PSI - Issue 84

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ScienceDirect

Procedia Structural Integrity 84 (2026) 97–102

© 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 Natural hazards such as landslides represent growing threats to critical infrastructure. Building upon the methodological foundation of the SGAM project, this work introduces enhancements to the Smart Geotechnical Asset Management (SGAM) framework, with particular emphasis on landslide hazard assessment in accordance with the 2020 Italian guidelines for risk classification and management, and bridges monitoring (MIT-n. 578 – 17/12/2020). SGAM integrates geotechnical monitoring, geohazard databases, Earth Observation (EO) data, and machine learning techniques to support predictive maintenance of wide linear infrastructures. In this work, a multi-component workflow was implemented, integrating ground motion data from satellite InSAR with thematic layers (e.g., topography, geology, land use, landslide maps and databases) through supervised learning algorithms and systematic parameter calculation. The spatialized outputs are then segmented and intersected with infrastructure elements to enable the classification of asset segments into hazard levels. Key enhancements include the implementation of the P Factor methodology, which incorporates activity state assessment (P a /P c ), velocity-based scoring (P v ), and magnitude evaluation (P m ), followed by assessment of model reliability and mitigation measures for final CdA (Attention Class) classification. A synthesis geospatial layer supports proactive risk mitigation by highlighting at-risk segments along linear infrastructure networks, thus prioritising their monitoring, maintenance, or intervention. This development enables direct application of data-driven landslide hazard assessment to linear infrastructure, providing risk-informed prioritisation of intervention areas along the road network, and enhancing operational readiness and resource allocation. It offers a practical output directly usable by infrastructure managers and planners while ensuring regulatory compliance. III Fabre Conference: Existing Bridges, Viaducts, and Tunnels: Research, Innovation, and Applications SGAM – Smart Geotechnical Asset Management: Enhancing predictive maintenance through data-driven insights and Earth Observation technologies Alessandro Brunetti a , Vera Costantini a , Maria Elena Di Renzo a,b , Michele Gaeta a , Paolo Mazzanti a,b , Giandomenico Mastrantoni a, *, Emanuela Valerio a a NHAZCA S.r.l., Via Vittorio Bachelet, 12 ,00185 Rome, Italy b Sapienza Università di Roma, Dipartimento di Scienze della Terra, Piazzale Aldo Moro, 5, 00185 Rome, Italy

* Corresponding author. Tel.: +39 06 95 065 820. E-mail address: Giandomenico.mastrantoni@nhazca.com

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.2026.06.014