Data-Driven Evaluation of Project Risk Registers

Geotechnical risk is an important part of managing large projects. This study developed a data-driven approach to tracking risk identification on major US transportation projects using textual analysis of historical project documents. In early phases of a project, the identification and assessment of risk is based largely on expert judgment. As a project moves through its life cycle, identified risks and their assessments evolve. Some risks are realized to become issues, some are mitigated, and some are retired as no longer important. The study investigated the comprehensiveness of early risk registers on 11 large transportation projects in comparison to how those risks changed as the projects progressed. Finite state automation methods similar to Markov chain models were used to track changes in risk attributes as the case-study projects matured. The objective was to be better able to anticipate how project risks change as projects mature and to be better able to forecast changes to risk registers through the project life cycle. Results suggest that fewer than 60% of initially identified risks, both geotechnical and other, ultimately occur in projects, while more than 40% are retired. Categorizing risk management styles illustrates that planning for geotechnical risks in the initial phase of the project is necessary but not sufficient for successful project delivery.