This paper proposes a framework of deep belief network (DBN) for studying the structural performance of cold-formed steel (CFS) channel sections with edge-stiffened/un-stiffened web holes, under axial compression. A total of 50,000 data points for training the DBN are generated from elasto plastic finite element analysis, which incorporates both initial geometric imperfections and residual stresses. A comparison against 23 experimental results was conducted, and it was found that the DBN predictions were conservative by 3% for columns with un-stiffened web holes, and 8% for columns with edge-stiffened web holes. When compared with Backpropagation Neural Network (a typical shallow artificial neural network) and linear regression model based on PaddlePaddle, it was found that the proposed DBN outperformed better than both the methods, using the same big training data used in this paper. When the same comparison was made for Effective Width Method and Direct Strength Method, the results from them were conservative by 5% and 12% against the experimental results, respectively for columns with un-stiffened web holes. Hole effects on the structural performance of channel sections under axial compression were also investigated. Based on the DBN output data, design recommendations of axial capacity enhancement/reduction factors were given for columns (stub, intermediate and slender) with edge-stiffened/un-stiffened web holes. Based on DBN prediction data, a comprehensive reliability analysis was conducted, which shows the proposed equations can predict the enhanced and reduced axial capacity of CFS channel sections with edge-stiffened/un-stiffened web holes accurately.

本文提出了一种深度置信网络 (DBN) 框架，用于研究带有边缘加劲/非加劲腹板孔的冷弯型钢 (CFS) 通道截面在轴向压缩下的结构性能。通过弹塑性有限元分析生成了总共 50,000 个用于训练 DBN 的数据点，其中包含初始几何缺陷和残余应力。对 23 个实验结果进行了比较，发现 DBN 预测对于带有非加强筋腹板孔的柱子保守了 3%，对于带有边缘加强筋腹板孔的柱子保守了 8%。当与反向传播神经网络（典型的浅层人工神经网络）和基于 PaddlePaddle 的线性回归模型进行比较时，发现所提出的 DBN 优于这两种方法，使用与本文相同的大训练数据。当对有效宽度法和直接强度法进行相同的比较时，对于具有未加劲肋腹板孔的柱，它们的结果分别与实验结果相比分别保守了 5% 和 12%。孔洞对轴压下通道截面结构性能的影响也进行了研究。根据 DBN 输出数据，针对带有边缘加劲/非加劲腹板孔的柱（短柱、中间柱和细长柱）给出了轴向承载力增强/减小系数的设计建议。基于DBN预测数据，进行了综合可靠性分析，结果表明，所提出的方程可以准确预测带有边缘加筋/非加筋腹板孔的CFS通道截面的轴向承载力的增加和减少。