AI-Enabled Digital Twins for Infrastructure Safety and Environmental Monitoring
Combining digital twins, artificial intelligence, and sensor data to monitor infrastructure, identify emerging risks, and understand environmental conditions.
This project develops AI-enabled digital twins that connect virtual models of physical infrastructure with data collected from sensors, inspections, and the surrounding environment. These continuously updated representations can help teams understand current conditions, anticipate change, and make better safety and maintenance decisions.
The research explores methods for:
- integrating heterogeneous sensor, image, spatial, and historical data;
- detecting anomalies and early signs of structural deterioration;
- forecasting infrastructure performance and environmental risk;
- visualizing complex systems and their changing conditions; and
- supporting inspection, maintenance, and emergency-response planning.
The goal is to provide timely, interpretable information that improves infrastructure resilience while supporting responsible monitoring of the environment around it.