AI기반 건설현장의 외국인 근로자 안전사고 예측을 위한 기본 연구

AI-based basic research to predict safety accidents for foreign workers at construction sites

  • 발행 : 2023.11.10

초록

Compared to other industries the construction industry experiences more casualties and property damage due to safety accidents. One of the reasons is the increasing number of foreign workers. For this reason, past studies have found that foreign workers at construction sites are more exposed to safety accidents than non-foreign workers. Nevertheless the proportion of foreign workers involved in safety accidents at construction sites is increasing, and there has been a lack of research to predict the risk of safety accidents at construction sites. Additionally, realistic safety management is lacking due to a lack of safety accident risk prediction research. Therefore, in this study, we would like to propose basic research that proposes an AI-based safety accident prediction model framework for predicting safety accidents of foreign workers at construction sites. The framework and results of this study will contribute to reducing and preventing the risk of safety accidents for foreign workers through risk prediction for safety management of foreign workers at construction sites.

키워드

과제정보

본 연구는 교육부 지원 한국연구재단(NRF) 기초과학연구사업(2022R1F1A106314112)의 지원을 받아 수행되었습니다.

참고문헌

  1. Gledson BJ, Greenwood D. The adoption of 4d bim in the UK construction industry: an innovation diffusion approach, Eng. Construct. Architect. Manag. 2017. Vol.24, No.6. p. 950-967. https://doi.org/10.1108/ECAM-03-2016-0066
  2. IPA. Transforming Infrastructure Performance. Infrastructure and Projects Authority. 2017.
  3. Kim JM, Son K, Yum SG, Ahn S. Analyzing the risk of safety accidents: The relative risks of migrant workers in construction industry. Sustainability. 2020. Vol.12, No.13. p. 5430.
  4. Lee JS, Kim MJ, Choi KH. An Efficient Safety Management through Mechanism Analysis on Disasters of Temporary Facilities. Journal of Architectural Institute of Korea, Architectural Institute of Korea. 2010. Vol. 26, No. 11. p. 129-136.
  5. Choi MC. Problems of AHP analysis and development of a modified weight model. 2020.
  6. Management and Information. Vol.39 No.2. p. 145-162.
  7. Alomari KA, Gambatese JA, Tymvios N. Risk perception comparison among construction safety professionals: Delphi perspective. Journal of construction engineering and management. 2018. Vol.144 No.12. p. 04018107.
  8. Alomari K, Gambatese J, Nnaji C, Tymvios N. Impact of Risk Factors on Construction Worker Safety: A Delphi Rating Study Based on Field Worker Perspective. Arabian Journal for Science and Engineering. 2020. Vol.45. p. 8041-8051. https://doi.org/10.1007/s13369-020-04591-7
  9. Weili F, Lieyun D, Hanbin L, Peter EL. Falls from heights: a computer vision-based approach for safety harness detection. Autom. ConStruct. 2018. Vol. 91. p. 53-61. https://doi.org/10.1016/j.autcon.2018.02.018