Bypaneni, S P K (2017) A Bayesian network-based decision framework for selecting project delivery methods in highway construction. PhD thesis, University of Kansas, USA.
Abstract
Transportation agencies currently have several options in delivering their highway construction projects. Selecting an appropriate project delivery method (PDM) is a complex decision-making process. Researchers and transportation industry practitioners have been striving to discover the knowledge and methodologies to enhance the project delivery decision. However, through conducting an extensive literature review of existing methodologies, it is found that quantitative approaches, implementing probabilistic comparisons, to project delivery decisions are not fully addressed or understood. To fill this gap, this research aims at developing a decision framework by implementing Bayesian Network (BN), an advanced statistical tool, for selecting an appropriate PDM in highway construction industry. The BN-based decision framework incorporates the decision driving factors such as project attributes, risk profiles, project complexity, cost, and time. In developing the BN-based decision framework, this dissertation employed several research methodologies and techniques, including content analysis, questionnaire, case studies, cluster analysis, ANOVA, correlation and reliability analysis, and cross-validation techniques. The dissertation follows a four-journal paper format. The first paper explores the impact of project size on highway design-bid-build (D-B-B) and design-build (D-B) projects. The second paper identifies and evaluates the risks involved in highway project delivery methods: D-B-B, D-B, and construction manager/general contractor (CM/GC). Building upon the findings and results from the first two papers, the third paper determines the probabilistic dependence between the decision factors and develops a theoretical decision framework using BNs for selecting an appropriate PDM. The fourth paper focuses on demonstrating the practical application of the proposed BN-based decision framework using case studies. Also, the final paper presents a k-fold (cross-validation) technique to test and verify the accuracy of the proposed BN-based decision framework. This dissertation contributes to the theoretical body of knowledge by introducing a new quantitative approach using BNs for PDM selection. The findings from this study indicate that implementing BNs facilitate the owner/decision maker in a better understanding of probabilistic comparison and selection of an appropriate PDM for highway construction projects. State transportation agency officials can utilize these findings as a supplemental tool for their project delivery decisions.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Tran, D |
| Uncontrolled Keywords: | accuracy; complexity; reliability; highway; decision framework; delivery method; project delivery; cluster analysis; content analysis; case studies |
| Index terms: | content analysis, literature review, general contractor, journal, accuracy, cluster analysis, methodology, questionnaire, validation, construction manager, practitioner, body of knowledge, project complexity, complexity, reliability analysis, case study, delivery method, owner, transportation agency, decision-making process, decision framework, design-bid-build, bayesian network, highway construction, research methodology, dissertation, option, project delivery |
| Subjects: | civil engineering, professional development, probabilistic model, decision analysis, sociology, delivery method, systems engineering, research design and methodology, data collection methods, profession, project delivery, data analysis and analytics, research methods, organizational theory, transportation engineering, research dissemination and communication, reliability engineering, practitioner, contractual arrangements, knowledge management |
| Topics: | Procurement, Risk Management, Engineering Principles, Project Management, Roles and Professions, Stakeholder Management, Information Management, Research Practice, Digital Applications |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here