Scalable AI computational framework for seismic response simulation of million-scale urban building clusters

Seismic response simulation of mega-scale urban building clusters is crucial for assessing urban resilience, but is hindered by data scarcity, prohibitive computational costs, and the limited scalability of conventional analytical methods. This paper presents an intelligent computational framework designed to overcome these limitations, enabling efficient and accurate seismic simulation for urban areas with hundreds of thousands to millions of buildings. The framework is composed of three integrated modules: (i) a parameter calibration module that infers key structural dynamic properties from readily available building information; (ii) an adaptive dynamic K-means clustering module to drastically reduce computational workload; (iii) a response prediction module that employs a novel graph neural network to learn from inter-entity relationships, enabling accurate regression from a minute fraction of known data. The framework’s efficacy is validated through a mega-scale case study, yielding 4.416 billion seismic response predictions for 1.15 million buildings in Shanghai under 3,840 ground motions. The results demonstrate exceptional performance. The framework’s predictions of engineering demand parameters achieved a response error of just 4.19%. Significantly, by learning from just 0.0087% of the total response space, the proposed framework reduces runtime from 12.2 years (estimated) with time history analysis to just 59.8 hours, offering a robust and scalable solution for quantitative seismic resilience assessment and disaster risk management in modern mega-cities.