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Abdenour, J I (2021) A cost estimation model for improving the budget estimates of industrial plant construction projects, Unpublished PhD Thesis, , The George Washington University.
Adoko, M T (2016) Developing a cost overrun predictive model for complex systems development projects, Unpublished PhD Thesis, , The George Washington University.
Alves, L F (2006) Stochastic approach to risk assessment of project finance structures under public private partnerships, Unpublished PhD Thesis, , The George Washington University.
Boyer, E J (2012) Building capacity for cross-sector collaboration: How transportation agencies develop skills and systems to manage public-private partnerships, Unpublished PhD Thesis, , The George Washington University.
Cho, S (2000) Sequential estimation and decision-making in project management: A Bayesian way and heuristic approaches, Unpublished PhD Thesis, , The George Washington University.
Farmer, C M (2018) Constructing program management offices for major defense acquisition programs: Factors to consider, Unpublished PhD Thesis, , The George Washington University.
Griffin, M G (2008) The lived experience of first line managers during planned organizational change: A phenomenological study of one firm in the residential construction industry, Unpublished PhD Thesis, , The George Washington University.
Innocent, M J F, Jr. (2018) Predicting military construction project time outcomes using data analytics, Unpublished PhD Thesis, , The George Washington University.
Kim, E (2000) A study on the effective implementation of earned value management methodology, Unpublished PhD Thesis, , The George Washington University.
Lounsbury, C R (1983) From craft to industry: The building process in North Carolina in the nineteenth century, Unpublished PhD Thesis, , The George Washington University.
Ngamthampunpol, D (2008) An assessment of safety management in the Thai construction industry, Unpublished PhD Thesis, , The George Washington University.
Park, J (2015) Essays on the delivery of public infrastructure projects: Empirical analyses on transportation projects in Florida, Unpublished PhD Thesis, , The George Washington University.
Schulte, W D, Jr. (1999) The effect of international corporate strategies and information and communication technologies on competitive advantage and firm performance: An exploratory study of the international engineering, procurement and construction (IEPC) industry, Unpublished PhD Thesis, , The George Washington University.
Shamma, E M (1988) A dynamic model for the growth of construction firms, Unpublished PhD Thesis, , The George Washington University.
Taku, A M (2021) Predicting modular efficiency in oil and gas capital projects using multi-criteria decision analysis, Unpublished PhD Thesis, , The George Washington University.
- Type: Thesis
- Keywords: construction activities; reasoning; construction site; logistics; modular construction; decision analysis; project stakeholder; stakeholder; critical success factor
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/2572603799
- Abstract:
The popularity of modularization as a strategy for capital projects in the engineering procurement and construction (EPC) industry is on the rise. Industry research has asserted that reducing the construction activities on the construction site reduces the overall project schedule and cost, thereby making modularization more cost and schedule efficient than nonmodular approaches. However, current literature is divided on the cost- and schedule-reducing effects of modular construction strategies. Primarily, this Praxis examines the efficiency and percentage differences between costs and schedules of Modular and Nonmodular approaches using multicriteria decision analysis (MCDA) methods. Then, the Praxis presents a predictive efficiency model to improve the benefits of using a modular strategy. The model is built from domain expert knowledge on modular critical success factors using Evidential Reasoning (ER). Critical success factors for modularity are identified and divided into four domains: Engineering, Supply Chain and Logistics, Modular Contractors, and Owner’s Involvement. The model aims to aid project stakeholders to better understand and manage the trade-offs between four influential project management domains, thereby enhancing the planning stages of modular projects. Sensitivity analysis techniques were used to identify the points where outcome probabilities are subject to change given different criteria. Finally, the research methodology was validated using a confusion matrix and ROC methods. Results indicated the model improved the prediction ability of modular projects by at least 10%.
Zhou, G (2021) Machine learning-based cost predictive model for better operating expenditure estimations of U.S. light rail transit projects, Unpublished PhD Thesis, , The George Washington University.