Abstracts – Browse Results
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Abdollahipour, S (2013) Multi-parameter bidding in highway construction and rehabilitation projects, Unpublished PhD Thesis, , Oklahoma State University.
Alroomi, A S (2013) Integrative framework for sustainable development of the cost estimating profession, Unpublished PhD Thesis, , Oklahoma State University.
Brown, B H J (1974) An econometric forecasting model for a segment of the construction market, Unpublished PhD Thesis, , Oklahoma State University.
Duffy, G A (2009) Linear scheduling of pipeline construction projects with varying production rates, Unpublished PhD Thesis, , Oklahoma State University.
Eldin, N N (1987) Methodology for project management control in the construction industry, Unpublished PhD Thesis, , Oklahoma State University.
Ghulman, B A (2000) Predicting construction cost growth in ODOT's paving projects using information available at the bidding time, Unpublished PhD Thesis, , Oklahoma State University.
Hajji, A M (2013) Development of a productivity-based economic, energy, environmental model for heavy duty diesel construction equipment, Unpublished PhD Thesis, , Oklahoma State University.
- Type: Thesis
- Keywords: accuracy; bias; construction sector; residential; working conditions; construction project; equipment; highway; specifications; environmental impact; estimating; infrastructure project; productivity; professional
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/1428390142
- Abstract:
The use of heavy duty diesel (HDD) equipment in infrastructure projects accounts for a large quantity of fuel consumption, pollutants emissions and the majority of the total cost of the project compared to others. Construction professionals need a tool that can be used to estimate not only the cost, but also the fuel use and emissions footprint of construction projects, particularly HDD equipment activities. The main purpose of this research is to develop an E3 tool to estimate the economic, energy, and environmental impact of bulldozers, scrapers, excavators, and dum trucks. The tool was developed by combining the multiple linear regression (MLR)-based productivity rate model of selected HDD equipment from RSMeans Heavy Construction Data with the US EPA's NONROAD model. The results showed that the overall productivity prediction models accounted for high percentage of variability in its respective data source; 95% for bulldozer, 99% for scraper, 92% for excavator, and 94% for dump truck. While the cost models also accounted for high percentage of variability, which are 97% for bulldozer, 99% for scraper, 70% for excavator, and 88% for dump truck. Since the productivity and cost models had high precision and accuracy with low bias, it can be used as the basis for estimating the total cost and fuel quantities that will be required and the total expected pollutant emissions for the project. The total fuel use and emissions estimates resulted from E3 model are also useful to observe its relationship with HDD equipment performance attributes, such as engine size and the attachments set up to the equipment (buckets or blades), and with various earthwork working conditions, such as type of soil, distance, depth, and cycle time. This tool can also be used to estimate emissions for various construction sectors. By using construction plans and specifications, the methodology and tool presented in this research can be used to estimate cost, fuel use, and emissions from commercial, residential, industrial, or heavy highway. Once all types of construction can be covered by this methodology, it is possible to develop new fuel use and emissions inventories for the construction industry in general.
Johnson, L R (1969) A competitive strategy model for highway construction proposals, Unpublished PhD Thesis, , Oklahoma State University.
Karimi, B (2018) Evaluation of emissions reduction strategies for heavy duty diesel construction equipment, Unpublished PhD Thesis, , Oklahoma State University.
Kim, J (2022) A multimodal approach to improve fire safety on construction sites, Unpublished PhD Thesis, , Oklahoma State University.
King-Lewis, A (2020) Diversity and inclusion of women in the construction industry, Unpublished PhD Thesis, , Oklahoma State University.
Lee, B-H (2011) Forecasting wheat yield and quality conditional on weather information and estimating construction costs of agricultural facilities, Unpublished PhD Thesis, , Oklahoma State University.
Lusby, A K (2003) The effect of increased public investment in transportation infrastructure on oklahoma's economic development, Unpublished PhD Thesis, , Oklahoma State University.
Miller, R H (1973) Life cycle system model for estimating construction equipment ownership costs, Unpublished PhD Thesis, , Oklahoma State University.
Rainer, R K (1968) Predicting productivity of one or two elevators for construction of high-rise buildings, Unpublished PhD Thesis, , Oklahoma State University.
Shararah, H H (1981) Evaluation of wall construction techniques based upon economical factors influencing labor and materials, Unpublished PhD Thesis, , Oklahoma State University.
Spencer, G R (1987) Integration of cost estimating with critical path scheduling, Unpublished PhD Thesis, , Oklahoma State University.
Syachrani, S (2010) Advanced sewer asset management using dynamic deterioration models, Unpublished PhD Thesis, , Oklahoma State University.
Tamimi, M F (2022) Reliability and sensitivity analysis of civil and marine structures using machine-learning-assisted simulation, Unpublished PhD Thesis, , Oklahoma State University.
Trost, S M (1998) A quantitative model for predicting the accuracy of early cost estimates for construction projects in the process industry, Unpublished PhD Thesis, , Oklahoma State University.
Zeitoun, A A (1992) Evaluation of cost and schedule growth trends during construction, Unpublished PhD Thesis, , Oklahoma State University.