Infrastructure Maintenance: Predictive and Proactive

By combining available asset data, IoT data, predictive models, and automated reasoning, AI models can provide new insights that drive a transition from reactive to a proactive maintenance approach. These continually aware models update risk assessments as new data arrives, detect early signs of degradation, and highlight where failures are most likely to occur so inspections, repairs and renewals can be prioritised before problems surface.

Federated Learning for Collaboration

Federated learning allows municipalities to collaborate securely, improving predictive accuracy while keeping sensitive data local.

The RAI Loop Framework

In parallel, the research and its practical implementation is grounded in an AI readiness framework, the RAI Loop (Readiness-Adoption-Impact) which helps organizations:

• Evaluate where they are today
• Identify bottlenecks in data, governance and competence
• Design realistic pilots and choose the right tools for their needs

This practical approach accelerates digital transformation while maintaining safety and accountability.

Outcome

The outcome is a smarter, more sustainable infrastructure system, one that saves resources, reduces disruption, and ensures safer communities.