PhD ThesisDisasters can have devastating effects on our communities and can cause great
suffering to the people who reside within them. Critical infrastructure underpins the
stable functioning of these communities and the severity of disasters is often linked to
failure of these systems.
Traditionally, the resilience of infrastructure systems is assessed by subjecting
physically based models to a range of hazard scenarios. The problem with this
approach is that it can only inform us of inadequacies in the system for the chosen
scenarios, potentially leaving us vulnerable to unforeseen events. This thesis
investigates whether network graph theory can be used to give us increased
confidence that the system will respond well in untested scenarios by developing a
framework that can identify generic system characteristics and hence describe the
underlying resilience of the network. The novelty in the work presented in this thesis
is that it overcomes a shortcoming in existing network graph theory by including the
effects of the spatial distribution of geographically dispersed systems.
To consider spatial influence, a new network generation algorithm was developed
which incorporated rules that connects system components based on both their
spatial distribution and topology. This algorithm was used to generate proxy networks
for the European, US and China air traffic networks and demonstrated that the
inclusion of this spatial component was crucial to form the highly connected hub
airports observed in these networks. The networks were then tested for hazard
tolerance and in the case of the European air traffic network validated using data from
the 2010 Eyjafjallajökull eruption. Hazard tolerance was assessed by subjecting the
networks to a series of random, but spatially coherent, hazards and showed that the
European air traffic network was the most vulnerable, having up to 25% more
connections disrupted compared to a benchmark random network. This contradicts
traditional network theory which states that these networks are resilient to random
hazards. To overcome this shortcoming, two strategies were employed to improve the
resilience of the network. One strategy ‘adaptively’ modified the topology (crises
management) while the other ‘permanently’ modified it (hazard mitigation). When
these modified networks were subjected to spatial hazards the ‘adaptive’ approach
Page i
produced the most resilient network, having up to 23% fewer cancelled air routes
compared to the original network, for only a 5% change in airport capacity. Finally, as
many infrastructure networks are flow based systems, an investigation into whether
graph theory could identify vulnerabilities in these systems was conducted. The
results demonstrated that by using a combination of both physically based and graph
theory metrics produced the best predictive skill in identifying vulnerable nodes in the
system.
This research has many important implications for the owners and operators of
infrastructure systems. It has demonstrated the European air traffic network to be
vulnerable to spatial hazard and shown that, because many infrastructure networks
possess similar properties, may therefore be equally vulnerable. It also provides a
method to identify generic system vulnerabilities and strategies to reduce these. It is
argued that as this research has considered generic networks it can not only increase
infrastructure resilience to known threats but also to previously unidentified ones and
therefore is a useful method to help protect these systems to large scale disasters and
reduce the suffering for the people in the communities who rely upon them.EPSR
