Using ANN to Estimate the Critical Buckling Load of Y Shaped Cross-Section Steel Columns

Accurate measurement of the critical buckling stress is crucial in the entire field of structural engineering. In this paper, the critical buckling load of Y-shaped cross-section steel columns was predicted by the Artificial Neural Network (ANN) using the Levenberg-Marquardt algorithm. The results of 57 buckling tests were used to generate the training and testing datasets. Seven input variables were considered, including the column length, column width, steel equal angles thickness, the width and thickness of the welded steel plate, and the total deviations following the Ox and Oy directions. The output was the critical buckling load of the columns. The accuracy assessment criteria used to evaluate the model were the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The selection of an appropriate structure of ANN was first addressed, followed by two investigations on the highest accuracy models. The first one consisted of the ANN model that gave the lowest values of MAE = 40.0835 and RMSE = 30.6669, whereas the second one gave the highest value of R = 0.98488. The results revealed that taking MAE and RMSE for model assessment was more accurate and reasonable than taking the R criterion. The RMSE and MAE criteria should be used in priority, compared with the correlation coefficient.