For data collection, the Joncadella Viaduct was selected as a representative case study of the network. As a protected cultural heritage structure, traditional material extraction for physical or mechanical testing is not permitted. Consequently, its structural characterization relies on Minor Destructive Testing (MDT) and Non-Destructive Testing (NDT), increasingly recognized as effective methods for assessing masonry structures. These techniques enable the evaluation of concrete, stone, brick, and mortar through penetration, pull-out, break-off, surface hardness, ultrasonic, and impact-echo testing.

In addition, seven LiDAR scans were conducted to capture high-resolution geometric data. The resulting point clouds were processed to generate the IFC model. Using our Grasshopper plug-in MatchFEM, Computational Design methods were then applied to produce precise IFC geometries.

Each asset of the railway network was integrated into a semantic Digital Twin that incorporates GIS data, providing a holistic view of the entire infrastructure system. The Digital Twin is powered by a knowledge graph database that leverages semantic ontologies to describe, represent, and interconnect multiple domains of expertise. This architecture enhances data accessibility, sharing, and analysis for infrastructure managers, enabling them to seamlessly navigate from a high-level GIS overview down to detailed asset information such as in-situ test results, damage conditions, and numerical models.