지속적으로 관찰되어 온 백두산 화산폭발 전조 현상들이 사회적 이슈가 되고 있으며 주변국인 일본의 화산활동 또한 활발한 추세이다. 국내와 500km 이상 떨어진 위 화산들은 국내에 직접적인 피해를 주기 어렵지만 화산 분화와 함께 분출되는 화산재의 경우 국내에 직간접적인 피해를 미칠 수 있다. 화산재 확산대응의 일환으로 수치해석 모델이 국내외로 사용되고 있으며 각 수치해석 모델 은 사용된 수치해석 방법에 따라 한계가 있다. 본 논문에서는 라그랑지안 방법을 기반으로 한 PUFF-UAF 모델을 분석하였으며, 초기 입자의 수에 대한 의존성의 문제점과 많은 입자개수를 사용함에도 불구하고 나타나는 화산재 농도 예측의 부정확성에 대한 문제점을 제기하였다. 이에 본 논문 연구를 통하여 라그랑지안 기법의 전산효용성을 이용하고 나타난 문제점을 해결하기 위하여 PUFF-UAF 모 델의 결과에 가우시안 확산 모델을 적용하여 결과를 보완하는 PUFF-Gaussian 모델을 개발하였다. 실제 화산분화로 부터 관측된 결과 와 본 연구로 예측된 결과를 비교한 결과 본 연구에서 제안한 방법의 효용성을 보였다.

Many reinforced concrete (RC) buildings constructed prior to 1980's lack important features guaranteeing ductile response under earthquake excitation. Structural components in such buildings, especially columns, do not satisfy the reinforcement details demanded by current seismic design codes. Columns with deficient reinforcement details may suffer significant damage when subjected to cyclic lateral loads. They can also experience rapid lateral strength degradation induced by shear failure. The objective of this study is to accurately simulate the load-deformation response of RC columns experiencing shear failure. In order to do so, model parameters are calibrated to the load-deformation response of 40 RC column specimens failed in shear. Multivariate stepwise regression analyses are conducted to develop the relationship between the model parameters and physical parameters of RC column specimens. It is shown that the proposed predictive equations successfully estimated the model parameters of RC column specimens with great accuracy. The proposed equations also showed better accuracy than the existing ones.

증발산은 순복사 에너지를 사용하여 잠열의 형태로 수증기를 대기 중으로 수송함으로써 지구에너지 순환에 있어 중요한 요소 중의 하나이며, 증발산량은 지표유출의 두 배 정도로서 지구 물 수지에서 차지하는 비중이 매우 크다. 증발산의 지상관측은 지점에 국한되기 때문에 공간연속면 상에서의 증발산량 산출을 위하여 격자형 기상자료와 위성자료를 이용한 모델링이 오랫동안 이루어져왔다. PM(Penman-Monteith) 방정식에 기초한 METRIC(Mapping Evapotranspiration with Internalized Calibration) 모델이나 PT(Priestley-Taylor) 방정식을 이용한 MS-PT(Modified Satellite-based Priestley-Taylor) 모델 등이 주로 사용되어 왔으나, 또 하나의 대안으로서 본 연구에서는 최근 부각되고 있는 딥러닝 기법인 DNN(deep neural network)을 이용한 증발산 모델링을 수행하였다. 은닉층 구조, 손실함수, 옵티마이저, 활성화함수, L1/L2 정규화, 드롭아웃 비율 등의 최적화 과정을 거쳐서 수립한 DNN 모델은 RMSE = 0.326mm/day, 상관계수 = 0.975의 매우 양호한 정확도를 나타내었다. 이는 DNN 최적화와 함께, 국지예보모델과 위성자료로부터 증발산 기작에 관여하는 인자들을 선택하여 입력자료로 적절히 사용하였기 때문이기도 하다. 향후과제로서 훈련자료의 종류와 양을 증가시켜서 DNN 모델을 보다 정교화하는 것은 반드시 필요하다고 사료된다.

An elliptic blending Reynolds stress transport equation model for Newtonian fluids has been extended to predict polymer-induced drag reduction FENE-P fluids. The conformation tensor equation which is related to the polymer stress is adopted from the model form of Resende et al., and the models of redistribution and dissipation rate terms for the Reynolds stress transport equation are considered by the elliptic blending equation. Also, the new model terms for viscoelastic turbulent transport and viscoelastic dissipation in the Reynolds stress transport equation are introduced to consider the polymer additives effect. The prediction results are directly compared to the DNS data to assess the performance of the present model predictions.

PURPOSES : Previously, airport concrete pavement was designed using only aircraft gear loading without consideration of environmental loading. In this study, a multiple-regression model was developed to predict maximum tensile stress of airport concrete pavement based on finite element analysis using both environmental and B777 aircraft gear loadings. METHODS: A finite element model of airport concrete pavement and B777 aircraft main gears were fabricated to perform finite element analysis. The geometric shape of the pavement, material properties of the layers, and the loading conditions were used as input parameters for the finite element model. The sensitivity of maximum tensile stress of a concrete slab according to the variation in each input parameter was investigated by setting the ranges of the input parameters and performing finite element analysis. Based on the sensitivity analysis results, influential factors affecting the maximum tensile stress were found to be used as independent variables of the multi regression model. The maximum tensile stresses predicted by both the multiple regression model and finite element model were compared to verify the validity of the model developed in this study. RESULTS: As a result of the finite element analysis, it was determined that the maximum tensile stress developed at the bottom of the slab edge where gear loading was applied in the case that environmental loading was small. In contrast, the maximum tensile stress developed at the top of the slab center situated between the main gears in the case that the environmental loading got larger. As a result of the sensitivity analysis and multiple regression analysis, a maximum tensile stress prediction model was developed. The independent variables used included the joint spacing, slab thickness, the equivalent linear temperature difference between the top and bottom of the slab, the maximum take-off weight of a B777 aircraft, and the composite modulus of the subgrade reaction. The model was validated by comparing the predicted maximum tensile stress to the result of the finite element analysis. CONCLUSIONS : The research shown in this paper can be utilized as a precedent study for airport concrete pavement design using environmental and aircraft gear loadings simultaneously.

본 연구에서는 한국기상청의 장기예측시스템 현업모형인 GloSea5의 성층권 극소용돌이 강화사례에 대한 예측성 을 진단 및 검증하였다. 진단에 사용된 통계량은 이상상관계수(ACC, Anomaly Correlation Coefficient)와 평균제곱근 예측성(MSSS, Mean Squared Skill Score)으로, 1991-2010년간 발생한 14개 극소용돌이 강화사례에 대한 GloSea5의 예측성한계는 ACC를 기준으로 13.6일, MSSS를 기준으로 18.5일로 나타났다. 모형의 평균제곱오차(MSE, Mean Squared Error)의 각 성분을 정량적으로 비교분석한 결과, 예측성을 저하시키는 가장 큰 요인은 맴돌이(에디)오차로, 그 중 에디의 위상오차가 전체 예측오차의 큰 부분을 차지하는 것으로 나타났다. 또한 극소용돌이 현상이 수평적으로 큰 규모를 가지는 만큼 동서파수 1의 에디와 관련한 오차가 더 작은 규모의 에디에 비해 가장 크게 예측오차에 기여하는 것으로 나타났다. 한편, 분석한 사례들에 대하여 GloSea5의 대류권 순환에 대한 예측성은 성층권 예측성과는 큰 관련이 없는 것으로 나타났다. 이는 단순히 GloSea5 모형이 성층권-대류권 접합과정을 잘 모의하지 못하기 때문에 나타난 결과로 유추할 수 있다. 하지만, 극소용돌이 강화에 의한 영향에 비해 대류권에서 내부변동성의 절대적인 크기가 종종 크게 나 타난다는 점을 감안하면, 모형에서 성층권-대류권 접합을 잘 모의하고 있더라도 극소용돌이 강화 자체만의 영향이 뚜렷 하게 나타나지 않았을 가능성 또한 간과하면 안 될 것이다.

Circular leaf spot(CLS) disease causes considerable economic damage to persimmon(Diospyros kaki) in South Korea. Mycosphaerella nawae ascospores are the primary inoculum for CLS epidemics. In this study, we investigated the seasonal fluctuation of spore release and its relationship to environmental factors, based on spore trapping. We evaluated the seasonal pattern of released M. nawae ascospores in South Korea. During three persimmon growing seasons(2010 to 2012), we recorded the release of ascospores in two regions, Jinju and Gimhae, which are major producing regions of sweet persimmon in South Korea. The release of CLS ascospores was from the first week of May to the end of July. The maximum release of spores was observed in late June. A computer model used accumulated degree days to simulate ascospore release. The overall mean accumulated degree days, from 01 May to 50% ascospore release for the observed data(1174℃ days), was not significantly different from the predicted value of 1144℃ days. The mean differences between predicted and observed release percentages for the sampling periods were not significantly different from zero.

본 연구는 사과 ‘후지’의 과실 횡경 예측 모델을 개발 하고 검증하고자, 수원지역에서 2000년부터 2014년까지의 연차간 과실 생장과 기상을 포함한 환경요인들을 분석하였다. 2000년부터 2014년까지의 평균 만개일은 4월 28일이었고, 만개 후 수확일까지의 성숙일수는 평균 181 일이었다. 만개 후 약 36일 이후부터의 과실 생장은 단일 S자 곡선이었으며, 과실 횡경에 영향을 미치는 환경 요인들은 BIO2, 9월강수량, 4월 최고·최저·평균기온, 8월 최저기온, 그리고 4월의 생육도일이었다. 그 중에서, 서로 간의 요인이 교차 상관관계를 나태내지 않는 BIO2와 9월 강수량을 각각 다른 요인들과 조합하여 모델을 만들었다. 선발된 모델 중에서 AICc가 92.61이며 보정된 R2 값이 0.53으로 가장 적합도가 높았던 모델을 선택하였으 며 그 최종 모델식은 19.33095+(5.76242×BIO2)-(0.01891×9월강수량)+(2.63046×4월최저온도)이었다. 이 모델을 2000 년부터 2014년까지의 실측치와 비교하였는데, 사과 ‘후지’과실 횡경의 실측치와 예측치의 평균차이는 ±2.9mm, 표준편차는 3.54였다.

PURPOSES : The purpose of this study is to develop a regression model to predict the International Roughness Index(IRI) and Surface Distress(SD) for the estimation of HPCI using Expressway Pavement Management System(PMS).METHODS : To develop an HPCI prediction model, prediction models of IRI and SD were developed in advance. The independent variables considered in the models were pavement age, Annual Average Daily Traffic Volume(AADT), the amount of deicing salt used, the severity of Alkali Silica Reaction(ASR), average temperature, annual temperature difference, number of days of precipitation, number of days of snowfall, number of days below zero temperature, and so on.RESULTS : The present IRI, age, AADT, annual temperature differential, number of days of precipitation and ASR severity were chosen as independent variables for the IRI prediction model. In addition, the present IRI, present SD, amount of deicing chemical used, and annual temperature differential were chosen as independent variables for the SD prediction model.CONCLUSIONS: The models for predicting IRI and SD were developed. The predicted HPCI can be calculated from the HPCI equation using the predicted IRI and SD.

Impact damage tolerance is an important design requirement for composite structures. In this study, the effect of post impact damage and hole size of the composite sandwich skin / sandwich with core specimen on compressive strength of the laminate was analyzed. Three specimen tests were performed in this research. Two tests were carried out on pure bending test specimens subjected to impact damage to the skin and specimen with a hole in one of its skin as a damage. Through this test, we compared the reduction of compressive strength due to the size of skin damage and the size of the hole. Also, core-free specimen with an open hole under uniaxial loading were tested to produce reference data for comparison with the series tested earlier. As results of the tests, the sandwich beams with damage size and open hole are almost identical, and we concluded that the prediction of compressive strength reduction after impact of the sandwich skin structure can be predicted using an analytical model assuming skin open hole as impact inputs.

In this study, a model was developed to predict for Disinfection By-Products (DBPs) generated in water supply networks and consumer premises, before and after the introduction of advanced water purification facilities. Based on two-way ANOVA, which was carried out to statistically verify the water quality difference in the water supply network according to introduce the advanced water treatment process. The water quality before and after advanced water purification was shown to have a statistically significant difference. A multiple regression model was developed to predict the concentration of DBPs in consumer premises before and after the introduction of advanced water purification facilities. The prediction model developed for the concentration of DBPs accurately simulated the actual measurements, as its coefficients of correlation with the actual measurements were all 0.88 or higher. In addition, the prediction for the period not used in the model development to verify the developed model also showed coefficients of correlation with the actual measurements of 0.96 or higher. As the prediction model developed in this study has an advantage in that the variables that compose the model are relatively simple when compared with those of models developed in previous studies, it is considered highly usable for further study and field application. The methodology proposed in this study and the study findings can be used to meet the level of consumer requirement related to DBPs and to analyze and set the service level when establishing a master plan for development of water supply, and a water supply facility asset management plan.

Through an integration of seasonal climate forecasts (SCF) and rice pest epidemiological models, a potential risk forrice pest epidemics can be predicted even before a cropping season starts. The objective of the study was to developand evaluate an epidemiological “rtdSim” model for tungro, a vector-born rice disease, aiming at predicting a seasonaltungro risk in the Bicol Region of the Philippines. Predicting tungro epidemics requires many components explaining thecomplex nature of the three-cornered pathosystems (virus, vector, and host) and their interactions with environmental variables.The rtdSim model successfully calculated number of rice hills infected with the rice tungro virus through its vector, thegreen leafhopper (GLH). The present study highlights the potential for developing a climate-based early warning systemfor rice pests, thus allowing better decision-making on a seasonal level.

Invasive pests have posed an ecological threat as climate change has been accelerated, suggesting early prediction ofinvasive pests is required to minimize damages by them. As one of predictive tools, CLIMEX has been effectively usedin a few regions, including US, Australia, and Europe. It allows us to predict a species distribution on a local area inresponse to climatic conditions: and thus, potential distribution of invasive species, risk assessment of agricultural pests,and suitability of biological control agents have been tested by CLIMEX. In this study, we introduced how to use CLIMEXfor predicting a species distribution differed by climate change in terms of its functions, required data, and examplesof its application.

본 연구진은 도로함몰 동공평가를 위한 선행연구를 바탕으로 LFWD 장비를 이용한 건전구간과 비건전구간의 처짐값을 비교하여 동공 유무 예측식을 개선하였다. 기존 연구의 경우, 도로함몰이 발생한 구간에서의 건전 구간과 비전건 구간의 도로의 처짐을 비교한 결과로 처짐비가 1.5∼2.5에 수렴하는 것으로 나타났으며. 대부분의 깊이비가 1.5이내의 제한을 가지고 있음을 알 수 있었다 또한 기존의 DB구간의 데이터가 아닌 타 구간의 데이터를 기존에 제시한 동공규모 관계식에 적용한 결과 동공규모 예측 시에 오차가 큼을 알 수 있었다. 본 연구진은 이러한 오차의 이유로 크게 두 가지의 이유로 구분하여 개선하고자 하였다. 첫 번째로, 기존 제안식에서 고려하지 않은 함몰평가 인자가 존재 할 수 있다는 점이었다. 따라서 본 연구진은 제안식 안의 여러 인자 분석을 진행하였으며, 분석결과 현재 처짐비의 제곱값과 동공깊이를 추가 고려한 인자로 채택하였을 때, 동공규모 예측을 개선할 수 있었다. 두 번째로는 깊이비가 1.5이상인 심도가 깊은 곳일 경우 건전구간과 비건전구간의 LFWD(Light Falling Weight Deflectometer) 처짐 값의 차이가 상이하지 않았다. 이에 처짐값의 차이를 비교 분석하여 깊이비가 1.5이상인 도로함몰에서의 LFWD 처짐값을 이용한 동공 존재 예측이 가능한지 여부에 대한 지속적인 연구가 필요함을 알 수 있었다.

최근 국내외에서는 수질안정성 향상 및 부지면적 저감을 위해 막여과 공정도입이 활발한 추세이며 특히, 정수처리 분야에서는 정밀여과(Microfiltration) 및 한외여과(Ultrafiltration) 공정이 많이 적용되고 있다. 막여과 공정의 경제성 향상을 위해서는 세정 시점의 예측 및 세정 주기 연장이 매우 중요한 요소이다. 따라서, 본 연구에서는 인공신경망(Artificial neural network)을 활용하여 UF 공정차압(Transmembrane pressure) 예측 모델을 개발하고자 한다. 입력변수로는 유입수 온도, pH, 탁도 등의 일평균값을 이용하였다.

The objective of this study was to select a model showing high-levels of interpretability which is high in R-squared value in terms of predicting the yield in the mixed pasture using the factors of fertilization, seeding rate and years after pasture establishment in steps, as well as the climate as a basic factor. The processes of constructing the yield prediction model for the mixed pasture were performed in the sequence of data collection (forage and climatic data), preparation, analysis, and model construction. Through this process, six models were constructed after considering climatic variables, fertilization management, seeding rates, and periods after pasture establishment years in steps, thereafter the optimum model was selected through considering the coincidence of the models to the forage production theories. As a result, Model VI (R squared = 53.8%) including climatic variables, fertilization amount, seeding rates, and periods after pasture establishment was considered as the optimum yield prediction model for mixed pastures in South Korea. The interpretability of independent variables in the model were decreased in the sequence of climatic variables(24.5%), fertilization amount(17.8%), seeding rates(10.7%), and periods after pasture establishment(0.8%). However, it is necessary to investigate the reasons of positive correlation between dry matter yield and days of summer depression (DSD) by considering cultivated locations and using other cumulative temperature related variables instead of DSD. Meanwhile the another research about the optimum levels of fertilization amounts and seeding rates is required using the quadratic term due to the certain value-centered distribution of these two variables