• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.79-80
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• 2022

In December 2017, a fire at the Jecheon Sports Center caused 29 deaths and 40 injuries, and about 2 billion won in property damage. It is a facility used by unspecified people as a publicly used establishment with a piloti structure. The damage was expanded due to the ignition of combustible exterior materials, Lack of fire protection in horizontal and vertical penetrations. Although legislation has improved since the fire, it is not retroactively applied, increasing the risk of fire in existing buildings. Accordingly, it is necessary to examine the case of the Jecheon Sports Center fire and draw out the problems. In this study, the fire simulation results and the Jecheon Sports Center fire are comparatively analyzed and used as basic data for fire reduction measures in publicly used establishment. As a result of the fire simulation drive, in the case of the second floor, the room temperature exceeded the human life safety standard of 60 ℃ after about 700 seconds had passed. In the case of three floor, it is predicted that the indoor temperature will exceed about 350 ℃. and temporary complexation will occur in the indoor combustibles.

• Fire Science and Engineering
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• v.34 no.1
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• pp.66-71
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• 2020

The process of discharging batteries using salt water, when used for the disposal of a lithium-ion (Li-ion) batteries, is likely to cause a fire. However, there is a dearth of studies in the literature on the risk of fire while discharging mobile phone batteries in salt water. In order to investigate the possibility of fire by elucidating the discharge characteristics and the generation of heat, we conducted experiments by varying the concentration of the salt water, number of overlapping batteries, and type of the mobile phone batteries used as experimental specimen. The discharging voltage and the temperature of the batteries were measured, and the fire risk was predicted by analyzing the data. The results of the experiment showed that the higher the salt water concentration, the greater the discharge value of the mobile phone battery and the higher the exothermic temperature. Moreover, the exothermic temperatures of the overlapping batteries were higher than that of the single battery submerged in salt water. The highest exothermic temperature points of the battery occurred at the positive and negative poles.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.869-878
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• 2021

This study is a study on 11-story apartments that increase the event of fires in old apartments where building-related laws and regulations are not retroactively applied. As a result of analyzing the risk of installing fire doors in Improvement Scenario 2-4, assuming that fire doors are installed as basic scenario 1 in the existing situation where fire doors are not installed at the entrance of direct stairs. In basic scenario 1, the visible distance to the entrance of the direct staircase due to the spread of smoke was 260 seconds. Improvement scenarios 3 to 4 with fire doors installed open 300 seconds after the fire was recognized, and when the fire doors were installed at the entrance of the direct stairs, the visibility to the entrance of the statistics team was less than 600 seconds. In this case, the visibility was 600 seconds at the time of installation of the fire door, and scenarios 3 to 4 increased 56.6% compared to scenario 1, lowering the risk of evacuation by more than 50%. In order to eliminate the risk of non-installation of direct statistical groups that increase the risk of smoke spread, building-related laws such as the Fire Fighting Act shall be retroactively applied when installing a direct stairway entrance or balcony folding evacuation system. The improvement caused by the installation of fire doors has numerically proven the necessity of fire doors during evacuation, and the importance of maintaining fire doors can be grasped.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.

• Fire Science and Engineering
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• v.21 no.2 s.66
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• pp.98-104
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• 2007

This study is related with the fire risk assessment for fire area at nuclear power plant by use of FDS (Fire Dynamics Simulator) that is a computational fluid dynamics (CFD) model of fire-driven fluid flow. The major purpose of this research is to analyze the sensitivity of the fire modeling when the heat release rate that is an important input variable is changed as well as when the grid size that is a critical factor of the fire model is modified. The result is presented at the conclusion with some comments for CFD model application.

• Nuclear Engineering and Technology
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• v.40 no.5
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• pp.349-364
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• 2008

Over the years, probabilistic risk assessment (PRA) research activities conducted at the U.S. Nuclear Regulatory Commission (NRC) have played an essential role in support of the agency's move towards risk-informed regulation. These research activities have provided the technical basis for NRC's regulatory activities in key areas; provided PRA methods, tools, and data enabling the agency to meet future challenges; supported the implementation of NRC's 1995 PRA Policy Statement by assessing key sources of risk; and supported the development of necessary technical and human resources supporting NRC's risk-informed activities. PRA research aimed at improving the NRC's understanding of risk can positively affect the agency's regulatory activities, as evidenced by three case studies involving research on fire PRA, human reliability analysis (HRA), and pressurized thermal shock (PTS) PRA. These case studies also show that such research can take a considerable amount of time, and that the incorporation of research results into regulatory practice can take even longer. The need for sustained effort and appropriate lead time is an important consideration in the development of a PRA research program aimed at helping the agency address key sources of risk for current and potential future facilities.

• Journal of The Korea Institute of Healthcare Architecture
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• v.28 no.4
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• pp.31-39
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• 2022

The purpose of this study is to analyse the hazard risk by examining the magnitude and severity of each type of hazard in order to mitigate and prepare for disasters in medical facilities. Methods: The hazard risk analysis for hazard types was surveyed for team leaders of medical facilities. The questionnaire analyzed data from 27 facilities, which were returned from 41 Local Medical Centers. Results: When looking at the 'Risk' by category type of hazard, the influence of health safety and fire/energy safety comes first, followed by natural disaster, facility safety, and crime safety. On the other hand, as for 'Magnitude', facility safety and crime safety come first, followed by health safety, fire/energy safety, and natural disasters. Most of the top types of disaster judged to have high hazard in medical facilities are health types. The top five priorities of hazard in medical facilities, they are affected by the geographical and industrial conditions of the treatment area. In the case of cities, the hazard was found to be high in the order of infectious disease, patient surge, and wind and flood damage. On the other hand, in rural areas, livestock diseases and infectious diseases showed the highest hazard. In the case of forest areas, the hazard was high in the order of wildfire, fire accident, lightning, tide, earthquake, and landslide, whereas in coastal areas of industrial complexes, the hazard was high due to fire, landslide, water pollution, marine pollution, and chemical spill accident. Implications: Through the research, standards will be established for the design of hospitals with disaster preparedness, and will contribute to the preparation of preemptive measures in terms of maintenance.

• Fire Science and Engineering
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• v.23 no.5
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• pp.110-116
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• 2009

This study is related with fire risk assessment for fire and smoke spread of double skin facade system by use of FDS (Fire Dynamics Simulator) which is a computational fluid dynamics (CFD) model of fire-driven fluid flow. For the study, fire scenario is intended to evaluate the impact of a fire spread for glazed office building. The major purpose of this study is to analyze the fire risk depending on the width of between inner skin and outer skin and to present fire prevention method regarding double skin facade system. The result of analysis presents fire spread more vertically as intermediate space becomes narrow. It is anticipated that fire can spread upper 2 stories above the fire floor if intermediate space with not more than 1m width. Therefore, prevention of vertical fire spread is required.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.351-361
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• 2021

The seriousness of fire is rising because fire causes enormous damage to property and human life. Therefore, this study aims to predict various risk factors affecting fire by fire type. The predictive analysis of fire factors was carried out targeting Gyeonggi-do, which has the highest number of fires in the country. For the analysis, using machine learning methods SVM (Support Vector Machine), RF (Random Forest), GBRT (Gradient Boosted Regression Tree) the accuracy of each model was presented with a high fit model through MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), and based on this, predictive analysis of fire factors in Gyeonggi-do was conducted. In addition, using machine learning methods such as SVM (Support Vector Machine), RF (Random Forest), and GBRT (Gradient Boosted Regression Tree), the accuracy of each model was presented with a high-fit model through MAE and RMSE. Predictive analysis of occurrence factors was achieved. Based on this, as a result of comparative analysis of three machine learning methods, the RF method showed a MAE = 1.765 and RMSE = 1.876, as well as the MAE and RMSE verification and test data were very similar with a difference between MAE = 0.046 and RMSE = 0.04 showing the best predictive results. The results of this study are expected to be used as useful data for fire safety management allowing decision makers to identify the sequence of dangers related to the factors affecting the occurrence of fire.

• Journal of the Korea Institute of Building Construction
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• v.22 no.3
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• pp.281-291
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• 2022

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.