Predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach rights and content


A novel predictive resilience assessment framework is proposed for interdependent water and transportation infrastructures considering physical, geospatial, and social dimensions.

Impacts of both random failures due to aging infrastructures, natural disasters, and their cascading failures is investigated.

In the water and transportation networks, areas prone to random failures, natural disasters, and their cascading failures within and across infrastructures are identified.

Areas with higher social vulnerability experience greater damage caused by both random failures and natural disasters.

The proposed framework supports resilience-informed infrastructure design and policymaking for a more holistic, collaborative, and equitable resilience planning.


Infrastructures are interdependent systems and their interdependency can influence their resilience to routine failures and extreme events. Even though infrastructure resilience has been widely explored, few studies have considered physical, spatial, and social dimensions simultaneously. In this paper, we propose a resilience assessment framework for interdependent water and transportation infrastructures. The framework incorporates the physical network of these infrastructures, social vulnerability indicators, and predictive analytics for a sociotechnical resilience assessment. It enables us to measure the impact of random failures due to aging infrastructures, natural disasters, and their cascading failures. We applied the proposed framework to the City of Tampa, FL. The results indicated that areas with higher social vulnerability are more prone to cascading failures caused by both random breakdowns and natural disasters. While natural disasters affect all land use classes similarly, random failures have a greater impact on residential and institutional land use. The findings of this study highlight that infrastructure interdependency and the consequences of cascading failures should be taken into account in a coordinated infrastructure resilience assessment and planning. Further, socioeconomic factors and land use features should be incorporated in interdependent resilience assessment for a more comprehensive and equitable resilience planning.


Infrastructure systems
Social vulnerability
Predictive analytics
Natural disasters
Cascading random failure
Community detection

Armin Rahimi-Golkhandan is a postdoctoral research fellow in the Center for Resilient and Sustainable Communities (C-RASC) at George Mason University. His experience is in transportation infrastructure and urban community resilience to natural disasters and routine failures. His research interests are in infrastructure resilience, human mobility, and smart cities.

Babak Aslani is a Ph.D. student in Systems Engineering and Operations Research at George Mason University. His research is focused on the resilience of interdependent infrastructure systems. His specific research interests include multi-objective optimization, evolutionary Algorithms, machine learning, and multi-criteria decision making.

Shima Mohebbi is an assistant professor in the Department of Systems Engineering and Operations Research and a core faculty of C-RASC (Center for Resilient and Sustainable Communities) at George Mason University. Her research interests include game theory, network optimization, simulation, and machine learning with applications in resilient infrastructure systems, sustainable water systems, and cyber-physical systems. Her research projects are supported by the National Science Foundation and the U.S. Department of Transportation.

View full text