PSI - Issue 78

Andrea Miano et al. / Procedia Structural Integrity 78 (2026) 1903–1910

1910

widgets enables the visualization of key indicators. These metrics are crucial for assessing the structural health of transport infrastructure, particularly in hydro-geologically sensitive areas affected by landslides, subsidence etc. (Frattini et al. 2013). A core feature of the platform is the exploitation of the automated classification of infrastructure elements based on the ground motion measurements (PS/DS), with a score obtained through the combined analysis of velocity, differential velocity, acceleration, and seasonality of PS located over these elementary cells. The elementary cells are thematized in a four-colors scheme based on the index: green - stable behaviour; yellow/orange - monitoring required to assess kinematic evolution; red - field inspection required with high priority, grey - areas lacking ground motion measurements. This methodology represents a prioritisation criterion, also enabling stakeholders to allocate inspection resources more efficiently (Nettis et al. 2023). In operational terms, the WebGIS platform serves as a scalable and transferable tool that can be adapted to various geographic and infrastructural contexts. Moreover, the system’s compatibility with standardized geospatial data formats and interoperability protocols (e.g., OGC services) facilitates its integration within broader national or regional spatial data infrastructures (SDIs). By fostering collaborative data sharing among institutions and operators, the platform supports a more holistic and coordinated approach to risk-informed asset management. Notably, the platform enables continuous monitoring of infrastructure segments, highlighting those that show anomalous displacement trends. Delivering a unified environment for data integration, visualization, classification, and reporting facilitates a comprehensive understanding of infrastructure behaviour under different conditions. Beyond the infrastructure domain, the platform also represents an asset for broader territorial monitoring (e.g., detecting ground instability, urban subsidence, Aiello et al. 2023)). Figure 7. Interface of the WebGIS platform. Interactive tools for visualizing and querying bridge segments, color-coded according to their IPS. 3. Conclusions This study presented a rapid large-scale methodology for the preliminary structural assessment of bridge networks using satellite-derived deformation data. The approach was applied to three different POCs developed within the RETURN extended partnership, utilizing MT-DInSAR measurements. By analyzing displacement trends of the Measurement Points (MPs), the methodology proposed a classification of the bridges. This classification can help stakeholders to identify most vulnerable bridges and to develop more targeted monitoring and maintenance strategies. Acknowledgements The research presented in this article was partially based on the activities developed in extended partnership RETURN and in the national project DPC-ReLUIS 2024-2026. References Aiello, A., Massimi, V., ... & Casucci, M., 2023. “A Land Monitoring Service for Local Public Administrations: The IRIDE EOS4LPA Lot 3 Project”. In Italian Conference on Geomatics and Geospatial Technologies (pp. 99-108). Cham: Springer Nature Switzerland. Frattini, P., Crosta, G. B., & Allievi, J., 2013. “Damage to buildings in large slope rock instabilities monitored with the PSInSAR™ technique”. Remote sensing, 5(10), 4753-4773. https://doi.org/10.3390/rs5104753 Mele, A., Crosetto, M., Miano, A., & Prota, A., 2023. “ADAfinder tool applied to EGMS data for the structural health monitoring of urban settlements”. Remote Sensing, 15(2), 324. Miano, A., Mele, A., Silla, M., Bonano, M., Striano, P., Lanari, R., ... & Prota, A., 2025. “Space-borne DInSAR measurements exploitation for risk classification of bridge networks”. Journal of Civil Structural Health Monitoring, 15(3), 731-744. Nettis, A., Massimi, V., Nutricato, R., Nitti, D. O., Samarelli, S., & Uva, G., 2023. “Satellite-based interferometry for monitoring structural deformations of bridge portfolios”. Automation in Construction, 147, 104707. https://doi.org/10.1016/j.autcon.2022.104707 Talledo, D.A.; Saetta, A., 2025. “A Multi-Level Semi-Automatic Procedure for the Monitoring of Bridges in Road Infrastructure Using MT DInSAR Data”. Remote Sens. 2025, 17, 2377. https://doi.org/10.3390/rs17142377 Teatini, P., et al., 2011, “Geomechanical response to seasonal gas storage in depleted reservoirs: A case study in the Po River basin, Italy”. Journal of Geophysical Research, 116, F02002, doi:10.1029/2010JF001793.