• Fire Science and Engineering
• /
• v.30 no.6
• /
• pp.130-136
• /
• 2016

A university needs to analyze and improve its curricula with the perspective of the consumer to develop a syllabus for the training of industry-demand customized human resources. Accordingly, this paper surveyed the demand of fire-related industry workers to evaluate the major fitness of the curriculum of fire risk prediction and assessment and carried out descriptive statistical analysis, factor analysis, cluster analysis, and one-way ANOVA based on the results. According to the analysis, fire-related industry workers reported that the curriculum of fire risk prediction and assessment is suitable for majors. In addition, they were greatly aware of the necessity of basic major and common major subjects among subjects of fire risk prediction and assessment. The results of this analysis will provide the basic data to improve the curriculum continuously in the future.

• Fire Science and Engineering
• /
• v.33 no.4
• /
• pp.50-58
• /
• 2019

The prediction performance of B-RISK was evaluated for the fire behaviors of combustibles in a compartment using Fire Dynamics Simulator (FDS). First of all, to predict the heat release rate (HRR) for two combustible sets, the HRR for one combustible set and the design fire curve were used as input values for B-RISK. Comparing results of B-RISK calculations with experimental data for two combustible sets, it was found that B-RISK results predicted insufficiently for fire growth rate of experimental data but there was good agreement for maximum HRR and total HRR with the experimental data. And the B-RISK results were used for input values of FDS to evaluate the fire behaviors of B-RISK results. Comparing results of FDS calculations with experimental data, the simulation results showed that the temperature and concentrations of O₂, CO₂ in the fire growth phase were different from the experimental data. However, when using the B-RISK result for percentile 70%, the simulation results sufficiently predicted the overall fire behaviors.

• Applied Chemistry for Engineering
• /
• v.35 no.3
• /
• pp.200-208
• /
• 2024

Chung's equations-IX and Chung's equation-XII were utilized to predict the fire risk and evaluate fire risk ratings for four types of wood: camphor, cherry, rubber, and elm trees. The combustion tests were conducted using a cone calorimeter test method by ISO 5660-1 standards. The fire risk and fire risk rating (FRR) were compared for Fire Risk Index-IX (FRI-IX) and Fire Risk Index-XII (FRI-XII). The results yielded Fire Performance Index-XI (FPI-XI) ranging from 0.08 to 11.48 and Fire Growth Index-XI (FGI-XI) ranging from 0.67 to 111.89. The Fire Risk Index-XII (FRI-XII), indicating fire risk rating, exhibited an increasing order of cherry (0.45): Grade A (Ranking 5) < PMMA (1): Grade A (Ranking 4) < elm (1.23): Grade A (Ranking 3) < rubber (1.56): Grade A (Ranking 2) << camphor (148.23): Grade G (Ranking 1). Additionally, the fire risk index-IX (FRI-IX) was cherry (0): Grade A (Ranking 3) ≈ rubber (0): Grade A (Ranking 3) ≈ elm tree (0): Grade A (Ranking 3) < PMMA (1): Grade A (Ranking 2) << camphor tree (66.67): Grade G (Ranking 1). In general, camphor was found to have the highest fire risk. In conclusion, although the expression of the index is different as shown based on the standards of FRI-IX and FRI-XII, predictions based on fire risk assessment of combustible materials showed similar trends.

• Applied Chemistry for Engineering
• /
• v.35 no.5
• /
• pp.458-467
• /
• 2024

Chung's equation-IX and Chung's equation-XII were utilized to predict the fire risk and evaluate fire risk ratings for five types of wood: white ash, hard maple, willow, fraxinus mandschurica, and sagent cherrys. The combustion tests were conducted using a cone calorimeter test method by ISO 5660-1 standards. The fire risk and fire risk rating (FRR) were compared with fire risk index-IX (FRI-IX) and fire risk index-XII (FRI-XII). The results yielded a fire performance index-XI (FPI-XI) ranging from 0.44 to 1.05 and a fire growth index-XI (FGI-XI) ranging from 0.89 to 3.11. Also, the fire risk index-XII (FRI-XII), indicated fire risk rating, exhibited an increasing order of sagent cherry (0.85): Grade A ≈ PMMA (1): Grade A ≈ white ash (1.22): Grade A ≈ hard maple (1.53): Grade A < willow (4.00): Grade C < faxinus mandschurica (7.07): Grade D. Additionally, the fire risk index-IX (FRI-IX) was PMMA (1): Grade A ≈ hard maple (2.28): Grade A ≈ sagent cherry (3.24): Grade A < white ash (5.73): Grade B < fraxinus mandschurica (10.29): Grade C ≪ willow (48.30): Grade G. In general, the willow and fraxinus mandschurica showed the highest fire risk. In conclusion, although the expression of the index is different as shown based on the criteria of FRI-IX and FRI-XII, predictions based on fire risk assessment of combustible materials showed a similar tendency.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.133-144
• /
• 2021

This paper derives the fire risk of buildings in Seoul through the prediction of property damage and the occurrence of fires. This study differs from prior research in that it utilizes variables that include not only a building's characteristics but also its affiliated administrative area as well as the accessibility of nearby fire-fighting facilities. We use Ensemble Voting techniques to merge different machine learning algorithms to predict property damage and fire occurrence, and to extract feature importance to produce fire risk. Fire risk prediction was made on 300 buildings in Seoul utilizing the established model, and it has been derived that with buildings at Level 1 for fire risks, there were a high number of households occupying the building, and the buildings had many factors that could contribute to increasing the size of the fire, including the lack of nearby fire-fighting facilities as well as the far location of the 119 Safety Center. On the other hand, in the case of Level 5 buildings, the number of buildings and businesses is large, but the 119 Safety Center in charge are located closest to the building, which can properly respond to fire.

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

• Journal of Internet Computing and Services
• /
• v.19 no.6
• /
• pp.101-112
• /
• 2018

A lot of manpower and budgets are being used to prevent fires, and only a small portion of the data generated during this process is used for disaster prevention activities. This study develops a prediction model of fire occurrence probability based on data mining in order to more actively use these data for disaster prevention activities. For this purpose, variables for predicting fire occurrence probability of various buildings were selected and data of construction administrative system, national fire information system, and Korea Fire Insurance Association were collected and integrated data set was constructed. After appropriate data cleansing and preprocessing, various data mining methodologies such as artificial neural network, decision trees, SVM, and Naive Bayesian were used to develop a prediction model of the fire occurrence probability of buildings. The most accurate model among the derived models is Linear SVM model which shows 68.42% as experimental data and 63.54% as verification data and it is the best model to predict fire occurrence probability of buildings. As this study develops the prediction model which uses only the set values of the specific ranges, future studies may explore more opportunites to use various setting values not shown in this study.

• Fire Science and Engineering
• /
• v.14 no.4
• /
• pp.7-16
• /
• 2000

In the study, three criteria(toxicity, fire & explosion, environment) and damage prediction method for each case was set up, and all these criteria were applied to the subject substance that was selected as hazardous level by integrating all criteria through Algorithm. Particularly, the environment criterion is a comprehensive concept, environment index modeling by combining USCG(United State Coast Guard) & MSDS(Material Safety Data Sheet) environment criteria classifications and the environment part of MFPA's health hazardousnes(Nh). And for damage prediction method of each criterion were adopted and they were applied to hazardous chemical substances in use or stored by chemical substance related enterprises located in each region that made possible to set up total hazard level of used substances(inflammability, poisonousness and counteraction on a unit substance, and hazard level & display modeling on environment) & damage prediction in case of accident & solidity setup(CPQRA: Chemical Process Quantitative Risk Assessment, IAEA: International Atomic Energy Agency, VZ eq: Vulnerable Zone) risk counter. Thus it is deemed that it can be applied to toxic substance leakage that can happen during any chemical processing & storage, application as a tool for prior safety evaluation through potential dangerousness computation of fire & explosion.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.781-791
• /
• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2020.06a
• /
• pp.115-116
• /
• 2020

Due to the nature of modern society, buildings are becoming larger and more complex. As a result, the design conditions of the building are changing. However, despite the complexities of buildings, the fire resistance performance is still equalized to one hour without considering fire engineering analysis in Korea, so there is a risk according to actual fire design conditions. Therefore, the purpose of this study is to calculate the required fire resistance time for actual fire through fire mechanics analysis and case study.