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Managing project risk is a key part of the successful implementation of anylarge project and is widely recognized as a best practice for public agenciesto deliver infrastructures. The conventional method of identifying andevaluating project risks involves getting input from subject matter experts atrisk workshops in the early phases of a project. As a project moves through itslife cycle, these identified risks and their assessments evolve. Some risks arerealized to become issues, some are mitigated, and some are retired as nolonger important. Despite the value provided by conventional expert-basedapproaches, several challenges remain due to the time-consuming and expensiveprocesses involved. Moreover, limited is known about how risks evolve fromex-ante to ex-post over time. How well does the project team identify andevaluate risks in the initial phase compared to what happens during projectexecution? Using historical data and artificial intelligence techniques, thisstudy addressed these limitations by introducing a data-driven framework toidentify risks automatically and to examine the quality of early risk registersand risk assessments. Risk registers from more than 70 U.S. majortransportation projects form the input dataset.

Data-Driven Risk Modeling for Infrastructure Projects Using Artificial Intelligence Techniques