Prediction of Critical Buckling Load of Web Tapered I-Section Steel Columns Using Artificial Neural Networks

The web tapered I-section steel (WTIS) columns have been widely used in civil and industrial steel structures. However, the existing theoretical and empirical equations demonstrate a significant discrepancy in estimating the critical axial load of the WTIS columns. This study aims to develop effecti...

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Bibliographic Details
Published in:International journal of steel structures 2021, 21(4), , pp.1159-1181
Main Authors: Nguyen, Trong-Ha, Tran, Ngoc-Long, Nguyen, Duy-Duan
Format: Article
Language:English
Subjects:
Artificial neural networks
Axial loads
Buckling
Civil Engineering
Empirical equations
Engineering
Finite element method
Graphical user interface
Materials Science
Mathematical models
Neural networks
Solid Mechanics
Steel columns
Steel structures
Webs
토목공학
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Summary:The web tapered I-section steel (WTIS) columns have been widely used in civil and industrial steel structures. However, the existing theoretical and empirical equations demonstrate a significant discrepancy in estimating the critical axial load of the WTIS columns. This study aims to develop effective artificial neural networks (ANNs) for predicting the critical buckling load of the WTIS columns. A database of 269 finite element models of WTIS columns was generated, after verifying with experimental results, to develop the ANN model. The results of the proposed ANN model were also compared with those of existing formulas, highlighting that the ANN model in this study predicts the critical buckling load of the WTIS columns more accurately than the existing formulas. Moreover, the influences of input parameters on the critical buckling load of the WTIS columns were thoroughly investigated. An ANN-based formula, which considers input variables, was thereafter proposed to estimate the critical buckling load of the WTIS columns. Additionally, a graphical user interface tool has been developed for simplifying the design practice of the WTIS columns.
ISSN:1598-2351
2093-6311
DOI:10.1007/s13296-021-00498-7