Abstract Summary
Population-based Structural Health Monitoring (PBSHM) aims to gain additional insights on the health of a structure when using data available across a population of similar structures, as compared to the insight available when using only data from a single structure. Before knowledge can be transferred across structures, the similarity between structures (or substructures), within the population must be established. The first paper in the series explored the use of Graph Neural Networks (GNNs) to compute similarity measures via an Irreducible Element (IE) model representation of structures stored within a PBSHM database. The second paper in the series explored the use of a comparison layer, using the Canonical Form representations of the associated structures to compare against. This paper builds upon the aforementioned research and explores the viability of using clustering algorithms to determine which population a structure belongs to within the network of structures from the CF similarity score.
