Search

The stability of a rock slope is strongly influenced by the pattern of groundwater flow through the fracture system, which may lead to an increase in the water pressure in partly open joints and the consequent decrease in the rock wall strength. The comprehension of the fracture pattern is a challenging but vital aspect in engineering geology since the fractures' spatial distribution, connectivity, and aperture guide both the water movement and flow quantity within the rock volume. In the literature, the most accepted methods to hydraulically characterise fractured rocks in situ are the single borehole packer test, the high-resolution flow meters for fractures, and the artificial tracer tests performed in boreholes. However, due to the high cost a borehole requires and the general absence of wells along coastal cliffs, these methods may not be appropriate in rockfall-prone areas. In this study, an unsaturated rocky cliff, strongly affected by rockfalls, was investigated by combining kinematic analysis, Discrete Fracture Network (DFN) modelling, and artificial tracer tests. The DFN model and potential rock block failure mechanisms were derived from high-resolution 3D virtual outcrop models via the Structure from Motion (SfM) photogrammetry technique. An artificial tracer was injected using a double ring infiltrometer atop the recharge zone of the slope to determine the infiltration rate and validate the DFN results. The DFN and tracer test methods are frequently used at different spatial scales and for different disciplines. However, the integration of digital photogrammetry, DFN, and tracer tests may represent a new step in rockfall and landslide studies. This approach made possible the identification of groundwater flow patterns within the fracture system and revealed about a 10-day tracer transit time from the injection area and the monitored slope, with similar conductivity values gathered from both the DFN and tracer test. Planar and wedge failures with volumes ranging from 0.1 and 1 m3 are the most probable failure mechanisms in the areas. The results were consistent with the delay between the intense rainfall and the slope failures previously documented in the study area and with their mechanisms. [ABSTRACT FROM AUTHOR]

Shallow landslide susceptibility is often assessed by means of physically-based models evaluating stability of slope deposits by the so-called infinite slope approach. In our experience, this approach has good predictive performance if the input datasets are well constrained by engineering geology field survey and laboratory data for the involved slope deposits. For these reasons we developed a procedure based on integration of bedrock geology, cluster analysis of morphometric variables and soil laboratory data which allowed us to obtain maps of depth and engineering geological properties of slope deposits for about. 200 km2 study area in the Garfagnana basin (Northern Apennines, Italy). Then, shallow landslide susceptibility was assessed by a probabilistic infinite slope model which provided AUROC values ranging from 0.75 to 0.78. Nevertheless, on-site checks highlighted that almost 70% of landslides involved both slope deposits and a weathered and fractured upper portion of the underlying bedrock. In order to investigate the engineering geology characteristics of the bedrock, we conducted a field survey aimed to classify rock masses inside, in the surroundings and far from shallow landslides. In the study area the Late Oligocene-Early Miocene arenaceous flysch of the Macigno is the most widespread formation, also showing the highest landslide density, hence we focused the survey within this formation for an area of about 100 km2. For each survey site, in order to obtain estimations of the Rock-mass Quality Index (RQI), we collected 200-400 Schmidt hammer rebound measures, bedding and joint data, samples for laboratory analyses (unit weight and slake durability test) and we also estimated degree of weathering as well as the Geological Strengh Index. The results allowed us to identify three different spatial domains discriminated by both landslide density and RQI, where we can observe that lower landslide density values are characterised by higher RQI. Moreover, low RQI values characterize those areas where high density of landslide involving bedrock occur. Comparing the susceptibility maps to shallow landslide distribution, we also observed that some landslides unexpectedly fall within very low susceptibility areas. Field observations allowed us to realize that these landslides are often characterized by thin slope deposits and involve low RQI bedrock. This suggests that better AUROC values could be obtained by implementing a multi-layer infinite slope model where also the shear strength parameters of the weathered and fractured bedrock are taken into account. [ABSTRACT FROM AUTHOR]