Machine learning‐based predictions of buckling behaviour of cold‐formed steel structural elements

Seyed Mohammad Mojtabaei*, Jurgen Becque, Rasoul Khandan, Iman Hajirasouliha

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Designing thin‐walled structural members is a complex process due to the possibility of multiple instabilities. This study aimed to develop machine learning algorithms to predict the buckling behavior of thin‐walled channel elements under axial compression or bending. The algorithms were trained using feed‐forward multi‐layer Artificial Neural Networks (ANNs), with the input variables including the cross‐sectional dimensions, the thickness, the presence and location of intermediate stiffeners, and the element length. The output data included the elastic critical buckling load or moment, as well as a modal decomposition of the buckled shape into the pure buckling mode categories: local, distortional and global buckling. The Finite Strip Method (FSM) and the Equivalent Nodal Force Method (ENFM) were used to prepare the sample output for training. To ensure the accuracy of the developed algorithms, the ANN models were subjected to a K‐fold cross‐validation technique and featured optimized hyperparameters. The results showed that the trained algorithms had a remarkable accuracy of 98% in predicting the elastic critical buckling loads and modal decomposition of the critical buckled shapes.
Original languageEnglish
Pages (from-to)843-847
Number of pages5
Issue number3-4
Publication statusPublished - 12 Sept 2023

Bibliographical note

© 2023 The Authors. Published by Ernst & Sohn GmbH.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.


  • Cold‐Formed Steel (CFS)
  • Buckling Mode
  • Machine Learning
  • Buckling Resistance
  • Artificial Intelligence (AI)


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