• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.113-122
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• 2017

Natural disasters caused by climate change are increasing globally. There are few studies on the quantitative analysis methods for predicting damages in the island area due to sea level rise. Therefore, it is necessary to study the damage prediction analysis method using the GIS which can quantitatively analyze. In this paper, we analyze the cause and status of sea level rise, quantify the vulnerability index, establish an integrated terrestrial modeling method of the ocean and land, and establish a method of analyzing the damage area and damage scale due to sea level rise using GIS and the method of making the damage prediction figure was studied. In order to extract the other affected areas to sea level rise are apart of the terrain model is generated by one requires a terrain modeling of target areas are offshore and vertical reference system differences in land, found the need for correction by a tidal observations and geoid model there was. Grading of terrain, coastline erosion rate, coastal slope, sea level rise rate, and even average by vulnerable factors due to sea level rise indicates that quantitative damage prediction is possible due to sea level rise in the island area. In the case of vulnerable areas extracted by GIS, residential areas and living areas are concentrated on the coastal area due to the nature of the book area, and field survey shows that coastal changes and erosion are caused by sea level rise or tsunami.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2747-2756
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• 2023

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.52-66
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• 2017

This study aims at designing standard metadata that can be incorporated for advanced utilization of damage prediction maps, and thereby constructing the standard meta-information management prototype system on the basis of the proposed design. Based on the ISO/TC 211 19115 international standard, which is considered as the most widely used standard (as per the results of a domestic and foreign metadata standard survey), the designing process for the standard metadata was established and the metadata was categorized into nine classes. Additionally, based on the output of the standard metadata design process, a standard meta-information management prototype system, capable of checking and downloading meta-property information, was constructed using the JAVASCRIPT language. By incorporating the obtained results, it is possible to maintain the quality of the constructed damage prediction map by establishing a standardized damage prediction map database. Furthermore, disaster response can be actuated through the provision and management of data for effective operation of the proposed damage prediction system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1881-1888
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• 2011

Red tide is a natural phenomenon to bloom harmful algal, which fish and shellfish die en masse. Red tide damage with respect to sea farming has been occurred each year. Red tide damage can be minimized by means of prediction of red tide blooms. Red tide prediction using naive bayes classifier can be achieve good prediction results. The result of naive bayes method only determine red tide blooms, whereas the method can not know how increasing of red tide algae density. In this paper, we proposed the red tide blooms prediction method using fuzzy reasoning and naive bayes classifier. The proposed method can enhance the precision of red tide prediction and forecast the increasing density of red tide algae.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.233-240
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• 2003

A GIS-based tunnelling risk management system (GIS-TURIMS) has been developed in this study The developed system uses ArcView 8.2 as a basic platform and the built-in interface(VBA) has been used to perform first-order simplified analyses for prediction of tunnelling-induced ground movements and building damage assessment. The main emphasis in this study was to develop a working framework that can be used in the perspective of tunnelling risk management. The developed system is capable of carrying out computationally intensive first-order analyses for ground movement prediction as well as building/utilities damage assessment with fully taking advantage of the GIS technologies. This paper describes the concept and details of the GIS-TURIMS development and implementation.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.311-318
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• 2002

A GIS-based tunnelling risk management system (GIS-TURIMS) has been developed in this study, The developed system uses ArcView 8.2 as a basic platform and the built-in interface (VBA) has been used to perform first-order simplified analyses for prediction of tunnelling-induced ground movements and building damage assessment. The main emphasis in this study was to develop a working framework that can be used in the perspective of tunnelling risk management. The developed system is capable of carrying out computationally intensive analyses for ground movement prediction as well as building/utilities damage assessment with fully taking advantage of the GIS technologies. This paper describes the concept and details of the GIS-TURIMS development and implementation

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.291-294
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• 2011

Red tide is a temporary natural phenomenon to change sea color by harmful algal blooms, which finfish and shellfish die en masse. There have been many studies on red tide due to increasing of harmful algae damage of fisheries in Korea. Particularly, red tide damage can be minimized by means of prediction of red tide blooms. However, the most of red tide research in Korea has been focused only classification of red tide which it is not enough for predicting red tide blooms. In this paper, we proposed the red tide blooms prediction method using fuzzy reasoning.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.259-262
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• 2007

In our country it develop damage reduction and prediction technology for prevention the danger of the rockfall and landslide which is repeated yearly. And it constructs integrated and efficient the misfortune management system it will be able to manage. So we will accomplish aims that is the rockfall and landslide damage occurrence reduction.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.5
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• pp.1455-1461
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• 1991

본 연구에서는 Kachanov의 재료손상(material damage)모델을 이용하여 새로운 수명예측식을 만들고, 이 수명예측식의 타당성을 조사하기 위하여, 최근에 발표된 크 립 수명과 기공분포와의 실험결과와 비교하였다.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.1
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• pp.75-85
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• 2021

Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.