AI-Powered Digital Twin Framework for Windstorm Emergency Management in Interconnected Critical Infrastructures

Extreme windstorms pose significant risks to interconnected critical infrastructures such as power, transportation, and telecommunication systems. Wind-induced damage to physical assets, including overhead lines and roadside vegetation, can trigger cascading failures across interdependent networks, leading to widespread service disruptions and societal impacts. Anticipating these cascading effects under uncertain and evolving windstorm conditions remains a major challenge for emergency and crisis management.

An AI-powered Digital Twin (DT) framework for windstorm emergency management is introduced in this presentation, focusing on interconnected critical infrastructures exposed to extreme wind hazards. The framework integrates physics-based windstorm simulation with cascading impact analysis within a unified digital environment, enabling systematic assessment of the interconnected infrastructure performance across a wide range of plausible windstorm scenarios. Rather than relying solely on historical events, physically informed models are used to generate synthetic windstorm scenarios that support preparedness planning and stress-testing under future extreme conditions.

Building on ensembles of simulated windstorm scenarios, the framework can incorporate Generative AI (GenAI) techniques as a post-simulation analytical layer for vulnerability and risk analysis. GenAI operates on the outputs of physics-based simulations, learning asset-level and system-level operational behaviors and vulnerability patterns from simulated impacts, rather than replacing the underlying hazard or infrastructure models. In this role, GenAI captures complex and nonlinear relationships between wind event characteristics and cascading infrastructure failures, enabling efficient synthesis and generalization across large scenario ensembles. This hybrid physics–AI approach supports rapid and accurate identification of vulnerable assets across interconnected infrastructures, spatial hotspots of risk, and conditions that may lead to cascading disruptions under future windstorm scenarios, while preserving the physical consistency of the Digital Twin.

The applicability of the proposed framework is demonstrated through representative case studies involving national-scale interconnected power, telecommunication, and transportation infrastructures in Cyprus, serving as an example implementation. The results illustrate how the AI-powered Digital Twin can support emergency and crisis management at a national level by enabling stress-testing of infrastructure systems, identification of highly vulnerable and critical assets in the Cyprus interconnected infrastructure, improving situational awareness on critical wind-induced cascading risks, and informing response and recovery strategies under severe windstorm conditions.

Overall, this work highlights the potential of hybrid physics-based and AI-enhanced Digital Twins as decision-support tools for windstorm emergency management in interconnected critical infrastructures, providing a flexible and extensible foundation for improving resilience to climate-driven hazards.