Highway foundation pit slope collapse detection method, system, equipment and storage medium

Abstract

The present invention relates to the technical field of engineering monitoring data analysis, specifically to a method, system, equipment, and storage medium for detecting collapse of highway foundation pit slopes. The method comprises the following steps: constructing boundary zones by analyzing resistivity data, adjusting modeling granularity by combining the density of fractures with the degree of rock fragmentation, analyzing construction loads and stress responses, evaluating disturbance intensity, extracting displacement data to screen groups of points with consistent directions, predicting the direction of displacement extension, and combining rainfall and infiltration data to detect signs of saturation and establish a saturated diffusion surface. In the present invention, resistivity changes are captured in real time by an electrode array, achieving continuous boundary identification and clarifying differences in structural units. Modeling accuracy is optimized by utilizing the degree of rock fragmentation and the density of fractures. Construction equipment load characteristics are captured and the trend of disturbance stress changes is simultaneously evaluated. Continuous displacement and boundary extension directions are clarified. Saturated accumulation areas are identified by combining rainfall evaporation data and rock and soil permeability characteristics, thereby improving the reliability of locating potential slope collapse risks.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering monitoring data analysis, and in particular to a method, system, equipment and storage medium for detecting highway foundation pit slope collapse. Background Art

[0002] The technical field of engineering monitoring data analysis includes the entire process of collecting, processing, modeling and analyzing monitoring data on the operating status of various engineering structures, aiming to achieve dynamic grasp of engineering status and risk warning, including sensor collection, data transmission, information fusion, anomaly identification and data mining of monitoring information. Through comprehensive calculation and analysis of spatiotemporal data, potential diseases or instability trends of structures can be identified, and it can be applied to geological disaster prevention, urban infrastructure safety assessment, transportation engineering safety assurance, etc. The engineering monitoring data analysis system relies on the integration of multiple technologies, such as sensor networks for measuring physical parameters, monitoring models based on time series analysis, structural deformation trend judgment algorithms and risk indicator calculation systems. It is an important part of ensuring the safety of major projects, among which, The highway foundation pit slope collapse detection method refers to a technical method for real-time monitoring and anomaly identification of unstable phenomena such as slippage and collapse that may occur in foundation pits and slopes formed along highway excavations during natural or construction processes. It involves surface displacement monitoring, slope deformation trend extraction, risk factor identification, and collapse precursor identification data modeling. Specifically, by deploying surface inclinometers, three-dimensional laser scanning systems, and image monitoring equipment, the structural geometry change and crack evolution information are collected. Based on the continuously changing data, the potential collapse location and time window are identified through threshold calculation method, acceleration evolution method, and curvature change analysis method. The method uses quantitative analysis sub-category means to construct a multi-source heterogeneous data fusion model to realize slope stability analysis and pre-collapse identification process based on physical field quantities.

[0003] Traditional highway foundation pit slope collapse detection technology uses equipment to directly measure the surface phenomena of structural deformation and crack changes. It is limited to responsive tracking of existing deformations, and it is difficult to timely detect abnormal changes in internal microstructures and the state of infiltration accumulation. It lacks in-depth correlation analysis of deformation causes. Under complex working conditions such as continuous rainfall or construction disturbances, it is difficult to identify collapse trends in advance by relying solely on surface data monitoring, resulting in delayed warnings and preventive measures, increasing the risk of engineering accidents. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a highway foundation pit slope collapse detection method, system, equipment and storage medium.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a method for detecting collapse of a highway foundation pit slope, comprising the following steps:

[0006] S1: Analyze the resistivity data collected from the vertical and horizontal electrode arrays using an electrode array. Based on the differences between adjacent measurement points, select candidate structural boundary nodes. Combined with the slope direction of the terrain surface, identify nodes with consistent directions, construct a continuous boundary zone, and generate a boundary difference distribution map.

[0007] S2: calling the boundary difference distribution map, analyzing the cross density of multiple unit fault lines in the area, adjusting the modeling granularity of multiple units based on the degree of rock fragmentation in the area, constructing a slope area model, and mapping the data collected by the stress sensor into the model;

[0008] S3: Using the slope regional model, analyze the construction behavior in the target area, identify the load parameters and spatial action coordinates generated by the construction equipment, evaluate the change trend of the stress response of each monitoring point, evaluate the disturbance intensity of various construction behaviors, and establish disturbance change trend information;

[0009] S4: Call the disturbance change trend information, extract the continuous displacement direction data recorded by the displacement monitoring points, calculate the displacement direction angle between adjacent monitoring points, screen the point group with consistent displacement direction, and compare it with the trend information of the continuity boundary zone to predict the displacement extension direction and establish the slope deformation extension line.

[0010] As a further solution of the present invention, the boundary difference distribution map includes the structural boundary position, resistivity change direction, and resistivity difference amplitude; the slope area model includes the structural partition range, modeling granularity level, and sensor mapping unit; the disturbance change trend information includes the construction load application position, the monitoring point stress change trend, and the disturbance impact spatial range; the slope deformation extension line includes the displacement direction path, the fracture direction matching section, and the deformation trend continuous area.

[0011] As a further solution of the present invention, the step of obtaining the boundary difference distribution map is specifically as follows:

[0012] S111: Acquire an electrode array, collect resistivity measurement data of each node of the vertically and horizontally arranged electrodes, number the measurement points and mark their spatial positions to form a basic resistivity sequence of the measurement points, and obtain archived data of the resistivity measurement points;

[0013] S112: extracting resistivity differences and spatial distances between adjacent measuring points based on the resistivity measuring point archive data, calculating difference significance scores for each pair of measuring points, screening candidate structural boundary nodes, and outputting a set of structural interface candidate nodes;

[0014] S113: Based on the structural interface candidate set, compare the angle between the resistivity change direction of each candidate node and the corresponding terrain slope direction, identify nodes with consistent directions, construct a continuous boundary zone, and obtain a boundary difference distribution map.

[0015] As a further solution of the present invention, the steps for obtaining the slope area model are specifically as follows:

[0016] S211: Calling the boundary difference distribution map, dividing the target area into multiple structural units, extracting the spatial distribution of fault lines in each unit, analyzing the intersection angles and number of intersections between adjacent fault lines, and evaluating the degree of structural interference in the unit based on the fault line distribution density to obtain a fault structure intersection distribution map;

[0017] S212: According to the fracture structure intersection distribution map, obtain the fracture intersection, rock layer fragmentation degree and structural feature deviation degree data corresponding to each unit, calculate the modeling particle size grade score of each unit, and obtain the modeling particle size grade distribution map;

[0018] S213: Based on the modeling particle size distribution map, a three-dimensional structural model of the target slope area is established, the structural model coordinates are called, and the real-time data of the corresponding sensors are aligned and mapped to the three-dimensional structural grid nodes in combination with the layout position information of the stress sensors to obtain the slope area model.

[0019] As a further solution of the present invention, the step of obtaining the disturbance change trend information is specifically as follows:

[0020] S311: calling the slope area model, collecting equipment load parameters and spatial action coordinates of construction behavior, recording the construction behavior time series, and establishing a load action information group based on the stress monitoring point distribution;

[0021] S312: Analyze the response change sequence of each stress monitoring point during the continuous construction period based on the load action information group, calculate the stress change amplitude, change rate and temporal and spatial correlation, and establish a stress response change trend group;

[0022] S313: Based on the load action information group and the stress response change trend group, the influence range of each type of construction behavior, the average load amplitude, the stress change rate, and the spatial discreteness of the monitoring points are used to calculate the disturbance intensity score, evaluate the disturbance intensity of various construction behaviors, and establish disturbance change trend information.

[0023] As a further solution of the present invention, the step of obtaining the slope deformation extension line is specifically as follows:

[0024] S411: calling the disturbance change trend information, collecting continuous displacement direction data recorded by multiple displacement monitoring points in the area, calculating the coordinate difference vectors between each monitoring point at consecutive moments, extracting the continuous displacement directions, and generating a displacement direction angle sequence;

[0025] S412: Obtain the displacement direction angle between each adjacent monitoring point according to the displacement direction angle sequence, select point groups with consistent displacement directions, and obtain point group selection records;

[0026] S413: Filtering records according to the point group, obtaining an extension vector of the point group, and comparing it with the strike information of the continuity boundary zone, predicting the displacement extension direction of the slope area, and establishing a slope deformation extension line.

[0027] As a further embodiment of the present invention, the method further comprises:

[0028] S5: Based on the slope deformation extension line, obtain monitoring data on rainfall and evaporation over multiple consecutive cycles in the construction area. Combined with surface permeability characteristics and rock and soil water holding capacity data, analyze the time difference and cumulative gap between rainfall input and ground water holding changes, identify areas of mismatch between the continuous permeability accumulation state and the slope absorption rate, detect signs of saturation accumulation, and establish an environmentally induced saturation diffusion surface.

[0029] The environmentally induced saturated diffusion surface includes rainfall infiltration distribution, rock and soil water retention variation range, and saturated accumulation response area.

[0030] As a further solution of the present invention, the step of obtaining the environment-induced saturated diffusion surface is specifically as follows:

[0031] S511: According to the slope deformation extension line, obtain the monitoring data of rainfall and evaporation in the construction area for multiple consecutive periods, calculate the difference between rainfall and evaporation in multiple time periods, and generate a moisture difference sequence;

[0032] S512: By analyzing the surface permeability characteristics and rock and soil water holding capacity of the target slope in combination with the moisture difference sequence, the cumulative difference between rainfall input and ground water holding capacity per unit time is evaluated to obtain a water permeability characteristic analysis result;

[0033] S513: Based on the seepage characteristic analysis results, identify the mismatch area between the continuous seepage accumulation state and the slope absorption rate, detect the accumulation signs of the saturation process, and establish the environment-induced saturation diffusion surface.

[0034] A highway foundation pit slope collapse detection system is used to implement the above-mentioned highway foundation pit slope collapse detection method, and the system includes:

[0035] The boundary identification module uses the vertical and horizontal electrode data collected by the electrode array to analyze the resistivity difference of each monitoring point, select the structural boundary nodes whose spatial coordinates are consistent with the slope direction, construct a continuous boundary zone, and generate a boundary difference distribution map;

[0036] The structural modeling module performs a spatial density analysis on the cross-density of the fault lines within each unit based on the boundary difference distribution map, adjusts the spatial division granularity of multiple units based on the degree of rock stratum fragmentation, spatially maps the monitoring data continuously collected by the stress sensor, and obtains a slope area model based on the three-dimensional structural model of the slope area;

[0037] Based on the slope area model, the disturbance assessment module collects construction equipment operation data within the target area, identifies load parameters and action coordinates, and performs time-series correlation between construction loads and stress monitoring point response data. It analyzes the disturbance effects of various types of construction activities within the spatial range, assesses the disturbance intensity of construction activities, and establishes disturbance change trend information.

[0038] Based on the disturbance change trend information, the deformation tracking module extracts the continuous directional change records of multiple displacement monitoring points in the area, calculates the spatial direction angle between adjacent monitoring points, selects point groups with consistent directions, and combines the direction of the continuous boundary zone to predict the displacement extension direction and establish the slope deformation extension line;

[0039] The infiltration and diffusion module collects rainfall and evaporation at each monitoring point within a continuous period based on the slope deformation extension line. Combined with the surface infiltration characteristics and rock and soil water holding capacity parameters, it analyzes the cumulative difference between rainfall input and water holding changes within the time period, locates the mismatch area between the infiltration accumulation rate and the water holding rate, detects signs of accumulation of the saturation process, and establishes the environment-induced saturation diffusion surface.

[0040] On the other hand, a highway foundation pit slope collapse detection device is provided, which includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned highway foundation pit slope collapse detection methods is implemented.

[0041] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for detecting highway foundation pit slope collapse.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are:

[0043] In the present invention, resistivity changes are captured in real time through an electrode array to achieve continuous boundary identification and clarify the differences in structural units. The degree of rock fragmentation and the dense distribution of fractures are used to optimize modeling accuracy, accurately capture the load characteristics of construction equipment and simultaneously evaluate the trend of disturbance stress changes, clarify the continuous displacement and boundary extension direction, and combine rainfall evaporation data and rock and soil permeability characteristics to accurately identify saturated accumulation areas, effectively improving the reliability of locating potential slope collapse risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0045] Figure 2 A flow chart for obtaining a boundary difference distribution map of the present invention;

[0046] Figure 3 Obtaining a flow chart for the slope area model of the present invention;

[0047] Figure 4 This is a flow chart for obtaining disturbance change trend information of the present invention;

[0048] Figure 5 A flow chart for obtaining a slope deformation extension line according to the present invention;

[0049] Figure 6 This is a flow chart for obtaining the environment-induced saturation diffusion surface of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0052] Example 1: Please refer to Figure 1 The present invention provides a technical solution: a method for detecting collapse of a highway foundation pit slope, comprising the following steps:

[0053] S1: Analyze the resistivity data collected from the vertical and horizontal electrode arrays using an electrode array. Based on the differences between adjacent measurement points, select candidate structural boundary nodes. Combined with the slope direction of the terrain surface, identify nodes with consistent directions, construct a continuous boundary zone, and generate a boundary difference distribution map.

[0054] S2: Call the boundary difference distribution map to analyze the density of cross-sectional fault lines of multiple units in the area. Combined with the degree of rock fragmentation in the area, adjust the modeling granularity of multiple units, build a slope area model, and map the data collected by the stress sensor into the model.

[0055] S3: Using the slope regional model, analyze the construction behavior in the target area, identify the load parameters and spatial action coordinates generated by the construction equipment, evaluate the change trend of the stress response of each monitoring point, evaluate the disturbance intensity of various construction behaviors, and establish disturbance change trend information;

[0056] S4: Call the disturbance change trend information, extract the continuous displacement direction data recorded by the displacement monitoring points, calculate the displacement direction angle between adjacent monitoring points, select the point group with consistent displacement direction, and compare it with the trend information of the continuous boundary zone to predict the displacement extension direction and establish the slope deformation extension line;

[0057] S5: Based on the slope deformation extension line, obtain monitoring data on rainfall and evaporation in the construction area for multiple consecutive cycles. Combined with the surface permeability characteristics and rock and soil water holding capacity data, analyze the time difference and cumulative gap between rainfall input and stratum water holding changes, identify the mismatch areas between the continuous permeability accumulation state and the slope absorption rate, detect signs of accumulation of the saturation process, and establish the environment-induced saturation diffusion surface.

[0058] The boundary difference distribution map includes the structural boundary position, resistivity change direction, and resistivity difference amplitude. The slope area model includes the structural partition range, modeling granularity level, and sensor mapping unit. The disturbance change trend information includes the construction load application position, the stress change trend of the monitoring point, and the spatial range of the disturbance impact. The slope deformation extension line includes the displacement direction path, the fracture direction matching section, and the deformation trend continuous area. The environment-induced saturated diffusion surface includes the rainfall infiltration distribution, the rock and soil water retention change range, and the saturated accumulation response area.

[0059] See also Figure 2 , the steps for obtaining the boundary difference distribution map are as follows:

[0060] S111: Acquire an electrode array, collect resistivity measurement data of each node of the vertically and horizontally arranged electrodes, number the measurement points and mark their spatial positions to form a basic resistivity sequence of the measurement points, and obtain archived data of the resistivity measurement points;

[0061] When acquiring the electrode array, the electrode array was first laid out in the target slope area in a grid pattern with a vertical and horizontal spacing of 1.5 m. Eight groups of vertical electrode nodes were set in the depth direction and 12 groups of nodes were set in the horizontal direction, forming a total of 96 cross-measurement points. Each measurement point was numbered and its three-dimensional spatial coordinates were recorded. The numbering sequence was extended horizontally from the left end of the top of the upslope to the right end of the bottom of the slope, forming a data structure matrix with a numbering rule. At the same time, a resistivity imager was used to perform a complete resistivity acquisition of all electrode measurement points. The current injection interval was set to 5 seconds, and the duration of each injection was 0.5 seconds. The measured resistivity value of each group of measurement points was recorded in sequence. After the acquisition was completed, the raw resistivity data was associated with the measurement point number and combined with the coordinate values ​​recorded by the GNSS positioning device to establish the basic data sequence of the resistivity measurement point. Subsequently, the resistivity values ​​were archived in a two-dimensional matrix according to the measurement point number. Each measurement point recorded a set of numbers, three-dimensional coordinates, raw resistivity values, and acquisition timestamps. Finally, a complete resistivity measurement point archive data set was formed for subsequent analysis.

[0062] S112: Based on the archived data of resistivity measurement points, the resistivity difference and spatial distance between adjacent measurement points are extracted using the formula:

[0063] ;

[0064] Calculate the difference significance score of each pair of measurement points, screen the candidate structural boundary nodes, and output the candidate set of structural interface;

[0065] in, For the The difference significance score of the test points, For the The normalized value of the resistivity difference between the measuring points is obtained by extracting the original resistivity values ​​of the pair of measuring points and calculating their difference, and then dividing it by the range of the resistivity difference of all measuring points. For the The normalized value of the spatial distance between the measuring points is obtained by obtaining the three-dimensional spatial coordinates of the pair of measuring points to calculate the Euclidean distance, and then normalizing all the distance data. For the For the moisture content of the area between the measuring points, the average moisture content value recorded by the humidity sensors deployed at the grid locations where the measuring points are located is used. For the The slope value of the area between the measuring points is calculated based on the terrain elevation data on the projected line segment between the measuring points. It is dimensionless. is the total number of measurement point pairs, k represents the corresponding measurement point pair number;

[0066] According to the archived data of resistivity measurement points, extract any two adjacent measurement point pairs The resistivity difference , the calculation method is: for the measuring point Extract their resistivity values ​​respectively , the difference between the two is , and then normalize it to get , the distance calculation part extracts the three-dimensional coordinates of the corresponding measuring points , the Euclidean distance is , and then normalize to get , humidity value The humidity sensor embedded in the grid where the pair of measuring points is located collects the data and records the average humidity value per unit time. By obtaining the elevation change on the projection line segment between two measuring points Horizontal distance Calculated, , after dimensionless conversion, it becomes the input value, and then the above parameters are substituted into the following formula:

[0067] ;

[0068] Taking a specific example, a pair of measuring points is numbered (k=5), and the corresponding measuring point resistivity is , ,but:

[0069] ;

[0070] In the sample dataset ,have to:

[0071] ;

[0072] coordinate , the distance is:

[0073] ;

[0074] During normalization, if ,have to:

[0075] ;

[0076] The average value of the regional moisture content sensor is , the slope value is , substituting it into the above formula, we get:

[0077] ;

[0078] When the denominator is known to add up to 134.2, we have:

[0079] ;

[0080] Among them, the difference significance score refers to the multi-factor normalized response intensity calculated for any two adjacent electrode measuring points. It is a probabilistic quantitative indicator used to characterize whether the measuring point pair is located in the potential structural boundary area. It is used to characterize the relative concentration of the mutation trend in the resistivity spatial field, and then used to construct a continuous path identification mechanism for the boundary line. In actual engineering applications, the measuring point pairs with higher scores mean that they have heterogeneous properties in structure, morphology, and hydrological three-dimensional structure, and are thus preferentially retained as candidate boundary nodes. Finally, the spatial distribution of the score sequence constitutes the basis of the entire boundary difference distribution map, which is used for the subsequent determination of modeling granularity, disturbance direction and structural evolution path. Through the above calculations, all measuring point pairs are processed in the same way to extract the significance score. The node pairs of compose the candidate boundary set.

[0081] Table 1 Resistivity measurement point normalization and significance score table:

[0082] ;

[0083] Table 1 lists the main physical parameters of different measurement point pairs and the calculated significance scores. The scores can be used to screen candidate nodes of the structural interface.

[0084] S113: Based on the set of candidate structural interfaces, the angle between the resistivity change direction of each candidate node and the corresponding terrain slope direction is compared, nodes with consistent directions are identified, a continuous boundary zone is constructed, and a boundary difference distribution map is obtained;

[0085] Based on the candidate set of structural interfaces, the resistivity change direction of each node is extracted one by one, that is, the resistivity change vector direction from the previous measuring point to the current measuring point, and the angle between this vector and the terrain slope direction vector is calculated. , using the space vector angle formula ,in is the resistivity change direction vector, is the slope direction vector, after calculating the angle, extract the vector that satisfies The nodes with the same direction are considered as a node group with the same direction. A boundary path with directional continuity is constructed, and the node group is connected to form a boundary belt. The two-dimensional boundary difference map is drawn based on their spatial distribution relationship. Taking the candidate nodes 5 and 6 after the above screening as an example, the resistivity vector direction is 15 degrees south-east, and the slope direction is calculated from the DEM elevation data to be 10 degrees south-east, then , which meets the continuity judgment conditions and is included in the construction of the continuity boundary path. Finally, all nodes that meet the direction consistency requirements constitute the boundary belt to support subsequent modeling.

[0086] See also Figure 3 , the steps for obtaining the slope area model are as follows:

[0087] S211: Calling the boundary difference distribution map, dividing the target area into multiple structural units, extracting the spatial distribution of fault lines in each unit, analyzing the intersection angles and number of intersections between adjacent fault lines, and evaluating the degree of structural interference within the unit based on the fault line distribution density to obtain a fault structure intersection distribution map;

[0088] When calling the boundary difference distribution map, the target slope area is first divided into several structural units according to the equivalent area. Each unit is a square area of ​​10m×10m, and a total of 64 structural units are divided. The numbering method is marked from S1 to S64 in order from the upper left to the lower right. The boundary zone trend and intensity distribution data identified in the boundary difference distribution map are superimposed on the geological structure layer to extract the spatial distribution data of all fault lines in each unit, including the starting and ending point coordinates, length, trend and dip information of the fault line. The fault lines are numbered and the intersection angle between each pair of fault lines is counted. The intersection angle is calculated as the distance between two fault lines. The absolute value of the angle between line vectors and the number of intersections are realized by calculating the intersection points of the fracture line projections. For example, three fracture lines f1, f2, and f3 are identified in the structural unit S17, where the intersection angle between f1 and f2 is 24°, and the intersection angle between f2 and f3 is 18°. There are two intersections, and the number of intersections is 2. After recording the intersection angles and number of intersections of all units, the fracture density is calculated as the ratio of the total length of the fracture line to the unit area, and all unit density values ​​are normalized. Finally, the average fracture intersection angle, number of intersections, and fracture density of each unit are integrated and output to form a fracture structure intersection distribution map as the input for subsequent particle size grade evaluation.

[0089] S212: According to the fracture structure intersection distribution map, the fracture intersection, rock layer fragmentation and structural feature deviation data corresponding to each unit are obtained using the formula:

[0090] ;

[0091] Calculate the modeling particle size grade score of each unit and obtain the modeling particle size grade distribution map;

[0092] in, is the modeling granularity score of the i-th unit, is the normalized fault line density of the i-th unit, which is obtained by normalizing the ratio of the number of fault lines in the unit to the unit area. is the average normalized fault line density of all units in the slope area. Taking the average, we get is the normalized longitudinal structural offset value of the i-th unit, which is obtained by calculating the offset angle between the fault line and the longitudinal main structural direction in the unit and normalizing it. is the normalized lateral structural offset value of the i-th unit, which is obtained by calculating the offset angle between the fault line and the lateral main structural direction in the unit and normalizing it. is the normalized rock fragmentation degree of the i-th unit, which is obtained by evaluating the integrity index of the core sampling results and performing normalization processing. The index number of the structural unit in the modeling area;

[0093] According to the fracture structure cross-degree distribution map, the first Fracture line density of structural units , for each unit, obtain the parameters by following the steps below: First calculate the fracture line density ,in is the total length of all fracture lines in the unit, For the unit area, all Normalized by minimum and maximum values , and then for all Calculate the average value to get Then, the angle between the strike direction of all fault lines in the unit and the main longitudinal structural direction of the region (set as 20° north-east) was extracted, and the absolute value was averaged and normalized to obtain The same method is used to calculate the normalized angle between the fault line and the horizontal main structural direction (set to be 15° east-southeast): , rock fragmentation degree The rock integrity index is calculated based on the core samples obtained from the geological drilling of the unit. Analysis, such as RQD = 55%, after normalization (Assuming the normalized range 0–1 corresponds to RQD100–0%), substitute the above parameters into the following modeling granularity calculation formula:

[0094] ;

[0095] Taking structural unit S17 as an example, , , , , , substituting into the formula we get:

[0096] ;

[0097] Among them, the modeling granularity grade score is a quantitative expression of the modeling accuracy requirements of each unit in the slope area, which comprehensively considers the complexity of fault line intersection (local structural disturbance risk), structural feature offset intensity (possible geometric anomaly trend) and rock layer integrity (medium continuity reliability). The higher the value, the more dense the grid modeling strategy needs to be adopted in the unit area to improve the region's ability to capture small stress responses and displacement changes; a lower value indicates that the region can appropriately adopt a coarse grid strategy to improve computational efficiency. This scoring mechanism can be directly used for grid division density determination, regional accuracy allocation in the modeling stage, and parameter input basis for subsequent deformation trend and disturbance response prediction. The granularity scores of all structural units are calculated using the same method. , and divide the modeling granularity level according to the value range, such as For low granularity, For medium particle size, For high granularity.

[0098] Table 2 Modeling particle size calculation parameters:

[0099] ;

[0100] As shown in Table 2, each unit obtains the modeling granularity score through unified normalization and vector angle analysis, which serves as the basis for modeling level division.

[0101] S213: Based on the modeled granularity distribution map, a three-dimensional structural model of the target slope area is established. The structural model coordinates are called, and in combination with the layout position information of the stress sensors, the real-time data of the corresponding sensors are aligned and mapped to the three-dimensional structural grid nodes to obtain the slope area model.

[0102] According to the modeling granularity distribution map, the target slope area is divided into three spatial levels according to high-granularity, medium-granularity, and low-granularity units. A three-dimensional modeling tool is used to construct a structural model. The minimum grid unit side length is set to 0.5m for high-granularity areas, 1m for medium-granularity areas, and 2m for low-granularity areas. A unified three-dimensional grid volume is generated, and a unique spatial index is established for each grid node. The coordinates are matched with the stress sensor layout table. The stress sensors arranged inside the structural unit are interpolated and matched to the structural grid nodes according to their spatial coordinates. If the matching accuracy error is less than 0.15m, the grid node is bound, and the stress data collected by the sensor is pushed and updated to the corresponding grid point attributes of the structural model in real time. The completed three-dimensional structural model contains the spatial distribution information of the fracture structure and the dynamic change information of the real-time stress data, forming a complete slope area model.

[0103] See also Figure 4 , the specific steps for obtaining disturbance change trend information are:

[0104] S311: Calling the slope area model, collecting equipment load parameters and spatial action coordinates of the construction behavior, recording the construction behavior time series, and establishing a load action information group based on the distribution of stress monitoring points;

[0105] When calling the slope area model, each construction area unit in the model is first located. Construction logs and equipment operation records are extracted to identify the current construction activity category (such as piling, excavation, and concrete pouring). The real-time positioning coordinates of the construction equipment recorded by the construction monitoring system are retrieved, and their spatial action point sets are recorded. Then, the vertical pressure data applied to the slope area per unit time during actual operation by this type of construction equipment (such as a crawler pile driver) is obtained and averaged as the load parameter value for this type of equipment. For example, during a certain piling operation, the average vertical load applied by the equipment was 27.5 kN. The spatial action coordinates of the equipment were matched to specific grid nodes in the structural model using the GNSS positioning system. The start and end times of the construction were marked as 08:30 and 09:10, respectively. A timestamp sequence was constructed for each construction period, and data such as the construction equipment number, type, load parameter, action coordinates, and time series were combined into a structured data table. Based on the monitoring point layout map, all stress sensors within the construction action area were spatially associated to establish a construction load action information group.

[0106] S312: Analyze the response change sequence of each stress monitoring point during the continuous construction period based on the load action information group, calculate the stress change amplitude, change rate and temporal and spatial correlation, and establish a stress response change trend group;

[0107] Based on the established load action information group, the stress monitoring point numbers and time series stress data in the action area during construction are extracted one by one, the stress change curves of each monitoring point in the three stages before, during and after construction are analyzed, and the change amplitude of the stress response is calculated. , rate of change ,in The stress value is the time interval between the stress peak and the starting point. The spatiotemporal correlation is evaluated by the consistency of the spatial distribution of the change amplitude between the measuring points in different time windows. For example, the stress of sensor S23 in the piling construction area increased from 42.3 kPa to 71.2 kPa from 08:30 to 09:10, with an amplitude of 28.9 kPa, a duration of 40 minutes, and a change rate of 0.7225 kPa / min. The change amplitudes of adjacent points S24 and S25 were 27.5 and 30.2 kPa, respectively. The correlation coefficient analysis was performed on the consistency of the changes between the three points. If the Pearson coefficient is greater than 0.75, it is recorded as a high spatiotemporal correlation. Finally, the change amplitude, rate, and correlation indicators of each monitoring point are classified and recorded according to the construction behavior to establish a stress response change trend group.

[0108] S313: Based on the load action information group and stress response change trend group, using the influence range of each type of construction behavior, average load amplitude, stress change rate, and spatial dispersion of monitoring points, the formula is used:

[0109] ;

[0110] Calculate disturbance intensity scores, evaluate the disturbance intensity of various construction behaviors, and establish disturbance change trend information;

[0111] in, For the The disturbance intensity score of construction-like behavior, For the The normalized value of the impact range area corresponding to the construction behavior is obtained by constructing the convex hull area through the extension boundary of the construction load space action point, calculating the actual area and performing maximum and minimum normalization processing. For the The normalized value of the average load amplitude corresponding to the type of construction behavior is obtained by collecting the single unit load amplitude value sequence generated by the type of construction equipment in the specified construction stage, taking the average and normalizing it. For the The normalized value of the stress change rate corresponding to the construction behavior is obtained by calculating the slope of the continuous response curve of the stress monitoring points in the affected area and taking the average value, and then performing normalization processing. For the The normalized value of the spatial dispersion of the stress response point under the influence of similar construction behavior is obtained by calculating the spatial dispersion of the point position through the variance of the nearest neighbor distance between the monitoring points and normalizing it. Category number indicating the construction activity;

[0112] Based on the load action information group and stress response change trend group, each type of construction behavior (numbered ) Count the scope of its impact area, construct the convex hull area of ​​the construction space action coordinates and calculate the area. For example, the convex hull area constructed by the coordinate distribution of the action point of type 1 construction (concrete pouring) is 168m². If the maximum and minimum normalized range is 50m² to 300m², the normalized result is: The normalization of the average load amplitude is similar. Assuming that the average unit load of this type of equipment is 12.8kN and the normalization range is 5–35kN, we get: ; Normalize the stress change rate. Assuming that its average change rate is 0.51 kPa / min and the maximum and minimum values ​​range from 0.1 to 1.2, then: The spatial dispersion is calculated by the variance of the nearest neighbor distance of the five sensors in the affected area, which is set to 0.042m², and the maximum and minimum normalized range is 0.01–0.12, which is: ; Substitute the above normalized results into the disturbance intensity formula:

[0113] ;

[0114] Calculating ,therefore:

[0115] ;

[0116] Among them, the disturbance intensity score is a numerical indicator that quantitatively describes the ability of each type of construction behavior to cause stress field disturbances in a specific area. It is a multi-factor fusion assessment parameter. The higher the value, the more significant the disturbance effect of this type of construction behavior under conditions of wide spatial range, strong load, sharp response of monitoring points, and high local concentration. It is used to assist in distinguishing the degree of interference of various construction methods on the stability risk of slope structures, and provides quantitative support for construction zoning management, interference impact warning, and stability control. In engineering applications, the disturbance intensity score will serve as the main driving quantity in the trend chart to mark the disturbance section and provide input basis for subsequent time series prediction, dynamic control and other tasks.

[0117] Table 3 Construction behavior disturbance intensity parameters and calculation results:

[0118] ;

[0119] As shown in Table 3, the disturbance intensity scores corresponding to different construction behaviors This can be used to compare the disturbance capacity of various behaviors on the slope area. The results show that piling has a stronger disturbance influence and its calculated score is the highest, providing a reference for the disturbance level for the next step of deformation prediction.

[0120] See also Figure 5 , the specific steps for obtaining the slope deformation extension line are:

[0121] S411: Calling disturbance change trend information, collecting continuous displacement direction data recorded by multiple displacement monitoring points in the area, calculating the coordinate difference vectors between consecutive moments of each monitoring point, extracting the continuous displacement direction, and generating a displacement direction angle sequence;

[0122] When calling for disturbance change trend information, we first extract real-time displacement data from multiple displacement monitoring points located within the slope area. In the specific implementation process, displacement sensors, such as GNSS equipment, perform continuous coordinate measurements every 5 minutes on site. For example, four adjacent monitoring points numbered P1, P2, P3, and P4 within the area are continuously recorded at three time points during a certain construction period: 、 、 , the coordinate change of monitoring points is as follows: the coordinates of monitoring point P1 change from (20.0m, 40.0m) to (20.3m, 40.1m), and then to (20.5m, 40.2m), and so on for other points. For each monitoring point, calculate the coordinate difference vector of adjacent moments, for example, monitoring point P1 at time node to The coordinate difference vector between them is (0.3m, 0.1m). The continuous displacement direction of the monitoring point is then extracted, and the direction angle is calculated based on the true north direction in the coordinate system. For example, the direction angle corresponding to the above vector is 18.4° north-east. Similarly, the continuous displacement direction of the coordinate difference vectors of all monitoring points at each time is calculated to form a complete displacement direction data set. The angle difference between each pair of displacement direction vectors between adjacent time nodes is then calculated and organized into a continuous displacement direction angle sequence for subsequent analysis.

[0123] S412: Obtain the displacement direction angle between each adjacent monitoring point according to the displacement direction angle sequence, select point groups with consistent displacement directions, and obtain point group selection records;

[0124] The displacement direction angle sequence is used for further analysis, and the angles between the displacement directions of adjacent monitoring points are obtained one by one. The spatial vector angle calculation method is used. For example, taking monitoring points P1 and P2 as examples, the displacement directions of P1 in adjacent time periods are 18.4° and 19.0° north-east, respectively, and those of P2 are 18.7° and 19.3° north-east, respectively. The displacement direction angle differences of adjacent points are |18.4°-18.7°|=0.3° and |19.0°-19.3°|=0.3°, respectively, indicating that the displacement directions between the two points are highly consistent. The calculation method is used to calculate the displacement direction of the two points. The method is applied to the entire monitoring point network, and an angle threshold is used for consistency judgment. The threshold is set to 5°. When the angle difference is less than the threshold, the displacement direction is considered to be consistent. For example, among the monitoring points in the entire area, if the angles between P1, P2, and P3 are 0.3° and 2.5° respectively, they are included in the same group to form a monitoring point group with the same movement trend; if the direction angle of monitoring point P4 is 6.5°, then this point is processed separately and not included in this point group. Finally, all monitoring points are classified, and a complete displacement direction consistency point group screening record is output for subsequent displacement direction trend analysis.

[0125] S413: Filtering records based on the point group, obtaining the extension vector of the point group, and comparing it with the strike information of the continuity boundary zone, predicting the displacement extension direction of the slope area, and establishing the slope deformation extension line;

[0126] Based on the point group records with consistent displacement directions obtained through screening, the direction of displacement extension in the slope area is further determined. First, the overall extension vector of each point group is calculated. The calculation method is to average the continuous displacement vectors of each monitoring point in each point group. For example, the displacement vectors of the point group with consistent displacement directions (P1, P2, P3) are averaged to form an extension vector (0.32m, 0.12m). This extension vector is then converted into a direction angle. Using the example data, assuming the average extension vector is (0.32m, 0.12m), the direction angle is 20.6° north-east. Next, the aforementioned continuity boundary zone trend information is called. For example, the continuity boundary zone trend is 22° north-east. The extension vector is compared with the boundary trend information to determine the consistency of the displacement extension trend. The angle difference is calculated to be |20.6°-22°|=1.4°, indicating that the displacement extension direction is highly consistent with the boundary zone trend. Therefore, it is confirmed that the displacement extension direction of this group is along the boundary zone direction. Based on this, a slope deformation extension line map is established to complete the regional displacement trend analysis and subsequent deformation warning.

[0127] Table 4 Calculation table of continuous displacement direction and angle of monitoring points:

[0128] ;

[0129] As shown in Table 4, the calculation results of the displacement difference vector, displacement direction angle and angle difference of the monitoring points clearly reflect the spatial consistency of the regional displacement direction, which is convenient for further extension direction judgment and slope deformation line drawing.

[0130] See also Figure 6 , the specific steps for obtaining the environment-induced saturated diffusion surface are:

[0131] S511: Based on the slope deformation extension line, obtain the monitoring data of rainfall and evaporation in the construction area for multiple consecutive periods, calculate the differences in rainfall and evaporation in multiple time periods, and generate a moisture difference sequence;

[0132] According to the slope deformation extension line, the rainfall and evaporation data of multiple consecutive cycles (each cycle is 7 days) recorded by the meteorological monitoring stations located in the key deformation area were extracted. The cycle was selected as four weeks (28 days) during the critical construction period. The monitoring data included daily rainfall (mm) and evaporation (mm). For example, the rainfall for 7 consecutive days in the first cycle was 6.2mm, 3.4mm, 7.1mm, 5.0mm, 2.3mm, 0mm, and 4.8mm respectively, and the evaporation was 3.5mm, 2.1mm, 3.2mm, 4.0mm, 5.0mm, 6.1mm, and 4.5mm respectively. Calculate the difference between daily rainfall and evaporation in each cycle. For example, the difference between rainfall and evaporation on the first day of the first cycle is 6.2mm-3.5mm=2.7mm. Calculate daily to obtain the daily moisture surplus and deficit value, and then summarize it into the cumulative moisture difference value of each cycle. For example, the cumulative moisture difference in the first cycle is 4.5mm. Calculate the cumulative value of each cycle in turn to form a complete 28-day periodic moisture difference sequence, which serves as an important basis for subsequent infiltration analysis.

[0133] S512: Combined with the moisture difference sequence, by analyzing the surface permeability characteristics and rock and soil water holding capacity of the target slope, the cumulative difference between rainfall input and ground water holding capacity per unit time is evaluated to obtain the water permeability analysis results;

[0134] Combined with the moisture difference sequence generated above, the surface permeability characteristics and rock and soil water holding capacity of the target slope area are further analyzed. First, the permeability coefficient of the target area is determined using the on-site in-situ permeability test and indoor soil test data. For example, the on-site permeability test results show that the permeability coefficient of the sandy clay on the surface of the slope is The permeability coefficient of deep rock and soil is about At the same time, the rock and soil water holding capacity was analyzed based on core tests obtained through drilling. For example, porosity measurements showed a porosity of 34% for the surface soil and 12% for the deep rock mass. Combined with pore water pressure sensor monitoring data, the gap between rainfall infiltration input per unit time and actual water holding capacity was evaluated. Taking each cycle as the analysis unit, for example, the cumulative moisture difference in the first cycle was 4.5 mm. Considering a surface runoff loss coefficient of 0.3, the actual rainfall entering the soil layer was approximately 3.15 mm. Comparing the permeability coefficient and water holding capacity, the actual stratum storage capacity was calculated to be approximately 2.5 mm, resulting in a cumulative gap of 0.65 mm, which was recorded as the core parameter for the permeability analysis of this cycle. After four cycles, continuous cumulative gap data was generated and output as the slope seepage characteristic analysis results.

[0135] S513: Based on the results of the seepage characteristics analysis, identify the mismatch between the continuous seepage accumulation state and the slope absorption rate, detect the accumulation signs of the saturation process, and establish the environmentally induced saturation diffusion surface;

[0136] Based on the results of the slope seepage analysis, a regional assessment of the cumulative discrepancy data was conducted to further analyze the degree of match between the seepage accumulation state and the actual slope absorption rate. Using data from the first cycle (0.65 mm), the second cycle (1.1 mm), the third cycle (1.8 mm), and the fourth cycle (2.2 mm), the authors determined that the regional seepage accumulation trend gradually increased, while the actual slope absorption rate showed a significant downward trend with increasing depth, indicating a significant mismatch in some areas. Pore water pressure data at different depths of the slope were evaluated. For example, the pore water pressures monitored at depths of 3 m, 5 m, and 7 m were 15 kPa, 24 kPa, and 33 kPa, respectively. The continued increase in pressure indicates significant signs of regional seepage saturation accumulation. This is particularly true in deep areas with low local permeability coefficients, where water cannot diffuse quickly. The rate of pore water pressure increase significantly exceeds the absorption capacity of the formation, identifying these areas as potential saturation accumulation zones. Combining this information, areas of significant mismatch were identified, their spatial distribution outlines clarified, and a complete environmentally induced saturation diffusion surface established, serving as a key basis for slope stability analysis and safety monitoring.

[0137] Table 5 Slope seepage accumulation and pore water pressure monitoring data:

[0138] ;

[0139] As shown in Table 5 , the increasing trend of the cumulative gap and pore water pressure in each period of the slope clearly reflects the gradual intensification of regional seepage saturation, supports the process of establishing the environmentally induced saturation diffusion surface, and clearly shows the existence of obvious mismatch and water accumulation in the slope area.

[0140] Highway foundation pit slope collapse detection system, the highway foundation pit slope collapse detection system is used to implement the above-mentioned highway foundation pit slope collapse detection method, the system includes:

[0141] The boundary identification module uses the vertical and horizontal electrode data collected by the electrode array to analyze the resistivity difference of each monitoring point, select the structural boundary nodes whose spatial coordinates are consistent with the slope direction, construct a continuous boundary zone, and generate a boundary difference distribution map;

[0142] The structural modeling module performs spatial density analysis on the cross-density of fault lines within each unit based on the boundary difference distribution map. It adjusts the spatial division granularity of multiple units based on the degree of rock fragmentation, spatially maps the monitoring data continuously collected by the stress sensors, and obtains the slope area model based on the three-dimensional structural model of the slope area.

[0143] The disturbance assessment module, based on the slope area model, collects construction equipment operation data within the target area, identifies load parameters and action coordinates, and performs time-series correlation between construction loads and stress monitoring point response data. It analyzes the disturbance impact of various types of construction activities within a spatial range, assesses the disturbance intensity of construction activities, and establishes disturbance change trend information.

[0144] The deformation tracking module extracts the continuous directional change records of multiple displacement monitoring points in the area based on the disturbance change trend information, calculates the spatial direction angle between adjacent monitoring points, selects point groups with consistent directions, and combines the direction of the continuous boundary zone to predict the displacement extension direction and establish the slope deformation extension line;

[0145] The infiltration and diffusion module collects rainfall and evaporation at each monitoring point within a continuous period based on the slope deformation extension line. Combined with the surface infiltration characteristics and rock and soil water holding capacity parameters, it analyzes the cumulative difference between rainfall input and water holding changes within the time period, locates the mismatch area between the infiltration accumulation rate and the water holding rate, detects signs of accumulation of the saturation process, and establishes the environment-induced saturation diffusion surface.

[0146] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for detecting collapse of a highway foundation pit slope, characterized in that: The following steps are involved: S1: Analyze the resistivity data collected from the vertical and horizontal electrode arrays using an electrode array. Based on the differences between adjacent measurement points, select candidate structural boundary nodes. Combined with the slope direction of the terrain surface, identify nodes with consistent directions, construct a continuous boundary zone, and generate a boundary difference distribution map. S2: calling the boundary difference distribution map, analyzing the cross density of multiple unit fault lines in the area, adjusting the modeling granularity of multiple units based on the degree of rock fragmentation in the area, constructing a slope area model, and mapping the data collected by the stress sensor into the model; S3: Using the slope regional model, analyze the construction behavior in the target area, identify the load parameters and spatial action coordinates generated by the construction equipment, evaluate the change trend of the stress response of each monitoring point, evaluate the disturbance intensity of various construction behaviors, and establish disturbance change trend information; S4: Call the disturbance change trend information, extract the continuous displacement direction data recorded by the displacement monitoring points, calculate the displacement direction angle between adjacent monitoring points, screen the point group with consistent displacement direction, and compare it with the trend information of the continuity boundary zone to predict the displacement extension direction and establish the slope deformation extension line.

2. The highway foundation pit slope collapse detection method according to claim 1, characterized in that: The steps for obtaining the boundary difference distribution map are specifically as follows: S111: Acquire an electrode array, collect resistivity measurement data of each node of the vertically and horizontally arranged electrodes, number the measurement points and mark their spatial positions to form a basic resistivity sequence of the measurement points, and obtain archived data of the resistivity measurement points; S112: extracting resistivity differences and spatial distances between adjacent measuring points based on the resistivity measuring point archive data, calculating difference significance scores for each pair of measuring points, screening candidate structural boundary nodes, and outputting a set of structural interface candidate nodes; S113: Based on the structural interface candidate set, compare the angle between the resistivity change direction of each candidate node and the corresponding terrain slope direction, identify nodes with consistent directions, construct a continuous boundary zone, and obtain a boundary difference distribution map.

3. The highway foundation pit slope collapse detection method according to claim 2, characterized in that: The steps for obtaining the slope area model are specifically as follows: S211: Calling the boundary difference distribution map, dividing the target area into multiple structural units, extracting the spatial distribution of fault lines in each unit, analyzing the intersection angles and number of intersections between adjacent fault lines, and evaluating the degree of structural interference in the unit based on the fault line distribution density to obtain a fault structure intersection distribution map; S212: According to the fracture structure intersection distribution map, obtain the fracture intersection, rock layer fragmentation degree and structural feature deviation degree data corresponding to each unit, calculate the modeling particle size grade score of each unit, and obtain the modeling particle size grade distribution map; S213: Based on the modeling particle size distribution map, a three-dimensional structural model of the target slope area is established, the structural model coordinates are called, and the real-time data of the corresponding sensors are aligned and mapped to the three-dimensional structural grid nodes in combination with the layout position information of the stress sensors to obtain the slope area model.

4. The highway foundation pit slope collapse detection method according to claim 3, characterized in that: The steps for obtaining the disturbance change trend information are specifically as follows: S311: calling the slope area model, collecting equipment load parameters and spatial action coordinates of construction behavior, recording the construction behavior time series, and establishing a load action information group based on the stress monitoring point distribution; S312: Analyze the response change sequence of each stress monitoring point during the continuous construction period based on the load action information group, calculate the stress change amplitude, change rate and temporal and spatial correlation, and establish a stress response change trend group; S313: Based on the load action information group and the stress response change trend group, the influence range of each type of construction behavior, the average load amplitude, the stress change rate, and the spatial discreteness of the monitoring points are used to calculate the disturbance intensity score, evaluate the disturbance intensity of various construction behaviors, and establish disturbance change trend information.

5. The highway foundation pit slope collapse detection method according to claim 4, characterized in that: The steps for obtaining the slope deformation extension line are specifically as follows: S411: calling the disturbance change trend information, collecting continuous displacement direction data recorded by multiple displacement monitoring points in the area, calculating the coordinate difference vectors between each monitoring point at consecutive moments, extracting the continuous displacement directions, and generating a displacement direction angle sequence; S412: Obtain the displacement direction angle between each adjacent monitoring point according to the displacement direction angle sequence, select point groups with consistent displacement directions, and obtain point group selection records; S413: Filtering records according to the point group, obtaining an extension vector of the point group, and comparing it with the strike information of the continuity boundary zone, predicting the displacement extension direction of the slope area, and establishing a slope deformation extension line.

6. The highway foundation pit slope collapse detection method according to claim 1, characterized in that: The method further comprises: S5: Based on the slope deformation extension line, obtain monitoring data on rainfall and evaporation in the construction area for multiple consecutive cycles. Combined with the surface permeability characteristics and rock and soil water holding capacity data, analyze the time difference and cumulative gap between rainfall input and stratum water holding changes, identify the mismatch areas between the continuous permeability accumulation state and the slope absorption rate, detect signs of accumulation of the saturation process, and establish the environment-induced saturation diffusion surface.

7. The highway foundation pit slope collapse detection method according to claim 6, characterized in that: The steps for obtaining the environment-induced saturated diffusion surface are specifically as follows: S511: According to the slope deformation extension line, obtain the monitoring data of rainfall and evaporation in the construction area for multiple consecutive periods, calculate the difference between rainfall and evaporation in multiple time periods, and generate a moisture difference sequence; S512: By analyzing the surface permeability characteristics and rock and soil water holding capacity of the target slope in combination with the moisture difference sequence, the cumulative difference between rainfall input and ground water holding capacity per unit time is evaluated to obtain a water permeability characteristic analysis result; S513: Based on the seepage characteristic analysis results, identify the mismatch area between the continuous seepage accumulation state and the slope absorption rate, detect the accumulation signs of the saturation process, and establish the environment-induced saturation diffusion surface.

8. A highway foundation pit slope collapse detection system, characterized in that: The system is used to implement the highway foundation pit slope collapse detection method according to any one of claims 1 to 7, and the system includes: The boundary identification module uses the vertical and horizontal electrode data collected by the electrode array to analyze the resistivity difference of each monitoring point, select the structural boundary nodes whose spatial coordinates are consistent with the slope direction, construct a continuous boundary zone, and generate a boundary difference distribution map; The structural modeling module performs a spatial density analysis on the cross-density of the fault lines within each unit based on the boundary difference distribution map, adjusts the spatial division granularity of multiple units based on the degree of rock stratum fragmentation, spatially maps the monitoring data continuously collected by the stress sensor, and obtains a slope area model based on the three-dimensional structural model of the slope area; Based on the slope area model, the disturbance assessment module collects construction equipment operation data within the target area, identifies load parameters and action coordinates, and performs time-series correlation between construction loads and stress monitoring point response data. It analyzes the disturbance effects of various types of construction activities within the spatial range, assesses the disturbance intensity of construction activities, and establishes disturbance change trend information. Based on the disturbance change trend information, the deformation tracking module extracts the continuous directional change records of multiple displacement monitoring points in the area, calculates the spatial direction angle between adjacent monitoring points, selects point groups with consistent directions, and combines the direction of the continuous boundary zone to predict the displacement extension direction and establish the slope deformation extension line; The infiltration and diffusion module collects rainfall and evaporation at each monitoring point within a continuous period based on the slope deformation extension line. Combined with the surface infiltration characteristics and rock and soil water holding capacity parameters, it analyzes the cumulative difference between rainfall input and water holding changes within the time period, locates the mismatch area between the infiltration accumulation rate and the water holding rate, detects signs of accumulation of the saturation process, and establishes the environment-induced saturation diffusion surface.

9. A highway foundation pit slope collapse detection device, characterized in that: The highway foundation pit slope collapse detection equipment includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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