The whole shear deformation of rock joints significantly affects the long-term behavior and safety of engineering projects. In this paper, a new damage constitutive model related to the Weibull distribution and statistical damage theory is proposed. This model considers the shear stiffness degradation, post-peak softening, and residual phase of rock joints in the whole shearing process. Main works include the three following aspects: First, the phase of initial damage is determined on the assumption that the joint shear failure is regarded as a result of damage evolution, according to the typical joint shear curve and the three-parameter Weibull distribution. Then, a statistical damage evolution model for the whole joint shearing process is introduced to make this model be capable of describing the residual phase of rock joints. Finally, a statistical constitutive model for the whole joint shearing process is proposed by statistical damage theory, and the calculated results of the models are compared to the experimental results. The results indicate that the proposed model shows a good agreement with the experimental examples, and the proposed model can distinctly reflect the effects of residual stress, peak stress, and shear stiffness. In addition, the model parameters can be mathematically confirmed and have distinct physical meanings.

This paper describes the influence of soil nail reinforcement on the stability of slopes through a numerical analysis that uses the strength reduction method. The stability of nailed slopes is evaluated in terms of factor of safety and potential slip surface. In this paper, the dual-spring structure element in the commercial package (FLAC^3D) based on an explicit finite difference formulation is adopted to simulate the characteristics of the nails. A 3D slope model is built to simulate the excavations of the slope and the reinforcement of soil nails. The effects of nail length, orientation, installation location and horizontal spacing on the factor of safety and potential slip surface of the slope, as well as on the nails’ mechanical responses, are numerically investigated.

A machine learning-based strategy is presented to estimate the contact force chains of uniformly sized spherical granular materials under triaxial compression, using particle kinematics and inter-particle contact evolution data measured by X-ray micro-tomography (μCT). To this end, a graph neural network (GNN) is introduced to predict the contact force chains of granular materials at the end of a shear increment based on the evolution of contact network and grain displacement during that shear increment. Meanwhile, discrete-element modelling (DEM) is performed for a glass bead specimen under triaxial compression with the use of in situ μCT scanning. The DEM model has the same initial conditions as the glass bead specimen and is validated by comparing the calculated stress–strain curves, particle kinematics and inter-particle contact fabric evolution of the numerical specimen with the experimental results. The DEM model is used to generate sufficient virtual data to train the GNN model, which is applied to the glass bead specimen to predict the evolution of contact force chains. The model-predicted results yield a power-law relationship between the above mean normalised particle maximum normal contact forces and the probability density function, which is consistent with the findings reported in previous numerical studies.