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인공신경망 모델 구축을 통한 건설장비별 이산화탄소 배출량 예측
임소민 ( Im Somin ),노상우 ( Ro Sangwoo ),김하윤 ( Kim Hayoon ),이민우 ( Lee Minwoo ),한승우 ( Han Seungwoo ) 한국건축시공학회 2020 한국건축시공학회 학술발표대회 논문집 Vol.20 No.1
In this paper, we intended to present a model for estimating carbon dioxide emissions by work of construction equipment using Artificial Neural Network(ANN) analysis. In this study, data of excavators and trucks are classified according to the work carried out, and carbon dioxide emissions are predicted through ANN based on equipment information and work information. As a result, the effect of each model was validated, and a carbon dioxide emission prediction model was derived for each work. This has the expected effect of establishig an eco-friendly process plan using this model from the construction planning stage.
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.
토공사 건설공정계획 지원을 위한 이산형 건설시뮬레이션과 인공신경망 기반 생산성 예측 방법론 개발
정다현 ( Jung Dahyun ),임소민 ( Im Somin ),오정환 ( Oh Jeonghwan ),이재우 ( Lee Jaewoo ),한승우 ( Han Seungwoo ) 한국건축시공학회 2021 한국건축시공학회 학술발표대회 논문집 Vol.21 No.1
Construction operation planning based on productivity analysis is essential for successful construction management in the construction industry. Up to this date, however, productivity analysis is not yet conducted with accurate data. Therefore, this study analyzes productivity depending on combinations of equipment and state of roads that dump trucks travel on, using construction simulation based on data collected in actual earthmoving construction sites, and develops methodology of predicting productivity for construction sites with varying conditions using Artificial Neural Network(ANN) model.