In the last three decades, structural health monitoring (SHM) has been a promising tool in management activities of bridges. The general idea has been the transformation of massive data obtained from monitoring systems and numerical models into meaningful information. To deal with large amounts of data and perform the damage identification automatically, SHM has been cast in the context of a statistical pattern recognition (SPR) paradigm, where machine learning plays an important role. Therefore, the main goal of this presentation is to point out key developments in research and applications of the SPR paradigm in the last three decades, including recent developments in stochastic finite element modeling, mobile sensing, transfer learning, and the impact of climate change on SHM.

Vàrem participar al seminari, on es van exposar línies de treball de molt interés. Felicitats a organitzadors i participants.