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Deterministic and stochastic flexural behaviors of laminated composite thin-walled I-beams using a sinusoidal higher-order shear deformation theory.

Authors :
Bui, Xuan-Bach
Nguyen, Trung-Kien
Source :
Mechanics Based Design of Structures & Machines. 2024, Vol. 52 Issue 10, p7349-7378. 30p.
Publication Year :
2024

Abstract

In this study, a novel sinusoidal higher-order shear deformation thin-walled beam theory is presented to examine the effects of material properties and external load uncertainty on static responses of laminated composite thin-walled beams with open sections. The solution for the deterministic flexural analysis is based on Hamilton's principle and Ritz-type exponential shape function series. Several mechanical parameters of laminated composite materials are randomized and plugged into the beam solver to investigate the thin-walled beam's stochastic flexural behaviors. The computational cost and accuracy of the polynomial chaos expansion (PCE) method with both projection and linear regression approaches are presented and evaluated by comparing its results with crude Monte Carlo simulation (MCS). This comparison allows for a thorough assessment of the PCE method's performance. Additionally, a sensitivity analysis is conducted to compare the relative significance of the uncertainty in material properties and loads on the stochastic responses. The supervised training of the artificial neural network based on the MCS beam data is also conducted and compared to the PCE and MCS methods. The findings about the stochastic outputs are introduced in various statistical metrics and illustrations to demonstrate the influences of material properties' randomness on different laminated composite thin-walled beam configurations. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15397734
Volume :
52
Issue :
10
Database :
Academic Search Index
Journal :
Mechanics Based Design of Structures & Machines
Publication Type :
Academic Journal
Accession number :
180330255
Full Text :
https://doi.org/10.1080/15397734.2023.2297840