ANN model for predicting the elastic critical buckling coefficients of prismatic tapered steel web plates under stress gradients

Abstract

Tapered steel plate girders are commonly used in large span industrial structures and composite bridges. The tapered thin steel web plates under stress gradients in such structures may be susceptible to elastic local buckling. An efficient and robust Artificial Neural Network (ANN) model is developed and presented in this paper for predicting the elastic critical buckling coefficients of prismatic tapered steel webs under stress gradients. More than 4000 samples are used in extensive parametric analyses of tapered web plates with simply supported edges. The ANN model recognizes important features associated with tapered plates, including the tapering ratio of the plate, aspect ratio, and stress gradient. A dataset containing 4665 records is developed for the training, validation, and testing of the ANN model developed. Two main ANN models are developed. The first model consists of a single hidden layer with neurons varying from 5 to 11. The second model consists of two hidden layers, with 8 neurons in the first layer and 6 neurons in the second layer. The accuracy of these models is thoroughly evaluated. It is found that the developed ANN model with two-hidden layers has superior performance than other models. The developed ANN can be employed to accurately predict the elastic critical buckling coefficients of prismatic tapered steel webs under stress gradients.