activity
20202024
most citedFoundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces

57 citations · 106 across the 9 of their papers we have counts for

collaborators

9 papers

stat.ML2024

On the topology and geometry of population-based SHM

Keith Worden, Tina A. Dardeno, Aidan J. Hughes +1

Population-Based Structural Health Monitoring (PBSHM), aims to leverage information across populations of structures in order to enhance diagnostics on those with sparse data. The…

cs.LG2024

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)…

cs.LG2023

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…

cs.LG2022★ 32 cited

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…

cs.LG2022★ 9 cited

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…

cs.LG2022★ 5 cited

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…

cs.LG2022★ 3 cited

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…

stat.ML2021★ 57 cited

Foundations of Population-Based SHM, Part IV: The Geometry of Spaces of Structures and their Feature Spaces

George Tsialiamanis, Charilaos Mylonas, Eleni Chatzi +3

One of the requirements of the population-based approach to Structural Health Monitoring (SHM) proposed in the earlier papers in this sequence, is that structures be represented by…

cs.CE2020

Bayesian graph neural networks for strain-based crack localization

Charilaos Mylonas, George Tsialiamanis, Keith Worden +1

A common shortcoming of vibration-based damage localization techniques is that localized damages, i.e. small cracks, have a limited influence on the spectral characteristics of a s…