Enabeling Comprehensive Querying of Environmental Context and Road Infrastructure Data Using a Graph-Based Approach

Abstract: Enabling predictive maintenance (PM) of road infrastructure by understanding condition developments requires accounting for internal and external influences. However, the corresponding data are highly heterogeneous and distributed, limiting comprehensive large-scale analysis. To bridge this gap, an existing Digital Twin (DT) approach is enhanced with environmental context data. Knowledge graphs enable scalable querying across heterogeneous subsystems, while a federated database addresses data distribution. The framework is validated using real-world data from Germany. Linking historical climate records with bridge condition development demonstrates comprehensive querying. By enabling scalable cross-system data association, the proposed DT framework enhances the practical applicability of existing PM methods.