High dimensional model representation for hybrid reliability analysis with dependent interval variables constrained within ellipsoids
Structural and Multidisciplinary Optimization, ISSN: 1615-1488, Vol: 56, Issue: 6, Page: 1493-1505
2017
- 20Citations
- 1Usage
- 11Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations20
- Citation Indexes20
- 20
- CrossRef13
- Usage1
- Abstract Views1
- Captures11
- Readers11
- 11
Article Description
Random variables could be dependent, and so could interval variables. To accommodate dependent interval variables, this work develops an efficient hybrid reliability analysis method to handle both random and dependent interval input variables. Due to the dependent interval variables, the reliability analysis needs to perform the probability analysis for every combination of dependent interval variables. This involves a nested double-loop procedure and dramatically decreases the efficiency. The proposed method decomposes the nested double loops into sequential probability analysis (PA) loop and interval analysis (IA) loop. An efficient IA algorithm based on the cut-HDMR (High Dimensional Model Representation) expansion is developed and a sampling strategy with the leave-one-out technique without extra calls of the limit-state function is proposed. The proposed method has good accuracy and efficiency as demonstrated by two engineering examples.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85029587387&origin=inward; http://dx.doi.org/10.1007/s00158-017-1806-1; http://link.springer.com/10.1007/s00158-017-1806-1; https://scholarsmine.mst.edu/mec_aereng_facwork/3846; https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=5372&context=mec_aereng_facwork; https://dx.doi.org/10.1007/s00158-017-1806-1; https://link.springer.com/article/10.1007/s00158-017-1806-1
Springer Science and Business Media LLC
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