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.

中文翻译：

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用ANN估算Y型截面钢柱的临界屈曲载荷

临界屈曲应力的准确测量在整个结构工程领域至关重要。本文利用人工神经网络（LNN），使用Levenberg-Marquardt算法，对Y型截面钢柱的临界屈曲载荷进行了预测。使用57个屈曲测试的结果来生成训练和测试数据集。考虑了七个输入变量，包括柱长，柱宽，钢等角厚度，焊接钢板的宽度和厚度以及沿Ox和Oy方向的总偏差。输出是列的临界屈曲载荷。用于评估模型的准确性评估标准是相关系数（R），均方根误差（RMSE）和平均绝对误差（MAE）。首先讨论了人工神经网络的适当结构的选择，然后是对最高精度模型的两次调查。第一个由ANN模型组成，其给出的MAE = 40.0835和RMSE = 30.6669的最小值，而第二个模型给出的最大值为R  ＝ 0.98488。结果表明，采用MAE和RMSE进行模型评估比采用R准则更为准确和合理。与相关系数相比，应优先使用RMSE和MAE标准。