A structural analysis model to represent the dynamic behavior of building structure is required to develop a semi-active seismic response control system. Although the finite element method (FEM) is the most widely used method for seismic response analysis, when the FEM is applied to the dynamic analysis of building structures with nonlinear semi-active control devices, the computational effort required for the simulation for optimal design of the semi-active control system can be considerable. To solve this problem, this paper used recurrent neural network (RNN) to make a time history response simulation model for building structures with a semi-active control system. Example structures were selected of an 11-story building structure with a semi-active tuned mass damper (TMD), and a 27-story building having a semi-active mid-story isolation system. A magnetorheological damper was used as the semi-active control device. Five historical earthquakes and five artificial ground motions were used as ground excitations to train the RNN model. Two artificial ground motions and one historical earthquake, which were not used for training, were used to verify the developed the RNN model. Compared to the FEM model, the developed RNN model could effectively provide very accurate seismic responses, with significantly reduced computational cost.

中文翻译：

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基于循环神经网络的半主动控制装置建筑结构地震响应仿真模型的开发

开发半主动地震响应控制系统需要一个结构分析模型来表示建筑结构的动态行为。尽管有限元法 (FEM) 是最广泛使用的地震响应分析方法，但当 FEM 应用于具有非线性半主动控制装置的建筑结构的动力分析时，模拟优化设计所需的计算工作量半主动控制系统可以是相当可观的。针对这一问题，本文采用递归神经网络（RNN）对半主动控制系统的建筑结构进行时程响应仿真模型。示例结构选自具有半主动调谐质量阻尼器 (TMD) 的 11 层建筑结构和具有半主动中层隔离系统的 27 层建筑。磁流变阻尼器用作半主动控制装置。五个历史地震和五个人工地面运动被用作地面激励来训练 RNN 模型。使用未用于训练的两次人工地面运动和一次历史地震来验证开发的 RNN 模型。与 FEM 模型相比，开发的 RNN 模型可以有效地提供非常准确的地震响应，并显着降低计算成本。