• Journal of the Society of Disaster Information
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• v.14 no.3
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• pp.296-304
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• 2018

Purpose: This study was carried out to evaluate the liquefaction potential of the land reclamation area in Incheon by using the ProShake program for long frequency Hachinohe seismic wave and short frequency Ofunato seismic waves to interpret ground response. Method: The interpretation results and the Modified Seed and Idriss method were used to evaluate the liquefaction potential. The liquefaction potential index which proposed by Iwasaki was calculated to be used as a guide line to represent the liquefaction evaluation results at the given location. The equivalent liquefaction factor of safety presented by Kang(1999) was used as a quantitative index to draw up the mapping of liquefaction potential. Results: This paper presents the mapping of liquefaction potential for the Incheon seaside reclamation area using both the liquefaction potential index and the equivalent liquefaction factor of safety. Conclution: As a result, the mapping of liquefaction based on the liquefaction potential index and equivalent liquefaction factor of safety shows similar distribution pattern.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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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.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.149-156
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• 2020

The genesis of ore bodies is a very diverse and complex process, and the target depth of mineral exploration increases. These create a need for predictive mineral exploration, which may be facilitated by the advancement of machine learning and geological database. In this study, we confirm that the faults and igneous rocks distributions and magnetic data can be used as input data for potential mapping using deep neural networks. When the input data are constructed with faults, igneous rocks, and magnetic data, we can build a potential mapping model of the metal deposit that has a predictive accuracy greater than 0.9. If detailed geological and geophysical data are obtained, this approach can be applied to the potential mapping on a mine scale. In addition, we confirm that the magnetic data, which provide the distribution of the underground igneous rock, can supplement the limited information from the surface igneous rock distribution. Therefore, rather than simply integrating various data sets, it will be more important to integrate information considering the geological correlation to genesis of minerals.

• Proceedings of the KSRS Conference
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• v.2
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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.

• Proceedings of the KSME Conference
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• 2000.11a
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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.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.04a
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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.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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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.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.497-500
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• 2008

Recently, the urban regeneration project has been performed actively at home and abroad. The stake-holders in urban regeneration project are various and complicated, and has large scale during a long life period. Also they show the characteristics of a mega-project and most mixed-use form. Therefore, the urban regeneration has a lot of potential risk factors from project beginning to completion. It means they need efficient and continuous risk management in terms of performance measurement. But the current domestic construction project does not reflect risk management in view of performance measurement. This study proposes the risk management methodology by mapping risk factors with major performance indexes of the urban regeneration project.

• Computers and Concrete
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• v.23 no.4
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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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• v.4 no.4
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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.