This study develops an artificial neural network (ANN) to estimate the critical buckling load (CBL) of corroded web-tapered steel I-section (WTSI) columns in pre-engineered steel buildings. A total of 387 datasets are employed to develop the ANN model. The datasets are generated from the proposed analytical model and Newton–Raphson method. The input parameters of the developed ANN model contain the cross-sectional dimensions of the steel column (i.e., the top and bottom flange width, top and bottom flange thickness, maximum section height, minimum section height, and web thickness), elastic modulus of material, and the column height. Meanwhile, the CBL is the output parameter of the ANN model. A predictive process for the CBL of the corroded WTSI columns has been proposed based on the ANN model and previous corrosion model. Results reveal that the ANN model showed an excellent performance in predicting the CBL of the corroded steel columns. The \({R}^{2}\) values of the training, testing, and validation data are 0.99975, 0.99916, and 0.99951, respectively. The root-mean-squared errors of the training, testing, and validation data are 96.705 \(\left(\mathrm{kN}\right)\), 103.402 \(\left(\mathrm{kN}\right)\), and 103.200 \(\left(\mathrm{kN}\right)\), respectively. Additionally, the a20-index is very close to 1.0. Moreover, a graphical user interface tool is constructed to facilitate the CBL calculation of the corroded WTSI columns.

本研究开发了一种人工神经网络（ANN）来估计预制钢结构建筑中腐蚀的腹板锥形钢工字形截面（WTSI）柱的临界屈曲载荷（CBL）。总共使用 387 个数据集来开发 ANN 模型。数据集是根据所提出的分析模型和牛顿-拉夫森方法生成的。所开发的ANN模型的输入参数包括钢柱的横截面尺寸（即顶部和底部翼缘宽度、顶部和底部翼缘厚度、最大截面高度、最小截面高度和腹板厚度）、弹性模量材料和柱高。同时，CBL是ANN模型的输出参数。基于 ANN 模型和先前的腐蚀模型，提出了腐蚀 WTSI 柱 CBL 的预测过程。结果表明，ANN 模型在预测腐蚀钢柱的 CBL 方面表现出优异的性能。这训练、测试和验证数据的\({R}^{2}\)值分别为 0.99975、0.99916 和 0.99951。训练、测试和验证数据的均方根误差为 96.705 \(\left(\mathrm{kN}\right)\)、 103.402 \(\left(\mathrm{kN}\right)\)，和 103.200 \(\left(\mathrm{kN}\right)\)，分别。此外，a20 指数非常接近 1.0。此外，还构建了图形用户界面工具以方便腐蚀的 WTSI 柱的 CBL 计算。