Seminar | Three decades of pattern recognition and new machine learning trends for SHM of bridges

08 FEBRUARY 2022

University of Twente, Enschede, Netherlands

Speaker: Eloi Figueiredo

Summary: Bridges play a crucial role in modern societies, regardless of their culture, geographical location, or economic development. The safest, economical and most resilient bridges are those that are well managed and maintained.

In the last three decades, structural health monitoring (SHM) has been a promising tool in management activities of bridges as potentially it permits one to perform condition assessment to reduce uncertainty in the planning and designing of maintenance activities as well as to increase the service performance and safety of operation. 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 the statistical pattern recognition (SPR) paradigm, where machine learning plays an important role.

Meanwhile, recent technologies have unveiled alternative sensing opportunities and new perspectives to manage and observe the response of bridges, but it is widely recognized that bridge SHM is not yet fully capable of producing reliable global information on the presence of damage. Therefore, the main goal of this presentation is to point out key developments in research and applications of the SPR paradigm observed in bridges in the last three decades, including developments in sensing and in machine learning for data analysis, and to identify current and future trends to promote more coordinated and interdisciplinary research in the SHM of bridges.