In this paper a two-phase neuro-modal solution for seismic analysis of skeletal structures was developed. Seismic analyses are required to design resisting structures against potential ground motions. Such analyses are, however, computationally intense because of coupled systems of differential equations, time-dependent analyses, uncertainty of seismic loads, and large number of degrees of freedom in high-rise structures. Here, through integration of modal analysis with Long Short-Term Memory neural networks, a method was developed to model and solve dynamic equations of motion more efficiently. Specifically, the method allowed us to convert a time-dependent problem to a recurrent neural network with fixed architecture, functions, and parameters so that the required CPU time for analysis of the problem reduced from order of 10⁻² s to the order of 10⁻⁵ s for a single degree-of-freedom system under a seismic load. The model was validated through comparison between model predictions and ground truth values obtained from simulated and real data. The correlation between predictions and target values was between 0.98626 and 1 for different loading conditions. This level of accuracy was equivalent to error values ranging from ~ 0 to 1.7% for predicted displacements of the structures under the seismic loads. The developed model can be used to tackle iterative procedures such as design optimizations, risk analysis, Monte Carlo simulations, etc. for large systems such as high-rise skeletal buildings in more efficient time. Also, the proposed platform can be extended to perform vibration and dynamic analyses for continuous systems, plates and shells, bridges, and offshore structures, and under loading conditions such as tornadic wind loads, moving Loads, wave and sea loads, etc.

本文提出了一种用于骨骼结构地震分析的两阶段神经模态解决方案。为了设计抵抗潜在地震动的结构，需要进行地震分析。但是，由于微分方程的耦合系统，与时间有关的分析，地震荷载的不确定性以及高层结构中的大量自由度，因此此类分析的计算量很大。在这里，通过将模态分析与长短期记忆神经网络相集成，开发了一种更有效地建模和求解运动动态方程的方法。具体来说，该方法使我们能够将时间相关的问题转换为具有固定体系结构，功能和参数的递归神经网络，从而使问题分析所需的CPU时间从10数量级减少^-2  s至10 ^-5  s的量级地震荷载下的单自由度系统。通过比较模型预测值和从模拟和真实数据获得的地面真实值，对模型进行了验证。对于不同的加载条件，预测值与目标值之间的相关性在0.98626与1之间。对于地震荷载下结构的预计位移，此精度水平等效于〜0至1.7％的误差值。所开发的模型可用于解决大型系统（例如高层骨架建筑）的迭代过程，如设计优化，风险分析，蒙特卡洛模拟等，从而更有效地解决问题。同样，可以扩展所提出的平台，以对连续系统，板和壳体，桥梁和海上结构进行振动和动态分析，