PSI - Issue 84

Nicola Perilli et al. / Procedia Structural Integrity 84 (2026) 813–820

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In summary, this approach fills the identified gaps, reinforces the implementation of LLG 2020, and ensures standardized, interoperable data with continuous updates linking conceptual definitions to operational procedures. through hierarchical spatial units (LIRA, DA, SGA, RA, AZ) within a dynamic WebGIS platform. It supports continuous data collection, iterative refinement of multi-scale susceptibility models, and enables inspectors and bridge owners to perform evidence-based CdA assessments and risk-based prioritization of periodic inspections. Building on these outcomes, this work provides a contribution to the literature by highlighting the key role of a scalable, dynamic platform capable of integrating periodic inspection results, machine learning-based susceptibility modeling, and operational decision-making, thereby enhancing inspector performance and supporting proactive bridge management in landslide-prone regions. 5. Conclusion Bridge-related landslide CdA assessment remains challenging, primarily due to fragmentation between back-office analyses and field surveys, and data management. LLG 2020 does not provide recommendations or practical support to guide inspectors and to support bridge owners in optimizing inspection prioritization. This study presents operational procedures and tools at reinforcing landslide-bridge survey in compliance with the LLG 2020 (Italian Guidelines for Risk Classification and Management of Existing Bridges). It translates conceptual definitions into actionable solutions for landslide CdA evaluation. The framework combines machine-learning–based susceptibility models to identify potentially unstable slopes, integrates regional-scale supplementary data, with local-scale field surveys, and updates landslide inventories to strengthen the assessment of primary and secondary parameters and improve inspector performance in detecting and distinguishing potentially unstable slopes, incipient landslides, and active landslides. The use of nested key areas—LIRA, DA, SGA, RA, AZ—together with matching supplementary and essential data, facilitates systematic data collection crucial for characterizing landslide–bridge interactions. By clarifying and bridging conceptual definitions and operational practice, the proposed procedures and tools improves inspector performance and supports bridge owners in planning risk-based prioritization of periodic inspections and decision-making. In addition, it fills a critical gap in the broader literature on landslide risk assessment by offering a structured, reproducible framework integrating hierarchical spatial units, regional and local data collection, and WebGIS-based analysis. Acknowledgements This study was supported by FABRE – Research consortium for the evaluation and monitoring of bridges, viaducts and other structures (www.consorziofabre.it/en). Any opinion expressed in the paper does not necessarily reflect the view of the funder. Additionally, this research was supported by the project Methodological Approaches for RIsk assessment in the framework of landslide bridge IntEraction (MARIE) . References LLG 2020. Ministero delle Infrastrutture e dei Trasporti, 2020. Adozione delle Linee Guida per la classificazione e gestione del rischio, la valutazione della sicurezza e il monitoraggio dei ponti esistenti . Decreto Ministeriale 17 dicembre 2020, n. 578. Roma, Italia. Crosta, G.B., & Frattini, P. (2013). Landslide hazard assessment: State of the art and perspectives. Engineering Geology, 123 , 65–83. Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., & Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology, 72 (1–4), 272–299. Perilli, N., Stacul, S., Lombardi, M., Nenci, N., & Squeglia, N. (2024a). Target areas to survey for the evaluation of the parameters required for the assessment of the Landslide Class of Attention of existing (Italian) bridges. In: Proceedings of the 6th Euro-Mediterranean Conference for Environmental Integration (EMCEI) , Marrakesh, Morocco, 15–18 May 2024. Perilli, N., Stacul, S., Lombardi, M., Nenci, N., & Squeglia, N. (2024b). Workflow and tasks for the data collection and the assessment of landslide susceptibility and landslide as required by Italian Guidelines for classifying the risk of existing bridges. Environmental Science and Engineering , Springer, Cham. (in press) Kouamou Njifen, S.R., Enyegue A Nyam, F.M., Perilli, N., Doglioni, A., Cimino, M.G.C., & Squeglia, N. (2024c). Mapping slope instability and areas prone to shallow landslides in Yaoundé, Cameroon, using Analytical Hierarchy Process and Machine Learning Algorithms. MEDGU 2025, Environmental Science and Engineering , Springer, Cham. (in press) Perilli, N., Fiorentini, N., Squeglia, N., & Losa, M. (2025a). A participatory framework for identifying landslide-prone areas in road asset management: AHP supporting Machine Learning. In: EMCEI 2025, Environmental Science and Engineering , Springer, Cham. Perilli, N., Lombardi, M., Squeglia, N., Stacul, S., & Pagliara, S. (2025b). Bridging gaps in landslide mapping: A semi-quantitative empirical framework for delineating key areas to improve collection of essential field-based and supplementary remote-based data. Infrastructures, 11 , 11. Perilli, N., Squeglia, N., Stacul, S., Doglioni, A., & Simeone, V. (2026). Enhancing landslide Class of Attention assessment in the Italian LLG