Junior MS04 - Toward novel theoretical methods and experimental techniques for structural health monitoring of civil structures

  • Ph.D. Domenico Camassa, Research-Assistant Professor, Department of Civil Engineering Sciences and Architecture (DICAR), Polytechnic University of Bari (Italy), domenico.camassa@poliba.it, +39 3278857565

One of the major current needs in structural engineering concerns the assessment of the structural integrity and safety of both contemporary and historical civil constructions over time. The scientific community has been working for several decades to develop methodologies for Structural Health Monitoring (SHM) of civil constructions. Nowadays, recent and continuing advancements in sensing technology offer new opportunities to this field and pose challenges whose solution will bring significant innovation to SHM.

The application of recent sensing technologies (MEMS, radar, satellites, smartphones, drones, GNSS, etc.) to SHM is very promising but still presents several issues. One of the major problems is that the modeling techniques, data analysis methods, and experimental approaches that have been developed for data acquired from traditional sensors generally turn out to be inadequate for recent technologies. There is a need to develop ad hoc theoretical methods and experimental techniques for acquiring, processing, and interpreting data. In addition, the integration of different types of sensors should be addressed by developing suitable data fusion techniques.

This mini-symposium aims to present research and stimulate discussion on this topic. The main focus concerns the development of novel ad hoc modeling techniques, data analysis methods, and experimental approaches. This topic requires a multidisciplinary approach involving both traditional and more recent knowledge from science and engineering. The topics here addressed include structural health monitoring, structural dynamic identification, damage identification, uncertainty quantification, model updating, and data sciences techniques (artificial intelligence, machine learning, neural networks, etc.).

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