期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 126, 期 -, 页码 505-520出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2019.02.050
关键词
Time-variant reliability analysis; High dimensions; Composite limit state; Generalized subset simulation; Series expansion methods; Cumulative failure probability curve
资金
- National Natural Science Foundation of China [U1533109]
- China Scholarship Council for his stay at Missouri University of Science and Technology
A new sampling-based method is proposed for high-dimensional time-variant reliability analysis with both random variables and random process as inputs. The new method employs the series expansion methods, e.g., the Karhunen-Loeve expansion, to represent the input random process into a set of random variables. Based on the concepts of composite limit state, the time-variant reliability analysis is converted into a series system reliability problem with multiple responses. Then the generalized subset simulation is applied to compute cumulative failure probabilities which are further used to interpolate a completely cumulative failure probability curve for a given time interval. The advantage of the proposed method is that only a single run can provide a cumulative failure probability curve instead of repeated runs. Two high-dimensional time-variant reliability problems with input random process are used to demonstrate the performance of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.
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