A model for predicting slope stability is developed using Categorical Boosting (CatBoost), which incorporates 6 slope features to characterize the state of slope stability. The model is trained using a symmetric tree as the base model, utilizing ordered boosting to replace gradient estimation, which enhances prediction accuracy. Comparative models including Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Logistic Regression (LR) were introduced. Five performance evaluation metrics are utilized to assess the predictive capabilities of the CatBoost model. Based on CatBoost model, the predicted probability of slope instability is calculated, and the early warning model of slope instability is further established. The results suggest that the CatBoost model demonstrates a 6.25% disparity in accuracy between the training and testing sets, achieving a precision of 100% and an Area Under Curve (AUC) value of 0.95. This indicates a high level of predictive accuracy and robust ordering capabilities, effectively mitigating the problem of overfitting. The slope instability warning model offers reasonable classifications for warning levels, providing valuable insights for both research and practical applications in the prediction of slope stability and instability warning.

Preferential flow is a non-equilibrium flow in unsaturated soil through which water infiltrates deep into the ground quickly. It has been studied in many fields, such as environment, agriculture, and hydrology. However, researchers from different disciplines have a different understanding of preferential flow, and it is difficult to grasp its development trends and research frontiers through qualitative analysis in a single field, while they can be quantitatively and objectively analyzed through bibliometrics with scientific knowledge map tools. This paper collects 3315 research studies on preferential flow in soil from the Web of Science (WoS) core collection database within 30 years, conducts a statistical analysis on keywords, countries, and research institutions of these studies based on CiteSpace, draws visualized scientific knowledge maps, and presents the development trends and research frontiers of preferential flow. Results showed that preferential flow is a multi-scale coexistence phenomenon, and researchers from different disciplines study preferential water flow movement and pollution at different research scales. New techniques and ideas are research hotspots and directions. Moreover, the difference between bibliometrics methods and review methods is analyzed. This paper is presented to provide a referable knowledge structure and new ideas for research in related fields and to help promote cross-integration between disciplines.

Habitat quality is a comprehensive index reflecting ecological conditions, land use impact, and human survival. Susceptibility to geological disasters is influenced by factors such as ecology, the geological environment, and human activities. Analyzing the effects of habitat quality on geological disaster susceptibility and its spatial dynamics is crucial for ecological protection and assessing geological disaster risks. This research focused on Pingshan County, using the InVEST 3.7.0 model and ArcGIS to evaluate habitat quality and geological disaster susceptibility for 2020. The spatial relationships were examined with GeoDa to investigate the impact of habitat quality on geological disaster susceptibility. The findings are as follows: (1) Pingshan County generally exhibits high habitat quality, showing significant spatial clustering with geological disaster susceptibility—predominantly high–high in the west and low–low in the east. (2) The geological environment significantly influences the relationship between habitat quality and geological disaster susceptibility, with an overall positive correlation but negative correlations in certain areas. Geological disaster susceptibility is primarily governed by geological factors rather than habitat quality. (3) In mountainous regions with comparable ecological and geological conditions, variations in geological disaster susceptibility are chiefly driven by human activities. Including human activities as a metric significantly enhances the evaluation accuracy. This study provides a scientific foundation for ecological protection, the assessment of geological disaster susceptibility, and the development of mitigation policies.

This study examines how coarse aggregate volume fraction (V_a) affects the double-K fracture toughness and fundamental mechanical properties of concrete. Wedge-splitting tests were conducted on specimens with six different V_a values: 19%, 25%, 31%, 37%, 43%, and 50%. The results indicate that compressive strength (f_c) and elastic modulus (E) consistently increase with V_a, reaching 59.8 MPa and 37.9 GPa at V_a = 50%, respectively. Conversely, tensile strength (f_t), double-K fracture toughness (including initiation toughness $K_{\mathrm{IC}}^{\mathrm{ini}}$ and unstable toughness $K_{\mathrm{IC}}^{\mathrm{un}}$), and fracture energy (G_IF) initially increase before decreasing, peaking at an optimal V_a of 37%. Specifically, $K_{\mathrm{IC}}^{\mathrm{ini}}$, $K_{\mathrm{IC}}^{\mathrm{un}}$, and G_IF reached their maximum values of 0.54 MPa·m^1/2, 1.20 MPa·m^1/2, and 225.0 N/m at V_a = 37%. The tortuosity of crack paths follows a similar trend, becoming more pronounced up to V_a = 37% before diminishing. Furthermore, quantitative exponential relationships were established between f_t and $K_{\mathrm{IC}}^{\mathrm{ini}}$, $K_{\mathrm{IC}}^{\mathrm{un}}$, and G_IF. A safety warning parameter (δ), derived from the double-K fracture toughness, was proposed to quantitatively assess the pre-peak ductility, with values ranging from 0.88 to 0.72 in this study. The findings offer valuable guidance for optimizing concrete mix design, suggesting that a V_a range of 25% to 31% provides an optimal balance between high crack initiation resistance and adequate safety warning capacity for critical engineering structures.

To study the influence law of effective peak acceleration (EPA) on the seismic response of soil slope, the finite element method was used to simulate the slope response under earthquake action with 100 actual seismic records were selected, the influence law of the EPA under four different definitions commonly used in domestic and foreign codes on the soil slope seismic response was discussed, and which was compared with the influence law of the peak acceleration (PGA). The results showed that the deformation and the maximum principal stress of soil slope both increased with the EPA and PGA, which had an obvious linear relationship, but the correlation degree were different with the parameters of PGA and EPA by the different definitions. EPA1 by the first definition has the highest correlation with the soil slope seismic response, followed by PGA, which was close to EPA1. Other parameters in order of correlation coefficient were EPA2, EPA3 and EPA4. In this example, EPA1 and PGA could better describe the response degree of soil slope in earthquake. The results are expected to provide a basis for the selection of seismic parameters in soil slope seismic stability evaluation.