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<p>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.</p>

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Accurate assessment technology of BIM bridge structure status using spatiotemporal transformation graph neural network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月20日

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  • Qianqian Zhen,
  • Wenming Su,
  • Yongquan Dong,
  • Ben Zhang

摘要

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.