PURPOSES: The aim of this study is to analyze overloading control effectiveness of enforcing overweighted vehicles using HS-WIM (High-Speed Weigh-in-Motion) at main lane of expressway. METHODS: To analyze the weight distribution statistically, HS-WIM system should has an appropriate weighing accuracy. Thus, the weighing accuracy of the two HS-WIM systems was estimated by applying European specifications and ASTM (American Standards for Testing and Materials) for WIM in this study. Based on the results of accuracy test, overweight enforcement system has been operated at main lanes of two expressway routes in order to provide weight informations of overweighted vehicle in real time for enforcement squad. To evaluate the overloading control effectiveness with enforcement, traffic volume and axle loads of trucks for two months at the right after beginning of the enforcement were compared with data set for same periods before the enforcement. RESULTS: As the results of weighing accuracy test, both WIM systems were accepted to the most precise type that can be useful to applicate not only statistical purpose but enforcing on overweight vehicles directly. After the enforcement, the rate of overweighted trucks that weighed over enforcement limits had been decreased by 27% compared with the rate before the enforcement. Especially, the rate of overweighted trucks that weighed over 48 tons had been decreased by 91%. On the other hand, in counterpoint to decrease of the overweighted vehicle, the rate of trucks that weighed under enforcement limits had been increased by 7%. CONCLUSIONS: From the results, it is quite clear that overloading has been controlled since the beginning of the enforcement.

인삼 재배포장에서 병원균인 Fusarium spp.의 밀도와 근부에 영향을 미치는 토양환경요인을 조사하였다. 2년근 포장의 토양을 토성별로 분류하여 결주율, Fusarium spp. 밀도, 화학적 성질을 비교해 본 결과 점토 함량이 들었고 결주율과 질산태 질소는 줄어드는 경향이었으며 다른 성질은 뚜렷한 차이가 없었다. 6년근 포장에서는 근부율과 유효인산 함량이 정의 상관(r=0.3162, p=0.05)을 보였으며 Fusarium spp. 밀도와 Streptomyces spp. 밀도는 고도의 부의 상관(r=-0.3976, p=0.01)을 나타내었다.

In meteorological data, various studies are being conducted to improve the prediction performance of rainfall with irregular patterns, unlike temperature and solar radiation with certain patterns. Especially in the case of the short-term forecast model for Dong-Nae Forecasts provided by the Korea Meteorological Administration (KMA), forecast data are provided at 6-hour intervals, and there is a limit to analyzing the impact of disasters. In this study, Hydrological Quantitative Precipitation Forecast (HQPF) information was generated by applying the machine learning method to Local ENsemble prediction system (LENS), Radar-AWS Rainrates (RAR), AWS and ASOS observation data and Dong-Nae Forecast provided by the KMA. Through the preprocessing process, the temporal and spatial resolutions of all the data were converted to the same resolution, and the predictor of machine learning was derived through the factor analysis of the predictor. Considering the processing speed and expandability, the XGBoost method of machine learning was applied, and the Probability Matching (PM) method was applied to improve the prediction accuracy of heavy rainfall. As a result of evaluating the HQPF performance produced for 14 heavy rainfall events that occurred in 2020, it was found that the predicted performance of HQPF was improved quantitatively and qualitatively.