1,720,994 research outputs found
Il contributo delle tecniche interferometriche DInSAR nello studio dei fenomeni franosi su area vasta.
No full textIncontro Annuale dei Ricercatori di Geotecnica - IARG 200
LARAM School 2019: the yearly doctoral school on “LAndslide Risk Assessment and Mitigation”
No full textInvestigating the Effects of Fire on Rooted Pyroclastic Soil Properties by Laboratory Burning Treatments
No full textAnalysis of Damage to Buildings in Urban Centers on Unstable Slopes via TerraSAR-X PSI Data: The Case Study of El Papiol Town (Spain)
No full textPersistent Scatterer Interferometry (PSI) data, deriving from the processing of SAR images acquired by high-resolution sensors such as TerraSAR-X, provide accurate measurements of displacements affecting structures (e.g., buildings) and linear infrastructure networks (e.g., roads, bridges, embankments, and pipelines). Such widespread displacements, when available on buildings on unstable slopes, offer new perspectives for their integration in procedures pursuing the analysis and the prediction of the physical vulnerability of exposed buildings. In this letter, both deterministic and probabilistic cause (differential settlements)-effects (damage) relationships are generated by using PSI-derived building settlements and the results of building damage surveys. The procedure is applied to El Papiol town (Spain), whose urban area has been suffering diffuse damage of different severity to buildings and roads due to extremely slow-moving landslide phenomena
Differential settlements affecting transition zones between bridges and road embankments on soft soils: Numerical analysis of maintenance scenarios by multi-source monitoring data assimilation
No full textDifferential settlements can affect transition zones between bridges and road embankments in countries where very compressible soft soil layers are widespread in the subsoil. The related damage makes these locations the most maintenance-prone locations, resulting in high direct and indirect costs for the road owner and the road users as well. Accordingly, approaches capable of analysing current conditions and forecasting future settlement scenarios associated with different possible maintenance operations can turn out to be a valuable tool for the road network management process. With reference to a case study representing typical conditions in the Netherlands, this paper proposes the proof of concept of a novel multi-source data-driven method that exploits the assimilation of settlement data – acquired by both conventional and satellite DInSAR monitoring techniques – in simplified geotechnical modelling. The forecasted settlement scenarios can support informed road maintenance decisions within risk mitigation strategies
Prospettive di monitoraggio per il controllo del comportamento di edifici in aree soggette a fenomeni di subsidenza.
No full textFull integration of geomorphological, geotechnical, A-DInSAR and damage data for detailed geometric-kinematic features of a slow-moving landslide in urban area
Full text linkThe reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period
Deep learning powered long-term warning systems for reservoir landslides
No full textThe reservoir landslides are characterized by repeated phases of acceleration and isokinetic de-formation under long-term periodic external forces. The state-of-the-art research lacks reliable prediction methods and judgment of their evolution stages. This work promotes the application of the deep learning algorithm and landslide evolution model in long-term warning systems. The test site is the Sifangbei landslide in the Three Gorges reservoir area of China. The main innova-tive features are: (i) the displacement of the landslide is considered as the prediction target, and the optimal model (i.e., conditioning factors and hyper-parameters combination) driven by the deep learning framework is used for spatial prediction; (ii) different warning methods (from both literature and current practice) are compared to single out the one that can best describe the evo-lution stage of the reservoir landslide; and (iii) deep learning model and adaptive evolution model are combined to analyze the temporal-spatial kinematic characteristics and evolution trend of the landslide under extreme scenarios related to rainfall and reservoir water levels. The results show that the predicted displacements of the lower and central part of the landslide are re-spectively controlled by reservoir water level and rainfall; the five-stage evolution model can cap-ture the long-term evolution trend of the Sifangbei landslide; under extreme scenarios, landslide deformation exhibits step-like characteristics and is more likely to start from the middle of the lower portion of the unstable area. These models represent the up-to-date steps of a long-term re-search plan. The gathered knowledge can be used to analyze the spatial evolution characteristics of landslides and promote the setup of long-term warning systems. Furthermore, the results show that combining the proposed deep learning and evolution methods provides forecast information that could help adjusting short-term warning strategies in such a complex risk area as the Three Gorges Reservoir
Experimental Analysis of the Fire-Induced Effects on the Physical, Mechanical, and Hydraulic Properties of Sloping Pyroclastic Soils
No full textThe paper investigates the changes in the physical, mechanical, and hydraulic properties of coarse-grained pyroclastic soils, considered under both wildfire-burned and laboratory heating conditions. The soil samples were collected on Mount “Le Porche” in the municipality of Siano (Campania Region, Southern Italy), hit by wildfires on 20 September 2019. The area is prone to fast-moving landslides, as testified by the disastrous events of 5–6 May 1998. The experimental results show that the analyzed surficial samples exhibited (i) grain size distribution variations due to the disaggregation of gravelly and sandy particles (mostly of pumice nature), (ii) chromatic changes ranging from black to reddish, (iii) changes in specific gravity in low-severity fire-burned soil samples different from those exposed to laboratory heating treatments; (iv) progressive reductions of shear strength, associated with a decrease in the cohesive contribution offered by the soil-root systems and, for more severe burns, even in the soil friction angle, and (v) changes in soil-water retention capacity. Although the analyses deserve further deepening, the appropriate knowledge on these issues could provide key inputs for geotechnical analyses dealing with landslide susceptibility on fire-affected slopes in unsaturated conditions
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