• 한국재난정보학회 논문집
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• 제14권3호
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• pp.296-304
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• 2018

연구목적: 이 논문에서는 인천 해안 매립 지반에 대한 액상화 평가를 위하여 장주기의 Hachinohe 지진파와 단주기의 Ofunato 지진파에 대하여 ProShake 프로그램을 사용하여 지반응답해석을 수행하였다. 연구방법: 지반응답해석 결과와 수정 Seed and Idriss의 방법을 이용하여 액상화 평가를 수행하였다. 각 지점의 액상화 평가 결과를 대표할 수 있는 지표로 Iwasaki가 제시한 액상화 가능성 지수를 산정하였다. 또한, 액상화 구역도 작성을 위한 정량적인 지표로서 등가 액상화 안전율을 이용하였다. 연구결과: 이 논문에서는 액상화 가능지수와 등가 액상화 안전율을 이용하여 인천 해안 매립지역을 대상으로 액상화 구역도를 작성하였다. 결론: 구역도 작성 결과, 액상화 가능지수와 등가 액상화 안전율을 이용하여 작성된 구역도가 유사한 분포 형태를 보여 이 논문에서 제시한 액상화 구역도 작성 지표로 인천 해안 매립 지역의 액상화 구역도를 작성할 경우 이용에 편리할 것으로 판단된다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
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• pp.1446-1453
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• 2008

This paper presents the mapping of liquefaction for the Incheon Song-do reclamation area using both the liquefaction potential index(LPI) and the equivalent liquefaction factor of safety(FE). As a result, the mapping of liquefaction based on LPI and FE shows similar distribution pattern. Therefore, the mapping of liquefaction presented in this study will be a convenient index for use when the mapping of liquefaction for the Incheon Song-do reclamation area is drawn up. It will make selection of area that needs specific estimation and areas with adaptation of liquefaction counteraction construction methods for the future reclaimed land with the economical soil investigation.

• 지구물리와물리탐사
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• 제23권3호
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• pp.149-156
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• 2020

복잡하고 다양한 광상 생성 과정과 부존 위치의 심부화로 인하여 광물자원 탐사의 효율을 높일 수 있는 예측탐사의 필요성이 점차 높아지고 있다. 최근의 머신러닝 기법의 활용성 증가와 광역 지질 데이터베이스의 구축 상황을 고려하면, 예측탐사의 기반인 광상 배태 가능성 평가 기술의 신뢰도는 점차 높아질 것으로 예상된다. 이번 연구에서는 심층신경망을 이용하여 화성암과 단층 및 자력탐사 정보의 광화 인자로의 활용 가능성을 확인하였다. 지질 정보의 수치화 기법으로 단층, 화성암, 자력 정보를 입력 자료로 구성하여 0.9 이상의 정확도를 가지며 예측 값이 안정적으로 수렴하는 금속 광상 예측 모델을 구축할 수 있었다. 이 기술은 추후 정밀한 지질 조사 결과와 물리탐사 정보가 확보된다면, 광화대 규모에서의 예측 탐사에도 활용할 수 있을 것으로 기대된다. 또한, 이 연구를 통해 지하의 화성암 정보를 제공하는 자력자료를 활용할 경우 지표의 화성암 정보를 보완하여 보다 높은 성능의 모델을 구축할 수 있는 것으로 확인되었다. 즉, 단순히 많은 자료를 융합하는 것 보다는 광체 성인과의 지질학적 상관관계를 고려하여 입력 자료를 구성하는 것이 보다 중요하다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.710-712
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• 2006

The aim of this study was to analyze limestone deposits potential using an artificial neural network and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential deposits in the Gangreung area, Korea. A spatial database considering deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The factors relating to 44 limestone deposits were the geological data, geochemical data and geophysical data. These factors were used with an artificial neural network to analyze mineral potential. Each factor’s weight was determined by the back-propagation training method. Training area was applied to analyze and verify the effect of training. Then the mineral deposit potential indices were calculated using the trained back-propagation weights, and potential map was constructed from GIS data. The mineral potential map was then verified by comparison with the known mineral deposit areas. The verification result gave accuracy of 87.31% for training area.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 추계학술대회논문집A
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• pp.117-122
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• 2000

In this paper the stress intensity factor under uniform pressure in the arbitrarily-shaped plane crack configuration transformed elliptic crack by Mobius mapping are determined. Using Dyson's formula Boussinesq-Papkovich potentials for mode I deformation are constructed. In the example the stress intensity factors are approximately calculated by least square method.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2000년도 춘계 학술발표회 논문집 Proceedings of EESK Conference-Spring
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• pp.141-150
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• 2000

In this study liquefaction hazard potential was assessed by modified Seed and Idriss method and maps of liquefaction hazard potential utilized by LPI(Liquefaction Potential Index) and FE(Equivalent Liquefaction Factor of Safety) were constructed in two dimensional space, Comparisons of liquefaction hazard maps assessed by LPI and FE are represented to verify the FE method proposed in this study. Based on the results of comparing liquefaction hazard map using LPI and FE there is similar distribution trend of zonation indices. from the result of comparison of liquefaction hazard maps of FE base using Hachinohe and ofunato PGA(Peak ground Acceleration) data at one site of port and harbor in Korea the values of FE in liquefaction hazard map using Hachinohe data are underestimated. And in the view of quantitative analysis FE is more convenient than LPI because types of results from FE are factor of safety that widely used in geotechnical practice and aseismic design standard for port and harbor in Korea.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2002년도 봄 학술발표회 논문집
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• pp.165-172
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• 2002

We presents an hazard zonation mapping technique in karst terrain and its assessment. From the detailed engineering geological mapping. Controlling factors of sink hole and limestone cave formation were discussed and 4 main hazard factors affecting hazard potential are identified as follows: prerequisite hazard factor(distributions of pre-existing sink holes and cavities), geomorphological hazard factors(slope gradient, vegetation, and drainage pattern etc.) geological hazard factors(lithology, fracture patterns and geological structures etc.) and hydraulic conditions(hydraulic head, annual fluctuation of ground water table and composition of g/w water). From the construction of hazard zonation map along the Jecheon-Maepo area, and vertical cross-sectional hazard zonations specific tunnel site we suggest hazard zonation rating systems.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2008년도 정기학술발표대회 논문집
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• pp.497-500
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• 2008

최근 국내외에서 도시재생사업이 활발히 진행되고 있다. 이러한 도시재생사업은 사업주체 및 이해관계자가 다양하고, 복잡하며, 대규모의 긴 생애주기를 가지고 있다. 또한 대부분 입체 복합공간 개발 형태의 메가프로젝트라는 특징을 나타내고 있다. 이러한 대규모의 도시재생사업은 사업수행과정에서 많은 위험요인들이 존재하며, 그와 더불어 효율적이고 지속적인 성과관리가 필요하다. 그러나 국내의 성과관리는 건설업의 특성을 반영하지 못하고 있으며, 위험과 연계된 연구는 미비하다. 그래서 본 연구는 효율적인 성과관리를 위한 위험과 연계된 성과측정지표를 도출하기 위한 기초연구로 맵핑(mapping)을 통해 성과측정을 위한 성과지표와 위험요인을 연계하는 방법을 제시하고자 한다.

• Computers and Concrete
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• 제23권4호
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• pp.255-265
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• 2019

The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제4권4호
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.