• Journal of Conservation Science
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• v.25 no.1
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• pp.25-38
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• 2009

The Gyeongju Seokbinggo (Treasure No. 66) is an ice-storing stone warehouse, consisting mainly of alkaligranite which shows milky white color and medium-grained textures with drusy cavities. As results of deterioration assessment, the deterioration rates were determined as crack (12.5%), disjoining (6.7%), breaking-out (25.1%), exfoliation (20.9%), efflorescence (6.5%), brown discoloration (9.8%), darkgray discoloration (2.0%) and biological discoloration (36.5%). Comprehensive physical deterioration rate and discoloration rate were calculated as 43.7% and 68.7%, respectively, that indicates the Seokbinggo has been severely weathered. Indoor relative humidity was above 90% except in winter season. Indoor microclimate was hardly fluctuating although indoor microclimate was dependent on the outdoor climate. The main cause of deterioration was high relative humidity and a long time of wetness due to penetration of rain, underground water and condensation. It was identified that the water brought out biological discoloration, dissolution of minerals, structural movement and efflorescence, and the dust from the ground soil in front of the entrance accelerated brown and dark gray discoloration on the stone surface.

• Journal of Wetlands Research
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• v.15 no.3
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• pp.289-300
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• 2013

In this study, two types of wetland plants, Eichhornia crassipes (a floating plant) and Ceratophyllum demersum (a submerged plant) were introduced to wetland mesocosms to understand how the water properties of wetlands such as pH, dissolved oxygen content, water temperature, oxidation reduction potential, and nutrient concentrations are affected by different types of wetland plant. The floating plant lives on the water surface and can block light penetration; it exhibited the lowest water temperature and temperature difference between lower and upper layers. After the addition of contaminants, the dissolved oxygen (DO) concentration decreased abruptly but recovered continuously in all mesocosms; especially the submerged plants, which photosynthesize in water, showed the largest increases in DO and diel periodicity DO, as well as in pH value. The oxidation-reduction potential in both water and sediment were affected by the presence of wetland plants and plant type and the results suggest that various aspects of wetland biogeochemistry are affected by the presence and type of wetland plants. The total nitrogen and phosphorous concentrations in water decreased in the following order: Water only < Water + Soil < Floating Plants < Submerged Plants. Although both floating and submerged plants can control algal concentrations, the effect was more prominent for floating plants.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of the Korean Geotechnical Society
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• v.23 no.2
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• pp.47-60
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• 2007

Conventional active surface wave measurements performed using a transient or continuous source are often limited in the maximum depth of penetration due to the difficulty of generating low-frequency energy with reasonably portable sources. This limitation may inhibit accurate seismic site response calculations because of the inability to define deeper subsurface structure. By measuring surface wave generated by passive sources including microtremors and cultural noise, it is possible to overcome this problem and develop soil stiffness profiles to much larger depth. Reliability of dispersion estimates from the passive surface wave measurements is critical to present reliable shear wave velocity profiles and can be improved by the measurements and analyses of passive surface waves based on correct understanding of systematic errors included in passive dispersion data. In this study, the systematic errors caused by poor wavenumber resolution and energy leakage into sidelobes in passive tests are mainly explored. Recommendations for reliable passive surface wave measurements and dispersion estimates are presented and illustrated at a site in San Jose, California, U.S.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3C
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• pp.143-155
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• 2006

SPT-Uphole tomography method was introduced for the evaluation of near subsurface shear wave velocity (Vs) distribution map. In SPT-Uphole method, SPT (Standard Penetration Test) which is common in geotechnical site investigation was used as a source and several surface geophones in line were used as receivers. Vs distribution map which is the triangular shape around the boring point can be developed by tomography inversion. To obtain the exact travel time information of shear wave component, a procedure using the magnitude summation of vertical and horizontal components was used based on the evaluation of particle motion at the surface. It was verified that proposed method could give reliable Vs distribution map through the numerical study using the FEM (Finite Element Method) model. Finally, SPT-Uphole tomography method was performed at the weathered soil site where several boring data with SPT-N values are available, and the feasibility of proposed method was verified in the field.

• Journal of Forest and Environmental Science
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• v.3 no.1
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• pp.1-9
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• 1983

The purpose of this study was to elucidate the ecophysiological habitat of natural Sorbus commixta forest at Mt. Seorak. The results obtained were as follows: 1. The Sorbus commixta trees mainly distributed from 900m to 1,500m altitude. In there, the warm index(WI) was about 42$3.2{\times}10^3$ to $9.2{\times}10^3$, cation exchange capacity(CEC) was 13.7 to 19.5mg/100g, N content 0.21 to 0.39%, $P_2O_5$ content was 22.6 to 38.7ppm, and pH value was 5.6 to 5.8 respectively. 4. The upper crown trees in Sorbus commixta communities were Abies nephrolepis, Taxus cuspidata, Betula platyphylla var. japonica, Quercus${\times}$grosseserrata, Acer mono, Prunus sargentii, Carpinus cordata, Tilia amurensis, and the under crown trees were Rhododendron brachycarpum, Acer pseudo-sieboldianum, Thuja olientalis, Corylus heterohpylla, Philadelphus schrenckii, Rhododendron schlippenbachii, Rhododendron mucronulatum, and Magnolia sieboldii. 5. The stand densities were 1,156 trees/ha at 1,160m and 3,600 trees/ha at 1,300m respectively. The coverages by the DBH basal area were 0.37 at 1,160m and 0.31 at 1,300m respectively, and the vegetation coverages by the crown projection area were 2.04 at 1,160m and 1.61 at 1,300m respectively. 6. The light extinction coefficient(k) in Beer-Lambert's law, showed the distance, F(z), from top canopy to aboveground, was 0.17. 7. The water relations parameters of Sorbus commixta shoot were obtained by the pressure chamber technique. The osmotic pressure, ${\pi}_o$, at maximum turgor was -16.2 bar, and VAT pressure was 14.5bar. The osmotic pressure, ${\pi}_p$, at incipient plasmolysis was -19.4bar. The relative water contents at incipient plasmolysis were 83.1% ($v_p/v_o$) and 87.1%($v_p/w_s$;$w_s$, total water at maximum turgor). 8. The bulk modulus of elasticity(E) of shoot was about 69.6. The total symplasmic water to total water in shoot was 67.7%, and the apoplastic water to total water was 32.3%.