
Author Profile
Tsukasa Mizutani
Institute of Industrial Science

Corresponding Author:[email protected]

Author Profile

Abstract

Bridge finger type expansion joints are widely used because of their durability. However, they are susceptible to damage from fatigue and corrosion, and making the development of efficient damage detection methods crucial for preventing serious accidents. This study proposes a framework for automatic damage detection by simply driving in a vehicle, consisting of two steps: (1) detection of expansion joints by applying object detection to smartphone video, and (2) calculation of damage scores by passing sound recorded by an on-vehicle microphone. YOLOv5 object detection successfully detected 99.8% of expansion joints in the first step. Regarding the second step, experiments were conducted using one intact and four specimens modeling incremental damage. Finally, the damage score was proposed to quantify the sound of contact between among damaged structural members. After applying the proposed method to expansion joints that are in service, those identified as needing urgent replacement (Urgent Replacement Needed or U.R.N specimens) scored in the top 0.1% of the distribution of scores, indicating low false positives.