Using Dynamic Bayesian Networks for Investigating the Impacts of Extreme Events

Investigating resiliency and interdependency of critical urban infrastructure has been the topic of interest in recent years (see for example, Zhang and Peeta 2011; Oh 2010). This is because of a surge in natural and man-made disasters over the last decade and limited resources available to cope with the resulting infrastructure failure. With an increased level of interdependencies among infrastructures, the potential for cascading failures are of a great concern. A cascading failure is one in which a failure in one infrastructure system causes the failure in one or more components of a second infrastructure (Rinaldi et al., 2001). Much of today’s emergency preparedness research is heavily focused on what is considered by many to be the eight “critical” infrastructures: (1) Telecommunications, (2) Electric Power Systems, (3) Natural Gas and Oil, (4) Banking and Finance, (5) Transportation, (6) Water Supply Systems, (7) Government Services and (8) Emergency Services. Within the United States’ transportation infrastructure systems, there exists approximately 5,000 public airports; 590,000 highway bridges; 120,000 miles of major railroad tracks; 2,000,000 miles of pipelines; 300 inland/coastal ports; 80,000 dams and 500 major urban public transit agencies. A large majority of these transportation infrastructure systems are highly interdependent with one another. The failure/collapse of one will more than likely cause the failure/collapse of another.

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