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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J I (2021) A cost estimation model for improving the budget estimates of industrial plant construction projects, Unpublished PhD Thesis, , The George Washington University.
Adoko, M T (2016) Developing a cost overrun predictive model for complex systems development projects, Unpublished PhD Thesis, , The George Washington University.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L F (2006) Stochastic approach to risk assessment of project finance structures under public private partnerships, Unpublished PhD Thesis, , The George Washington University.
Bersson, T F (2012) A framework for application of system engineering process models to sustainable design of high performance buildings, Unpublished PhD Thesis, , George Washington University.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collaboration: How transportation agencies develop skills and systems to manage public-private partnerships, Unpublished PhD Thesis, , The George Washington University.
Charoenphol, D (2017) Using robust statistical methodology to evaluate the performance of project delivery systems: A case study of horizontal construction, Unpublished PhD Thesis, , George Washington University.
Cho, S (2000) Sequential estimation and decision-making in project management: A Bayesian way and heuristic approaches, Unpublished PhD Thesis, , The George Washington University.
Elsherbiny, A (2021) Prediction of design error rework cost in EPC industrial projects, Unpublished PhD Thesis, , George Washington University.
- Type: Thesis
- Keywords: failure; construction engineering; construction phase; disputes; investment; learning; subcontractor; case study; machine learning
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/2471026465
- Abstract:
Design errors constitute the major part of rework in construction engineering projects. Due to the significant overlap between engineering, design and construction works industrial Engineering, Procurement and Construction (EPC) projects have high propensity of encountering design errors. The reviewed literature revealed that design error rework on industrial projects can reach 12. 4% of projects value. Given the high investment costs of industrial projects in the USA, design error rework cost could reach $4. 6Bn per annum. Design error rework costs and the failure to agree on their magnitude have been reported as one of the major disputes causes in construction engineering projects. The goal of this study is to examine the design error rework costs encountered in industrial EPC projects in Texas and to develop a quantitative predictive model to assist EPC contractors to obtain accurate estimates of design error rework cost in industrial EPC projects. To accomplish this, the research objectives are to (1) identify the main contributors of design error rework cost through analyzing change orders in industrial EPC projects identifying the causes of the change orders, and pinpointing the percentage and cost of design errors, (2) develop a predictive model to quantify the design error rework cost, (3) use the developed predictive model to provide accurate estimates of design error rework that will be used by EPC contractors to negotiate down the proposed subcontractor’s design error price, thus reducing the cost of design errors. Accordingly, the predictive model will also reduce the disputes related to design error cost through providing reliable estimates that satisfy the expectations of both subcontractors and EPC contractor. The analysis of fifty (50) contracts revealed that mean design error rework cost accounted for around 15. 5% of the projects value and for 62% of the rework cost. Utilizing actual historical data, three (3) machine learning algorithms were deployed to predict the design error rework cost during the detailed engineering and construction phase of industrial EPC projects. The results were compared with those of multiple linear regression models. The machine learning algorithms results outperformed the multiple linear regression models results. Support Vector Regression (SVR) algorithm utilizing the important parameters revealed by Random Forest (RF) algorithm outperformed ll other machine learning models and was the most accurate algorithm in predicting design error rework cost. The SVR algorithm had coefficient of determination (R2) of 98. 3%, Root Mean Square Error (RMSE) of 104,876. 70 and Mean Absolute Error (MAE) of 7,195. 11 on the testing dataset. The SVR algorithm capability to assist the EPC contractor in negotiating down the subcontractors’ design error rework price was demonstrated through a case study. Using the SVR algorithm, the EPC contractor successfully negotiated down the design error rework price of subcontractors by 19%.
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.
Holmlin, R M (2016) Pre-design methodology for establishing scope-budget and scope-duration alignment for capital projects, Unpublished PhD Thesis, , 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.
Joao, Z R (2021) Road construction assessment model (RC-AM) to prevent contract overbilling in Angola, Unpublished PhD Thesis, , George Washington University.
Kim, E (2000) A study on the effective implementation of earned value management methodology, Unpublished PhD Thesis, , The George Washington University.
Krone, S J (1991) Decreasing the impact of changes, Unpublished PhD Thesis, , 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.
McDavid, H A (1996) Construction and economic development: A stimulus or constraint in developing countries, Unpublished PhD Thesis, , George Washington University.
Momtazi, S (2021) Reducing cost overruns in construction projects using lean methodology, Unpublished PhD Thesis, , 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.
Reid, J S (2019) Evaluating the impact of using agile methodologies in heavy-civil construction, Unpublished PhD Thesis, , 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.
Stuban, S M F (2011) Employing risk management to control military construction costs, Unpublished PhD Thesis, , 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.
Toliver, B L (2018) Implementing project management plans to control construction costs on military projects in Korea, Unpublished PhD Thesis, , George Washington University.
Tonimoghadam, F (2021) A predictive model for reducing cost and time for the detection of clashes in residential and commercial constructions projects, Unpublished PhD Thesis, , George Washington University.
Wenzelberger, J P (1987) The economic analysis of tall buildings (Milwaukee, Wisconsin; Boston, Massachusetts; Kansas City, Missouri), Unpublished PhD Thesis, , George Washington University.
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.