Numerical evaluation of the upright columns with partial reinforcement along with the utilisation of neural networks with combining feature-selection method to predict the load and displacement

This study evaluated the axial capacity of cold-formed racking upright sections strength-ened with an innovative reinforcement method by finite element modelling and artificial intelligence techniques. At the first stage, several specimens with different lengths, thicknesses and reinforcement spacings were modelled in ABAQUS. The finite element method (FEM) was employed to increase the available datasets and evaluate the proposed reinforcement method in different geometrical types of sections. The most influential factors on the axial strength were investigated using a feature-selection (FS) method within a multi-layer perceptron (MLP) algorithm. The MLP algorithm was developed by particle swarm optimization (PSO) and FEM results as input. In terms of accuracy evaluation, some of the rolling criteria including results showed that geometrical parameters have almost the same contribution in compression capacity and displacement of the specimens. According to the performance evaluation indexes, the best model was detected and specified in the paper and opti-mised by tuning other parameters of the algorithm. As a result, the normalised ultimate load and displacement were predicted successfully.