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.

翻译：项目风险管理是大型项目成功实施的关键环节，被广泛认为是公共机构交付基础设施的最佳实践。传统风险识别与评估方法需要在项目早期阶段组织专家研讨会，由领域专家提供专业意见。随着项目生命周期推进，已识别风险及其评估结果会动态演变：部分风险转化为实际问题，部分得到缓解，部分因不再重要而被淘汰。尽管传统的基于专家方法具有重要价值，但由于其耗时昂贵的过程仍存在诸多挑战。此外，关于风险如何随时间从事前评估演变为事后结果的研究尚不充分——项目团队在初始阶段的风险识别与评估能力，与项目执行期间的实际表现相比究竟如何？本研究利用历史数据与人工智能技术，通过构建数据驱动框架来突破上述局限，实现风险自动识别，并评估早期风险登记簿及风险评估的质量。本研究的输入数据集源自美国70余个重大交通项目的风险登记簿。