A method for spatial partitioning of swelling potential of pavement area swelling soil

Abstract

This application relates to the field of expansive soil ecological analysis technology, and in particular to a method for spatial zoning of the expansive potential of expansive soil in pavement areas. The method involves determining anomaly screening data from borehole sampling points in the pavement influence area using anomaly screening indices, obtaining the results; determining the pavement influence area to be zoned and its discriminant indices; performing first-level expansive potential zoning on the pavement influence area based on the discriminant indices, obtaining the first-level zoning results; reclassifying to obtain the reclassification results of the expansive potential of the pavement influence area; and performing optimization of the expansive potential zoning process to output the optimized zoning results of the expansive potential of the pavement influence area. This invention solves the problem that discrete sample data and a single discriminant indices cannot accurately characterize the spatial variability of expansive soil through spatial interpolation and zoning, thereby refining engineering design and construction and reducing engineering costs.

Description

Technical Field

[0001] This application relates to the field of expansive soil ecological analysis technology, and in particular to a spatial zoning method for the expansive potential of expansive soil in pavement areas. Background Technology

[0002] Expansive soil is a special type of soil rock, characterized by significant water absorption and expansion, and water loss and shrinkage. It is widely distributed in most parts of southern and eastern my country. In the field of civil airport construction, according to the relevant provisions of the "Code for Geotechnical Design of Civil Airports" MH / T 5027-2013, to ensure the stability and safety of the airport pavement foundation, expansive soil within the thickness range of the airport pavement (the subgrade portion within 0.8m below the pavement surface) requires over-excavation treatment. Especially for excavated foundations of expansive soil with strong expansion potential, the replacement depth is required to be increased to 1.0m~1.5m and not less than the depth of the atmospheric influence layer. This results in significant depth differences in foundation treatment design within a large pavement influence area due to variations in expansion potential across different regions.

[0003] Regarding the spatial classification of expansion potential, the "Code for Surveying and Mapping Civil Airports" (MH / T 5025-2011) stipulates that the determination and classification of expansion potential grades of expansive soil should be carried out in accordance with the "Code for Investigation of Geotechnical Engineering" (GB50021). However, according to the requirements of the "Code for Investigation of Geotechnical Engineering" (GB 50021-2001 (2009 edition)), the calculation and classification methods of expansion potential should comply with the provisions of the current national standard "Code for Construction Technology in Expansive Soil Areas" (GB112). The "Code for Construction Technology in Expansive Soil Areas" (GB 50112-2013) proposes that after preliminary geological assessment, the free expansion rate index should be used to determine and classify the expansion potential of expansive soil. When necessary, verification should also be based on tests of the soil's mineral composition (mainly montmorillonite content) and cation exchange capacity. Engineering experience also tells us that relying on a single index to classify the spatial zoning of expansion potential can lead to significant differences in the expansion potential zones classified by different indexes, resulting in pavement engineering safety issues. Therefore, in the existing technology, there is a lack of a spatial partitioning method for pavement expansion potential that can comprehensively consider multiple indicators. Summary of the Invention

[0004] To achieve the above objectives, this application provides the following technical solution: According to a first aspect of the present invention, the present invention claims protection for a method for spatially zoning the expansion potential of expansive soil in pavement areas, comprising: Determine the expansion potential discrimination index, and based on the expansion potential discrimination index, perform clustering anomaly data screening on the expansive soil at the borehole sampling points of the pavement influence area to obtain the clustering anomaly data screening results of the expansive soil. The pavement impact zone to be divided is determined, and the discrimination index data of the pavement impact zone is determined based on the cluster anomaly data screening results of the expansive soil. Based on the discrimination index data, the expansion potential of the pavement influence area is divided into the first partition, and the expansion potential first partition result is obtained. Based on the first partitioning result of the expansion potential, a reclassification is performed to obtain the expansion potential reclassification result of the pavement influence area. The expansion potential reclassification results are optimized by expansion potential partitioning, and the expansion potential partitioning optimization results of the pavement influence area are output.

[0005] Furthermore, the step of determining the swelling potential discrimination index, and based on the swelling potential discrimination index, performing clustering anomaly data screening on the expansive soil at the borehole sampling points of the pavement influence area to obtain the clustering anomaly data screening results for the expansive soil, further includes: The free swelling rate and montmorillonite content of expansive soil were obtained as indicators for judging swelling potential. The cluster radius for clustering expansive soil is determined based on the threshold of the expansibility potential discrimination index, and each cluster center is determined. Based on the cluster radius and cluster center, K-means clustering was used to filter out cluster anomalies in expansive soil, and outliers were removed to obtain the results of the cluster anomaly filtering for expansive soil.

[0006] Furthermore, the step of determining the pavement impact zone to be partitioned, and determining the discriminant index data of the pavement impact zone based on the clustering anomaly data screening results of the expansive soil, also includes: Based on on-site measurement data and terrain design data, determine the pavement influence area to be divided, and import the pavement influence area into ArcGIS to generate a pavement influence area vector file; Obtain borehole sampling points in the affected area of ​​the pavement, and obtain the discrimination index data for each borehole sampling point.

[0007] Furthermore, the step of performing expansion potential first partitioning processing on the pavement influence area based on the discrimination index data to obtain the expansion potential first partitioning result further includes: Based on the aforementioned expansion potential discrimination index, expansion potential levels are distinguished, and an expansion potential level table is determined. Based on the expansion potential level table and the discrimination index data, the Kriging spatial interpolation method is used to perform expansion potential first partitioning on the pavement influence area to obtain the pavement influence area spatial interpolation results under each expansion potential discrimination index, which are used as the expansion potential first partitioning results.

[0008] Furthermore, the reclassification based on the first partitioning result of the expansion potential to obtain the reclassification result of the expansion potential of the pavement influence area also includes: Using a reclassification tool, the first partition result of the expansion potential is reclassified to determine the reclassification code of each partition result; The expansion potential reclassification results of the pavement influence area were converted into surface vector data using a conversion tool. A surface vector file is generated by combining the surface vector data determined by each expansion potential discrimination index.

[0009] Furthermore, the step of optimizing the expansion potential reclassification result by partitioning the expansion potential and outputting the optimized expansion potential partitioning result of the pavement influence area also includes: Obtain the discriminant index data of the expansion potential reclassification result, perform single-index partitioning integration of the expansion potential of the pavement influence area on the discriminant index data, and optimize the merged surface vector file; Based on the elimination function of the data management tool, the local small-area expansion potential zone of the pavement influence area is eliminated; Export the surface vector attribute table data of the merged surface vector file; Based on the reclassification code, the expansion potential discrimination index is selected as the final expansion potential code to determine the expansion potential discrimination result; The exported form of the processed surface vector attribute table data is converted into a table and associated with the surface vector file after the expansion potential discrimination index is merged. The expansion potential zoning optimization results of the pavement influence area are determined based on the expansion potential discrimination results after data processing.

[0010] The present invention has the following technical effects: (1) High discrimination accuracy Multiple expansibility potential discrimination indices can comprehensively reflect the expansibility potential characteristics of expansive soils. The spatial discrimination results have small errors and can accurately divide different expansibility potential zones, thereby improving subsequent engineering design and safety assessment.

[0011] (2) Multi-factor comprehensive evaluation method It can effectively integrate the spatial distribution characteristics of the swelling and shrinkage of expansive soil, and the judgment results are comprehensive, which can reflect the complex situation in actual working conditions, thus improving the scientific nature and practical value of the judgment method.

[0012] (3) High work efficiency The discrimination method relies on global index judgment, automatically performs manual division of expansion potential zones, has systematic spatial analysis means, and has a simple workflow and short processing time.

[0013] (4) Fully consider spatial heterogeneity The low spatial variability of multiple discrimination indicators results in low spatial variability of the expansive soil swelling potential discrimination results. By fully considering their spatial correlation and hierarchical structure, it is possible to accurately divide different swelling potential blocks in a large area of ​​the pavement, thereby reducing the uncertainty and economic cost of foundation treatment design. Attached Figure Description

[0014] Figure 1 A flowchart illustrating the process of a spatial zoning method for the expansion potential of expansive soil in pavement areas, as claimed in an embodiment of this application. Figure 2 A schematic diagram of K-Means machine learning clustering results for a spatial partitioning method of the expansion potential of expansive soil in pavement areas, as claimed in an embodiment of this application. Figure 3 A vector range diagram of the influence zone of an airport pavement with expansive soil, which is a spatial zoning method for the expansive potential of expansive soil in the pavement area, as claimed in the embodiments of this application. Figure 4 A schematic diagram of spatial interpolation of the free expansion rate of the pavement influence zone for a spatial zoning method for the expansion potential of expansive soil in pavement areas, as claimed in an embodiment of this application. Figure 5 A schematic diagram of spatial interpolation of the pavement influence zone based on montmorillonite content, for a spatial zoning method of expansion potential for expansive soil in pavement areas, as claimed in an embodiment of this application. Figure 6 A schematic diagram of spatial interpolation reclassification of the pavement influence zone based on the free expansion rate of a spatial zoning method for the expansion potential of expansive soil in pavement areas, as claimed in an embodiment of this application. Figure 7 A schematic diagram of spatial interpolation reclassification of the pavement influence zone based on montmorillonite content, which is a spatial zoning method for the expansion potential of expansive soil in pavement areas, as claimed in an embodiment of this application. Figure 8 A schematic diagram showing the spatial merging of free swelling rate and montmorillonite content in a spatial zoning method for the swelling potential of expansive soil in pavement areas, as claimed in an embodiment of this application. Figure 9 This is a schematic diagram of the spatial zoning of the pavement influence zone for the comprehensive discrimination of expansion potential of expansive soil in the pavement area, as claimed in the embodiments of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0016] The terms "first," "second," and "third" in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0017] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0018] According to the first embodiment of the present invention, referring to Figure 1 This invention claims protection for a method for spatially partitioning the expansion potential of expansive soil in pavement areas, comprising: Determine the expansion potential discrimination index, and based on the expansion potential discrimination index, perform clustering anomaly data screening on the expansive soil at the borehole sampling points of the pavement influence area to obtain the clustering anomaly data screening results of the expansive soil. The pavement impact zone to be divided is determined, and the discrimination index data of the pavement impact zone is determined based on the cluster anomaly data screening results of the expansive soil. Based on the discrimination index data, the expansion potential of the pavement influence area is divided into the first partition, and the expansion potential first partition result is obtained. Based on the first partitioning result of the expansion potential, a reclassification is performed to obtain the expansion potential reclassification result of the pavement influence area. The expansion potential reclassification results are optimized by expansion potential partitioning, and the expansion potential partitioning optimization results of the pavement influence area are output.

[0019] Furthermore, the step of determining the swelling potential discrimination index and performing cluster anomaly data screening on expansive soil based on the swelling potential discrimination index to obtain the cluster anomaly data screening results for expansive soil further includes: The free swelling rate and montmorillonite content of expansive soil were obtained as indicators for judging swelling potential. The cluster radius for clustering expansive soil is determined based on the threshold of the expansibility potential discrimination index, and each cluster center is determined. Based on the cluster radius and cluster center, K-means clustering was used to filter out cluster anomalies in expansive soil, and outliers were removed to obtain the results of the cluster anomaly filtering for expansive soil.

[0020] In this embodiment, K-Means clustering is a classic unsupervised learning algorithm widely used in data grouping and pattern recognition. Its core idea is to divide data into different clusters and iteratively optimize the location of the cluster centers to maximize the similarity of data within a cluster while minimizing the similarity between different clusters.

[0021] In outlier identification, the K-Means clustering method analyzes the distribution characteristics of data to discover data points that do not conform to the usual patterns. Normal data points usually cluster near the cluster centers, while outliers, due to their uniqueness, are often far from the cluster centers. By calculating the distance of each data point to its cluster center, points whose distance exceeds a certain threshold can be identified as outliers.

[0022] Reference Figure 2 Using the free expansion rate and montmorillonite content required by national standards as indicators of expansion potential, spatial discrimination of the expansion potential in the pavement impact zone was conducted. A threshold (maximum cluster radius of Cluster I) was determined by the maximum cluster radius of the optimal cluster, and this threshold was used to delineate each cluster region (with the cluster center as the center and the threshold as the radius). Data outside the cluster regions were then identified as outliers, and the cluster analysis results for this data are as follows: Figure 1 As shown, S32, S45, S62, S64, S67, and S188 are outliers. Therefore, the six outliers identified by the K-Means machine learning method were removed, and this data was used as the basis for subsequent analysis.

[0023] Furthermore, the step of determining the pavement impact zone to be partitioned, and determining the discriminant index data of the pavement impact zone based on the clustering anomaly data screening results of the expansive soil, also includes: Based on on-site measurement data and terrain design data, determine the pavement influence area to be divided, and import the pavement influence area into ArcGIS to generate a pavement influence area vector file; Obtain borehole sampling points in the affected area of ​​the pavement, and obtain the discrimination index data for each borehole sampling point.

[0024] In this embodiment, reference is made to Figure 3 Based on on-site measurement data and terrain design data, the scope of the pavement impact zone is determined, and the data is imported into ArcGIS to generate a pavement impact zone surface vector file; Measurement data is essential foundational data for all engineering sites. Based on topographic data, airport pavement design and other related disciplines consider factors such as visibility, airspace clearance, fill-cut balance, and investment to design the airport's orientation and elevation, which in turn determines the location and elevation of the pavement impact zone. Contour lines measured in CAD can be transferred to GIS for processing.

[0025] The specific data points obtained from 10 sampling points are shown in Table 1; Table 1. Data Table of Sampling Point Discrimination Indicators

[0026] The borehole sampling locations from the exploration phase and the geotechnical test swelling and shrinkage parameter data processed by machine learning are imported into ArcGIS (in this example, the criteria are free swelling rate and montmorillonite content) as the discrimination indicators for spatial swelling potential zoning.

[0027] Specific steps: Open ArcToolBox—Data Management Tools—Layers and Table Views—Create XY Event Layer—"Enter Borehole Sampling Point Data".

[0028] Furthermore, the step of performing expansion potential first partitioning processing on the pavement influence area based on the discrimination index data to obtain the expansion potential first partitioning result further includes: Based on the aforementioned expansion potential discrimination index, expansion potential levels are distinguished, and an expansion potential level table is determined. Based on the expansion potential level table and the discrimination index data, the Kriging spatial interpolation method is used to perform expansion potential first partitioning on the pavement influence area to obtain the pavement influence area spatial interpolation results under each expansion potential discrimination index, which are used as the expansion potential first partitioning results.

[0029] In this embodiment, the "Code for Surveying and Mapping Civil Airports" (MH / T 5025-2011) stipulates that the determination and classification of swelling and shrinkage grades of expansive soil shall be carried out in accordance with the "Code for Investigation of Geotechnical Engineering" (GB50021). However, according to the requirements of the "Code for Investigation of Geotechnical Engineering" (GB50021-2001 (2009 edition)), the calculation and classification methods of swelling potential should comply with the provisions of the current national standard "Code for Construction Technology in Expansive Soil Areas" (GB112). The national standard "Code for Construction Technology in Expansive Soil Areas" GB 50112-2013 proposes that after the initial geological assessment, the free swelling rate index should be used to determine and classify the swelling potential of expansive soil. When necessary, it should also be verified by tests based on the mineral composition and cation exchange capacity of the soil, as shown in Table 2. Table 2. Relationship between free swelling rate and montmorillonite content and cation exchange capacity in the "Technical Specifications for Building Construction in Expansive Soil Areas"

[0030] Based on the free expansion rate and montmorillonite content, kriging space interpolation was performed on the sampling points to obtain the spatial partitioning of the expansion potential of the pavement influence zone. (Refer to...) Figure 4 , Figure 5 Since the data relied upon in this embodiment does not include cation exchange capacity data, the free expansion rate and montmorillonite content are used as two indicators for comprehensive judgment of expansion potential. In actual operation, according to the specifications of various industries, indicators such as loaded expansion rate, standard hygroscopic moisture content, and total expansion and contraction rate can be selected for comprehensive spatial judgment and zoning.

[0031] Specific steps: Open ArcToolBox—Spatial Analyst—Interpolation Analysis—Kriging Furthermore, the reclassification based on the first partitioning result of the expansion potential to obtain the reclassification result of the expansion potential of the pavement influence area also includes: Using a reclassification tool, the first partition result of the expansion potential is reclassified to determine the reclassification code of each partition result; The expansion potential reclassification results of the pavement influence area were converted into surface vector data using a conversion tool. A surface vector file is generated by combining the surface vector data determined by each expansion potential discrimination index.

[0032] In this embodiment, reference is made to Figure 6 , Figure 7 Using a reclassification tool, the expansion potential space of the pavement area is reclassified, with non-expansive soil defined as 1; weakly expansive soil defined as 2; and moderately expansive soil defined as 3. Open ArcToolBox—Spatial Analyst Tools—Reclassification Tool—Reclassification.

[0033] The reclassification data of pavement influence areas, including free expansion rate and montmorillonite content, were converted into surface vector data using a transformation tool. Open ArcToolBox—Convert Tool—Export from Raster—Convert from Raster to Polygon The surface vector files for each indicator are combined into a single surface vector file. Open ArcToolBox—Analysis Tools—Overlay Analysis—Union.

[0034] Furthermore, the step of optimizing the expansion potential reclassification result by partitioning the expansion potential and outputting the optimized expansion potential partitioning result of the pavement influence area also includes: Obtain the discriminant index data of the expansion potential reclassification result, perform single-index partitioning integration of the expansion potential of the pavement influence area on the discriminant index data, and optimize the merged surface vector file; Based on the elimination function of the data management tool, the local small-area expansion potential zone of the pavement influence area is eliminated; Export the surface vector attribute table data of the merged surface vector file; Based on the reclassification code, different reclassification definition rules select different large or small code values, and select the final inflation potential code from the inflation potential discrimination index to determine the inflation potential discrimination result; The exported form of the processed surface vector attribute table data is converted into a table and associated with the surface vector file after the expansion potential discrimination index is merged. The expansion potential zoning optimization results of the pavement influence area are determined based on the expansion potential discrimination results after data processing.

[0035] In this embodiment, the spatial interpolation results of the free swelling rate and montmorillonite content in the pavement impact zone show a significant difference in the swelling potential of the expansive soil judged by the two indicators. Since airport projects are important transportation hubs, the pavement impact zone is often considered under worse conditions during actual design and construction. Therefore, the judgment results need to comprehensively consider both indicators and refer to... Figure 8 The single-index partitioning of the pavement influence zone expansion potential is integrated, and the merged surface vector file is optimized.

[0036] To facilitate calculations and consider actual conditions, the elimination function of the data management tool is used to eliminate small, localized expansion potential zones (in this example, this small area is considered to be less than 1‰ of the pavement impact area). The pavement impact area of ​​this airport is 2,460,000 m². 2 Therefore, 1‰ of it is 2460m. 2 ).

[0037] Open ArcToolBox—Data Management Tools—Cartography and Generalization—Eliminate.

[0038] Export the surface vector attribute table data, which combines the spatial partitions of free swelling rate and montmorillonite content, to Excel for easier data processing in the next step. Based on the above reclassification definition, non-expanding soil is designated as 1, weakly expansive soil as 2, and moderately expansive soil as 3. Utilizing the rules of the reclassification definition, different data processing methods are selected based on different reclassification definitions to choose a value from free swelling rate and montmorillonite content as the final swelling potential code, thus determining the swelling potential, as shown in Table 3.

[0039] Open ArcToolBox—Conversion Tools—Excel—Table to Excel.

[0040] Table 3 Spatial Zoning Merged Attribute Table of Free Expansion Rate and Montmorillonite Content

[0041] The processed Excel file is converted into a table, which is then linked to a polygon vector file that combines the free expansion rate and montmorillonite content. Finally, as shown... Figure 9 As shown, the expansion potential spatial partition can be determined based on the expansion potential discrimination results after data processing.

[0042] Open ArcToolBox—Conversion Tools—Excel—Excel to Table.

[0043] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0044] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0045] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A method for spatial partitioning of swelling potential of pavement area expansive soil, characterized in that, include: Determine the expansion potential discrimination index, and based on the expansion potential discrimination index, perform cluster anomaly data screening on the expansive soil at the borehole sampling points in the pavement influence area to obtain the cluster anomaly data screening results of the expansive soil. The pavement impact zone to be divided is determined, and the discrimination index data of the pavement impact zone is determined based on the cluster anomaly data screening results of the expansive soil. Based on the discrimination index data, the expansion potential of the pavement influence area is divided into the first partition, and the expansion potential first partition result is obtained. Based on the first partitioning result of the expansion potential, a reclassification is performed to obtain the expansion potential reclassification result of the pavement influence area. Using a reclassification tool, the first partition result of the expansion potential is reclassified to determine the reclassification code of each partition result; The expansion potential reclassification results of the pavement influence area were converted into surface vector data using a conversion tool. The surface vector data determined by each expansion potential discrimination index are combined to generate a surface vector file; The expansion potential reclassification results are optimized by expansion potential partitioning, and the expansion potential partitioning optimization results of the pavement influence area are output. The process of determining the swelling potential discrimination index, and performing clustering anomaly data screening on the expansive soil at borehole sampling points in the pavement influence area based on the swelling potential discrimination index to obtain the clustering anomaly data screening results for the expansive soil, further includes: The free swelling rate and montmorillonite content of expansive soil were obtained as indicators for judging swelling potential. The cluster radius for clustering expansive soil is determined based on the threshold of the expansibility potential discrimination index, and each cluster center is determined. Based on the cluster radius and cluster center, K-means clustering was used to filter out cluster anomalies in expansive soil, and outliers were removed to obtain the results of the cluster anomaly filtering for expansive soil. The step of optimizing the expansion potential reclassification result by partitioning the expansion potential and outputting the optimized expansion potential partitioning result of the pavement influence area further includes: Obtain the discriminant index data of the expansion potential reclassification result, perform single-index partitioning integration of the expansion potential of the pavement influence area on the discriminant index data, and optimize the merged surface vector file; Based on the elimination function of the data management tool, the local small-area expansion potential zone of the pavement influence area is eliminated; Export the surface vector attribute table data of the merged surface vector file; Based on the reclassification code, the expansion potential discrimination index is selected as the final expansion potential code to determine the expansion potential discrimination result; The exported form of the processed surface vector attribute table data is converted into a table and associated with the surface vector file after the expansion potential discrimination index is merged. The expansion potential zoning optimization results of the pavement influence area are determined based on the expansion potential discrimination results after data processing.

2. The method for spatial zoning of swelling potential of pavements area swelling soils according to claim 1, characterized by that, The process of determining the pavement impact zone to be partitioned, and determining the discriminant index data of the pavement impact zone based on the clustering anomaly data screening results of the expansive soil, further includes: Based on on-site measurement data and terrain design data, determine the pavement influence area to be divided, and import the pavement influence area into ArcGIS to generate a pavement influence area vector file; Obtain borehole sampling points in the affected area of ​​the pavement, and obtain the discrimination index data for each borehole sampling point.

3. The method for spatially zoning the expansion potential of expansive soil in pavement areas according to claim 2, characterized in that, The step of performing expansion potential first-level partitioning on the pavement influence area based on the discriminant index data to obtain the expansion potential first-level partitioning result further includes: Based on the aforementioned expansion potential discrimination index, expansion potential levels are distinguished, and an expansion potential level table is determined. Based on the expansion potential level table and the discrimination index data, the Kriging spatial interpolation method is used to perform expansion potential first partitioning on the pavement influence area to obtain the pavement influence area spatial interpolation results under each expansion potential discrimination index, which are used as the expansion potential first partitioning results.

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