• Journal of the Korean Geotechnical Society
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• v.38 no.1
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• pp.17-33
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• 2022

This study describes the effect of ground water table on the dynamic analysis of single piles subjected to earthquake loading. The dynamic numerical analysis was performed for different dry and saturated soils with varying the relative densities of surrounding weathered soils (SM). The test soil was a weathered soil encountered in the engineering field and bender element tests were conducted to estimate the dynamic properties of test soil. The Mohr-Coulomb model and Finn model were used for soil, dry and saturated conditions, respectively. These models validated with results of centrifuge tests. When compared with the results from the soil conditions, saturated cases showed more lateral displacement and bending moment of piles than dry cases, and this difference caused from the generation of excess porewater pressure. It means that the kinematic effect of the soil decreased as the excess pore water pressure was generated, and it was changed to the inertial behavior of the pile.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1295-1299
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• 2008

Soil erosion is a natural process and has been occurring in most areas in the watershed. However, accelerated soil erosion rates have been causing numerous environmental impacts in recent years. To reduce soil erosion and sediment inflow into the water bodies, site-specific soil erosion best management practices (BMPs) need to be established and implemented. The most commonly used soil erosion model is the Universal Soil Loss Equation (USLE), which have been used in many countries over 30 years. The Sediment Assessment Tool for Effective Erosion Control (SATEEC) ArcView GIS system has been developed and enhanced to estimate the soil erosion and sediment yield from the watershed using the USLE input data. In the last decade, the Soil and Water Assessment Tool (SWAT) model also has been widely used to estimate soil erosion and sediment yield at a watershed scale. The SATEEC system estimates the LS factor using the equation suggested by Moore and Burch, while the SWAT model estimates the LS factor based on the relationship between sub watershed average slope and slope length. Thus the SATEEC and SWAT estimated soil erosion values were compared in this study. The differences in LS factor estimation methods in the SATEEC and SWAT caused significant difference in estimated soil erosion. In this study, the difference was -51.9%(default threshold)$\sim$-54.5%(min. threshold) between SATEEC and non-patched SWAT, and -7.8%(default threshold)$\sim$+3.8%(min. threshold) between SATEEC and patched SWAT estimated soil erosion.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.1
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• pp.3-12
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• 2008

Soil erosion is a natural process and has been occurring in most areas in the watershed. However, accelerated soil erosion rates have been causing numerous environmental impacts in recent years. To reduce soil erosion and sediment inflow into the water bodies, site-specific soil erosion best management practices(BMPs) need to be established and implemented. The most commonly used soil erosion model is the Universal Soil Loss Equation(USLE), which have been used in many countries over 30 years. The Sediment Assessment Tool for Effective Erosion Control(SATEEC) ArcView GIS system has been developed and enhanced to estimate the soil erosion and sediment yield trom the watershed using the USLE input data. In the last decade, the Soil and Water Assessment Tool(SWAT) model also has been widely used to estimate soil erosion and sediment yield at a watershed scale. The SATEEC system estimates the LS factor using the equation suggested by Moore and Burch, while the SWAT model estimates the LS factor based on the relationship between sub watershed average slope and slope length. Thus the SATEEC and SWAT estimated soil erosion values were compared in this study. The differences in LS factor estimation methods in the SATEEC and SWAT caused significant difference in estimated soil erosion. In this study, the difference was -51.9%(default threshold)${\sim}-54.5%$(min. threshold) between SATEEC and non-patched SWAT, and -7.8%(default threshold)${\sim}+3.8%$(min. threshold) between SATEEC and patched SWAT estimated soil erosion.

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.3
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• pp.205-212
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• 2014

Chronological changes of soil organic carbon (SOC) must be prepared by IPCC guidelines for national greenhouse gas inventories. IPCC suggested default reference SOC stocks for mineral soils and relative stock factors for different management activities where country own factors were not prepared. 3.4 million data were downloaded from agricultural soil information system and analyzed to get chronological changes of SOC for some counties and for land use in Korea. SOC content of orchard soil was higher than the other soils but chronological SOC changes of all land use had no tendency in differences with high standard deviation. SOC contents of counties depended on their own management activities and chronological SOC changes of districts also had no tendency in differences. Thus, Korea should survey the official records and relative stock factors on management activities such as land use, tillage and input of organic matter to calculate SOC stocks correctly. Otherwise, Korea should establish a model for predicting SOC by analyzing selected representative fields and by calculating SOC differences from comparing management activities of lands with those of representative fields.

• Journal of The Geomorphological Association of Korea
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• v.23 no.3
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• pp.67-78
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• 2016

Soil carbon(C) is an essential property for characterizing soil quality. Understanding spatial patterns of soil C is particularly limited for mountain areas. This study aims to predict the spatial pattern of soil C using terrain analysis in a steep mountainous area. Specifically, model performances and prediction uncertainties were investigated based on the number of resampling repetitions. Further, important predictors for soil C were also identified. Finally, the spatial distribution of uncertainty was analyzed. A total of 91 soil samples were collected via conditioned latin hypercube sampling and a digital soil C map was developed using support vector regression which is one of the powerful machine learning methods. Results showed that there were no distinct differences of model performances depending on the number of repetitions except for 10-fold cross validation. For soil C, elevation and surface curvature were selected as important predictors by recursive feature elimination. Soil C showed higher values in higher elevation and concave slopes. The spatial pattern of soil C might possibly reflect lateral movement of water and materials along the surface configuration of the study area. The higher values of uncertainty in higher elevation and concave slopes might be related to geomorphological characteristics of the research area and the sampling design. This study is believed to provide a better understanding of the relationship between geomorphology and soil C in the mountainous ecosystem.

• Journal of the Korean Geosynthetics Society
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• v.12 no.4
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• pp.35-44
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• 2013

This paper presents the slope wall technique using soil improvement material for reinforced soil slope through laboratory scale model tests, and verifies the experimental results comparing with numerical analysis. In additional, case study in field has performed to investigate the deformation of reinforced soil slope for 6 months. As a result of laboratory scale model test, numerical analysis, and case study, the reinforcement effect of the slope wall technique using soil improvement material is sufficient to be constructed as reinforced soil slope. The technique shows the stable ratio (0.4%) of horizontal to vertical deformation in the surface loading.

• Journal of Biosystems Engineering
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• v.14 no.3
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• pp.158-167
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• 1989

This study was conducted to investigate the applicability of true model theory in a powered lugged wheel-soil system and to examine the possibility of using principles of similitude in investigating the dimensions of soil parameters pertinent to a powered lugged wheel-soil system concerning the sinkage prediction. The following conclusions were derived from the study; 1) The sinkage of prototype wheels proved to be predicted by those of the model wheels for the range of the dynamic weight tested. 2) A conditional equation which can be used for the prediction of sinkage of prototype by model test was derived as $n_f=n{_\ell}{^{-b}}$. The range of the numerical value of b, which is the exponent on the length dimension of the soil property ${\alpha}$, was found to be -1.48~-2.54. 3) Considering a relatively wide variation of b values, it was concluded that there are several soil properties which are pertinent to the powered lugged-wheel soil system concerning the sinkage prediction.

• Structural Engineering and Mechanics
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• v.78 no.4
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• pp.425-437
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• 2021

Under foundation shock mats have been used in the current practice in order to reduce/damp vibrations received by buildings through the surrounding environment. Although some investigations have been made on under foundation shock mats performance, their effectiveness in the reduction of railway induced-vibrations has not been fully studied, particularly with the consideration of underneath soil media. In this regard, this research is aimed at investigating performance of shock mat used beneath building foundation for reduction of railway induced-vibrations, taking into account soil-structure interaction. For this purpose, a 2D finite/infinite element model of a building and its surrounding soil media was developed. It includes an elastic soil media, a railway embankment, a shock mat, and the building. The model results were validated using an analytical solution reported in the literature. The performance of shock mats was examined by an extensive parametric analysis on the soil type, bedding modulus of shock mat and dominant excitation frequency. The results obtained indicated that although the shock mat can substantially reduce the building vibrations, its performance is significantly influenced by its underneath soil media. The softer the soil, the lower the shock mat efficiency. Also, as the train excitation frequency increases, a better performance of shock-mats is observed. A simplified model/method was developed for prediction of shock mat effectiveness in reduction of railway-induced vibrations, making use of the results obtained.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.4
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• pp.108-114
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• 1980

A deterministic conceptual erosion model which simulates detachment, entrainment, transport and deposition of eroded soil particles by rainfall impact and flowing water is presented. Both upland and channel phases of sediment yield are incorporated into the erosion model. The algorithms for the soil erosion and sedimentation processes including land and crop management effects are taken from the literature and then solved using a digital computer. The erosion model is used in conjunction with the modified Kentucky Watershed Model which simulates the hydrologic characteristics from watershed data. The two models are linked together by using the appropriate computer code. Calibrations for both the watershed and erosion model parameters are made by comparing the simulated results with actual field measurements in the Four Mile Creek watershed near Traer, Iowa using 1976 and 1977 water year data. Two water years, 1970 and 1978 are used as test years for model verification. There is good agreement between the mean daily simulated and recorded streamflow and between the simulated and recorded suspended sediment load except few partial differences. The following conclusions were drawn from the results after testing the watershed and erosion model. 1. The watershed and erosion model is a deterministic lumped parameter model, and is capable of simulating the daily mean streamflow and suspended sediment load within a 20 percent error, when the correct watershed and erosion parameters are supplied. 2. It is found that soil erosion is sensitive to errors in simulation of occurrence and intensity of precipitation and of overland flow. Therefore, representative precipitation data and a watershed model which provides an accurate simulation of soil moisture and resulting overland flow are essential for the accurate simulation of soil erosion and subsequent sediment transport prediction. 3. Erroneous prediction of snowmelt in terms of time and magnitute in conjunction with The frozen ground could be the reason for the poor simulation of streamflow as well as sediment yield in the snowmelt period. More elaborate and accurate snowmelt submodels will greatly improve accuracy. 4. Poor simulation results can be attributed to deficiencies in erosion model and to errors in the observed data such as the recorded daily streamflow and the sediment concentration. 5. Crop management and tillage operations are two major factors that have a great effect on soil erosion simulation. The erosion model attempts to evaluate the impact of crop management and tillage effects on sediment production. These effects on sediment yield appear to be somewhat equivalent to the effect of overland flow. 6. Application and testing of the watershed and erosion model on watersheds in a variety of regions with different soils and meteorological characteristics may be recommended to verify its general applicability and to detact the deficiencies of the model. Futhermore, by further modification and expansion with additional data, the watershed and erosion model developed through this study can be used as a planning tool for watershed management and for solving agricultural non-point pollution problems.