Academic Journal
Optimal design of frame structures equipped with viscous dampers using machine learning techniques.
| Τίτλος: | Optimal design of frame structures equipped with viscous dampers using machine learning techniques. |
|---|---|
| Συγγραφείς: | Wen, Yi, Wang, Jianze, Xu, Jun, Dai, Kaoshan, Shi, Yuanfeng, Sharbati, Reza |
| Πηγή: | Journal of Asian Architecture & Building Engineering; May2026, Vol. 25 Issue 3, p1994-2014, 21p |
| Θεματικοί όροι: | Machine learning, Dampers (Mechanical devices), Seismic response, Dynamic programming, Structural frames, Boosting algorithms, Building design & construction, Structural optimization |
| Περίληψη: | It is widely accepted that the use of supplemental damping system is an effective measure to improve seismic performance of buildings. This study aims to develop an automated optimization design method for structures with damping systems to rapidly determine the optimal parameters and placement of viscous dampers in such structures. To achieve this, the proposed method involves the Machine Learning (ML) techniques of Extreme Gradient Boosting (XGBoost) and a Dynamic Programming (DP) algorithm. To generate the dataset for the XGBoost model development, an automated modeling and simulation tool for damping structures was developed. The optimization performance of the framework was validated using three different structural examples (3-story, 6-story, and 8-story). The results demonstrate that the XGBoost is capable of predicting the story drift response of a building structure under any damper arrangement. The built models achieved an average R2 of 0.955 for the testing dataset, indicating a high level of predictive accuracy. Then, with the target of minimizing seismic responses of the structural system, the developed DP-based optimization algorithm effectively identifies the optimal damper arrangement for the considered three structure cases. With varying initial conditions and parameters, the algorithm consistently converges toward the optimal state, demonstrating substantial robustness. The findings validate the efficacy of the proposed methodology in optimizing the seismic design of structures with viscous dampers. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Asian Architecture & Building Engineering is the property of Taylor & Francis Ltd 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 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=13467581&ISBN=&volume=25&issue=3&date=20260501&spage=1994&pages=1994-2014&title=Journal of Asian Architecture & Building Engineering&atitle=Optimal%20design%20of%20frame%20structures%20equipped%20with%20viscous%20dampers%20using%20machine%20learning%20techniques.&aulast=Wen%2C%20Yi&id=DOI:10.1080/13467581.2025.2481244 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 193489608 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.76245117188 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Optimal design of frame structures equipped with viscous dampers using machine learning techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wen%2C+Yi%22">Wen, Yi</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jianze%22">Wang, Jianze</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Jun%22">Xu, Jun</searchLink><br /><searchLink fieldCode="AR" term="%22Dai%2C+Kaoshan%22">Dai, Kaoshan</searchLink><br /><searchLink fieldCode="AR" term="%22Shi%2C+Yuanfeng%22">Shi, Yuanfeng</searchLink><br /><searchLink fieldCode="AR" term="%22Sharbati%2C+Reza%22">Sharbati, Reza</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Asian Architecture & Building Engineering; May2026, Vol. 25 Issue 3, p1994-2014, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Dampers+%28Mechanical+devices%29%22">Dampers (Mechanical devices)</searchLink><br /><searchLink fieldCode="DE" term="%22Seismic+response%22">Seismic response</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+programming%22">Dynamic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+frames%22">Structural frames</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Building+design+%26+construction%22">Building design & construction</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+optimization%22">Structural optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: It is widely accepted that the use of supplemental damping system is an effective measure to improve seismic performance of buildings. This study aims to develop an automated optimization design method for structures with damping systems to rapidly determine the optimal parameters and placement of viscous dampers in such structures. To achieve this, the proposed method involves the Machine Learning (ML) techniques of Extreme Gradient Boosting (XGBoost) and a Dynamic Programming (DP) algorithm. To generate the dataset for the XGBoost model development, an automated modeling and simulation tool for damping structures was developed. The optimization performance of the framework was validated using three different structural examples (3-story, 6-story, and 8-story). The results demonstrate that the XGBoost is capable of predicting the story drift response of a building structure under any damper arrangement. The built models achieved an average R<superscript>2</superscript> of 0.955 for the testing dataset, indicating a high level of predictive accuracy. Then, with the target of minimizing seismic responses of the structural system, the developed DP-based optimization algorithm effectively identifies the optimal damper arrangement for the considered three structure cases. With varying initial conditions and parameters, the algorithm consistently converges toward the optimal state, demonstrating substantial robustness. The findings validate the efficacy of the proposed methodology in optimizing the seismic design of structures with viscous dampers. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Asian Architecture & Building Engineering is the property of Taylor & Francis Ltd 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=193489608 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/13467581.2025.2481244 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1994 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Dampers (Mechanical devices) Type: general – SubjectFull: Seismic response Type: general – SubjectFull: Dynamic programming Type: general – SubjectFull: Structural frames Type: general – SubjectFull: Boosting algorithms Type: general – SubjectFull: Building design & construction Type: general – SubjectFull: Structural optimization Type: general Titles: – TitleFull: Optimal design of frame structures equipped with viscous dampers using machine learning techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wen, Yi – PersonEntity: Name: NameFull: Wang, Jianze – PersonEntity: Name: NameFull: Xu, Jun – PersonEntity: Name: NameFull: Dai, Kaoshan – PersonEntity: Name: NameFull: Shi, Yuanfeng – PersonEntity: Name: NameFull: Sharbati, Reza IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13467581 Numbering: – Type: volume Value: 25 – Type: issue Value: 3 Titles: – TitleFull: Journal of Asian Architecture & Building Engineering Type: main |
| ResultId | 1 |