Infrastructure projects often encounter performance challenges, such as cost overruns and safety issues, due to complex risk interactions and systemic risks. Existing literature treats risk interactions and systemic risks separately and relies on models that struggle with nonlinearities, adaptability, and practical applications, leading to suboptimal risk management. To address this gap, this paper uses machine learning (ML) algorithms to analyze historical project data and predict the impacts of risk interactions and systemic risks on future projects. The results show that ML-based models provide accurate and practical data-driven predictions of project performance under risk interactions and systemic risks. These findings are valuable for infrastructure project managers seeking to improve risk mitigation strategies and project outcomes. The paper lays also the foundation for future research on leveraging advanced predictive analytics in managing complex project risks more effectively.

中文翻译：

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使用机器学习预测和管理基础设施项目中的风险交互和系统性风险


由于复杂的风险交互和系统性风险，基础设施项目经常会遇到性能挑战，例如成本超支和安全问题。现有文献将风险相互作用和系统性风险分开处理，并依赖于难以应对非线性、适应性和实际应用的模型，导致风险管理欠佳。为了解决这一差距，本文使用机器学习 （ML） 算法来分析历史项目数据，并预测风险交互和系统性风险对未来项目的影响。结果表明，基于 ML 的模型提供了在风险交互和系统性风险下对项目绩效的准确和实用的数据驱动预测。这些发现对于寻求改进风险缓解策略和项目成果的基础设施项目经理来说非常有价值。该论文还为未来利用高级预测分析更有效地管理复杂项目风险的研究奠定了基础。