Abstract This paper discusses the implementation of an artificial neural network (ANN) for predicting the critical flutter velocity of suspension bridges with closed box deck sections. Deck chord length, bridge weight and structural damping were varied. The ANN model was derived and trained using a dataset of critical flutter velocities, calculated using flutter derivatives (FDs) from experiments and by varying geometrical and mechanical parameters. The ANN model was derived by training and comparing two different, preliminary ANNs. The first one was based on thirty sets of experimental FDs. This first set was subsequently used to calibrate the second model, based on surrogate FDs obtained by curve fitting of the experimental data. The surrogate FD dataset was subsequently expanded by Nataf-model Monte Carlo (MC) and Polynomial Chaos Expansion (PCE)-model MC simulation. Finally, the ANN was employed to synthetically generate a larger dataset of critical flutter velocities and estimate the corresponding probability distribution.

中文翻译：

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预测悬索桥颤振速度的人工神经网络模型

摘要 本文讨论了人工神经网络 (ANN) 的实现，用于预测具有封闭箱形桥面板截面的悬索桥的临界颤振速度。甲板弦长、桥梁重量和结构阻尼各不相同。ANN 模型是使用临界颤振速度数据集导出和训练的，该数据集使用来自实验的颤振导数 (FD) 并通过改变几何和机械参数进行计算。ANN 模型是通过训练和比较两个不同的初步 ANN 得出的。第一个基于 30 组实验 FD。该第一组随后用于校准第二个模型，基于通过实验数据的曲线拟合获得的替代 FD。随后通过 Nataf 模型蒙特卡罗 (MC) 和多项式混沌扩展 (PCE) 模型 MC 模拟扩展了代理 FD 数据集。最后，人工神经网络被用来综合生成一个更大的临界颤振速度数据集并估计相应的概率分布。