Framework for modeling interdependencies between households, businesses, and infrastructure system, and their response to disruptions-application

Understanding the impact of disruptions to infrastructure systems, households, and businesses is critical to assess resilience of urban systems. However, modeling these complex interdependent systems and applying these models to real-world areas and disruption scenarios is challenging. To address these challenges, we present a workflow for application of a framework for modeling interdependencies between households, businesses, and infrastructure systems, and their impact on disruptions. Furthermore, we identify challenges in applying this framework to an area of Singapore and analyze the resulting cost of 8 disruption scenarios using metrics that we described. Our results showed and confirmed that our model and the devised metrics can be used to assess resilience of systems. In particular, they showed that the multi-system failures have the largest impact on urban areas, and that disruptions affect small vulnerable areas disproportionately. Moreover, they revealed that disruptions to utility systems were costlier than to easily reconfigurable businesses or transportation networks. We also identified and outlined key factors influencing application of the framework to an urban area. We identified some areas for future research focused on investigating a larger set of urban areas and systems.

Automated summary

Understanding the impact of disruptions on infrastructure systems, households, and businesses is crucial for assessing the resilience of urban systems. The study presents a framework for modeling interdependencies between households, businesses, and infrastructure systems, and explores the cost of 8 disruption scenarios in a specific area of Singapore. Results confirm that multi-system failures have the largest impact on urban areas, and disruptions disproportionately affect small vulnerable areas. Additionally, disruptions to utility systems are found to be more costly than to easily reconfigurable businesses or transportation networks, with key factors influencing the application of the framework identified for future research.