An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios
Abstract This study proposes a novel Bayesian damage identification method that uses an Improved Elemental Modal Strain Energy Ratio (IEMSER) to guide a sparse prior distribution. Measured frequencies and mode shapes develop the IEMSER indicator for preliminary damage assessment, forming the basis f...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84315-1 |
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| author | Li Chen Hui Chen Lu-ling Liu |
| author_facet | Li Chen Hui Chen Lu-ling Liu |
| author_sort | Li Chen |
| collection | DOAJ |
| description | Abstract This study proposes a novel Bayesian damage identification method that uses an Improved Elemental Modal Strain Energy Ratio (IEMSER) to guide a sparse prior distribution. Measured frequencies and mode shapes develop the IEMSER indicator for preliminary damage assessment, forming the basis for a sparse prior distribution. Using the sparse prior and initial damage estimates, Markov Chain Monte Carlo (MCMC) sampling computes the posterior Probability Density Functions (PDFs) of damage parameters to determine the Maximum A Posteriori (MAP) estimates. The proposed method better utilizes the advantages of prior information in the Bayesian method, making the identified damage more accurate. A numerical case of a steel truss bridge shows that IEMSER’s preliminary damage estimates closely match actual damage, yielding a reliable sparse prior and significantly improving identification accuracy. The method’s effectiveness is further validated using modal test data from an 18-story frame structure, confirming its applicability to real structures. |
| format | Article |
| id | doaj-art-adf29017cc9d4e8a9d46ffbfc67213a8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-adf29017cc9d4e8a9d46ffbfc67213a82025-01-05T12:16:56ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-024-84315-1An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratiosLi Chen0Hui Chen1Lu-ling Liu2School of Computer Science and Engineering Artificial Intelligence, Wuhan Institute of TechnologyCollege of Post and Telecommunication, Wuhan Institute of TechnologyCollege of Post and Telecommunication, Wuhan Institute of TechnologyAbstract This study proposes a novel Bayesian damage identification method that uses an Improved Elemental Modal Strain Energy Ratio (IEMSER) to guide a sparse prior distribution. Measured frequencies and mode shapes develop the IEMSER indicator for preliminary damage assessment, forming the basis for a sparse prior distribution. Using the sparse prior and initial damage estimates, Markov Chain Monte Carlo (MCMC) sampling computes the posterior Probability Density Functions (PDFs) of damage parameters to determine the Maximum A Posteriori (MAP) estimates. The proposed method better utilizes the advantages of prior information in the Bayesian method, making the identified damage more accurate. A numerical case of a steel truss bridge shows that IEMSER’s preliminary damage estimates closely match actual damage, yielding a reliable sparse prior and significantly improving identification accuracy. The method’s effectiveness is further validated using modal test data from an 18-story frame structure, confirming its applicability to real structures.https://doi.org/10.1038/s41598-024-84315-1Damage identificationBayesianModal strain energy ratioSparse prior |
| spellingShingle | Li Chen Hui Chen Lu-ling Liu An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios Scientific Reports Damage identification Bayesian Modal strain energy ratio Sparse prior |
| title | An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios |
| title_full | An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios |
| title_fullStr | An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios |
| title_full_unstemmed | An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios |
| title_short | An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios |
| title_sort | enhanced bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios |
| topic | Damage identification Bayesian Modal strain energy ratio Sparse prior |
| url | https://doi.org/10.1038/s41598-024-84315-1 |
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