• International Journal of Highway Engineering
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• v.11 no.4
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• pp.107-115
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• 2009

This study was conducted to investigate the effect of the slab thickness on the tensioning design and to determine the optimal slab thickness of the post-tensioned concrete pavement (PTCP). The tensile stresses due to the vehicle and environmental loads were obtained using a finite element analysis model and the tensioning stress was calculated employing an allowable flexural strength. The environmental loads of both the constant temperature gradient and the constant temperature difference between top and bottom of the slab were considered. The tensioning designs for various slab thicknesses were performed considering prestressing losses. The comparison results showed that generally as the thickness increased, the number of tendons became larger. Consequently, the design was not economical for a thicker slab thickness. Even though the number of tendons became smaller with an increase in the thickness under the small environmental load, a thicker PTCP slab was not economical because of a higher cost of concrete than that of steel. Therefore, the slab thickness should be kept in minimum within the construction available thicknesses.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.67-74
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• 2017

PURPOSES : In this study, a three-dimensional nonlinear finite element analysis (FEA) model for airport concrete pavement was developed using the commercial program ABAQUS. Users can select an analysis method and set the range of input parameters to reflect actual conditions such as environmental loading. METHODS : The geometrical shape of the FEA model was chosen by considering the concrete pavement located in the third-stage construction site of Incheon International Airport. Incompatible eight-node elements were used for the FEA model. Laboratory test results for the concrete specimens fabricated at the construction site were used as material properties of the concrete slab. The material properties of the cement-treated base suggested by the Federal Aviation Administration(FAA) manual were used as those of the lean concrete subbase. In addition, preceding studies and pavement evaluation reports of Incheon International Airport were referred for the material properties of asphalt base and subgrade. The kinetic friction coefficient between the concrete slab and asphalt base acquired from a preceding study was used for the friction coefficient between the layers. A nonlinear temperature gradient according to slab depth was used as an input parameter of environmental loading, and a quasistatic method was used to analyze traffic loading. The average load transfer efficiency obtained from an Heavy falling Weight Deflectomete(HWD) test was converted to a spring constant between adjacent slabs to be used as an input parameter. The reliability of the FEA model developed in this study was verified by comparing its analysis results to those of the FEAFAA model. RESULTS : A series of analyses were performed for environmental loading, traffic loading, and combined loading by using both the model developed in this study and the FEAFAA model under the same conditions. The stresses of the concrete slab obtained by both analysis models were almost the same. An HWD test was simulated and analyzed using the FEA model developed in this study. As a result, the actual deflections at the center, mid-edge, and corner of the slab caused by the HWD loading were similar to those obtained by the analysis. CONCLUSIONS : The FEA model developed in this study was judged to be utilized sufficiently in the prediction of behavior of airport concrete pavement.

• Proceedings of the Korea Association of Crystal Growth Conference
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• 1996.06a
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• pp.258-292
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• 1996

In the last year great interest appears to YBCO thin films preparation on different substrate materials. Preparation of epitaxial film is a very difficult problem. There are many requirements to substrate materials that must be fullfilled. Main problems are lattice mismatch (misfit) and similarity of structure. From paper [1] or follows that difference in interatomic distances and angles of substrate and film is mire important problem than similarity of structure. In this work we present interatomic distances and angle relations between substrate materials belonging to ABCO4 group (where A-Sr or Ca, B-rare earth element, C-Al or Ga) of different orientations and YBCO thin films. There are many materials used as substrates for HTsC thin films. ABCO4 group of compounds is characterized by small dielectric constants (it is necessary for microwave applications of HTsC films), absence of twins and small misfit [2]. There most interesting compounds CaNdAlO4, SrLaAlO4 and SrLaGaO4 were investigated. All these compounds are of pseudo-perovskite structure with space group 14/mmm. This structure is very similar to structure of YBCO. SLG substrate has the lowest misfit (0.3%) and dielectric constant. For preparation of then films of substrates of this group of compound plane of <100> orientation are mainly used. Good quality films of <001> orientations are obtained [3]. In this case not only a-a misfit play role, but c-3b misfit is very important too. Sometimes, for preparation of thin films substrates of <001> and <110> orientations were manufactured [3]. Different misfits for different YBCO faces have been analyzed. It has been found that the mismatching factor for (100) face is very similar to that for (001) face so there is possibility of preparation of thin films on both orientations. SrLaAlO4(SLA) and SrLaGaO4(SLG) crystals of general formula ABCO4 have been grown by the Czochralski method. The quality of SLA and SLG crystals strongly depends on axial gradient of temperature and growth and rotation rates. High quality crystals were obtained at axial gradient of temperature near crystal-melt interface lower than 50℃/cm, growth rate 1-3 mm/h and the rotation rate changing from 10-20pm[4]. Strong anisotropy in morphology of SLA and SLG single crystals grown by the Czochralski method is clearly visible. On the basics of our considerations for ABCO4 type of the tetragonal crystals there can appear {001}, {101}, and {110} faces for ionic type model [5]. Morphology of these crystals depend on ionic-covalent character of bonding and crystal growth parameters. Point defects are observed in crystals and they are reflected in color changes (colorless, yellow, green). Point defects are detected in directions perpendicular to oxide planes and are connected with instability of oxygen position in lattice. To investigate facets formations crystals were doped with Cr3+, Er3+, Pr3+, Ba2+. Chromium greater size ion which is substituted for Al3+ clearly induces faceting. There appear easy {110} faces and SLA crystals crack even then the amount of Cr is below 0.3at.% SLG single crystals are not so sensitive to the content of chromium ions. It was also found that if {110} face appears at the beginning of growth process the crystal changes its color on the plane {110} but it happens only on the shoulder part. The projection of {110} face has a great amount of oxygen positions which can be easy defected. Pure and doped SLA and SLG crystals measured by EPR in the<110> direction show more intensive lines than in other directions which allows to suggest that the amount of oxygen defects on the {110} plane is higher. In order to find the origin of colors and their relation with the crystal stability, a set of SLA and SLG crystals were investigated using optical spectroscopy. The colored samples exhibit an absorption band stretching from the UV absorption edge of the crystal, from about 240 nm to about 550 m. In the case of colorless sample, the absorption spectrum consists of a relatively weak band in the UV region. The spectral position and intensities of absorption bands of SLA are typical for imperfection similar to color centers which may be created in most of oxide crystals by UV and X-radiation. It is pointed out that crystal growth process of polycomponent oxide crystals by Czochralski method depends on the preparation of melt and its stoichiometry, orientation of seed, gradient of temperature at crystal-melt interface, parameters of growth (rotation and pulling rate) and control of red-ox atmosphere during seeding and growth (rotation and pulling rate) and control of red-ox atmosphere during seeding and growth. Growth parameters have an influence on the morphology of crystal-melt interface, type and concentration of defects.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.403-414
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• 1994

Nonlinear analysis of RC containment structure under thermal load and pressure is presented to trace the behaviour after an assumed LOCA. The temperature distribution varying with time through the wall thickness is determined by transient finite element analysis with the two time level scheme in time domain. The layered shell finite elements are used to represent the containment structures in nuclear power plants. Both geometric and material nonlinearities are taken into account in the finite element formulation. The constitutive relation of concrete is modeled according to Drucker-Prager yield criteria in compression. Tension stiffening model is used to represent the tensile behaviour of concrete including bond effect. The reinforcing bars are modeled by smeared layer at the location of reinforcements accounting elasto-plastic axial behaviors. The steel liner model under Von Mises yield criteria is adopted to represent elastic-perfect plastic behaviour. Geometric nonlinearity is formulated to consider the large displacement effect. Thermal stress components are determined by the initial strain concept during each time step. The temperature differential between any two consecutive time steps is considered as a load incremental. The numerical results from this study reveal that nonlinear temperature gradient based on transient thermal analysis will produces excessive large displacement. Nonlinear behavior of containment structures up to ultimate stage can be traced reallistically. The present study allows more realistic analysis of concrete containment structures in nuclear power plants.

• Atmosphere
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• v.23 no.4
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• pp.377-387
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• 2013

Projections of changes in the low latitude atmospheric circulation under global warming are investigated using the results of the CMIP5 ensemble mean. For this purpose, 30-yr periods for the present day (1971~2000) and the end of the $21^{st}$ century (2071~2100) according to the RCP emission scenarios are compared. The wintertime subtropical jet is projected to strengthen on the upper side of the jet due to increase in meridional temperature gradient induced by warming in the tropical upper-troposphere and cooling in the stratosphere except for the RCP2.6. It is also found that a strengthening of the upper side of the wintertime subtropical jet in the RCP2.6 due to tropical upper-tropospheric warmings. Model-based projection shows a weakening of the mean intensity of the Hadley cell, an upward shift of cell, and poleward shift of the Hadley circulation for the winter cell in both hemispheres. A weakening of the Walker circulation, which is one of the most robust atmospheric responses to global warming, is also projected. These results are consistent with findings in the previous studies based on CMIP3 data sets. A weakening of the Walker circulation is accompanied with decrease (increase) in precipitation over the Indo-Pacific warm pool region (the equatorial central and east Pacific). In addition, model simulation shows a decrease in precipitation over subtropical regions where the descending branch of the winter Hadley cell in both hemispheres is strengthened.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Horticultural Science & Technology
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• v.28 no.5
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• pp.768-776
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• 2010

This study was conducted to predict number and fresh weight of leaves, and total leaf area of a single-stemmed rose 'Vital' based on the accumulated thermal units, and to develop a model of shoot development for the prediction of the time when the flowering shoot reaches a phenological stage in a plant factory system. The base temperature ($T_b$), optimum temperature ($T_{opt}$), and maximum temperature ($T_{max}$) were estimated by regressing the rate of shoot development against the temperature gradient. The rate of shoot development ($R$, $d^{-1}$) for the phase from cutting to bud break (CT-BB) was best described by a linear model $R_b$ ($d^{-1}$) = -0.0089 + $0.0016{\cdot}temp$. The rate of shoot development for the phase from bud break to harvest (BB-HV) was fitted to the parabolic model $R_h$ ($d^{-1}$) = $-0.0001{\cdot}temp^2$ + $0.0054{\cdot}temp$ - 0.0484. The $T_b$, $T_{opt}$, and $T_{max}$ values were 5.56, 27.0, and $42.7^{\circ}C$, respectively. The $T_b$ value was used in the thermal unit computations for the shoot development. Number of leaves, leaf area (LA), and leaf fresh weight showed sigmoidal curves regardless of the cut time. The shoot development and leaf area model was described as a sigmoidal function using thermal units. Leaf area was described as LA = 578.7 $[1+(thermal units/956.1)^{-8.54}]^{-1}$. Estimated and observed shoot length and leaf fresh weight showed a reasonably good fit with 1.060 ($R^2=0.976^{***}$) and 1.043 ($R^2=0.955^{***}$), respectively. The average thermal units required from cutting to transplant and from transplant to harvest stages were $426{\pm}42^{\circ}C{\cdot}d$ and $783{\pm}24^{\circ}C{\cdot}d$, respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.