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Reliability and sensitivity assessment of laminated composite plates with high-dimensional uncertainty variables using active learning-based ensemble metamodels / Zemed, Najib (CC BY)

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fullscreen : Reliability and sensitivity assessment of laminated composite plates with high-dimensional uncertainty variables using active learning-based ensemble metamodels / Zemed, Najib (CC BY)

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Reliability and sensitivity assessment of laminated composite plates with high-dimensional uncertainty variables using active learning-based ensemble metamodels / Zemed, Najib (CC BY)

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Reliability and sensitivity assessment of laminated composite plates with high-dimensional uncertainty variables using active learning-based ensemble metamodels / Zemed, Najib (CC BY)

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Zeitschrift

Titel:
Curved and layered structures
Erschienen:
Berlin: De Gruyter
Fußnote:
Gesehen am 20. Juli 2016
Open Access
Namensnennung 4.0 International
Umfang:
Online-Ressource
ISSN:
2353-7396
ZDB-ID:
2862627-8 ZDB
VÖBB-Katalog:
35314090
Schlagworte:
Zeitschrift
ZLB-Systematik:
Mathematik
Sammlung:
Mathematik
Copyright:
Rechte vorbehalten
Zugriffsberechtigung:
Freier Zugang
Titel:
Curved and layered structures
Erschienen:
Berlin: De Gruyter
Fußnote:
Gesehen am 20. Juli 2016
Open Access
Namensnennung 4.0 International
Umfang:
Online-Ressource
ISSN:
2353-7396
ZDB-ID:
2862627-8 ZDB
VÖBB-Katalog:
35314090
Schlagworte:
Zeitschrift
ZLB-Systematik:
Mathematik
Sammlung:
Mathematik
Copyright:
Rechte vorbehalten
Zugriffsberechtigung:
Freier Zugang

Aufsatz

Verfasser:
Zemed, Najib
Mouzoun, Kaoutar
Moulay Abdelali, Hanane
Cherradi, Toufik
Bouyahyaoui, Azzeddine
Titel:
Reliability and sensitivity assessment of laminated composite plates with high-dimensional uncertainty variables using active learning-based ensemble metamodels
Erschienen:
Berlin: De Gruyter, 2024
Sprache:
Englisch
Zusammenfassung:
Abstract: Laminated composite materials play a crucial role in various engineering applications due to their exceptional mechanical properties. This article explores the analysis of reliability and the local sensitivity of failure probability associated with laminated composite materials. It delves into the impact of uncertainties on the performance of these materials and presents an active learning-based reliability methodology. This methodology combines Monte Carlo simulation with a metamodel derived from the combination of three metamodels: artificial neural networks, support vector regression, and Kriging. The article illustrates this methodology through two practical applications on laminated composite plates and compares its performance with other reliability methods. This approach offers valuable insights to enhance the analysis of reliability, strengthen the design process, and facilitate decision-making while fully considering uncertainties related to material properties.
Umfang:
Online-Ressource
Fußnote:
Open Access
Archivierung/Langzeitarchivierung gewährleistet
Schlagworte:
composite material reliability ; reliability sensitivity ; artificial neural network ; support vector regression ; Kriging ; Monte Carlo simulation ; active learning
ZLB-Systematik:
Mathematik
Sonstiges
URN:
urn:nbn:de:101:1-2024040915403976248734
Sammlung:
Mathematik
Sonstiges
Copyright:
CC BY
Zugriffsberechtigung:
Freier Zugang
Verfasser:
Zemed, Najib
Mouzoun, Kaoutar
Moulay Abdelali, Hanane
Cherradi, Toufik
Bouyahyaoui, Azzeddine
Titel:
Reliability and sensitivity assessment of laminated composite plates with high-dimensional uncertainty variables using active learning-based ensemble metamodels
Erschienen:
Berlin: De Gruyter, 2024
Sprache:
Englisch
Zusammenfassung:
Abstract: Laminated composite materials play a crucial role in various engineering applications due to their exceptional mechanical properties. This article explores the analysis of reliability and the local sensitivity of failure probability associated with laminated composite materials. It delves into the impact of uncertainties on the performance of these materials and presents an active learning-based reliability methodology. This methodology combines Monte Carlo simulation with a metamodel derived from the combination of three metamodels: artificial neural networks, support vector regression, and Kriging. The article illustrates this methodology through two practical applications on laminated composite plates and compares its performance with other reliability methods. This approach offers valuable insights to enhance the analysis of reliability, strengthen the design process, and facilitate decision-making while fully considering uncertainties related to material properties.
Umfang:
Online-Ressource
Fußnote:
Open Access
Archivierung/Langzeitarchivierung gewährleistet
Schlagworte:
composite material reliability ; reliability sensitivity ; artificial neural network ; support vector regression ; Kriging ; Monte Carlo simulation ; active learning
ZLB-Systematik:
Mathematik
Sonstiges
URN:
urn:nbn:de:101:1-2024040915403976248734
Sammlung:
Mathematik
Sonstiges
Copyright:
CC BY
Zugriffsberechtigung:
Freier Zugang

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