Infrastructure resilience is generally concerned with how a system's functionality changes over time in response to a disruption. When illustrated, the resilience curve framing mechanism has been adopted throughout critical infrastructure resilience literature for both conceptualization and quantified analysis. Through modeling and simulation, metrics derived from resilience curves are often used to evaluate resilience improvements, such as targeted investment and recovery sequencing. There are significant challenges with this approach, including: establishing appropriate performance measures, defining baseline or target performance levels, converting measures to metrics via value propositions, and translating measures and metrics between interconnected systems.

This research is focused on four overarching questions. Together, they consider methods in which resilience curves can provide actionable analysis for stakeholders in infrastructure systems.

  1. How can stakeholders' value systems be represeted and quantified in resilience curves for interdepedent infrastructure systerms?
  2. How can resilience curves incorporate complexity, uncertainty, and unknowns?
  3. When modeling infrastructure systems for resilience, how might the modeler identify factors that necessitate additional complexity?
  4. In addition to system metrics, how can simulated resilience curves provide stakeholders with insights for actionable decisions?

To answer these questions, research is broken into three efforts:

  1. A Sytematic Search and Review of Resilience Curves to synthesize the implementation of resilience curves across current literature
  2. Modeling Assertions for Expanding Complexity to guide the incorporation of resilience curves into infrastructure simulation
  3. Multiverse Simulation to provide a novel modeling approach for identifying stakeholder value and actions
Conference Papers
[1]C. Poulin and M. Kane, "Identifying Heterogeneous Infrastructure Interdependencies through Multiverse Simulation", In 2019 Resilience Week (RWS), pp. 123-131, 2019. [bibtex] [pdf] [doi]