Finite Element and Artificial Neural Network Analysis of Thin-Walled Steel Perforated Sections in Compression
Meng Wu, Zhijun Lyu, Qian Xiang, Yiming Song, Hongliang Li
Available Online April 2017.
- 10.2991/eame-17.2017.37How to use a DOI?
- artificial neural network (ANN); finite element method (FEM); steel perforated sections; ultimate load
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.
- © 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/).
Cite this article
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 -