• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.17-27
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• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.128-135
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• 2021

Thin-walled tubes have been widely used as energy absorbing devices because they are light and have high energy-absorption efficiency. However, the downside is that conventional thin-walled tubes usually exhibit an excessive initial peak crushing force (IPCF) and a large fluctuation in the load-displacement curve, and thus lack stability as energy absorbing devices. Corrugated tubes were introduced to reduce IPCF and to increase the stability of collision energy-absorbing devices. Since the performance of corrugated tubes is highly influence by geometry, design optimization methods can be utilized to optimize the performance of corrugated tubes. In this paper, we utilize shape design optimization based on an adaptive surrogate model for crashworthiness analysis. The amplitude and wavelength of the corrugation, as well as curvature changes in the features, are the design variables. A morphing methodology is adopted to perform shape design parameterization. Through numerical examples, we compare optimal design results based on the adaptive surrogate model, with optimal results based on conventional surrogate models, and we show that direct optimal design methods produce more efficient results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.23-28
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• 2019

The structural integrity of a surface metal coating was evaluated through numerical results to improve the efficiency and reduce the damage caused by cavitation in ships and marine plants. The goal was to ensure structural strength and performance, even if the thickness of the wing is reduced to reduce the weight of the material and surface coating. Analytical methods were used for four models: a non-coating model, one with the same thickness after coating, one with a thickness reduction of 3% after coating, and one with thickness reduction of 5% after coating. With a thickness reduction of 5% after coating, the stress was increased to 12%, and the safety factor was 0.99%, so the structural integrity was insufficient. However, a better material or a thicker coating could allow a sufficient safety factor to be secured. The structural integrity was improved by the coating, and even when the weight was reduced up to 5%, the structural integrity could be sufficiently secured due to the coating effect.

• Journal of Climate Change Research
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• v.3 no.1
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• pp.13-24
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• 2012

CCGIS (Climate Change Adaptation Toolkit based on GIS) was developed to use as a tool for the climate change assessment and any relevant tasks involving climate change adaptation policy over Korean peninsula. The main objective of CCGIS is to facilitate an efficient and relevant information for the estimation of climate change vulnerability index by providing key information in the climate change adaptation process. In particular, the atmospheric modeling system implemented in CCGIS, which is composed of climate and meteorological numerical model and the atmospheric environmental models, were used as a tool to generate the climate and environmental IPCC SRES (A2, B1, A1B, A1T, A1FI, and A1 scenarios) climate data for the year of 2000, 2020, 2050, and 2100. This article introduces the components of CCGIS and describes its application to the Korean peninsula. Some examples of the CCGIS and its use for both climate change adaptation and estimation of vulnerability index applied to Korean provinces are presented and discussed here.

• Journal of the Korea Organic Resources Recycling Association
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• v.28 no.4
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• pp.43-52
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• 2020

As the membrane gas separation technology grows, various models were developed by numerous researchers to describe the separation process. In this work, the counter-current model was compared thoroughly with experimental data. Experimentally, hollow fiber membrane using CA module was prepared for the separation of biogas. The pure gas permeation properties of membrane module for methane, nitrogen, oxygen, and carbon dioxide were measured. The permeance of CO2 and CH4 were 25.82 GPU and 0.65 GPU, respectively. The high CO2/CH4 selectivity of 39.7 was obtained. the separation test for three different simulated mixed gases were carried out after pure gas test, and the gas concentration of the permeate at various stage-cut were measured from CA membrane module. Results showed that the experimental data agreed with the numerical simulation. A mathematical model has implemented in this study for the separation of biogas using a membrane module. The finite difference method (FDM) is applied to calculate the membrane biogas separation behaviors. Futhermore, the counter-current model can be considered as a convenient model for biogas separation process.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.51-58
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• 2021

In this paper, a multi-scale finite element (FE) modeling methodology for three-dimensional (3D) needle-punched (NP) C/SiC with a complex microstructure is presented. The variations of the material properties induced by the needle-punching process and complex geometrical features could pose challenges when estimating the material behavior. For considering these features of composites, a 3D microscopic FE approach is introduced based on micro-CT technology to produce a 3D high fidelity FE model. The image processing techniques of micro-CT are utilized to generate discrete-gray images and reconstruct the high fidelity model. Furthermore, a subcell modeling technique is developed for the 3D NP C/SiC based on the high fidelity FE model to expand to the macro-scale structural problem. A numerical homogenization approach under periodic boundary conditions (PBCs) is employed to estimate the equivalent behavior of the high fidelity model and effective properties of subcell components, considering geometry continuity effects. For verification, proposed models compare excellently with experimental results for the mechanical behavior of tensile, shear, and bending under static loading conditions.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.167-179
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• 2021

In order to characterize the mixing process of confluence for understanding the impacts of a river on the other river, it has been crucial to analyze the spatial mixing patterns for main streams depending on various inflow conditions of tributaries. However, most conventional studies have mostly relied upon hydraulic or water quality numerical models for understanding mixing pattern analysis of confluences, due to the difficulties to acquire a wide spatial range of in-situ data for characterizing mixing process. In this study, backscatters (or SNR) measured from ADCPs were particularly used to track sediment mixing assuming that it could be a surrogate to estimate the suspended sediment concentration. Raw backscatter data were corrected by considering the beam spreading and absorption by water. Also, an optical Laser diffraction instrument (LISST) was used to verify the method of acoustic backscatter and to collect the particle size distribution of main stream and tributary. In addition, image-based spatial distributions of sediment mixture in the confluence were monitored in various flow conditions by using an unmanned aerial vehicle (UAV), which were compared with the spatial distribution of acoustic backscatter. As results, we found that when acoustic backscatter by ADCPs were well processed, they could be proper indicators to identify the spatial patterns of the three-dimensional mixing process between two rivers. For this study, flow and sediment mixing characteristics were investigated in the confluence between Nakdong and Nam river.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.222-234
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• 2021

Airborne-pests can be introduced into Korea from overseas areas by wind, which can cause considerable damage to major crops. Meteorological models have been used to estimate the wind trajectories of airborne insects. The objective of this study is to analyze the effect of input settings on the prediction of areas where airborne pests arrive by wind. The wind trajectories were predicted using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The HYSPLIT model was used to track the wind dispersal path of particles under the assumption that brown plant hopper (Nilaparvata lugens) was introduced into Korea from sites where the pest was reported in China. Meteorological input data including instantaneous and average wind speed were generated using meso-scale numerical weather model outputs for the domain where China, Korea, and Japan were included. In addition, the calculation time intervals were set to 1, 30, and 60 minutes for the wind trajectory calculation during early June in 2019 and 2020. It was found that the use of instantaneous and average wind speed data resulted in a considerably large difference between the arrival areas of airborne pests. In contrast, the spatial distribution of arrival areas had a relatively high degree of similarity when the time intervals were set to be 1 minute. Furthermore, these dispersal patterns predicted using the instantaneous wind speed were similar to the regions where the given pest was observed in Korea. These results suggest that the impact assessment of input settings on wind trajectory prediction would be needed to improve the reliability of an approach to predict regions where airborne-pest could be introduced.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.2
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• pp.73-82
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• 2022

The study presents a three-dimensional approach to simulate the nonlinear behavior of a 72 m long Ultra High Performance Fiber Reinforced Concrete (UHPFRC) pre-stressed box girder for a pedestrian bridge in Busan, South Korea. The concrete damage plasticity (CDP) model is adopted to model the non-linear behavior of the UHPFRC material, in which the material properties are obtained from uniaxial compressive and tensile tests. The simulation model based on the proposed stress-strain curve is validated by the results of four-point bending model tests of a 50 m UHPFRC pre-stressed box girder. The results from the simulation models agree with the experimental observations and predict the flexural behavior of the 50 m UHPFRC pre-stressed box girder accurately. Afterward, the validated model is utilized to investigate the flexural behavior of the 72 m UHPFRC pre-stressed box girder. Here, the load-deflection curve, stress status of the girder at various load levels, and connection details is analyzed. The load-deflection curve is also compared with design load to demonstrate the great benefit of the slender UHPFRC box girder. The obtained results demonstrate the applicability of the nonlinear finite element method as an appropriate option to analyze the flexural behavior of pre-stressed long-span girders.