Thi Minh Hue Le

Reliability of heterogeneous slopes with cross-correlated shear strength parameters

Spatial variabilities of the soil shear strength parameters (friction angle and cohesion coefficient) have long been recognised as an important factor influencing the reliability of geo-structures including slopes. However, these two parameters are frequently considered separately in research studies even though, in natural soils, both parameters are likely to vary simultaneously with existence of cross-correlation between them. This study stochastically investigates the reliability of a slope constructed in soil having spatially varying both friction angle and cohesion coefficient, and compares that with the scenarios where each soil parameter varies individually. The finite element method is merged with the random field theory to probabilistically evaluate the factor of safety and probability of failure of the slope via Monte Carlo simulations. A simple procedure to create perfect cross-correlation is discussed. The results show that the variabilities of both friction angle and cohesion coefficient even without cross-correlation can elevate the probability of failure relative to the cases where each of them varies individually. If a perfectly positive cross-correlation exists, the probability of failure increases significantly due to increasing occurrences of local failures.

Cone penetration data classification with Bayesian Mixture Analysis

ABSTRACT This paper presents an application of the Bayesian Mixture Analysis (BMA) to deal with the classification of spatially variable soil data from the cone penetration test. The cone penetration data classification postulates a problem where a set of cone penetration measurements is used to identify “hidden or unobserved” soil classes. The problem is formulated as an incomplete-data Gaussian mixture where the observed data are defined by the natural logarithm-transformed values of the normalized friction and the normalized cone resistance, while the soil classes to be identified are considered as hidden data. The solution for the incomplete-data problem which consists of class-dependent mixture probabilities and Gaussian distribution parameters is defined in a Bayesian framework. The implementation of conjugate priors for the Gaussian mixtures enables an efficient sampling of the posterior parameters by the Gibbs algorithm of the Markov Chain Monte Carlo method. When compared to the well-established Robertson classification charts, the BMA formulation has an advantage due to the Bayesian framework which enables the definition of soil classes through mixture priors, class-dependent posterior parameter estimates, and a probabilistic soil classification. The presented approach is applied to the cone penetration data from the Sheringham Shoal Offshore Wind Farm site.

Probabilistic modelling of auto-correlation characteristics of heterogeneous slopes

Spatial variability of soil materials has long been recognised as an important factor influencing the reliability of geo-structures. This study stochastically investigates the influence of spatial variability of shear strength on the stability of heterogeneous slopes, focusing on the auto-correlation function, auto-correlation distance and cross-correlation between soil parameters. The finite element method is merged with the random field theory to probabilistically evaluate factor of safety and probability of failure via Monte-Carlo simulations. The simulation procedure is explained in detail with suggestions on improving efficiency of the Monte-Carlo process. A simple procedure to create cross-correlation between random variables, which allows direct comparison of the influence of each strength variable, is discussed. The results show that the auto-correlation distance and cross-correlation can significantly influence slope stability, while the choice of auto-correlation function only has a minor effect. An equation relating the probability of failure with the auto-correlation distance is suggested in light of the analyses performed in this work and other results from the literature.

Probabilistic Study of Rainfall-Triggered Instabilities in Randomly Heterogeneous Unsaturated Finite Slopes

Water infiltration destabilises unsaturated soil slopes by reducing matric suction, which produces a decrease of material cohesion. If the porosity of the soil is spatially heterogeneous, a degree of uncertainty is added to the problem as water tends to follow preferential paths and produces an irregular spatial distribution of suction. This study employs the finite element method together with Monte Carlo simulations to quantify the effect of random porosity on the uncertainty of both the factor of safety and failure size of an unsaturated finite slope during and after a rainfall event. The random porosity is modelled using a univariate random field. Results show that, under partially saturated conditions, the random heterogeneity leads to a complex statistical variation of both factor of safety and failure size during the rainfall event. At any given time, the uncertainty about failure size is directly linked to the uncertainty about the position of the wetting front generated by infiltration. Interestingly, the statistical mean of the failed area is smallest when the mean of the factor of safety is lowest. In other words, the slope becomes more likely to fail, but the size of the failure mass tends to be limited. The study also investigates the sensitivity of failure uncertainty to external hydraulic parameters (i.e. initial water table depth, rainfall intensity) and internal soil parameters (i.e. permeability and water retention characteristics). In general, the sensitivity increases when the effect of these parameters on the spatial variation of suction is stronger.

Thermomechanical Erosion Modelling of Baydaratskaya Bay, Russia with COSMOS

Rapid coastal erosion threatens Arctic coastal infrastructure, including communities and industrial installations. Erosion of permafrost depends on numerous processes, including thermal and mechanical behaviour of frozen and unfrozen soil, nearshore hydrodynamics, atmospheric forcing, and the presence of sea ice. The quantification and numerical modelling of these processes is essential to predicting Arctic coastal erosion. This paper presents a case study of Baydaratskaya Bay, Russia, using the COSMOS numerical model to predict thermal-mechanical erosion. In particular, this study focuses on thermoabrasional rather than thermodenudational processes. A field dataset of onshore thermal and mechanical soil characteristics was supplemented by sources from the literature to serve as input for the model. A detailed sensitivity analysis has been conducted to determine the influence of key parameters on coastal erosion rates at the study site. This case study highlights the need for expanded data collection on Arctic coastlines and provides direction for future investigations.

Characterisation of Residual Shear Strength at the Sheringham Shoal Offshore Wind Farm

For offshore foundations, the residual shear strength is an important soil parameter for the evaluation of installation resistance and axial pile capacity (for jacket foundation). Estimation of residual shear strength can be conducted in a shear box test in the conventional way, or with the introduction of an interface to evaluate the change in residual shear strength under influence of friction between soil and the interface. In addition, the residual effective friction angle can be measured in the ring shear test using the Bromhead apparatus. In this study, the three above-mentioned methods are employed to estimate the values of residual shear strength of two soil units: the Swarte Bank Formation and the Chalk Unit sampled from the Sheringham Shoal offshore wind farms. The Swarte Bank Formation is dominated by heavily over-consolidated stiff clay, while the Chalk Unit is characterized by putty white chalk which behaves in a similar manner to stiff clay if weathered, or to soft rock if unweathered. These soil units are located at the bottom of the soil profile at the Sheringham Shoal wind farm and hence are important in providing axial capacity to the foundation.Samples from the two soil units are tested and compared at different rates of shearing to evaluate the change in axial capacity and installation resistance of the offshore wind turbine foundations under various possible loading and drainage conditions. Comparison is also made between residual shear strength with and without a reconsolidation period to assess the potential for soil set-up and its influence on the soil capacity. The results show that, for both the clay and the chalk, the estimated residual shear strengths are quite similar between the conventional and interface shear tests and tend to increase with increasing shearing rate. This can be attributed to the increasing dominance of the turbulent shearing mode. Relative to the peak shear strength, the values of residual shear strength are approximately 5 to 35% lower in most cases. Reconsolidation for a period of 24 hours appears to have, if any, marginal positive effect on residual shear strength of the two soils in both shear box and interface shear box tests. The residual friction angles derived from the shear box and ring shear tests are comparable and fall in the immediate range of shear strength. The various test results imply that the pile foundations at the Sheringham Shoal would have considerably large axial capacity, assuming that the horizontal stress is similar to the normal stress used in testing. The test data however should be used with caution and combined with piling experience in comparable soils where possible. The study aims to provide a source of reference for design of pile foundations for sites with similar soil conditions.Copyright