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 e
展开▼
