Abstract Accurate and rapid structural-dynamics modeling is critical for structural design, disaster mitigation, and resilience assessment, yet existing computational frameworks rely almost exclusively on nonlinear finite-element analysis. Conventional finite-element analysis approaches require substantial computational resources, with individual simulations typically taking hours to days to complete, making real-time or city-wide structural assessments impractical. To overcome this fundamental limitation, we introduce SeisGPT, a physics-informed foundation model designed specifically to enable high-fidelity, real-time structural response prediction across extensive building portfolios encompassing diverse structural types and topologies. SeisGPT integrates structural mechanics principles with advanced deep-learning methodologies, including a physics-informed graph neural network encoder, a simplified dynamic-response embedding module, and a generative Transformer-based decoder. The model is pretrained on a large-scale dataset comprising over 2 million nonlinear elastoplastic FEA simulations—covering 270,000 AI-generated, code-compliant structural designs created via an automated generative workflow, as well as 694 real-world buildings—totaling more than 10 billion discrete response time-steps. For previously unseen buildings subjected to external loads, SeisGPT achieves displacement and acceleration predictions with less than 5% normalized error while providing an approximately 40,000-fold computational speedup over conventional FEA methods. Furthermore, by assimilating sparse sensor measurements, SeisGPT’s physics-guided latent representations refine prediction accuracy beyond that achievable with conventional FEA simulations, enabling real-time structural-health monitoring and damage localization. By integrating physics-informed modeling with scalable inference, SeisGPT establishes a widely applicable computational paradigm, paving the way for transformative advancements in structural dynamics.
Zhou et al. (Thu,) studied this question.