A method and device for testing frost heaving of a subway connecting passage

Abstract

The present application relates to the technical field of low temperature test, in particular to a kind of subway communication passage frost heaving test method and device, comprising: subway communication passage is divided into multiple monitoring areas according to tunnel structure, constructs the displacement field of each monitoring area;Retrieve the displacement of each displacement field at horizontal position and vertical position, in the form of displacement amplitude and wavelength, define the displacement characteristics of each displacement field;Based on the displacement characteristics of each displacement field, check the displacement direction and pressure change of each monitoring area under boundary constraint, construct the local displacement change trend of each monitoring area.Based on the local displacement change trend of each monitoring area, the trend is integrated to each monitoring area, and the integrated displacement distribution trend is obtained.The slope value of each point in displacement distribution trend is used to determine the risk point corresponding to each monitoring area, and the risk transmission path after connecting risk point is used to determine the treatment measures of each monitoring area;It improves the accuracy and efficiency of frost heaving risk identification.

Description

Technical Field

[0001] This invention relates to the field of low-temperature testing technology, specifically a method and apparatus for testing the frost heave of subway connecting passages. Background Technology

[0002] Frost heave is caused by the expansion of soil due to the freezing of water in the soil and the growth of ice, resulting in uneven uplift of the ground surface. For subway connecting passages, frost heave can cause cracks and misalignments in the connecting passages and adjacent tunnel segments, and in severe cases, structural instability. Currently, when conducting frost heave analysis for subway connecting passages, silty clay, due to its special particle size distribution and physical properties, is considered a high-risk stratum for frost heave disasters, requiring analysis and assessment of the degree of frost heave in the corresponding tunnel structures.

[0003] For example, Chinese Patent Publication No. CN119023942A discloses a method and system for assessing the frost heave of airport concrete pavement, which relates to the field of pavement analysis technology. The method discloses the following steps: Step 1: Divide the airport concrete pavement into multiple monitoring areas and periodically acquire frost heave monitoring logs for each monitoring area; Step 2: Evaluate the monitoring areas as core frost heave areas or ordinary areas based on the frost heave monitoring logs; Step 3: Analyze the potential related areas of the core frost heave areas, and then assess the frost heave of the airport concrete pavement.

[0004] For example, Chinese Patent Publication No. CN119064412A discloses a method and device for testing temperature conduction and frost heave strain in concrete-lined channels, which relates to the field of low-temperature material testing technology. The specific steps include: collecting temperature and strain change data inside the lining channel, constructing time-temperature and time-strain function expressions, and calculating their average rate of change; obtaining corresponding expressions based on data such as the size, thermal conductivity, and average temperature gradient of the lining channel, and training a machine learning model; collecting external environmental parameters and generating environmental influence coefficients; and combining temperature and strain influence coefficients to process the average temperature change rate and strain change rate to obtain the true values, thereby accurately obtaining the true temperature and strain of the lining channel.

[0005] Existing technologies assess road frost heave by counting the number of associated areas and obtain real strain through the coupling of time, temperature, and pressure to test and process frost heave strain. However, these processes are biased towards time-dimensional data analysis, which can easily lead to the loss of monitoring of frost heave displacement in the spatial dimension of the monitored area due to changes in the time dimension. This results in the lack of analysis of the correlation of frost heave in different areas at the same time when subsequent data is rolled back and regional monitoring is carried out, making it impossible to identify the spatial risk transmission in a timely manner. Summary of the Invention

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method and apparatus for testing the frost heave of a subway connecting passage, comprising: S1, acquiring displacement, temperature, pressure and moisture data during the test, dividing the subway connecting passage into multiple monitoring areas according to the tunnel structure, and constructing the displacement field of each monitoring area based on the displacement change of each monitoring area during frost heave.

[0007] S2 retrieves the displacements of each displacement field at the horizontal and vertical positions, and defines the displacement characteristics of each displacement field in the form of displacement amplitude and wavelength.

[0008] S3. Based on the displacement characteristics of each displacement field, determine the displacement direction and pressure changes of each monitoring area under boundary constraints, and construct the local displacement change trend of each monitoring area.

[0009] S4. Based on the local displacement change trend of each monitoring area, extract the trend label of each monitoring area, calculate the trend correlation between adjacent monitoring areas through the trend label, and integrate the trends of each monitoring area to obtain the integrated displacement distribution trend.

[0010] S5. Based on the slope value of each point in the displacement distribution trend, determine the risk points corresponding to each monitoring area, and determine the treatment measures for each monitoring area based on the risk transmission path after connecting the risk points.

[0011] A frost heave testing device for a subway connecting passage includes: a region division module for acquiring displacement, temperature, pressure and moisture data during the test, dividing the subway connecting passage into multiple monitoring areas according to the tunnel structure, and constructing the displacement field of each monitoring area based on the displacement change of each monitoring area during frost heave.

[0012] The feature recognition module is used to retrieve the displacement of each displacement field at the horizontal and vertical positions, and define the displacement characteristics of each displacement field in the form of displacement amplitude and wavelength.

[0013] The local displacement analysis module is used to determine the displacement direction and pressure changes of each monitoring area under boundary constraints based on the displacement characteristics of each displacement field, and to construct the local displacement change trend of each monitoring area.

[0014] The displacement integration module is used to extract trend labels for each monitoring area based on the local displacement change trend of each monitoring area, calculate the trend correlation between adjacent monitoring areas through the trend labels, and integrate the trends of each monitoring area to obtain the integrated displacement distribution trend.

[0015] The risk identification module is used to determine the risk points corresponding to each monitoring area based on the slope value of each point in the displacement distribution trend, and to determine the treatment measures for each monitoring area based on the risk transmission path after connecting the risk points.

[0016] The beneficial effects of this invention are as follows: First, this invention divides the connecting passage into a ring-shaped monitoring area according to the geometric characteristics of the tunnel structure and the distribution rules of the freeze-thaw zone. Then, based on the temperature diffusion rate, structural stiffness, and boundary distance, the risk coefficient of the test points is quantified, and multiple sets of test points are selected. At the same time, based on the temperature threshold, the unfrozen area, frozen area, and lining support area are divided, the inner and outer rings of the frozen area are marked, and the displacement time history data of the outer ring of the frozen area is extracted to construct a displacement field. Through the targeted deployment of weak points, the core area of ​​frost heave and the weak parts of the structure are defined, realizing the location of frost heave displacement in each area, and providing data support for subsequent analysis.

[0017] Second, this invention extracts horizontal and vertical displacement data from the displacement field, sets the displacement amplitude and wavelength distribution range, and identifies the characteristic patterns after matching the two through cluster analysis; it also verifies the consistency of displacement, pressure and boundary under each boundary type and marks the reliability of the trend; under the premise of reliable trend, the displacement direction is synthesized by horizontal and vertical displacement vectors, so that the local displacement trend analysis is upgraded from single displacement dependence to boundary-pressure-displacement coupling verification, ensuring that the output displacement direction, frost heave displacement evolution and other trend characteristics are consistent with the actual stress state of the project; thus improving the accuracy of data processing.

[0018] Third, this invention extracts multi-dimensional trend labels such as displacement amplitude range, wavelength range, azimuth angle, and amplitude growth rate; calculates the matching degree of trend labels in adjacent areas; and selects a third-order polynomial, linear, or piecewise independent fitting method based on the matching degree; and fits the displacement amplitude at the same moment into a continuous displacement distribution trend along the tunnel's central axis and the vertical direction of the ground surface. This transforms local trends into a global vertical and longitudinal trend integration, ensuring the continuity of trends in related areas while preserving the segmentation of weakly related areas, avoiding the distortion of patterns caused by forced global uniformity, and providing a complete spatial perspective for risk identification.

[0019] IV. This invention filters high, medium, and low-risk points based on the slope value of displacement distribution trends; constructs a risk transmission path starting from high-risk points and using medium-risk points as transmission nodes; verifies the moisture data of risk points and determines the type of inducing factor by the overlap ratio with high-moisture areas; and configures differentiated treatment measures based on the path location and inducing factor type. This clarifies the core location, diffusion direction, and root cause of frost heave risk, enabling the determination of treatment measures at various locations in the subway connecting passage based on its root cause during frost heave analysis, effectively avoiding blind construction and reducing project costs and construction complexity. Attached Figure Description

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] Figure 1 This is a flowchart illustrating a method and apparatus for testing the frost heave of subway connecting passageways.

[0022] Figure 2 This is a flowchart illustrating step S1 of a method and apparatus for testing the frost heave of a subway connecting passage.

[0023] Figure 3 This is a flowchart illustrating step S2 of a method and apparatus for testing the frost heave of a subway connecting passage.

[0024] Figure 4 This is a flowchart illustrating step S3 of a method and apparatus for testing the frost heave of a subway connecting passage.

[0025] Figure 5 This is a flowchart illustrating step S4 of a method and apparatus for testing the frost heave of a subway connecting passage.

[0026] Figure 6 This is a flowchart illustrating step S5 of a method and apparatus for testing the frost heave of a subway connecting passage.

[0027] Figure 7 This is a schematic diagram of a system for testing the frost heave of a subway connecting passage. Detailed Implementation

[0028] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, they shall be performed in accordance with the techniques or conditions described in the literature in the art or in accordance with the product manual.

[0029] See Figure 1 A method and apparatus for testing the frost heave of a subway connecting passage includes: S1, acquiring displacement, temperature, pressure and moisture data during the test, dividing the subway connecting passage into multiple monitoring areas according to the tunnel structure, and constructing the displacement field of each monitoring area based on the displacement change of each monitoring area during frost heave.

[0030] S2 retrieves the displacements of each displacement field at the horizontal and vertical positions, and defines the displacement characteristics of each displacement field in the form of displacement amplitude and wavelength.

[0031] S3. Based on the displacement characteristics of each displacement field, determine the displacement direction and pressure changes of each monitoring area under boundary constraints, and construct the local displacement change trend of each monitoring area.

[0032] S4. Based on the local displacement change trend of each monitoring area, extract the trend label of each monitoring area, calculate the trend correlation between adjacent monitoring areas through the trend label, and integrate the trends of each monitoring area to obtain the integrated displacement distribution trend.

[0033] S5. Based on the slope value of each point in the displacement distribution trend, determine the risk points corresponding to each monitoring area, and determine the treatment measures for each monitoring area based on the risk transmission path after connecting the risk points.

[0034] Currently, frost heave tests can be conducted using a global frost heave model of the freeze-thaw zone to identify the displacement field of frost heave. This model treats the tunnel structure as a circular tunnel within an infinite domain, dividing it into an unfrozen region, a frozen region, and a lining / support region. It analyzes the freezing and thawing range of the soil near the tunnel, and then studies the evolution of displacement and stress in the frozen region. This model focuses on the response of the surrounding rock structure to temperature gradients, treating each region as a whole. It assumes that the frost heave force originates from the overall expansion of the frozen region, with no displacement occurring in the inner circle and the outer circle expanding outwards.

[0035] During processing, temperature sensors are deployed to mark the frozen area, used to delineate the unfrozen area, frozen area, and lining support area, and to correlate the temperature with displacement and pressure measurement points, enabling synchronous analysis of temperature, displacement, and pressure. Displacement sensors are deployed to record the displacement during the frost heave test. Displacement measurement points set on the lining structure surface, the tunnel radial and axial directions, and the ground surface support the identification of displacement distribution trends in the central axis and vertical direction, taking into account both local and overall displacement measurement. Pressure sensors are deployed to measure the pressure data from the frost heave test, used to correlate the coupling relationship between displacement and pressure under subsequent tunnel structure changes. Moisture sensors are deployed to measure the moisture content at multiple displacement measurement points to identify the causes of significant frost heave at each location, improving the accuracy of the maximum allowable displacement calculation.

[0036] like Figure 2 As shown, the implementation of step S1 also includes: S11, based on the geometric characteristics of the tunnel structure and the distribution rules of the freeze-thaw zone, the connecting passage is divided into multiple annular monitoring areas along the axial direction, such as the arch, arch shoulder, side wall, and invert arch.

[0037] S12. Based on the point selection principles for each monitoring area, determine the displacement measuring points, temperature measuring points, pressure measuring points, and moisture measuring points to be deployed in each monitoring area.

[0038] The principle of point selection refers to the location of the measuring points. Generally, the measuring points can be arranged in the following way.

[0039] Temperature measuring points are arranged radially on the inner and outer sides of the lining (arch crown, arch waist, arch bottom, arch foot) and the surrounding rock in each monitoring area, precisely corresponding to the boundary of the monitoring area and the stratigraphic interface.

[0040] Displacement measuring points and temperature measuring points are laid out on the same cross section (6 points on the lining surface), and are simultaneously laid out in the radial direction of the surrounding rock (at the same depth as the temperature measuring points), the central axis of the connecting passage, and the vertical direction of the ground surface.

[0041] Pressure measuring points are set up at the contact surfaces between the lining and the surrounding rock (arch waist, arch foot, arch bottom), the inner and outer boundaries of the frozen zone, and the stratum interface, and their coordinates are consistent with those of the displacement measuring points in the same area.

[0042] Moisture measuring points: These points are set up radially in the surrounding rock, at the outer boundary of the frozen zone, and on the outer side of the lining to ensure that moisture migration is captured synchronously with temperature measuring points.

[0043] In order to capture the weak points during frost heave changes, when setting the measuring points, step S12 is further implemented as follows: S121, retrieve the temperature diffusion rate during the current frost heave, and select the corresponding target baseline and selection interval based on the temperature diffusion rate.

[0044] First, the temperature diffusion rate of each monitoring area is calculated from the real-time data of the temperature sensor. Based on the diffusion rate, high-rate, medium-rate, and low-rate zones are divided. 0.5℃ / m·h and 0.2℃ / m·h can be selected as the dividing criteria. The area greater than 0.5℃ / m·h is regarded as the high-rate zone, where frost heave is active and its frost heave displacement and pressure changes are more obvious. The medium-rate zone, which is between 0.5℃ / m·h and 0.2℃ / m·h, shows more stable frost heave. The low-rate zone has weak frost heave. The target baselines for these three zones will be adjusted according to the temperature diffusion rate.

[0045] The target baseline will include a longitudinal baseline (L-axis), a transverse baseline (T-axis), and a vertical baseline (V-axis), with measuring points configured using a grid-based system. The longitudinal baseline is a straight line along the central axis of the connecting passage, serving as the primary coordinate axis of the current grid. The transverse baseline is a horizontal straight line perpendicular to the L-axis, located within the tunnel cross-section. The vertical baseline is a straight line perpendicular to the LT plane, pointing towards the Earth's center as the positive direction, and the measured displacement is determined using an absolute elevation measurement method.

[0046] Two sets of orthogonal target baselines are set up in the medium-speed and low-speed zones. An additional set of oblique target baselines will be added in the high-speed zone. At the same time, the selection spacing in the high-speed zone is reduced to 0.2-0.3m, in the medium-speed zone it remains at 0.5m, and in the low-speed zone it is enlarged to 0.8-1m. These adjusted values ​​can also be scaled down proportionally according to the size of the current test space and the specifications of normal tests to simulate the displacement changes of the connecting channel during the frost heave process.

[0047] S122, perform intersection calculations based on the target baseline and selected spacing to determine the intersection points of multiple target baselines, and establish test points for each monitoring area according to the normal projection of each intersection point.

[0048] At this point, the target baselines within the same monitoring area are intersected to obtain multiple intersection points. Taking each intersection point as the origin, the data is projected along the structural normal of the monitoring area (such as the normal of the lining surface, the radial direction of the surrounding rock, and the vertical direction of the ground surface) onto the actual structure or stratum to form test points. The test points are then bound to the previously set LTV three-dimensional mesh, and the unique coordinates of each test point are marked.

[0049] S123, based on the linear analysis results of the test points, obtain the weak points including a preset number, and set displacement test points, temperature test points, pressure test points and moisture test points at the location of the weak points.

[0050] When performing linear analysis on the test points, step S123 is further implemented by: judging each test point based on the temperature diffusion rate corresponding to the test point, the structural stiffness of the location, and the distance from the boundary, quantifying the risk coefficient of each test point by weighted sum under linear correlation, thereby identifying the severely affected points, and setting up multiple sets of test points at the locations of these points.

[0051] At this point, the temperature diffusion rate is normalized to quantify the index value of temperature diffusion. Based on the tunnel structure corresponding to the test point, the ratio of the lining thickness to the design standard thickness is taken in the lining part, and the ratio of the elastic modulus of the stratum to the regional average elastic modulus is taken in the surrounding rock part. The obtained ratio is subtracted from 1 to identify the index value of structural stiffness. Then, it is determined whether the test point is close to the simulated constant load boundary, constant displacement boundary, or semi-rigid boundary. For example, when the distance from the boundary is ≤0.5m, the index value is 1; when the distance from the boundary is 0.5-1m, the index value is 0.5; and when the distance from the boundary is >1m, the index value is 0, thus obtaining the index value of the stratum boundary.

[0052] When the risk coefficient is obtained by weighting the three factors, since the risk of frost heave is mainly driven by the activity level, followed by the structural resistance to deformation, the weights are set to 0.4, 0.35 and 0.25 respectively, according to the order of temperature diffusion rate, structural stiffness and stratum boundary. This is to emphasize the main displacement change trend of the measuring points set at the corresponding weak points of the structure under the development of frost heave in the scenario where temperature diffusion is the main influence, thereby completing the analysis of the impact of frost heave in the connecting passage.

[0053] When determining the risk coefficient, the ranges [0.8, 1.0], [0.6, 0.8], [0.4, 0.6], and [0, 0.4] will be used to classify points into extremely high risk, high risk, medium risk, and low risk, respectively. Extremely high risk points are the core areas of frost heave deformation, stress concentration, and moisture migration, requiring priority placement of measuring points and the reservation of backup measuring points to achieve displacement monitoring of frost heave changes in the corresponding monitoring area. High risk points are areas with active frost heave and unfavorable structural and geological conditions, and are the main areas for weak point measurement, requiring the placement of four types of measuring points according to their location. Medium risk points are areas with relatively stable frost heave or relatively stable structures, and are supplemented when the number of measuring points in the monitoring area is insufficient. Low risk points represent weak frost heave and do not require the placement of measuring points.

[0054] All test points are sorted in descending order according to their risk coefficients, and the top-ranked test points are selected according to a preset number for each monitoring area to identify the weak points that need to be addressed.

[0055] At this point, the preset quantity will be obtained from the database according to the location of each monitoring area, ensuring that the weak points cover the risky locations in the monitoring area.

[0056] S13. Using the acquired temperature measurement points and the temperature threshold when the soil freezes (usually 0℃), the unfrozen area, frozen area, and lining support area are divided into each monitoring area. After clarifying the spatial range and boundary coordinates of each area, the inner circle (near the lining side) and outer circle (near the unfrozen area) of the frozen area are marked to determine the current main displacement measurement and identification points.

[0057] S14. Combining the displacement measuring points located on the outer edge of the frozen area, extract the displacement time history data in the horizontal and vertical directions to construct the displacement field corresponding to each monitoring area.

[0058] At this point, in accordance with the assumption of the overall frost heave model of the freeze-thaw zone, the displacement data of each monitoring area are combined into displacement time history data according to their time, so as to reveal the displacement change trend of the outer side of the frozen area under each time period and form an independent displacement field for each monitoring area, providing a data basis for subsequent displacement feature extraction and tunnel structure analysis.

[0059] In one embodiment of the present invention, the obtained displacement amplitude represents the maximum displacement of a certain measuring point or monitoring area, reflecting the intensity characteristics of frost heave deformation; the wavelength represents the periodic repetition distance of the displacement in space, such as a displacement peak appearing every 3m along the tunnel axis, and the current wavelength is represented by the peak spacing. These two obtained features will reflect the quantitative value of the whole soil in the frost heave analysis obtained from the local soil.

[0060] like Figure 3As shown, the implementation of step S2 includes: S21, extracting displacement data of each monitoring area in the horizontal and vertical directions from the displacement field data, and setting the displacement amplitude distribution range and wavelength distribution range based on the displacement amplitude and wavelength at any time.

[0061] The distribution range of displacement amplitude will be statistically analyzed in both the horizontal and vertical directions. The wavelength distribution range will be statistically analyzed based on the wavelength values ​​at any given time, and these values ​​are directly related to the uniformity, localization, and potential risks of frost heave deformation.

[0062] S22, Match the displacement amplitude distribution range with the wavelength distribution range to determine the characteristic patterns of displacement amplitude and wavelength at each moment.

[0063] When matching the displacement amplitude distribution range with the wavelength distribution range, the implementation methods include: binding the displacement amplitude and wavelength values ​​at the same time to obtain multiple sets of data pairs, performing cluster analysis on the data pairs in each monitoring area, and identifying the characteristic patterns after clustering displacement amplitude and wavelength, such as the local deformation pattern of high amplitude and short wavelength, and the overall deformation pattern of low amplitude and long wavelength.

[0064] During cluster analysis, displacement amplitude and wavelength values ​​are standardized, and the horizontal and vertical displacement amplitudes and corresponding wavelengths of each monitoring point at the same time are bound to form four-dimensional data pairs. After converting the data pairs into vectors, the Euclidean distance between the vectors is used as the clustering index value, and the DBSCAN algorithm is used for clustering. Unsupervised clustering is performed on all data pairs. Based on the clustered displacement amplitude and wavelength (e.g., based on the quantile of the displacement amplitude and the dominant wavelength), feature pattern labels (such as high amplitude-short wavelength, low amplitude-long wavelength, etc.) are extracted from the database to interpret the spatial distribution law and dominant deformation mechanism of frost heave deformation at the corresponding time.

[0065] Each feature pattern's label covers a set interval, which will rely on the confidence interval of multiple batches of frost heave analysis. The interval is determined by the global statistical mean ± 3 standard deviations, and then labeled with tags such as high amplitude and low amplitude.

[0066] For example, in multiple batches of data, the global mean of the largest horizontal amplitude is 15mm, the global standard deviation is 5mm, and the confidence interval is 0-30mm; labels are divided according to this interval: low amplitude (0-10mm), medium amplitude (10-20mm), and high amplitude (20-30mm); thus completing the labeling of the corresponding feature patterns.

[0067] S23, based on the characteristic patterns of displacement amplitude and wavelength, the data corresponding to the characteristic patterns are regarded as the output displacement features. Each set of output displacement features represents a set of data under a characteristic pattern. For example, the displacement data of the monitoring area where the high amplitude and short wavelength are located is one set, and the displacement data of the monitoring area where the medium amplitude and medium wavelength are located is another set. Each set of data is judged and processed in turn.

[0068] The output displacement features will be output according to the characteristic patterns corresponding to the displacement amplitude and wavelength after clustering. These displacement features can accurately describe the intensity and spatial distribution characteristics of frost heave deformation and are used to verify the development trend of frost heave.

[0069] In one embodiment of the present invention, the boundary constraints are simulated constant load boundaries, constant displacement boundaries, or semi-rigid boundaries; these three types of boundaries are boundary features used in conventional tests to verify the distribution of frost heave changes.

[0070] The constant load boundary simulates long-term forces such as hydrostatic pressure and earth pressure, reflecting the continuous constraint of the strata on the tunnel structure; the constant displacement boundary simulates rigid constraint conditions (such as bedrock and diaphragm walls), restricting the overall movement of the structure or soil, and is used to analyze the structural response under displacement control conditions, such as frost heave deformation when the tunnel ends are fixed; the semi-rigid boundary simulates the mechanical behavior of non-perfectly rigid contact between the lining and the soil interface, joint surfaces, etc., such as frost heave causing shear deformation at the interface between the lining and the soil.

[0071] In step S3, the continuous changes in the dominant displacement direction, displacement amplitude, and wavelength under boundary constraints will be recorded. These changes will be compared with the pressure at the corresponding locations to eliminate false abrupt changes in the frost heave analysis, thereby determining the actual stress state of frost heave at each location and completing the local trend analysis of displacement changes at each monitoring area.

[0072] like Figure 4 As shown, the implementation of step S3 includes: S31, obtaining the boundary type of each monitoring area, and establishing a matching rule that includes displacement characteristics, pressure verification and boundary constraints based on the boundary type.

[0073] At this point, the matching rules can be formed into a logical chain based on the constant load boundary + high amplitude + short wavelength + stable pressure to describe the matching situation of displacement characteristics under boundary constraints. These matching rules will take the currently input boundary constraints, displacement characteristics and pressure data as the main body. The boundary constraints will explain the changes in displacement and pressure under the three boundary types of constant load boundary, constant displacement boundary and semi-rigid boundary, and whether their values ​​meet their constraint changes.

[0074] S32, for each monitoring area, the displacement, pressure and boundary covered in the matching rules are verified in sequence to mark the reliability of the trend of each monitoring area.

[0075] When verifying the credibility of trends, the displacement amplitude, wavelength and pressure values ​​extracted from historical data are used as the retrieval index, and the average value of the corresponding feature mode is taken as the benchmark value. With three times the standard deviation as the reasonable deviation, the displacement, pressure and boundary covered in the matching rules are verified in turn.

[0076] For example, displacement verification can be performed using the following three main aspects: 1. Whether the current horizontal or vertical maximum amplitude is within the range corresponding to the reference value ± reasonable deviation; 2. Whether the current wavelength is within the range corresponding to the reference value ± reasonable deviation; 3. Whether the wavelength, horizontal and vertical maximum amplitudes all fall within the range corresponding to the reference value ± reasonable deviation. Based on these three aspects, the data at multiple time points can be verified.

[0077] If all three conditions are met, the displacement matches; if two conditions are met, the displacement basically matches; if less than one condition is met, the displacement does not match. Based on the retrieval results of boundary type and feature pattern, the parameter value range under the boundary type restriction is checked, and the reliability of the displacement data at multiple time points is identified in turn.

[0078] Pressure verification primarily involves the following three aspects: 1. Whether the current pressure matches the expected matching rules. For example, in scenarios with high amplitude, short wavelength, and constant load, the average pressure value of the contact surface should match the design value of the constant load. This means verifying whether the current pressure change corresponds to the initially set boundary type. 2. Whether the current pressure change rate is synchronized with the displacement change. For example, if the displacement amplitude increases, is the pressure change rate positive? 3. Whether the pressure value falls within the reference value ± reasonable deviation. If all three aspects are met, the pressure matches. If two aspects are met, the pressure basically matches. If less than one aspect is met, the displacement does not match. Boundary verification is performed similarly.

[0079] The boundary type can be verified based on the following two items: 1. Whether the current boundary type is consistent with the matching rule setting, such as whether the current processing is for dead load and whether it is actually a dead load; 2. Whether the current boundary parameters are consistent with the benchmark value, such as whether the dead load value is consistent with the benchmark value; if both items are satisfied, the boundary fits; if one item is satisfied, the boundary basically fits; if neither item is satisfied, the boundary does not fit.

[0080] Based on the fit of various rules, the reliability of the current monitoring area's trend can be identified. For example, a fit of displacement, pressure, and boundary is considered highly reliable, indicating that the data perfectly matches the historical baseline. A fit of displacement, pressure, and basic boundary is considered moderately reliable, indicating a slight deviation in secondary dimensions, requiring reference to other data. Low reliability is a fit of displacement, non-fitting pressure, and fitting boundary, or a fit of basic displacement, basic fitting pressure, and fitting boundary, where key dimensions are mismatched, and the trend of frost heave is uncertain, requiring data verification to determine the frost heave process. If any two or more mismatches occur, the data is considered unreliable, and the trend data is difficult to extract, requiring recalibration of the sensor or boundary parameters to determine the displacement and wavelength collected under the current test.

[0081] S33, when the trend is reliable, the displacement direction of each monitoring area is obtained by vector synthesis of the horizontal and vertical displacements during frost heave.

[0082] When setting the displacement direction, the displacement value is set as the displacement vector according to the horizontal or vertical position, and the dominant direction during the current frost heave is determined according to the ratio of the magnitude of the displacement vector in the horizontal or vertical direction to the magnitude of the synthesized vector. At the same time, the direction angle after vector synthesis is recorded to determine the displacement direction at multiple times.

[0083] S34. Based on the mapping relationship between displacement direction, displacement amplitude, and wavelength, the local displacement change trend of each monitoring area is constructed.

[0084] The mapping relationship in this step includes the evolution of the current trend, such as horizontal outward displacement + high amplitude + short wavelength + continuously increasing pressure; the subsequent trend is that the horizontal outward deformation will continue to increase, and due to the characteristics of short wavelength, the deformation is concentrated in the current area. At this time, according to the output mapping relationship, the label of the monitoring area at the corresponding time can be recorded, and the displacement value and other values ​​can be combined into a trend curve in the form of time to illustrate the curve form of each area conforming to the boundary constraints at each time.

[0085] In one embodiment of the present invention, in step S4, trend labels are formed according to the changing trend at each tunnel structure, such as the dominant displacement direction (horizontal outward / vertical upward, etc.) + the evolution of displacement amplitude and wavelength after mode abrupt change (enhancing / stable / weakening), etc. The trend labels of adjacent regions are compared, and regions with strong correlation, moderate correlation, weak correlation and no correlation are identified. Then, the displacement values ​​of these regions are integrated to complete the integration of displacement trends from local to global and from static to dynamic.

[0086] like Figure 5As shown, the implementation of step S4 includes: S41, extracting the displacement amplitude range, wavelength range, displacement direction angle, displacement amplitude, and amplitude growth rate corresponding to the local displacement change trend as trend labels; the displacement amplitude range represents high amplitude, medium amplitude, or low amplitude, the wavelength range represents short wavelength, medium wavelength, or long wavelength, the direction angle is the direction angle when the displacement direction is output, and the amplitude growth rate represents the growth rate of the maximum displacement value in the monitoring area at continuous time.

[0087] S42 uses trend labels to calculate the matching degree between adjacent monitoring areas, and selects the fitting method between adjacent monitoring areas based on the matching degree.

[0088] When calculating the matching degree between adjacent monitoring areas, the values ​​of each component in the trend label will be compared in turn. An index value will be set based on the comparison of displacement amplitude range and wavelength range, an index value will be set based on the comparison of displacement direction angle, and an index value will be set based on the comparison of amplitude growth rate. The weighted sum of these three index values ​​will be used as the current matching degree.

[0089] First, determine whether the descriptions of adjacent monitoring areas are the same in the displacement amplitude range and wavelength range. If they are all the same, set it to 1; if one is the same, set it to 0.5; if they are all different, set it to 0. Then, determine the ratio of the difference in displacement direction angle between adjacent monitoring areas to 90°, and subtract this value from 1. Finally, determine the ratio of the difference in displacement amplitude growth rate between adjacent monitoring areas to the maximum amplitude growth rate in all areas, and subtract this value from 1 as well. After setting the three index values, you can choose 0.7, 0.2, and 0.1 as their weights to emphasize the similarity between frost heave intensity between continuous monitoring intervals, and obtain the matching degree value between adjacent monitoring areas.

[0090] The chosen fitting method will be based on the matching degree value, with 0.8 and 0.6 as the dividing intervals. The part with a matching degree greater than or equal to 0.8 is considered strongly correlated, and the displacement amplitude of this part is fitted using a cubic polynomial method to set a smooth and continuous curve. The data with a matching degree between 0.6 and 0.8 is considered moderately correlated, and the corresponding displacement amplitude is connected by a linear fitting method. The part with a matching degree less than 0.6 is considered weakly correlated, and a piecewise independent fitting method is selected. The spatial boundaries of adjacent monitoring areas are used to divide the area and form multiple independent curves to explain the displacement distribution trend after the integration of all adjacent areas.

[0091] S43, based on the selected fitting method, the displacement amplitude is fitted along the tunnel centerline and the direction perpendicular to the ground surface, respectively, and the displacement amplitude at the same moment is fitted into the displacement distribution trend.

[0092] At this point, the overall spatial displacement trend will be integrated, ultimately completing the integration of vertical and longitudinal trends.

[0093] In step S5, based on the spatial differences in frost heave, the prevention and control decisions for the current monitoring area will be determined in the spatial dimension by means of risk clustering and path transmission. At the same time, the causes of risk clustering will be verified by using the moisture data on the risk transmission path, and then the configurable treatment measures for each monitoring area will be selected.

[0094] like Figure 6 As shown, the implementation of step S5 includes: S51, selecting at least one risk point from the displacement distribution trend based on the slope value range of each point; when selecting risk points, the calculated slope value is equivalent to the deformation per unit length, which can be selected as mm / m or... Using microstrain as the unit, high-risk points with a displacement distribution trend of >5 mm / m can be selected in the longitudinal direction, and medium-risk and low-risk points can be distinguished by 3 mm / m; high-risk points with a displacement distribution trend of >8 mm / m can be selected in the vertical direction, and medium-risk and low-risk points can be distinguished by 5 mm / m. These data are only illustrative. The actual values ​​can be based on the confidence intervals during multiple regressions. Values ​​exceeding the upper and lower limits of the confidence interval can be used to distinguish between high and low risk. The confidence interval can be in the form of the average value ± 3 times the standard deviation, or other confidence levels can be selected.

[0095] S52 connects the screened risk points, starting with high-risk points and using medium-risk points as transmission points to construct risk transmission paths; the risk transmission paths use high-risk points as extension starting points and connect medium-risk points one by one to form multiple risk transmission paths, and mark the identification information of each path according to its connection position.

[0096] If there are no high-risk points at present, the point with the largest slope value among the medium-risk points is used as the starting point to construct a risk transmission path.

[0097] S53, verify the moisture data of each risk point in the risk transmission path, determine the overlap ratio between the risk transmission path and the high moisture area, and define the cause type of each risk point based on the overlap ratio.

[0098] Assuming areas with a moisture content greater than 15% are considered high-moisture regions, an overlap ratio is set based on the ratio of risk points in high-moisture regions to the total number of risk points along the path. When the overlap ratio is ≥70%, it indicates that most risk points are located within high-moisture regions, and the displacement caused by frost heave is primarily due to excessive moisture; this is marked as a moisture-dominant cause. When the overlap ratio is 30% ≤ overlap ratio < 70%, it indicates that moisture is a secondary cause contributing to excessive displacement at the current risk points. When the overlap ratio is < 30%, it indicates that the current risk transmission path is not directly affected by moisture; the cause of excessive displacement may be uneven formation stiffness or differences in structural constraints. Dominant cause, secondary cause, uneven formation stiffness, and differences in structural constraints are the cause types labeled in this case.

[0099] It should be noted that high moisture areas can be identified by linear regression, which can be used to select the average value of frost heave displacement dominated by moisture from historical data or multiple sets of data.

[0100] S54, Configure handling measures based on the cause type and path location of each risk point.

[0101] When configuring treatment measures, measures are selected from the database based on the type of cause, location of the risk point, and risk transmission path. For example, if the current risk point is characterized by high risk, isolation, and moisture dominance, measures such as local drilling for drainage, injection of water-repellent agent to reduce water content, and local lining thickening can be selected. The displacement changes of frost heave are checked according to the corresponding measures to determine the test results of frost heave under the current structure.

[0102] like Figure 7 As shown, the present invention also provides a frost heave test device for subway connecting passages, comprising: an output end of a region division module connected to a feature recognition module, an output end of a feature recognition module connected to a local displacement analysis module, an output end of a local displacement analysis module connected to a displacement integration module, and an output end of a displacement integration module connected to a risk identification module.

[0103] The area division module is used to acquire displacement, temperature, pressure and moisture data during the test. The subway connecting passage is divided into multiple monitoring areas according to the tunnel structure. The displacement field of each monitoring area is constructed based on the displacement changes of each monitoring area during frost heave.

[0104] The feature recognition module is used to retrieve the displacement of each displacement field at the horizontal and vertical positions, and define the displacement characteristics of each displacement field in the form of displacement amplitude and wavelength.

[0105] The local displacement analysis module is used to determine the displacement direction and pressure changes of each monitoring area under boundary constraints based on the displacement characteristics of each displacement field, and to construct the local displacement change trend of each monitoring area.

[0106] The displacement integration module is used to extract trend labels for each monitoring area based on the local displacement change trend of each monitoring area, calculate the trend correlation between adjacent monitoring areas through the trend labels, and integrate the trends of each monitoring area to obtain the integrated displacement distribution trend.

[0107] The risk identification module is used to determine the risk points corresponding to each monitoring area based on the slope value of each point in the displacement distribution trend, and to determine the treatment measures for each monitoring area based on the risk transmission path after connecting the risk points.

[0108] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered within the protection scope of the present invention.

Claims

1. A method for testing the frost heave of subway connecting passages, characterized in that, include: S1, acquire displacement, temperature, pressure and moisture data during the test, divide the subway connecting passage into multiple monitoring areas according to the tunnel structure, and construct the displacement field of each monitoring area based on the displacement change of each monitoring area during frost heave; S2, retrieve the displacement of each displacement field at the horizontal and vertical positions, and define the displacement characteristics of each displacement field in the form of displacement amplitude and wavelength; S3. Based on the displacement characteristics of each displacement field, determine the displacement direction and pressure change of each monitoring area under boundary constraints, and construct the local displacement change trend of each monitoring area. S4. Based on the local displacement change trend of each monitoring area, extract the trend label of each monitoring area, calculate the trend correlation between adjacent monitoring areas through the trend label, and integrate the trends of each monitoring area to obtain the integrated displacement distribution trend. S41, extract the displacement amplitude range, wavelength range, displacement direction angle, displacement amplitude and amplitude growth rate corresponding to the local displacement change trend as trend labels; S42, using trend labels, calculate the matching degree between adjacent monitoring areas, and select the fitting method between adjacent monitoring areas based on the matching degree; S43, based on the selected fitting method, fit the displacement amplitude along the tunnel central axis and the direction perpendicular to the ground surface, and fit the displacement amplitude at the same time as the displacement distribution trend. S5. Based on the slope value of each point in the displacement distribution trend, determine the risk points corresponding to each monitoring area, and based on the risk transmission path after connecting the risk points, determine the treatment measures for each monitoring area. S51, based on the slope value range of each point in the displacement distribution trend, at least one risk point is selected from the displacement distribution trend; S52, connect the selected risk points, with the high-risk point as the starting point and the medium-risk point as the transmission point, to construct a risk transmission path; S53, verify the moisture data of each risk point in the risk transmission path, determine the overlap ratio between the risk transmission path and the high moisture area, and define the cause type of each risk point based on the overlap ratio. S54, Configure handling measures based on the cause type and path location of each risk point.

2. The method for testing the frost heave of a subway connecting passage according to claim 1, characterized in that, The implementation of step S1 also includes: S11, based on the geometric characteristics of the tunnel structure and the distribution rules of the freeze-thaw zone, the connecting passage is divided into multiple ring-shaped monitoring areas along the axial direction; S12, Based on the point selection principle of each monitoring area, determine the displacement measuring points, temperature measuring points, pressure measuring points and moisture measuring points to be set up in each monitoring area; S13. Using the acquired temperature measurement points and the temperature threshold when the soil freezes, divide each monitoring area into unfrozen area, frozen area and lining support area, and mark the inner and outer circles of the frozen area. S14. Combining the displacement measuring points located on the outer edge of the frozen area, extract the displacement time history data in the horizontal and vertical directions to construct the displacement field corresponding to each monitoring area.

3. The method for testing the frost heave of a subway connecting passage according to claim 2, characterized in that, The implementation of step S12 also includes: S121, retrieve the temperature diffusion rate during the current frost heave, and select the corresponding target baseline and selection interval based on the temperature diffusion rate; S122, perform intersection calculations based on the target baseline and selected spacing to determine the intersection points of multiple target baselines, and establish test points for each monitoring area according to the normal projection of each intersection point; S123, based on the linear analysis results of the test points, obtain the weak points including a preset number, and set displacement test points, temperature test points, pressure test points and moisture test points at the location of the weak points.

4. The method for testing the frost heave of a subway connecting passage according to claim 3, characterized in that, The implementation of step S123 also includes: Based on the temperature diffusion rate corresponding to the test point, the structural stiffness of the location, and the distance from the boundary, each test point is judged, and the risk coefficient of each test point is quantified by a weighted sum under linear correlation. All test points are sorted in descending order according to their risk coefficients, and the top-ranked test points are selected according to a preset number for each monitoring area to identify the weak points that need to be addressed.

5. The method for testing the frost heave of a subway connecting passage according to claim 1, characterized in that, Step S2 can be implemented in the following ways: S21, extract displacement data of each monitoring area in the horizontal and vertical directions from the displacement field data, and set the displacement amplitude distribution range and wavelength distribution range based on the displacement amplitude and wavelength at any time. S22, Match the displacement amplitude distribution range with the wavelength distribution range to determine the characteristic patterns of displacement amplitude and wavelength at each moment; S23, based on the characteristic patterns of displacement amplitude and wavelength, uses the data corresponding to the characteristic patterns as the output displacement features.

6. The method for testing the frost heave of a subway connecting passage according to claim 5, characterized in that, When matching the amplitude distribution range of the advance displacement with the wavelength distribution range, the following methods are used: By binding the displacement amplitude and wavelength values ​​at the same time, multiple sets of data pairs are obtained. Cluster analysis is then performed on the data pairs in each monitoring area to identify the characteristic patterns after clustering displacement amplitude and wavelength.

7. The method for testing the frost heave of a subway connecting passage according to claim 1, characterized in that, Step S3 can be implemented in the following ways: S31. Obtain the boundary type of each monitoring area, and establish matching rules that include displacement characteristics, pressure verification and boundary constraints based on the boundary type. S32, for each monitoring area, the displacement, pressure and boundary covered in the matching rules are verified in sequence, and the reliability of the trend of each monitoring area is marked; S33, when the trend is reliable, the displacement direction of each monitoring area is obtained by vector synthesis of the horizontal and vertical displacements during frost heave; S34. Based on the mapping relationship between displacement direction, displacement amplitude, and wavelength, the local displacement change trend of each monitoring area is constructed.

8. A frost heave testing apparatus for a subway connecting passage, used to perform the steps in the frost heave testing method for a subway connecting passage according to any one of claims 1-7, characterized in that, include: The area division module is used to acquire displacement, temperature, pressure and moisture data during the test. The subway connecting passage is divided into multiple monitoring areas according to the tunnel structure. The displacement field of each monitoring area is constructed based on the displacement change of each monitoring area during frost heave. The feature recognition module is used to retrieve the displacement of each displacement field at the horizontal and vertical positions, and define the displacement characteristics of each displacement field in the form of displacement amplitude and wavelength. The local displacement analysis module is used to determine the displacement direction and pressure changes of each monitoring area under boundary constraints based on the displacement characteristics of each displacement field, and to construct the local displacement change trend of each monitoring area. The displacement integration module is used to extract trend labels for each monitoring area based on the local displacement change trend of each monitoring area, calculate the trend correlation between adjacent monitoring areas through the trend labels, and integrate the trends of each monitoring area to obtain the integrated displacement distribution trend. The risk identification module is used to determine the risk points corresponding to each monitoring area based on the slope value of each point in the displacement distribution trend, and to determine the treatment measures for each monitoring area based on the risk transmission path after connecting the risk points.

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