Fractured rocks in the subsurface are ubiquitous, and the dynamics of mass transfer in fractured rocks plays an important role in understanding the problem in engineering geology and environmental geology. In this study, the influence of shear displacement on fluid flow and solute transport in a 3D rough fracture was investigated. A 3D self-affine rough fracture was generated using the modified successive random addition (SRA) technology, and three sheared fractures with different shear displacements were constructed based on the mechanistic model. A direct numerical model based on the Navier-Stokes equation and the advection-diffusion equation was developed to solve the fluid flow and the solute transport. The results showed that shear displacement had a significant influence not only on the fluid flow but also on the solute transport. A global measure of the spatial variability of the flow velocity showed that the heterogeneity became weaker with decreasing shear displacement. All measured BTCs deviated from the Gaussian profile and exhibited the typical anomalous behaviors, such as the long tail and the early arrival. Although the best-fitted results of the advection-dispersion equation (ADE) model and mobile-immobile model (MIM) were generally consistent with those of the BTCs, the MIM was more capable than the ADE model for characterizing the shear-induced anomalous behavior of the BTCs. It was found that the mass exchange process between the immobile and mobile domains was enhanced in the sheared fractures while the fraction of the advection-dominant mobile domain decreased as the shear displacement increased. Furthermore, the deviation of the Taylor dispersion coefficient from the fitted dispersion coefficient by the ADE model and MIM in the sheared fractures was confirmed due to the influence of shear displacement.

Although numerous studies have been paid much attention to rainfall-induced instability of multilayered slopes, the interface between layers is generally considered to be “zero thickness”, and the layer transition zone between layers is neglected. In this study, the influence of the layer transition zone on the rainfall-induced instability of multilayered slope was investigated. A model was developed to simulate the rainfall infiltration process, the distribution of pore water pressure, and the stability of multilayered slope by coupling the unsaturated seepage model and the slope stability analysis method. Based on the analysis of the multilayered slopes with the different thickness ratios of the layer transition zone, a method for determining the critical thickness of the layer transition zone was proposed. The results showed that the layer transition zone had a significant influence on the stability of multilayered slope. It was found that the presence of the layer transition zone in the multilayered slope reduced the hydraulic conductivity of the slope and increased the rate of formation of transient saturated zone, which contributed to excess pore water pressure at the toe of the slope. The analysis of the local factor of safety (LFS) showed that when the thickness ratios of the layer transition zone were between 2.5% and 5%, the corresponding hydraulic conductivity of the slope decreased by 1%-2.5% and the maximum failure area of the slope during the rainfall was 25% of the slope. Our study highlighted the importance of the layer transition zone for the rainfall-induced instability of the multilayered slope.

Soil pollution is a serious threat to human life and development. Different remedial measures are applied to soils with different levels of contamination. The degree of soil contamination in different areas is generally evaluated and categorised based on the analysis of samples. Regional soil sampling sites are generally sparse because of the cost of sampling and other factors, which makes it difficult to accurately assess the extent of regional soil contamination. In this study, a spatial classification model was established for the Di(2-ethylhexyl) phthalate (DEHP) pollution level using a Bi-directional Long Short-Term Memory (Bi-LSTM) neural network considering that the sampling information gradually diminishes with increasing distance between the sampling and prediction points. In this study, a method is proposed for the prediction of the spatial distribution of soil pollution categories based on sparse samples. We also established a model for the spatial distribution of organic pollution categories. The analysis of an actual contaminated area shows that the DEHP concentrations at different locations can be effectively predicted with the proposed method by categorising the contamination levels of specific DEHP samples. The results show that the method can be used to classify the degree of light/severe DEHP contamination. The results are in good agreement with the actual situation, verifying the validity of the method. This method is important for the rapid assessment of the spatial distribution of soil contamination levels based on sparse sampling.