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Steel and Composite Structures Volume 51, Number 4, May 25 2024 , pages 441-456 DOI: https://doi.org/10.12989/scs.2024.51.4.441 |
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Predicting restraining effects in CFS channels: A machine learning approach |
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Seyed Mohammad Mojtabaei, Rasoul Khandan and Iman Hajirasouliha
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| Abstract | ||
| This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flangerestrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flangerestrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications. | ||
| Key Words | ||
| Artificial Neural Network (ANN); Cold-Formed Steel (CFS); elastic distortional buckling resistance; Finite Element Method (FEM); Finite Strip Method (FSM); flange-restrained channels | ||
| Address | ||
| Seyed Mohammad Mojtabaei:School of Architecture, Building and Civil Engineering, Loughborough University, Leicestershire LE11 3TU, UK Rasoul Khandan:Faculty of Engineering and Science, University of Greenwich, Kent ME4 4TB, UK Iman Hajirasouliha:Department of Civil and Structural Engineering, The University of Sheffield, Sheffield S1 3JD, UK | ||