In the past, soil-layer delineation methods can usually only take a single type of input data, e.g., soil-type data at boreholes. However, this does not fit in the geotechnical engineering practice where multiple types of data are usually available during site investigation (e.g., borehole data and cone penetration test data are both available). This paper adopts a novel data-driven method for soil-layer delineation that accommodates multiple types of site investigation data. The basic idea is to include liquid limit (LL), plasticity index (PI), and fines content (FC) into the soil parameters of analysis. According to the Unified Soil Classification System (USCS), the information of (LL, PI, FC) can be used to determine whether the soil is sand, silt, or clay. As a result, the conditional random field simulation results for (LL, PI, FC) can be used to delineate sand, silt, and clay layers. If extra soil parameters (such as cone penetration test results) are incorporated, the novel method can accommodate multiple types of site investigation data. A real example of the Fucino Basin in Italy is adopted to demonstrate the application of the novel data-driven soil-delineation method.@en

Using CPTu profiles for subsoil characterisation, transformation equations must be used to obtain the hydro-mechanical properties for structures and infrastructure designing. Additionally, the uncertainty and the spatial variability of measured parameters must be taken into account for a reliable geotechnical design. In this work, we used a Stochastic Simulation approach to define reliable 3D models of two geotechnical designing variables for granular soils (friction angle–ϕ’ and the Darcy permeability coefficient–k) from tip resistance (qc), sleeve friction (fs), and pore pressure (u2) profiles. The selected method – the Sequential Gaussian Co-Simulation (SGCS) – provided reliable optimized 3D models of the spatial distribution of the variables of interest and allowed quantifying the propagation of the estimation uncertainty associated with the raw measurement models through the transformation equations. Overestimation (OE) and Underestimation (UE) percentages for a confidence interval of 68% were calculated throughout the 3D model: granular soils showed a larger uncertainty than fine soils concerning the measured variables (qc, fs, and u2). In granular soils, the measured variable uncertainty varies up to 100% but the derived variables show different behavior: ϕ’ shows UE and OE less than 25% while k reaches 100%. These differences in the propagated uncertainties depend on the transformation equations and the measured variable dependence. @en

The Empirical Rainfall Thresholds (ERTs) for shallow landslide initiation are commonly devised worldwide mostly to be implemented within landslide early warning systems. Nonetheless, since the pioneering works on ERTs in the 1980s, only meteorological variables - that are cumulated E or intensity I and duration D values of rainfalls that are likely to trigger landslides - have been used to predict landslide occurrence, even though they are characterized by a large uncertainty. Over time, many efforts have been devoted to constrain ERT to geo-morphological characters of the landslide locations but, since nowadays, they did not get to a sound new method to derive ERT and strengthen its ability to forecast future rainfall-induced landslide. In this study, local geo-morphological characters have been taken into account by means of the co-kriging technique to constrain the E and D mean values of a regional ERT and their confidence intervals. The study area, where the proposed method was trained, is the hilly side of the Abruzzo region (Italy). Here, 62 shallow landslides have been analyzed in the time span of 2013–2017 by collecting 62 (D,E) pairs related to the rainfalls that were likely to trigger them. The relevant geo-morphological features for the considered territory have been selected through the principal component analysis. Then, the Multi-Collocated Co-Kriging technique, through ISATIS Geovariances software, has been applied to derive the spatial variability structures of E and D values conditioned by the selected geo-morphological parameters. Therefore, threshold values of E and D and their confidence intervals have been calculated generating a new shape of regional ERT, consisting of maps of continuous estimated threshold values of (D,E) and confidence interval values suitable for being used in early warning systems for shallow landslide initiation.

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The Cone Penetration Test (CPT) measures enable to recognize vertical lithological sequence at each investigated point. From the tip resistance q_c and sleeve resistance f_s profiles, the Soil Behavior Type index I_SBT has been calculated, in order to identify the lithotypes alongside depth. The present study focuses on the combination of different variables to provide a lithological and mechanical subsoil characterization. The main objectives of the paper are: (1) to model the 3D spatial variability structure of the soil lithotypes and mechanical properties using q_c and f_s profiles; 2) to evaluate the uncertainties of the estimates for designing purposes. 182 CPTs were collected in a 900 km² area (corresponding to a subsoil volume of about 12 km³) located in the study site in the Bologna province (Italy). The study area is made up of fine-grained soils, silt and clay mixtures that are intercalated at different depths by sandy and gravelly soils. These variations of each soil fraction affect the engineering properties of these alluvial deposits. For 3D modeling, two geostatistical methods, Ordinary Kriging (stationary method), and Intrinsic Random Function theory (non-stationary method) have been used. The results show that the non-stationary method allows to obtain more reliable q_c and f_s predicted values. The final stochastic mechanical and lithological model enables engineers and geologists to detect the emerging of fan and paleochannels bodies where mean resistance values can abruptly change in terms of bearing capacity, liquefaction potential and static and dynamic settlement occurrence.

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