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Framing Critical Infrastructure Resilience Using Systems Engineering Methods Open Access

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The last two decades have brought several natural and man-made impact events that have affected the way-of-life in the United States and other countries around the world. Many of the impacts have been against critical infrastructures—the backbone that sustains most industrialized countries. Because of this, establishing policies, programs, and operational initiatives for critical infrastructure resilience has been a global priority. Substantial progress has been made over the last decade to better articulate and address the critical infrastructure resilience challenge; however, there is still considerable room for improved problem framing and quantifiable approaches, with decision-making traceability. Given these global challenges surrounding critical infrastructure resilience, the following real-world problems emerge:Problem 1: There is a need for proven extensible critical infrastructure resilience problem-solving frameworks.Problem 2: There are inadequate traceable resource allocation methodologies to facilitate critical infrastructure resilience decision-making.Problem 3: There is a lack of verified or validated modeling approaches to demonstrate critical infrastructure resilience resource decision-making.This praxis describes concepts that lay the foundation for an extensible, holistic approach to analyzing the resilience of critical infrastructure that can help decision-makers allocate resources and estimate functional performance. The research presented here applies systems engineering methods by mapping the current resilience lexicons to create an overall framework for analysis and resource decision-making (Problem 1). The inherent utility of the systems engineering method then enables the allocation of resources and performance from mission, to capability, and then to the system, to directly link resource decisions to overall critical infrastructure resilience (Problem 2). These concepts are applied to a commuter rail critical infrastructure system case-study that has sufficient data to support the resource allocation concepts, along with the accompanying functional performance output. Through statistical resource allocation estimates, a dynamic diffusion model is used to estimate and link critical infrastructure functional performance for the commuter rail system. The model estimates are then compared to the actual commuter rail system verifying a resource allocation modeling technique at a 95% confidence level (Problem 3).

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