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Abdollahipour, S (2013) Multi-parameter bidding in highway construction and rehabilitation projects, Unpublished PhD Thesis, , Oklahoma State University.
Alroomi, A S (2013) Integrative framework for sustainable development of the cost estimating profession, Unpublished PhD Thesis, , Oklahoma State University.
Brown, B H J (1974) An econometric forecasting model for a segment of the construction market, Unpublished PhD Thesis, , Oklahoma State University.
Duffy, G A (2009) Linear scheduling of pipeline construction projects with varying production rates, Unpublished PhD Thesis, , Oklahoma State University.
Eldin, N N (1987) Methodology for project management control in the construction industry, Unpublished PhD Thesis, , Oklahoma State University.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A (2000) Predicting construction cost growth in ODOT's paving projects using information available at the bidding time, Unpublished PhD Thesis, , Oklahoma State University.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Development of a productivity-based economic, energy, environmental model for heavy duty diesel construction equipment, Unpublished PhD Thesis, , Oklahoma State University.
Johnson, L R (1969) A competitive strategy model for highway construction proposals, Unpublished PhD Thesis, , Oklahoma State University.
Karimi, B (2018) Evaluation of emissions reduction strategies for heavy duty diesel construction equipment, Unpublished PhD Thesis, , Oklahoma State University.
Kim, J (2022) A multimodal approach to improve fire safety on construction sites, Unpublished PhD Thesis, , Oklahoma State University.
King-Lewis, A (2020) Diversity and inclusion of women in the construction industry, Unpublished PhD Thesis, , Oklahoma State University.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yield and quality conditional on weather information and estimating construction costs of agricultural facilities, Unpublished PhD Thesis, , Oklahoma State University.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K (2003) The effect of increased public investment in transportation infrastructure on oklahoma's economic development, Unpublished PhD Thesis, , Oklahoma State University.
Miller, R H (1973) Life cycle system model for estimating construction equipment ownership costs, Unpublished PhD Thesis, , Oklahoma State University.
Rainer, R K (1968) Predicting productivity of one or two elevators for construction of high-rise buildings, Unpublished PhD Thesis, , Oklahoma State University.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.
- Type: Thesis
- Keywords: accuracy; project team; bidding; cost information; factor analysis; regression analysis; construction project; estimator
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/304472287
- Abstract:
Purpose and objectives. The importance of accurate estimates during the early stages of capital projects has been widely recognized for many years. Early project estimates represent a key ingredient in business unit decisions and often become the basis for a project's ultimate finding. However, a stark contrast arises when comparing the importance of early estimates with the amount of information typically available during the preparation of an early estimate. Such limited scope definition often leads to questionable estimate accuracy. Even so, very few quantitative methods are available that enable estimators and business managers to objectively evaluate the accuracy of early estimates. The primary objective of this study was to establish such a model. To accomplish this objective, quantitative data were collected from completed construction projects in the process industry. Methods of analysis. Each of the respondents was asked to assign a one-to-five rating for each of forty-five potential drivers of estimate accuracy for a given estimate. The data were analyzed using factor analysis and regression analysis. The factor analysis was used to group the forty-five elements into eleven orthogonal factors. Regression analysis was performed on the eleven factors to determine a suitable model for predicting estimate accuracy. The resulting model, known as the Estimate Score procedure, allows die project team to score an estimate and then predict its accuracy based on the Estimate Score. In addition, a computer software tool, the Estimate Score Program, or ESP, was developed to automate the Estimate Score procedure. Findings and conclusions. The regression analysis identified five of the eleven factors that were significant at the α = 10% level. The five factors, in order of significance, were basic process design, team experience and cost information, time allowed to prepare the estimate, site requirements and bidding and labor climate. These five factors represent twenty-three of the forty-five elements and together account for almost seventy-six percent of the Estimate Score.
Zeitoun, A A (1992) Evaluation of cost and schedule growth trends during construction, Unpublished PhD Thesis, , Oklahoma State University.