[논문]빅데이터를 활용한 건축물 화재위험도 평가 지표 결정

주홍준; 최윤정; 옥치열; 안재홍

doi:10.5345/jkibc.2022.22.3.281

빅데이터를 활용한 건축물 화재위험도 평가 지표 결정
Determination of Fire Risk Assessment Indicators for Building using Big Data 원문보기

한국건축시공학회지 = Journal of the Korea Institute of Building Construction, v.22 no.3, 2022년, pp.281 - 291

주홍준 (Department of Construction Test & Assessment Center, Korea Institute of Civil Engineering and Building Technology) , 최윤정 (Department of Construction Test & Assessment Center, Korea Institute of Civil Engineering and Building Technology) , 옥치열 (Department of Construction Test & Assessment Center, Korea Institute of Civil Engineering and Building Technology) , 안재홍 (Department of Construction Test & Assessment Center, Korea Institute of Civil Engineering and Building Technology)

초록
AI-Helper

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.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (8)

그림 Figure 1. Research process & Flow chart
그림 Figure 2. Big data types & examples
표 Table 1. Examples of stopwords
표 Table 2. Search and collection of professional literature
표 Table 3. Search Review of previous studies related to disaster risk
그림 Figure 3. Internet search engine usage rate(2010~2020)
표 Table 4. Key words extraction
표 Table 5. Determination of building fire risk assessment indicators

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