Extensive simulations of complex computational models are typically required to acquire accurate prediction of structural responses under seismic loading. Traditional mechanics-based or phenomenological models however often struggle to capture the behavior of complex components. This study proposes integrating real-time hybrid simulation (RTHS) with deep gated recurrent units (GRU) network for accurate dynamic response prediction of structural systems with parts challenging for modeling. The GRU network, trained using RTHS data, enables accurate assessments of structural responses. To optimize training of the GRU network, a cross-validation (CV)-Voronoi adaptive sampling method is introduced to minimize the number of RTHS experiments. A two-story steel frame equipped with self-centering viscous dampers (SC-VD) is designed as a proof-of-concept to validate the proposed method. To account for the effects of uncertainty, a non-stationary stochastic earthquake ground motion model is used to generate the seismic waves for training. The final GRU network is then validated against additional tests. The results demonstrate that the proposed method holds promise for efficient and accurate prediction of dynamic response of structural systems.

中文翻译：

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使用信息物理实时混合仿真，通过经过实验训练的深度门控循环单元网络进行动态时程响应预测


通常需要对复杂的计算模型进行广泛的仿真，以获得对地震载荷下结构响应的准确预测。然而，传统的基于力学或现象学的模型往往难以捕捉复杂组件的行为。本研究提出将实时混合仿真 （RTHS） 与深度门控循环单元 （GRU） 网络集成，以准确预测结构系统的动态响应，其中部件难以建模。使用 RTHS 数据训练的 GRU 网络能够准确评估结构响应。为了优化 GRU 网络的训练，引入了交叉验证 （CV）-Voronoi 自适应采样方法，以最大限度地减少 RTHS 实验的数量。设计了配备自定心粘性阻尼器 （SC-VD） 的两层钢框架作为概念验证，以验证所提出的方法。为了考虑不确定性的影响，使用非平稳随机地震地震动模型来生成地震波以进行训练。然后，根据其他测试验证最终的 GRU 网络。结果表明，所提方法有望高效、准确地预测结构系统的动态响应。