Characterizing the robustness of power-law networks that experience spatially-correlated failures

Abstract

Knowing the ability of networked infrastructure to maintain operability following a spatially distributed hazard (e.g. an earthquake or a hurricane) is paramount to managing risk and planning for recovery. Leveraging topological properties of the network, along with characteristics of the hazard field, may be an expedient way of predicting network robustness compared to more computationally-intensive simulation methods. Prior work has shown that the topological properties are insightful for predicting robustness, considered here to be measured by the relative size of the largest connected subgraph after failures, especially for networks experiencing random failures. While this does not equate to full engineering-based performance, it does provide an indication of the robustness of the network. In this work, we consider the effect that spatially-correlated failures have on network robustness using only spatial properties of the hazard and topological properties of networks. The results show that the spatial properties of the hazard together with the mean nodal degree, mean clustering coefficient, clustering coefficient standard deviation and path length standard deviation are the most influential factors in characterizing the network robustness. Using the results, recommendations are made for infrastructure management/owners to consider when improving existing systems, or designing new infrastructure. Recommendations include examining the known possible locations of potential hazards in relation to the system and considering the level of redundancy within the system.