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In the framework of landslides, the hydrogeological features play an essential role in slope stability, governing water movement and thus resulting in modification of the effective stress in the soil. In this framework, the hydrogeological conceptualization of landslide areas and the identification of groundwater origin are key points to developing risk mitigation measures. In fact, groundwater recharge cannot always be attributed to local precipitation alone. Mixing processes between water derived from local infiltration and deep water upflow along tectonic lineaments or anthropogenic water can affect the groundwater balance on a local scale. This study aims to define the potential groundwater origin of one of the highest risk urban landslides in central Italy and to define a hydrogeological conceptual model by exploiting its existing drainage system network. This research is based on a multiple-techniques approach based on hydrological water balance, artificial tracer tests during recharge period, seasonal monitoring of the water stable-isotope content, hydro-chemical survey during low-flow periods, and analysis of the piezometric level fluctuation. All these analyses are coupled with a detailed reconstruction of the geology of the area depicted from boreholes and drill holes. Two groundwater bodies have been evidenced from the study. The shallower one is located in the landslide unstable zone and is hydraulically connected to a deeper groundwater body hosted in the underlying bedrock. Results highlighted that the local rainfall regime could not fully explain the hydro-chemical facies. Local water contributions to the landslide area coming from leakage of the urban sewerage system have been evidenced, excluding deep groundwater upflow from the fault system. [ABSTRACT FROM AUTHOR]

This study wants to give a contribution to the semi-automatic evaluation of rock mass discontinuities, orientation and spacing, as important parameters used in Engineering. In complex and inaccessible study areas, a traditional geological survey is hard to conduct, therefore, remote sensing techniques have proven to be a very useful tool for discontinuity analysis. However, critical expert judgment is necessary to make reliable analyses. For this reason, the open-source Python tool named DCS (Discontinuities Classification and Spacing) was developed to manage point cloud data. The tool is written in Python and is based on semi-supervised clustering. By this approach the users can: (a) estimate the number of discontinuity sets (here referred to as "clusters") using the Error Sum of Squares (SSE) method and the K-means algorithm; (b) evaluate step by step the quality of the classification visualizing the stereonet and the scatterplot of dip vs. dip direction from the clustering; (c) supervise the clustering procedure through a manual initialization of centroids; (d) calculate the normal spacing. In contrast to other algorithms available in the literature, the DCS method does not require complex parameters as inputs for the classification and permits the users to supervise the procedure at each step. The DCS approach was tested on the steep coastal cliff of Ancona town (Italy), called the Cardeto–Passetto cliff, which is characterized by a complex fracturing and is largely affected by rockfall phenomena. The results of discontinuity orientation were validated with the field survey and compared with the ones of the FACETS plug-in of CloudCompare. In addition, the algorithm was tested and validated on regular surfaces of an anthropic wall located at the bottom of the cliff. Eventually, a kinematic analysis of rock slope stability was performed, discussing the advantages and limitations of the methods considered and making fundamental considerations on their use. [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]