57 citations · 106 across the 9 of their papers we have counts for
6 papers · 1 filter
Towards an active-learning approach to resource allocation for population-based damage prognosis
George Tsialiamanis, Keith Worden, Nikolaos Dervilis +1
Damage prognosis is, arguably, one of the most difficult tasks of structural health monitoring (SHM). To address common problems of damage prognosis, a population-based SHM (PBSHM)…
On the Detection and Quantification of Nonlinearity via Statistics of the Gradients of a Black-Box Model
G. Tsialiamanis, C. R. Farrar
Detection and identification of nonlinearity is a task of high importance for structural dynamics. Detecting nonlinearity in a structure, which has been designed to operate in its…
On generative models as the basis for digital twins
G. Tsialiamanis, D. J. Wagg, N. Dervilis +1
A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account f…
On partitioning of an SHM problem and parallels with transfer learning
G. Tsialiamanis, D. J. Wagg, P. A. Gardner +2
In the current work, a problem-splitting approach and a scheme motivated by transfer learning is applied to a structural health monitoring problem. The specific problem in this cas…
On an application of graph neural networks in population based SHM
G. Tsialiamanis, C. Mylonas, E. Chatzi +3
Attempts have been made recently in the field of population-based structural health monitoring (PBSHM), to transfer knowledge between SHM models of different structures. The attemp…
On generating parametrised structural data using conditional generative adversarial networks
G. Tsialiamanis, D. J. Wagg, N. Dervilis +1
A powerful approach, and one of the most common ones in structural health monitoring (SHM), is to use data-driven models to make predictions and inferences about structures and the…