• Fire Science and Engineering
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• v.33 no.1
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• pp.188-193
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• 2019

In the past, the risk of fire and the rate of fire occurrence has increased gradually as the quality of life improved due to rapid economic growth, and the government enacted Fire Prevention Act. The existing inspection method was revised considering the rapid increase in the number of fire-fighting objects(hereinafter referred to as specific fire-fighting objects) that require the installation of fire-fighting facilities, and has been applied to this day. On the other hand, unlike the rapid increase in specific fire-fighting objects and the development of fire prevention technologies, the scope of work and inspections by unsuitable inspectors caused a large fire accident, which required improvement of the related laws. This study evaluated, the Act on the relationship of firefighting facilities, which had been implemented previously to identify fire victims, save lives, secure independence of fire inspection agencies, and ensure the accuracy of fire prevention actions.

• Structural Engineering and Mechanics
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• v.7 no.2
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• pp.127-145
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• 1999

This paper deals with the development of an approach for evaluating the squash load and rigidity of unprotected concrete filled steel columns at elevated temperatures. The current approach of evaluating these properties is reviewed. It is shown that with a non-uniform temperature distribution, over the composite cross-section, the calculations for the squash load and rigidity are tedious in the current method. A simplified approach is proposed to evaluate the temperature distribution, squash load, and rigidity of composite columns. This approach is based on the model in Eurocode 4 and can conveniently be used to calculate the resistance to axial compression of a concrete filled steel column for any fire resistance time. The accuracy of the proposed approach is assessed by comparing the predicted strengths against the results of fire tests on concrete filled circular and square steel columns. The applicability of the proposed approach to a design situation is illustrated through a numerical example.

• Land and Housing Review
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• v.11 no.4
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• pp.99-104
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• 2020

This paper presents the recent research developments identified through a review of literature on the application of artificial intelligence in developing automated designs of fire protection facilities. The literature review covered research related to image recognition and applicable neural networks. Firstly, it was found that convolutional neural network (CNN) may be applied to the development of automating the design of fire protection facilities. It requires a high level of object detection accuracy necessitating the classification of each object making up the image. Secondly, to ensure accurate object detection and building information, the data need to be pulled from architectural drawings. Thirdly, by applying image recognition and classification, this can be done by extracting wall and surface information using dimension lines and pixels. All combined, the current review of literature strongly indicates that it is possible to develop automated designs for fire protection utilizing artificial intelligence.

• Journal of Korean Society of Forest Science
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• v.99 no.2
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• pp.163-171
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• 2010

Forest fires have potentials to change the structure and function of forest ecosystems and significantly influence on atmosphere and biogeochemical cycles. Forest fire also affects the quality of public benefits such as carbon sequestration, soil fertility, grazing value, biodiversity, or tourism. The prediction of fire occurrence and its spread is critical to the forest managers for allocating resources and developing the forest fire danger rating system. Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behaviors and its spread. Thus, meteorological factors as well as social factors were considered in the fire danger rating systems. A total of 298 forest fires occurred during the fall season from 2002 to 2006 in South Korea were considered for developing a logistic model of forest fire occurrence. The results of statistical analysis show that only effective humidity and temperature significantly affected the logistic models (p<0.05). The results of ROC curve analysis showed that the probability of randomly selected fires ranges from 0.739 to 0.876, which represent a relatively high accuracy of the developed model. These findings would be necessary for the policy makers in South Korea for the prevention of forest fires.

• Computers and Concrete
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• v.19 no.5
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• pp.545-553
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• 2017

In the authors' previous study, an axial force-flexural moment (P-M) interaction curve model was proposed to evaluate fire-resisting performances of reinforced concrete (RC) column members. The proposed method appeared to properly consider the axial and flexural strength degradations including the secondary moment effects in RC columns due to fire damage. However, the detailed P-M interaction curve model proposed in the authors' previous study requires somewhat complex computational procedures and iterative calculations, which makes it difficult to be used for practical design in its current form. Thus, the aim of this study was to develop a simplified P-M interaction curve model of RC columns exposed to fire considering the effects of fire damage on the material performances and magnitudes of secondary moments. The simplified P-M interaction model proposed in this study was verified using 66 column fire test results collected from literature, and the verification results showed that the proposed simplified method can provide an adequate analysis accuracy of the failure loads and fire-resisting times of the RC column specimens.

• Fire Science and Engineering
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• v.17 no.1
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• pp.40-45
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• 2003

In this paper many vulnerable areas of the present fire PSA methodology were revealed to apply risk-informed fire protection to nuclear power plants. The results and insights from the fro PSA should be used as a part of a risk-informed decision making process rather than the complete technical basis for decision making. The degree of support and scope of applications is dependent on the accuracy and validity of the model used in the fire PSA. Accordingly; the usefulness of the fire PSA will increase as ongoing research and development efforts lead to improvements in the state of the art technology and as improvements in the implementation of the state of the art technology lead to more consistent results.

• Fire Science and Engineering
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• v.29 no.4
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• pp.39-48
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• 2015

This research was done to develop a duct-dedicated intelligent fire detection system to prevent fires and minimize fire damage of the industrial duct having a high fire risk. To understand the fire hazards of the ducts, the analysis was centered on the Daegu Textile Industrial Complex, where industrial ducts are used frequently. With this in the background, dedicated fire detectors and fire alarm control panel, which can prevent fires and to minimize fire damages to the ducts, were designed and produced, after which the performance was confirmed. As a result of performance experiments, it was shown that a duct-dedicated intelligent fire detection system had excellent adaptability and temperature accuracy. Through real-time temperature monitoring of the inside of the ducts, it was confirmed that duct fires could be efficiently extinguished by stepwise control of linkage facilities according to the setting temperature.

• Computers and Concrete
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• v.32 no.6
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• pp.625-637
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• 2023

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.351-357
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• 2019

UAV (Unmanned Aerial Vehicle) images can be exploited for rapid forest fire damage assessment by virtue of UAV systems' advantages. In 2019, catastrophic forest fire occurred in Goseong and Sokcho, Korea and burned 1,757 hectares of forests. We visited the town in Goseong where suffered the most severe damage and conducted UAV flights for forest fire damage assessment. In this study, economic and rapid damage assessment method for forest fire has been proposed using UAV systems equipped with only a RGB sensor. First, forest masking was performed using automatic elevation thresholding to extract forest area. Then ExG (Excess Green) vegetation index which can be calculated without near-infrared band was adopted to extract damaged forests. In addition, entropy filtering was applied to ExG for better differentiation between damaged and non-damaged forest. We could confirm that the proposed forest masking can screen out non-forest land covers such as bare soil, agriculture lands, and artificial objects. In addition, entropy filtering enhanced the ExG homogeneity difference between damaged and non-damaged forests. The automatically detected damaged forests of the proposed method showed high accuracy of 87%.

• 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.