One of the main problems in data-based Structural Health Monitoring (SHM), is
the scarcity of measured data corresponding to damage states in the structures
of interest. One approach to solving this problem is to develop methods of
transferring health inferences and information between structures in an identified
population—Popul ...
One of the main problems in data-based Structural Health Monitoring (SHM), is
the scarcity of measured data corresponding to damage states in the structures
of interest. One approach to solving this problem is to develop methods of
transferring health inferences and information between structures in an identified
population—Population-based SHM (PBSHM). In the case of homogenous populations
(sets of nominally-identical structures, like in a wind farm), the idea of the form has
been proposed which encodes information about the ideal or typical structure together
with information about variations across the population. In the case of sets of disparate
structures—heterogeneous populations—transfer learning appears to be a powerful
tool for sharing inferences, and is also applicable in the homogenous case. In order
to assess the likelihood of transference being meaningful, it has proved useful to
develop an abstract representation framework for spaces of structures, so that similarities
between structures can formally be assessed; this framework exploits tools from graph
theory. The current paper discusses all of these very recent developments and provides
illustrative examples
