Analysis of Risk Identification and Relationships for Major Engineering and Construction Transportation Projects

Abstract

Risk management has been integrated into transportation infrastructure construction projects for more than two decades. Risk management seeks to identify possible events that could impact the performance of a project, and allow the project team to influence those events to either reduce the severity of negative possibilities or increase the severity of positive possibilities. Construction risk management literature has focused on each step of the risk management process with the aim of making the next incremental improvement in project performance. Researchers focusing on risk identification have done so primarily by proposing different categorization schemes and the primary factors on which project teams should place their focus. While these studies introduce an important starting point for project managers, they nearly always start with identification of risk factors through a literature review, and measure importance through some version of an expert survey or questionnaire. This approach lacks sufficient rigor as it relies on the evaluation of risks facing hypothetical projects in the abstract. Additionally, the relationship between risk factors is often ignored under the assumption that each risk is independent. Further, there is no existing research addressing the disconnect between risk identification tools and the inability to identify risks in practice. This dissertation provides a comprehensive examination of the risks facing State Transportation Agencies (STA) and the challenges in identifying those risks. Content analysis of more than 5000 risk statements from 289 transportation construction projects revealed 34 common risk factors. The risk factors were reviewed by industry professionals and compared with the factors frequently mentioned in the literature. Additionally, the risks were examined for their relationships to other risks, leading to the identification of 60 significant relationships among the risk factors. The risk relationships were determined by examining the sentiment within risk statements and whether they were identified together in the same project, and validated by a panel of risk practitioners. The final study included 12 interviews with professionals managing one or more projects that experienced incomplete risk identification. The study identified 10 contributing factors through a thematic analysis of the interviews including cognitive biases, communication and alignment, facilitator expertise, imagination, experience, level of detail, management support, process standardization, stakeholder participation, and time constraints. The findings of this dissertation contribute to the body of knowledge in construction risk management. It is the largest empirical study of risk registers, identifying the most common risk factors and their relationships. The findings also have practical implications for public agencies managing transportation infrastructure projects.