In this paper, a new deep learning framework named encoding convolution long short-term memory (encoding ConvLSTM) is proposed to build a surrogate structural model with spatiotemporal evolution of structure, estimate the structural spatiotemporal state and predict the dynamic response under similar future dynamic load conditions. The main work of this study includes: (a) The spatiotemporal response tensor database is developed using discrete-time history data of structural dynamic response. (b) As an extension of LSTM, convolution operation is combined with LSTM network to construct structural surrogate model from the spatiotemporal evolution structural performance. (c) To enhance the anti-interference ability of structural surrogate models, a new three-layer encoding layer is added for denoising autoencoders of the hybrid network. The influence of building types and input noise on the accuracy and antinoise performance of the surrogate models are analyzed through the dynamic response prediction of a frame-shear wall, a cylindrical, and a spherical reticulated shell structure. As a testbed for the proposed network, a case study is performed on a laboratory stadium structure. The results demonstrate that the developed surrogate model can predict the structural dynamic response precisely with more under 30% noise interference.

中文翻译：

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考虑时间和空间演化的结构代理模型和动态响应预测：编码器-解码器 ConvLSTM 网络

本文提出了一种名为编码卷积长短期记忆（encoding ConvLSTM）的新型深度学习框架，用于构建具有结构时空演化的替代结构模型，估计结构时空状态并预测类似未来动态载荷下的动态响应状况。本研究的主要工作包括： (a) 利用结构动力响应的离散时间历史数据开发了时空响应张量数据库。(b) 作为 LSTM 的扩展，卷积运算与 LSTM 网络相结合，从时空演化结构性能构建结构代理模型。(c) 为了增强结构代理模型的抗干扰能力，为混合网络的去噪自编码器添加了一个新的三层编码层。通过框架剪力墙、圆柱体和球面网壳结构的动力响应预测，分析了建筑类型和输入噪声对替代模型精度和抗噪性能的影响。作为拟议网络的测试平台，我们在实验室体育场结构上进行了案例研究。结果表明，所开发的代理模型可以精确地预测结构动态响应，噪声干扰低于 30%。