Model-Based Reasoning for Aviation Safety Risk Assessments

This paper presents a probabilistic approach for using the model-based reasoning of Bayesian Belief Networks (BBNs) to perform risk assessments of new aviation safety products. Sponsored by NASA's Aviation Safety and Security Program [1], the author is leading a research team at Rutgers University in the creation of aircraft accident models in order to assess the projected relative risk reductions of an aeronautics technology portfolio. The modeling approach uses elements from a case study architecture, inductive reasoning and analytic generalization. Aspects of the modeling approach, including knowledge capture and sensitivity analyses are emphasized and preliminary results discussed.