Artificial Intelligence for Dynamic Characterization of Composite Panel Structures: A Structured Review.
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| Title: | Artificial Intelligence for Dynamic Characterization of Composite Panel Structures: A Structured Review. |
|---|---|
| Authors: | Sarfarazi, Sina1 (AUTHOR) Sina.sarfarazi@unina.it, Modano, Mariano1 (AUTHOR), Fulgione, Marcello1 (AUTHOR) |
| Source: | Mechanics Research Communications. Feb2026, Vol. 151, pN.PAG-N.PAG. 1p. |
| Subjects: | Artificial intelligence, Composite structures, Modular construction, Mathematical optimization, Dynamic models, Reduced-order models |
| Abstract: | Dynamic characterisation of composite panels in modular construction requires approaches that can capture scale effects, variability in manufacturing, and uncertain restraints. To investigate how artificial intelligence (AI) contributes in this field, we compiled a dataset of 2,085 papers from Scopus (2001–2025) and classified them into five methodological families: surrogate models, sequence learning, physics-informed neural networks (PINNs), inverse and optimisation approaches, and explainable AI (XAI). The dataset shows strong growth after 2016. Inverse and metaheuristic optimisation are the most common, especially for laminate lay-up, core design, and finite-element (FE) model updating. Surrogate models are widely used to emulate FE runs and accelerate calibration. Sequence models and PINNs remain smaller but are expanding, supporting time-history prediction and physics-consistent parameter identification under sparse data. XAI is the least developed area and is mostly used for feature attribution in optimisation or health monitoring tasks. In addition to classification, the review presents a clear framework hat connects each method to specific dynamic tasks and provides an interactive web-based GUI where readers can filter, inspect, and export the screened corpus. This combination of systematic mapping and open access to the database creates a transparent reference point for further studies and supports the development of reliable and scalable dynamic analysis frameworks for modular composite panels. [ABSTRACT FROM AUTHOR] |
| Copyright of Mechanics Research Communications is the property of Pergamon Press - An Imprint of Elsevier Science 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: 190795656 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial Intelligence for Dynamic Characterization of Composite Panel Structures: A Structured Review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sarfarazi%2C+Sina%22">Sarfarazi, Sina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Sina.sarfarazi@unina.it</i><br /><searchLink fieldCode="AR" term="%22Modano%2C+Mariano%22">Modano, Mariano</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fulgione%2C+Marcello%22">Fulgione, Marcello</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mechanics+Research+Communications%22">Mechanics Research Communications</searchLink>. Feb2026, Vol. 151, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Composite+structures%22">Composite structures</searchLink><br /><searchLink fieldCode="DE" term="%22Modular+construction%22">Modular construction</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+models%22">Dynamic models</searchLink><br /><searchLink fieldCode="DE" term="%22Reduced-order+models%22">Reduced-order models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Dynamic characterisation of composite panels in modular construction requires approaches that can capture scale effects, variability in manufacturing, and uncertain restraints. To investigate how artificial intelligence (AI) contributes in this field, we compiled a dataset of 2,085 papers from Scopus (2001–2025) and classified them into five methodological families: surrogate models, sequence learning, physics-informed neural networks (PINNs), inverse and optimisation approaches, and explainable AI (XAI). The dataset shows strong growth after 2016. Inverse and metaheuristic optimisation are the most common, especially for laminate lay-up, core design, and finite-element (FE) model updating. Surrogate models are widely used to emulate FE runs and accelerate calibration. Sequence models and PINNs remain smaller but are expanding, supporting time-history prediction and physics-consistent parameter identification under sparse data. XAI is the least developed area and is mostly used for feature attribution in optimisation or health monitoring tasks. In addition to classification, the review presents a clear framework hat connects each method to specific dynamic tasks and provides an interactive web-based GUI where readers can filter, inspect, and export the screened corpus. This combination of systematic mapping and open access to the database creates a transparent reference point for further studies and supports the development of reliable and scalable dynamic analysis frameworks for modular composite panels. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mechanics Research Communications is the property of Pergamon Press - An Imprint of Elsevier Science 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: 10.1016/j.mechrescom.2025.104607 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Composite structures Type: general – SubjectFull: Modular construction Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Dynamic models Type: general – SubjectFull: Reduced-order models Type: general Titles: – TitleFull: Artificial Intelligence for Dynamic Characterization of Composite Panel Structures: A Structured Review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sarfarazi, Sina – PersonEntity: Name: NameFull: Modano, Mariano – PersonEntity: Name: NameFull: Fulgione, Marcello IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00936413 Numbering: – Type: volume Value: 151 Titles: – TitleFull: Mechanics Research Communications Type: main |
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