Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Framework for Estimating Greenhouse Gas Emissions Due to Asphalt Pavement Construction
Greenhouse gas (GRG) emissions are an important primary criteria for environmental evaluation. As a project evaluation criterion, this study developed a framework that could estimate GHG emissions in asphalt pavement construction based on the limited information available in the feasibility study phase. A thorough literature review and in-depth interviews with domain experts in the field of road engineering were conducted to identify the input variables for this framework. By considering the characteristics of the input variables, such as material type, geometric shape of the structure, and earthwork quantities, an artificial neural network and a parametric calculation were used to develop the framework for estimating GHG emissions. When applied to real-life asphalt pavement projects in the Republic of Korea, the framework produced an average estimation error of 11.2%, which was considered sufficiently accurate for the planning phase. The proposed framework is expected to help decision makers easily evaluate the GHG emission potential of asphalt pavement projects in their preliminary phase such that project viability and priority can be determined in a more holistic manner.
Framework for Estimating Greenhouse Gas Emissions Due to Asphalt Pavement Construction
Greenhouse gas (GRG) emissions are an important primary criteria for environmental evaluation. As a project evaluation criterion, this study developed a framework that could estimate GHG emissions in asphalt pavement construction based on the limited information available in the feasibility study phase. A thorough literature review and in-depth interviews with domain experts in the field of road engineering were conducted to identify the input variables for this framework. By considering the characteristics of the input variables, such as material type, geometric shape of the structure, and earthwork quantities, an artificial neural network and a parametric calculation were used to develop the framework for estimating GHG emissions. When applied to real-life asphalt pavement projects in the Republic of Korea, the framework produced an average estimation error of 11.2%, which was considered sufficiently accurate for the planning phase. The proposed framework is expected to help decision makers easily evaluate the GHG emission potential of asphalt pavement projects in their preliminary phase such that project viability and priority can be determined in a more holistic manner.
Framework for Estimating Greenhouse Gas Emissions Due to Asphalt Pavement Construction
Kim, Byungil (Autor:in) / Lee, Hyounkyu (Autor:in) / Park, Hyungbae (Autor:in) / Kim, Hyoungkwan (Autor:in)
Journal of Construction Engineering and Management ; 138 ; 1312-1321
03.03.2012
102012-01-01 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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