ENDORSING PARTNERS

Modelling
Infrastructure Systems
for Resilience and
Sustainability

The following are confirmed contri...
MODELLING INFRASTRUCTURE
SYSTEMS FOR RESILIENCE AND
SUSTAINABILITY
Sarah Dunn, Sean Wilkinson, Gaihua Fu and Richard Dawso...
ANALYSIS OF INFRASTRUCTURE SYSTEMS

Underlying
Network
Architecture
Vejvodova (2006)
SCALE-FREE NETWORKS

EXPONENTIAL NETWORKS

• The Internet

• Electrical Distribution Systems

• The World-Wide-Web

• Airl...
NETWORK GENERATION ALGORITHMS


Scale-free network generation algorithm:

Barabasi, A. L. and Albert, R. (1999). "Emergen...
NETWORK GENERATION ALGORITHMS


Exponential network generation algorithm:

Wilkinson, S. M., Dunn, S., and Ma, S., (2012)...
TOPOLOGICAL HAZARD TOLERANCE


Infrastructure networks have been shown to be:


Resilient to random hazard
TOPOLOGICAL HAZARD TOLERANCE


Infrastructure networks have been shown to be:


Vulnerable to targeted attack
DEVELOPMENT OF SPATIAL NETWORK MODEL


Starts with the input of initial conditions




Seed node locations and their ge...
DEVELOPMENT OF SPATIAL NETWORK MODEL

CD = 100

CD = 300
PROXY FOR REAL-WORLD NODAL LAYOUT
EXPONENTIAL SYNTHETIC NETWORKS

CD = 100

CD = 200

CD = 300
TOPOLOGICAL HAZARD TOLERANCE


Assessed by subjecting the networks to:
Degree attack strategy

Random hazard
SPATIAL HAZARD TOLERANCE


Assessed by using a ‘central attack’ spatial hazard


Remove nodes in order of their distance...
SPATIAL HAZARD TOLERANCE
SPATIAL HAZARD TOLERANCE
CONCLUSION


To date little work has been done on spatially distributed
networks



Space has a small but important effe...
THANK YOU.
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SMART International Symposium for Next Generation Infrastructure: Modelling Infrastructure Systems for Resilience and Sustainability

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A presentation conducted by Miss Sarah Dunn, Newcastle University.
Presented on Thursday the 3rd of October 2013.

Modern infrastructure systems are vital to the functioning of modern society.They promote social well-being, support economic development and are crucial in mitigating the effects of natural hazards. While there is some understanding of their mitigation role, there has been little quantifiable work on how they support our societies or how they stimulate economic development. Some recent analysis of infrastructure
systems have shown that many of these seemingly different systems display similar architectures to each other leading to the hypothesis that the evolution of these systems is a result of underlying drivers that are common to all. This paper presents a network model that captures the growth of infrastructure networks in terms of architecture, hazard tolerance and geographical characteristics. The results presented in the paper suggest that the model may be the basis for an enhanced understanding of the role that infrastructure plays in sustaining our communities

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SMART International Symposium for Next Generation Infrastructure: Modelling Infrastructure Systems for Resilience and Sustainability

  1. 1. ENDORSING PARTNERS Modelling Infrastructure Systems for Resilience and Sustainability The following are confirmed contributors to the business and policy dialogue in Sydney: • Rick Sawers (National Australia Bank) • Nick Greiner (Chairman (Infrastructure NSW) Monday, 30th September 2013: Business & policy Dialogue Tuesday 1 October to Thursday, 3rd October: Academic and Policy Dialogue Presented by: Miss Sarah Dunn, Newcastle University www.isngi.org www.isngi.org
  2. 2. MODELLING INFRASTRUCTURE SYSTEMS FOR RESILIENCE AND SUSTAINABILITY Sarah Dunn, Sean Wilkinson, Gaihua Fu and Richard Dawson
  3. 3. ANALYSIS OF INFRASTRUCTURE SYSTEMS Underlying Network Architecture Vejvodova (2006)
  4. 4. SCALE-FREE NETWORKS EXPONENTIAL NETWORKS • The Internet • Electrical Distribution Systems • The World-Wide-Web • Airline Networks (Albert, et al. Nature: 1999) (Albert, et al. Nature: 2000) Newman (2003) (Sole, et al. 2008) (Wilkinson, et al. 2012) Carvalho, et al. (2009)
  5. 5. NETWORK GENERATION ALGORITHMS  Scale-free network generation algorithm: Barabasi, A. L. and Albert, R. (1999). "Emergence of scaling in random networks." Science 286(5439): 509-512.
  6. 6. NETWORK GENERATION ALGORITHMS  Exponential network generation algorithm: Wilkinson, S. M., Dunn, S., and Ma, S., (2012) ‘The Vulnerability of the European Air Traffic Network to Spatial Hazards’ Natural Hazards. 60(3): 1027-1036.
  7. 7. TOPOLOGICAL HAZARD TOLERANCE  Infrastructure networks have been shown to be:  Resilient to random hazard
  8. 8. TOPOLOGICAL HAZARD TOLERANCE  Infrastructure networks have been shown to be:  Vulnerable to targeted attack
  9. 9. DEVELOPMENT OF SPATIAL NETWORK MODEL  Starts with the input of initial conditions   Seed node locations and their geographic influence The remaining nodes are then added individually Each cluster can attract new nodes  New nodes located randomly within cluster  Each cluster expands with added nodes  𝑃 𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜 𝑛𝑛𝑛𝑛𝑛 𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑟𝑟𝑟𝑟𝑟𝑟 𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑅𝑅𝑅𝑅𝑅𝑅 = 𝐶 𝐷 ln 𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜 𝑛𝑛𝑛𝑛𝑛 + 1
  10. 10. DEVELOPMENT OF SPATIAL NETWORK MODEL CD = 100 CD = 300
  11. 11. PROXY FOR REAL-WORLD NODAL LAYOUT
  12. 12. EXPONENTIAL SYNTHETIC NETWORKS CD = 100 CD = 200 CD = 300
  13. 13. TOPOLOGICAL HAZARD TOLERANCE  Assessed by subjecting the networks to: Degree attack strategy Random hazard
  14. 14. SPATIAL HAZARD TOLERANCE  Assessed by using a ‘central attack’ spatial hazard  Remove nodes in order of their distance from the centre of the network
  15. 15. SPATIAL HAZARD TOLERANCE
  16. 16. SPATIAL HAZARD TOLERANCE
  17. 17. CONCLUSION  To date little work has been done on spatially distributed networks  Space has a small but important effect on the degree distribution of a network and can have a significant effect on the hazard tolerance  We have developed a network model which is capable of capturing the growth of infrastructure systems in terms of both their network architecture and geographical distribution  This can be used to form an assessment of their hazard tolerance to spatial hazards
  18. 18. THANK YOU.

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