A Physics-Informed Heterogeneous Graph Transformer Model for Managing Heterogeneous Big Data in Urban Construction Projects

This research aims to enhance predictive maintenance and inspection planning in urban construction projects. Recent advances in graph neural networks and graph transformer architecture have demonstrated significant potential for modeling complex lifecycle processes of building systems. However, most existing approaches remain predominantly data-driven and lack integration of physics-informed modeling and real-time data, which limits their applicability in large-scale urban environments. This research addresses this gap by proposing an approach for managing heterogeneous big data in urban construction projects, enabling prediction of the technical condition and inspection needs of structural elements. The core contribution is the development of a physics-informed heterogeneous graph transformer model that integrates domain-specific physical knowledge into the learning process through physics-based features and regularization mechanisms. The results confirm that all validation criteria are simultaneously satisfied: the difference between training and validation accuracy remains within the threshold (=0.05); The overall classification accuracy exceeds 92.06%; area under ROC curve above 0.8; F1-score is above 0.8 for all major classes; Physics-alignment error is lower than 0.15; and a strong Spearman correlation is observed between model predictions and physics-based indicators. The novelty of the proposed approach lies in the development of a physics-informed graph learning paradigm which enables the integration of structural mechanics, degradation processes, and heterogeneous data sources within a unified predictive framework.