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Curve Fitting을 이용한 건설장비 CO<sub>2</sub> 배출량 예측 모델
노재윤 ( Noh Jaeyun ),김유진 ( Kim Yujin ),이지연 ( Lee Jiyeon ),이민우 ( Lee Minwoo ),한승우 ( Han Seungwoo ) 한국건축시공학회 2020 한국건축시공학회 학술발표대회 논문집 Vol.20 No.1
The severity of the global climate crisis is increasing due to greenhouse gases caused by human activities. As a result, countries and industries are making efforts to reduce carbon dioxide emissions, the biggest cause of global warming. Many studies have been conducted to predict carbon emissions in the construction sector to reduce this, but they have not actually produced a highly usable formula in the field. Therefore, the two variables 'Curve Fitting' were performed based on the data of excavators and trucks measured at the field. As a result, we have obtained a carbon dioxide emission prediction model for construction equipment, and we would like to use it to help establish an eco-friendly process plan.
건설장비의 배출가스 데이터 기반 대기오염물질 배출량 예측 시스템
노재윤 ( Noh¸ Jaeyun ),김유진 ( Kim¸ Yujin ),김수민 ( Kim¸ Sumin ),한승우 ( Han¸ Seungwoo ) 한국건축시공학회 2021 한국건축시공학회 학술발표대회 논문집 Vol.21 No.2
As non-road mobile pollutants such as construction equipment are emerging as the main cause of air pollutants emission, construction equipment regulations are gradually strengthening. Research was conducted by correcting the emission coefficient to calculate and predict air pollutant emissions of construction equipment, but it did not reflect site variables such as field and equipment conditions that affect actual emissions. This study derived an Artificial Neural Network emission prediction model based on the actual emission data of excavators and trucks measured at the site and proposed a platform to predict the emission of air pollutants at the site according to the working size and conditions. Through this, it is possible to establish an eco-friendly process plan using a model from the construction plan.
이산형 이벤트 시뮬레이션 기반 최적의 건설장비 조합 도출 방법 제시 - 표준품셈 건설기계 시공능력 산식을 기반으로 -
고용호,키앙,노재윤,김유진,한승우,Ko, Yongho,Ngov, Kheang,Noh, Jaeyun,Kim, Yujin,Han, Seungwoo 한국건설관리학회 2022 한국건설관리학회 논문집 Vol.23 No.6
Productivity estimation of construction operations is crucial to successful project delivery. Especially in the preconstruction phase, the adequacy and effectiveness of plans directly affect the actual performance of operations. Currently, productivity estimation is conducted by referring to existing references such as the Construction Standard Production Rate. However, it is difficult to promptly apply changing conditions of operations when using such references. Moreover, it is difficult to deduce the optimal combination of construction machinery for the given condition. This paper presents a simple simulation model that can be used to generate productivity data that considers site conditions and construction equipment combination. The suggested method is expected to be used as a decision making assisting tool for practitioners who rely on estimations using the Construction Standard Production Rate when establishing construction plans using heavy machinery such as backhoes, loaders and dumptrucks.
인공신경망 및 비선형 회귀분석을 이용한 건설장비의 CO<sub>2</sub> 배출량 예측 모델 개발
임소민 ( Im Somin ),노재윤 ( Noh Jaeyun ),노상우 ( Ro Sangwoo ),이민우 ( Lee Minwoo ),한승우 ( Han Seungwoo ) 한국건축시공학회 2019 한국건축시공학회 학술발표대회 논문집 Vol.19 No.2
In order to measure the amount of carbon dioxide emitted from the construction sites, many literature which have been conducted have proposed methodologies for calculating coefficients based on actual data collections for estimating the emission formula. The existing data collected under controlled conditions not on site measurement were too limited to apply in actual sites. The purpose of this study is to conduct analysis based on the data measured in fields and to present predictive models using artificial neural network and nonlinear regression analysis for appropriate predictions and practical applications.
조건별 세부공종 시뮬레이션을 통한 아스팔트 포장공사의 생산성 분석
김유진 ( Kim Yujin ),이수민 ( Lee Sumin ),노재윤 ( Noh Jaeyun ),한승우 ( Han Seungwoo ) 한국건축시공학회 2021 한국건축시공학회 학술발표대회 논문집 Vol.21 No.1
Currently, the Korean Smart Construction Corporation aims to stimulate overseas expansion of smart construction technology centered on Expressway development. In addition, the integrated classification system of construction information with Work Breakdown Structure(WBS) is currently being established in Korea, but its application to the construction industry is limited. In this study, data generation using simulation is carried out at the lowest level of WBS presented by the Korea Expressway Corporation, and detailed process productivity is predicted by site conditions.