Polyphase uncertainty analysis through virtual modelling technique
Abstract
A virtual model aided non-deterministic static analysis (including linear and nonlinear analyses) with polyphase uncertainty is presented in this paper. Within an uncertain system, the polyphase uncertainty integrates both probabilistic and non-probabilistic uncertainties, which is more sophisticated than the conventional uncertainty modelling through a single type. To further improve the computational stableness and robustness of the virtual model, a kernel-based machine learning technique, namely Twin Extended Support Vector Regression (T-X-SVR), is newly developed. The feature of auto-learning is fulfilled through the Bayesian optimization. The proposed approach is capable of providing sufficient statistical information, including the membership functions of mean and standard deviation, fuzzy-valued probabilistic density function (PDF) and cumulative distribution function (CDF) for the upper and lower bounds of the concerned structural response. To demonstrate the effectiveness and computational efficiency of the proposed approach, a verification case, where analytical solutions are available, is tested first. Then, two practically stimulated engineering applications are fully investigated.
Details
- Organisationseinheit(en)
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Institut für Risiko und Zuverlässigkeit
- Externe Organisation(en)
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University of New South Wales (UNSW)
University of Technology Sydney
The University of Liverpool
Tongji University
Future Innovative Technology Pty Ltd
- Typ
- Artikel
- Journal
- Mechanical Systems and Signal Processing
- Band
- 162
- ISSN
- 0888-3270
- Publikationsdatum
- 01.01.2022
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Steuerungs- und Systemtechnik, Signalverarbeitung, Tief- und Ingenieurbau, Luft- und Raumfahrttechnik, Maschinenbau, Angewandte Informatik
- Elektronische Version(en)
-
https://doi.org/10.1016/j.ymssp.2021.108013 (Zugang:
Geschlossen
)
