Closely Spaced Mode Identification of Skyscraper Buildings via Variational Mode Decomposition with Multiple-Signal Classification Algorithm and Recursive Hilbert Transform.
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| Title: | Closely Spaced Mode Identification of Skyscraper Buildings via Variational Mode Decomposition with Multiple-Signal Classification Algorithm and Recursive Hilbert Transform. |
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| Authors: | Hu, Feng1,2 (AUTHOR) fenghu@mail.hfut.edu.cn, Zhi, Lunhai1,2 (AUTHOR) zhilunhai1979@163.com, Hu, Zhixiang1,2 (AUTHOR) huzhixiang@hfut.edu.cn, Li, Qiusheng3 (AUTHOR) bcqsli@cityu.edu.hk |
| Source: | International Journal of Structural Stability & Dynamics. 5/30/2026, Vol. 26 Issue 11, p1-20. 20p. |
| Subjects: | Multiple Signal Classification, Hilbert transform, Modal analysis, Structural health monitoring, Mode shapes, Skyscrapers, Signal processing |
| Abstract: | Addressing the limitations of traditional modal analysis methods, which struggle with closely spaced modal frequencies and noise interference, this paper introduces a novel approach that integrates variational mode decomposition (VMD) with multiple signal classification (MUSIC) and the recursive Hilbert transform (RHT). This integration leverages the adaptability of VMD in signal decomposition and exploits the high-resolution spectral identification capabilities of MUSIC to decompose the closely spaced modes accurately. Additionally, the RHT is applied to analyze the instantaneous properties of the decomposed signals. The proposed method was validated through a numerical study on a 2DOF model, demonstrating its effectiveness and robustness in identifying modal parameters under simulated conditions. Further validation was conducted through field measurements from a 420 m high skyscraper building during Super Typhoon Nesat, confirming the practical applicability and effectiveness of the approach in actual engineering cases. The findings indicate a substantial improvement in the accuracy of closely spaced modal parameter identification, thus enhancing the reliability of structural health monitoring (SHM) systems in skyscraper buildings. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Structural Stability & Dynamics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192347532 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Closely Spaced Mode Identification of Skyscraper Buildings via Variational Mode Decomposition with Multiple-Signal Classification Algorithm and Recursive Hilbert Transform. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hu%2C+Feng%22">Hu, Feng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> fenghu@mail.hfut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhi%2C+Lunhai%22">Zhi, Lunhai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zhilunhai1979@163.com</i><br /><searchLink fieldCode="AR" term="%22Hu%2C+Zhixiang%22">Hu, Zhixiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> huzhixiang@hfut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Qiusheng%22">Li, Qiusheng</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> bcqsli@cityu.edu.hk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Structural+Stability+%26+Dynamics%22">International Journal of Structural Stability & Dynamics</searchLink>. 5/30/2026, Vol. 26 Issue 11, p1-20. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multiple+Signal+Classification%22">Multiple Signal Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Hilbert+transform%22">Hilbert transform</searchLink><br /><searchLink fieldCode="DE" term="%22Modal+analysis%22">Modal analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Mode+shapes%22">Mode shapes</searchLink><br /><searchLink fieldCode="DE" term="%22Skyscrapers%22">Skyscrapers</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Addressing the limitations of traditional modal analysis methods, which struggle with closely spaced modal frequencies and noise interference, this paper introduces a novel approach that integrates variational mode decomposition (VMD) with multiple signal classification (MUSIC) and the recursive Hilbert transform (RHT). This integration leverages the adaptability of VMD in signal decomposition and exploits the high-resolution spectral identification capabilities of MUSIC to decompose the closely spaced modes accurately. Additionally, the RHT is applied to analyze the instantaneous properties of the decomposed signals. The proposed method was validated through a numerical study on a 2DOF model, demonstrating its effectiveness and robustness in identifying modal parameters under simulated conditions. Further validation was conducted through field measurements from a 420 m high skyscraper building during Super Typhoon Nesat, confirming the practical applicability and effectiveness of the approach in actual engineering cases. The findings indicate a substantial improvement in the accuracy of closely spaced modal parameter identification, thus enhancing the reliability of structural health monitoring (SHM) systems in skyscraper buildings. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Structural Stability & Dynamics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0219455426500768 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Multiple Signal Classification Type: general – SubjectFull: Hilbert transform Type: general – SubjectFull: Modal analysis Type: general – SubjectFull: Structural health monitoring Type: general – SubjectFull: Mode shapes Type: general – SubjectFull: Skyscrapers Type: general – SubjectFull: Signal processing Type: general Titles: – TitleFull: Closely Spaced Mode Identification of Skyscraper Buildings via Variational Mode Decomposition with Multiple-Signal Classification Algorithm and Recursive Hilbert Transform. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hu, Feng – PersonEntity: Name: NameFull: Zhi, Lunhai – PersonEntity: Name: NameFull: Hu, Zhixiang – PersonEntity: Name: NameFull: Li, Qiusheng IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 05 Text: 5/30/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02194554 Numbering: – Type: volume Value: 26 – Type: issue Value: 11 Titles: – TitleFull: International Journal of Structural Stability & Dynamics Type: main |
| ResultId | 1 |
