This study performed the seismic response analysis of an LNG storage tank supported by a disconnected piled raft foundation (DPRF) with a load transfer platform (LTP). For this purpose, a precise analytical model with simultaneous consideration of Fluid-Structure Interaction (FSI) and Soil-Structure Interaction (SSI) was used. The effect of the LTP characteristics (thickness, stiffness) of the DPRF system on the seismic response of the superstructure (inner and outer tanks) and piles was analyzed. The analytical results were compared with the response of the piled raft foundation (PRF) system. The following conclusions can be drawn from the numerical results: (1) The DPRF system has a smaller bending moment and axial force at the head of the pile than the PRF system, even if the thickness and stiffness of the LTP change; (2) The DPRF system has a slight stiffness of the LTP and the superstructure member force can increase with increasing thickness. This is because as the stiffness of the LTP decreases and the thickness increases, the natural frequency of the LTP becomes closer to the natural frequency of the superstructure, which may affect the response of the superstructure. Therefore, when applying the DPRF system, it is recommended that the sensitivity analysis of the seismic response to the thickness and stiffness of the LTP must be performed.

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier’s abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

PURPOSES: Evaluation of input parameters determination procedure for dynamic analysis of aggregates in DEM. METHODS: In this research, the aggregate slump test and angularity test were performed as fundamental laboratory tests to determine input parameters of spherical particles in DEM. The heights spreads, weights of the simple tests were measured and used to calibrate rolling and static friction coefficients of particles. RESULTS : The DEM simulations with calibrated parameters showed good agreement with the laboratory test results for given dynamic condition. CONCLUSIONS: It is concluded that the employed calibration method can be applicable to determine rolling friction coefficient of DEM simulation for given dynamic conditions. However, further research is necessary to connect the result to the behavior of aggregate in packing and mixing process and to refine static friction coefficient.

국내 심부지질환경조건을 반영한 처분안전성 평가에 필요한 입력자료를 제공하기위해, 그 동안 국내 지하시험시설(KURT)환경조건에서 많은 실험을 수행해 왔다. 안전성평가코드에 사용되는 많은 입력변수 들 중 중요성이 부각되는 입력변수들을 선정하여, 각 변수별로 수집한 자료를 통계처리를 하여 값 분포 특성을 기술하고, 외국자료 값과 비교평가를 통해 값의 타당성을 검토하였다. 다룬 입력변수로서 용기물 성분야에서 용기수명, 초기파손률을, 완충재물성분야에서는 핵종용해도, 완충재의 공극률, 밀도, 확산계 수, 핵종분배계수를, 암반 및 원계영역에서는 수리전도도, 지하수유속, 핵종분배계수, 확산깊이, 암반균 열폭, 주지하수유동통로까지 거리, 핵종이동오염운의 너비 등이다.

To prevent increasing instances of heat-related illnesses due to heat waves generated by climate change, a customized thermal environment index should be developed for outdoor workers. In this study, we conducted sensitivity analysis of the Masan harbor during a heat wave period (August 9th to 15th, 2013) using the MENEX model with metabolic rate and clothing-insulation data, in order to obtain realistic information about the thermal environment. This study shows that accurate input data are essential to gather information for thermophysiological indices (PST, DhR, and OhR). PST is sensitive to clothing insulation as a function of clothing. OhR is more sensitive to clothing insulation during the day and to the metabolic rate at night. From these results, it appears that when exposed to high-temperature thermal environments in summer, wearing highly insulated clothing and getting enough rest (to lower the metabolic rate) can aid in preventing heat-related illnesses. Moreover, in the case of high-intensity harbor work, quantification of allowed working time (OhR) during heat waves is significant for human health sciences.

본 연구에서는 극한강우를 고려한 산사태 및 토석류 재해 실시간 예측 및 대응을 위하여 필요한 원천기술들 중, 토석류 전이규준 개발 및 이를 통한 발생부 토사량 추정 기법, 그리고 전이 이후 토석류 확산의 정량적 예측 분석에 결정적으로 중요한 인자들인 연행 증가율 및 유변학적 인자들에 대한 입력값을 결정하는 기법에 대하여 연구하였다. 또한 본 원천기술들을 실시간으로 연계 및 적용 가능하도록, GIS(Geographic Information System) 및 Web 기반의 산사태 및 토석류 관련 D/B 시스템을 경기도 용인지역에 시범 구축하였다. 이 시스템은 클라우드 환경에서 산사태 및 토석류 유발인자 관련 지리 공간적 컨텐츠를 저장하고 게시하여 Web 상에서 D/B를 공동으로 활용하고 분석할 수 있다. 토석류 전이규준에서 발생부 토사량 추정으로 이어지는 연구는 두 가지 접근법을 통해 이루어졌다. 이는 지형학적 인자들을 바탕으로 통계학적인 규준값을 설정하는 접근법, 그리고 국내의 전형적인 사면파괴 형태인 강우침투로 인한 침윤전선(wetting depth)의 하강과 같은 실제 메커니즘을 고려하는 물리적 기반의 접근법 등이다. 한 편, 국내 과거 토석류 발생 지역 데이터들을 바탕으로 다중 회귀분석을 실시하여 DAN3D 모델을 통한 토석류 확산 예측 분석에서의 연행 증가율에 대한 추정식을 개발하였다. 본 연구결과들은 궁극적으로 국내의 실시간 산사태 재해 예·경보 및 종합적 리스크 평가 시스템 구축을 위한 핵심 구성 원천기술들로 활용될 수 있을 것으로 기대된다.

In order to how well predict ISCST3(Industrial Source Complex Short Term version 3) model dispersion of air pollutant at point source, sensitivity was analysed necessary parameters change. ISCST3 model is Gaussian plume model. Model calculation was performed with change of the wind speed, atmospheric stability and mixing height while the wind direction and ambient temperature are fixed. Fixed factors are wind direction as the south wind(180˚) and temperature as 298 K(25℃). Model's sensitivity is analyzed as wind speed, atmospheric stability and mixing height change. Data of stack are input by inner diameter of 2m, stack height of 30m, emission temperature of 40℃, outlet velocity of 10m/s. On the whole, main factor which affects in atmospheric dispersion is wind speed and atmospheric stability at ISCST3 model. However it is effect of atmospheric stability rather than effect of distance downwind. Factor that exert big influence in determining point of maximum concentration is wind speed. Meanwhile, influence of mixing height is a little or almost not.

신호교차로는 도로조건, 교통조건, 신호조건 능 방대한 입력자료를 바탕으로 용량분석을 시행하고 이 과정을 토대로 주요 효과척도인 지체를 산정하여 신호교차로의 서비스 수준을 판단한다. 하지만 이러한 용량 및 서비스 수준 결정에 있어 바탕이 되는 현장 데이터(회전 교통량, 도로의 기하구조, 신호시간, 접근로 구배, 중차량비, 첨두시간계수, 차량도착형태 등) 입력자료의 불확실성으로 인해 초래되는 결과의 오류에 대해서는 고려되지 않고 있는 실정이다. 이로 인해 추정된 용량 및 서비스수준에 대한 신뢰성을 검증할 수 없는 문제를 내포하고 있다. 따라서 본 연구에서는 해당 교차로 접근로의 교통량과 중차량 비율 및 도로의 기하구조 등 입력자료의 불확실성이 용량해석과 서비스수준 결정에 끼치는 영향을 고려해보고 이틀에 의한 영향을 최소화할 수 있는 방안을 제시하고자 한다.

This study has two objectives. One is developing the runoff model for Hoe-Dong Reservoir basin located at the upstream of Su-Young River in Pusan. To develop the runoff model, basic hydrological parameters - curve number to find effective rainfall, and storage coefficient, etc. - should be estimated. In this study, the effective rainfall was calculated by the SCS method, and the storage coefficient used in the Clark watershed routing was cited from the report of P.E.B. The other is the derivation of transfer function for Hoe-Doug Reservoir basin. The linear, discrete, input-output model which contained six parameters was selected, and the parameters were estimated by the least square method and the correlation function method, respectively. Throughout this study, rainfall and flood discharge data were based on the field observation in 1981.8.22 - 8.23 (typhoon Gladys). It was observed that the Clark watershed routing regenerated the flood hydrograph of typhoon Gladys very well, and this fact showed that the estimated hydrological parameters were relatively correct. Also, the calculated hydrograph by the linear, discrete, input-output model showed good agreement with the regenerated hydrograph at Hoe-Dong Dam site, so this model can be applicable to other small urban areas.