Vulnerability of Interdependent Urban Infrastructure Networks: Equilibrium after Failure Propagation and Cascading Impacts. (25th January 2018)

• Record Type: Journal Article
• Authors: Lu, Liqun; Wang, Xin; Ouyang, Yanfeng; Roningen, Jeanne; Myers, Natalie; Calfas, George
• Editors: Waller, S. Travis; Machemehl, Randy
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 33:Number 4(2018:Apr.)
• Page Start: 300
• Page End: 315
• Publication Date: 2018-01-25
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12347
• Languages: English
• ISSNs: 1093-9687

Abstract

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.