• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.17 no.1
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• pp.1-13
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• 2019

The accident at the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) resulted in the deposition of large quantities of radionuclides over parts of eastern Japan. Radioactive contaminants have been observed over a large area including forests, cities, rivers and lakes. Due to the strong adsorption of radioactive cesium by soil particles, radioactive cesium migrates with the eroded soil, follows the surface flow paths, and is delivered downstream of population-rich regions and eventually to coastal areas. In this study, we developed a model to simulate the transport of contaminated sediment in a watershed hydrological system and this model was compared with observation data from eroded soil observation instruments located at the Korea Atomic Energy Research Institute. Two methods were applied to analyze the soil particle size distribution of the collected soil samples, including standardized sieve analysis and image analysis methods. Numerical models were developed to simulate the movement of soil along with actual rainfall considering initial saturation, rainfall infiltration, multilayer and rain splash. In the 2019 study, a numerical model will be used to add rainfall shield effect by trees, evaporation effect and shield effects of surface water. An eroded soil observation instrument has been installed near the Wolsong nuclear power plant since 2018 and observation data are being continuously collected. Based on these observations data, we will develop the numerical model to analyze long-term behavior of radionuclides on land as they move from land to rivers, lakes and coastal areas.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1307-1315
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• 2021

The Changjiang Diluted Water (CDW) spreads over the East China Sea every summer and significantly affects the sea surface salinity changes in the seas around Jeju Island and the southern coast of Korea peninsula. Sometimes its effect extends to the eastern coast of Korea peninsula through the Korea Strait. Specifically, the CDW has a significant impact on marine physics and ecology and causes damage to fisheries and aquaculture. However, due to the limited field surveys, continuous observation of the CDW in the East China Sea is practically difficult. Many studies have been conducted using satellite measurements to monitor CDW distribution in near-real time. In this study, an algorithm for estimating Sea Surface Salinity (SSS) in the East China Sea was developed using the Geostationary Ocean Color Imager (GOCI). The Multilayer Perceptron Neural Network (MPNN) method was employed for developing an algorithm, and Soil Moisture Active Passive (SMAP) SSS data was selected for the output. In the previous study, an algorithm for estimating SSS using GOCI was trained by 2016 observation data. By comparison, the train data period was extended from 2015 to 2020 to improve the algorithm performance. The validation results with the National Institute of Fisheries Science (NIFS) serial oceanographic observation data from 2011 to 2019 show 0.61 of coefficient of determination (R²) and 1.08 psu of Root Mean Square Errors (RMSE). This study was carried out to develop an algorithm for monitoring the surface salinity of the East China Sea using GOCI and is expected to contribute to the development of the algorithm for estimating SSS by using GOCI-II.

• Journal of the Korean Society for Railway
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• v.11 no.1
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• pp.54-60
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• 2008

In the trackbed design using elastic multilayer model, the stress-dependent resilient modulus $(E_R)$ is an important input parameter, that is, reflects substructure performance under repeated traffic loading. However, the evaluation method for resilient modulus using repeated loading triaxial test is not fully developed for practical purpose, because of costly equipment and the significantly fluctuated values depending on the testing equipment and laboratory personnel. The this study, the paper will present an indirect method to estimate the resilient modulus using dynamic properties. The resilient modulus of crushed stone, which is the typical material of sub-ballast, was calculated with the measured dynamic properties and the range of stress level of the sub-ballast, and approximated with the power model combined with bulk and deviatoric stresses. The resilient modulus of coarse grained material decreases with increasing deviatoric stress at a confining pressure, and increases with increasing bulk stress. Sandy soil (SM classified from Unified Soil Classification System) of subgrade was also evaluated and best fitted with the power model of deviatoric stress only.

• Geomechanics and Engineering
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• v.9 no.6
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• pp.757-774
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• 2015

Reinforcing soils with the geosynthetics have been shown to be an effective method for improving the uplift capacity of granular soils. The pull-out resistance of the reinforcing elements is one of the most notable factors in increasing the uplift capacity. In this paper, a new reinforcing element including the elements (anchors) attached to the ordinary geogrid for increasing the pull-out resistance of the reinforcement, is used. Thus, the reinforcement consists of the geogrid and anchors with the cylindrical plastic elements attached to it, namely grid-anchors. A three-dimensional numerical study, employing the commercial finite difference software FLAC-3D, was performed to investigate the uplift capacity of the pipelines buried in sand reinforced with this system. The models were used to investigate the effect of the pipe diameter, burial depth, soil density, number of the reinforcement layers, width of the reinforcement layer, and the stiffness of geogrid and anchors on the uplift resistance of the sandy soils. The outcomes reveal that, due to a developed longer failure surface, inclusion of grid-anchor system in a soil deposit outstandingly increases the uplift capacity. Compared to the multilayer reinforcement, the single layer reinforcement was more effective in enhancing the uplift capacity. Moreover, the efficiency of the reinforcement layer inclusion for uplift resistance in loose sand is higher than dense sand. Besides, the efficiency of reinforcement layer inclusion for uplift resistance in lower embedment ratios is higher. In addition, by increasing the pipe diameter, the efficiency of the reinforcement layer inclusion will be lower. Results demonstrate that, for the pipes with an outer diameter of 50 mm, the grid-anchor system of reinforcing can increase the uplift capacity 2.18 times greater than that for an ordinary geogrid and 3.20 times greater than that for non-reinforced sand.

• Geomechanics and Engineering
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• v.6 no.1
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• pp.79-98
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• 2014

This paper presents an experimental and numerical study on the evaluation of a thermal response test using a precast high-strength concrete (PHC) energy pile and a closed vertical system with W-type ground heat exchangers (GHEs). Field thermal response tests (TRTs) were conducted on a PHC energy pile and on a general vertical GHE installed in a multiple layered soil ground. The equivalent ground thermal conductivity was determined by using the results from TRTs. A simple analytical solution is suggested in this research to derive an equivalent ground thermal conductivity of the multilayered soils for vertically buried GHEs. The PHC energy pile and general vertical system were numerically modeled using a three dimensional finite element method to compare the results with TRTs'. Borehole thermal resistance values were also obtained from the numerical results, and they were compared with various analytical solutions. Additionally, the effect of ground thermal conductivity on the borehole thermal resistance was analyzed.

• Smart Structures and Systems
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• v.26 no.3
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• pp.263-275
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• 2020

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

• Smart Structures and Systems
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• v.30 no.4
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• pp.421-432
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• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• Horticultural Science & Technology
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• v.34 no.2
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• pp.342-353
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• 2016

This study was initiated to evaluate green color retention under three different soil systems. Several turfgrasses were evaluated in multi-layer, USGA, and mono-layer systems. Turfgrass entries were comprised of three cultivars of Korean lawngrass (Zoysia japonica Steud.) as warm-season grass (WSG) and three blends and three mixtures of Kentucky bluegrass (KB, Poa pratensis L.), perennial ryegrass (PR, Lolium perenne L.), and tall fescue (TF, Festuca arundinacea Schreb.) as cool-season grass (CSG). Significant differences were observed in visual turf color and green color retention among soil systems and turfgrasses. Both the multi-layer and USGA systems were highly associated with better color ratings and longer color retention, as compared with the mono-layer system. Seasonal variation of visual turf color greatly occurred from late December to early spring. CSG exhibited longer color retention than did WSG. The latter maintained green color for approximately 6 months, regardless of the soil system. Spring green-up of Korean lawngrass occurred from early to middle May, while it underwent discoloration from late October to early November. Among the CSGs green-up occurred between early March and early April and leaf color was maintained until middle December to early February. Therefore, the CSGs were green for 8.5 to 11 months, depending on turfgrass and soil system. The mean period of green color duration across all soil systems was approximately 10-11, 9-10 and 8.5-9.0 months for PR, KB and TF, respectively. As for the CSG mixtures, the greater the proportion of PR, the longer the green color retention, while the higher the proportion of TF, the shorter the color retention. There was greater variation in green color duration among the CSGs than the WSGs. Across soil systems, color retention differences of 2 to 6 days were observed for the Korean lawngrass, but 7 to 36 days for the CSGs. These results demonstrate that green color retention varied greatly according to soil systems and also among turfgrasses. Selections of turfgrass and soil system should be made using a concept-oriented approach, when establishing garden, park, soccer field, golf course and other sports field. Information obtained in this study can be used to select soil systems and turfgrasses based on the expected degree of leaf color retention.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2C
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• pp.71-79
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• 2009

This study is to develope the resilient modulus prediction model, which is the function of mean effective principal stress and axial strain, for three types of railroad trackbed materials such as crushed stone, weathered granite soil, and crushed-rock soil mixture. The model consists of the maximum Young's modulus and nonlinear values for higher strain, analogous to dynamic shear modulus. The maximum value is modeled by model parameters, $A_E$ and the power of mean effective principal stress, $n_E$. The nonlinear portion is represented by modified hyperbolic model, with the model parameters of reference strain, ${\varepsilon}_r$ and curvature coefficient, a. To assess the performance of the prediction models proposed herein, the elastic response of a test trackbed near PyeongTaek, Korea, was evaluated using a 3-D elastic multilayer computer program (GEOTRACK). The results were compared with measured elastic vertical displacement during the passages of freight and passenger trains at two locations, whose sub-ballasts were crushed stone and weathered granite soil, respectively. The calculated vertical displacements of the sub-ballasts are within the order of 0.6mm, and agree well with measured values. The prediction models are thus concluded to work properly in the preliminary investigation.

• Korean Journal of Soil Science and Fertilizer
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• v.25 no.4
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• pp.342-356
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• 1992

Kinetic studies using stirred-flow methods were conducted with the Luisiana soil at three pH levels(pH 5, 6.5, and 8) and three temperature levels(10, 25, and $40^{\circ}C$) to explore effects on the rate of silica retention and release and to find out reaction mechanisms. In this study the maximum silica retention could not be obtained for long enough experimental time. The silica sorption isorption was C type fitted well to Freundlich equation. The pH of the soil suspension increased by the silica release process at low pH treatments(pH 5 and 6.5), while decreased at high pH treatment(pH 8). From the above findings It can be deduced that the mechanism of silica retention is a multilayer forming process to change the ligand form depending on pH condition. In the proposed mechanism the sorbed silica provide new binding sites for additional sorption of silica, while the activation energy for the formation of subsequent layers increases correspondingly. The silica retention and release process were well described by first-order and parabolic diffusion equation. However, clear interpretation for silica sorption mechanism using these equations could not be made. The validity of the fraction term (Fa and Fd) included in first-order and parabolic diffusion equation requires further examinations because the temperature effect on apparent rate constant shows no constant trends among temperature treatments, while there was a good trend in Elovich and modified Freundlich equation where the fraction term was not included.