期刊
APPLIED MATHEMATICAL MODELLING
卷 77, 期 -, 页码 773-787出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2019.08.010
关键词
Non-probabilistic reliability analysis; Exponential convex model; Dimension reduction minimum volume method; Relaxed exponential nominal value method; Perturbation method
资金
- National Natural Science Foundation of China [11972143, 11602076]
- Natural Science Foundation of Anhui Province [1708085QA06]
- Fundamental Research Funds for the Central Universities of China [JZ2018HGTB0231, PA2018GDQT0008]
The convex model is commonly applied to quantify the uncertain-but-bounded parameters. However, the typical interval and ellipsoid models may lead to inaccurate approximation for existing experimental data, which may incur either too risk or conservative for safety assessment. To this end, this study aims to create a novel data-driven exponential convex model to achieve accurate approximation for experiment data, in which the dimension reduction minimum volume method plays the key role. Furthermore, a novel relaxed exponential nominal value method (RENVM) is developed to evaluate the corresponding non-probabilistic reliability index robustly and efficiently, and the sensitivities are also derived based on the straight forward perturbation method to guarantee its efficiency. Through numerical and experimental studies, the accuracy and validity of the proposed data-driven exponential convex model are validated compared to the interval and ellipsoid models, and the robustness and efficiency of the proposed RENVM are also demonstrated for solving both linear and nonlinear problems. (C) 2019 Elsevier Inc. All rights reserved.
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