Abstract
In this paper, we present a new methodology for quantifying the reliability of complex systems, using techniques from network graph theory. In recent years, network theory has been applied to many areas of research and has allowed us to gain insight into the behaviour of real systems that would otherwise be difficult or impossible to analyse, for example increasingly complex infrastructure systems. Although this work has made great advances in understanding complex systems, the vast majority of these studies only consider a systems topological reliability and largely ignore their spatial component. It has been shown that the omission of this spatial component can have potentially devastating consequences. In this paper, we propose a number of algorithms for generating a range of synthetic spatial networks with different topological and spatial characteristics and identify real-world networks that share the same characteristics. We assess the influence of nodal location and the spatial distribution of highly connected nodes on hazard tolerance by comparing our generic networks to benchmark networks. We discuss the relevance of these findings for real world networks and show that the combination of topological and spatial configurations renders many real world networks vulnerable to certain spatial hazards.
Highlights
Infrastructure systems, including water, electricity, transportation and telecommunication, are of critical importance to our modern communities
We consider the impact that node introduction order has to the resulting hazard tolerance of the spatial scale-free and exponential networks; secondly, we consider the impact that nodal configuration has to hazard tolerance and we consider the combinations of hazard, node introduction order and nodal configuration that produces the ‘worst’ and ‘best’ resilience and compare this to the results of ‘traditional’ topological hazard
We quantify the reliability/resilience of networks with different characteristics using a modified version of the Relative Spatial Vulnerability Index (RSVI) of Li et al [24], which is a formalised measure of the methodology derived by Wilkinson et al [38], given by Eq (1)
Summary
Infrastructure systems, including water, electricity, transportation and telecommunication, are of critical importance to our modern communities. The reliability of these physical assets and the services they provide are vital for ensuring national security, public health and productivity [20]. It is no surprise that the reliability of these systems has received a great deal of attention in recent years [23] These systems are becoming increasingly complex and interdependent, meaning that they rely on each other to function normally [15,21], and this increased complexity and reliance is making these networked infrastructure systems harder to manage and assess [33]. One possible solution is to use a network graph theory approach to quantify the reliability of these complex infrastructure systems
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