Abstract
Population-based Structural Health Monitoring's (PBSHM) aspiration is to accumulate a deeper knowledge of a structure's health, by harvesting available data across a range of similar structures (or substructures). Whilst each structure is represented via a unified language called an Irreducible Element (IE) model, the comparison and similarity metrics are computed within a graph space. As such, each model is subsequently converted into an Attributed Graph (AG) before being placed into the PBSHM Framework's comparison graph space; the network. This paper looks into the application of using the PBSHM Framework's comparison graph space as a complex network and determining if the theories of communities and community structures from network science, can be applied to the PBSHM Framework's network, to determine similarity and define yet unknown populations.
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