Improving the Resilience of Urban Critical Infrastructures with Digital Twins: Challenges, Concepts, and Modeling

Abstract

The increasing number of crisis situations, like natural disasters or military conflicts, emphasize that the reliability and security of critical infrastructures (CIs) is essential for daily life of people. Therefore, it is necessary to continuously assess and ensure the resilience of CIs. However, CIs exhibit interdependencies across different infrastructure domains (e.g. energy, water, transportation) and the interaction with humans forms a complex, dynamic, and self-organized system-of-systems, which is hard to understand and analyze with traditional, isolated approaches. In crises or catastrophes, these interdependencies are revealed and can lead to catastrophic, unforeseen cascade effects. The Digital Twin (DT) is a promising approach to deal with these challenges, and can be used as a tool for the smart city of the future to assist in all phases of the resilience cycle. The DT can, for example, establish real-time analysis, allow for the simulation of “what-if” scenarios, and provide decision support, during normal operation as well as during a crisis, thus increasing the resilience of CIs. In this context, we focus on the application of the DT concept for urban CIs and the role of DTs for supporting crisis management. The particular focus of this work lies in the modeling of cities with their CIs as socio-technical systems. We discuss the main challenges for the construction of a DT, including data acquisition, model validation, simulation, and technical challenges. Further, we present major concepts that are used to address the complexity of the system. We investigate, how interdependencies of CIs and human behavior, for example synchronization, can be considered and modeled. Furthermore, we present examples of technical and social models, considering especially the energy, water, and mobility domain. With this, certain scenarios can be simulated to assess their consequences for the population in advance and measure resilience in a holistic framework.