A method for dynamically monitoring and evaluating freezing effect of a composite stratum of a subway

Abstract

The application provides a method for dynamically monitoring and evaluating freezing effect of a subway composite stratum, a multi-dimensional integrated monitoring device is installed in a single freezing pipe; freezing construction parameters are obtained by using a subsystem of the multi-dimensional integrated monitoring device; the subsystem respectively evaluates freezing stratum construction effect; four subsystem evaluation indexes of a temperature effect index, a connected area of a freezing average temperature, a radar freezing effect index and a frost heaving amount are obtained; a comprehensive evaluation system of overall freezing stratum construction effect is established; within a certain monitoring time range, if the correlation coefficients of the four subsystem evaluation indexes are all greater than or equal to 0.9, the freezing effect is qualified, and freezing construction is ended; otherwise, the freezing effect is unqualified, and freezing is continuously implemented. The freezing soil construction effect evaluation is quantified, various modes of analysis and evaluation are used, the evaluation result is more accurate, and the deficiency of the construction effect evaluation method in the field of freezing soil construction is made up.

Description

Technical Field

[0001] This application relates to the field of monitoring the freezing construction of subway tunnel boring machines, and more specifically to a method for dynamically monitoring and evaluating the freezing effect of subway composite strata. Background Technology

[0002] In the construction of subway connecting passages in complex geological formations, the freezing method is frequently used for reinforcement. This method utilizes refrigeration technology to reinforce the strata surrounding the subway construction area, creating a frozen wall with sufficient load-bearing capacity and water resistance. However, due to the uneven hardness characteristics of the complex geological formations encountered in current subway construction—such as soft tops and hard bottoms, hard tops and soft bottoms, soft left and hard right, or hard left and soft right—the quality of the frozen wall reinforced by the freezing method often fails to meet expectations. This frequently leads to major engineering accidents during construction, such as water inrush, sand inrush, ground subsidence, and damage to existing tunnels. Therefore, freezing construction in complex geological formations is a significant hazard in subway construction, and effectively monitoring the dynamic effects of freezing is a pressing problem that needs to be solved in current subway construction.

[0003] Monitoring of the current freezing method in subway composite strata construction mainly relies on qualitative assessments using temperature measurement wells, pressure relief wells, brine return temperature, and active freezing time. Temperature sensors and camera systems are deployed around the freezing pipes to monitor temperature changes in the permafrost. However, deploying sensors requires tracks and trolleys for moving them, which is labor-intensive in practice and inconvenient for sensor movement. Furthermore, existing sensors cannot monitor frost heave stress and the freezing condition outside the freezing pipes, which are crucial parameters for freezing construction. Therefore, monitoring these two factors is essential for successful freezing construction.

[0004] Because the burial depth of freezing pipes in subway connecting passages is generally between 25m and 30m, and the number of freezing pipes is relatively large, while the number of temperature measuring holes is very limited and unevenly distributed, it is difficult to quantitatively monitor the thickness of the frozen wall of the freezing pipes and the intersection of the frozen walls of multiple freezing pipes. It is also impossible to monitor the displacement of the composite soil layer caused by frost heave, or to evaluate the strength of the frozen soil after freezing. In particular, it is impossible to visualize and quantitatively track the entire freezing process of subway connecting passages, and it is impossible to accurately assess the dynamic effect of curtain freezing intersections under different freezing times and locations. Therefore, it is necessary to monitor the main construction parameters around the freezing pipes (such as freezing temperature, freezing deformation, and frost heave stress) to ensure the effectiveness of frost heave construction. Summary of the Invention

[0005] To overcome the shortcomings of the existing technology, the purpose of this invention is to provide a method for dynamically monitoring and evaluating the freezing effect of subway composite strata. This method dynamically monitors multiple factors affecting freezing construction, such as the soil image around the freezing pipe, the freezing state outside the freezing pipe, the soil temperature around the freezing pipe, and the soil stress around the freezing pipe, from different spatial locations. The method analyzes and judges the freezing effect of the strata from these multiple factors, providing data services for freezing construction.

[0006] The technical solution of the present invention is as follows:

[0007] A method for dynamically monitoring and evaluating the freezing effect of composite strata in subways, characterized by including:

[0008] Step 1: Install a multi-dimensional integrated monitoring device inside a single freezing tube, including four subsystems: temperature sensor group 5, camera system 3, radar detector group 4, and frost heave stress monitor group 6.

[0009] Step 2: The multi-dimensional integrated monitoring device uses the subsystems of temperature sensor group 5, camera system 3, radar detector group 4, and frost heave stress monitor group 6 to monitor and acquire freezing temperature value, original freezing image, electromagnetic parameter change, and frost heave stress value, and provides these monitoring data to the core controller 101.

[0010] Step 3: The subsystems evaluate the construction effect in the frozen strata respectively;

[0011] The temperature sensor group 5, camera system 3, radar detector group 4, and frost heave stress monitor group 6 of the subsystems respectively evaluate the construction effect of the frozen strata and obtain four subsystem evaluation indicators: temperature effect index, area of ​​the connected region with average freezing temperature, radar freezing effect index, and frost heave amount.

[0012] Step 4: Establish a comprehensive evaluation system for the overall construction effect in frozen strata;

[0013] Within a certain monitoring time range, the core controller 101 determines the correlation coefficients of the four subsystem evaluation indicators based on the monitored data. If the correlation coefficients of the four subsystem evaluation indicators are all greater than or equal to 0.9, the freezing effect is qualified and the freezing construction ends. Otherwise, it is unqualified, the freezing pipe continues to be frozen and the monitoring and judgment continue.

[0014] In step 3, the method for evaluating the construction effect of the temperature sensor group 5 in the frozen stratum is as follows:

[0015] The freezing effect can also be evaluated using temperature sensor group 5. The freezing temperatures measured by temperature sensors 501 to 505 in the core controller 101 are obtained, a quantitative temperature evaluation model is established, and a quantitative evaluation is performed. The specific evaluation process is as follows:

[0016] S1: Collect freezing temperature. Export the freezing temperatures measured by temperature sensors 501 to 505 in the core controller 101. Temperature data within any time period can be selected as evaluation indicators according to evaluation needs.

[0017] S2: Establish a quantitative temperature evaluation model. The established evaluation model is as follows:

[0018]

[0019] In the above formula, I represents the temperature effect index; Q i ω is the normalized value of the temperature data collected for the i-th index; i is the weighting coefficient of the i-th indicator; n is the number of temperature sensors, which is 5.

[0020] In step 3, the camera system 3 evaluates the construction effect of the frozen stratum:

[0021] On the one hand, temperature values ​​are obtained from the temperature sensor group 5 subsystem and the average freezing temperature is calculated;

[0022] On the other hand, the original image of the frozen strata stored in the core controller 101 is first exported; then the original image of the frozen strata is grayscaled using the weighted average method; then the background subtraction algorithm is used to establish the correspondence between the grayscale values ​​of the frozen image and the average freezing temperature; then, based on the correspondence between the grayscale values ​​of the frozen image and the average freezing temperature, the K-means clustering algorithm is used to determine the area of ​​the connected regions that reach the average freezing temperature.

[0023] In step 3, the radar detector group 4 evaluates the construction effect of the frozen stratum:

[0024] R1: Collect and analyze the relevant indicators of freezing effect. The five indicators of freezing effect are soil dielectric constant z, electromagnetic wave propagation velocity v, electromagnetic wave reflection signal intensity difference Δ, freezing range s, and number of reflection strips m of frost heave deformation of freezing pipe. According to the evaluation needs, the data measured by radar detector group 5 in core controller 101 at any time can be selected as the evaluation indicators.

[0025] The electromagnetic parameter values ​​measured by the radar detector group 5 in the core controller 101 are obtained. After calculation and analysis by the core controller 101, the dielectric constant of the soil layer, the propagation speed of electromagnetic waves in frozen soil, the difference in the intensity of electromagnetic wave reflection signal, the freezing range, and the reflection strip of the freezing pipe frost heave deformation are obtained.

[0026] Soil dielectric constant z: When the dielectric constant is less than 3.2, it indicates that the soil layer is in the frozen soil range s; otherwise, it is in the unfrozen soil range. The core controller 101 records the soil dielectric constant.

[0027] Electromagnetic wave propagation velocity in frozen soil, v: When the electromagnetic wave propagation velocity in frozen soil is greater than 0.17 m / ns, it indicates that the stratum is in the frozen soil range; otherwise, it is in the unfrozen soil range. The core controller 101 records the electromagnetic wave propagation velocity in frozen soil.

[0028] Electromagnetic wave reflection signal intensity difference Δ: This calculates the difference between the electromagnetic wave reflection signal intensity within the frozen soil area and the electromagnetic wave reflection signal intensity in adjacent areas outside the frozen soil area. The core controller 101 records this electromagnetic wave reflection signal intensity difference.

[0029] Calculation of the freezing range s: The freezing range s is calculated by using the two-way travel time T of the electromagnetic wave from emission to return and the average propagation speed V1 of the electromagnetic wave in the ice. The calculation formula is s = T * V1 / 2.

[0030] The number m of the frost heave deformation reflection stripes of the frozen pipe is determined: If other frozen pipes around it break, the water content of the strata around the other frozen pipes is high and it is difficult to freeze. The electromagnetic wave speed is correspondingly low. The electromagnetic wave suddenly appears as frost heave deformation reflection stripes around the other frozen pipes. The core controller 101 records the number of frost heave deformation reflection stripes of the frozen pipes.

[0031] R2: Establish a quantitative evaluation model for radar freezing effects. The established evaluation model is as follows:

[0032]

[0033] In the above formula, A represents the radar freeze effect index; B i C is the normalized value of the radar freeze data collected for the i-th indicator; i The weight coefficient of the i-th indicator can be established through testing and verification during the experimental stage before engineering application; n is the number of indicators, which is 5.

[0034] In step 3, the method for calculating the frost heave amount based on the frost heave stress monitored by the frost heave stress monitoring device group 6 is as follows:

[0035] First, the frost heave stress values ​​are obtained from three locations in the core controller 101: the top (monitoring values ​​of frost heave stress monitor 1 601 and frost heave stress monitor 2 602), the middle (monitoring values ​​of frost heave stress monitor 3 603 and frost heave stress monitor 4 604), and the bottom (monitoring values ​​of frost heave stress monitor 5 605 and frost heave stress monitor 6 606). Second, a calculation formula is established between the frost heave amount and the frost heave stress value. Finally, the frost heave stress value is substituted into the calculation formula to obtain the frost heave amount.

[0036] The displacement caused by frost heave stress is calculated as follows:

[0037] The displacement caused by frost heave is called frost heave amount. The formula for calculating the frost heave amount η is:

[0038] η = η max -δ (Formula 1)

[0039] Where, η max δ represents the free frost heave, which can be calculated using Formula 2; δ represents the constrained frost heave, which can be calculated using Formula 8.

[0040]

[0041] Wherein, ε can be calculated using Formula 3, ε x ε represents the strain value in the x-direction. y ε represents the strain value in the y-direction; γ xy The result can be obtained by simultaneously solving equations four through seven; R can be expressed as ω is a constant representing the angle of rotation of the frozen body, and η0 is the initial frost heave. x represents the frost heave development path along the length of the freezing pipe; y represents the frost heave development path perpendicular to the length of the freezing pipe.

[0042]

[0043] Where, σ max The value is determined as follows: for the upper frost heave stress monitor 601 and 602, the larger of the two measured values ​​is set as σ. max Alternatively, select the frost heave stress monitor 3603 and frost heave stress monitor 4604 in the middle section, and set the larger of the two measurements as σ. max Alternatively, select either the frost heave stress monitor 605 or 606 at the bottom, and set the larger of the two measurements as σ. max E represents the elastic modulus of frozen soil.

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] In the above formula, p represents the freezing strength, which is the maximum value among the frost heave stress monitors 601, 602, 603, 604, 605, and 606; κ is the designed Poisson's ratio of the frozen soil; π is pi; E is the designed elastic modulus of the frozen soil; and r is the designed frozen wall thickness.

[0050] Beneficial effects

[0051] This invention utilizes a multi-dimensional integrated monitoring device to monitor freezing temperature, original freezing images, electromagnetic parameter changes, and frost heave stress values. Based on these five monitoring dimensions, different analysis methods are established to evaluate the construction effect of frozen soil, making the evaluation of frozen soil construction effect quantitative. Furthermore, the multiple analysis and evaluation methods result in more accurate evaluation results, thus making up for the deficiencies in the evaluation methods for construction effect in the field of frozen soil construction. Attached image description:

[0052] Figure 1 This is a schematic diagram of the front view section of the multi-dimensional integrated monitoring device.

[0053] Figure 2 This is a schematic diagram of the test cross-section of the multi-dimensional integrated monitoring device.

[0054] Figure 3 This is a cross-sectional view of the multi-dimensional integrated monitoring device rotated 45° counterclockwise.

[0055] Figure 4 This is a cross-sectional view of the multi-dimensional integrated monitoring device rotated 135° counterclockwise.

[0056] Figure 5 for Figure 1 Top view of the cross section along the tangent line A-A';

[0057] Figure 6 This is a process diagram of a monitoring method using a multi-dimensional integrated monitoring device;

[0058] Figure 7 A flowchart for evaluating the construction effect of frozen strata using camera system 3;

[0059] Figure 8 This is a fitting graph of the correlation coefficient of the temperature effect index in Example 1;

[0060] Figure 9 This is a fitting graph of the correlation coefficient between the area of ​​the connected regions at the average freezing temperature in Example 1;

[0061] Figure 10 This is a fitting graph of the correlation coefficient of the radar freezing effect index in Example 1;

[0062] Figure 11 This is a fitting graph of the correlation coefficient of frost heave in Example 1;

[0063] Numerical marker annotations:

[0064] Central Control Processing System 1: Core Controller 101, Data Transmission Line 102, External Power Interface 103, Transmission Channel 1 104, Transmission Channel 2 105, Transmission Channel 3 106, Transmission Channel 4 107; Lifting System 2: Lifter 1 201, Lifter 2 202, Lifter 3 203, Lifter 4 204, Suspension Rope 205, Monitoring Area 1 206, Monitoring Area 2 207, Monitoring Area 3 208, Monitoring Area 4 209, Motor 1 210, Motor 2 211, Motor 3 212, Motor 4 213, Fixing Frame 214; Camera System 3: Camera 1 301, Camera 2 302, Cold Light Source 1 303, Cold Light Source 2 304; Radar Detector Group 4: Radar Detector 1 401 Radar detector 402; Temperature sensor group 5, Temperature sensor 1 501, Temperature sensor 2 502, Temperature sensor 3 503, Temperature sensor 4 504, Temperature sensor 505; Frost heave stress monitoring device group 6, Frost heave stress monitoring device 1 601, Frost heave stress monitoring device 2 602, Frost heave stress monitoring device 3 603, Frost heave stress monitoring device 4 604, Frost heave stress monitoring device 5 605, Frost heave stress monitoring device 6 606; Refrigeration circulation system 7, Central water inlet pipe 701, Water outlet pipe 702, Central refrigeration chamber 703, Liquid inlet controller 704, Liquid outlet controller 705, Refrigerant storage tank 706, Hose 707; Device housing 8, Transparent tempered glass shell 801, Wear-resistant conical head 802; Soil 9, Soft soil 901, Hard soil 902. Detailed implementation method:

[0065] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0066] Examples, not limitations. Figures 1-5The multi-dimensional integrated monitoring device shown is arranged within the soil mass 9 and includes a central control and processing system 1, a lifting system 2, a camera system 3, a radar detector group 4, a temperature sensor group 5, a frost heave stress monitoring device group 6, a refrigeration circulation system 7, and a device housing 8. The central control and processing system 1 includes a core controller 101, a data transmission line 102, and an external power interface 103; the lifting system 2 includes a lifting device group, a hoisting rope 205, a monitoring area group, a motor group, and a fixing frame 214; the camera system 3 includes two camera devices; the radar detector group 4 includes radar detector 401 and radar detector 402; the temperature sensor group 5 includes temperature sensor 501, temperature sensor 502, temperature sensor 503, temperature sensor 504, and temperature sensor 505; the frost heave stress monitoring device group 6 includes a frost heave stress monitoring device group 7, a refrigeration circulation system 7, and a device housing 8. The central control and processing system 1 includes a core controller 101, a data transmission line 102, and an external power interface 103; the lifting system 2 includes a lifting device group 105, a hoisting rope 205, a monitoring area group 102, a motor group 103, and a fixing frame 214; the camera system 3 includes two camera devices; the radar detector group 4 includes radar detector 401 and radar detector 402; the temperature sensor group 5 includes temperature sensor 501, temperature sensor 502, temperature sensor 503, temperature sensor 504, and temperature sensor 505; the frost heave stress monitoring device group 6 includes a frost heave stress monitoring device group 7, a frost heave stress monitoring device group 8, a frost heave stress monitoring device group 9, a frost heave stress monitoring device group 105, a frost heave The monitoring group 6 includes a frost heave stress monitor 601, a frost heave stress monitor 602, a frost heave stress monitor 603, a frost heave stress monitor 604, a frost heave stress monitor 605, and a frost heave stress monitor 606; the refrigeration circulation system 7 includes a central water inlet pipe 701, a water outlet pipe 702, a central freezing chamber 703, a liquid inlet controller 704, a liquid outlet controller 705, a freezing liquid storage tank 706, and a hose 707; the device housing 8 includes a transparent tempered glass shell 801 and a wear-resistant conical head 802.

[0067] The outer casing 8 of the device is the outer casing of the monitoring device, and its side is a transparent tempered glass shell 801, which is cylindrical; the wear-resistant conical head 802 is installed on the top of the transparent tempered glass shell 801, which is conical.

[0068] The central control processing system 1 is installed inside the device housing 8 and near the top. The core controller 101 is the center for monitoring data collection and controlling each subsystem. An external power interface 103 is installed on its upper part. The upper end of the core controller 101 in the central control processing system is connected to an external power source through the external power interface 103. The lower end of the core controller 101 is connected to the data transmission line 102. The data transmission line 102 is connected to the lifting system 2, the camera system 3, the radar detector group 4, the temperature sensor group 5, the frost heave stress monitor group 6, and the freezing cycle system 7, respectively. It is used to transmit the control signals of the core controller 101 to the lifting system 2 and the freezing cycle system 7, as well as to transmit the measurement data of the camera system 3, the radar detector group 4, the temperature sensor group 5, and the frost heave stress monitor group 6 to the core controller 101.

[0069] The lifting system 2's lifting assembly is mounted on the upper part of the central refrigeration chamber 703 via a fixing frame 214. The motor assembly is connected to the lifting assembly, and the motor assembly is connected to the core controller 101 via a data transmission line 102. The core controller 101 controls the rotation of the lifting assembly. The monitoring area is distributed between the refrigeration circulation system 7 and the device housing 8, forming a cuboid space, which provides vertical movement space for the camera system 3 and the radar detector group 4. The camera system 3 and the radar detector group 4 are connected to the lifting assembly via suspension ropes 205.

[0070] Each camera device in the camera system 3 includes a camera and a cold light source, used to capture the frozen state of the soil 9 in the left and right directions of the device housing 8; the cold light source is installed on the camera and is used to provide the camera with the cold light source required to capture the frozen state of the soil 9; the upper part of the cold light source is connected to the lifting device group by a suspension rope 205.

[0071] The radar detector group 4 includes radar detector 401 and radar detector 402, which are used to monitor the freezing status of the soil 9 in the front and rear directions of the device housing 8.

[0072] The temperature sensor group 5 is vertically distributed in the upper, middle and lower parts of the multi-dimensional integrated monitoring device, and is symmetrically distributed. Spatially, it is arranged on the cross-section of the main view of the multi-dimensional integrated monitoring device rotated 45° counterclockwise. Among them, temperature sensor 1 501 and temperature sensor 2 502 are installed in the upper part of the multi-dimensional integrated monitoring device; temperature sensor 3 503 and temperature sensor 4 504 are installed in the middle part of the multi-dimensional integrated monitoring device; and temperature sensor 505 is installed in the lower part of the multi-dimensional integrated monitoring device and is in the shape of a frustum.

[0073] The frost heave stress monitoring device group 6 is vertically distributed in three parts—upper, middle, and lower—of the multi-dimensional integrated monitoring device, and is symmetrically distributed. Spatially, it is arranged on a cross-section rotated 135° counterclockwise from the main view of the multi-dimensional integrated monitoring device. Specifically, frost heave stress monitoring device one (601) and frost heave stress monitoring device two (602) are installed in the upper part of the multi-dimensional integrated monitoring device; frost heave stress monitoring device three (603) and frost heave stress monitoring device four (604) are installed in the middle part of the multi-dimensional integrated monitoring device; and frost heave stress monitoring device five (605) and frost heave stress monitoring device six (606) are installed in the lower part of the multi-dimensional integrated monitoring device. In this embodiment, each of the above frost heave stress monitoring devices is specifically selected as a vibrating wire earth pressure cell, with an operating temperature of -25℃ to +60℃.

[0074] The central water inlet pipe 701 in the refrigeration circulation system 7 is connected to the refrigerant storage tank 706 via a liquid inlet controller 704 at its upper end. The central refrigeration chamber 703 has a columnar structure. The central water inlet pipe 701 passes through the upper part of the central refrigeration chamber 703, and its bottom is set at a certain distance from the bottom of the central refrigeration chamber 703. The central refrigeration chamber 703 is connected to the refrigerant storage tank 706 in sequence via a water outlet pipe 702, a liquid outlet controller 705, and a hose 707 to form a circulation loop. Refrigerant is provided in the refrigeration circulation system 7 to provide a cold source to the soil 9 outside the device casing 8.

[0075] The aforementioned monitoring device is used for dynamic monitoring and evaluation of the freezing effect of composite strata in subways, such as... Figure 6 The method is as follows:

[0076] Step 1: Install a multi-dimensional integrated monitoring device inside a single freezing tube, including four subsystems: temperature sensor group 5, camera system 3, radar detector group 4, and frost heave stress monitor group 6.

[0077] Step 2: The multi-dimensional integrated monitoring device uses the subsystems of temperature sensor group 5, camera system 3, radar detector group 4, and frost heave stress monitor group 6 to monitor and acquire freezing temperature value, original freezing image, electromagnetic parameter change, and frost heave stress value, and provides these monitoring data to the core controller 101.

[0078] Step 3: The subsystems evaluate the construction effect in the frozen strata respectively;

[0079] The temperature sensor group 5, camera system 3, radar detector group 4, and frost heave stress monitor group 6 of the subsystems respectively evaluate the construction effect of the frozen strata and obtain four subsystem evaluation indicators: temperature effect index, area of ​​the connected region with average freezing temperature, radar freezing effect index, and frost heave amount.

[0080] Step 4: Establish a comprehensive evaluation system for the overall construction effect in frozen strata;

[0081] Within a certain monitoring time range, the core controller 101 determines the correlation coefficients of the four subsystem evaluation indicators based on the monitored data. If the correlation coefficients of the four subsystem evaluation indicators are all greater than or equal to 0.9, the freezing effect is qualified and the freezing construction ends. Otherwise, it is unqualified, the freezing pipe continues to be frozen and the monitoring and judgment continue.

[0082] In step 3, the method for evaluating the construction effect of the temperature sensor group 5 in the frozen stratum is as follows:

[0083] The freezing effect can also be evaluated using temperature sensor group 5. The freezing temperatures measured by temperature sensors 501 to 505 in the core controller 101 are obtained, a quantitative temperature evaluation model is established, and a quantitative evaluation is performed. The specific evaluation process is as follows:

[0084] S1: Collect freezing temperature. Export the freezing temperatures measured by temperature sensors 501 to 505 in the core controller 101. Temperature data within any time period can be selected as evaluation indicators according to evaluation needs.

[0085] S2: Establish a quantitative temperature evaluation model. The established evaluation model is as follows:

[0086]

[0087] In the above formula, I represents the temperature effect index; Q i ω is the normalized value of the temperature data collected for the i-th index; i is the weighting coefficient of the i-th indicator; n is the number of temperature sensors, which is 5.

[0088] In step 3, the camera system 3 evaluates the construction effect of the frozen stratum:

[0089] On the one hand, temperature values ​​are obtained from the temperature sensor group 5 subsystem and the average freezing temperature is calculated;

[0090] On the other hand, firstly, the original image of the frozen soil layer stored in the core controller 101 is exported; secondly, the original image of the frozen soil layer is processed into grayscale using a weighted average method; then, a background subtraction algorithm is used to establish the correspondence between the grayscale values ​​of the frozen image and the average freezing temperature; then, based on the correspondence between the grayscale values ​​of the frozen image and the average freezing temperature, a K-means clustering algorithm is used to determine the area of ​​connected regions that reach the average freezing temperature. The weighted average method, background subtraction algorithm, and K-means clustering algorithm are all well-known algorithms in the field of computer image processing, and are used here for image analysis of frozen soil.

[0091] In step 3, the radar detector group 4 evaluates the construction effect of the frozen stratum:

[0092] R1: Collect and analyze the relevant indicators of freezing effect. The five indicators of freezing effect are soil dielectric constant z, electromagnetic wave propagation velocity v, electromagnetic wave reflection signal intensity difference Δ, freezing range s, and number of reflection strips m of frost heave deformation of freezing pipe. According to the evaluation needs, the data measured by radar detector group 5 in core controller 101 at any time can be selected as the evaluation indicators.

[0093] The electromagnetic parameter values ​​measured by the radar detector group 5 in the core controller 101 are obtained. After calculation and analysis by the core controller 101, the dielectric constant of the soil layer, the propagation speed of electromagnetic waves in frozen soil, the difference in the intensity of electromagnetic wave reflection signal, the freezing range, and the reflection strip of the freezing pipe frost heave deformation are obtained.

[0094] Soil dielectric constant z: When the dielectric constant is less than 3.2, it indicates that the soil layer is in the frozen soil range s; otherwise, it is in the unfrozen soil range. The core controller 101 records the soil dielectric constant.

[0095] Electromagnetic wave propagation velocity in frozen soil, v: When the electromagnetic wave propagation velocity in frozen soil is greater than 0.17 m / ns, it indicates that the stratum is in the frozen soil range; otherwise, it is in the unfrozen soil range. The core controller 101 records the electromagnetic wave propagation velocity in frozen soil.

[0096] Electromagnetic wave reflection signal intensity difference Δ: This calculates the difference between the electromagnetic wave reflection signal intensity within the frozen soil area and the electromagnetic wave reflection signal intensity in adjacent areas outside the frozen soil area. The core controller 101 records this electromagnetic wave reflection signal intensity difference.

[0097] Calculation of the freezing range s: The freezing range s is calculated by using the two-way travel time T of the electromagnetic wave from emission to return and the average propagation speed V1 of the electromagnetic wave in the ice. The calculation formula is H=T*V1.

[0098] The number m of the frost heave deformation reflection stripes of the frozen pipe is determined: If other frozen pipes around it break, the water content of the strata around the other frozen pipes is high and it is difficult to freeze. The electromagnetic wave speed is correspondingly low. The electromagnetic wave suddenly appears as frost heave deformation reflection stripes around the other frozen pipes. The core controller 101 records the number of frost heave deformation reflection stripes of the frozen pipes.

[0099] R2: Establish a quantitative evaluation model for radar freezing effects. The established evaluation model is as follows:

[0100]

[0101] In the above formula, A represents the radar freeze effect index; B i C is the normalized value of the radar freeze data collected for the i-th indicator; i The weight coefficient of the i-th indicator can be established through testing and verification during the experimental stage before engineering application; n is the number of indicators, which is 5.

[0102] In step 3, the method for calculating the frost heave amount based on the frost heave stress monitored by the frost heave stress monitoring device group 6 is as follows:

[0103] First, the frost heave stress values ​​are obtained from three locations in the core controller 101: the top (monitoring values ​​of frost heave stress monitor 1 601 and frost heave stress monitor 2 602), the middle (monitoring values ​​of frost heave stress monitor 3 603 and frost heave stress monitor 4 604), and the bottom (monitoring values ​​of frost heave stress monitor 5 605 and frost heave stress monitor 6 606). Second, a calculation formula is established between the frost heave amount and the frost heave stress value. Finally, the frost heave stress value is substituted into the calculation formula to obtain the frost heave amount.

[0104] The displacement caused by frost heave stress is calculated as follows:

[0105] The displacement caused by frost heave is called frost heave amount. The formula for calculating the frost heave amount η is:

[0106] η = η max -δ (Formula 1)

[0107] Where, η max δ represents the free frost heave, which can be calculated using Formula 2; δ represents the constrained frost heave, which can be calculated using Formula 8.

[0108]

[0109] Wherein, ε can be calculated using Formula 3, ε x ε represents the strain value in the x-direction. y ε represents the strain value in the y-direction; γ xy The result can be obtained by simultaneously solving equations four through seven; R can be expressed as ω is a constant representing the angle of rotation of the frozen body, and η0 is the initial frost heave.

[0110]

[0111] Where, σ max The value is determined as follows: for the upper frost heave stress monitor 601 and 602, the larger of the two measured values ​​is set as σ. max Alternatively, select the frost heave stress monitor 3603 and frost heave stress monitor 4604 in the middle section, and set the larger of the two measurements as σ. max Alternatively, select either the frost heave stress monitor 605 or 606 at the bottom, and set the larger of the two measurements as σ. max E represents the elastic modulus of frozen soil.

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] In the above formula, p represents the freezing strength, which is the maximum value among the frost heave stress monitors 601, 602, 603, 604, 605, and 606; κ is the designed Poisson's ratio of the frozen soil; π is pi; E is the designed elastic modulus of the frozen soil; and r is the designed frozen wall thickness.

[0118] Example 1:

[0119] Step 1: Install a multi-dimensional integrated monitoring device inside a single freezing tube, including four subsystems: temperature sensor group (5), camera system (3), radar detector group (4), and frost heave stress monitor group (6).

[0120] Step 2: The subsystems of the multi-dimensional integrated monitoring device, namely the temperature sensor group (5), the camera system (3), the radar detector group (4), and the frost heave stress monitor group (6), are used to monitor and acquire the freezing temperature value, the original freezing image, the change of electromagnetic parameters, and the frost heave stress value, and provide these monitoring data to the core controller (101).

[0121] Step 3: The subsystems evaluate the construction effect in the frozen strata respectively;

[0122] The monitoring system is continuously in a monitoring state. For example, when the refrigeration cycle system 7 is running for 14 hours, the specific subsystems are as follows:

[0123] Temperature sensor group 5 subsystem:

[0124] S1: Export the measurement data of temperature sensors 1 to 5 in the core controller 101. At the time point of 14 hours, the measurement values ​​of temperature sensors 1 to 5 are -5℃, -8℃, -15℃, -4℃, and -20℃, respectively. Dividing the five collected temperature data by -35℃ (design value) gives: 0.14, 0.23, 0.43, 0.11, and 0.57. After normalization, the values ​​are 0.10, 0.15, 0.29, 0.08, and 0.38.

[0125] S2: Since the freezing effect at any point inside the freezing tube is crucial, the temperature weighting coefficients of the five temperature sensors are all equal, set to 0.2. Substituting the known data into Formula 9, we get I = 0.10 × 0.2 + 0.15 × 0.2 + 0.29 × 0.2 + 0.08 × 0.2 + 0.38 × 0.2 = 0.20.

[0126] At the same time point, subsystem radar detector group 4:

[0127] R1: Five data points were collected within a certain time period: soil dielectric constant, electromagnetic wave propagation velocity in frozen soil, difference in electromagnetic wave reflection signal intensity, freezing range, and number of reflection strips from frost heave deformation of frozen pipes. The values ​​were 3, 0.18, 150, 3, and 2, respectively. The design parameters for these five parameters were 3.2, 0.17, 100, 2, and 1, respectively. First, the five parameters were divided by the design values ​​and then normalized, resulting in the following values: 0.13, 0.15, 0.21, 0.21, and 0.29.

[0128] Dielectric constant of soil: The dielectric constant mainly reflects the change in water content in the soil. The dielectric constant of water is 81, the dielectric constant of ice is 3.2, the dielectric constant of general soil is between 3 and 7, and the dielectric constant of tunnel segments is 13. When the dielectric constant of the radar is less than 3.2, it indicates that the stratum is in the frozen soil range, and vice versa.

[0129] Electromagnetic wave propagation speed in frozen soil: The propagation speed of electromagnetic waves in water is 0.033 m / ns, in ice it is 0.17 m / ns, in soil it is 0.06 m / ns, and in tunnel segments it is 0.08 m / ns. Therefore, when the propagation speed of electromagnetic waves is greater than 0.17 m / ns, it indicates that the stratum is in the frozen soil range, and vice versa.

[0130] Electromagnetic wave reflection signal intensity difference: This is mainly judged by the characteristics of electromagnetic wave reflection signals. If the reflection signal intensity of a certain frozen soil area is about -150dBm, but the emission signal intensity of a nearby area or a small area within this area is about -20dBm, it indicates that there is a significant difference in the reflection signal intensity between the two. When the difference is -130dBm, it indicates that the area with a large difference is an anomalous stratum, and vice versa.

[0131] Calculation of the freezing range: If the two-way travel time T of the electromagnetic wave from transmission to return is 50 ns, and the average propagation speed V1 of the electromagnetic wave in the ice is 0.17 m / ns, then the freezing range s is 4.25 m; if the two-way travel time T of the electromagnetic wave from transmission to return is 5 ns, and the average propagation speed V1 of the electromagnetic wave in the ice is 0.17 m / ns, then the freezing range s is 0.425 m.

[0132] Determining the number of reflection strips caused by the freezing tube's frost heave deformation: The freezing tube had been running for 14 hours, during which time there were no reflection strips of electromagnetic waves around the freezing tube. At the 14th hour, the radar image showed that at the same location, electromagnetic waves suddenly showed two or more continuous reflection strips around the freezing tube, indicating that the freezing tube had broken at that location.

[0133] R2: Since the weights of the five indicators—soil dielectric constant, electromagnetic wave propagation velocity in frozen soil, difference in electromagnetic wave reflected signal intensity, freezing range, and number of reflection strips due to frost heave deformation of frozen pipes—are not entirely consistent, the weights of the five indicators are calculated using the analytic hierarchy process (AHP). The weight coefficients for the five indicators are: soil dielectric constant, electromagnetic wave propagation velocity in frozen soil, difference in electromagnetic wave reflected signal intensity, freezing range, and number of reflection strips due to frost heave deformation of frozen pipes, respectively: 0.0833, 0.4167, 0.1154, 0.0385, and 0.3462. Substituting the known data into Formula 10, we obtain the radar freezing effect index A = 0.13 × 0.0833 + 0.15 × 0.4167 + 0.21 × 0.1154 + 0.21 × 0.0385 + 0.29 × 0.3462 ≈ 0.21.

[0134] Step 4: Establish a comprehensive evaluation system for the overall construction effect in frozen strata;

[0135] Within the 14-hour monitoring period, the core controller (101) determines the correlation coefficients of the four subsystem evaluation indicators based on the monitored data. If the correlation coefficients of the four subsystem evaluation indicators are all greater than or equal to 0.9, the freezing effect is qualified and the freezing construction ends. Otherwise, it is unqualified, and the freezing pipe continues to be frozen and monitored for judgment.

[0136] Specifically, the correlation coefficients of the evaluation indicators for each subsystem are as follows:

[0137] ① If the temperature freezing effect index collected over 1t, 2t, 3t, 4t, 5t, 6t, 7t, 8t, 9t, and 14t periods are 0.2, 0.32, 0.36, 0.42, 0.43, 0.47, 0.51, 0.56, 0.61, and 0.76 respectively, plot the temperature freezing effect index at different time periods using the collected data, as shown below. Figure 8 As shown:

[0138] Depend on Figure 8 It can be seen that the correlation coefficient of the temperature effect index is 0.96.

[0139] ② Set the area of ​​the connected region at the average freezing temperature. The parameters collected at times 1t, 2t, 3t, 4t, 5t, 6t, 7t, 8t, 9t, and 14t are 1m², 1.2m², 1.3m², 1.4m², 1.5m², 1.8m², 2.4m², 4m², 5m², and 7.8m², respectively. Plot the area of ​​the connected region at the average freezing temperature over different time periods based on the collected data, such as... Figure 9 As shown:

[0140] Depend on Figure 9 It can be seen that the correlation coefficient of the area of ​​the connected region with the average freezing temperature is 0.89.

[0141] ③ The radar freezing effect index was set, and the indexes collected at 1t, 2t, 3t, 4t, 5t, 6t, 7t, 8t, 9t, and 14t were 0.67, 0.62, 0.56, 0.45, 0.42, 0.35, 0.31, 0.24, 0.22, and 0.13, respectively. Graphs showing the radar freezing effect index at different time periods were plotted on the collected data. Figure 10 As shown:

[0142] Depend on Figure 10 It can be seen that the correlation coefficient of the radar freezing effect index is 0.90.

[0143] ④ Set the frost heave amount. The indicators collected at 1t, 2t, 3t, 4t, 5t, 6t, 7t, 8t, 9t, and 14t are 0.76, 0.53, 0.38, 0.45, 0.32, 0.073, 0.065, 0.06, 0.04, and 0.02, respectively. Plot the frost heave amount correlation graph for the collected data at different time periods, such as... Figure 11 As shown:

[0144] By Figure 11 It can be seen that the correlation coefficient of frost heave is 0.68.

[0145] In summary, the correlation coefficients of the four indicators—average freezing temperature connected area, radar freezing effect index, temperature effect index, and freezing heave—are 0.89, 0.96, 0.9, and 0.68, respectively. None of them simultaneously met the requirement of a correlation coefficient greater than 0.9 within the same time period. Therefore, the dynamic performance during this time period is unqualified, and the entire freezing tube unit needs to continue freezing operation.

Claims

1. A method for dynamically monitoring and evaluating the freezing effect of composite strata in subway systems, characterized in that, include: Step 1: Modify the traditional freezing tube and install a multi-dimensional integrated monitoring device in a single freezing tube, including four subsystems: temperature sensor group (5), camera system (3), radar detector group (4), frost heave stress monitor group (6) and core controller (101). Step 2: The subsystems of the multi-dimensional integrated monitoring device, namely the temperature sensor group (5), the camera system (3), the radar detector group (4), and the frost heave stress monitor group (6), are used to monitor and acquire the freezing temperature value, the original freezing image, the change of electromagnetic parameters, and the frost heave stress value, and provide these monitoring data to the core controller (101). Step 3: The subsystems evaluate the construction effect in the frozen strata respectively; The temperature sensor group (5), camera system (3), radar detector group (4), and frost heave stress monitor group (6) of the subsystem evaluate the construction effect of the frozen stratum, and obtain four subsystem evaluation indicators: temperature effect index, area of ​​the connected region with average freezing temperature, radar freezing effect index, and frost heave amount. Step 4: Establish a comprehensive evaluation system for the overall construction effect of frozen strata using the evaluation indicators from Step 3; Within a certain monitoring time accumulation range, the core controller (101) determines the correlation coefficients of the four subsystem evaluation indicators based on the monitored evaluation indicator data; if the correlation coefficients of the four subsystem evaluation indicators are all greater than or equal to 0.9, the freezing effect is qualified and the freezing construction ends; otherwise, it is unqualified, the freezing pipe continues to be frozen and monitoring continues to determine the result. In step 3, the method for evaluating the construction effect of the temperature sensor group (5) in the frozen stratum is as follows: The freezing effect can also be evaluated by the temperature sensor group (5). The freezing temperature measured by temperature sensor one (501) to temperature sensor five (505) in the core controller (101) is obtained, a quantitative temperature evaluation model is established, and a quantitative evaluation is carried out. The specific evaluation process is as follows: S1: Collect freezing temperature; export the freezing temperature measured by temperature sensor 1 (501) to temperature sensor 5 (505) in the core controller (101). The temperature data at any time can be selected and provided to S2 as an evaluation index according to the evaluation needs. S2: Establish a quantitative temperature evaluation model; The established evaluation model is Formula Nine: In the above formula, Temperature effect index; For the first The normalized value of the temperature data collected for each indicator; For the first The weighting coefficient of each indicator; n is the number of temperature sensors, which is 5; In step 3, the camera system (3) evaluates the construction effect of the frozen stratum: On the one hand, the temperature value is obtained from the temperature sensor group (5) subsystem and the average freezing temperature is calculated; on the other hand, firstly, the core controller (101) uses the stored original image of the frozen stratum to perform grayscale processing on the original image of the frozen stratum using the weighted average method; then, the background subtraction algorithm is used to establish the correspondence between the grayscale value of the frozen image and the average freezing temperature; then, based on the correspondence between the grayscale value of the frozen image and the average freezing temperature, the K-means clustering algorithm is used to determine the area of ​​the connected region that reaches the average freezing temperature. In step 3, the radar detector group (4) evaluates the construction effect of the frozen stratum: R1: Collect and analyze relevant indicators of freezing effect; The five indicators related to the freezing effect are the soil dielectric constant z, the electromagnetic wave propagation speed v in frozen soil, the difference in electromagnetic wave reflection signal intensity Δ, the freezing range s, and the number of reflection strips of the freezing pipe frost heave deformation m. According to the evaluation needs, the data measured by the radar detector group 5 in the core controller (101) at any time can be selected as the evaluation indicators. The electromagnetic parameter values ​​measured by radar detector group 5 in the core controller (101) are obtained. After calculation and analysis by the core controller (101), the dielectric constant of the soil layer z, the propagation velocity of electromagnetic waves in frozen soil v, the difference in electromagnetic wave reflection signal intensity Δ, the freezing range s, and the reflection strip of frost heave deformation of the frozen pipe are obtained, where: Soil dielectric constant z: When the dielectric constant is less than 3.2, it indicates that the soil is in the frozen soil range s, otherwise it is in the unfrozen soil range; the core controller (101) records the soil dielectric constant; Electromagnetic wave propagation speed v in frozen soil: When the electromagnetic wave propagation speed in frozen soil is greater than 0.17 m / ns, it indicates that the stratum is in the frozen soil range, otherwise it is in the unfrozen soil range; the core controller (101) records the electromagnetic wave propagation speed in frozen soil. Electromagnetic wave reflection signal intensity difference △: Calculate the difference between the electromagnetic wave reflection signal intensity within the frozen soil area and the electromagnetic wave reflection signal intensity in the adjacent area outside the frozen soil area; the core controller (101) records the electromagnetic wave reflection signal intensity difference; Calculation of the freezing range s: The freezing range s is calculated by using the two-way travel time T of the electromagnetic wave from emission to return and the average propagation speed V1 of the electromagnetic wave in the ice. The calculation formula is s=T*V1 / 2. The number m of the frost heave deformation reflection strips of the frozen pipe is determined: If other frozen pipes around it break, the water content of the strata around the other frozen pipes is high and it is difficult to freeze, the electromagnetic wave speed is correspondingly low, and the electromagnetic wave suddenly appears the frost heave deformation reflection strips of the frozen pipes around the other frozen pipes. The core controller (101) records the number m of the frost heave deformation reflection strips of the frozen pipes. R2: Establish a quantitative evaluation model for radar freezing effect; the established evaluation model is as follows: In the above formula, The radar freezing effect index; For the first The normalized value of the radar freeze data collected for each indicator; For the first The weighting coefficients of each indicator can be established through testing and verification during the experimental phase before engineering application; n is the number of indicators, which is 5. In step 3, the method for calculating the frost heave amount based on the frost heave stress monitored by the frost heave stress monitoring device group (6) is as follows: First, the frost heave stress monitoring group (6) in the core controller (101) obtains the frost heave stress values ​​at different spatial locations in the upper, middle and lower parts; wherein, the frost heave stress value in the upper part includes the monitoring values ​​of frost heave stress monitoring device one (601) and frost heave stress monitoring device two (602), the frost heave stress value in the middle part includes the monitoring values ​​of frost heave stress monitoring device three (603) and frost heave stress monitoring device four (604), and the frost heave stress value in the lower part includes the monitoring values ​​of frost heave stress monitoring device five (605) and frost heave stress monitoring device six (606); Secondly, a calculation formula is established between the frost heave amount and the frost heave stress value; finally, the frost heave stress value is substituted into the calculation formula to obtain the frost heave amount. The displacement caused by frost heave stress is calculated as follows: The displacement caused by frost heave is called frost heave amount; frost heave amount The calculation formula is Formula 1: in, The free frost heave is calculated according to Formula 2; To constrain frost heave, it is calculated using Formula 8; in, It can be calculated using Formula 3. express The frost heave strain value in the x-direction, where x-direction is along the length of the frozen tube. express The frost heave strain value in the y-direction, where the y-direction is along the thickness of the frozen wall, i.e., perpendicular to the length of the frozen tube. We obtain the result by simultaneously solving formulas four through seven; Represented as ; The constant represents the angle of rotation of the frozen body. y represents the initial frost heave; x represents the frost heave development path along the length of the freezing pipe; y represents the frost heave development path perpendicular to the length of the freezing pipe. in, The method of value selection is as follows: for the upper frost heave stress monitor 1 (601) and frost heave stress monitor 2 (602), the larger of the two measured values ​​is set as the value. Alternatively, select the frost heave stress monitor three (603) and frost heave stress monitor four (604) in the middle, and set the value with the larger measurement value. Alternatively, select the frost heave stress monitor five (605) and frost heave stress monitor six (606) at the bottom, and set the value with the larger measurement value. ; The elastic modulus of frozen soil; Formula 2: Formula 3: Formula 4: ; Formula 5: ; Formula Six: ; Formula 7: ; Formula 8: In the above formula, The value of freezing intensity is taken from the values ​​of frost heave stress monitor 1 (601), frost heave stress monitor 2 (602), frost heave stress monitor 3 (603), frost heave stress monitor 4 (604), frost heave stress monitor 5 (605), and frost heave stress monitor 6 (606). The Poisson's ratio for the designed frozen soil; π is pi. For the designed elastic modulus of frozen soil, The designed frozen wall thickness.

Images