Development of deep neural network model to predict the compressive strength of FRCM confined columns

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 2095-2449

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Development of deep neural network model to predict the compressive strength of FRCM confined columns ; volume:16 ; number:10 ; day:5 ; month:12 ; year:2022 ; pages:1213-1232 ; date:12.2022
Frontiers of structural and civil engineering ; 16, Heft 10 (5.12.2022), 1213-1232, 12.2022

Creator: Le-Nguyen, Khuong
Minh, Quyen Cao
Ahmad, Afaq
Ho, Lanh Si

Contributor: SpringerLink (Online service)

DOI: 10.1007/s11709-022-0880-7

URN: urn:nbn:de:101:1-2023031909070256016741

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 11:02 AM CEST

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Le-Nguyen, Khuong
Minh, Quyen Cao
Ahmad, Afaq
Ho, Lanh Si
SpringerLink (Online service)

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Development of deep neural network model to predict the compressive strength of FRCM confined columns

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Other Objects (12)

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Ductility of RC Columns confined with FRP Sheets

Ductility of RC Columns confined with FRP Sheets

Concentrically Loaded Circular RC Columns Partially Confined with FRP

The compressive strength of crumpled matter

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Ductility of RC Columns confined with FRP Sheets

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Concentrically Loaded Circular RC Columns Partially Confined with FRP

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Predicting compressive strength of RCFST columns under different loading scenarios using machine learning optimization

Ductility of RC Columns confined with FRP Sheets

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Concentrically Loaded Circular RC Columns Partially Confined with FRP

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