Prediction of Buckling Load of Columns Using Artificial Neural Networks

Abstract

A number of investigators have proposed semiempirical formulas for the critical buckling load of slender columns. The departure from the assumptions of the elastic-plastic theory makes the task of incorporating all the features of real-life columns into a single formula very difficult. As a result, semiempirical formulas, adopted for design specifications often follow a lower bound to experimental observations to include a variety of column types. Therefore, a significant portion of the actual column strength remains unutilized, when such a lower bound is adopted in the design of axially compressed members. This technical note reports development of a tool for the prediction of buckling load of columns, which requires minimum assumptions using neural computing techniques. This concept can be extended to include a variety of column types in a single model for the buckling load of columns. This concept can also be further extended for reliability analysis as the network can also predict the standard deviation in the column strength.