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Polyphase uncertainty analysis through virtual modelling technique
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DOI：10.1016/j.ymssp.2021.108013.png)
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摘要
En 中文
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.
Keyword:
Polyphase uncertainty
Static linear and nonlinear analyses
Virtual modelling technique
Engineering application
期刊
IF:
8.9
论文数:
1.3W
被引数:
6.6W
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