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of 381
pro vyhledávání: '"Worden, Keith"'
In traditional approaches to structural health monitoring, challenges often arise associated with the availability of labelled data. Population-based structural health monitoring seeks to overcomes these challenges by leveraging data/information from
Externí odkaz:
http://arxiv.org/abs/2407.14342
Autor:
Kamariotis, Antonios, Chatzi, Eleni, Straub, Daniel, Dervilis, Nikolaos, Goebel, Kai, Hughes, Aidan J., Lombaert, Geert, Papadimitriou, Costas, Papakonstantinou, Konstantinos G., Pozzi, Matteo, Todd, Michael, Worden, Keith
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 paper, we offer perspectives on the synergy between SHM and decision-m
Externí odkaz:
http://arxiv.org/abs/2402.00021
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 population-based approach to SHM is that information can be shared between suf
Externí odkaz:
http://arxiv.org/abs/2311.03083
Autor:
Clarkson, Daniel R., Bull, Lawrence A., Dardeno, Tina A., Wickramarachchi, Chandula T., Cross, Elizabeth J., Rogers, Timothy J., Worden, Keith, Dervilis, Nikolaos, Hughes, Aidan J.
At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model
Externí odkaz:
http://arxiv.org/abs/2310.05807
Decision-support for the operation and maintenance of structures provides significant motivation for the development and implementation of structural health monitoring (SHM) systems. Unfortunately, the limited availability of labelled training data h
Externí odkaz:
http://arxiv.org/abs/2307.06978
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 the context of structural health monitoring (SHM): (i) the natural occur
Externí odkaz:
http://arxiv.org/abs/2305.09425
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 (SHM) systems. A probabilistic risk-based framework for decision-maki
Externí odkaz:
http://arxiv.org/abs/2303.13533
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 in temperature, and this will serve as the main focal point of this wo
Externí odkaz:
http://arxiv.org/abs/2212.11727
Topological methods are very rarely used in structural health monitoring (SHM), or indeed in structural dynamics generally, especially when considering the structure and topology of observed data. Topological methods can provide a way of proposing ne
Externí odkaz:
http://arxiv.org/abs/2209.05484
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 persistent homology; this is a means of measuring the shape of data, from th
Externí odkaz:
http://arxiv.org/abs/2209.06155