期刊
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
卷 51, 期 1, 页码 1-21出版社
SPRINGER
DOI: 10.1007/s00158-014-1132-9
关键词
Reliability; Approximation; Stochastic process
资金
- National Science Foundation [CMMI 1234855]
- Intelligent Systems Center at the Missouri University of Science and Technology
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1234855] Funding Source: National Science Foundation
Time-variant reliability is often evaluated by Rice's formula combined with the First Order Reliability Method (FORM). To improve the accuracy and efficiency of the Rice/FORM method, this work develops a new simulation method with the first order approximation and series expansions. The approximation maps the general stochastic process of the response into a Gaussian process, whose samples are then generated by the Expansion Optimal Linear Estimation if the response is stationary or by the Orthogonal Series Expansion if the response is non-stationary. As the computational cost largely comes from estimating the covariance of the response at expansion points, a cheaper surrogate model of the covariance is built and allows for significant reduction in computational cost. In addition to its superior accuracy and efficiency over the Rice/FORM method, the proposed method can also produce the failure rate and probability of failure with respect to time for a given period of time within only one reliability analysis.
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