Details
| Original language | English |
|---|---|
| Article number | 108013 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 162 |
| Early online date | 14 May 2021 |
| Publication status | Published - 1 Jan 2022 |
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.
Keywords
- Engineering application, Polyphase uncertainty, Static linear and nonlinear analyses, Virtual modelling technique
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Mechanical Systems and Signal Processing, Vol. 162, 108013, 01.01.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Polyphase uncertainty analysis through virtual modelling technique
AU - Wang, Qihan
AU - Feng, Yuan
AU - Wu, Di
AU - Yang, Chengwei
AU - Yu, Yuguo
AU - Li, Guoyin
AU - Beer, Michael
AU - Gao, Wei
N1 - Funding Information: The work presented in this paper has been supported by the Australian Research Council projects IH150100006 and IH200100010.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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.
KW - Engineering application
KW - Polyphase uncertainty
KW - Static linear and nonlinear analyses
KW - Virtual modelling technique
UR - http://www.scopus.com/inward/record.url?scp=85111030523&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2021.108013
DO - 10.1016/j.ymssp.2021.108013
M3 - Article
AN - SCOPUS:85111030523
VL - 162
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 108013
ER -