A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis
The reliability analysis system is currently evolving, and reliability analysis efforts are also focusing more on correctness and efficiency. The effectiveness of the active learning Kriging metamodel for the investigation of structural system reliability has been demonstrated. In order to effective...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/2/1036 |
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| author | Zhian Li Xiao Li Chen Li Jiangqin Ge Yi Qiu |
| author_facet | Zhian Li Xiao Li Chen Li Jiangqin Ge Yi Qiu |
| author_sort | Zhian Li |
| collection | DOAJ |
| description | The reliability analysis system is currently evolving, and reliability analysis efforts are also focusing more on correctness and efficiency. The effectiveness of the active learning Kriging metamodel for the investigation of structural system reliability has been demonstrated. In order to effectively predict failure probability, a semi-parallel active learning method based on Kriging (SPAK) is developed in this study. The process creates a novel learning function called <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>U</mi><mi mathvariant="normal">A</mi></msub></mrow></semantics></math></inline-formula>, which takes the correlation between training points and samples into account. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>U</mi><mi mathvariant="normal">A</mi></msub></mrow></semantics></math></inline-formula> function has been developed from the U function but is distinct from it. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>U</mi><mi mathvariant="normal">A</mi></msub></mrow></semantics></math></inline-formula> function improves the original U function, which pays too much attention to the area near the threshold and the accuracy of the surrogate model is improved. The semi-parallel learning method is then put forth, and it works since <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>U</mi><mi mathvariant="normal">A</mi></msub></mrow></semantics></math></inline-formula> and U functions are correlated. One or two training points will be added sparingly during the model learning iteration. It effectively lowers the required training points and iteration durations and increases the effectiveness of model building. Finally, three numerical examples and one engineering application are carried out to show the precision and effectiveness of the suggested method. In application, evaluation efficiency is increased by at least 14.5% and iteration efficiency increased by 35.7%. It can be found that the proposed algorithm is valuable for engineering applications. |
| first_indexed | 2024-03-09T13:42:20Z |
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| id | doaj.art-75aab2b5d0bf46d5af4c307ecd9ebfff |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:42:20Z |
| publishDate | 2023-01-01 |
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| spelling | doaj.art-75aab2b5d0bf46d5af4c307ecd9ebfff2023-11-30T21:05:16ZengMDPI AGApplied Sciences2076-34172023-01-01132103610.3390/app13021036A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability AnalysisZhian Li0Xiao Li1Chen Li2Jiangqin Ge3Yi Qiu4College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaPuhui Zhizao Technology Co., Ltd., Hangzhou 310020, ChinaCollege of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, ChinaCollege of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, ChinaZhejiang Tean Inspection & Technology Co., Ltd., Hangzhou 310020, ChinaThe reliability analysis system is currently evolving, and reliability analysis efforts are also focusing more on correctness and efficiency. The effectiveness of the active learning Kriging metamodel for the investigation of structural system reliability has been demonstrated. In order to effectively predict failure probability, a semi-parallel active learning method based on Kriging (SPAK) is developed in this study. The process creates a novel learning function called <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>U</mi><mi mathvariant="normal">A</mi></msub></mrow></semantics></math></inline-formula>, which takes the correlation between training points and samples into account. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>U</mi><mi mathvariant="normal">A</mi></msub></mrow></semantics></math></inline-formula> function has been developed from the U function but is distinct from it. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>U</mi><mi mathvariant="normal">A</mi></msub></mrow></semantics></math></inline-formula> function improves the original U function, which pays too much attention to the area near the threshold and the accuracy of the surrogate model is improved. The semi-parallel learning method is then put forth, and it works since <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>U</mi><mi mathvariant="normal">A</mi></msub></mrow></semantics></math></inline-formula> and U functions are correlated. One or two training points will be added sparingly during the model learning iteration. It effectively lowers the required training points and iteration durations and increases the effectiveness of model building. Finally, three numerical examples and one engineering application are carried out to show the precision and effectiveness of the suggested method. In application, evaluation efficiency is increased by at least 14.5% and iteration efficiency increased by 35.7%. It can be found that the proposed algorithm is valuable for engineering applications.https://www.mdpi.com/2076-3417/13/2/1036failure probabilityactive learningsemi-parallelKrigingMonte Carlo |
| spellingShingle | Zhian Li Xiao Li Chen Li Jiangqin Ge Yi Qiu A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis Applied Sciences failure probability active learning semi-parallel Kriging Monte Carlo |
| title | A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis |
| title_full | A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis |
| title_fullStr | A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis |
| title_full_unstemmed | A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis |
| title_short | A Semi-Parallel Active Learning Method Based on Kriging for Structural Reliability Analysis |
| title_sort | semi parallel active learning method based on kriging for structural reliability analysis |
| topic | failure probability active learning semi-parallel Kriging Monte Carlo |
| url | https://www.mdpi.com/2076-3417/13/2/1036 |
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