Multi-Scale Modeling of Interdependent Critical Infrastructure System Performance During Hurricanes

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Critical infrastructure systems such as water and electric power networks provide essential services that underlie the economic prosperity, security, and public health of the U.S. These complex, interdependent systems are prone to failure during hurricanes. Improved modeling of the ability of these systems to meet the needs of society after a hurricane makes landfall would substantially improve our ability to manage the risk of these systems failing. However, there are fundamental research needs of both conceptual and computational natures in the area of risk analysis for critical infrastructure systems in hurricane-prone areas. Conceptually, we do not yet have modeling frameworks that allow for accurate prediction of the performance of large-scale interdependent infrastructure systems during hurricanes, a necessary starting point for accurate risk assessment and management. Computationally, many of the available tools that aim to model infrastructure performance at the scale of large metropolitan areas require long run times on large computer clusters, limiting their usefulness for practical infrastructure planning and management. Recent advances in both statistical methods and computing based on graphical processing units (?graphics cards?) enable advances that can address both the conceptual and computational limitations inherent in current approaches for risk analysis for interdependent infrastructure systems in hurricane-prone areas. The focus of this project is on developing methods for accurate performance and risk modeling for interdependent infrastructure systems, methods that are practical for infrastructure managers to use. While the focus of this project is on coupled water and power systems, the advances will have application much more broadly.This project will enable significantly more accurate and rapid risk analysis for interdependent infrastructure systems, allowing highly limited public infrastructure funds to be spent more efficiently and helping to better protect economic and public health during disasters. The models developed in this project will be practical for use on desktop computers with existing higher-end graphics cards, greatly enhancing the ability of infrastructure managers to run these models on their existing computer hardware. In addition, this project will yield insights into the factors that lead interdependent infrastructure systems to be more resilient during a hurricane, helping engineers and utility system managers better understand how to strengthen their systems. In parallel with the research efforts, this project will aim to interest students traditionally underrepresented in engineering programs in pursuing engineering as a career. This will be done through outreach at multiple educational levels.