This is like putting smart sensors and a digital doctor on bridges, tunnels, and buildings so they can continuously tell us how they’re feeling, warn us when something is going wrong, and help schedule repairs before anything becomes dangerous or very expensive.
Traditional inspection of critical infrastructure (bridges, buildings, tunnels, dams) is periodic, manual, and expensive, which means issues can be missed until they are severe, creating safety risks and high unplanned maintenance costs. AI-based structural health monitoring turns scattered sensor data into early warnings and actionable maintenance decisions.
Domain-specific sensor data and historical structural performance records, combined with tuned ML models and integration into existing asset management workflows, can create a defensible advantage over generic AI solutions.
Hybrid
Time-Series DB
High (Custom Models/Infra)
High-volume, high-frequency sensor time-series data ingestion and storage, plus the need for reliable real-time inference under edge or constrained connectivity conditions.
Early Majority
Focus on applying AI specifically to continuous structural health monitoring and predictive maintenance for infrastructure, combining sensor data, engineering models, and safety thresholds rather than generic anomaly detection or generic construction analytics.
2 use cases in this application