Polyphase uncertainty analysis through virtual modelling technique
- authored by
- Qihan Wang, Yuan Feng, Di Wu, Chengwei Yang, Yuguo Yu, Guoyin Li, Michael Beer, Wei Gao
- 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.
- Organisation(s)
-
Institute for Risk and Reliability
- External Organisation(s)
-
University of New South Wales (UNSW)
UTS University of Technology Sydney
University of Liverpool
Tongji University
Future Innovative Technology Pty Ltd
- Type
- Article
- Journal
- Mechanical Systems and Signal Processing
- Volume
- 162
- ISSN
- 0888-3270
- Publication date
- 01.01.2022
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Control and Systems Engineering, Signal Processing, Civil and Structural Engineering, Aerospace Engineering, Mechanical Engineering, Computer Science Applications
- Electronic version(s)
-
https://doi.org/10.1016/j.ymssp.2021.108013 (Access:
Closed)
