Finite Element and Artificial Neural Network Analysis of Thin-Walled Steel Perforated Sections in Compression

DOI

10.2991/eame-17.2017.37How to use a DOI?

Keywords

artificial neural network (ANN); finite element method (FEM); steel perforated sections; ultimate load

Abstract

The analysis of perforated members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections can't be used for the perforated steel member design. In this study finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Meng Wu
AU  - Zhijun Lyu
AU  - Qian Xiang
AU  - Yiming Song
AU  - Hongliang Li
PY  - 2017/04
DA  - 2017/04
TI  - Finite Element and Artificial Neural Network Analysis of Thin-Walled Steel Perforated Sections in Compression
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)
PB  - Atlantis Press
SP  - 151
EP  - 154
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-17.2017.37
DO  - 10.2991/eame-17.2017.37
ID  - Wu2017/04
ER  -