• Journal of the Korean Geotechnical Society
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• v.25 no.11
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• pp.39-51
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• 2009

This is a study on rectangular-shaped passive row piles in inclined sand-ground by model tests. The experiment controlled the angle of inclination of ground and induced the ground destruction. We also measured the behavior of row piles, by adjusting the shape, position and spacing of piles. As a result, we confirmed the earth pressure, the lateral resistance, and the effect of depressing on the ground variation working on passive pile. The effect of B-type pile of which the front width is wide is bigger than that of H-type pile of which the side width is wide. We can find out the failure angle of slope, the shared force of pile and soil by using the lateral resistance graph based on slope angle.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.330-330
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• 2021

본 연구에서는 새롭게 개발 중인 SSP 시나리오의 일단위 강수량과 온도 자료를 활용하여 청미천 유역의 미래 가뭄의 예측 및 분석을 실시하였다. SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5에 따른 새롭게 개발 중인 CMIP6 (Coupled Model Intercomparison Project) GCM (General Circulation Models) 중 ACCESS-ESM1.5(Australian Community Climate and Earth System Simulator model)를 이용하였다. GCM 자료는 Quantile Mapping 방법을 사용하여 편이보정 되었고, 유출분석은 SWAT(Soil and Water Assessment Tool) 모형을 사용하여 청미천 유역에 대해 수행하였다. 청미천 유역의 가뭄분석을 위해 기상학적 가뭄지수인 SPI(Standardized Precipitation Index)와 SPEI(Standardized Precipitation Evapotranspiration Index), 수문학적 가뭄지수인 SDI(Standardized Streamflow Index)를 산정하였다. 그 후, 시간에 따른 가뭄의 특성을 분석하기 위해 가까운 미래 (2025-2064)와 먼 미래 (2065-2100) 로 구분하여 분석을 진행하였다. 그 결과, 청미천 유역의 가뭄 발생은 SSP시나리오, 가뭄지수에 따라 차이점을 확인할 수 있었다. SSP 시나리오의 경우 SSP5-8.5에서 가장 심각한 가뭄이 발생하였다. 가뭄지수의 경우 강수만을 고려한 SPI는 먼 미래에 비해 가까운 미래에서 더욱 심각한 가뭄이 발생하였다. SDI의 경우 강수량의 변동이 일반적으로 하천의 흐름에 영향을 미치기에 SPI와 비슷한 양상을 나타내었다. SPEI의 경우 시간에 따른 기온상승으로 먼 미래에 심각한 가뭄이 발생하였다.

• Geomechanics and Engineering
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• v.34 no.5
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• pp.507-527
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• 2023

Accurately estimation of the geo-mechanical parameters in Artificial Ground Freezing (AGF) is a most important scientific topic in soil improvement and geotechnical engineering. In order for this, one way is using classical and conventional constitutive models based on different theories like critical state theory, Hooke's law, and so on, which are time-consuming, costly, and troublous. The others are the application of artificial intelligence (AI) techniques to predict considered parameters and behaviors accurately. This study presents a comprehensive data-mining-based model for predicting the Young's Modulus of frozen sand under the triaxial test. For this aim, several single and hybrid models were considered including additive regression, bagging, M5-Rules, M5P, random forests (RF), support vector regression (SVR), locally weighted linear (LWL), gaussian process regression (GPR), and multi-layered perceptron neural network (MLP). In the present study, cell pressure, strain rate, temperature, time, and strain were considered as the input variables, where the Young's Modulus was recognized as target. The results showed that all selected single and hybrid predicting models have acceptable agreement with measured experimental results. Especially, hybrid Additive Regression-Gaussian Process Regression and Bagging-Gaussian Process Regression have the best accuracy based on Model performance assessment criteria.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.84-84
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• 2021

전 지구적 기후변화로 세계 곳곳은 이례적인 홍수와 가뭄 등으로 많은 재산 및 인명 피해가 발생하고 있다. 우리나라의 경우, 강우 강도가 크고 단기간 많은 양의 비가 내리는 집중호우의 빈도가 급격히 증가하고 있다. 또한, 우리나라의 국토는 약 70%가 산지로 이루어져 있고, 경사가 험준한 지형을 지니고 있어 강우 시 유출이 급격히 발생하는 것이 특징이다. 이러한 기후 패턴과 지형적 특성으로 인하여 토양침식이 가중되고 있으며, 그중 강원도의 경우 산지 곳곳에 위치한 고랭지 밭으로 인해 강우 시 많은 양의 토사가 유실되어 농경지가 감소하고 있으며, 유실된 토사의 하천 유입으로 인한 하천 통수능력의 저하와 수질 악화 등 다양한 문제를 발생시키고 있다. 본 연구에서는 물리기반 모형인 SSEM (SSORii Erosion Model)을 이용하여 강원도 평창군에 위치한 도암댐 유역의 필지를 중심으로 침식과 퇴적의 양상을 분석해보고자 하였다. SSEM은 단기 강우 사상을 모의할 수 있고, 침식과 퇴적의 시·공간적 변동성을 반영할 수 있어 침식이 언제, 어디서, 얼마나 발생하였는지 식별이 가능한 모형이다. 연구분석 결과, 대부분의 필지에서 침식과 퇴적이 발생하였으며, 그중 도심지 주변에 위치한 필지에서 많은 토양침식이 발생하는 것으로 분석되었다. 이는 본 유역의 현장 조사 당시 육안으로 확인한 침식의 실태와 상당 부분 일치하고 있음을 보여준다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.287-289
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• 2021

Smart farming education service based on network are important factors for farming sector. The lack of time and space has to lead to their limited appliance to farmers. Limited information support and low background knowledge in farming production is a lot of trial and error in farming production. Smart farming education as a service based on cloud provide the farming information that is the farming knowledge, farming skill, and farmer's experiences and knowhow, etc. And real-time information such as climate change, soil environment and market trends is very important. This paper proposes a framework for applying farming education service based on cloud. It consists of smart farming function and smart farming education function

• Journal of Drive and Control
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• v.20 no.4
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• pp.100-106
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• 2023

We developed a crawler-based driving platform that can perform harvesting, transportation, pest control, and rotary operation by equipping it with various implements, and analyzed its performance. This single platform was developed to perform as pepper harvester, peanut harvester, and transporter with a 46-kW engine. A simulation model was developed to study the specifications of the platform, and the accuracy was also analyzed. The absolute percentage error ranged from 0.2 to 5.9%, which made it possible to predict the platform performance using simulation model. In T-test, both torque and speed on field and asphalt showed a significant difference (1%). Driving torque required differed depending on the nature of the field, and the speeds also changed based on soil load. The developed platform has the advantage of being equipped with a variety of working tools, expected to be used to harvest root crops in the future.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.629-641
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• 2024

The presence of excavations or cavities beneath the foundations of a building can have a significant impact on their stability and cause extensive damage. Traditional methods for calculating the bearing capacity and subsidence of foundations over cavities can be complex and time-consuming, particularly when dealing with conditions that vary. In such situations, machine learning (ML) and deep learning (DL) techniques provide effective alternatives. This study concentrates on constructing a prediction model based on the performance of ML and DL algorithms that can be applied in real-world settings. The efficacy of eight algorithms, including Regression Analysis, k-Nearest Neighbor, Decision Tree, Random Forest, Multivariate Regression Spline, Artificial Neural Network, and Deep Neural Network, was evaluated. Using a Python-assisted automation technique integrated with the PLAXIS 2D platform, a dataset containing 272 cases with eight input parameters and one target variable was generated. In general, the DL model performed better than the ML models, and all models, except the regression models, attained outstanding results with an R² greater than 0.90. These models can also be used as surrogate models in reliability analysis to evaluate failure risks and probabilities.

• Geomechanics and Engineering
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• v.38 no.3
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• pp.285-305
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• 2024

This study investigates the behaviour of hunchback retaining walls supporting unsaturated sandy backfill under active earth pressure conditions. Utilizing a horizontal slice method and a unified effective stress methodology, the influence of various factors on lateral earth pressure, including the position of the hunch along the wall, friction angles, and wall heights, is explored. The results suggest that relocating the hunch position from close to the wall's top to near its base leads to a significant decrease (ranging from 54% to 81%) in lateral earth pressure. However, as the hunch position transitions from near the top to mid-height, the point of application of active thrust shifts upward initially, then slightly downward as the hunch position approaches the toe. Notably, the reduction in lateral earth pressure is more pronounced for shorter wall heights and higher friction angles. Building upon these findings, an Artificial Neural Network (ANN)-based model is developed to accurately predict the lateral earth pressure coefficient and point of application, achieving R² values of 0.94 and 0.93, respectively. In addition, an analytical model based on Coulomb's earth pressure theory is presented and compared with ANN models. These models are anticipated to assist designers and practitioners in optimizing hunchback retaining walls for unsaturated backfill.

• Korean Chemical Engineering Research
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• v.56 no.4
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• pp.479-495
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• 2018

This study was carried out to research the multimedia fate modeling, concentration distribution and impact assessment of polycyclic aromatic hydrocarbons (PAHs) emitted from automobiles, which are known as carcinogenic and mutation chemicals. The amount of emissions of PAHs was determined based on the census data of automobiles at a target S-city and emission factors of PAHs, where multimedia fugacity modeling was conducted by the restriction of PAHs transfer between air-soil at the impervious area. PAHs' Concentrations and their distributions at several environmental media were predicted by multimedia fugacity model (level III). The residual amounts and the distributions of PAHs through mass transfer of PAHs between environment media were used to assess the health risk of PAHs at unsteady state (level IV), where the sensitivity analyses of the model parameter of each variable were conducted based on Monte Carlo simulation. The experimental result at S-city showed that Fluoranthene among PAHs substances are the highest residual concentrations (60%, 53%, 32% and 34%) at all mediums (atmospheric, water, soil, sediment), respectively, where most of the PAHs were highly accumulated in the sediment media (more than 80%). A result of PAHs concentration changes in S-city over the past 34 years identified that PAHs emissions from all environmental media increased from 1983 to 2005 and decreased until 2016, where the emission of heavy-duty vehicle including truck revealed the largest contribution to the automotive emissions of PAHs at all environment media. The PAHs concentrations in soil and water for the last 34 years showed the less value than the legal standards of PAHs, but the PAHs in air exceeded the air quality standards from 1996 to 2016. The result of this study is expected to contribute the effective management and monitoring of toxic chemicals of PAHs at various environment media of Metropolitan city.

• Korean Journal of Soil Science and Fertilizer
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• v.9 no.3
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• pp.165-181
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• 1976

Fertilizer(N,P,K) demand for crop production in 1980, 1990 and 2000 was estimated according to the two proposed models, one of which is fertilizer use efficiency model expressed in $Fn=(Y/E){\cdot}(1-Cs)Eu$, where Fn:fertilizer demand, Y:Crop production estimated, E:nutrient efficiency, Cs:fraction of natural resource nutrient in plant, Eu:fertilizer use efficiency and the other fertilization efficiency model expressed in Fn=Y(1-Cys)/Fe, where Cys:fraction of yield without fertilizer, Fe:fertilization efficiency. Total crop uptake of nutrient and its noncycling portion were estimated as criteria for fertilizer demand and nutrient maintenance. Total crop uptake of N,P,K was 600,000 M/T in 1965 700,000 M/T in 1974 and estimated to 880,000 M/T in 1980, 1,170,000 M/T in 1990 and 1,410,000 M/T in 2000. Fertilizer demand appeares to be about 90% of total crop uptake according to fertilizer use efficiency model and about 87% according to fertilization efficiency model. The noncycling nutrient was about 29% of total crop uptake. Fertilizer demand was almost same to the uptake amount in nitrogen, 1.5 times of uptake in phosphorus and half of uptake in potassium. Varietal development, improvement of soil fertility and cultivation method and development of fertilizer forms appears to decrease fertilizer demand by increasing efficiency term in two models while environmental stress such as low temperature appears to give reverse effect resulting in higher fertilizer demand. Fertilizer consumption in 1974 seemed to be unreasonably high especially in nitrogen and phosphorus and thus the effective use of fertilizer appeared as an urgent problem considering that large fields are still remained in lower fertility.