Traditional bridge structural condition assessments struggle to integrate the physical topological information of BIM models with the spatiotemporal evolution of long-term monitoring data, resulting in low assessment accuracy and a lack of damage localization capabilities. To address this, this paper proposes a precise assessment framework based on a spatiotemporal transformer graph neural network (ST-Transformer GNN). Component attributes and connectivity relationships are automatically extracted from the IFC (Industry Foundation Classes) model of BIM (Building Information Modeling), constructing a dynamic graph structure with physical weights. Multi-source sensor data is mapped to corresponding nodes based on spatial location, forming a temporal input sequence. A physics-guided spatiotemporal attention module based on the Transformer architecture is then designed. This module, through a stiffness-constrained spatial propagation mechanism and temporal dynamic weighting, enables joint modeling of inter-component load responses. Finally, by combining multi-task learning with gradient interpretability analysis, component-level health status is output, and a damage heatmap is generated. Experimental results demonstrate that this method achieves an average component-level health status classification accuracy of 96.3%, and achieves a peak error of 0.8με in temporal response prediction, significantly outperforming competing models such as ST-GCN and DCRNN. The conclusions suggest that the deep integration of BIM and the physically enhanced spatiotemporal transform graph neural network (ST-Transformer GNN) can effectively enhance the intelligence level of bridge condition assessment.
Zhen et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: