This study presents distribution of naturally occurring radioactive materials in groundwater in Jeju island. Radon (222Rn) and potassium (40K) concentrations were performed by using Liquid Scintillation Counter and Ion Chromatograph respectively. In addition, the activities of uranium and thorium nuclides were analyzed by Inductively Coupled Plasma Mass Spectroscopy. Groundwater samples were collected from 9 sites of water intake facilities for wide area supply in Jeju island from September 2022 to September 2023. The 40K concentrations of groundwater ranged between 0.050 and 0.400 Bq·L-1. The radon concentrations in groundwater were in the range of 0 to 60 Bq L-1, and there was no groundwater exceeding the range of 148 Bq L-1 proposed by the US EPA. The distribution of uranium and thorium in groundwater varied from 0 to 500 ng L-1 and 0 to 2.4 ng L-1, respectively. The concentrations of uranium did not exceed 30 μg L-1, thresholds indicated by the US EPA. By analyzing the concentrations of 40K, 222Rn, 238U and 232Th, the annual effective dose of residents can be assessed. The evaluated residents’ effective dose from natural radionuclides due to intake of drinking water is less than the recommended value of 100 μSv y-1. Consequently, this study indicates that the cancer risks of the residents in Jeju island from naturally occurring radioactive materials ingested with water is insignificant.

In order to apply indirect methods (such as scaling factors) to assess the radionuclide inventory of waste generated by nuclear power plants, it is essential to first evaluate the correlation coefficient between key radionuclides and those that are difficult to measure (DTM). The benchmark for the correlation coefficient (r) applied in indirect assessments is set at 0.6, and its significance can vary based on both its value and the size of the dataset. For instance, deriving a correlation coefficient using three data points versus utilizing a dataset with a hundred data points would yield different implications. This study addresses the variance in correlation coefficients based on data selection and presents a methodology for validating the significance of these coefficients. Additionally, we will discuss how these variances may impact the results of indirect assessments, such as scaling factor evaluations.

When an earthquake occurs, the severity of damage is determined by natural factors such as the magnitude of the earthquake, the epicenter distance, soil properties, and type of the structures in the affected area, as well as the socio-economic factors such as the population, disaster prevention measures, and economic power of the community. This study evaluated the direct economic loss due to building damage and the community’s recovery ability. Building damage was estimated using fragility functions due to the design earthquake by the seismic design code. The usage of the building was determined from the information in the building registrar. Direct economic loss was evaluated using the standard unit price and estimated building damage. The standard unit price was obtained from the Korean Real Estate Board. The community’s recovery capacity was calculated using nine indicators selected from regional statistical data. After appropriate normalization and factor analysis, the recovery ability score was calculated through relative evaluation with neighboring cities.

증산은 적정 관수 관리에 중요한 역할을 하므로 수분 스트레스에 취약한 토마토와 같은 작물의 관개 수요에 대한 지식이 필요하다. 관수량을 결정하는 한 가지 방법은 증산량을 측정하는 것인데, 이는 환경이나 생육 수준의 영향을 받는다. 본 연구는 분단위 데이터를 통해 수학적 모델과 딥러닝 모델을 활용하여 토마토의 증발량을 추정하 고 적합한 모델을 찾는 것을 목표로 한다. 라이시미터 데이터는 1분 간격으로 배지무게 변화를 측정함으로써 증산 량을 직접 측정했다. 피어슨 상관관계는 관찰된 환경 변수가 작물 증산과 유의미한 상관관계가 있음을 보여주었다. 온실온도와 태양복사는 증산량과 양의 상관관계를 보인 반면, 상대습도는 음의 상관관계를 보였다. 다중 선형 회귀 (MLR), 다항 회귀 모델, 인공 신경망(ANN), Long short-term memory(LSTM), Gated Recurrent Unit(GRU) 모델을 구 축하고 정확도를 비교했다. 모든 모델은 테스트 데이터 세트에서 0.770－0.948 범위의 R2 값과 0.495mm/min－ 1.038mm/min의 RMSE로 증산을 잠재적으로 추정하였다. 딥러닝 모델은 수학적 모델보다 성능이 뛰어났다. GRU 는 0.948의 R2 및 0.495mm/min의 RMSE로 테스트 데이터에서 최고의 성능을 보여주었다. LSTM과 ANN은 R2 값이 각각 0.946과 0.944, RMSE가 각각 0.504m/min과 0.511로 그 뒤를 이었다. GRU 모델은 단기 예측에서 우수한 성능 을 보였고 LSTM은 장기 예측에서 우수한 성능을 보였지만 대규모 데이터 셋을 사용한 추가 검증이 필요하다. FAO56 Penman-Monteith(PM) 방정식과 비교하여 PM은 MLR 및 다항식 모델 2차 및 3차보다 RMSE가 0.598mm/min으로 낮지만 분단위 증산의 변동성을 포착하는 데 있어 모든 모델 중에서 가장 성능이 낮다. 따라서 본 연구 결과는 온실 내 토마토 증산을 단기적으로 추정하기 위해 GRU 및 LSTM 모델을 권장한다.

The blockage rate for three kinds of nets commonly used in agricultural facilities was assessed by using the image acquisition and its relevant processing. By using both empirical relations presented by Idel’chik and Richards and Robinson, and the blockage rate obtained from the image processing, the pressure drop through the nets was predicted and also compared with wind tunnel experiment results. The results of the study showed that the blockage rate of the net was discriminated according to such factors as the magnitude of nets, the existence of inside threads, the thickness and number of threads. In addition, the blockage rate for the incident angle of 0° when the wind blew at the front had the range of 0.22－0.29 (0.22－0.32 when considering whole incident angles from 0° to 45° by 15°). For the nets with the blockage rate of about 30% or below, the prediction by the empirical relations of by Idel’chik and Richards and Robinson showed a little higher pressure drop overall than that of the wind tunnel test, but the use of the empirical relations and the blockage rate could be thought of as providing effectively meaningful guidelines for the safe design of agricultural facilities including nets because the wind tunnel test has been tedious and expensive. Further research and potential application on the prediction technique of the pressure drop, regarding both a subtle deformation by the wind and manufacturing methods with regard to the level of knots and the existence of inside threads, needs to be done for the nets with higher blockage rate.

PURPOSES : In this study, a model was developed to estimate the concentrations of particulate matter (PM2.5 and PM10) in expressway tunnel sections. METHODS : A statistical model was constructed by collecting data on particulate matter (PM2.5 and PM10), weather, environment, and traffic volume in the tunnel section. The model was developed after accurately analyzing the factors influencing the PM concentration. RESULTS : A machine learning-based PM concentration estimation model was developed. Three models, namely linear regression, convolutional neural network, and random forest models, were compared, and the random forest model was proposed as the best model. CONCLUSIONS : The evaluation revealed that the random forest model displayed the least error in the concentration estimation model for (PM2.5 and PM10) in all tunnel section cases. In addition, a practical application plan for the model developed in this study is proposed.

PURPOSES : In this study, energy-consuming processes in asphalt plants were evaluated, and the drying and mixing processes were characterized using a thermal equilibrium equation-based model to quantitatively estimate the amount of energy consumed during the production of mixtures in asphalt concrete plants. METHODS : An energy consumption model based on the thermal equilibrium equation was used to estimate the energy consumption of the aggregate drying process that consumes the maximum energy; the energy consumed for material transportation, storage, and operation of other facilities was cited from the literature. The results were compared with the actual results obtained for recycled hot asphalt mixtures and recycled warm mix asphalt mixtures, and a sensitivity analysis was performed by varying the conditions. RESULTS : An analysis of the main processes required to produce asphalt mixtures showed that the water content had the largest impact on energy consumption (approximately 80%). This quantitatively supports the opinion of field practitioners that maximum energy is consumed during aggregate drying. Although some discrepancies were observed, the results were found to be reasonable and within the range of typical measurements. CONCLUSIONS : The thermal energy consumption estimation model provides consistent results that reflect the characteristics of the mixture and can be used to derive the thermal energy consumption rates for individual materials, such as aggregates and binders. This can be used to identify the priorities for process optimization within a plant.

PURPOSES : Previously, the expansion state of the concrete pavement in which AAR occurred could not be determined. Because the current situation has not been evaluated, it has been difficult to prepare an appropriate response. In this study, a method for calculating the expansion amount of concrete pavement using the stiffness damage test (SDT) is proposed. METHODS : The SDT method was examined through a literature review. For the laboratory tests, specimens that generated AAR were produced based on the mix design (2018) of the Korea Expressway Corporation. SDT was used to calculate various mechanical properties, and their correlation with the expansion amount was reviewed. RESULTS : Using the SDT, various mechanical properties(elastic modulus, hysteresis area, plastic deformation, plastic deformation index, stiffness damage index, and nonlinear index) were calculated based on the expansion rate of the AAR. The elastic modulus was evaluated as the best predictor of the expansion rate. Thus, if the elastic modulus is calculated using SDT, a prediction equation can be used to calculate the amount of AAR expansion. This equation will need to be supplemented by further research. CONCLUSIONS : SDT was used to confirm that the expansion state due to the AAR of the concrete pavement could be indirectly evaluated. Among the mechanical properties related to SDT, the elastic modulus was found to be the most suitable for predicting the amount of expansion.

본 연구는 난대상록수종인 종가시나무에 대한 수간곡선식의 도출과 재적표의 작성 및 탄소배출계수를 이용하여 탄소저장, 흡수량을 추정하고자 수행하였다. 분석에 사용된 공시목은 전남, 경남, 제주 등에서 수집된 468본이며, 수간형태를 도출하기 위하여 적용한 수간곡선 모형은 Kozak 모델이다. 이 모델의 적합도는 0.9452, 편의는 0.0807, 추정치 표준오차의 백분율은 1.7145, 평균절대편차는 1.2655로 각각 나타났다. 종가시나무의 개체목 재적은 Kozak 수간곡선 모델에 Smalian 재적식을 적용시켜 산출하였으며, 수고와 흉고직경급별로 재적표를 작성하였다. 그리고 붉가시나무 재적표와 이번에 만든 종가시나무 재적표를 서로 비교한 결과(t-test), 통계적으로 두 집단 간에 차이가 없는 것으로 나타났다. 따라서 이들 재적표는 두 수종 중 하나만 이용하거나, 둘을 하나로 통합하여도 문제가 없을 것으로 판단된다. 한편 종가시나무림의 탄소저장 및 흡수량은 산림바이오소재연 구소 시험림의 조사구를 대상으로 하였다. 이들 조사구를 대상으로 탄소저장 및 흡수량을 계산한 결과, 생육상태가 양호한 시험구(A)에서 탄소저장량 은 93.17 C ton/ha, 그리고 탄소흡수량은 13.14 CO2 ton/ha/yr 인 것으로 나타났다. 반면에 생육이 저조한 시험구(B)의 탄소 저장 및 흡수량은 양호한 시험구보다 약 1/3 정도 낮게 나타났다.