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  • 1.
    Jassim, Hassanean
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Krantz, Jan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Lu, Weizhuo
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Olofsson, Thomas
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    A Cradle-to-Gate Framework for Optimizing Material Production in Road Construction2016In: IABSE CONGRESS, STOCKHOLM, 2016: Challenges in Design and Construction of an Innovativeand Sustainable Built Environment / [ed] Lennart Elfgren, Johan Jonsson, Mats Karlsson, Lahja Rydberg-Forssbeck and Britt Sigfrid, CH - 8093 Zürich, Switzerland, 2016, no 19, p. 758-764Conference paper (Refereed)
    Abstract [en]

    Abstract

    In road construction, large quantities of raw materials are extracted and transported duringseveral stages of its life cycle. Consequently, processing and preparation of raw materials fordifferent purposes inevitably result in considerable amount of energy use and emissions of airpollutants. The Swedish Transportation Administration has an ambition to minimizeenvironmental impacts from transport infrastructure projects by, for instance, reducing the energyuse and emissions of greenhouse gases. This can be achieved by implementing specific strategiesand techniques during various stages throughout the life cycle of the project. In this paper aframework is proposed to manage the energy use and greenhouse gases emissions from rawmaterials extraction processes in road construction projects. A prototype is developed based onthe framework and demonstrated in a small case study.

  • 2.
    Jassim, Hassanean
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Lu, Weizhuo
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Olofsson, Thomas
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Predicting energy consumption and CO2 emissions of excavators in earthwork operations: An artificial neural network model2017In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 9, no 7, article id 1257Article in journal (Refereed)
    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.

  • 3.
    Jassim, Hassanean S. H.
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Lu, Weizhuo
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Olofsson, Thomas
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    A Practical Method for Assessing the Energy Consumption and CO2 Emissions of Mass Haulers2016In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 9, no 10, article id 802Article in journal (Refereed)
    Abstract [en]

    Mass hauling operations play central roles in construction projects. They typically use many haulers that consume large amounts of energy and emit significant quantities of CO2. However, practical methods for estimating the energy consumption and CO2 emissions of such operations during the project planning stage are scarce, while most of the previous methods focus on construction stage or after the construction stages which limited the practical adoption of reduction strategy in the early planning phase. This paper presents a detailed model for estimating the energy consumption and CO2 emissions of mass haulers that integrates the mass hauling plan with a set of predictive equations. The mass hauling plan is generated using a planning program such as DynaRoad in conjunction with data on the productivity of selected haulers and the amount of material to be hauled during cutting, filling, borrowing, and disposal operations. This plan is then used as input for estimating the energy consumption and CO2 emissions of the selected hauling fleet. The proposed model will help planners to assess the energy and environmental performance of mass hauling plans, and to select hauler and fleet configurations that will minimize these quantities. The model was applied in a case study, demonstrating that it can reliably predict energy consumption, CO2 emissions, and hauler productivity as functions of the hauling distance for individual haulers and entire hauling fleets.

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