• 한국재난관리표준학회지
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• 제1권4호
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• pp.65-71
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• 2008

본 논문은 국지성 호우에 따른 성토사변의 내부 침식의 특성에 대한 것이다. 기존 성토재료의 필터조건 분석방법에 대하여 검토하였다. 근접 성토재료로부터 재료업자의 이동을 억제하기 위한 필터층의 조건을 이론적 배경을 기초로 새로운 분석법을 제안하였다. 이러한 필터거동에 대한 새로운 분석기법에 따라 성토재료 내 침식에 의한 입자간의 거동 특성을 규명하기 위하여 사면의 내부 침식에 영향을 주는 주요원인인자(강우강도, 사면구배, 성토재료 특성 등)를 변화하여 총 3가지 Case의 축소모형실험을 실시하였다. 본 연구로부터 새로운 필터조건 분석법이 실질적인 성토구배 내의 성토재료에 대한 필터 설계시보다 더 적용성이 높은 방법이라는 결론을 얻었다. 이에 따라 새로운 필터조건검토 기준 또는 방법을 제안하였다.

• 한국터널지하공간학회 논문집
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• 제2권2호
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• pp.3-10
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• 2000

고속열차가 터널에 진입할 때 압축파가 터널 내부로 전파된다. 이 압축파와 연관된 터널출구 미기압파는 고속철도 열차-터널 인터페이스에서 발생되는 독특한 물리현상이다. 미기압파를 저감시키는 방법중에 터널 입 출구부에 공기역학적인 구조물을 사용하여 압축파의 시간에 대한 구배를 지연시키는 방법이 있다. 본 연구의 목적은 터널주행 열차모형 시험기로 최적의 경사갱구를 개발하는 것이다. 경사갱구의 경사각도에 따른 시험을 통한 시험결과에서 터널 입구부에 $45^{\circ}$ 경사갱구를 적용했을 때 미기압파 최대 피크값이 19.2 %가 저감되었다. 터널 입 출입구 양쪽에 $45^{\circ}$ 경사갱구를 적용할 경우는 41.2% 저감되었다. 또한 터널 입 출입구 양쪽에 $30^{\circ}$ 경사갱구를 적용할 경우는 미기압파 최대 피크값이 34.6% 저감되었다.

• 한국도로학회논문집
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• 제14권2호
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• pp.45-53
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• 2012

지중구조물 주위는 다짐이 잘 되지 않아 지반이 장기 침하하므로 콘크리트 포장 하부에 공동이 발생하기 쉬우며, 이로 인해 지지력이 저하되기 쉽다. 여기에 하중이 가해지면 설계 시 기대한 것보다 큰 응력이 도입되어 포장에 파손이 발생하고 수명이 감소하게 된다. 본 논문에서는 한국도로공사 시험도로의 박스형 암거 상부 콘크리트 포장 슬래브의 파손을 조사하였다. 토피고가 다른 상행선과 하행선의 암거 위치에 발생한 슬래브의 횡방향 균열을 비교하였다. 시험도로의 횡방향 균열을 검증하기 위해 토피고가 없는 박스형 암거와 콘크리트 포장을 유한요소 방법으로 모형화하고 해석하였다. 포장의 자중을 고려하고 시험도로가 위치한 경기도 여주 지역 콘크리트 슬래브의 온도구배를 적용한 후 윤하중을 재하하였다. 각 하중조합에 대해 최대인장 응력이 발생하는 위치와 이때의 윤하중 위치를 찾아냈다. 이를 통해 최대인장응력을 감소시킬 수 있는 줄눈 위치를 찾아내고 암거 크기 별로 상부에 위치하는 슬래브의 적정 길이를 제안하였다.

• 대한조선학회논문집
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• 제51권5호
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• pp.419-428
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• 2014

This paper presents a numerical simulation method for the flow around advancing ships in regular waves by using a rectilinear grid system. Because the grid lines do not consist with body surface in the rectilinear grid system, the body geometries are defined by the interaction points of those grid lines and the body surface. For the satisfaction of body boundary conditions, no-slip and divergence free conditions are imposed on the body surface and body boundary cells, respectively. Meanwhile, free surface is defined with the modified marker density method. The pressure on the free surface is determined to make the pressure gradient terms of the governing equations continuous, and the velocity around the free surface is calculated with the pressure on the free surface. To validate the present numerical method, a vortex induced vibration (VIV) phenomenon and flows around an advancing Wigley III ship model in various regular waves are simulated, and the results are compared with existing and corresponding research data. Also, to check the applicability to practical ship model, flows around KRISO Container Ship (KCS) model advancing in calm water are numerically simulated. On the simulations, the trim and the sinkage are set free to compare the running attitude with some other experimental data. Moreover, flows around the KCS model in regular waves are also simulated.

• 대한조선학회논문집
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• 제53권5호
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• pp.371-379
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• 2016

In the ship design process, ship motion and propulsion performance in sea waves became very important issues. Especially, prediction of ship propulsion performance during real operation is an important challenge to ship owners for economic operation in terms of fuel consumption and route-time evaluation. Therefore, it should be considered in the early design stages of the ship. It is thought that the averaged value and fluctuation of effective inflow velocity to the propeller have a great effect on the propulsion performance in waves. However, even for the nominal velocity distribution, very few results have been presented due to some technical difficulties in experiments. In this study, flow measurements near the propeller plane using a stereo PIV system were performed. Phase-averaged flow fields on the propeller plane of a KVLCC2 model ship in waves were measured in the towing tank by using the stereo PIV system and a phase synchronizer with heave motion. The experiment was carried out at fully loaded condition with making surge, heave and pitch motions free at a forward speed corresponding to Fr=0.142 (Re=2.55×106) in various head waves and calm water condition. The phase averaged nominal velocity fields obtained from the measurements are discussed with respect to effects of wave orbital velocity and ship motion. The low velocity region is affected by pressure gradient and ship motion.

• 상하수도학회지
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• 제34권1호
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• The Korean Journal of Legal Medicine
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• 제41권2호
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• pp.32-40
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• 2017

In addition to identifying genetic differences between target populations, it is also important to determine the impact of genetic differences with regard to the respective target populations. In recent years, there has been an increasing number of cases where this approach is needed, and thus various statistical methods must be considered. In this study, genetic data from populations of Southeast and Southwest Asia were collected, and several statistical approaches were evaluated on the Y-chromosome short tandem repeat data. In order to develop a more accurate and practical classification model, we applied gradient boosting and ensemble techniques. To infer between the Southeast and Southwest Asian populations, the overall performance of the classification models was better than that of the decision trees and regression models used in the past. In conclusion, this study suggests that additional statistical approaches, such as data mining techniques, could provide more useful interpretations for forensic analyses. These trials are expected to be the basis for further studies extending from target regions to the entire continent of Asia as well as the use of additional genes such as mitochondrial genes.

• 상하수도학회지
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• 제35권1호
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• pp.83-91
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• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• 한국농공학회논문집
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• 제63권1호
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• pp.11-25
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• 2021

The objective of this study was to analyze the impact of activation functions on flood forecasting model based on Artificial neural networks (ANNs). The traditional activation functions, the sigmoid and tanh functions, were compared with the functions which have been recently recommended for deep neural networks; the ReLU, leaky ReLU, and ELU functions. The flood forecasting model based on ANNs was designed to predict real-time runoff for 1 to 6-h lead time using the rainfall and runoff data of the past nine hours. The statistical measures such as R2, Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), the error of peak time (ETp), and the error of peak discharge (EQp) were used to evaluate the model accuracy. The tanh and ELU functions were most accurate with R2=0.97 and RMSE=30.1 (㎥/s) for 1-h lead time and R2=0.56 and RMSE=124.6~124.8 (㎥/s) for 6-h lead time. We also evaluated the learning speed by using the number of epochs that minimizes errors. The sigmoid function had the slowest learning speed due to the 'vanishing gradient problem' and the limited direction of weight update. The learning speed of the ELU function was 1.2 times faster than the tanh function. As a result, the ELU function most effectively improved the accuracy and speed of the ANNs model, so it was determined to be the best activation function for ANNs-based flood forecasting.

• 대한토목학회논문집
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• 제28권4B호
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• pp.429-439
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• 2008

본 연구에서는 잠제의 제원(천단수심, 천단폭, 사면경사, 제장)에 따라 해빈상을 전파하는 풍파의 처오름 높이 변화특성을 논의하기 위하여, 기존의 수리모형 실험치와 계산치를 비교 검토를 통해 타당성과 유효성이 검증된 수치모델로서, 파 투과성구조물 해빈의 상호간섭을 직접 해석할 수 있는 3D-수치모델(LES-WASS-3D; 허와 이, 2007)을 이용하여 잠제 2기의 제원에 따른 수치시뮬레이션을 실시하였다. 그 결과 천단수심, 천단폭, 사면경사, 제장의 변화가 연안에서의 처오름 높이에 미치는 영향에 관하여 검토하였으며, 아울러, 잠제 주변의 파고분포 및 상층흐름특성과의 관계에 대해서도 논의하였다.