Work

A Graph Network–Based Real‐Time Precise Predicting Model for Progressive Collapse of Steel Frame Structures

Year: 2026

Type: article

Abstract: Progressive collapse is initiated by local component failure and evolves into global structural instability through continuous damage propagation. Rapid prediction of the structural response before an actual collapse-inducing event occurs is important for collapse-resistant design, pre-disaster consequence assessment, and safety evaluation. However, challenges remain in predicting three-dimensional and multi-component collapse scenarios, including geometry representation, topology changes, and generalization across different failure conditions. To address these issues, this study proposes a graph neural network model for fast prediction of progressive collapse in steel-framed buildings after component failure. The model adopts a graph encoder-decoder architecture to represent component geometry and structural connectivity, employs multi-scale message passing to infer global damage patterns from local failures, and incorporates a multi-source feature joint training strategy for single-, double-, and multi-column failure scenarios. Validation results show that the model achieves 94% accuracy compared with simulation data and a 6900-fold acceleration, enabling real-time collapse prediction for pre-disaster consequence forecasting. This approach improves the balance between simulation accuracy and efficiency and provides a new pathway for large-scale collapse analysis.

Source: Annals of the New York Academy of Sciences

DOI: 10.1111/nyas.70371