6 papers · 1 filter
Transfer learning via interpolating structures
T. A. Dardeno, A. J. Hughes, L. A. Bull +3
Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a…
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)…
Cost-informed dimensionality reduction for structural digital twin technologies
Aidan J. Hughes, Keith Worden, Nikolaos Dervilis +1
Classification models are a key component of structural digital twin technologies used for supporting asset management decision-making. An important consideration when developing c…
Active learning for regression in engineering populations: A risk-informed approach
Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi +5
Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applic…
Multitask learning for improved scour detection: A dynamic wave tank study
Simon M. Brealy, Aidan J. Hughes, Tina A. Dardeno +4
Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of…
Quantifying the value of positive transfer: An experimental case study
Aidan J. Hughes, Giulia Delo, Jack Poole +2
In traditional approaches to structural health monitoring, challenges often arise associated with the availability of labelled data. Population-based structural health monitoring s…