• Structural Engineering and Mechanics
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• v.18 no.3
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• pp.303-314
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• 2004

An artificial neural network (ANN) application is presented for flexural and axial vibration analysis of elastic beams with various support conditions. The first three natural frequencies of beams are obtained using multi layer neural network based back-propagation error learning algorithm. The natural frequencies of beams are calculated for six different boundary conditions via direct solution of governing differential equations of beams and Rayleigh's approximate method. The training of the network has been made using these data only flexural vibration case. The trained neural network, however, had been tested for cantilever beam (C-F), and both end free (F-F) in case the axial vibration, and clamped-clamped (C-C), and Guided-Pinned (G-P) support condition in case the flexural vibrations which were not included in the training set. The results found by using artificial neural network are sufficiently close to the theoretical results. It has been demonstrated that the artificial neural network approach applied in this study is highly successful for the purposes of free vibration analysis of elastic beams.

• Steel and Composite Structures
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• v.4 no.1
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• pp.49-75
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• 2004

The paper derives, validates and illustrates the application of GBT-based formulae to estimate distortional critical lengths and bifurcation stress resultants in cold-formed steel rack-section columns, beams and beam-columns with arbitrarily inclined mid-stiffeners and four support conditions. After a brief review of the Generalised Beam Theory (GBT) basics, the main concepts and procedures employed to obtain the formulae are addressed. Then, the GBT-based estimates are compared with exact results and, when possible, also with values yielded by formulae due to Lau and Hancock, Hancock and Teng et al. A few remarks on novel aspects of the rack-section beam-column distortional buckling behaviour, unveiled by the GBT-based approach, are also included.