• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.69-75
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• 2019

The frequency of natural disasters and the scale of damage are increasing due to the abnormal weather phenomenon that occurs worldwide. Especially, damage caused by natural disasters in coastal areas around the world such as Earthquake in Japan, Hurricane Katrina in the United States, and Typhoon Maemi in Korea are huge. If we can predict the damage scale in response to disasters, we can respond quickly and reduce damage. In this study, we developed damage prediction functions for Wind waves caused by sea breezes and waves during various natural disasters. The disaster report (1991 ~ 2017) has collected the history of storm and typhoon damage in coastal areas in Korea, and the amount of damage has been converted as of 2017 to reflect inflation. In addition, data on marine weather factors were collected in the event of storm and typhoon damage. Regression analysis was performed through collected data, Finally, predictive function of the sea turbulent damage by the sea area in 74 regions of the country were developed. It is deemed that preliminary damage prediction can be possible through the wind damage prediction function developed and is expected to be utilized to improve laws and systems related to disaster statistics.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.217-220
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• 2024

Predicting earthquake occurrences accurately is challenging, and preparing all buildings with seismic design for such random events is a difficult task. Analyzing building features to predict potential damage and reinforcing vulnerabilities based on this analysis can minimize damages even in buildings without seismic design. Therefore, research analyzing the efficiency of building damage prediction models is essential. In this paper, we compare the accuracy of earthquake damage prediction models using machine learning classification algorithms, including Random Forest, Extreme Gradient Boosting, LightGBM, and CatBoost, utilizing data from buildings damaged during the 2015 Nepal earthquake.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.128-129
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• 2016

This paper evaluates experimentally the explosion proof of fiber reinforced cement composite(FRCC) panels with various fibers of 2 % volume fraction subjected to contact explosions using an emulsion explosive. As a results, the proportion of the total damage in FRCC panels is not biased scabbing on the rear side with contrast to plain panels, which means that the local damage of FRCC panels was significantly controlled. The experimental results presented useful information for prediction of limited thickness on the local damage subjected to contact explosions through comparison with existing damage evaluation prediction equations.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.461-462
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• 2002

Rolling element bearing fatigue life can be significantly reduced by debris particles in lubricants. The debris particles cause raceway surface dents that initiate early fatigue damage. Optical interferometry has been found to be the best method for characterizing bearing raceway debris dent damage. This technique is used to determine the important features, sizes and density of dents. The resulting data file is then used to determine bearing fatigue life. Tests show that bearings manufactured by different processes and material types are affected differently by debris damage and that these differences must be considered by life prediction methodologies. Bearings made by a specific enhanced process can significantly resist the deleterious effects of debris damage and outperform bearings made by other means.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.271-278
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• 2011

Storm and flood such as torrential rains and major typhoons has often caused damages on a large scale in Korea and damages from storm and flood have been increasing by climate change and warming. Therefore, it is an essential work to maneuver preemptively against risks and damages from storm and flood by predicting the possibility and scale of the disaster. Generally the research on numerical model based on statistical methods, the KDF model of TCDIS developed by NIDP, for analyzing and predicting disaster risks and damages has been mainstreamed. In this paper, we introduced the model for prediction of damage cost from storm and flood by the neural network algorithm which outstandingly implements the pattern recognition. Also, we compared the performance of the neural network model with that of KDF model of TCDIS. We come to the conclusion that the robustness and accuracy of prediction of damage cost on TCDIS will increase by adapting the neural network model rather than the KDF model.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.8
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• pp.818-823
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• 2012

In order to predict necking behaviour of aluminium sheets, a crystal plasticity model is introduced in the finite element analysis of tensile test. Due to the computational limits of time and memory, only a small part of tensile specimen is subjected to the analysis. Grains having different orientations are subjected to numerical tensile tests and each grain is discretized by many elements. In order to predict the sudden drop of load carrying capacity after necking, a well-known Cockcroft-Latham damage model is introduced. The mismatch of grain orientation causes stress concentration at several points and damage is evolved at these points. This phenomenon is similar to void nucleation. In the same way, void growth and void coalescence behaviours are well predicted in the analysis. For the comparison of prediction capability of necking, same model is subjected to finite element analysis using uniform material properties of polycrystal with and without damage. As a result, it is shown that the crystal plasticity model can be used in prediction of necking and fracture behavior of materials accurately.

• Journal of Korea Foundry Society
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• v.33 no.2
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• pp.63-68
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• 2013

Generally, metal is the most important material used in many engineering applications. Therefore, it is important to understand and predict the damage of metal as result of the impact. The objective of this research is to evaluate the damage criterion on the impact performance of A356 Al-alloy castings. Both experimental method and computational analysis were used to achieve the research objective. In this paper, we performed impact test according to various impact velocities to the A356 cast aluminium specimen for damage prediction. Impact computational simulation was done by applying properties obtained from the tensile test, and damages was predicted according to the damage criteria based plastic work. The good agreement of the results between the experiment and computer simulation shows that the reliability of the proposed FE simulation method to predict fracture of A356 casting components by impact.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.453-461
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• 1999

The estimation of fatigue life at the design stage is very important in order to arrive at feasible and cost effective solutions considering the total lifetime of the structure and machinery compo-nents. In this study the practical procedure of prediction of fatigue life by use of cumulative damage factors based on Miner-Palmgren hypothesis and probability density function is shown with a $135,000m^3$ LNG tank being used as an example. In particular the parameters of Weibull distribution taht determine the stress spectrum are dis-cussed. At the end some of uncertainties associated with fatigue life prediction are discussed. The main results obtained from this study are as follows: 1. The practical procedure of prediction of fatigue life by use of cumulative damage factors expressed in combination of probability density function and S-N data is proposed. 2. The calculated fatigue life is influenced by the shape parameter and stress block. The conser-vative fatigue design can be achieved when using higher value of shape parameter and the stress blocks divded into more stress blocks.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.3-5
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• 2008

An increase in oil and gas plants caused by development of process industry have brought into the increase in use of flammable and toxic materials in the complex process under high temperature and pressure. There is always possibility of fire and explosion of dangerous chemicals, which exist as raw materials, intermediates, and finished goods whether used or stored in the industrial plants. Since there is the need of efforts on disaster damage reduction or mitigation process, we have been conducting a research to relate explosion model on the background of real 3D terrain model. By predicting the extent of damage caused by recent disasters, we will be able to improve efficiency of recovery and, sure, to take preventive measure and emergency counterplan in response to unprepared disaster. For disaster damage prediction, it is general to conduct quantitative risk assessment, using engineering model for environmental description of the target area. There are different engineering models, according to type of disaster, to be used for industry disaster such as UVCE (Unconfined Vapour Cloud Explosion), BLEVE (Boiling Liquid Evaporation Vapour Explosion), Fireball and so on, among them, we estimate explosion damage through UVCE model which is used in the event of explosion of high frequency and severe damage. When flammable gas in a tank is released to the air, firing it brings about explosion, then we can assess the effect of explosion. As 3D terrain information data is utilized to predict and estimate the extent of damage for each human and material. 3D terrain data with synthetic environment (SEDRIS) gives us more accurate damage prediction for industrial disaster and this research will show appropriate prediction results.

• 한국지역지리학회:학술대회
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• 2009.08a
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• pp.108-115
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• 2009

An increase in oil and gas plants caused by development of process industry have brought into the increase in use of flammable and toxic materials in the complex process under high temperature and pressure. There is always possibility of fire and explosion of dangerous chemicals, which exist as raw materials, intermediates, and finished goods whether used or stored in the industrial plants. Since there is the need of efforts on disaster damage reduction or mitigation process, we have been conducting a research to relate explosion model on the background of real 3D terrain model. By predicting the extent of damage caused by recent disasters, we will be able to improve efficiency of recovery and, sure, to take preventive measure and emergency counterplan in response to unprepared disaster. For disaster damage prediction, it is general to conduct quantitative risk assessment, using engineering model for environmentaldescription of the target area. There are different engineering models, according to type of disaster, to be used for industry disaster such as UVCE (Unconfined Vapor Cloud Explosion), BLEVE (Boiling Liquid Evaporation Vapor Explosion), Fireball and so on, among them.we estimate explosion damage through UVCE model which is used in the event of explosion of high frequency and severe damage. When flammable gas in a tank is released to the air, firing it brings about explosion, then we can assess the effect of explosion. As 3D terrain information data is utilized to predict and estimate the extent of damage for each human and material. 3D terrain data with synthetic environment (SEDRIS) gives us more accurate damage prediction for industrial disaster and this research will show appropriate prediction results.