Comparison of Two Models for Estimating Equipment Productivity for a Sustainability Quantification Tool

As the construction industry moves toward more sustainable activities, it is imperative to accurately quantify fuel consumption and air pollutant emissions for heavy-duty diesel equipment. Total emissions are dependent upon the total engine fuel consumption and the fuel consumption rates are dependent upon the productivity of the equipment. Using data from two reliable construction estimating sources - the Caterpillar Performance Handbook and RS Means Heavy Construction Cost Data - multiple linear regression (MLR) was used to develop two models (one for each source) for estimating equipment productivity of a bulldozer performing a bulk excavation activity. The key variables affecting the equipment productivity include engine size, blade capacity, material type, dozing distance, operational efficiency, and operator skill level. Results indicate that each model had high precision and accuracy and relatively low bias. A proposed methodology for validating the model results via field testing is presented. Recommendations for using these models as the foundation of a sustainability quantification tool for estimating the economic, energy, and environmental impacts of the equipment are provided.