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Most landslides occurring in Italy consist of shallowtranslational movements, which involve fine, essentially clayey material. They are usually characterized by low velocities, typically of few centimeters per year. The main triggering factor is hydrologic, since movements are usually strictly connected to groundwater level fluctuations. This slow and periodical trend can be interpreted by a viscous soil response, and in order to catch the actual kinematics of the soil mass behavior, a dynamic analysis should be adopted. This paper discusses the case of the Alverà mudslide, located in the Northern Alps (Italy), for which a very detailed and almost 9-year-long monitoring database, including displacements and groundwater levels records, is available. A welldefined dynamic viscoplastic model, capable of returning a displacement prediction and a mobilized shear strength angle estimate from a groundwater level input, was considered. A first deterministic calibration proved the ability of the model to reproduce the mudslide overall displacements trend if a suitable reduction of the mobilized angle ϕ 0 0 is allowed. Then, an uncertainty quantification analysis was performed by measuring the model parameters variability, and all parameters could be represented using a probability density function and a correlation structure. As a consequence, it was possible to define a degree of uncertainty for model predictions, so that an assessment of the model reliability was obtained. The final outcome is believed to represent an important advancement in relation to hazard assessment and for future landslide risk management.

Unknown bridge foundations pose a significant safety risk due to stream scour and erosion. Records from older structures may be non‐existent, incomplete or incorrect. We evaluate 2D and 3D electrical resistivity imaging (ERI) as a means to reliably identify the depth of unknown bridge foundations. A survey procedure is described for mixed terrain/water environments in the presence of rough terrain. Some electrodes are installed on the stream banks while others are adapted for underwater use. Tests were conducted at five field sites, including three roadway bridges, a geotechnical test site and a railway bridge, containing drilled shafts and spread footings of both known and unknown depth extent. The 2D data acquisition was carried out in the dipole‐dipole configuration. The 2D ERI method resolved the shape and depth extent of the larger bridge foundations but, with less accuracy, the shape and depth extent of the smaller foundations. The 3D ERI method is time‐consuming and does not add sufficient additional value over 2D ERI to become a practical tool for unknown bridge foundation investigations. The 2D ERI method is a cost‐effective geophysical method that is relatively easy to use by bridge engineers.

A comprehensive experimental database containing results of a series of dry vacuum-consolidated triaxial compression tests was populated. The tests were performed on sand specimens and conducted under similar experimental conditions, in which specimens’ boundary deformation was captured using a three-dimensional digital image correlation analysis (3D-DIC). The use of a standard triaxial device along with the 3D-DIC technology allowed the specimens’ global and local boundary displacement fields to be computed from start to end of the compression phase. By repeating each test under the same experimental conditions and building the specimens using the same type of sand, the boundary deformation patterns could be identified, and the statistics associated with both global and local displacement fields could be assessed. Making this experimental database available to others should serve to calibrate as well as develop new forward models to account for effects associated with the specimens’ local displacements and material heterogeneity and include statistics to represent a specimen’s random response. Moreover, this work will serve as a basis for the statistical characterization of spatio-temporal boundary localization effects used to develop stochastic models and machine-learning models, and simulate virtual triaxial tests.