Abstract The aim of the present study is to develop a deep convolutional neural network (DCNN) to predict geometrically and physically nonlinear structural deformations. Training data is obtained by short-time measurements in shock tubes, wherein metal plates are subjected to impulsive loadings, leading to viscoplastic vibrations and inelastic deflections. Due to the fact that, in literature, feed forward neural networks (FFNN) are more distributed for applications in structural mechanics, comparative calculations are presented between structural deformations based on DCNNs and FFNNs. Special attention is focused on the ability of DCNNs to capture also path-dependent deformations inside the network, which is an essential feature for inelastic material behaviour.

中文翻译：

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考虑粘塑性材料行为的结构动力学中的深度卷积神经网络

摘要 本研究的目的是开发一种深度卷积神经网络 (DCNN) 来预测几何和物理非线性结构变形。训练数据是通过在激波管中的短时间测量获得的，其中金属板受到冲击载荷，导致粘塑性振动和非弹性偏转。由于在文献中，前馈神经网络 (FFNN) 在结构力学中的应用更加分散，因此在基于 DCNN 和 FFNN 的结构变形之间进行了比较计算。特别关注 DCNN 捕获网络内部路径相关变形的能力，这是非弹性材料行为的基本特征。