PSI - Issue 84

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 84 (2026) 813–820

© 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Conference Abstract The proposed framework, composed of three components, addresses the fragmentation between remote sensing analyses, field surveys, and data management, offering useful solutions that enhances inspectors’ performance in assessing landslide Attention Class (CdA) and supports bridge owners in prioritizing periodic inspections. The proposed procedures and tools are based on hierarchical spatial units: Landslide Inventory Reference Area (LIRA), Diagnostic Area, Geomorphologically Significant Area, Relevant Area, and Approach Zone. These areas enable data collection remotely or in the field, integrating essential local- and slope-scale information with supplementary broader-scale data. To improve data collection and decision-making in prioritizing periodic inspections, machine learning–based susceptibility models, trained on the IFFI landslide inventory, are progressively updated with data gathered during periodic inspections. The use of a validated glossary, supplemented by closed and open vocabularies, ensures data standardization, progressive enrichment of the WebGIS platform, and the availability of consistent and interoperable information that can be reliably shared across systems. This allows rapid access, efficient consultation, and querying, improving inspection efficiency and the reliability of the Attention Class (CdA). By linking the conceptual definitions of slope susceptibility and slope instability , as well as potential slope instability and incipient landslides , to operational procedures, it is possible to perform a standardized , reproducible, and proactive assessment of primary and secondary parameters. The integration of hierarchical spatial units, dynamic susceptibility models, and multi-level inventories represents a solid and innovative approach, applicable to different and complex geomorphological, lithological, hydrographic/hydrological, environmental, and anthropic contexts. This method optimizes data collection and quality, supporting the effective prioritization of bridge inspections in landslide-prone areas, fully leveraging the procedures and tools developed in this work, in accordance with LLG 2020. III Fabre Conference: Existing Bridges, Viaducts, and Tunnels: Research, Innovation, and Applications Framework to Enhance Landslide-Bridge Inspections According to Italian Guidelines (LLG 2020) Nicola Perilli a *, Nunziante Squeglia a , Vincenzo Gervasi b , Filippo Forlani c a Department of Civil and Industrial Engineering, University of Pisa, Largo L. Lazzarino 1, Pisa 56122, Italy b Department of of Computer Science, University of Pisa, Largo L. Lazzarino 1, Pisa 56122, Italy 2 SGAI srl. Via Mariotti 18, Morciano di Romagna, 4783 , Rimini 70125, Italy

* Corresponding author. Tel.: :+39-05022148 E-mail address: Nicola.perilli@unipi.it.

2452-3216 © 2026 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Conference 10.1016/j.prostr.2026.06.104