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.

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.

Strengthening the application of big data technology in data analysis can effectively improve the service capability and level of relevant statistics, and provide comprehensive and reliable information support for macro decision-making and trend analysis. This paper comprehensively reviews the research status of big data technology in the field of college students' mental health at home and abroad. Combining with the characteristics of college students' mental health statistical data and the weaknesses in statistical analysis, the feasibility of using knowledge mapping technology is demonstrated. On this basis, the blood relationship graph and influence analysis among the statistical indicators of college students' mental health were constructed through the knowledge map. The application of the knowledge map of college students' mental health statistical indicators in statistical data analysis, statistical indicator identification and statistical data quality management is proposed. Specifically, based on the concept of big data, we can establish a decision analysis platform for college students' mental health. Based on the big data technology, the data mining and analysis ability can be enhanced. In addition, it can change the traditional thinking of college students' mental health statistics and strengthen the construction of statistical team.