한국건축시공학회지 (Journal of the Korea Institute of Building Construction)

  • 제22권3호
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  • Pages.281-291
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  • 2022
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  • 1598-2033(pISSN)
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  • 2233-5706(eISSN)
한국건축시공학회 (The Korean Institute of Building Construction)
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빅데이터를 활용한 건축물 화재위험도 평가 지표 결정

Determination of Fire Risk Assessment Indicators for Building using Big Data

  • Joo, Hong-Jun (Department of Construction Test & Assessment Center, Korea Institute of Civil Engineering and Building Technology) ;
  • Choi, Yun-Jeong (Department of Construction Test & Assessment Center, Korea Institute of Civil Engineering and Building Technology) ;
  • Ok, Chi-Yeol (Department of Construction Test & Assessment Center, Korea Institute of Civil Engineering and Building Technology) ;
  • An, Jae-Hong (Department of Construction Test & Assessment Center, Korea Institute of Civil Engineering and Building Technology)
  • 투고 : 2022.04.30
  • 심사 : 2022.05.26
  • 발행 : 2022.06.20
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초록

본 연구에서는 빅데이터를 활용하여 건축물의 화재위험도 평가에 필요한 지표를 결정하였다. 건축물에서 화재위험도에 영향을 미치는 원인은 대부분 건축물만을 고려한 지표로 고착화되어 있기 때문에 제한적이고 주관적인 평가가 수행되어왔다. 따라서, 빅데이터를 활용하여 다양한 내·외부 지표를 고려한다면 건축물의 화재위험도 저감을 위한 효과적인 대책을 도모할 수 있다. 지표 결정에 필요한 데이터를 수집하기 위해 먼저 질의어를 선정하고, 웹 크롤링 기법을 이용하여 비정형 데이터 형식의 전문 문헌을 수집하였다. 문헌 내 단어를 수집하기 위해 사용자 용어사전 등록, 중복 문헌 및 불용어 제거의 전처리 과정을 수행하였으며, 선행 연구를 검토하여 단어를 4개의 요소로 분류하고 각 요소에서 위험도와 관련된 대표 키워드를 선정하였다. 그리고 대표 키워드의 연관검색어 분석을 통해 파생되는 위험도 관련 지표를 수집하였다. 지표의 선정 기준에 따라 수집된 지표를 검토한 결과, 20개의 건축물 화재위험도 지표를 결정할 수 있었다. 본 연구 방법론은 건축물 화재위험의 저감 대책 수립을 위한 빅데이터 분석의 적용 가능성을 나타내며, 결정된 지표는 건축물 화재위험도 평가를 위한 참고자료로 사용될 수 있을 것이다.

This study attempts to use big data to determine the indicators necessary for a fire risk assessment of buildings. Because most of the causes affecting the fire risk of buildings are fixed as indicators considering only the building itself, previously only limited and subjective assessment has been performed. Therefore, if various internal and external indicators can be considered using big data, effective measures can be taken to reduce the fire risk of buildings. To collect the data necessary to determine indicators, a query language was first selected, and professional literature was collected in the form of unstructured data using a web crawling technique. To collect the words in the literature, pre-processing was performed such as user dictionary registration, duplicate literature, and stopwords. Then, through a review of previous research, words were classified into four components, and representative keywords related to risk were selected from each component. Risk-related indicators were collected through analysis of related words of representative keywords. By examining the indicators according to their selection criteria, 20 indicators could be determined. This research methodology indicates the applicability of big data analysis for establishing measures to reduce fire risk in buildings, and the determined risk indicators can be used as reference materials for assessment.

키워드

과제정보

This research was supported by Quality and Certification project(No. 20220035-001).

참고문헌

  1. National Fire Agency. 2020 National fire agency statistical year book. Sejong (Korea): National Fire Agency; 2021. 470 p.
  2. Mi H, Liu Yaling, Wang W, Xiao G. An integrated method for fire risk assessment in residential buildings. Mathematical Problems in Engineering. 2020 Aug;2020:9392467. https://doi.org/10.1155/2020/9392467
  3. National Fire Protection Association. NFPA 101A: Guide on Alternative Approaches to Life Safety. Quincy (MA): National Fire Protection Association; 2007. 96 p.
  4. Bryant P. Fire Strategies-Strategic Thinking. London: Kingfell; 2013. 186 p.
  5. Frantzich H. Risk analysis and fire safety engineering. Fire Safety Journal. 1998 Nov;31(4):313-29. https://doi.org/10.1016/S0379-7112(98)00021-6
  6. Heo JE, Jeon GY, Hong WH. Study on the fire risk assessment in CBD based on the characteristic features of fire damage. Journal of The Architectural Institute of Korea Planning & Design. 2009 Mar;25(3):247-54.
  7. Hwang HY, Baek KY, Park BH, Lee MH, Hwang JH, Ryu EL, Kim TH. Empirical application for the urban disaster risk assessment: fire, facility and escape cases in cheongju city. Journal of The Korean Society of Hazard Mitigation. 2001 Sep;1(2):123-37.
  8. Kim YJ, Shin SY. Developing a risk assessment method for the mitigation of urban disasters. Seoul (Korea): Seoul Development Institute; 2009. 231 p.
  9. Kim YS. Development of resilience indicator based on big data analysis under climate change [dissertation]. [Incheon (Korea)]: Inha University; 2016. 452 p.
  10. Desouza KC. Realizing the promise of big data: Implementing big data projects. WA: IBM Center for The Business of Government. 2014. 42 p.
  11. Park ES, Min SH. Standardization of fire factor for big data. Journal of Korean Society of Hazard Mitigation. 2019 Aug;19(4):143-9. https://doi.org/10.9798/KOSHAM.2019.19.4.143
  12. Kim DH, Jo BW. A study on the development of a fire site risk prediction model based on initial information using big data analysis. Journal of the Society of Disaster Information. 2021 Jun;17(2):245-53. https://doi.org/10.15683/kosdi.2021.6.30.245
  13. Kim YS, Choi CH, Bae YH, Kim DH, Kim DH, Kim HS. Indicator development and evaluation of storm and flood resilience using big data analysis: (1) Development of Resilience Indicators. Journal of Korean Society of Hazard Mitigation. 2019 Aug;18(4):97-107. https://doi.org/10.9798/KOSHAM.2018.18.4.97
  14. Sirisuriya SCM. A comparative study on web scraping. Proceedings of 8th International Research Conference; 2015 Nov 18-20; Seville, Spain. Valencia (Spain): General Sir John Kotelawala Defence University; 2015. p. 135-40.
  15. Park GC. Big data analysis for civil complaints using text mining technique: Gangnam-gu. Seoul (Korea): Seoul Digital Foundation. 2020. 30 p.
  16. Silva C, Ribeiro B. The importance of stop word removal on recall values in text categorization. 2003 Proceedings of the International Joint Conference on Neural Networks.2003 Jul 20-24; Portland, OR. New York(NY): Institute of Electrical and Electronics Engineers; 2003. p. 1661-6.
  17. Li M, Ch'ng E, Chong A, See S. Twitter sentiment analysis of the 2016 U.S. Presidential Election Using an Emoji Training Heuristic. Applied Informatics and Technology Innovation Conference; 2016 Nov 22-24; Newcastle, Australia. New York (NY): Springer; 2016. p. 1-16.
  18. Kim YS, Kang NR, Jung JW, Kim HS. A review on the management of water resources information based on big data and cloud computing. Journal of Wetlands Research. 2016 Feb;18(1):100-12. https://doi.org/10.17663/JWR.2016.18.1.100
  19. Dole D. Ranks NL [Internet]. Massat (France): RANKS NL; 1998 Jan 1 [updated 2014 Jan 1; cited 2021 Oct 13]. Availabel from: https://www.ranks.nl/stopwords/korean.
  20. Watts JM. SFPE Handbook of Fire Protection Engineering: Fire risk indexing. Quincy (Il): National Fire Protection Association; 2016. p. 5-125. https://doi.org/10.1007/978-1-4939-2565-0_82
  21. Shaw R, Takeuchi Y, Joerin J, Fernandez G, Tjandradewi BI, Chosadillia, Wataya E, McDonald B, Fukui R, Sharma A, Tsunozaki E, Matsuoka Y. Climate and disaster resilience initiative capacity-building program. Tokyo (Japan): United Nations International Strategy for Disaster Reduction; 2010. 28p.
  22. Cutter SL. Burton CG. Emrich CT. Disaster Resilience Indicators for Benchmarking Baseline Conditions. Journal of Homeland Security and Emergency Management. 2010 Aug;7(1):1-22. https://doi.org/10.2202/1547-7355.1732
  23. Esnard AM. Sapat A. Mitsova D. An index of relative displacement risk to hurricanes. Natural Hazards. 2011 Apr;59(2):833-59. https://doi.org/10.1007/s11069-011-9799-3
  24. Burton CG. A validation of metrics for community resilience to natural hazards and disasters using the recovery from hurricane katrina as a case study. Annals of the Association of American Geographers. 2014 Nov 4;105(1):67-86. https://doi.org/10.1080/00045608.2014.960039
  25. Beck M. Shepard C. Birkmann J. Rhyner J. Welle T. Witting M. Wolfertz J. Martens J. Maurer K. Mucke P. World Risk Report 2012. Berlin (Germany): Alliance Development Works; 2012. 74 p.
  26. Ko CS. A Study for the Improvement of Disaster Management Systems in Korea [dissertation]. [Seoul (Korea)]: Kyunghee University; 2012. 253 p.
  27. Internet Trend [Internet]. Seoul (Korea): Bizspring. 2010 - [cited 2021 Nov 22]. Availabel from: http://www.internettrend.co.kr/trendForward.tsp