PSI - Issue 84

Francesca Ceccato et al. / Procedia Structural Integrity 84 (2026) 599–606

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Rather than providing a deterministic or quantitative risk assessment, the framework functions as a decision support and prioritization tool, supporting the attribution of landslide-related attention classes in accordance with the Italian Guidelines. It offers a robust basis for directing detailed investigations, monitoring, and mitigation efforts, particularly in mountainous and foothill areas. Future developments will extend the framework to additional landslide types and refine the definition of the ISI to further enhance its operational applicability. Acknowledgements This study was funded by the European Union—NextGenerationEU, Mission 4, Component 2, in the framework of the GRINS-Growing Resilient, INclusive and Sustainable project (GRINS PE00000018—CUP C93C22005270001). The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union, nor can the European Union be held responsible for them. This study was also 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 founder. References Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., …, Kempen, B., 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS one 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748 Huang, F., Yin, K., Huang, J., Gui, L., Wang, P., 2017. Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Engineering Geology 223, 11–22. https://doi.org/10.1016/j.enggeo.2017.04.013 Hungr, O., Leroueil, S., Picarelli, L., 2014. The Varnes classification of landslide types, an update. Landslides 11(2), 167–194. https://doi.org/10.1007/s10346-013-0436-y IFFI. (n.d.). IFFI – Inventory of landslides in Italy. ISPRA. https://www.isprambiente.it ITALICA. (2023). ITALICA – Spatio-temporal catalogue of rainfall-induced landslides in Italy. Zenodo. https://doi.org/10.5281/zenodo.8009366 Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., Abderrahmane, B., 2020. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews 207, 103225. https://doi.org/10.1016/j.earscirev.2020.103225 Ministry for Sustainable Infrastructure and Mobility (MIMS), Ministerial decree 204 on 01.07.2022. Guidelines for the classification and management of risk, the safety assessment and the monitoring of existing bridges, Italy. G.U. Serie Generale n. 196 (2022) 8–23, 2022. Mowll, R., Anderson, M. J., Logan, T. M., Becker, J. S., Wotherspoon, L. M., Stewart, C., Johnston, D., Neely, D., 2024. A new mapping tool to visualise critical infrastructure levels of service following a major earthquake. Progress in Disaster Science 21, 100312. https://doi.org/10.1016/j.pdisas.2024.100312 Nirandjan, S., Koks, E. E., Ward, P. J., Aerts, J. C., 2022. A spatially explicit harmonized global dataset of critical infrastructure. Scientific Data 9(1), 150. https://doi.org/10.1038/s41597-022-01218-4 Prakash, N., Manconi, A., Loew, S., 2020. Mapping landslides on EO data: Performance of deep learning models vs. traditional machine learning models. Remote Sensing 12(3), 346. https://doi.org/10.3390/rs12030346 Proske, D., 2019. Comparison of the collapse frequency and the probability of failure of bridges, in “Proceedings of the Institution of Civil Engineers-Bridge Engineering” 172(1), pp. 27-40. Thomas Telford Ltd. doi: https://doi.org/10.1680/jbren.18.00002 Qadri, J., Ceccato, F., 2024. Exploring the interaction between landslides and carbon stocks in Italy. Sustainability 16(24), 11273. https://doi.org/10.3390/su162411273 Scala, A., Niero, L., Brezzi, L., Gabrieli, F., Gibin, F., Pellegrino, C., Simonini, P., Sarhosis, V., Zampieri, P., 2025. Extreme natural events and bridge collapses: statistical insights in Italy. International Journal of Disaster Risk Reduction, 105880. https://doi.org/10.1016/j.ijdrr.2025.105880 Shahabi, H., Hashim, M., 2015. Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environments. Scientific Reports 5, 9899. https://doi.org/10.1038/srep09899 Sim, K. B., Lee, M. L., Wong, S. Y., 2022. A review of landslide acceptable risk and tolerable risk. Geoenvironmental Disasters 9, 3. https://doi.org/10.1186/s40677-022-00205-6 Cruden, D. M., Varnes, D. J., 1996. Landslide types and processes. In: A. K. Turner & R. L. Schuster (Eds.). Landslides: Investigation and mitigation, pp. 36–75. Transportation Research Board, National Research Council, Special Report 247, Washington, DC.