Academic Journal
A novel cepstrum-based and evolutionary optimization hybrid framework for structural parameter identification.
| Τίτλος: | A novel cepstrum-based and evolutionary optimization hybrid framework for structural parameter identification. |
|---|---|
| Συγγραφείς: | Li, Lechen, Shen, Zhenzhong, Gan, Lei, Xu, Liqun, Zhang, Hongwei, Ye, Wenbin |
| Πηγή: | Advances in Structural Engineering; Aug2026, Vol. 29 Issue 11, p2248-2267, 20p |
| Θεματικοί όροι: | Cepstrum analysis (Mechanics), Evolutionary algorithms, Parameter estimation, Differential evolution, Signal processing, Structural health monitoring |
| Περίληψη: | The accuracy and practicality of structural parameter identification remain critical challenges in Structural Health Monitoring (SHM), as many existing modal updating techniques are limited by noise sensitivity, reliance on expert judgment, and complex system identification procedures. This study introduces a novel model updating method that redefines structural parameter identification process by utilizing Cepstral Coefficients (CCs) of structural responses, which provide a compact, robust, and noise-tolerant representation of dynamic characteristics. This study pioneers the use of response-cepstrum representations in structural model updating. Unlike conventional modal characteristics, the CCs are directly and efficiently extracted from time-domain responses through signal-processing techniques, avoiding the expertise-intensive and laborious modal identification process while significantly accelerating the analysis. Exploiting the unique characteristic representation of the CCs, a customized evolutionary optimization framework is developed based on an advanced variant of the Differential Evolution (DE) algorithm for parameter identification. This development is supported by a systematic investigation of multiple effective evolutionary optimization strategies to ensure robust and accurate matching of CCs between measured and simulated responses. The proposed method was validated through both numerical simulations and experimental tests, demonstrating strong performance in identification accuracy, computational efficiency, and robustness to measurement noise as well as parameter interdependence. This method offers an efficient and scalable alternative to modal updating and establishes a new direction for feature-driven, data-centric SHM systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 195472209 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42456054688 |
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| Items | – Name: Title Label: Title Group: Ti Data: A novel cepstrum-based and evolutionary optimization hybrid framework for structural parameter identification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Lechen%22">Li, Lechen</searchLink><br /><searchLink fieldCode="AR" term="%22Shen%2C+Zhenzhong%22">Shen, Zhenzhong</searchLink><br /><searchLink fieldCode="AR" term="%22Gan%2C+Lei%22">Gan, Lei</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Liqun%22">Xu, Liqun</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hongwei%22">Zhang, Hongwei</searchLink><br /><searchLink fieldCode="AR" term="%22Ye%2C+Wenbin%22">Ye, Wenbin</searchLink> – Name: TitleSource Label: Source Group: Src Data: Advances in Structural Engineering; Aug2026, Vol. 29 Issue 11, p2248-2267, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Cepstrum+analysis+%28Mechanics%29%22">Cepstrum analysis (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The accuracy and practicality of structural parameter identification remain critical challenges in Structural Health Monitoring (SHM), as many existing modal updating techniques are limited by noise sensitivity, reliance on expert judgment, and complex system identification procedures. This study introduces a novel model updating method that redefines structural parameter identification process by utilizing Cepstral Coefficients (CCs) of structural responses, which provide a compact, robust, and noise-tolerant representation of dynamic characteristics. This study pioneers the use of response-cepstrum representations in structural model updating. Unlike conventional modal characteristics, the CCs are directly and efficiently extracted from time-domain responses through signal-processing techniques, avoiding the expertise-intensive and laborious modal identification process while significantly accelerating the analysis. Exploiting the unique characteristic representation of the CCs, a customized evolutionary optimization framework is developed based on an advanced variant of the Differential Evolution (DE) algorithm for parameter identification. This development is supported by a systematic investigation of multiple effective evolutionary optimization strategies to ensure robust and accurate matching of CCs between measured and simulated responses. The proposed method was validated through both numerical simulations and experimental tests, demonstrating strong performance in identification accuracy, computational efficiency, and robustness to measurement noise as well as parameter interdependence. This method offers an efficient and scalable alternative to modal updating and establishes a new direction for feature-driven, data-centric SHM systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Advances in Structural Engineering is the property of Sage Publications Inc. 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: 10.1177/13694332251405899 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 2248 Subjects: – SubjectFull: Cepstrum analysis (Mechanics) Type: general – SubjectFull: Evolutionary algorithms Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Differential evolution Type: general – SubjectFull: Signal processing Type: general – SubjectFull: Structural health monitoring Type: general Titles: – TitleFull: A novel cepstrum-based and evolutionary optimization hybrid framework for structural parameter identification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Lechen – PersonEntity: Name: NameFull: Shen, Zhenzhong – PersonEntity: Name: NameFull: Gan, Lei – PersonEntity: Name: NameFull: Xu, Liqun – PersonEntity: Name: NameFull: Zhang, Hongwei – PersonEntity: Name: NameFull: Ye, Wenbin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13694332 Numbering: – Type: volume Value: 29 – Type: issue Value: 11 Titles: – TitleFull: Advances in Structural Engineering Type: main |
| ResultId | 1 |