International conference on construction engineering and project management (국제학술발표논문집)

  • 2024.07a
  • /
  • Pages.731-737
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  • 2024
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  • 2508-9048(eISSN)
Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Automated Data Collection and Intelligent Management System for Construction Site Disaster Prevention

  • Chang-Yong Yi (Intelligent Construction Automation Center, Kyungpook National University) ;
  • Young-Jun Park (Intelligent Construction Automation Center, Kyungpook National University) ;
  • Tae-Yong Go (Intelligent Construction Automation Center, Kyungpook National University) ;
  • Jin-Young Park (Intelligent Construction Automation Center, Kyungpook National University) ;
  • Hyung-Keun Park (Intelligent Construction Automation Center, Kyungpook National University) ;
  • Dong-Eun Lee (School of Architecture, Civil, Environment and Energy Engineering, Kyungpook National University)
  • Published : 2024.07.29
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Abstract

The accident rate in the South Korean construction industry has increased by 50% over the past ten years, reaching seven times the average growth rate of the entire industry. However, the number of management personnel at construction sites is decreasing, making it increasingly difficult to establish a safety monitoring system through professional personnel. This study aims to develop an intelligent control system to address the problem of insufficient management personnel and support the establishment of a continuous safety monitoring system. This system consists of a mobile information collection robot (S-BOT) and an intelligent algorithm. The visual information collected by S-BOT can be analyzed in real-time using computer vision-based intelligent algorithms to detect unsafe situations. The results of this study will contribute to preventing unnecessary social and economic losses by maximizing safety management efficiency and supporting timely decision-making through the sharing of information provided by the intelligent control system.

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Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2018R1A5A1025137), by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS2023-00249407), by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2023-00251002), and by Korea Innovation Foundation through the Ministry of Science and ICT (2023-DG-RD-0022).

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