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On average, natural hazards are responsible for 鈧�15 billion/year in economic losses in Europe, affecting infrastructure vital to safe societies. Infrastructure system resilience has become a cutting-edge research topic and AI can offer new insights, however its use remains inadequate due to limited data availability. Furthermore, there is a dearth of data-driven investigations that elucidate the mechanisms underlying the resilience of interdependent social-technical infrastructure systems (ISTIS).

The ResilAI aims to investigating how AI techniques can support research to deliver more resilient and sustainable infrastructure. The objectives are: (1) to devise a synthetic approach to collecting data for studying the resilience of ISTIS; (2) to propose the principles of data requirements for resilience prediction using machine learning (ML) approaches; (3) to identify the key drivers and reveal the mechanisms of the resilience of ISTIS.听

Correspondingly, the ResilAI will: (1) combine conventional data collection and data generation methods to comprehensively gather data on ISTIS; (2) examine the performance of different ML approaches to predict the resilience of ISTIS with different availability of data and establish fundamental principles for data requirements; (3) find the key factors that contribute to the resilience of ISTIS and the formation mechanisms of resilience based on explainable AI. The outcomes will provide academia and industry with a database on infrastructure system resilience as well as the foundation for further data-driven research and practice in this field.

Researcher: Dr. Zaishang Li 听听听 听听听 听
Supervisor: Prof Dina D'Ayala
Start date: 1st Aug 2024 听听 听听听 听听听 听
Duration: 24 months

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