Author Description

In recent years, low-probability, high-impact natural hazard events have become an increasingly serious threat to infrastructure systems and communities. This trend is attributable to several factors, including climate change, population growth and urbanization in hazard-prone areas, among others. In addition, systems and communities are exposed to other exogenous and endogenous factors (e.g., uncertain economic and political circumstances, unpredictable human beings' behaviors) affecting their long-term performance in a positive or negative way. If the effects of these factors are not addressed in long-term system performance assessment and decision-making appropriately, it can result in a significant deviation from the system's intended performance, leading to unexpected expenditures and consequences. While it is important to characterize and understand various risk factors in effective risk management planning, most existing studies have focused on the assessment of system performance against natural hazards. To enable system or community to main performance and reduce the consequences of various types of uncertain events and/or conditions (i.e., risk factors), this dissertation provides a set of quantitative tools and frameworks that can assess the effects of risk factors on system performance. First, this dissertation proposes both theoretical and empirical approaches to quantifying the role of catastrophe risk insurance in community resilience planning. Second, a multi-component resilience assessment framework for a large-scale supply chain system is developed to predict the long-term system performance exposed to multiple uncertain events and conditions. Finally, this dissertation proposes a simulation framework that quantitatively assesses the direct and indirect effects of CAVs on a supply chain system. In summary, this dissertation provides a comprehensive understanding of how various risk factors can affect system performance. The tools and framework prop