• Journal of Biosystems Engineering
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• v.37 no.1
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• pp.51-57
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• 2012

Purpose: The compression strength of corrugated fiberboard containers used to package agricultural products rapidly decreases owing to various environmental factors encountered during the distribution of unitized products. The main factors affecting compression strength are moisture absorption, long-term top load, and fatigue caused by shock and vibration during transport. This study characterized the durability of corrugated fiberboard containers for packaging fruits and vegetables under simulated transportation conditions. Methods: Compression tests were done after corrugated fiberboard containers containing fruit were vibrated by an electro-dynamic vibration test system using the power spectral density of routes typically traveled to transport fruits and vegetables in South Korea. Results: To predict loss of compression strength owing to vibration fatigue, a multiple nonlinear regression equation ($r^2=0.9217$, $RMSE=0.6347$) was developed using three independent variables of initial container compression strength, namely top stacked weight, loading weight, and vibration time. To test the applicability of our model, we compared our experimental results with those obtained during a road test in which peaches were transported in corrugated containers. Conclusions: The comparison revealed a highly significant ($p{\leq}0.05$) relationship between the experimental and road-test results.

• Journal of the Korean Geotechnical Society
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• v.25 no.7
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• pp.47-54
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• 2009

Better understanding of in-situ mechanical behavior of pavement foundations is very important to predict long-term effects on the system performance of transport infrastructure. For this purpose resilient stiffness characterization of geomaterials is needed to properly adopt such mechanistic analysis under both traffic and environmental loadings. In this paper in-situ monitoring data from KHC test road were used to analyze the non-linear response using finite element method for a selected constitutive model of foundation geomaterials, and the results were compared with the field data.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.211-218
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• 2010

Volume of concrete slab changes by temperature and moisture effects. At that time, tensile stress develops because the slab volume change is restrained by friction resistance between the slab and subbase, and then crack occurs occasionally. Accordingly, researchers have made efforts to figure out the friction characteristics between the slab and subbase by performing push-off tests. Lately, researches to analyze concrete pavement behavior by the friction characteristics have been performed by finite element method. In this study, The friction characteristics between the slab and subbase were investigated based on the friction test results for lean concrete, aggregate, and asphalt subase widely used in Korean concrete pavements. The energy method bilinearizing relation between nonlinear friction resistance and displacement were suggested. The friction test was modeled by 3-D finite element program, ABAQUS, and the model was verified by comparing the analyzed results to the test results. The bilinear model developed by the energy method was validated by comparing analysis results obtained by using the nonlinear and bilinear friction resistance displacement relation as input data. A typical Korean concrete pavement was modeled by ABAQUS and EverFE and analyzed results were compared to evaluate applicability of the bilinear model.

• Computers and Concrete
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• v.25 no.4
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• pp.311-325
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• 2020

In this research work, the hygrothermal and mechanical buckling responses of simply supported FG sandwich plate seated on Winkler-Pasternak elastic foundation are investigated using a novel shear deformation theory. The current model take into consideration the shear deformation effects and ensures the zero shear stresses on the free surfaces of the FG-sandwich plate without requiring the correction factors "Ks". The material properties of the faces sheets of the FG-sandwich plate are assumed varies as power law function "P-FGM" and the core is isotropic (purely ceramic). From the virtual work principle, the stability equations are deduced and resolved via Navier model. The hygrothermal effects are considered varies as a nonlinear, linear and uniform distribution across the thickness of the FG-sandwich plate. To check and confirm the accuracy of the current model, a several comparison has been made with other models found in the literature. The effects the temperature, moisture concentration, parameters of elastic foundation, side-to-thickness ratio, aspect ratio and the inhomogeneity parameter on the critical buckling of FG sandwich plates are also investigated.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.343-348
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• 2014

We developed a simple model to predict emergence time and emergence rate of southern type garlic using the daily mean temperature. Emergence rate of garlic was decreased and emergence time was delayed on higher temperature than optimum temperature of $12.7^{\circ}C$. In the model, firstly daily emergence rate was calculated using a beta function to input daily mean temperature, then the percentage of garlic emergence was calculated using a nonlinear model with accumulated emergence rate. The model was good to describe the experimental data of growth cabinet. Also it can explain well the experimental data using temperature gradient tunnel, designed for verification of model performance. But there are 5 days of deviation between estimated and measured time of garlic emergence on the field experiment. More research is needed to develop an advanced model considering other factors, such as soil moisture.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.2
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• pp.197-215
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• 2015

Modeling water flow in variably saturated, porous media is important in many branches of science and engineering. Highly nonlinear relationships between water content and hydraulic conductivity and soil-water pressure result in very steep wetting fronts causing numerical problems. These include poor efficiency when modeling water infiltration into very dry porous media, and numerical oscillation near a steep wetting front. A one-dimensional finite element formulation is developed for the numerical simulation of variably saturated flow systems. First order backward Euler implicit and second order Crank-Nicolson time discretization schemes are adopted as a solution strategy in this formulation based on Picard and Newton iterative techniques. Five examples are used to investigate the numerical performance of two approaches and the different factors are highlighted that can affect their convergence and efficiency. The first test case deals with sharp moisture front that infiltrates into the soil column. It shows the capability of providing a mass-conservative behavior. Saturated conditions are not developed in the second test case. Involving of dry initial condition and steep wetting front are the main numerical complexity of the third test example. Fourth test case is a rapid infiltration of water from the surface, followed by a period of redistribution of the water due to the dynamic boundary condition. The last one-dimensional test case involves flow into a layered soil with variable initial conditions. The numerical results indicate that the Crank-Nicolson scheme is inefficient compared to fully implicit backward Euler scheme for the layered soil problem but offers same accuracy for the other homogeneous soil cases.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.1115-1123
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• 2008

When constructing projects such as road embankments, bridge approaches, dikes or buildings on soft, compressible soils, significant settlements may occur due to the consolidation of these soils under the superimposed loads. The compressibility of the soil skeleton of a soft clay is influenced by such factors as structure and fabric, stress path, temperature and loading rate. Although it is possible to determine appropriate relations and the corresponding material parameters in the laboratory, it is well known that sample disturbance due to stress release, temperature change and moisture content change can have a profound effect on the compressibility of a clay. The early research of Tezaghi and Casagrande has had a lasting influence on our interpretation of consolidation data. The 24 hour, incremental load, oedometer test has become, more or less, the standard procedure for determining the one-dimensional, stress-strain behavior of clays. An important notion relates to the interpretation of the data is the ore-consolidation pressure ${\sigma}_p$, which is located approximately at the break in the slope on the curve. From a practical point of view, this pressure is usually viewed as corresponding to the maximum past effective stress supported by the soil. Researchers have shown, however, that the value of ${\sigma}_p$ depends on the test procedure. furthermore, owing to sampling disturbance, the results of the laboratory consolidation test must be corrected to better capture the in-situ compressibility characteristics. The corrections apply, strictly speaking, to soils where the relation between strain and effective stress is time independent. An important assumption in Terzaghi's one-dimensional theory of consolidation is that the soil skeleton behaves elastically. On the other hand, Buisman recognized that creep deformations in settlement analysis can be important. this has led to extensions to Terzaghi's theory by various investigators, including the applicant and coworkers. The main object of this study is to suggestion the modified compression index value to predict settlements by back calculating the $C_c$ from different numerical models, which are giving best prediction settlements for multi layers including very thick soft clay.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.469-483
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• 2006

An algorithm was developed to estimate stability indices (SI) over the Korean peninsula using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) infrared brightness temperatures (TBs). The SI is defined as the stability of the atmosphere in the hydrostatic equilibrium with respect to the vertical displacements and is used as an index for the potential severe storm development. Using atmosphere temperature and moisture profiles from Regional Data Assimilation and Prediction System (RDAPS) as initial guess data for a nonlinear physical relaxation method, K index (KI), KO Index (KO), lifted index (LI), and maximum buoyancy (MB) were estimated. A fast radiative transfer model, RTTOV-7, is utilized for reducing the computational burden related to the physical relaxation method. The estimated TBs from the radiative transfer simulation are in good agreement with observed MODIS TBs. To test usefulness for the short-term forecast of severe storms, the algorithm is applied to the rapidly developed convective storms. Compared with the SIs from the RDAPS forecasts and NASA products, the MODIS SI obtained in this research predicts the instability better over the pre-convection areas. Thus, it is expected that the nowcasting and short-term forecast can be improved by utilizing the algorithms developed in this study.