The inverse-distance weighting interpolation is widely used in 3D geological modeling and directly affects the accuracy of models. With the development of “smart” or “intelligent” geology, classical inverse-distance weighting interpolation cannot meet the accuracy, reliability, and efficiency requirements of large-scale 3D geological models in these fields. Although the improved inverse-distance weighting interpolation can basically meet the requirements of accuracy and reliability, it cannot meet the requirements of efficiency at the same time. In response to these limitations, the adaptive inverse-distance weighting interpolation method based on geological attribute spatial differentiation and geological attribute feature adaptation was proposed. This method takes into account the spatial differentiation of geological attributes to improve the accuracy and considers the first-order neighborhood selection strategy to adaptively improve efficiency to meet above requirements of large-scale geological modeling. The proposed method was applied to an area in eastern China, and the results of the proposed method, compared to the results of classical inverse-distance weighting interpolation and improved inverse-distance weighting interpolation, suggest that the problems encountered above in large-scale geological modeling can be solved with the proposed method. The method can provide effective support for large-scale 3D geological modeling in smart geology.

Simulation of a geostratigraphic unit is of vital importance for the study of geoinformatics, as well as geoengineering planning and design. A traditional method depends on the guidance of expert experience, which is subjective and limited, thereby making the effective evaluation of a stratum simulation quite impossible. To solve this problem, this study proposes a machine learning method for a geostratigraphic series simulation. On the basis of a recurrent neural network, a sequence model of the stratum type and a sequence model of the stratum thickness is successively established. The performance of the model is improved in combination with expert-driven learning. Finally, a machine learning model is established for a geostratigraphic series simulation, and a three-dimensional (3D) geological modeling evaluation method is proposed which considers the stratum type and thickness. The results show that we can use machine learning in the simulation of a series. The series model based on machine learning can describe the real situation at wells, and it is a complimentary tool to the traditional 3D geological model. The prediction ability of the model is improved to a certain extent by including expert-driven learning. This study provides a novel approach for the simulation and prediction of a series by 3D geological modeling.

Pervasive, unavoidable and uncontrollable creep failure generated in soft-rock engineering occasionally happens and therefore attracts extensive attention recently. However, due to soft rock’s multi-factor creep mechanism, it is still difficult to simulate the full-stage creep with the Distinct Element Method (DEM). In this study, we proposed an improved simulation method based on the classical Burger’s model and the Parallel Bonded model in Particle Flow Code (PFC). We apply the abovementioned models together to simulate the full-stage creep process in soft rock. The proposed process has considered the mesoscopic mechanical characteristics of DEM carefully and finally resulted in a parallel physical model, which is called Burger’s Bonded model in this paper. The DEM simulation test using Burger’s Bonded model was designed to compare with experiments. The experiments include a normal creep test and a uniaxial loading test with prefabricated cracks. In contrast to experimental results, the numerical results show that the average error during the whole creep process is less than 3%; the stress–strain curves and crack development process show great agreement. It is also found that the wing crack coalescence in soft rock is independent of the prefabricated crack angle, propagating with a fixed dip angle. The results show that the numerical method proposed in this paper can simulate the multi-factor-caused full stage (attenuated, steady, accelerated) creep process of soft rock in DEM, which provides new insights for theoretical research and engineering design.

The rheological deformation of soft rock resulting from tunnel excavation can lead to significant construction and safety challenges. In this study, a multiphase numerical model was developed to simulate the rheological deformation of soft rock surrounding a tunnel after excavation. The developed model considers the coupled meso/macroscale creep and damage processes of the rock using the coupled discrete element method–finite element method (DEM–FEM). In particular, the damage and deformation accumulation at the mesoscale (i.e., initial phase before excavation, loading phase due to the disturbance of the excavation and creep-induced damage phase leading to large deformation) were incorporated into the model. The model predictions were validated using field monitoring data. By incorporating the coupled meso/macroscale deformation process of the rock into the model, the predicted time-dependent displacements of the tunnel face agree reasonably well with the monitoring data. In addition, the results demonstrate that tunnel brittle damage accumulated in mineral clusters severely leads to instantaneous deformation, which becomes less important in the creep evolution stage. Furthermore, the results indicate that the final deformation is characterized by a high sensitivity to the value of mesoscale modeling parameters.

Red beds are Meso–Cenozoic continental sedimentary strata that are mainly composed of gravel stone, sandstone, siltstone, mudstone, and shale and occasionally have interlayers of limestone, halite, and gypsum. As a typical rock mass, red beds are widely distributed throughout South China. In a typical tropical and subtropical continental environment, red beds are the product of multiple sedimentary cycles, which have resulted in complicated rock mass structures that play an important role in rock mass stability. It is thus of great significance to investigate the influence of different rock mass structures on the stability of red-bed slopes. In this paper, the geological formation history of red beds in South China is described. The main features of red-bed rock mass slopes in South China are discussed. The main combinations of inner geomechanical structures comprise: (1) mega-thick soft rock structures; (2) mega-thick hard rock structures; (3) thick hard rock structures with weak intercalation; and (4) soft–hard interbedded structures. In addition, the features of slope failure are analyzed, and four common failure modes are identified from the statistical data: (a) weathering spalling and scouring; (b) rock falls; (c) landslides; and (d) tensile dumping.