• Journal of Positioning, Navigation, and Timing
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• v.9 no.4
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• pp.347-356
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• 2020

In this study, Inclined Geosynchronous Orbit (IGSO) and Geostationary Orbit (GEO) of BeiDou System (BDS) and Quasi Zenith Satellite System (QZSS) satellites positions and clock errors calculated by broadcast ephemeris and compared with Multi-GNSS Experiment (MGEX) products provided by five Analysis Centers (ACs). Root Mean Square Errors (RMSE) calculated for satellite position error. The IGSO results showed that 1.82 m, 0.91 m, 1.28 m in BDS and 1.34 m 0.36 m 0.49 m in QZSS and the GEO results showed that 2.85 m, 6.34 m, 6.42 m in BDS and 0.47 m, 4.79 m, 5.82 m in QZSS in the direction of radial, along-track and cross-track respectively. RMS calculated for satellite clock error. The IGSO result showed that 2.08 ns and 1.24 ns and the GEO result showed that 1.28 ns and 1.12 ns in BDS and QZSS respectively.

• Computers and Concrete
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• v.27 no.3
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• pp.225-239
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• 2021

Carbon fiber reinforced polymer (CFRP) has extensive use in strengthening reinforced concrete structures due to its high strength and elastic modulus, low weight, fast and easy application, and excellent durability performance. Many studies have been carried out to determine the performance of the CFRP confined concrete cylinder. Although studies about the prediction of confined compressive strength using ANN are in the literature, the insufficiency of the studies to predict the strain of confined concrete cylinder using ANN, which is the most appropriate analysis method for nonlinear and complex problems, draws attention. Therefore, to predict both strengths and also strain values, two different ANNs were created using an extensive experimental database. The strength and strain networks were evaluated with the statistical parameters of correlation coefficients (R2), root mean square error (RMSE), and mean absolute error (MAE). The estimated values were found to be close to the experimental results. Mathematical equations to predict the strength and strain values were derived using networks prepared for convenience in engineering applications. The sensitivity analysis of mathematical models was performed by considering the inputs with the highest importance factors. Considering the limit values obtained from the sensitivity analysis of the parameters, the performances of the proposed models were evaluated by using the test data determined from the experimental database. Model performances were evaluated comparatively with other analytical models most commonly used in the literature, and it was found that the closest results to experimental data were obtained from the proposed strength and strain models.

• Advances in environmental research
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• v.9 no.4
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• pp.251-273
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• 2020

Soil degradation has become a major worldwide environmental problem, particularly in arid and semi-arid climate zones due to irregular rainfall and the intensity of storms that frequently generate heavy flooding. The main objective of this study is the use of geographic information system and remote sensing techniques to quantify and to map the soil losses in the Wadi Saida watershed (624 ㎢) through the revised universal soil loss equation model and a proposed model based on the surface erosive runoff. The results Analysis revealed that the Wadi Saida watershed showed moderate to moderately high soil loss, between 0 and 1000 t/㎢/year. In the northern part of the basin in the region of Sidi Boubkeur and the mountains of Daia; which are characterized by steep slopes, values can reach up to 3000 t/㎢/year. The two models in comparison showed a good correlation with R = 0.95 and RMSE = 0.43; the use of the erosive surface runoff parameter is effective to estimate the rate of soil loss in the watersheds. The problem of soil erosion requires serious interventions, particularly in basins with disturbances and aggressive climatic parameters. Good agricultural practices and forest preservation areas play an important role in soil conservation.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.671-680
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• 2022

The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R²) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC.

• Geomechanics and Engineering
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• v.31 no.5
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• pp.453-460
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• 2022

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R² value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.463-463
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• 2023

국내 수도관 보급률이 증가하면서 기존 노후화된 수도관들과 추가로 노후화된 수도관들이 증가하고 있다. 경과년수가 오래된 시설이 증가하는 것은 잠재적인 사고발생 위험을 증가시킨다. 실제 노후화된 상수도 시설물로 인해 단수, 누수, 수질오염, 지반함몰 발생이 증가하는 추세이다. 이러한 현상들은 시민들의 생활과 안전, 경제활동에 직접적인 영향을 끼치기 때문에 이에 대한 대책 마련이 시급한 상태이다. 본 연구에서는 AI를 기반으로 상수도관의 노후도 및 위험도를 예측하는 모델을 설계하고자 하였다. 대상지역을 인천광역시 서구로 선정하여 유량과 수질의 실시간 계측데이터를 수집하였다. 딥러닝 기법 중 하나인 LSTM(Long Short-Term Memory)을 이용하여 데이터를 예측하였고, 결정계수(R²)와 RMSE(Root Mean Square Error)로 학습데이터와 검증데이터의 비율을 정하여 예측도를 평가하였다. 유량과 수질 데이터 중 80%는 학습데이터로 20%는 검증 데이터로 분리하였고, LSTM의 셀과레이어 수를 해석에 적합한 범위로 설정한 결과, 실제값과 예측값이 높은 상관성을 보이는 것으로 나타났다. 예측된 유량 및 수질의 결과는 상수도 관리에 중요한 정보를 제공하며, 사고 위험도 평가와 관 노후화에 따른 대응력을 향상시키는 데 도움이 될 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.191-191
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• 2023

기후변화 대응을 위한 탄소 중립의 중요성이 대두되는 요즘, 생태계의 가장 큰 메탄 저장소로서 지구의 탄소 순환에 주요한 영향을 미치는 습지에 대한 이해는 필수적이다. 전지구 지면 모델인 Community Land Model(CLM)에 Functionally Assembled Terrestial Ecosystem Simulator(FATES) 외부 모듈을 함께 구동한 지면 생태계 모델 CLM-FATES는 지면 heterogeneity와 다양한 식생 종류를 고려하여 에너지 플럭스, 토양 수문, 생화학적 과정 등을 모의함으로써 탄소 동태 변화를 포함한 장기적 생태계 동태 변화 모의를 가능하게 한다. 본 연구는 CLM-FATES 모델을 미국 캘리포니아주 Mayberry Wetland (US-Myb)와 Twitchell East End Wetland (US-Tw4)에 적용하였다. 모델의 대기 입력 자료로는 FLUXNET-CH4에서 제공하는 에디 공분산 기반 플럭스 관측 자료를 사용하였다. 기존 CLM-FATES 모델은 토양이 장기간 포화 혹은 침수되어 지표 위 혹은 지표면 가까이 발달한 지하수면을 가지고 있는 습지의 수문학적 특성을 잘 반영하지 못해 정밀한 습지 생태계 동태 변화 모의에 한계를 가지고 있다. 본 연구에서는 CLM-FATES를 통한 보다 정확한 습지 생태계 모의를 위해 모델 내 토양 수문 관련 모듈을 수정하여 모델이 습지의 수문학적 특성을 반영할 수 있도록 하였다. 모델 구동 결과 도출한 잠열, 총일차생산량(Gross Primary Production: GPP)과 순생태계생산량(Net Ecosystem Production, NEP) 플럭스, 메탄 플럭스, 엽면적지수(Leaf Area Index; LAI)와 지표수 높이에 대해 관측값 대비 RMSE 및 R² 값을 계산하여 모의 결과의 적절성을 분석하였다. 이러한 모델 개선 경험을 바탕으로 추후 우리나라 습지 사이트에 모델을 적용하여 습지 탄소 동태 예측에 활용할 계획이다.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.342-342
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• 2022

본 연구에서는 금강 최상류 용담댐에 유입되는 오염부하를 효과적으로 관리하기 위하여 용담댐 상류 유역에 수문-수질 모델을 적용하고 보정한 결과를 보고하고자 한다. 유역의 유출유량과 오염물질 부하를 산정하기 위해 미국환경부의 Hydrological Simulation Program-Fortran(HSPF)을 적용하였고 강우에 의한 용담댐 유역의 유입유량과 오염물질 부하 특성을 분석하였다. 용담댐은 금강수계 최상류에 있는 다목적댐으로 국내에서 5번째로 규모가 큰 댐으로서 도수터널을 이용한 유역변경을 통해 전라북도 지역의 용수를 공급하고 있다. 용담댐 유역을 환경부의 소유역 구분에 따라 51개의 소유역으로 세분화한 후 티센망을 고려하여 12개의 그룹으로 범주화하였고, 강우 관측지점 12곳의 2017-2021년 강우 자료를 각 소유역에 입력하였다. 강우를 제외한 여타 기상 자료는 자료의 한계로 인해 장수기상청과 전주기상청의 동일 기간의 것을 사용하였다. 수질 모의 항목은 총부유물질(TSS), 총인(TP)과 총질소(TN)이며, 유량과 수질 항목의 보정은 측정망의 2017~2021년 실측자료에 대하여 시행하였다. 보정 결과는 R², RMSE, MAE로 평가하였다. 본 연구의 결과는 용담댐 내부의 수질을 예측할 수 있는 3차원 수리-수질 모델의 입력자료로 활용될 예정이며, 추후 기후변화 영향을 고려한 상류 유역의 수질관리 계획 수립에 기여할 것으로 기대된다.

• Journal of Korean Society of Rural Planning
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• v.28 no.4
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• pp.105-117
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• 2022

This study tested SVM(support vector machine), RF(random forest), and ANN(artificial neural network) machine-learning models that can predict net irrigation water requirements in paddy fields. For the Jeonju and Jeongeup meteorological stations, the net irrigation water requirement was calculated using K-HAS from 1981 to 2021 and set as the label. For each algorithm, twelve models were constructed based on cumulative precipitation, precipitation, crop evapotranspiration, and month. Compared to the CE model, the R² of the CEP model was higher, and MAE, RMSE, and MSE were lower. Comprehensively considering learning performance and learning time, it is judged that the RF algorithm has the best usability and predictive power of five-days is better than three-days. The results of this study are expected to provide the scientific information necessary for the decision-making of on-site water managers is expected to be possible through the connection with weather forecast data. In the future, if the actual amount of irrigation and supply are measured, it is necessary to develop a learning model that reflects this.