Vulnerability of Interdependent Urban Infrastructure Networks: Equilibrium after Failure Propagation and Cascading Impacts

Abstract

The functionality of modern cities relies heavily on interdependent infrastructure systems such as those for water, power, and transportation. Disruptions often propagate within and across physical infrastructure networks and result in catastrophic consequences. The reaction of communities to disasters (e.g., seeking alternative resource sources) may further transfer and aggravate the burden on surviving infrastructures, which may facilitate cascading secondary disruptions. Hence, a holistic analysis framework that integrates infrastructure interdependencies and human community behaviors is needed to evaluate a city's vulnerability to disruptions and to assess the impact of a disaster. To this end, we develop a game‐theoretical equilibrium model in a multilayer infrastructure network to systematically investigate the mutual influence between the infrastructures and the communities. Two types of infrastructure failure patterns are formulated to capture general network interdependencies; network equilibrium is extended into infrastructure and community systems to address redistribution of demand for life‐supporting resources; the societal impact of disasters is estimated based on communities’ resource demand loss, cost increase, as well as total infrastructure failures. A real‐world case study based on Maiduguri, Nigeria, is implemented to demonstrate the proposed model and algorithm, and to reveal insights.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 25, 2018
Source ID
10.1111/mice.12347

Entities

People

  • George Calfas
  • Jeanne Roningen
  • Liqun Lu
  • Natalie Myers
  • Xin Wang
  • Yanfeng Ouyang

Organizations

  • Cold Regions Research and Engineering Laboratory
  • Engineer Research and Development Center
  • United States Army Corps of Engineers
  • University of Illinois Urbana–Champaign
  • University of Wisconsin–Madison

Tags

Fields of Study

  • Computer science

Readers

  • Emergency Management and Homeland Security.
  • Neural Network Machine Learning.