Generation of a digital structured database for the modelling of landslide mechanisms: two prototypes designed for different geo-mechanical contexts

This research presents a methodology for diagnosing landslides, demonstrated through two prototypes designed for distinct geo-mechanical environments. The study addresses both the highly complex, geo-hydro-mechanical context of the Pianello slope in Bovino, located in the Daunia Apennines, and the less complex, but potentially more disastrous, quick clay hazard case. The methodology integrates multidisciplinary data collection, advanced modelling techniques, and real-time monitoring to effectively diagnose landslide behaviour. It begins with the collection of critical data, such as borehole logs, geotechnical laboratory tests, in-situ tests, and geophysical surveys, which provide insights into underground conditions, including lithological heterogeneity, shear strength, and groundwater dynamics. The methodology then incorporates field observations, structural damage surveys, and multi-temporal remote sensing data, using historical maps, aerial photographs, and LiDAR-derived digital elevation models (DEMs) to track the evolution of landslide bodies over time, while inclinometers and piezometers monitor ongoing displacements and porewater pressures. This combination of historical and real-time data is essential for identifying predisposing factors such as tectonic fracturing and anthropogenic activities, as well as triggering mechanisms like rainfall infiltration. These datasets are synthesized into models that include geological cross-sections, geotechnical models, and ERT profiles, which provide a detailed understanding of slope behaviour. In the case of Pianello, these models reveal deep-seated, roto-translational landslides and shallow earthflows, each with distinct movement patterns. DEM differencing and building inclination vectors are used to track surface deformations, while the GIS-based dashboard enables real-time visualization and analysis. The iterative process of feedback between data collection, modelling and monitoring is an essential aspect of the methodology, ensuring adaptive diagnostics as new data is acquired. Additionally, the quick clay hazard case where the main target is the mapping the occurrences of the target soil material, though less complex, demonstrates the versatility of the methodology in managing more catastrophic landslide risks. The approach proves scalable and adaptable to different geomechanical contexts, offering a framework for landslide risk management in diverse regions. In conclusion, the proposed methodology offers an advancement in landslide diagnostics, integrating geological, geotechnical, hydrogeological, and geomorphological data into a digital twin that serves as a digital structured database, for site-scale geotechnical soil characterisation.