Abstract
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.
Highlights
In the field of modern construction today, steel materials are used for most constructions such as infrastructure work, bridges, towers, and airports, thanks to its advantages compared to other types of objects of other materials [1, 2]
Experimental works have been conducted in many studies to characterize the instability of structural components under compression, for instance, in the work of Shi et al [12] on the steel structure under axial load. e welding of 460 MPa stainless steel plates created four specimens of the square box Scientific Programming section with an identical slenderness ratio. e results showed that the stability of steel tubes decreased compared with conventional design codes
It is worth noticing that 200 simulations were performed in each case, where the indexes of samples were randomly taken to establish the training dataset. e results with respect to R, root mean square error (RMSE), and mean absolute error (MAE) are plotted under probability density functions over 200 simulations
Summary
In the field of modern construction today, steel materials are used for most constructions such as infrastructure work, bridges, towers, and airports, thanks to its advantages compared to other types of objects of other materials [1, 2]. In many studies, such as Shi et al [12, 13] for rectangular steel tubes, Yang et al [14] for I cross-section columns, or Jiang et al [21] for hollow circular tubes made of nickel-titanium alloy, the behavior of structural members under compression was analyzed Software such as ANSYS [22] or ABAQUS [23] was used for problems with input parameters such as the geometry of the cross-section, weight, mechanical properties, loading, and other considerations. Erefore, in this study, with 57 experimental results collected from the available literature, the authors propose an approach using an artificial neural network (ANN) to accurately predict the critical buckling load (Fcr) of Y-section steel columns. A reliability analysis is performed, combined with the three mentioned criteria to deduce the best ANN black-box for the prediction problem
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