The traditional susceptibility evaluation of geological hazards usually comprises a global susceptibility evaluation of the entire study area but ignores the differences between the local areas caused by spatial non-stationarity. In view of this, the geographically weighted regression model (GWR) was used to divide the study area at regional scale. Seven local areas were obtained with low spatial auto-correlation of each evaluation factor. Additionally, 11 evaluation factors, including the aspect, elevation, curvature, ground roughness, relief amplitude, slope, lithology, distance from the fault, height of the cut slope, multiyear average rainfall and the normalized difference vegetation index (NDVI) were selected to establish the evaluation index system of the geological hazard susceptibility. The Pearson coefficient was used to remove the evaluation factors with high correlation. The global and seven local areas were evaluated for susceptibility using the information value model and the global and regional division susceptibility evaluation results were obtained. The results show that the regional division information value model had better prediction performance (AUC = 0.893) and better accuracy. This model adequately considers the influence of the geological hazard impact factors in the different local areas on geological hazard susceptibility and weakens the influence of some factors that have higher influence in the global model but lower influence in local areas on the evaluation results. Therefore, the use of the regional division information value model for susceptibility evaluation is more consistent with the actual situation in the study area and is more suitable for guiding risk management and hazard prevention and mitigation.

The appropriate selection of machine learning samples forms the foundation for utilizing machine learning models. However, in landslide susceptibility evaluation, discrepancies arise when non-landslide samples are positioned within areas prone to landslides or demonstrate spatial biases, leading to differences in model predictions. To address the impact of non-landslide sample selection on landslide susceptibility predictions, this study uses the western region of Henan Province as a case study. Utilizing historical data, remote sensing interpretation, and field surveys, a sample dataset comprising 834 landslide points is obtained. Ten environmental factors, including elevation, slope, aspect, profile curvature, land cover, lithology, topographic wetness index, distance from river, distance from faults, and distance from road, are chosen to establish an evaluation index system. Negative sample sampling areas are delineated based on the susceptibility assessment outcomes derived from the information value model. Two sampling strategies, whole-region random sampling (I) and partition-based random sampling (II), are employed. Random Forest (RF) and Back Propagation Neural Network (BPNN) models are used to forecast and delineate landslide susceptibility in the western region of Henan Province, with prediction accuracy evaluated. The model prediction accuracy is ranked as follows: II-BPNN (AUC = 0.9522) > II-RF (AUC = 0.9464) > I-RF (AUC = 0.8247) > I-BPNN (AUC = 0.8068). Under the Receiver Operating Characteristic (AUC) curve and accuracy, the II-RF and II-BPNN models exhibit increases in the region by 12.17% and 15.61%, respectively, compared to the I-RF and I-BPNN models. Moreover, the II-BPNN model shows improvements over the I-BPNN model with increases in AUC and accuracy by 14.54% and 16.52%, respectively. This indicates enhancements in model performance and predictive capability. In terms of recall and specificity, the II-RF and II-BPNN models demonstrate increases in recall by 15.09% and 17.47%, respectively, and in specificity by 15.80% and 14.99%, respectively. These findings suggest that the optimized models have better predictive capabilities for identifying landslide and non-landslide areas, effectively reducing the uncertainty introduced by point data in landslide risk prediction.

The tight sandstone reservoir in the Qianfoya formation of well PL-3 of the Puguang gas field in Sichuan, China, obtained a high-yield gas flow after a volume fracturing treatment. However, the stimulated reservoir volume (SRV), fracture morphology, scale and formation law still remain unclear. Based on particle flow discrete-element theory in this paper, we carried out a few trials of the Brazilian splitting test, uniaxial compression and triaxial compression of rock mechanics. Meanwhile, the research also testified to the conversion relationship between macroparameters and microparameters, established the numerical simulation on hydraulic fracturing through PFC^2D discrete element software, and finally analyzed the influence of difference coefficients on the fracturing effect, in terms of different in-situ stresses. The conclusions are as follows: firstly, the influence of in-situ stress is essential for the direction, shape and quantity of fracture propagation, and the fractures generated by hydraulic fracturing are mainly tension fractures, accounting for over 90% of the total longitudinal fractures. Secondly, it is indicated that when the difference coefficient is small in the in-situ stress, the fractures formed by hydraulic fracturing expand randomly around the wellbore. When the difference coefficient K_h of in-situ stress is above 0.6, the development of hydraulic fractures is mainly controlled by in-situ stress; as a result, the fractures tend to expand in the vertical direction of the minimum horizontal principal stress and the fracture shape is relatively singular. When the difference coefficient of in-situ stress was 0.3, in total, 3121 fractures were generated by fracturing, and the fractal dimension D value of the fracture network complexity was 1.60. In this case, this fracturing effect was the best and it is the easiest to achieve for the purpose of economical and effective development on large-scale volume fracturing.

In large-scale water conservancy and hydropower projects, complex rock structures are considered to be the main factor controlling the stability of hydraulic structures. The classification of rock mass structure plays an important role in the safety of all kinds of large buildings, especially underground engineering buildings. As a quantitative classification index of rock mass, the rock block index is very common in the classification of borehole and dam foundation rock mass structures. However, there are few studies on the classification of underground engineering rock masses. Moreover, their classification criteria have disadvantages in spatial dimension. Therefore, this paper takes the long exploratory cave CPD1 in the water transmission and power generation system of the Qingtian pumped storage power station in Zhejiang Province as the research object and launches a study on the structural classification of the rock mass of a flat cave based on the 3D rock block index. According to the group distribution of joints, the sections are statistically homogeneous. Additionally, the Monte Carlo method is used to carry out random simulations to generate a three-dimensional joint network model. The virtual survey lines are arranged along the center of the shape of the three different orthogonal planes of the 3D joint network model to represent the boreholes, and the RBI values of the virtual survey lines on each orthogonal plane are counted to classify the rock mass structure of the flat cave in a refined manner using the rock block index of the rock mass in 3D. The above method realizes the application of the 3D rock block index in underground engineering and overcomes the limitations of traditional rock mass classification methods in terms of classification index and dimension. The results show that: (1) Three-dimensional joint network simulations built on statistical and probabilistic foundations can visualize the structure of the rock mass and more accurately reflect the structural characteristics of the actual rock mass. (2) Based on the 3D rock block index, the rock mass structure of the long-tunnel CPD1 is classified, from that of a continuous structure to a blocky structure, corresponding to the integrity of the rock mass from complete to relatively complete. The classification results are consistent with the evaluation results of horizontal tunnel seismic wave geophysical exploration. (3) Based on the 3D joint network model, it is reasonable and feasible to use the 3D rock block index as a quantitative evaluation index to determine the structure type of flat cave rock masses. The above method is helpful and significant in the classification of underground engineering rock mass structures.

Slate, characterized by its layered structure, exhibits mechanical properties predominantly influenced by structural planes. To investigate the mechanical behavior and failure modes of slate under varying dip angles of structural planes, uniaxial compression tests were conducted on slate specimens. The study systematically examined the effects of dip angles on compressive strength, stress-strain curves, and failure patterns. Additionally, a particle flow model was established using PFC-2D to simulate the failure process of slate, with detailed analysis of stress responses under different dip angles. The results indicate that the compressive strength of slate exhibits significant anisotropy across different bedding inclinations. Overall, the strength distribution follows a “U-shaped” curve as the inclination angle varies. During uniaxial compression, the deformation process of slate specimens progresses through four distinct stages: compaction, elastic deformation, elastoplastic deformation, and failure. Three primary failure modes were identified: composite shear-tensile failure along bedding planes, shear failure along bedding planes, and compression-induced tensile failure involving both bedding planes and the rock matrix. Numerical simulations of uniaxial compression tests on slate with varying inclination angles were conducted using the particle flow code PFC-2D. The simulated stress response clearly reflects the mechanical behavior, and the failure modes observed in the models consistently match those from laboratory tests. Based on detailed analysis of stress evolution, the critical boundaries between different failure modes were accurately determined.

The shear strength parameters of landslide zones are the necessary data basis for landslide stability evaluation and landslide surge disaster chain research. It is important to determine the physical and mechanical parameters of landslide zones scientifically and reasonably. In this study, four small residual landslide deposits near the Hei Duo Village road in Diebu County, Gansu Province, were investigated. The research involved detailed field investigations, the construction of landslide engineering geological models, and the use of the transfer coefficient method for simultaneous/inverse inversion and sensitivity analysis of the strength parameters of the four landslides. Based on the inversion results, an analysis of the landslide formation mechanism was conducted. The inversion results yielded the shear strength parameters of the sliding surface soil as c = 30.12 kPa and φ = 21.08°. It was found that the excavation at the base of the slope is the direct triggering factor for the landslides, with the 3# landslide being the most affected by the base excavation. In terms of the type of movement, all four landslides belong to the retrogressive landslide, with the maximum shear strain increment mainly concentrated at the slope angle after excavation. The slope body experiences shear failure, which is in good agreement with the field conditions. The study provides reference for stability prediction and disaster prevention and control of reservoir bank slope.