45 citations · 217 across the 37 of their papers we have counts for
37 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…
Inter-turbine Modelling of Wind-Farm Power using Multi-task Learning
Simon M. Brealy, Lawrence A. Bull, Pauline Beltrando +3
Because of the global need to increase power production from renewable energy resources, developments in the online monitoring of the associated infrastructure is of interest to re…
Regularising NARX models with multi-task learning
Sarah Bee, Lawrence Bull, Nikolaos Dervilis +1
A Nonlinear Auto-Regressive with eXogenous inputs (NARX) model can be used to describe time-varying processes; where the output depends on both previous outputs and current/previou…
When does a bridge become an aeroplane?
Tina A. Dardeno, Lawrence A. Bull, Nikolaos Dervilis +1
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…
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…
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…
Towards a population-informed approach to the definition of data-driven models for structural dynamics
G. Tsialiamanis, N. Dervilis, D. J. Wagg +1
Machine learning has affected the way in which many phenomena for various domains are modelled, one of these domains being that of structural dynamics. However, because machine-lea…
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…
When is an SHM problem a Multi-Task-Learning problem?
Sarah Bee, Lawrence Bull, Nikolas Dervilis +1
Multi-task neural networks learn tasks simultaneously to improve individual task performance. There are three mechanisms of multi-task learning (MTL) which are explored here for th…
A probabilistic approach for acoustic emission based monitoring techniques: with application to structural health monitoring
C. A. Lindley, M. R. Jones, T. J. Rogers +4
It has been demonstrated that acoustic-emission (AE), inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an…
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…
A Meta-Learning Approach to Population-Based Modelling of Structures
G. Tsialiamanis, N. Dervilis, D. J. Wagg +1
A major problem of machine-learning approaches in structural dynamics is the frequent lack of structural data. Inspired by the recently-emerging field of population-based structura…
A topological analysis of cointegrated data: a Z24 Bridge case study
Tristan Gowdridge, Elizabeth Cross, Nikolaos Dervilis +1
The paper studies the topological changes from before and after cointegration, for the natural frequencies of the Z24 Bridge. The second natural frequency is known to be nonlinear…
On the application of topological data analysis: a Z24 Bridge case study
Tristan Gowdridge, Nikolaos Dervilis, Keith Worden
Topological methods are very rarely used in structural health monitoring (SHM), or indeed in structural dynamics generally, especially when considering the structure and topology o…
On topological data analysis for structural dynamics: an introduction to persistent homology
Tristan Gowdridge, Nikolaos Dervilis, Keith Worden
Topological methods can provide a way of proposing new metrics and methods of scrutinising data, that otherwise may be overlooked. In this work, a method of quantifying the shape o…
A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves
Marcus Haywood-Alexander, Nikolaos Dervilis, Keith Worden +3
Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully-define…
On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation
G. Tsialiamanis, D. Wagg, N. Dervilis +1
A major problem of structural health monitoring (SHM) has been the prognosis of damage and the definition of the remaining useful life of a structure. Both tasks depend on many par…
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…