A decision framework for selecting information-transfer strategies in
  population-based SHM
Aidan J. Hughes Jack Poole Nikolaos Dervilis Paul Gardner Keith Worden
Decision-support for the operation and maintenance of structures provides
significant motivation for the development and implementation of structural
health monitoring (SHM) systems. Unfortunately, the limited availability of
labelled training data hinders the development of the statistical models on
which these decision-support systems rely. Population-based SHM seeks to
mitigate the impact of data scarcity by using transfer learning techniques to
share information between individual structures within a population. The
current paper proposes a decision framework for selecting transfer strategies
based upon a novel concept -- the expected value of information transfer --
such that negative transfer is avoided. By avoiding negative transfer, and by
optimising information transfer strategies using the transfer-decision
framework, one can reduce the costs associated with operating and maintaining
structures, and improve safety.