Predicting the buckling behaviour of thin-walled structural elements using machine learning methods-Reference-Cited by-同舟云学术

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Predicting the buckling behaviour of thin-walled structural elements using machine learning methods

Published:2023-03 Issue: Volume:184 Page:110518
ISSN:0263-8231
Container-title:Thin-Walled Structures
language:en
Short-container-title:Thin-Walled Structures

Author:

Mojtabaei Seyed Mohammad^ORCID,Becque Jurgen,Hajirasouliha Iman^ORCID,Khandan Rasoul

Funder

Engineering and Physical Sciences Research Council

Publisher

Elsevier BV

Subject

Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference44 articles.

1. Coupled element and structural level optimisation framework for cold-formed steel frames;Phan;J. Construct. Steel Res.,2019

2. Local buckling in cold-formed steel moment-resisting bolted connections: Behavior, capacity, and design;Mojtabaei;J. Struct. Eng.,2020

3. The strength of thin plates in compression;von Karman;ASME Appl. Mech. Trans.,1932

4. Eurocode 3: Design of Steel Structures, Part 1.3: General Rules—Supplementary Rules for Cold Formed Members and Sheeting;CEN,2005

5. North American Specification for the Design of Cold-Formed Steel Structural Members;AISI S100- 16,2016

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1. Spatial tuning of the positive and negative Poisson's ratio of metamaterials through gradient design;Thin-Walled Structures;2024-12

2. Bayesian optimization-based Model-Agnostic Meta-Learning: Application to predict maximum cyclic moment resistance of steel bolted T-stub connections;Thin-Walled Structures;2024-11

3. Neural networks for critical load factors of unstiffened plates according to the reduced stresses method of EN 1993-1-5;Thin-Walled Structures;2024-10

4. Predicting the load-bearing capacity of lipped channel section cold formed steel profiles under combined effects using machine learning;Structures;2024-08

5. Neural networks-based line element method for large deflection frame analysis;Computers & Structures;2024-08

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