Infrastructure is becoming increasingly vulnerable to natural hazards, which have the potential to precipitate severe economic and social disruptions. The Smart Geotechnical Asset Management (SGAM) project offers a forward-looking solution by integrating advanced monitoring and analysis techniques to manage these risks. SGAM integrates geotechnical data, remote sensing technologies, and data fusion methodologies to facilitate the development of early warning systems and targeted maintenance for linear infrastructure. The multi-hazard framework utilises satellite interferometry and machine learning algorithms to detect and analyse hazards such as landslides, subsidence, and liquefaction. These insights are synthesised into a comprehensive geospatial layer that identifies areas of elevated risk, thus guiding timely and efficient interventions. The incorporation of artificial intelligence and high-quality datasets into SGAM serves to enhance the capacity to evaluate geohazards, thereby ensuring that infrastructure systems become more resilient and sustainable in the face of environmental challenges.
SGAM – Smart Geotechnical Asset Management / Brunetti, A.; Di Renzo, M. E.; Gaeta, M.; Mazzanti, P.; Mastrantoni, G.; Valerio, E.. - (2025), pp. 1-5. (Intervento presentato al convegno NSG 2025: 1st Conference on Geohazards Assessment and Risk Mitigation Conference tenutosi a Naples, Italy) [10.3997/2214-4609.202520152].
SGAM – Smart Geotechnical Asset Management
Di Renzo, M. E.;Mazzanti, P.;Mastrantoni, G.;Valerio, E.
2025
Abstract
Infrastructure is becoming increasingly vulnerable to natural hazards, which have the potential to precipitate severe economic and social disruptions. The Smart Geotechnical Asset Management (SGAM) project offers a forward-looking solution by integrating advanced monitoring and analysis techniques to manage these risks. SGAM integrates geotechnical data, remote sensing technologies, and data fusion methodologies to facilitate the development of early warning systems and targeted maintenance for linear infrastructure. The multi-hazard framework utilises satellite interferometry and machine learning algorithms to detect and analyse hazards such as landslides, subsidence, and liquefaction. These insights are synthesised into a comprehensive geospatial layer that identifies areas of elevated risk, thus guiding timely and efficient interventions. The incorporation of artificial intelligence and high-quality datasets into SGAM serves to enhance the capacity to evaluate geohazards, thereby ensuring that infrastructure systems become more resilient and sustainable in the face of environmental challenges.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


