Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Analysis of Fire-Accident Factors Using Big-Data Analysis Method for Construction Areas
Abstract 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.
Analysis of Fire-Accident Factors Using Big-Data Analysis Method for Construction Areas
Abstract 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.
Analysis of Fire-Accident Factors Using Big-Data Analysis Method for Construction Areas
Kim, Joon-Soo (Autor:in) / Kim, Byung-Soo (Autor:in)
KSCE Journal of Civil Engineering ; 22 ; 1535-1543
15.08.2017
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Analysis of Fire-Accident Factors Using Big-Data Analysis Method for Construction Areas
| Online Contents | 2018
| DOAJ | 2021
Road Accident Analysis Factors
| British Library Conference Proceedings | 2013
Analysis of safety risk factors of modular construction to identify accident trends
| Taylor & Francis Verlag | 2022
Analysis of safety risk factors of modular construction to identify accident trends
| DOAJ | 2022