The buckling analysis of axially loaded columns with artificial neural networks

Abstract

The determination of effective design values in structural analysis is important. Axially loaded columns are designed according to the their buckling load capacity. In this study, a multi-layer artificial neural network is trained to give critical load for axially loaded columns and various support conditions. Backpropagation training algorithms are used considering the circular, square, rectangular, and I cross-sections. The artificial neural network, with is trained for circular and rectangular sections for three support conditions, is tested for the fourth support condition, square and I which is simply supported at both ends. For the fourth support condition column cross-sections are chosen, which were not included in the training set. The results found using trained neural networks are sufficiently close to the theoretical solution. It is emphasized that logical programming has application potential in this area.