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Assessing energy consumption and carbon dioxide emissions of off-highway trucks in earthwork operations: an artificial neural network model
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction. Department of Civil Engineering, College of Engineering, University of Babylon.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: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 198, p. 364-380Article in journal (Refereed) Published
Abstract [en]

Methods capable of predicting the energy use and CO2 emissions of off-highway trucks, especially in the initial planning phase, are rare. This study proposed an artificial neural networks (ANN) model to assess such energy use and CO2 emissions for each unit volume of hauled materials associated with each hauling distance. Data from discrete event simulations (DES), an off-highway truck database, and different site conditions were simultaneously analyzed to train and test the proposed ANN model. Six independent quantities (i.e., truck utilization rate, haul distance, loading time, swelling factor, truck capacity, and grade horsepower) were used as the input parameters for each model. The developed model is an efficient tool capable of assessing the energy use and CO2 emissions of off-highway trucks in the initial planning stage. The results revealed that the grade horsepower and haul distances yield a significant increase in the environmental impact of the trucks. In addition, the results demonstrated that, for a given set of project conditions, the environmental impact of trucks can reduced by improving their utilization rate and reducing the loading time.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 198, p. 364-380
Keywords [en]
Off-highway truck; energy consumption; CO2 emission; Simulation; ANN prediction model; initial planning stage.
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
URN: urn:nbn:se:ltu:diva-70115DOI: 10.1016/j.jclepro.2018.07.002OAI: oai:DiVA.org:ltu-70115DiVA, id: diva2:1232685
Note

Validerad;2018;Nivå 2;2018-08-08 (andbra)

Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2018-08-08Bibliographically approved

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Publisher's full texthttps://doi.org/10.1016/j.jclepro.2018.07.002

Authority records BETA

Jassim, Hassanean S.H.Lu, WeizhuoOlofsson, Thomas

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