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The onset of static liquefaction in anisotropically consolidated soils is of relevance in assessing the performance of geotechnical systems. Previous studies have also highlighted the role of inherent soil fabric. This study derives an analytical instability criterion for granular materials under undrained loading by using the relatively new anisotropic critical state theory (ACST). The criterion is established using the SANISAND-F model, and it is amenable to incorporating consolidation anisotropy and fabric effects. We assess different numerical strategies for simulating the instability onset on materials sheared from initially anisotropic conditions. Our assessments indicate that simulations that consider consolidation followed by shear better represent the response observed in laboratory tests. It is observed that the degree of anisotropic consolidation has no significant effect on the instability stress ratio, but a very high degree of anisotropic consolidation results in a spontaneous collapse. It is also observed that the anisotropic consolidated specimens have a higher instability stress ratio in triaxial compression than in triaxial extension, highlighting the effect of loading direction relative to the existing fabric. 

Static liquefaction has been associated with the failure of several tailing storage facilities (e.g., the Brumadinho failure in 2019) and has been a persistent topic of discussion in the mining and tailings communities. Experimental studies have suggested that the onset of static liquefaction is dependent on the initial state (void ratio and confinement) and fabric anisotropy. In this context, traditional constitutive models developed under the critical state theory (CST) have been used to investigate the onset of static liquefaction for several complex loading paths. However, these models do not capture the effect of soil fabric anisotropy (inherent and induced) that are relevant in field conditions. In this study, the Anisotropic Critical State Theory (ACST) framework is used to assess the onset of static liquefaction in particulate materials, incorporating the effects of inherent and induced fabric. Our assessments derive an analytical criterion to assess static liquefaction that can be applied to screening assessments. The derived analytical criterion is a function of material properties, state, and fabric anisotropy, which couple the effects of fabric and loading direction. The use of the derived criteria in particulate materials is illustrated, and the implications of assessing the static liquefaction of mine tailings under generalized loading is also discussed. 

Estimating soil properties from the mechanical reaction to a displacement is a common strategy, used not only in in situ soil characterization (e.g., pressuremeter and dilatometer tests) but also by biological organisms (e.g., roots, earthworms, razor clams), which sense stresses to explore the subsurface. Still, the absence of analytical solutions to predict the stress and deformation fields around cavities subject to geostatic stress, has prevented the development of characterization methods that resemble the strategies adopted by nature. We use the finite element method (FEM) to model the displacement-controlled expansion of cavities under a wide range of stress conditions and soil properties. The radial stress distribution at the cavity wall during expansion is extracted. Then, methods are proposed to prepare, transform and use such stress distributions to back-calculate the far field stresses and the mechanical parameters of the material around the cavity (Mohr-Coulomb friction angle$$\phi $$$\varphi$, Young’s modulusE). Results show that: (i) The initial stress distribution around the cavity can be fitted to a sum of cosines to estimate the far field stresses; (ii) By encoding the stress distribution as intensity images, in addition to certain scalar parameters, convolutional neural networks can consistently and accurately back-calculate the friction angle and Young’s modulus of the soil.

 

Bedrock weakening is of wide interest because it influences landscape evolution, chemical weathering, and subsurface hydrology. A longstanding hypothesis states that bedrock weakening is driven by chemical weathering of minerals like biotite, which expand as they weather and create stresses sufficient to fracture rock. We build on recent advances in rock damage mechanics to develop a model for the influence of multimineral chemical weathering on bedrock damage, which is defined as the reduction in bedrock stiffness. We use biotite chemical weathering as an example application of this model to explore how the abundance, aspect ratio, and orientation affect the time‐dependent evolution of bedrock damage during biotite chemical weathering. Our simulations suggest that biotite abundance and aspect ratio have a profound effect on the evolution of bedrock damage during biotite chemical weathering. These characteristics exert particularly strong influences on the timing of the onset of damage, which occurs earlier under higher biotite abundances and smaller biotite aspect ratios. Biotite orientation, by contrast, exerts a relatively weak influence on damage. Our simulations further show that damage development is strongly influenced by the boundary conditions, with damage initiating earlier under laterally confined boundaries than under unconfined boundaries. These simulations suggest that relatively minor differences in biotite populations can drive significant differences in the progression of rock weakening. This highlights the need for observations of biotite abundance, aspect ratio, and orientation at the mineral and field scales and motivates efforts to upscale this microscale model to investigate the evolution of the macroscale fracture network.