45 citations · 155 across the 24 of their papers we have counts for
24 papers
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 Additive Gaussian Processes for Wind Farm Power Prediction
Simon M. Brealy, Lawrence A. Bull, Daniel S. Brennan +4
Population-based Structural Health Monitoring (PBSHM) aims to share information between similar machines or structures. This paper takes a population-level perspective, exploring t…
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…
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…
Towards Multilevel Modelling of Train Passing Events on the Staffordshire Bridge
Lawrence A. Bull, Chiho Jeon, Mark Girolami +3
We suggest a multilevel model, to represent aggregate train-passing events from the Staffordshire bridge monitoring system. We formulate a combined model from simple units, represe…
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…
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…
On the hierarchical Bayesian modelling of frequency response functions
T. A. Dardeno, K. Worden, N. Dervilis +2
For situations that may benefit from information sharing among datasets, e.g., population-based SHM of similar structures, the hierarchical Bayesian approach provides a useful mode…
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…
Meta-models for transfer learning in source localisation
Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross +2
In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent…
Better Together: Using Multi-task Learning to Improve Feature Selection within Structural Datasets
S. C. Bee, E. Papatheou, M Haywood-Alexander +4
There have been recent efforts to move to population-based structural health monitoring (PBSHM) systems. One area of PBSHM which has been recognised for potential development is th…
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…
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes
Tina A. Dardeno, Lawrence A. Bull, Nikolaos Dervilis +1
Reductions in natural frequency are often used as a damage indicator for structural health monitoring (SHM) purposes. However, fluctuations in operational and environmental conditi…
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 statistic alignment for domain adaptation in structural health monitoring
Jack Poole, Paul Gardner, Nikolaos Dervilis +2
The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data. Transfer learning - specifically in the form of domain adapta…
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning
L. A. Bull, D. Di Francesco, M. Dhada +5
A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilising an interpretable hierarchical Bayesian ap…
Modelling variability in vibration-based PBSHM via a generalised population form
Tina A Dardeno, Lawrence A Bull, Robin S Mills +2
Structural health monitoring (SHM) has been an active research area for the last three decades, and has accumulated a number of critical advances over that period, as can be seen i…
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…
Bayesian Modelling of Multivalued Power Curves from an Operational Wind Farm
L. A. Bull, P. A. Gardner, T. J. Rogers +6
Power curves capture the relationship between wind speed and output power for a specific wind turbine. Accurate regression models of this function prove useful in monitoring, maint…
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…
Probabilistic Inference for Structural Health Monitoring: New Modes of Learning from Data
Lawrence A. Bull, Paul Gardner, Timothy J. Rogers +3
In data-driven SHM, the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are…