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An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models
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| Zeitschriftentitel: | Periodica Polytechnica Civil Engineering |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Periodica Polytechnica Civil Engineering, 2019 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Periodica Polytechnica Budapest University of Technology and Economics
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| Schlagwörter: |
| author_facet |
Vahedi, Jafar Ghasemi, Mohammad Reza Miri, Mahmoud Vahedi, Jafar Ghasemi, Mohammad Reza Miri, Mahmoud |
|---|---|
| author |
Vahedi, Jafar Ghasemi, Mohammad Reza Miri, Mahmoud |
| spellingShingle |
Vahedi, Jafar Ghasemi, Mohammad Reza Miri, Mahmoud Periodica Polytechnica Civil Engineering An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models Geotechnical Engineering and Engineering Geology Civil and Structural Engineering |
| author_sort |
vahedi, jafar |
| spelling |
Vahedi, Jafar Ghasemi, Mohammad Reza Miri, Mahmoud 1587-3773 0553-6626 Periodica Polytechnica Budapest University of Technology and Economics Geotechnical Engineering and Engineering Geology Civil and Structural Engineering http://dx.doi.org/10.3311/ppci.12747 <jats:p>Meta-models or surrogate models are convenient tools for reliability assessment of problems with time-consuming numerical models. Recently, an adaptive method called AK-MCS has been widely used for reliability analysis by combining Mont-Carlo simulation method and Kriging surrogate model. The AK-MCS method usually uses constant regression as a Kriging trend. However, other regression trends may have better performance for some problems. So, a method is proposed by combining multiple Kriging meta-models with various trends. The proposed method is based on the maximum entropy of predictions to select training samples. Using multiple Kriging models can reduce the sensitivity to the regression trend. So, the propped method can have better performance for different problems. The proposed method is applied to some examples to show its efficiency.</jats:p> An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models Periodica Polytechnica Civil Engineering |
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Periodica Polytechnica Civil Engineering |
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| title |
An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_unstemmed |
An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_full |
An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_fullStr |
An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_full_unstemmed |
An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_short |
An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_sort |
an efficient entropy-based method for reliability assessment by combining kriging meta-models |
| topic |
Geotechnical Engineering and Engineering Geology Civil and Structural Engineering |
| url |
http://dx.doi.org/10.3311/ppci.12747 |
| publishDate |
2019 |
| physical |
|
| description |
<jats:p>Meta-models or surrogate models are convenient tools for reliability assessment of problems with time-consuming numerical models. Recently, an adaptive method called AK-MCS has been widely used for reliability analysis by combining Mont-Carlo simulation method and Kriging surrogate model. The AK-MCS method usually uses constant regression as a Kriging trend. However, other regression trends may have better performance for some problems. So, a method is proposed by combining multiple Kriging meta-models with various trends. The proposed method is based on the maximum entropy of predictions to select training samples. Using multiple Kriging models can reduce the sensitivity to the regression trend. So, the propped method can have better performance for different problems. The proposed method is applied to some examples to show its efficiency.</jats:p> |
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| author | Vahedi, Jafar, Ghasemi, Mohammad Reza, Miri, Mahmoud |
| author_facet | Vahedi, Jafar, Ghasemi, Mohammad Reza, Miri, Mahmoud, Vahedi, Jafar, Ghasemi, Mohammad Reza, Miri, Mahmoud |
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| description | <jats:p>Meta-models or surrogate models are convenient tools for reliability assessment of problems with time-consuming numerical models. Recently, an adaptive method called AK-MCS has been widely used for reliability analysis by combining Mont-Carlo simulation method and Kriging surrogate model. The AK-MCS method usually uses constant regression as a Kriging trend. However, other regression trends may have better performance for some problems. So, a method is proposed by combining multiple Kriging meta-models with various trends. The proposed method is based on the maximum entropy of predictions to select training samples. Using multiple Kriging models can reduce the sensitivity to the regression trend. So, the propped method can have better performance for different problems. The proposed method is applied to some examples to show its efficiency.</jats:p> |
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| spelling | Vahedi, Jafar Ghasemi, Mohammad Reza Miri, Mahmoud 1587-3773 0553-6626 Periodica Polytechnica Budapest University of Technology and Economics Geotechnical Engineering and Engineering Geology Civil and Structural Engineering http://dx.doi.org/10.3311/ppci.12747 <jats:p>Meta-models or surrogate models are convenient tools for reliability assessment of problems with time-consuming numerical models. Recently, an adaptive method called AK-MCS has been widely used for reliability analysis by combining Mont-Carlo simulation method and Kriging surrogate model. The AK-MCS method usually uses constant regression as a Kriging trend. However, other regression trends may have better performance for some problems. So, a method is proposed by combining multiple Kriging meta-models with various trends. The proposed method is based on the maximum entropy of predictions to select training samples. Using multiple Kriging models can reduce the sensitivity to the regression trend. So, the propped method can have better performance for different problems. The proposed method is applied to some examples to show its efficiency.</jats:p> An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models Periodica Polytechnica Civil Engineering |
| spellingShingle | Vahedi, Jafar, Ghasemi, Mohammad Reza, Miri, Mahmoud, Periodica Polytechnica Civil Engineering, An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models, Geotechnical Engineering and Engineering Geology, Civil and Structural Engineering |
| title | An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_full | An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_fullStr | An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_full_unstemmed | An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_short | An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| title_sort | an efficient entropy-based method for reliability assessment by combining kriging meta-models |
| title_unstemmed | An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models |
| topic | Geotechnical Engineering and Engineering Geology, Civil and Structural Engineering |
| url | http://dx.doi.org/10.3311/ppci.12747 |