Rainfall-induced differential settlements of foundations on heterogeneous unsaturated soils

This study stochastically investigates the rainfall-induced differential settlement of a centrally loaded, rigid strip foundation on an unsaturated soil with spatially varying values of either preconsolidation stress or porosity. The differential settlement (between the two foundation ends) is calculated at various times during rainfall by way of a coupled, hydro-mechanical, finite-element analysis. The Barcelona basic model describes the mechanical behaviour of the soil, and the van Genuchten relationships describe water retention and permeability. The variability of soil properties is modelled by means of random fields with spatial correlation in the framework of a Monte Carlo simulation. The study demonstrates that the occurrence of rainfall-induced differential settlements can be consistently analysed using concepts of unsaturated soil mechanics and random field theory. Results show that differential settlements can be vastly underpredicted (or even completely missed) if random heterogeneity and partial saturation are not simultaneously considered. The variation of differential settlements and their statistics during the rainfall depend on the magnitude of the applied load and the statistics of soil variability. Moreover, the transient phase of infiltration and a spatial correlation length equal to the width of the foundation pose the highest risk of differential settlement

Statistics of highly heterogeneous flow fields confined to three-dimensional random porous media

We present a strong relationship between the microstructural characteristics of, and the fluid velocity fields confined to, three-dimensional random porous materials. The relationship is revealed through simultaneously extracting correlation functions R-uu (r) of the spatial (Eulerian) velocity fields and microstructural two-point correlation functions S-2(r) of the random porous heterogeneous materials. This demonstrates that the effective physical transport properties depend on the characteristics of complex pore structure owing to the relationship between R-uu (r) and S-2(r) revealed in this study. Further, the mean excess plot was used to investigate the right tail of the streamwise velocity component that was found to obey light-tail distributions. Based on the mean excess plot, a generalized Pareto distribution can be used to approximate the positive streamwise velocity distribution

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data

Grain size, composition, porosity and permeability contrasts within cross-bedded sandstones in Tertiary fluvial deposits, central Spain

Permeability measured with a portable probe pcrmcflmeter on outcrops of cross·bcdded sandstones ranges betwecn 0·9 and 19 O. The highest pcrmeability (2-19 0 with an avcrage of8·5 D) occurs in thc coarsest grained foresct laminae (CFL). intermediate values (2-120 with an average of 5·3 D) occur in fincr grained foreset laminac (FFL) and thc lowest values (0·9-1 0 0 with un avcrage of 4·8 D) occur in boltomsct laycrs (BL). In the cross·bcds the uverage grain sizc rangcs from medium graincd sand in the CFL to finc grained s,lnd in thc FFL and BL. In all three subf'lcies. the average size of the primary pores is approximately It/> unit smaller than the averagc grain size. The abundance of unstablc carbonatc clasts correlatcs with incrcasing avcrage grain size, micritic clasts being most abundant in the CFL. Converscly. quartz content incrcascs with decreasing grain size and is highest in the FFL and BL. Oiagenetic destruction of primary porosity by compact ion and cementation. as well �IS generation of sccondary porosity through dissolution, were controlled by the original mineralogical composition of the sand. Contrasts in grain size detcrmine the primary pore size contrasts and differences in composition bctwecn CFL. FFL and BL. Permeability contrasts reflect variations in averagc primary pore size rather than differenccs in total porosity. Probe permeability contrasts between eFL. FFL and BL depend on contrasts in llverage pore size and contrasts in mineralogical composition between the subfacies