Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders

DOI10.1007/s00707-020-02878-2zbMath1476.74149OpenAlexW3120310365MaRDI QIDQ2234140

N. Geran Malek, Armin Dadras Eslamlou, S. M. Javadi, Ali Kaveh

Publication date: 18 October 2021

Published in: Acta Mechanica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00707-020-02878-2

zbMATH Keywords

deep learning modelbuckling capacitycylinder bendingfiber angle effect

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Composite and mixture properties (74E30) Bifurcation and buckling (74G60) Numerical and other methods in solid mechanics (74S99)

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Uses Software

• ABAQUS

Cites Work

• Unnamed Item
• A distribution-free approach to inducing rank correlation among input variables
• A coupled integral-differential quadrature and B-spline-based multi-step technique for transient analysis of VSCL plates
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• Multi-fidelity optimization via surrogate modelling
• Large Sample Properties of Simulations Using Latin Hypercube Sampling

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