Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Tsialiamanis, G."'
Publikováno v:
Mechanical Systems and Signal Processing, Volume 200, 1 October 2023, 110581
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-learning algorithms are problem-specific, they often fail to perform effi
Externí odkaz:
http://arxiv.org/abs/2307.09862
Autor:
Tsialiamanis, G., Farrar, C. R.
Detection and identification of nonlinearity is a task of high importance for structural dynamics. Detecting nonlinearity in a structure, which has been designed to operate in its linear region, might indicate the existence of damage. Therefore, it i
Externí odkaz:
http://arxiv.org/abs/2302.07986
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 structural health monitoring (PBSHM), and the use of transfer learning in this
Externí odkaz:
http://arxiv.org/abs/2302.07980
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 parameters, many of which are often uncertain. Many models have been deve
Externí odkaz:
http://arxiv.org/abs/2208.08666
Publikováno v:
Data-Centric Engineering , Volume 2 , 2021
A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modelling applications.
Externí odkaz:
http://arxiv.org/abs/2203.04384
Publikováno v:
Topics in Modal Analysis & Testing, Volume 8 pp 41-50, 2020
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 case is that of localising damage on an aircraft wing. The original exper
Externí odkaz:
http://arxiv.org/abs/2203.01655
Publikováno v:
Data Science in Engineering, Volume 9 pp 47-63, 2021
Attempts have been made recently in the field of population-based structural health monitoring (PBSHM), to transfer knowledge between SHM models of different structures. The attempts have been focussed on homogeneous and heterogeneous populations. A
Externí odkaz:
http://arxiv.org/abs/2203.01646
Publikováno v:
Data Science in Engineering, Volume 9 pp 35-46, 2021
A powerful approach, and one of the most common ones in structural health monitoring (SHM), is to use data-driven models to make predictions and inferences about structures and their condition. Such methods almost exclusively rely on the quality of t
Externí odkaz:
http://arxiv.org/abs/2203.01641
Publikováno v:
Mechanical Systems and Signal Processing, Volume 166, 1 March 2022, 108473
Linear modal analysis is a useful and effective tool for the design and analysis of structures. However, a comprehensive basis for nonlinear modal analysis remains to be developed. In the current work, a machine learning scheme is proposed with a vie
Externí odkaz:
http://arxiv.org/abs/2203.01229
Publikováno v:
In Mechanical Systems and Signal Processing 1 March 2024 209
