  Managing project risk is a key part of the successful implementation of any
large project and is widely recognized as a best practice for public agencies
to deliver infrastructures. The conventional method of identifying and
evaluating project risks involves getting input from subject matter experts at
risk workshops in the early phases of a project. As a project moves through its
life cycle, these identified risks and their assessments evolve. Some risks are
realized to become issues, some are mitigated, and some are retired as no
longer important. Despite the value provided by conventional expert-based
approaches, several challenges remain due to the time-consuming and expensive
processes involved. Moreover, limited is known about how risks evolve from
ex-ante to ex-post over time. How well does the project team identify and
evaluate risks in the initial phase compared to what happens during project
execution? Using historical data and artificial intelligence techniques, this
study addressed these limitations by introducing a data-driven framework to
identify risks automatically and to examine the quality of early risk registers
and risk assessments. Risk registers from more than 70 U.S. major
transportation projects form the input dataset.
