Academic Journal
Optimization Strategy of Tunnel Lining Structural Analysis Using Parametric Design-Driven MINN Surrogate Model.
| Τίτλος: | Optimization Strategy of Tunnel Lining Structural Analysis Using Parametric Design-Driven MINN Surrogate Model. |
|---|---|
| Συγγραφείς: | Li, Linhao, Luo, Hanbin |
| Πηγή: | Buildings (2075-5309); Jun2026, Vol. 16 Issue 11, p2172, 26p |
| Θεματικοί όροι: | Structural analysis (Engineering), Parametric modeling, Finite element method, Structural optimization, Model validation, Artificial neural networks, Tunnel lining |
| Περίληψη: | Traditional methods for the structural analysis of tunnel linings are often hampered by complexity and significant time consumption, creating the need for a more efficient analysis workflow. To address this issue, this study develops an application oriented surrogate modeling framework centered on parametric design-driven MINN. The framework of PD-driven MINN leverages parametric design to systematically generate a comprehensive dataset of 2000 parameter groups from FEA, providing a robust foundation for model training. Meanwhile, the MINN architecture is tailored to process these diverse inputs, effectively capturing complex parameter interactions while balancing computational speed and modeling accuracy. To validate the proposed strategy, the model's predictions were rigorously compared against real-world engineering data from pre-buried sensor measurements in an actual transportation tunnel project. The results indicate the reliability of a parametric design-driven MINN surrogate model. A comparative analysis demonstrates its superior fitting performance and convergence over traditional artificial neural networks. This study demonstrates the practical value of adapting existing neural network techniques and integrating them with parametric design to support more efficient tunnel lining structural analysis. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20755309&ISBN=&volume=16&issue=11&date=20260601&spage=2172&pages=2172-2197&title=Buildings (2075-5309)&atitle=Optimization%20Strategy%20of%20Tunnel%20Lining%20Structural%20Analysis%20Using%20Parametric%20Design-Driven%20MINN%20Surrogate%20Model.&aulast=Li%2C%20Linhao&id=DOI:10.3390/buildings16112172 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimization Strategy of Tunnel Lining Structural Analysis Using Parametric Design-Driven MINN Surrogate Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Linhao%22">Li, Linhao</searchLink><br /><searchLink fieldCode="AR" term="%22Luo%2C+Hanbin%22">Luo, Hanbin</searchLink> – Name: TitleSource Label: Source Group: Src Data: Buildings (2075-5309); Jun2026, Vol. 16 Issue 11, p2172, 26p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Structural+analysis+%28Engineering%29%22">Structural analysis (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Parametric+modeling%22">Parametric modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+optimization%22">Structural optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Tunnel+lining%22">Tunnel lining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Traditional methods for the structural analysis of tunnel linings are often hampered by complexity and significant time consumption, creating the need for a more efficient analysis workflow. To address this issue, this study develops an application oriented surrogate modeling framework centered on parametric design-driven MINN. The framework of PD-driven MINN leverages parametric design to systematically generate a comprehensive dataset of 2000 parameter groups from FEA, providing a robust foundation for model training. Meanwhile, the MINN architecture is tailored to process these diverse inputs, effectively capturing complex parameter interactions while balancing computational speed and modeling accuracy. To validate the proposed strategy, the model's predictions were rigorously compared against real-world engineering data from pre-buried sensor measurements in an actual transportation tunnel project. The results indicate the reliability of a parametric design-driven MINN surrogate model. A comparative analysis demonstrates its superior fitting performance and convergence over traditional artificial neural networks. This study demonstrates the practical value of adapting existing neural network techniques and integrating them with parametric design to support more efficient tunnel lining structural analysis. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Buildings (2075-5309) is the property of MDPI 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: 10.3390/buildings16112172 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 2172 Subjects: – SubjectFull: Structural analysis (Engineering) Type: general – SubjectFull: Parametric modeling Type: general – SubjectFull: Finite element method Type: general – SubjectFull: Structural optimization Type: general – SubjectFull: Model validation Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Tunnel lining Type: general Titles: – TitleFull: Optimization Strategy of Tunnel Lining Structural Analysis Using Parametric Design-Driven MINN Surrogate Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Linhao – PersonEntity: Name: NameFull: Luo, Hanbin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20755309 Numbering: – Type: volume Value: 16 – Type: issue Value: 11 Titles: – TitleFull: Buildings (2075-5309) Type: main |
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