Mechanical Aspects of Flow-Like Movements in Granular and Fine Grained Soils

Summary Experience shows that slope movements occurring in similar geomorphogical contexts may display very different styles and magnitude. This has important practical implications, since the risk associated with a landslide depends just on its magnitude. The paper discusses the mechanics of slope failure in coarse-grained and in fine-grained soils with particular reference to flow-like landslides, showing that even small details can affect their movement pattern. Author’s address: Prof. Luciano Picarelli, Seconda Università di Napoli, Aversa, Italy     

A simulation chain for early prediction of rainfall-induced landslides

A new simulation chain for early prediction of rainfall-induced landslides in unsaturated soils is presented. It includes a special computational weather code for forecasting the evolution of the synoptic weather and its changes due to interaction with the Earth’s surface (rainfall pattern), and a hydro-mechanical code to analyse rainfall effects on slope stability by computing degree of saturation and pore pressure changes due to rainwater infiltration. The linkage between these two numerical codes is ensured by an interface with the aim of bringing the data provided by the first code, which operates at basin or slope scale. The simulation chain can work in computational times that may be considered suitable for civil protection operations.     

A reduced-order model for Monte Carlo simulations of stochastic groundwater flow

We explore the ability of the greedy algorithm to serve as an effective tool for the construction of reduced-order models for the solution of fully saturated groundwater flow in the presence of randomly distributed transmissivities. The use of a reduced model is particularly appealing in the context of numerical Monte Carlo (MC) simulations that are typically performed, e.g., within environmental risk assessment protocols. In this context, model order reduction techniques enable one to construct a surrogate model to reduce the computational burden associated with the solution of the partial differential equation governing the evolution of the system. These techniques approximate the model solution with a linear combination of spatially distributed basis functions calculated from a small set of full model simulations. The number and the spatial behavior of these basis functions determine the computational efficiency of the reduced model and the accuracy of the approximated solution. The greedy algorithm provides a deterministic procedure to select the basis functions and build the reduced-order model. Starting from a single basis function, the algorithm enriches the set of basis functions until the largest error between the full and the reduced model solutions is lower than a predefined tolerance. The comparison between the standard MC and the reduced-order approach is performed through a two-dimensional steady-state groundwater flow scenario in the presence of a uniform (in the mean) hydraulic head gradient. The natural logarithm of the aquifer transmissivity is modeled as a second-order stationary Gaussian random field. The accuracy of the reduced basis model is assessed as a function of the correlation scale and variance of the log-transmissivity. We explore the performance of the reduced model in terms of the number of iterations of the greedy algorithm and selected metrics quantifying the discrepancy between the sample distributions of hydraulic heads computed with the full and the reduced model. Our results show that the reduced model is accurate and is highly efficient in the presence of a small variance and/or a large correlation length of the log-transmissivity field. The flow scenarios associated with large variances and small correlation lengths require an increased number of basis functions to accurately describe the collection of the MC solutions, thus reducing significantly the computational advantages associated with the reduced model.