Buckling analysis of a beam–column using multilayer perceptron neural network technique

Abstract

We present the mathematical model and an artificial neural network method for calculating the buckling load of a beam column with different end conditions. A trial solution of the beam column equation is written as a sum of two parts, in which first part satisfies the boundary conditions and the second part represents the feed forward neural network containing adjustable parameters, weights and biases. We prepared the Error function by using the beam column equation and its boundary conditions, which is used in the back propagation method with deflection term to update the network parameters. It is found that the artificial neural network method is capable for calculating deflection of a beam column as a part of the training process. To ascertain the soundness, efficiency and accuracy of the proposed method the results are compared to the Euler critical load.