Collaborative Research: Bayesian Methods for the Data-Driven Recovery of Networks: Measuring Impact and Building Resilience in Infrastructures and Communities

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The US government has increasingly emphasized resilience planning for critical infrastructure, where the combination of withstanding and recovering from disruptions that exacerbate our aging and vulnerable infrastructure systems, constitutes resilience. According to the Department of Homeland Security, the resilient operation of critical infrastructures is "essential to the Nation's security, public health and safety, economic vitality, and way of life." Of particular interest recently is an emphasis on the resilience of communities after a disruptive event, acknowledging that infrastructures do not exist for their own sake but serve society (e.g., citizens, industries), and in some cases, resilient communities assist in protecting the built environment. A resilient community would ideally be able to use the physical infrastructure to effectively communicate risk and coordinate recovery strategies to respond to and recover from disruptions, and ultimately adapt to change and learn from past disruptions. The objective of this work is to develop a new data-driven optimization framework to improve (i) the ability to model the performance of infrastructure networks, and (ii) the ability to plan for the recovery of these networks after a disruption, with an emphasis on community resilience and economic productivity. The research approach is composed of three components. The first component develops a new statistical technique, the hierarchical Bayesian kernel method, which integrates the Bayesian property of improving predictive accuracy as data are dynamically obtained, the kernel function that adds specificity to the model and can make nonlinear data more manageable, and the hierarchical property of borrowing information from different sources in sparse and diverse data situations which are common in disruptive events scenarios. The second component develops an infrastructure network recovery optimization formulation that minimizes the larger impact of infrastructure network performance with data-driven (and dynamically updated) hierarchical Bayesian kernel parameters of infrastructure recovery, along with solution techniques that account for the size and dynamic nature of model parameters. The application of the first two integrated components to electric power networks (where impact is measured on the safety and resilience of the community) and inland waterways (where impact is measured on economic productivity across multiple industries), constitutes the third component, offering two application perspectives on the impact of infrastructure network resilience and recovery.