Seismic analysis of concrete-filled steel tube (CFST) arch bridge based on finite element method is a time-consuming work. Especially when uncertainty of material and structural parameters are involved, the computational requirements may exceed the computational power of high performance computers. In this paper, a seismic analysis method of CFST arch bridge based on artificial neural network is presented. The ANN is trained by these seismic damage and corresponding sample parameters based on finite element analysis. In order to obtain more efficient training samples, a uniform design method is used to select sample parameters. By comparing the damage probabilities under different seismic intensities, it is found that the damage probabilities of the neural network method and the finite element method are basically the same. The method based on ANN can save a lot of computing time.

中文翻译：

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基于人工神经网络的钢管混凝土拱桥地震分析方法

基于有限元法的钢管混凝土拱桥的抗震分析是一项耗时的工作。特别是当涉及材料和结构参数的不确定性时，计算要求可能会超出高性能计算机的计算能力。提出了一种基于人工神经网络的钢管混凝土拱桥地震分析方法。基于有限元分析，通过这些地震破坏和相应的样本参数对ANN进行训练。为了获得更有效的训练样本，使用统一的设计方法来选择样本参数。通过比较不同地震烈度下的破坏概率，发现神经网络法和有限元法的破坏概率基本相同。