Abstract Cold-formed steel channels are made with staggered courses of slots for reduced thermal conductivity and improved energy efficiency of cold-formed steel buildings. The reduced shear strength of the slotted channels must be accurately evaluated in the design to ensure the safety of the buildings with such members. This paper proposes artificial neural networks for predicting the elastic shear buckling loads and the ultimate shear strengths of the channels with slotted webs. The neural networks were trained using a large dataset consisting of 3512 finite element simulation results and a ten-fold cross-validation method. Hyperparameter tuning with a grid search was performed to determine the optimal hyperparameters of the models. The effects of the channel properties on the elastic shear buckling loads and the ultimate shear strengths of the channels were evaluated using the SHAP method. The selected neural networks with optimal hyperparameters showed excellent agreements with the finite element simulation results and exceeded the accuracy of the available design equations.

中文翻译：

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用于预测开槽腹板 CFS 通道剪切强度的神经网络

摘要 冷弯型钢槽道采用交错排列的槽道，以降低冷弯型钢建筑的热导率，提高能效。必须在设计中准确评估开槽通道的抗剪强度降低，以确保具有此类构件的建筑物的安全。本文提出了人工神经网络来预测弹性剪切屈曲载荷和开槽腹板通道的极限剪切强度。神经网络使用由 3512 个有限元模拟结果和十倍交叉验证方法组成的大型数据集进行训练。使用网格搜索进行超参数调整以确定模型的最佳超参数。使用SHAP方法评估通道特性对弹性剪切屈曲载荷和通道的极限剪切强度的影响。选定的具有最佳超参数的神经网络与有限元仿真结果非常吻合，并且超出了可用设计方程的准确性。