TY - JOUR
T1 - Analysis of Fire-Accident Factors Using Big-Data Analysis Method for Construction Areas
AU - Kim, Joon Soo
AU - Kim, Byung Soo
N1 - Publisher Copyright:
© 2018, Korean Society of Civil Engineers.
PY - 2018/5/1
Y1 - 2018/5/1
N2 - The Ministry of Employment and Labor releases its annual report on the present conditions of industrial disasters by aggregating and summarizing negligent accidents that occur at construction sites. Industry-specific accident and fatality rates, and disaster classification and statistics are aggregated in this report, but its effectiveness is low. This is due to the fact that it does not sufficiently present the direct causes of accidents or related information on their causal relation. However, this study utilizes a big-data method that has recently gained significant attention throughout all industrial and academic areas to collect Internet articles on fire-accidents that have occurred at construction sites over the last decade. In addition, principal component analysis was conducted to deduce season-specific factors according to time, location, inducer, and accident pattern. Based on this analysis, as for common factors, direct spark and oil mist were deduced. As work-related factors, negligent supervision and violations of the safety regulations were shown to cause fire-accidents, illustrating the man-made nature of such accidents. It was also found that secondary accidents such as collapses, burials, explosions, and suffocation have occurred when fires have broken out. The big-data analysis method utilized in this study is considered to be very effective and can be successfully utilized in the future for deducing high volumes of text data.
AB - The Ministry of Employment and Labor releases its annual report on the present conditions of industrial disasters by aggregating and summarizing negligent accidents that occur at construction sites. Industry-specific accident and fatality rates, and disaster classification and statistics are aggregated in this report, but its effectiveness is low. This is due to the fact that it does not sufficiently present the direct causes of accidents or related information on their causal relation. However, this study utilizes a big-data method that has recently gained significant attention throughout all industrial and academic areas to collect Internet articles on fire-accidents that have occurred at construction sites over the last decade. In addition, principal component analysis was conducted to deduce season-specific factors according to time, location, inducer, and accident pattern. Based on this analysis, as for common factors, direct spark and oil mist were deduced. As work-related factors, negligent supervision and violations of the safety regulations were shown to cause fire-accidents, illustrating the man-made nature of such accidents. It was also found that secondary accidents such as collapses, burials, explosions, and suffocation have occurred when fires have broken out. The big-data analysis method utilized in this study is considered to be very effective and can be successfully utilized in the future for deducing high volumes of text data.
KW - big-data
KW - data mining
KW - negligent accident
KW - principal component analysis
KW - text mining
KW - web crawling
UR - http://www.scopus.com/inward/record.url?scp=85026999451&partnerID=8YFLogxK
U2 - 10.1007/s12205-017-0767-7
DO - 10.1007/s12205-017-0767-7
M3 - Article
AN - SCOPUS:85026999451
SN - 1226-7988
VL - 22
SP - 1535
EP - 1543
JO - KSCE Journal of Civil Engineering
JF - KSCE Journal of Civil Engineering
IS - 5
ER -