• Fire Science and Engineering
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• v.33 no.2
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• pp.47-55
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• 2019

The prediction performance of design fire curves was evaluated using a Fire dynamics simulator (FDS) for a solid fuel fire in a building space by comparing the results with experimental data. EDC 2-step mixing controlled combustion model was used in the FDS simulations and the previously suggested 2-stage design fire (TDF), Quadratic and Exponential design fire curves were used as the FDS inputs. The simulation results showed that smoke propagation in the building space was significantly affected by the design fire curves. The predictions of simulations using design fire curves for the experimental temperatures in the building space were reasonable, but the TDF was found to be the most acceptable for predicting temperature. The predictions with each design fire curve of species concentrations showed insufficient agreement with the experiments. This suggests that the combustion model used in this study was not optimized for the simulation of a solid fuel fire, and additional studies will be needed to examine the combustion model on the FDS prediction of solid fires.

• Fire Science and Engineering
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• v.33 no.6
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• pp.20-27
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• 2019

In this study, a new function with higher accuracy for fire heat release rate prediction was developed. The 'αt²' curve, which is the major exponential function currently used for fire engineering calculations, must be improved to minimize the prediction gap that causes fire system engineering inefficiency and lower cost-effectiveness. The newly developed prediction function was designed to cover the initial fire stage that features rapid growth based on logistic function theory, which has a more logical background and graphical similarity compared to conventional exponential function methods for 'αt²'. The new function developed in this study showed apparently higher prediction accuracy over wider range of fire growth durations. With the progress of fire growth pattern studies, the results presented herein will contribute towards more effective fire protection engineering.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.86-92
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• 2008

On this study, modeling works using Cone tools simulation method were made for the prediction of real fire test results such as small to large scale fire tests including ISO 5660-1, EN 13823 and ISO 13784-1. For those simulation prediction, three real fire tests were performed in advance. In addition, Real data from ISO 5660-1 test were applied to this simulation modeling. Finally, the comparative analysis between Real fire tests and Simulation results were made out. Also, the Classifying evaluation by EURO Class using EN 13501-1 were taken off.

• Fire Science and Engineering
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• v.3 no.1
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• pp.19-28
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• 1989

It has been recognized that the escape facility planning is very important for effective evacuation of accupants on fire event. The ultimate goal of the escape facility planning is to evacuate occupants rapidly from building fires to the safe areas. In fire event, occupants usually gather, utilize and finally act upon information about state transient of building fire system, which is consisted of components of fire, building and accupant during the ralatively short period of the fire event. That is, occupants' egress behavior is largely dependent upon building fire system. Therefore, comprehensive study for the relationship between building fire system and occupants' egress behavior is needed. This study aims to suggest the pre -occupancy evaluation method of the life safety performance for the architectural design based on prediction of occupants' egress behavior during building fires with computer simulation.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.469-472
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• 2005

With all ensuring the fire resistance structure as a method of setting the required cover thickness to fire, the RC is significantly affected from the standpoint of its structural stability that the compressive strength and elastic modulus is reduced by fire. Normally, the degradation of concrete member exposed to fire is largely dependent on the fire scale and fire condition. There is therefore a need to precisely predict the deterioration and fire damage of the exposed member. Thus, this work estimated the temperature distribution inside a member taking into consideration of the thermal properties by means of finite element method(FEM). The estimation results in a little higher prediction value than the experimental value in surface layer and is almost coincident with the experiment as the heating depth increase. From this work it can be known that the simulation application of FEM using the thermal properties of concrete member in high temperature gives rise to the confident prediction in the prediction of temperature distribution.

• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.56-65
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• 2020

Forecasting of coming fire accidents is quite a challenging problem cause normally fire accidents occur for a variety of reasons and seem randomness. However, if fire accidents that cause critical losses can be forecasted, it can expect to minimize losses through preemptive action. Classifications using machine learning were determined as appropriate classification criteria for the forecasting cause it classified as a constant damage scale and proportion. In addition, the analysis of the periodicity of a critical fire accident showed a certain pattern, but showed a high deviation. So it seems possible to forecast critical fire accidents using advanced prediction techniques rather than simple prediction techniques.

• Journal of Korean Society of Steel Construction
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• v.25 no.6
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• pp.587-599
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• 2013

A simple but accurate analytical method to evaluate the fire resistance of unprotected concrete filled tubular (CFT) columns under standard fire condition is proposed based on the fire design framework of EC4. To this end, the accuracy of the current tabulation method for the temperature prediction proposed by Lawson et al. was first critically evaluated, and a new prediction equation for the temperature gradient across the CFT section was then proposed based on available test and finite element analysis results. Overall, the axial strength predicted by using the proposed equation under the general fire design framework of EC4 was more accurate than that based on existing methods and appeared reasonable for design purposes. The results of this study are directly usable for the more rational fire analysis and design of unprotected CFT columns.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.305-310
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• 1997

There are many parameters in prediction of forest fire spread. The variables such as fuel moisture, fuel loading, wind velocity, wind direction, relative humidity, slope, and solar aspect have important effects on fire. Particularly, wind and slope factors are considered to be the most important parameters in propagation of forest fire. Generally, slope effect cause different wind distribution in mountain area. However, this effect is disregarded in complex geometry. In this paper, wind is estimated by applying computational fluid dynamics to the forest geometry. Wind velocity data is obtained by using CFD code with Newtonian model and slope is calculated with geometrical data. These data are applied fer 2-dimentional forest fire spreading algorithm with Korean ROS(Rate Of Spread). Finally, the comparison between the simulation and the real forest fire is made. The algorithm spread of forest fire will help fire fighter to get the basic data far fire suppression and the prediction to behavior of forest fire.