PURPOSES : In this study, surface distress (SD), rutting depth (RD), and international roughness index (IRI) prediction models are developed based on the zones of Incheon and road classes using regression analysis. Regression analysis is conducted based on a correlation analysis between the pavement performance and influencing factors. METHODS : First, Incheon was categorized by zone such as industrial, port, and residential areas, and the roads were categorized into major and sub-major roads. A weather station triangle network for Incheon was developed using the Delaunay triangulation based on the position of the weather station to match the road sections in Incheon and environmental factors. The influencing factors of the road sections were matched Based on the developed triangular network. Meanwhile, based on the matched influencing factors, a model of the current performance of the road pavement in Incheon was developed by performing multiple regression analysis. Sensitivity analysis was conducted using the developed model to determine the influencing factor that affected each performance factor the most significantly. RESULTS : For the SD model, frost days, daily temperature range, rainy days, tropical nights, and minimum temperatures are used as independent variables. Meanwhile, the truck ratio, freeze–thaw days, precipitation days, annual temperature range, and average temperatures are used for the RD model. For the IRI model, the maximum temperature, freeze–thaw days, average temperature, annual precipitation, and wet days are used. Results from the sensitivity analysis show that frost days for the SD model, precipitation days and freeze–thaw days for the RD model, and wet days for the IRI model impose the most significant effects. CONCLUSIONS : We developed a road pavement performance prediction model using multiple regression analysis based on zones in Incheon and road classes. The developed model allows the influencing factors and circumstances to be predicted, thus facilitating road management.

PURPOSES : To efficiently manage pavements, a systematic pavement management system must be established based on regional characteristics. Suppose that the future conditions of a pavement section can be predicted based on data obtained at present. In this case, a more reasonable road maintenance strategy should be established. Hence, a prediction model of the annual surface distress (SD) change for national highway pavements in Gangwon-do, Korea is developed based on influencing factors. METHODS : To develop the model, pavement performance data and influencing factors were obtained. Exploratory data analysis was performed to analyze the data acquired, and the results show that the data were preprocessed. The variables used for model development were selected via correlation analysis, where variables such as surface distress, international roughness index, daily temperature range, and heat wave days were used. Best subset regression was performed, where the candidate model was selected from all possible subsets based on certain criteria. The final model was selected based on an algorithm developed for rational model selection. The sensitivity of the annual SD change was analyzed based on the variables of the final model. RESULTS : The result of the sensitivity analysis shows that the annual SD change is affected by the variables in the following order: surface distress ˃ heat wave days ˃ daily temperature range ˃ international roughness index. CONCLUSIONS : An annual SD change prediction model is developed by considering the present performance, traffic volume, and climatic conditions. The model can facilitate the establishment of a reasonable road maintenance strategy. The prediction accuracy can be improved by obtaining additional data, such as the construction quality, material properties, and pavement thickness.

사출성형공정은 열가소성 수지를 가열하여 유동상태로 만들어 금형의 공동부에 가압 주입한 후에 금형 내에서 냉각시키는 공정으로, 금형의 공동모양과 동일한 제품을 만드는 방법이다. 대량생산이 가능하고, 복잡한 모양이 가능한 공정으로, 수지온도, 금형온도, 사출속도, 압력 등 다양한 요소들이 제품의 품질에 영향을 미친다. 제조현장에서 수집되는 데이터는 양품과 관련된 데이터는 많은 반면, 불량품과 관련된 데이터는 적어서 데이터불균형이 심각하다. 이러한 데이터불균형을 효율적으로 해결하기 위하여 언더샘플링, 오버샘플링, 복합샘플링 등이 적용되고 있다. 본 연구에서는 랜덤오버샘플링(ROS), 소수 클래스 오버 샘플링(SMOTE), ADASTN 등의 소수클래스의 데이터를 다수클래스만큼 증폭시키는 오버샘플링 기법을 활용하고, 데이터마이닝 기법을 활용하여 품질예측을 하고자 한다.

For a plastic diffusion lens to uniformly diffuse light, it is important to minimize deformation that may occur during injection molding and to minimize deformation. It is essential to control the injection molding condition precisely. In addition, as the number of meshes increases, there is a limitation in that the time required for analysis increases. Therefore, We applied machine learning algorithms for faster and more precise control of molding conditions. This study attempts to predict the deformation of a plastic diffusion lens using the Decision Tree regression algorithm. As the variables of injection molding, melt temperature, packing pressure, packing time, and ram speed were set as variables, and the dependent variable was set as the deformation value. A total of 256 injection molding analyses were conducted. We evaluated the prediction model's performance after learning the Decision Tree regression model based on the result data of 256 injection molding analyses. In addition, We confirmed the prediction model's reliability by comparing the injection molding analysis results.

낙동강 하구 기수생태 복원이 본격으로 논의가 진행 전인 2016년까지는 하류 수위의 예측을 위해 하구에서 수km 떨어진 기존 조위관측소(부산 및 가덕도)의 측정 자료를 활용하여 분석을 수행하였지만, 조위와 위상 차이로 인해 예측이 용이하지 않았다. 따라서, 낙 동강 하굿둑 인접 외해역에서 조석 영향을 받는 수위관측치를 이용하여 조석조화분해를 통한 정밀한 조위 예측 산정의 필요성이 대두되 어 본 연구를 수행하였다. 연구의 방법으로는 낙동강하굿둑 인근 외해역에서 10분 간격으로 기간별 관측자료의 저장상태 및 이상자료 유 무를 확인하고, 조석조화분해 프로그램인 TASK2000(Tidal Analysis Software Kit) Package를 이용하여 관측조위와 예측조위를 1대 1 비교하여 회귀상관분석을 수행하였다. 분석 결과, 관측조위와 예측조위간의 상관도는 0.9334로 높게 나타났으며, 당해 연도의 조위예측 분석시 직전 연도의 1년 조석관측 자료를 조화분해하여 산출된 조화상수를 이용하여 조위예측을 실시하면 보다 정확한 결과를 산출할 수 있음을 확인 하였다. 이를 바탕으로 2022년 예측조위를 생성하여 낙동강 하구 기수생태 복원의 해수유입량의 산정에 활용 중이다.

The lane departure warning device can not detect the lane to be driven in the future by sensing the departure of the lane passing by during driving and warning the driver. Considering the safe operation of the truck, it is also expected that the departure of the future lanes according to the dynamic weight and speed of the current truck should be predicted. This study attempted to predict whether or not to deviate from the lanes of curved roads to be driven in the future according to the current dynamic driving weight and speed in consideration of the safe driving of trucks.

오대산국립공원 내 뱀류 로드킬의 발생 경향 파악 및 예방을 위하여 2006-2017년 사이 공원 내에서 발생한 뱀류 로드킬 자료를 확보 및 분석하였고, 잠재적 발생지 예측을 위하여 종분포모델을 제작하였다. 연구기간 동안 뱀류 로드킬 은 600m 대의 양쪽 환경이 산림-수계인 도로에서 가장 많이 발생하였다. 모델링 결과에서 뱀류 로드킬 발생 가능성은 고도 700m 이하의 하천과의 거리가 25m 부근인 완만한 경사의 도로의 로드킬 발생확률이 높게 나타났다. 국립공원 내 주요 로드킬발생 예측지역은 국도 6호선 도로 위 공원 남쪽 경계로부터 약 2.2㎞ 지역과 약 11.7㎞ 지역이, 지방도 446호선 도로 위 공원 남쪽 경계로부터 약 3.44㎞ 지역이었다. 본 연구결과는 해발고도 700m 이하 수계와 인접한 도로 주변에 우선적으로 대체 일광욕 장소, 생태통로 및 도로의 유입을 막는 울타리의 설치가 산림에서 뱀류 로드킬을 줄이는 효과적인 방안이 될 것을 제시한다.

본 논문은 철계형상기억합금(Fe-SMA) stirrup으로 전단보강된 철근콘크리트 보의 전단거동을 예측하기 위한 해석적 연구이다. 전단거동 예측을 위해 변형률 적합조건 및 하중 평형조건을 만족하는 휨해석이 수행되었으며, 이후 수정압축장이론 (MCFT)에 기반한 전단해석이 수행되었다. 부재의 처짐을 계산하기 위해 휨 및 전단에 의해 발생된 처짐이 모두 고려되었다. 제 안된 해석모델의 검증은 Ji et al.(2022)의 실험결과를 비교하여 수행되었다. 비교결과 제안된 해석모델과 실험결과의 극한하중, 극한하중 시 처짐의 오차는 평균 4.77%, 6.62%로 Fe-SMA Stirrup으로 전단보강된 철근콘크리트 보의 전단 거동을 비교적 정확 하게 예측하는 것으로 나타났다.

As the zircaloy cladding absorbs an excessive amount of hydrogen and cooled down under hoop stress, radial hydride may be precipitated by hydride reorientation phenomenon. There have been many previous studies about the threshold stress of the reorientation, but it is known that the quantitative degree of hydride reorientation rather than the threshold is important for the prediction of mechanical properties. A thermodynamic model for Radial Hydride Fraction (RHF) prediction has been developed in this study. The model calculates RHF with respect to temperature, cooling rate, hydrogen content, and applied stresses. Once the cooling rate is given, the solid solution concentration at each temperature is determined by Hydrogen-Nucleation-Growth-Dissolution model. Subsequently, the increment of radial hydride is derived by nucleation and growth theory. The code based on the thermodynamic theory can provide the prediction of RHF under hoop stress, as well as a change in precipitation behavior over time. RHF of the zircaloy cladding in long-term dry storage can be obtained by the implementation of the code and the degradation of the cladding is directly estimated according to the correlation between RHF and mechanical properties. Ongoing experimental validation of the developed model is discussed.