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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
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction. Babylon University.
    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.
    Quantification of Energy Consumption and Carbon Dioxide Emissions During Excavator Operations2018In: 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 (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

  • 4.
    Jassim, Hassanean S. H.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Artificial Neural Networks as a Technique in Construction Engineering and Management: Predicting Hourly Air Pollutant of Excavator in the Earthworks2018Report (Other academic)
  • 5.
    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.

  • 6.
    Jassim, Hassanean S.H.
    et al.
    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.
    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.
    Assessing energy consumption and carbon dioxide emissions of off-highway trucks in earthwork operations: an artificial neural network model2018In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 198, p. 364-380Article in journal (Refereed)
    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.

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