Early structural anomalies identification allows to hold maintenance activities that avoid loss of both
economic resources and human life. This is extremely important for crucial infrastructures like railway
bridges. This paper illustrates the structural health monitoring approach applied to a simply supported
prestressed concrete railway bridge. In the framework of long-term monitoring, both static quantities
(displacements, strains, and rotations) and environmental measurements (temperatures) have been
recorded. Machine learning techniques, Extreme Gradient boosting machine and Multi-Layer
Perceptron, have been exploited to build regression correlation models associated with the undamaged structural condition after adequate pre-processing operations. In this way, alarm thresholds
based on the expected residuals between the predicted structural quantities and the measured ones,
have been defined. The thresholds turned out to be able to catch early-stage anomalies not pointed
out by traditional damage thresholds based on the design values. The proposed damage index is
chosen as the moving median of the residuals, allowing a significant reduction of false alarms. The
used correlation models and the obtained results represent a starting point for the generalization of
this approach to the bridges belonging to the same static typology
