단독주택 유형별 폐기단계 CO2 배출량 특성에 관한 연구

차기욱,홍원화,김진호 대한건축학회 2017 大韓建築學會論文集 : 構造系 Vol.33 No.3

Recently, construction industry has been seeking various countermeasures to realize eco-friendly and sustainable development and resource recycling construction industry. Meanwhile, currently, detached houses that a lifetime limit comes account for a considerable proportion in Korea. For such reason, the problem with the increase in construction waste and environmental pollution are expected. Therefore, this research carried out a study on indicators of demolition waste generation and CO2 emissions in end-of-life(EOL) phase for 854 detached houses. For that, this study calculated demolition waste generation by detached house type using volume of material data in buildings investigated before dismantling. In addition, the data of the specifications on the equipment applied in EOL phase of the building through field investigation and existing literature was constructed. As a result of this study, it was found that demolition waste generation indicators by type were RC house: 2048.65kg/m2, brick house: 1170.74kg/m2, block house: 1053.59kg/m2, wooden house: 999.79kg/m2. And CO2 emissions by house type were found to be RC house: 11.273kg/m2, brick house: 6.847kg/m2, block house: 4.629kg/m2, wooden house: 4.864kg/m2. But, the CO2 emission in the demolition and transportation stage of RC/brick house was similar levels to the value. On the other hand, the CO2 emission in the transportation stage of block/wooden house was higher than one of demolition stage in EOL phase.

가뭄재해관련 광동댐 운영 및 수자원확보 방안

차기욱,Cha, Gi-Uk 한국수자원학회 2009 물과 미래(한국수자원학회지) Vol.42 No.6

가뭄 극복과 2016년 다목적댐 운영방향

차기욱,Cha, Gi-Uk 한국방재학회 2016 한국방재학회지 Vol.16 No.2

재개발 지역의 해체폐기물 발생량 예측을 위한 최적 k-earest neighbors (KNN) 모델 개발

차기욱,홍원화 한국생활환경학회 2023 한국생활환경학회지 Vol.30 No.1

Due to the rapid increase in waste generation, smart waste management technology has become important in recent years. In this study, an optimal demolition waste generation rate (DWGR) prediction model was developed by applying various distance metrics of the K-Nearest Neighbors (KNN) algorithm. The optimal K value and the prediction model were determined through mean square error (MSE), mean absolute error (MAE), coefficient of determination (R2), and coefficient of variation of the root mean square error (CVRMSE) for Euclidean, Manhattan, and Chebyshev as KNN metrics. As a result of this study, it was found that the Manhattan-KNN (k=5) model (R2 value of 0.789) had better predictive performance than the Euclidean-KNN (k=6) model (R2 value of 0.685) and the Chebyshev-KNN (k=12) model (R2 value of 0.627) in predicting DWGR. And the mean of the observed values was 987.181 kg·m-2, and the mean of the predictive values of the Manhattan, Euclidean, and Chebyshev models were 992.307 kg·m-2, 993.144 kg·m-2 and 994.050 kg·m-2, respectively.

가변형 차양장치 적용에 따른 하절기 냉방부하 저감 및빛환경 개선효과 분석

차기욱,문현준,김호정,홍원화,백용규 한국생활환경학회 2017 한국생활환경학회지 Vol.24 No.6

The envelope is important for sustainable building. Recent commercial buildings are causing thermal degradationand cooling load due to the increase of the area of the windows. Therefore, this research studied kineticshading system which can improve energy saving and visual environment in summer. For that, this study proposednew shading system and shape considering the orientation of the building and the location of the sun. Based on this,this study analyzed the effectiveness on energy reduction and improvement of visual environment by applying thekinetic shading system proposed in this study. As the results of this study, energy reduction rate was 35% in the east,22.9% in the south, and 30.7% in the west depending on the application location. Also, as the result of the illuminanceanalysis, it was found that the effect of achieving uniformity ratio of illumination was considerable.

가뭄재해관련 광동댐 운영 및 수자원확보 방안

차기욱 한국수자원학회 2009 한국수자원학회논문집 Vol.42 No.6

LCIA(Life Cycle Impact Assessment)를 위한 온실가스 발생량과 Global Warming Potential 산출-폐목재 처리방식에 따라

차기욱,손병훈,홍원화 대한건축학회지회연합회 2010 대한건축학회지회연합회 학술발표대회논문집 Vol.2010 No.1

단독주택 유형별 폐기단계 CO₂ 배출량 특성에 관한 연구

차기욱,홍원화,김진호 대한건축학회 2017 대한건축학회논문집 Vol.33 No.3

Recently, construction industry has been seeking various countermeasures to realize eco-friendly and sustainable development and resource recycling construction industry. Meanwhile, currently, detached houses that a lifetime limit comes account for a considerable proportion in Korea. For such reason, the problem with the increase in construction waste and environmental pollution are expected. Therefore, this research carried out a study on indicators of demolition waste generation and <TEX>$CO_2$</TEX> emissions in end-of-life(EOL) phase for 854 detached houses. For that, this study calculated demolition waste generation by detached house type using volume of material data in buildings investigated before dismantling. In addition, the data of the specifications on the equipment applied in EOL phase of the building through field investigation and existing literature was constructed. As a result of this study, it was found that demolition waste generation indicators by type were RC house: <TEX>$2048.65kg/m^2$</TEX>, brick house: <TEX>$1170.74kg/m^2$</TEX>, block house: <TEX>$1053.59kg/m^2$</TEX>, wooden house: <TEX>$999.79kg/m^2$</TEX>. And <TEX>$CO_2$</TEX> emissions by house type were found to be RC house: <TEX>$11.273kg/m^2$</TEX>, brick house: <TEX>$6.847kg/m^2$</TEX>, block house: <TEX>$4.629kg/m^2$</TEX>, wooden house: <TEX>$4.864kg/m^2$</TEX>. But, the <TEX>$CO^2$</TEX> emission in the demolition and transportation stage of RC/brick house was similar levels to the value. On the other hand, the <TEX>$CO_2$</TEX> emission in the transportation stage of block/wooden house was higher than one of demolition stage in EOL phase.

결정 트리 기반 알고리즘을 활용한 해체폐기물 발생량 예측모델 개발

차기욱,홍원화 대한건축학회 2023 대한건축학회논문집 Vol.39 No.3

Management of demolition waste (DW), which accounts for a large portion of waste generation (WG), is a very important issue. Therefore,many researchers tried to apply various ML algorithms to predict WG, and tried to find the decisive factors affecting WG. This studyconducted a study on the development of optimal ML model for predicting demolition waste generation (DWG). In this study, decision tree(DT), random forest (RF), and gradient boost machine (GBM) algorithms were applied to develop ML models to predictive DWG. For this,data preprocessing was performed and the optimal hyper parameter was searched for each algorithm to derive an optimal ML model. Inconsideration of dataset size, leave one out cross validation (LOOCV) was applied to the model validation and mean absolute error (MAE),root mean square error (RMSE), coefficient of determination (R squared), and mean square error (MSE) were used as the performanceevaluation index of the models. As a result of this study, it was found that the predictive performance of the RF model (MAE 72.837, MSE12198.236, RMSE 110.446, R2 0.880) was better than one of DT (MAE 87.081, MSE 17348.052, RMSE 131.712, R2 0.829) and GBM(MAE 87.883, MSE 18175.125, RMSE 134.815, R2 0.821) models. The error from the observed mean (987.1806 kg m-2) was 8.82%, 7.38%,and 8.90% for the DT, RF, and GBM models, respectively. Therefore, it can be seen that the ML model using the DT-based algorithms isvery good at predicting DWG. Finally, this study presented a reliable and optimal ML model for predicting DWG for a domestic wastemanagement strategy. 폐기물 발생의 많은 부분을 차지하는 해체폐기물의 관리는 매우 중요한 문제이다. 따라서 많은 연구자들은 폐기물 발생량 예측을 위해 다양한 기계학습 모델 개발을 통해 폐기물 발생량에 영향을 미치는 주요 요인들을 밝히고자 하였다. 본 연구에서는 해체폐기물 발생량 예측을 위한 최적 기계학습 모델 개발에 관한 연구를 수행하였다. 이 연구에서는 DT(Decision Tree), RF(Random Forest) 및 GBM(Gradient Boost Machine) 알고리즘을 적용하여 해체폐기물 발생량 예측모델을 개발하였다. 이를 위해 각 알고리즘들을 대상으로 최적의 하이퍼 파라미터를 도출하여 모델 개발에 적용하였다. 모델 검증은 LOOCV(Leve One Out Cross validation)를 적용하였으며, 평균절대오차(MAE), 평균제곱근오차(RMSE), 결정계수(R제곱), 평균제곱오차(MSE)을 모델의 성능평가 지표로 사용하였다. 본 연구의 결과, RF 모델(MAE 72.837, MSE 12198.236, RMSE 110.446, R2 0.880)의 예측 성능이 DT(MAE 87.081, MSE 17348.052,RMSE 131.712, R2 0.829), GBM(MAE 87.883, MSE 18175.125, RMSE 134.815, R2 0.821) 모델보다 우수한 것으로 나타났다. DT, RF 및 GBM 모델들의 관측값 평균(987.1806kg m-2)과의 오차는 각각 8.82%, 7.38% 및 8.90%로 나타나 DT 기반 알고리즘을 사용하는 ML 모델은 해체폐기물 발생량 예측에 우수한 성능 결과를 보여주었다. 본 연구는 해체폐기물 관리를 위해 신뢰할 수 있는 DT 기반의 최적 기계학습 예측모델을 제시하였다.

토지이용특성에 따른 도시 역세권의 에너지소비 특성에 관한 연구

차기욱(Cha Gi-Wook),전규엽(Jeon Kyu-Yeob),홍원화(Hong Won-Hwa) 대한건축학회 2011 대한건축학회 학술발표대회 논문집 - 계획계/구조계 Vol.31 No.2(계획계)

The propose of this paper is to analyse an energy consumption according to characteristic of land use within subway adjacent area. First of all, GIS is used to analyze subway adjacent area’s land use quality. Then, Cluster Analysis was performed with land use area and construction area that is within 500m around station. Through those processes, several types are classified subway adjacent area of city. The characteristics of energy consumption are realized according to building-to-land ratio, floor area ratio and energy unit. By research finding, Railway Station Area are divided into four groups. And it shows distinct differences between final energy consumption and consumption structure by Railway Station Area types.