87 citations · 378 across the 11 of their papers we have counts for
11 papers
On topological data analysis for SHM; an introduction to persistent homology
Tristan Gowdridge, Nikolaos Devilis, Keith Worden
This paper aims to discuss a method of quantifying the 'shape' of data, via a methodology called topological data analysis. The main tool within topological data analysis is persis…
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…
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…
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…
Damage detection in operational wind turbine blades using a new approach based on machine learning
Kartik Chandrasekhar, Nevena Stevanovic, Elizabeth J. Cross +2
The application of reliable structural health monitoring (SHM) technologies to operational wind turbine blades is a challenging task, due to the uncertain nature of the environment…
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…
Structured Machine Learning Tools for Modelling Characteristics of Guided Waves
Marcus Haywood-Alexander, Nikolaos Dervilis, Keith Worden +3
The use of ultrasonic guided waves to probe the materials/structures for damage continues to increase in popularity for non-destructive evaluation (NDE) and structural health monit…
A Bayesian methodology for localising acoustic emission sources in complex structures
Matthew R. Jones, Tim J. Rogers, Keith Worden +1
In the field of structural health monitoring (SHM), the acquisition of acoustic emissions to localise damage sources has emerged as a popular approach. Despite recent advances, the…
On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression
Rajdip Nayek, Ramon Fuentes, Keith Worden +1
This paper presents the use of spike-and-slab (SS) priors for discovering governing differential equations of motion of nonlinear structural dynamic systems. The problem of discove…
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…
Considering discrepancy when calibrating a mechanistic electrophysiology model
Chon Lok Lei, Sanmitra Ghosh, Dominic G. Whittaker +14
Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ me…