scholarly journals Development of deep learning-based joint elements for thin-walled beam structures

2022 ◽  
Vol 260 ◽  
pp. 106714
Author(s):  
Jaemin Jeon ◽  
Jaeyong Kim ◽  
Jong Jun Lee ◽  
Dongil Shin ◽  
Yoon Young Kim
1994 ◽  
Vol 30 (1) ◽  
pp. 43-54 ◽  
Author(s):  
J. Altenbach ◽  
H. Altenbach ◽  
V. Matzdorf

Scanning ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Long Yanze ◽  
Zhang Ke ◽  
Shi Huaitao ◽  
Li Songhua ◽  
Zhang Xiaochen

Thin-walled curved box beam structures especially rectangular members are widely used in mechanical and architectural structures and other engineering fields because of their high strength-to-weight ratios. In this paper, we present experimental and theoretical analysis methods for the static analysis of thin-walled curved rectangular-box beams under in-plane bending based on 11 feature deformation modes. As to the numerical investigations, we explored the convergence and accuracy analysis by normal finite element analysis, higher-order assumed strain plane element, deep collocation method element, and inverse finite element method, respectively. The out-of-plane and in-plane characteristic deformation vector modes derived by the theoretical formula are superimposed by transforming the axial, tangential, and the normal deformation values into scalar tensile and compression amounts. A one-dimensional deformation experimental test theory is first proposed, formulating the specific contributions of various deformation modes. In this way, the magnitude and trend of the influence of each low-order deformation mode on the distortion and warping in the actual deformation are determined, and the significance of distortion and warping in the actual curved beams subjected to the in-plane loads is verified. This study strengthens the deformation theory of rectangular box-type thin-walled curved beams under in-plane bending, thus providing a reference for analyzing the mechanical properties of curved-beam structures.


Sign in / Sign up

Export Citation Format

Share Document