The three Cluster D projects contribute to the development of automated structural health monitoring for bridges that detects changes in condition at an early stage, interprets them physically, and can be utilized in digital twins. To this end, AI-supported methods for damage detection and model adaptation are applied in order to systematically take uncertainties into account and optimize sensor networks. The approaches developed are validated on the reference structures.