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This paper introduces a novel assessment framework for bridge structures that integrates Building Information Modeling (BIM) with a spatiotemporal transformer graph neural network (ST-Transformer GNN). The proposed method enhances the accuracy of health assessments by automatically extracting component attributes from BIM models and mapping sensor data to a dynamic graph structure, achieving a classification accuracy of 96.3%. The framework addresses limitations of existing methods by embedding physical mec

Author

juan camilo manotas espinosa

Language

EN