Considering the geological hazards attributed to the highway slope, using a common simple model cannot accurately assess the stability of the slope. First, principal component analysis (PCA) was conducted to extract the principal components of six factors (namely, bulk density, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio) affecting the slope stability. Second, four principal components were adopted as input variables of the support vector machine (SVM) model optimized by genetic algorithm (GA). The output variable was slope stability. Lastly, the assessing model of highway slope stability based on PCA-GA-SVM is established. The maximal absolute error of the model is 0.0921 and the maximal relative error is 9.21% by comparing the assessment value and the practical value of the test sample. The above studies are conducive to enrich the assessing model of highway slope stability and provide some reference for highway slope engineering treatment.

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.

Spatial variability of soil parameter distribution is crucial to calculating the pile foundation failure probability. Traditional reliability design methods describe the dispersion degree of soil parameters with their point variance without considering the influence of correlation distance. In this paper, static cone penetration test data of a project site are used, and random field theory is introduced to describe the average spatial characteristics of soil parameters. Then, the method of spatial average is used to calculate the correlation distance of soil parameters in each foundation soil layer. Given the influence of the correlation distance, a variance reduction function is determined to convert point variance to spatial mean-variance and further calculate the failure probability of pile foundation with the Monte Carlo method to study the influence of correlation distance on pile foundation failure probability. Results show that the spatial variability of parameters can be better reflected, and project cost can be reduced by considering the influence of correlation distance during the pile foundation design process. These results lay a foundation for further research on the pile foundation reliability design method.

This study aims to investigate the contradiction between the growing demand for ecosystem services and the degradation of these services due to urbanization. Within the context of ecological civilization construction and the “dual carbon” goals, research on the carbon sequestration function of terrestrial ecosystems is particularly significant. Utilizing land-use data from 2000 to 2022, this study employs Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model’s Carbon module to estimate the carbon stock in Pingshan County, using it as a representative example. Geological environmental factors, including elevation, slope, aspect, and degree of relief, were considered with the distribution of geological hazard points and the implementation of relevant policies. Both qualitative and quantitative analyses were undertaken to examine the variations in carbon stock in Pingshan County. The results demonstrate a consistent increase in carbon stock from 2000 to 2022. The geological environment exerts the most significant influence on carbon stock, followed by land-use types, while geological disasters have the least impact. This paper studies the changes in carbon storage and its driving factors in Pingshan County from multiple perspectives. It not only provides theoretical basis and new ideas for the ecological environment protection of Pingshan County, enriching the research results on the carbon cycle of terrestrial ecosystems, but also offers guidance for promoting human-land harmonious development, advancing ecological civilization construction, and achieving sustainable development.

State-of-the-arts in the study of urban land subsidence in our country, including its occurrence mechanism, prediction and control have been reviewed in recent years. The land subsidence is a geological hazard with a reduction of regional elevation owing to the compact of soil which is given rise to the natural and human factors. It is found that the engineering construction is getting more and more impact on land subsidence. The relationship between the construction characteristics, such as the size of buildings, the volume ratio and the degree of dispersion, and land subsidence was revealed, which is helpful to the environmental protection and the sustained development of the city.