高精度、高效率的结构响应预测对于建筑结构的智能防灾减灾，包括震后损坏评估、结构健康监测和建筑物抗震能力评估等至关重要。为了提高结构响应预测的精度和效率，本研究提出了一种基于物理信息深度学习的新型实时结构响应预测方法，该方法通过数据驱动的训练方法和自回归的训练策略，可以预测结构中的大量节点响应。该方法包含一个Phy-Seisformer模型，该模型可将结构的物理信息纳入模型中，从而实现更高精度的预测。实验对象包括4层砌体结构、11层钢筋混凝土不规则结构和21层钢筋混凝土框架结构，以验证所提方法的精度和效率。此外，通过消融实验验证了Phy-Seisformer模型中结构的有效性。通过对比实验，研究了地震波振幅范围对预测精度的影响。实验结果表明，对于不同类型的建筑结构，本文提出的方法可以达到非常高的精度，计算速度比有限元计算至少快5000倍。

High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures, including post-earthquake damage assessment, structural health monitoring, and seismic resilience assessment of buildings. To improve the accuracy and efficiency of structural response prediction, this study proposes a novel physics-informed deep-learning-based real-time structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy. The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model, thereby enabling higher-precision predictions. Experiments were conducted on a four-story masonry structure, an eleven-story reinforced concrete irregular structure, and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method. In addition, the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study. Furthermore, by conducting a comparative experiment, the impact of the range of seismic wave amplitudes on the prediction accuracy was studied. The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.