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Mobile Device-Based Struck-By Hazard Recognition in Construction Using a High-Frequency Sound.

Jaehoon Lee1, Kanghyeok Yang1

  • 1School of Architecture, College of Engineering, Chonnam National University, Gwangju 61186, Korea.

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|May 20, 2022
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Summary

This study introduces a new technology using sound recognition and Convolutional Neural Networks (CNN) to detect struck-by hazards in construction. The system accurately identifies worker movement and near-misses, enhancing worksite safety.

Keywords:
Convolutional Neural NetworkDoppler effectconstruction safetyhigh-frequency soundstruck-by accident

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Area of Science:

  • Construction Safety
  • Machine Learning Applications
  • Acoustic Sensing

Background:

  • Construction sites have the highest casualty rates from safety accidents.
  • Existing safety management studies increasingly integrate Machine Learning (ML).
  • Struck-by hazards between equipment and workers remain a critical safety concern.

Purpose of the Study:

  • To propose a novel technology for recognizing struck-by hazards.
  • To combine Convolutional Neural Networks (CNN) and sound recognition for hazard detection.
  • To analyze Doppler effect changes caused by subject movement for safety.

Main Methods:

  • Developed a system combining CNN and sound recognition.
  • Utilized Doppler effect analysis to detect subject movement.
  • Conducted experiments in indoor and outdoor environments.
  • Evaluated performance based on movement state, direction, speed, and near-misses.

Main Results:

  • Accurate classification of movement direction (84.4-97.4%) and speed (84.4-97.4%).
  • Effective recognition of near-miss situations with 78.9% accuracy.
  • Demonstrated feasibility using smartphone microphone data.

Conclusions:

  • The proposed technology is highly applicable for real-time hazard detection.
  • It enhances worker awareness of potential struck-by hazards near construction equipment.
  • Findings are expected to aid in preventing struck-by accidents on construction sites.