Sensitivity Analysis of Interdependency Parameters Using Probabilistic System Models

Abstract

Comprehensive models of infrastructure networks feature many parameters characterizing the complex interdependencies that exist between systems. Most of these parameters are uncertain. Conducting sensitivity analyses is one way to characterize uncertainty in estimations of system-level performance based on component and interdependency parameters. Doing so provides an assessment of the importance of varying parameters and informs how to achieve targeted system outcomes through component- and system-level changes. To do this over interdependent infrastructure networks, we conduct inference over probabilistic Bayesian network-based models of these systems. We have developed a framework along with accompanying algorithms to conduct computationally tractable exact inference over the network model. Through a series of these analyses, we are able to analyze the impacts of changes in parameters on estimations of system-level performance. We apply the framework to a water distribution system including its dependencies with power and transportation networks. The results of the analyses show the effect of varying system parameters on probabilities of providing service across the network. We investigate the impacts on system performance of adding redundant power supplies, changing link configurations, and increased or reduced probabilities of component failures. The use of the sensitivity analysis results to support performance-based design based on system-level reliability measures is discussed.