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- Jassim, Hassanean, 1978-, et al.
(författare)
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A Cradle-to-Gate Framework for Optimizing Material Production in Road Construction
- 2016
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Ingår i: IABSE CONGRESS, STOCKHOLM, 2016. - CH - 8093 Zürich, Switzerland. - 9783857481444 ; , s. 758-764
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Konferensbidrag (refereegranskat)abstract
- AbstractIn 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.
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| 2. |
- Jassim, Hassanean, 1978-, et al.
(författare)
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Determining the environmental impact of material hauling with wheel loaders during earthmoving operations
- 2019
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Ingår i: Journal of the Air and Waste Management Association. - : Taylor & Francis. - 1096-2247 .- 2162-2906. ; 69:10, s. 1195-1214
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Tidskriftsartikel (refereegranskat)abstract
- A method has been developed to estimate the environmental impact of wheel loaders used in earthmoving operations. The impact is evaluated in terms of energy use and emissions of air pollutants (CO2, CO, NOx, CH4, VOC, and PM) based on the fuel consumption per cubic meter of hauled material. In addition, the effects of selected operational factors on emissions during earthmoving activities were investigated to provide better guidance for practitioners during the early planning stage of construction projects. The relationships between six independent parameters relating to wheel loaders and jobsite conditions (namely loader utilization rates, loading time, bucket payload, horsepower, load factor, and server capacity) were analyzed using artificial neural networks, machine performance data from manufacturer’s handbooks, and discrete event simulations of selected earthmoving scenarios. A sensitivity analysis showed that the load factor is the largest contributor to air pollutant emissions, and that the best way to minimize environmental impact is to maximize the wheel loaders’ effective utilization rates. The new method will enable planners and contractors to accurately assess the environmental impact of wheel loaders and/or hauling activities during earthmoving operations in the early stages of construction projects.Implications: There is an urgent need for effective ways of benchmarking and mitigating emissions due to construction operations, and particularly those due to construction equipment, during the pre-construction phase of construction projects. Artificial Neural Networks (ANN) are shown to be powerful tools for analyzing the complex relationships that determine the environmental impact of construction operations and for developing simple models that can be used in the early stages of project planning to select machine configurations and work plans that minimize emissions and energy consumption. Using such a model, it is shown that the fuel consumption and emissions of wheel loaders are primarily determined by their engine load, utilization rate, and bucket payload. Moreover, project planners can minimize the environmental impact of wheel loader operations by selecting work plans and equipment configurations that minimize wheel loaders’ idle time and avoid bucket payloads that exceed the upper limits specified by the equipment manufacturer.
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| 3. |
- Jassim, Hassanean, 1978-, et al.
(författare)
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Predicting energy consumption and CO2 emissions of excavators in earthwork operations : An artificial neural network model
- 2017
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Ingår i: Sustainability. - Basel : MDPI. - 2071-1050. ; 9:7
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Jassim, Hassanean, 1978-, et al.
(författare)
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Quantification of Energy Consumption and Carbon Dioxide Emissions During Excavator Operations
- 2018
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Ingår i: Advanced Computing Strategies for Engineering. - Cham : Springer International Publishing. - 9783319916347 - 9783319916354 ; , s. 431-453
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Konferensbidrag (refereegranskat)abstract
- 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
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| 5. |
- Jassim, Hassanean S. H., 1978-, et al.
(författare)
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A Practical Method for Assessing the Energy Consumption and CO2 Emissions of Mass Haulers
- 2016
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Ingår i: Energies. - Basel, Switzerland : MDPI AG. - 1996-1073. ; 9:10
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 6. |
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| 7. |
- Jassim, Hassanean S.H, 1978-
(författare)
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Assessing Energy Use and Carbon Emissions to Support Planning of Environmentally Sustainable Earthmoving Operations
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Road and infrastructure projects have significant environmental impacts due to their high energy consumption and CO2 emissions. Among it, earthmoving operations contribute disproportionately to these impacts because of their intensive use of heavy machinery. However, little is known about how different equipment configurations and/or operational management strategies affect the environmental impact of earthmoving operations. Specifically, there is• a lack of tools that enables stakeholders to understand and assess environment impacts of per unit volume of earth handled,• a lack of integrated method taking into account both environmental and economic impacts in the planning of earthmoving operations.This work aims to facilitate the adoption of sustainable earthmoving practices in construction by providing methods for selecting environmentally costeffective equipment configurations for earthmoving operations. Based on these considerations, three research questions were formulated:• How can planners and construction managers of earthmoving projects estimate the energy use and carbon emissions of earthmoving machines per functional unit of material handled? • Which factors relating to earthworks operations have the greatest impact on energy use and carbon emissions?• How can stakeholders optimize equipment configurations with respect to the trade-off between the carbon emissions, time, and cost of earthwork operations?To answer these questions, an exploratory research approach involving multiple case studies was adopted. This resulted in the generation of a large body of experimental data and made it possible to test new methods for predicting and minimizing emissions due to earthmoving operations during the planning phases of construction projects. Throughout, the work was guided by the results of comprehensive literature reviews. Key findings of the work presented here include:• The combination of Discrete Event Simulations (DES) and mass haul optimization (MHO) can be used to assess environmental impacts during project planning stage. Artificial Neural Networks (ANNs) provides an effective approach to model the relationships between input variables relating to the earthmoving equipment and project conditions and output variables relating to energy use and CO2 emissions per unit volume of hauled materials,• The environmental performance of an item of equipment during earthmoving operations can be expressed as a function of the equipment’s operational characteristics and the job-site conditions such as digging depth, density of hauled materials and/or the topography of haulage surface. These factors all have important effects on the environmental impacts of earthmoving operations and the efficiency of the work,• As expected, improving equipment utilization rates and/or cycle times significantly reduces energy use and CO2 emissions per unit volume of material handled. This also increases the equipment’s usage efficiency in terms of fuel consumption per unit volume of material hauled. A high usage efficiency (evaluated in terms of utilization rates and/or cycle times) thus minimizes both the emissions and the costs of earthmoving operations.• Planning tools that account for costs and durations when assessing the carbon emissions of earthmoving operations make it possible to select optimal earthmoving equipment configurations that minimize emissions and costs (or at least do not increases costs).In summary, this thesis identifies key factors that facilitate the assessment and reduction of energy consumption and carbon emissions in earthmoving projects. The developed approaches allow construction managers to benchmark the emissions of different equipment configurations during project planning.The most important outcome of this work is the development of new methods for assessing energy use and CO2 emissions per unit volume of materials handled based on equipment characteristics and project conditions. These methods can be used to compare equipment configurations during the early stages of projects, and also for benchmarking/monitoring purposes during the construction stage. In particular, their use in the planning stages could help planners and construction managers to identify optimal equipment configurations that will minimize the environmental and economic impacts simultaneously.
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