Artificial neural network (ANN) models were applied for simulating and predicting the ultimate capacities of cellular steel beams. To do this, at the first, different neural networks by various learning algorithms and number of neurons in the hidden layer were simulated. The required data for networks in training, validating, and testing state were obtained from a reliable database. Next, the best network according to its predictive performance was chosen, and a new formula was derived to predict the failure loads of cellular steel beams subjected to LTB. The attempt was done to evaluate the most exact practical formula using different algorithm and method for LTB strength assessment of cellular beams. Next, a comparison was made between the ANN-based formula and a formula based on the stepwise regression (SR) to show the predictive power of the ANN model. The results provided some evidence that ANN model obtained more accurate predictions than SR model. At the end, a sensitivity analysis was developed using Garson’s algorithm to determine the importance of each input parameter which was used in the proposed ANN formulation.

中文翻译：

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蜂窝钢梁极限承载力的神经网络模型研究

人工神经网络（ANN）模型被应用于模拟和预测蜂窝钢梁的极限承载力。为此，首先，通过各种学习算法和隐藏层中的神经元数量来模拟不同的神经网络。网络在训练、验证和测试状态所需的数据是从可靠的数据库中获得的。接下来，根据其预测性能选择最佳网络，并推导出一个新公式来预测受 LTB 影响的蜂窝钢梁的破坏载荷。尝试使用不同的算法和方法来评估最精确的实用公式，用于蜂窝梁的 LTB 强度评估。下一个，在基于 ANN 的公式和基于逐步回归 (SR) 的公式之间进行了比较，以显示 ANN 模型的预测能力。结果提供了一些证据，表明 ANN 模型比 SR 模型获得了更准确的预测。最后，使用 Garson 算法开发了灵敏度分析，以确定在建议的 ANN 公式中使用的每个输入参数的重要性。