60 citations · 133 across the 19 of their papers we have counts for
19 papers
Latent variable models for simultaneous EOV identification and removal in population-based SHM
M. D. Champneys, M. R. Jones, A. J. Hughes +3
The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM). The difficulty is compounded…
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…
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…
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…
Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling
S. M. Smith, A. J. Hughes, T. A. Dardeno +3
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…
Monitoring-Supported Value Generation for Managing Structures and Infrastructure Systems
Antonios Kamariotis, Eleni Chatzi, Daniel Straub +9
To maximize its value, the design, development and implementation of Structural Health Monitoring (SHM) should focus on its role in facilitating decision support. In this position…
Quantifying the value of information transfer in population-based SHM
Aidan J. Hughes, Jack Poole, Nikolaos Dervilis +2
Population-based structural health monitoring (PBSHM), seeks to address some of the limitations associated with data scarcity that arise in traditional SHM. A tenet of the populati…
Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
Daniel R. Clarkson, Lawrence A. Bull, Tina A. Dardeno +6
At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental cos…
A decision framework for selecting information-transfer strategies in population-based SHM
Aidan J. Hughes, Jack Poole, Nikolaos Dervilis +2
Decision-support for the operation and maintenance of structures provides significant motivation for the development and implementation of structural health monitoring (SHM) system…
Towards risk-informed PBSHM: Populations as hierarchical systems
Aidan J. Hughes, Paul Gardner, Keith Worden
The prospect of informed and optimal decision-making regarding the operation and maintenance (O&M) of structures provides impetus to the development of structural health monitoring…
Mitigating sampling bias in risk-based active learning via an EM algorithm
Aidan J. Hughes, Lawrence A. Bull, Paul Gardner +2
Risk-based active learning is an approach to developing statistical classifiers for online decision-support. In this approach, data-label querying is guided according to the expect…
Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers
Aidan J. Hughes, Paul Gardner, Lawrence A. Bull +2
Gaining the ability to make informed decisions on operation and maintenance of structures provides motivation for the implementation of structural health monitoring (SHM) systems.…
On robust risk-based active-learning algorithms for enhanced decision support
Aidan J. Hughes, Lawrence A. Bull, Paul Gardner +2
Classification models are a fundamental component of physical-asset management technologies such as structural health monitoring (SHM) systems and digital twins. Previous work intr…
On risk-based active learning for structural health monitoring
A. J. Hughes, L. A. Bull, P. Gardner +3
A primary motivation for the development and implementation of structural health monitoring systems, is the prospect of gaining the ability to make informed decisions regarding the…
A probabilistic risk-based decision framework for structural health monitoring
Aidan J. Hughes, Robert J. Barthorpe, N. Dervilis +2
Obtaining the ability to make informed decisions regarding the operation and maintenance of structures, provides a major incentive for the implementation of structural health monit…