• Geomechanics and Engineering
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• v.28 no.5
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• pp.505-520
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• 2022

In recent years, geotextile-encased gravel columns (usually called stone columns) have become a popular method to increasing soil shear strength, decreasing the settlement, acceleration of the rate of consolidation, reducing the liquefaction potential and increasing the bearing capacity of foundations. The behavior of improved loose base-soil with gravel columns under shear loading and the shear stress-horizontal displacement curves got from large scale direct shear test are of great importance in understanding the performance of this method. In the present study, by performing 36 large-scale direct shear tests on sandy base-soil with different fine-content of zero to 30% in both not improved and improved with gravel columns, the effect of the presence of gravel columns in the loose soils were investigated. The results were used to predict the shear stress-horizontal displacement curve of these samples using support vector machines (SVM). Variables such as the non-plastic fine content of base-soil (FC), the area replacement ratio of the gravel column (Arr), the geotextile encasement and the normal stress on the sample were effective factors in the shear stress-horizontal displacement curve of the samples. The training and testing data of the model showed higher power of SVM compared to multilayer perceptron (MLP) neural network in predicting shear stress-horizontal displacement curve. After ensuring the accuracy of the model evaluation, by introducing different samples to the model, the effect of different variables on the maximum shear stress of the samples was investigated. The results showed that by adding a gravel column and increasing the Arr, the friction angle (ϕ) and cohesion (c) of the samples increase. This increase is less in base-soil with more FC, and in a proportion of the same Arr, with increasing FC, internal friction angle and cohesion decreases.

• Geomechanics and Engineering
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• v.10 no.3
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• pp.269-284
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• 2016

Simplified techniques based on in situ testing methods are commonly used to assess seismic liquefaction potential. Many of these simplified methods were developed by analyzing liquefaction case histories from which the liquefaction boundary (limit state) separating two categories (the occurrence or non-occurrence of liquefaction) is determined. As the liquefaction classification problem is highly nonlinear in nature, it is difficult to develop a comprehensive model using conventional modeling techniques that take into consideration all the independent variables, such as the seismic and soil properties. In this study, a modification of the Multivariate Adaptive Regression Splines (MARS) approach based on Logistic Regression (LR) LR_MARS is used to evaluate seismic liquefaction potential based on actual field records. Three different LR_MARS models were used to analyze three different field liquefaction databases and the results are compared with the neural network approaches. The developed spline functions and the limit state functions obtained reveal that the LR_MARS models can capture and describe the intrinsic, complex relationship between seismic parameters, soil parameters, and the liquefaction potential without having to make any assumptions about the underlying relationship between the various variables. Considering its computational efficiency, simplicity of interpretation, predictive accuracy, its data-driven and adaptive nature and its ability to map the interaction between variables, the use of LR_MARS model in assessing seismic liquefaction potential is promising.

• Korean Journal of Microbiology
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• v.51 no.2
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• pp.133-140
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• 2015

We have examined the correlation between the physicochemical and microbiological environment variables for the different layers of oak forest soil in Mt. Gyeryong, Korea. The result shows that there is a high correlation in the environment variables between the soil parameters of the fermented (F) layer and humus (H) layer. In particular, the pH level in the F layer shows a high correlation with C and N, while the various organic acids of the H layer turns out to be closely correlated with soil bacteria density. As we evaluated phylogenetic characteristics of bacterial populations by DGGE analysis with DNA extracted. Total of 175 bands including 43 bands from litter (L) layer, 42 bands from F layer, 43 bands from H layer and 47 bands from rhizosphere (A) layer were selected as the major DGGE band of oak forest soil. Based on the 16S rRNA gene sequences, 175 DGGE bands were classified into 32 orders in 7 phylum. The heat map was analyzed in order to compare the quantity of the base sequences of each order and based on the clustering of the different layers of oak forest soil, the result confirms that the F layer and H layer belong to a different cluster from that of L layer and A layer. Furthermore, it also showed that approximately 50% of the total microbial population in different layers is ${\alpha}$-proteobacteria, which indicates that they belong to the dominant system group. In particular, Rhizobiales, Burkholderiales and Actinobacteriales were observed in all the seasons and layers of oak forest soil, which confirms that they are the indigenous soil bacterial community in oak forest soil.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.239-247
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• 2022

The physico-chemical properties of agricultural soil are factors that affect crop productivity and soil fertility. In Korea, agricultural environment change surveys have been conducted every four years since 1999. The purpose of this study is to investigate the status and changes in the physico-chemical properties of agricultural soil in Chungcheongnam-do. Samples were collected from the exact location of the aforementioned environment survey, and environmental variables (land use, type of crop cultivated) were investigated in relation to the samples. Soil collection was conducted using a core sampler and a single gouge auger. The bulk density of the soil generally increased up to a depth of sampling of 40 cm but decreased thereafter to a depth of 60 cm. Additionally, the bulk density was highest in the upland soil case and stood at 1.59 g·cm^-3, while the lowest value of 1.52 g·cm^-3 was obtained from orchard soil samples. Conversely, the porosity and moisture gradually decreased at soil depths of 0 - 40 cm and increased at depths of 40 - 60 cm. Most of the soil chemical properties generally decreased with an increase the soil depth from 0 to 70 cm, but electrical conductivity (EC) increased up to a depth of 40 cm. Therefore, it is judged that it is necessary to lower the bulk density by supplying organic matter for agricultural land in Chungcheongnam-do.

• Journal of Forest and Environmental Science
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• v.39 no.1
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• pp.13-19
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• 2023

We investigated Soil respiration in Bluepine forest of western Bhutan, in relation to soil temperature, moisture content and soil pH and it was aimed at establishing variability in space and time. The Bluepine forest thrives in the typical shallow dry valleys in the inter-montane Bhutan Himalaya, which is formed by ascending wind from the valley bottom, which carries moisture from the river away to the mountain ridges. Stratified random sampling was applied and the study site was classified into top, mid, low slope and further randomized sample of n=20 from 30 m×30 m from each altitude. The overall soil respiration mean for the forest was found 2248.17 CO₂ g yr^-1 and it is ~613.58 C g yr^-1. The RS from three sites showed a marginal variation amongst sites, lower slope (2,309 m) was 4.64 μ mol m^-2 s^-1, mid slope (2,631 m) was 6.78 μ mol m^-2 s^-1 and top slope (3,027 m) was 6.33 μ mol m^-2 s^-1 and mean of 5.92 μ mol m^-2 s^-1, SE=0.25 for the forest. Temporal distribution and variations were observed more pronounced than in the space variation. Soil respiration was found highest during March and lowest in September. Soil temperature had almost inverse trend against soil respiration and dropped a low in February and peak in July. The moisture in the soil changed across months with precipitation and pH remained almost consistent across the period. The soil respiration and soil temperature had significant relationship R²=-0.61, p=0.027 and other variables were found insignificant. Similar relationship are reported for dry season in a tropical forest soil respiration. Soil temperature was found to have most pronounced effect on the soil respiration of the forest under study.

• KIEAE Journal
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• v.16 no.1
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• pp.73-79
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• 2016

This study aimed to investigate the flood prevention effect expected from the afforestation of a large area metal roof of an industrial complex located in an area prone to floods in the rainwater outflow reduction aspect through computer simulation based on soil, which is a key element of the system. In order to conduct a more realistic simulation, the properties of the surveyed soil were generated through substantive analysis, soil texture analysis, and saxton method. A comparative performance evaluation was conducted by using soil depth and ponding depth, which are key elements of the system, as variables. The study result showed that during the heavy rainfall period, the bottom ash artificial soil had 61% rainwater outflow reduction effect, which was 11% higher than the SWMM standard sand.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.5
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• pp.798-804
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• 2012

Although siderophores are induced primarily in response to iron deficiency, soil and other ecological factors can affect on this process. This study was to evaluate the production of siderophores by different fungal species isolated from heavy metal contaminated and uncontaminated soils. More than thirty fungal strains were isolated from heavy metal contaminated and rhizosphere uncontaminated soils. Chrome azurol sulfonate (CAS) was used for both quantitative and qualitative evaluation of siderophores production. No significant correlations were observed between the tested variables such as ultraviolet (UV) irradiation method and CAS-agar plate and heavy metal concentration in both soils. The production of siderophores in rhizosphere fungi was higher than those isolated from the contaminated soil; however, the difference was not significant. The siderophore production (%) by fungi isolated from heavy metal contaminated soil using UV irradiation method was positively correlated with the qualitative values using CAS-plate method (P<0.05). Pearson correlation test indicated a positive correlation between the quantitative and qualitative methods of detection for fungi isolated from rhizosphere and also those isolated from heavy metal contaminated soil.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.85-98
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• 2003

There has recently been an increasing interest of the watershed management as a solution to a wide range of problems related water environment, therefore this study attempted to construct the environment information system to monitor the Geumho River watershed, and to evaluate the impacts of the watershed characteristics on stream water quality. A detailed GIS database to analyze the environmental characteristics at the subwatershed units, including 1:25,000 scale topographical maps, detailed soil maps, land use, 10m-resolution DEMs, roads, streams, vegetation index(NDVI) calculated from Landsat TM imagery, rainfall, and soil loss using RUSLE, is compiled for the study area. The set of variables representing watershed urbanization or industrialization, residential and commercial landuse, industrial landuse, and road area have significantly negative(-) relationship with water quality variables(BOD, COD, SS, T-N, T-P). On the other hand, watershed indicators related to natural environmental conditions, forest cover and vegetation index(NDVI) in each subwatershed were significantly positive(+) relationship with water quality. Three other variables, agricultural landuse, amount of fertilizer and pesticides, and potential soil loss, were not significant in explaining the correlations between watershed environment and stream water quality.

• Interaction and multiscale mechanics
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• v.6 no.4
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• pp.339-356
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• 2013

This paper investigates the optimized parameters of Tuned Mass Dampers (TMDs) for vibration control of high-rise structures including Soil Structure Interaction (SSI). The Artificial Bee Colony (ABC) method is employed for optimization. The TMD Mass, damping coefficient and spring stiffness are assumed as the design variables of the controller; and the objective is set as the reduction of both the maximum displacement and acceleration of the building. The time domain analysis based on Newmark method is employed to obtain the displacement, velocity and acceleration of different stories and TMD in response to 6 types of far field earthquakes. The optimized mass, frequency and damping ratio are then formulated for different soil types; and employed for the design of TMD for the 40 and 15 story buildings and 10 different earthquakes, and well results are achieved. This study leads the researchers to the better understanding and designing of TMDs as passive controllers for the mitigation of earthquake oscillations.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.1159-1164
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• 2006

In this paper an artificial neural network model is developed to estimate the unconfined compression strength of Deep Cement Mixing(DCM) treated soil. A database which consists of a number of unconfined compression test result compiled from 9 clay sites is used to train and test of the artificial neural network model. Developed neural network model requires water content of soil, unit weight of soil, passing percent of #200 sieve, weight of cement, w-c ratio as input variables. It is found that the developed artificial neural network model can predict more precise and reliable unconfined compression strength than the conventional empirical models.