PSI - Issue 84

Mirza Adeel Zeb et al. / Procedia Structural Integrity 84 (2026) 248–255

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(12–15), Medium High (16–19) and High (19-25). where the final susceptibility class is obtained by summing the individual scores of landslide activity, velocity and magnitude previously calculated, in accordance with the Italian Guidelines. Out of the total bridges assessed, 188 (75%) were classified as Low, 20 (8%) as Low Medium, 14 (6%) as Medium, 23 (9%) as Medium High and 5 (2%) as High susceptible (Fig. 5). The scores ranged between 5 and 25, indicating substantial variability in risk levels across the bridge portfolio. Favourably, only a limited portion of the analyzed bridges, corresponding to approximately 11% of the total bridges, falls within the Medium-High and High susceptibility classes, indicating that the number of potentially critical bridges is relatively small at the provincial scale. High-risk bridges tended to occur in spatial clusters, typically characterized by complex geology, steep slopes and known landslide occurrences, and were predominantly located in the northern sector of the Caserta province, corresponding to inland mountainous and hilly areas. This clustering could be considered to prioritize resource allocation for detailed investigations and targeted mitigation measures, in line with the objective of the national guidelines. These results represent the most critical outcome of the study, as they enable the identification of a limited number of bridges, corresponding to less than 30 bridges from the analyzed structures, that represents the ones classified in the Medium-High and High susceptibility classes. These bridges require crucial attention within a large infrastructure network, allowing available resources to be focused on a restricted set of critical structures.

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Fig. 5. (a) Slope instability map of the study area; (b) Landslide susceptibility assessment graph for the bridges in the study area.

For bridges in the High susceptibility classes, visual inspections and detailed site investigations are recommended as the next steps in bridge management. The implementation of continuous monitoring systems and targeted mitigation measures may also be appropriate. 5. Conclusion This study highlights the importance of the Italian national framework for bridge risk classification and provides a replicable methodology for evaluating landslide hazards affecting transportation infrastructure. By integrating multiple data sources, including the official IFFI landslide inventory, satellite-based ground deformation data from the European Ground Motion Service, and an empirical volume–area relationship, it was possible to characterize landslide activity, velocity, and magnitude across the entire province of Caserta, while also adopting specific evaluation criteria. The results revealed a clear spatial clustering of bridges classified within the Medium-High and High susceptibility classes, mainly located in geomorphological settings characterized by complex geology, steep slopes, and known landslide occurrences. This clustering allows the identification of a limited number of critical bridges within a large infrastructure stock, supporting risk-based prioritization. The proposed methodology is fully compliant with the Italian national guidelines and demonstrates how geospatial technologies can effectively support