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Predicting energy consumption and CO2 emissions of excavators in earthwork operations: An artificial neural network model
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.ORCID iD: 0000-0003-0465-8304
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.ORCID iD: 0000-0002-5661-5237
2017 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 9, no 7, 1257Article in journal (Refereed) Published
Abstract [en]

Excavators are one of the most energy-intensive elements of earthwork operations. Predicting the energy consumption and CO2 emissions of excavators is therefore critical in order to mitigate the environmental impact of earthwork operations. However, there is a lack of method for estimating such energy consumption and CO2 emissions, especially during the early planning stages of these activities. This research proposes a model using an artificial neural network (ANN) to predict an excavator's hourly energy consumption and CO2 emissions under different site conditions. The proposed ANN model includes five input parameters: digging depth, cycle time, bucket payload, engine horsepower, and load factor. The Caterpillar handbook's data, that included operational characteristics of twenty-five models of excavators, were used to develop the training and testing sets for the ANN model. The proposed ANN models were also designed to identify which factors from all the input parameters have the greatest impact on energy and emissions, based on partitioning weight analysis. The results showed that the proposed ANN models can provide an accurate estimating tool for the early planning stage to predict the energy consumption and CO2 emissions of excavators. Analyses have revealed that, within all the input parameters, cycle time has the greatest impact on energy consumption and CO2 emissions. The findings from the research enable the control of crucial factors which significantly impact on energy consumption and CO2 emissions.

Place, publisher, year, edition, pages
MDPI AG , 2017. Vol. 9, no 7, 1257
National Category
Construction Management
Research subject
Construction Engineering and Management
Identifiers
URN: urn:nbn:se:ltu:diva-65072DOI: 10.3390/su9071257ISI: 000406709500184Scopus ID: 2-s2.0-85025160264OAI: oai:DiVA.org:ltu-65072DiVA: diva2:1131544
Note

Validerad; 2017; Nivå 2; 2017-08-15 (andbra)

Available from: 2017-08-15 Created: 2017-08-15 Last updated: 2017-09-22Bibliographically approved

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