Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures
This study introduces a reliability analysis methodology employing Kriging modeling enriched by a hybrid active learning process. Emphasizing noise integration into structural response predictions, this research presents a framework that combines Kriging modeling with regression to handle noisy data...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/882 |
| _version_ | 1797340106234789888 |
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| author | Junho Chun |
| author_facet | Junho Chun |
| author_sort | Junho Chun |
| collection | DOAJ |
| description | This study introduces a reliability analysis methodology employing Kriging modeling enriched by a hybrid active learning process. Emphasizing noise integration into structural response predictions, this research presents a framework that combines Kriging modeling with regression to handle noisy data. The framework accommodates either constant variance of noise for all observed responses or varying, uncorrelated noise variances. Hyperparameters and the variance of the Kriging model with noisy data are determined through maximum likelihood estimation to address inherent uncertainties in structural predictions. An adaptive hybrid learning function guides design of experiment (DoE) point identification through an iterative enrichment process. This function strategically targets points near the limit-state approximation, farthest from existing training points, and explores candidate points to maximize the probability of misclassification. The framework’s application is demonstrated through metamodel-based reliability analysis for continuum and discrete structures with relatively large degrees of freedom, employing subset simulations. Numerical examples validate the framework’s effectiveness, highlighting its potential for accurate and efficient reliability assessments in complex structural systems. |
| first_indexed | 2024-03-08T09:58:10Z |
| format | Article |
| id | doaj.art-cd256d4a775a41dd93ed2b4c9fb76f7f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:58:10Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cd256d4a775a41dd93ed2b4c9fb76f7f2024-01-29T13:45:35ZengMDPI AGApplied Sciences2076-34172024-01-0114288210.3390/app14020882Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of StructuresJunho Chun0School of Architecture, Syracuse University, Syracuse, NY 13244, USAThis study introduces a reliability analysis methodology employing Kriging modeling enriched by a hybrid active learning process. Emphasizing noise integration into structural response predictions, this research presents a framework that combines Kriging modeling with regression to handle noisy data. The framework accommodates either constant variance of noise for all observed responses or varying, uncorrelated noise variances. Hyperparameters and the variance of the Kriging model with noisy data are determined through maximum likelihood estimation to address inherent uncertainties in structural predictions. An adaptive hybrid learning function guides design of experiment (DoE) point identification through an iterative enrichment process. This function strategically targets points near the limit-state approximation, farthest from existing training points, and explores candidate points to maximize the probability of misclassification. The framework’s application is demonstrated through metamodel-based reliability analysis for continuum and discrete structures with relatively large degrees of freedom, employing subset simulations. Numerical examples validate the framework’s effectiveness, highlighting its potential for accurate and efficient reliability assessments in complex structural systems.https://www.mdpi.com/2076-3417/14/2/882active learningKrigingreliability analysissurrogate modelsubset simulation |
| spellingShingle | Junho Chun Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures Applied Sciences active learning Kriging reliability analysis surrogate model subset simulation |
| title | Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures |
| title_full | Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures |
| title_fullStr | Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures |
| title_full_unstemmed | Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures |
| title_short | Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures |
| title_sort | active learning based kriging model with noise responses and its application to reliability analysis of structures |
| topic | active learning Kriging reliability analysis surrogate model subset simulation |
| url | https://www.mdpi.com/2076-3417/14/2/882 |
| work_keys_str_mv | AT junhochun activelearningbasedkrigingmodelwithnoiseresponsesanditsapplicationtoreliabilityanalysisofstructures |