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Quantification of Energy Consumption and Carbon Dioxide Emissions During Excavator Operations
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction. Babylon University.ORCID iD: 0000-0003-0465-8304
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.ORCID iD: 0000-0002-4695-5369
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.ORCID iD: 0000-0002-5661-5237
2018 (English)In: Advanced Computing Strategies for Engineering: 25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018, Proceedings, Part I, Cham, 2018, p. 431-453Conference paper, Published paper (Refereed)
Abstract [en]

A number of studies have assessed the energy consumed and carbon dioxide emitted by construction machinery during earthwork operations. However, little attention has been paid to predicting these variables during planning phases of such operations, which could help efforts to identify the best options for minimizing environmental impacts. Excavators are widely used in earthwork operations and consume considerable amounts of fuel, thereby generating large quantities of carbon dioxide. Therefore, rigorous evaluation of the energy consumption and emissions of different excavators during planning stages of project, based on characteristics of the excavators and projects, would facilitate selection of optimal excavators for specific projects, thereby reducing associated environmental impacts. Here we describe use of artificial neural networks (ANNs), developed using data from Caterpillar’s handbook, to model the energy consumption and CO2 emissions of different excavators per unit volume of earth handled. We also report a sensitivity analysis conducted to determine effects of key parameters (utilization rate, digging depth, cycle time, bucket payload, horsepower, load factor, and hauler capacity) on excavators’ energy consumption and CO2 emissions. Our analysis shows that environmental impacts of excavators can be most significantly reduced by improving their utilization rates and/or cycle times, and reducing their engine load factor. We believe our ANN models can potentially improve estimates of energy consumption and CO2 emissions by excavators. Their use in planning stages of earthworks projects could help planners make informed decisions about optimal excavator(s) to use, and contractors to evaluate environmental impacts of their activities. Finally, we describe a case study, based on a road construction project in Sweden, in which we use empirical data on the quantities and nature of the materials to be excavated, to estimate the environmental impact of using different excavators for the project

Place, publisher, year, edition, pages
Cham, 2018. p. 431-453
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10863
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
URN: urn:nbn:se:ltu:diva-69572DOI: 10.1007/978-3-319-91635-4_22Scopus ID: 2-s2.0-85049074506ISBN: 978-3-319-91634-7 (print)ISBN: 978-3-319-91635-4 (electronic)OAI: oai:DiVA.org:ltu-69572DiVA, id: diva2:1219043
Conference
25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018
Available from: 2018-06-15 Created: 2018-06-15 Last updated: 2018-08-08Bibliographically approved

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Jassim, HassaneanLu, WeizhuoOlofsson, Thomas

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