Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

This study was conducted to determine the possibility of estimating the daily mean temperature for a specific location based on the climatic data collected from the nearby Automated Synoptic Observing System (ASOS) and Automated Weather System(AWS) to improve the accuracy of the climate data in forage yield prediction model. To perform this study, the annual mean temperature and monthly mean temperature were checked for normality, correlation with location information (Longitude, Latitude, and Altitude) and multiple regression analysis, respectively. The altitude was found to have a continuous effect on the annual mean temperature and the monthly mean temperature, while the latitude was found to have an effect on the monthly mean temperature excluding June. Longitude affected monthly mean temperature in June, July, August, September, October, and November. Based on the above results and years of experience with climate-related research, the daily mean temperature estimation was determined to be possible using longitude, latitude, and altitude. In this study, it is possible to estimate the daily mean temperature using climate data from all over the country, but in order to improve the accuracy of daily mean temperature, climatic data needs to applied to each city and province.

PURPOSES : The purpose of this study is to compare applicability, explanation power, and flexibility of traffic accident models between estimating model using the statistical method and the machine learning method. METHODS: In order to compare and analyze traffic accident models between model estimated using the statistical method and machine learning method, data acquisition was conducted, and traffic accident models were estimated using statistical methods such as negative binomial regression model, and machine learning methods such as a generalized regression neural network (GRNN). Then, the fitness of model as R2, root mean square error (RMSE), mean absolute percentage error (MAPE), accuracy, etc., were determined to compare the traffic accident models. RESULTS: The results showed that the annual average daily traffic (AADT), speed limits, number of lanes, land usage, exclusive right turn lanes, and front signals were significant for both traffic accident models. The GRNN model of total traffic accidents had been better statistical significant with R2: 0.829, RMSE: 2.495, MAPE: 32.158, and Accuracy: 66.761 compared with the negative binomial regression model with R2: 0.363, RMSE: 9.033, MAPE: 68.987, and Accuracy: 8.807. The GRNN model of injury traffic accidents also showed similar results of model’s statistical significance. CONCLUSIONS: Traffic accident models estimated with GRNN had better statistical significance compared with models estimated with statistical methods such as negative binomial regression model.

풍력 자원의 단기 예측 가능성은 풍력 발전 단지의 경제적 타당성을 평가하는 중요한 요소이다. 본 연구에서는 풍력 자원의 단기 예측 가능성을 향상시키는 방법의 하나로 베이지안 칼만 필터를 후처리 과정으로 적용하였다. 이때 추정된 모델과 관측 데이터의 상관관계를 평가하기 위하여 일정 시간 동안 베이지안 칼만 훈련 기간이 요구된다. 본 연구는 여러 훈련 기간에 따라 예측 특성을 정량적으로 분석하였다. 태백 지역에서는 3일 단기 베이지안 칼만 훈련으 로 기온과 풍속을 예측하는 것이 다른 훈련 기간을 적용할 때보다 우수한 예측 성능을 보였다. 반면 이어도는 6일 이 상의 베이지안 칼만 필터의 훈련 기간을 적용한 경우 가장 좋은 예측 성능을 나타낸다. WRF 예측 성능이 떨어지는 사 례에서 베이지안 칼만 필터의 예측 성능향상이 뚜렷하게 나타나며, 반대로 WRF 예측이 정확한 지점에서는 필터적용에 따른 성능향상 정도가 약한 경향을 가진다.

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation (R2 cv) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, R2 cv, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy (R2 cv 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

PURPOSES : The travel times of expressway buses have been estimated using the travel time data between entrance tollgates and exit tollgates, which are produced by the Toll Collections System (TCS). However, the travel time data from TCS has a few critical problems. For example, the travel time data include the travel times of trucks as well as those of buses. Therefore, the travel time estimation of expressway buses using TCS data may be implicitly and explicitly incorrect. The goal of this study is to improve the accuracy of the expressway bus travel time estimation using DSRC-based travel time by identifying the appropriate analysis period of input data. METHODS : All expressway buses are equipped with the Hi-Pass transponders so that the travel times of only expressway buses can be extracted now using DSRC. Thus, this study analyzed the operational characteristics as well as travel time patterns of the expressway buses operating between Seoul and Dajeon. And then, this study determined the most appropriate analysis period of input data for the expressway bus travel time estimation model in order to improve the accuracy of the model. RESULTS: As a result of feasibility analysis according to the analysis period, overall MAPE values were found to be similar. However, the MAPE values of the cases using similar volume patterns outperformed other cases. CONCLUSIONS: The best input period was that of the case which uses the travel time pattern of the days whose total expressway traffic volumes are similar to that of one day before the day during which the travel times of expressway buses must be estimated.

최근 강교량이나 선박과 같은 강구조물에 있어서 여러 가지 환경요인에 의해 균열 및 부식 등의 문제가 다수 발생되어지고 있다. 이러한 문제를 해결하기 위해 보수용접을 사용할 수 있다. 이러한 보수용접은 절단이라는 과정을 필연적으로 수반하고 있다. 따라서 이러한 절단 줄 얻어지는 잔류응력의 예측은 구조물의 안전이라는 측면에서 중요하다고 할 수 있다. 본 연구에서는 2차원 및 3차원 유한요소 해석을 수행하여 가스절단에 의해 얻어진 절단잔류응력을 구하였으며, 2차원 및 3차원 해석기법의 정도를 명확히 하였다. 2차원 및 3차원 해석을 수행하여 얻은 절단잔류응력의 분포 및 그 절대치는 유사한 값을 나타내었다.

구조물의 손상예측정확도를 모텔불확실성의 함수로 산정하는 방법론이 제시되었다. 먼저， 구조물의 손상발 생위치와 크기를 결정할 수 있는 알고리즘이 요약되고 모델불확실성과 손상발견정확도를 측정하는 방법들이 제시되었다. 다음으로， 실폰구조물의 손상발견정확도에 미치는 모델불확실성의 영향을 산정하는 방법론이 제 시되었다. 마지막으로， 한개의 진동모드가 측정된 Plate-Girder 교량올 사용하여 이같은 산정방법론의 적합 성이 예증되었다.

The assessment of wind resources must be carried out to choose wind farm sites adequately. Additionally, input data on surface roughness maps and topographic maps are required to evaluate wind resources, where input data accuracy determines the accuracy of their overall analysis. To estimate this accuracy, we used met-mast data in Jeju and produced the ground roughness value for the Jeju region. To determine these values, an unsupervised classification method using SPOT-5 images was carried out for image classification. The wind resources of the northeastern part of Jeju were predicted, and the ground roughness map of the region was calculated by the WindPRO software. The wind speed of the Pyeongdae region of Jeju from the ground roughness map was calculated using WindPRO as 8.51 m/s. The wind speed calculated using the remote sensing technology presented in this study was 8.69 m/s. To assess the accuracy of the measured WindPro and the remote sensing technology values, we compared these results to the observed values in the Pyeongdae region using met-mast. This comparison shows that remote sensing data are more accurate than the WindPro data. We also found that the ground roughness map calculated in this study is useful for generating an accurate wind resource map of Jeju Island.

Carbonation of reinforced concrete is a major factor in the deterioration of reinforced concrete, and prediction of the resistance to carbonation is important in determining the durability life of reinforced concrete structures. In this study, basic research on the prediction of carbonation penetration depth of concrete using Deep Learning algorithm among artificial neural network theory was carried out. The data used in the experiment were analyzed by deep running algorithm by setting W/B, cement and blast furnace slag, fly ash content, relative humidity of the carbonated laboratory, temperature, CO2 concentration, Deep learning algorithms were used to study 60,000 times, and the analysis of the number of hidden layers was compared.

우리나라에서는 도시 개발사업을 위한 환경영향평가를 실시하는데 있어 개발 전․중․ 후의 강우유출량을 분석하도록 규정하고 있다. 도시개발에 따른 수문학적 변화를 분석하고 대책을 수립하기 위해 수문모델이 사용되고 있으나 대부분의 경우 현장의 자료가 충분하지 않은 관계로 그 산정결 과의 신뢰도가 문제될 수 있다. 본 연구에서는 대전의 관평천 일부유역에서 2015년 7월 부터 2016년 7월 까지 자동 모니터링 장치을 이용하고 또 한 및 현장 측정을 통해 확보된 강우량 및 유출유량의 연속자료를 활용하여 SWMM을 이용하는 경우 강우 유출량 예측의 정확도를 제고하고자 하 였다. 토양침투량 산정을 위해 대표적으로 사용되는 Curve Number 방법, Horton 방법 및 Green-Ampt 방법들을 사용한 경우에 대해서 투수지 역과 불투수 지역에 대해 각각 최적의 Manning 조도계수와 지표면 저류깊이를 산정하여 제시하였다. 본 연구의 결과는 우리나라의 도시 유역에 서 실측자료를 이용하여 강우 유출 모델을 보정하였다는 면에서 의미가 있다고 판단되며 추후 유역의 개발등의 상황에 대해는 강우 시 유출량 및 수질현상을 더욱 정확하게 예측하고 나아가서 향후의 유역 내 수문조건 변화 요인에 대한 영향을 분석하는 데 정확도를 향상시킬 수 있을 것으로 기대된다.

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

구조물은 시공 단계에서의 작업환경과 시공품질 그리고 자연환경과 불확실한 하중 등 수많은 변수들에 의해 해석 모델과 큰 차이를 보인다. 따라서, 구조물에 센서들을 설치하여 계측된 값으로 Structural Health Monitoring (SHM)을 실시하여 구조물의 안전성을 진단하고 있다. 하지만 대형화, 비정형화 되어가고 있는 건축 구조물에서 부분적으로 계측한 데이터로 전체 안전성에 대한 평가는 현실적으로 힘든 상황이다. 정확한 구조물 평가를 위해서는 보다 많은 센서의 개수가 필요하며, 장기간의 계측 기간이 요구된다. 그러나 재정적 문제 및 현장 여건 등으로 인해 설치되는 센서의 수 및 계측 기간은 제한이 될 수밖에 없다. 따라서 요구되는 구조물 진단의 정확성을 확보하면서 소요되는 비용을 최소화할 필요가 있다. 이를 위해서는 먼저 구조물 진단의 정확성과 비용과의 관계를 파악할 필요가 있다. 본 연구에서는 부분적으로 계측한 변위 값을 이용하여 구조물 전체의 변위를 예측하는 알고리즘을 제시하고, 계측 기간에 따른 알고리즘의 정확도를 평가한다. 이를 통해 요구되는 신뢰도를 가지면서 최소의 계측 기간을 파악할 수 있다. 이는 유지관리 비용을 절감하는 비용효과를 가진다.