摘要
Aiming at the problems of implicit and highly nonlinear limit state function in the process of reliability analysis of mechanical products, a reliability analysis method of mechanical structures based on Kriging model and improved EGO active learning strategy is proposed. For the problem that the traditional EGO method cannot effectively select points in the limit state surface region, an improved EGO method is proposed. By dealing with the predicted values of sample point model with absolute values and assume that the distribution state of response values remains the same, the work focus of active learning selection points is moved to the vicinity, where the points are with larger prediction variance or close to the limit state surface. Three examples show that, compared with the classical active learning method, the proposed method has good global and local search ability, and can estimate the exact failure probability value under the condition of less calculation of the limit state function.
投稿的翻译标题 | Structural Reliability Algorithms of Kriging Model Based on Improved Learning Strategy |
---|---|
源语言 | 繁体中文 |
页(从-至) | 412-419 |
页数 | 8 |
期刊 | Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University |
卷 | 38 |
期 | 2 |
DOI | |
出版状态 | 已出版 - 1 4月 2020 |
关键词
- Active learning function
- Algorithm
- Failure probability
- Kriging model
- Monte Carlo method
- Structural reliability