New publication on SHM of Bridges

24 MARCH 2022

This is another step to move forward the integration of physics-based modeling into SHM of bridges, and  to support the unsupervised learning strategies of machine learning algorithms for damage detection under operational and environmental variability.

Bud, M.; Moldovan, I.; Radu, L.; Nedelcu, M.; Figueiredo, E. (2022). Reliability of probabilistic numerical data for training machine learning algorithms to detect damage in bridges. Structural Control and Health Monitoring. e2950. DOI: 10.1002/stc.2950.