Subway tunnel gap monitoring method
By monitoring the thermal images of subway tunnel walls with an infrared imager and performing binarization processing, combined with the carbon black-doped tunnel segment material, the problems of high misjudgment rate, low efficiency and high cost in subway tunnel gap monitoring were solved, achieving efficient and accurate gap monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-23
- Publication Date
- 2026-03-31
AI Technical Summary
Existing methods for monitoring gaps in subway tunnels suffer from problems such as high misjudgment rate, low monitoring efficiency, high cost, and shallow depth, and are prone to misjudgment, especially in complex external environments.
Infrared imagers were used to monitor thermal images of subway tunnel walls. After binarization, the crack change trend was analyzed based on the location of the cracks in the tunnel segments. Carbon black-doped tunnel segment materials were used to improve monitoring accuracy.
It enables non-contact, continuous, and low-cost gap monitoring, improving monitoring effectiveness and accuracy, especially in the ability to identify minute gaps.
Smart Images
Figure CN121762544A_ABST
Abstract
Description
Technical Field
[0001] This application relates to a method for monitoring gaps in subway tunnels. Background Technology
[0002] Subway tunnels are typically constructed using the tunnel boring machine (TBM) method. This involves excavating the tunnel using a TBM while simultaneously laying tunnel segments to create the overall tunnel structure. Tracks are then laid within the tunnel. After construction, continuous monitoring of the subway tunnel is crucial due to its underground location and complex external environment. Currently, there are two main monitoring methods: image recognition, which acquires images and compares them to identify surface cracks; and ultrasonic monitoring, which provides non-contact, non-destructive testing. Image recognition is prone to misjudgments and has relatively low efficiency. Ultrasonic monitoring has stringent requirements for equipment and the monitoring environment, and is susceptible to external interference, especially under varying environmental conditions. While infrared monitoring is low-cost and simple in structure, current methods have shallow detection depths and are prone to missed detections. Summary of the Invention
[0003] To address the aforementioned problems, this application proposes a method for monitoring cracks in subway tunnels, comprising the following steps: using an infrared imager to monitor the subway tunnel wall and obtain thermal images; preprocessing the thermal images to obtain a base image; using the joint between tunnel segments as a location reference point, and obtaining the trend of crack changes based on the location reference point; and determining whether intervention is necessary based on the trend of crack changes. This application uses an infrared imager to generate thermal images of the subway tunnel wall, then processes them to obtain a base image, and analyzes the cracks in the tunnel segments based on this image. This method enables non-contact, continuous monitoring with good monitoring results and low monitoring costs.
[0004] Preferably, the preprocessing is binarization.
[0005] Preferably, the length and width of the crack are obtained by analyzing the image after binarization.
[0006] Preferably, the binarization process is performed in the following manner: First, convert the thermal image to a grayscale image; Obtain the grayscale value s of each pixel in a grayscale image i The maximum value is s max The minimum value is s min ; Calculate the average gray value S of all pixels in a grayscale image; Get the basic white marker pixels: For grayscale value s i In [s max -(s max -S)*0.1,s max The grayscale value of the pixels within the brackets is set to 255. For grayscale value s i In [s min s min +(Ss min The grayscale value of the pixels within [0.1] is set to 0; Then, secondary assignment is performed on the grayscale values of other pixels adjacent to the already assigned pixels. The binarization process in this application first obtains the extreme values, and then assigns values to the adjacent pixels based on the extreme values. The gaps obtained by this binarization method are more accurate, and some point-like defects can be corrected into non-cracks, thus optimizing the recognition capability of the thermal image after binarization.
[0007] Preferably, the secondary assignment is set in the following manner: The selection of other pixels is based on the size of the number N of adjacent pixels that have already been assigned a value. First, the pixel with the largest N is assigned a value in the following manner. First, calculate the average value of the surrounding pixels. If the gray value of the point is greater than the average value of the surrounding pixels, then set the gray value of the point to 255; otherwise, set it to 0. Then, based on the number N of adjacent pixels that have already been assigned values, other pixels are selected, and the pixel with the largest N is assigned a value; this process is repeated until the binarization of all pixels is completed.
[0008] Preferably, the subway tunnel wall is assembled from tunnel segments.
[0009] Preferably, the location reference point is set at the corner of the segment, and the segment is located according to the segment joint formed by the abutment between the segments. Before monitoring, preliminary positioning can be performed by recording the starting position of infrared imaging or the specific number of the segment. After the preliminary positioning is completed, monitoring is then carried out to ensure that the obtained position can be compared with the previous monitoring result to ensure that changes in the gap during the monitoring process can be obtained.
[0010] Preferably, carbon material is doped into the raw material of the tube sheet; the carbon material is carbon black.
[0011] Preferably, the raw material for the pipe segments comprises the following components in parts by weight: cement: 300-350 parts; fly ash: 50-60 parts; sand: 600-650 parts; crushed stone: 400-500 parts; water-reducing agent: 10-15 parts; carbon material: 8-10 parts; and a water-cement ratio of 0.25-0.3.
[0012] Preferably, in the process of synthesizing the raw materials for the pipe segments, the carbon black is first fully mixed with fly ash, and then mixed with cement, sand, crushed stone, water-reducing agent and water to obtain a slurry, which is then poured into a mold, compacted, cured and demolded to obtain the pipe segments; The carbon black and fly ash are mixed in the following manner: First, dissolve carbon black in 6-8 times its weight of water, then heat to 80-90℃ and stir until homogeneous. Add this mixture to fly ash and continue stirring for at least 1 hour. The slurry can be used directly or spray-dried to obtain a solid mixture of carbon black and fly ash. This application uses a composite of fly ash and carbon black as a raw material for pipe segments. The resulting pipe segments, when subjected to infrared monitoring, facilitate the identification of minute gaps, and provide more accurate monitoring results for gap width.
[0013] This application can bring the following beneficial effects: 1. This application uses an infrared imager to generate thermal images of the subway tunnel wall, then processes them to obtain a basic image, and analyzes the segment cracks based on this image. This method enables non-contact continuous monitoring with good monitoring effect and low monitoring cost.
[0014] 2. The binarization process of this application first obtains the extreme values, and then assigns values to the neighboring pixels based on the extreme values. The gaps obtained by this binarization method are more accurate, and some point-like defects can be corrected into non-cracks, thus optimizing the recognition capability of the thermal image after binarization.
[0015] 3. In this application, fly ash and carbon black are combined and then used as raw materials for tube segments. When infrared monitoring is performed on the tube segments prepared, it is beneficial to identify tiny gaps and the monitoring results of the gap width are more accurate. Attached Figure Description
[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of Example 1; Figure 2 This is a schematic diagram of the experimental setup. Detailed Implementation
[0017] To clearly illustrate the technical features of this solution, the following detailed description of specific implementation methods will be provided.
[0018] This application, if Figure 1 As shown, a method for monitoring gaps in subway tunnels includes the following steps: S1. Use an infrared imager to monitor the walls of the subway tunnel and obtain thermal images; The subway tunnel walls are assembled from tunnel segments.
[0019] The location reference point is set at the corner of the segment, and the segment is positioned according to the segment joint formed by the abutment between the segments.
[0020] Before monitoring, preliminary positioning is achieved by recording the starting position of infrared imaging or the specific segment number. After preliminary positioning, thermal images are acquired to ensure that the obtained position corresponds to the previous monitoring results and to ensure that changes in segment cracks during the monitoring process can be captured. This application is mainly used for monitoring the stability of tunnel segments.
[0021] S2. Preprocess the thermal image to obtain the base image; The preprocessing is binarization.
[0022] The length and width of the crack were obtained by analyzing the image after binarization.
[0023] The binarization process is performed as follows: First, convert the thermal image to a grayscale image; Obtain the grayscale value s of each pixel in a grayscale image i The maximum value is s max The minimum value is s min ; Calculate the average gray value S of all pixels in a grayscale image; Get the basic white marker pixels: For grayscale value s i In [s max -(s max -S)*0.1,s max The grayscale value of the pixels within the brackets is set to 255. For grayscale value s i In [s min s min +(Ss min The grayscale value of the pixels within [0.1] is set to 0; Then, secondary assignments are performed on the grayscale values of other pixels that are adjacent to pixels that have already been assigned values.
[0024] The secondary assignment is set as follows: The selection of other pixels is based on the size of the number N of adjacent pixels that have already been assigned a value. First, the pixel with the largest N is assigned a value in the following manner. First, calculate the average value of the surrounding pixels. If the gray value of the point is greater than the average value of the surrounding pixels, then set the gray value of the point to 255; otherwise, set it to 0. Then, based on the number N of adjacent pixels that have already been assigned values, other pixels are selected, and the pixel with the largest N is assigned a value; this process is repeated until the binarization of all pixels is completed.
[0025] S3. Using the joint between the segments as the location reference point, the trend of segment crack changes is obtained based on the location reference point; Based on the time records of each measurement, the development trend of segment cracks is generally a process from the occurrence of segment cracks to their gradual increase.
[0026] S4. Determine whether intervention is necessary based on the changing trend of segment cracks.
[0027] If the increase is too rapid or the width of the gap exceeds the limit, manual retesting is required. If the gap still exceeds the limit after retesting, repair or other methods are needed.
[0028] Carbon material is doped into the raw material of the tube sheet; the carbon material is carbon black.
[0029] The raw material for the pipe segments comprises the following components in parts by weight: cement: 300-350 parts; fly ash: 50-60 parts; sand: 600-650 parts; crushed stone: 400-500 parts; water-reducing agent: 10-15 parts; carbon materials: 8-10 parts; and a water-cement ratio of 0.25-0.3.
[0030] In the process of synthesizing raw materials for pipe segments, the carbon black is first fully mixed with fly ash, and then mixed with cement, sand, crushed stone, water-reducing agent and water to obtain a slurry. The slurry is then poured into a mold, compacted, cured and demolded to obtain pipe segments. The carbon black and fly ash are mixed in the following manner: First, dissolve the carbon black in 6-8 times its weight of water, then heat it to 80-90℃ and stir it evenly. Then add it to the fly ash. Continue stirring for at least 1 hour. The slurry can be used directly or spray-dried to obtain a solid mixture of carbon black and fly ash for later use.
[0031] To prove the validity of this application, such as Figure 2 As shown, this application sets up a support plane 1, and three tube segments 2 are set on the support plane 1. The tube segments are connected by snap-fit. The length of the tube segments is 2m and the corresponding center angle is 30°. A sliding track 3 is set at the center of the support plane 1, and the infrared imager 4 is slidably set on the sliding track 3 to acquire thermal images.
[0032] To demonstrate the effectiveness of the technical solution adopted in this application, the following examples are provided: Example 1: S11. Structural fabrication: S1101 segment processing Mix the following components in parts by weight: cement: 300 parts; fly ash: 50 parts; sand: 600 parts; crushed stone: 400 parts; water-reducing agent: 10 parts, with a water-cement ratio of 0.25. After mixing evenly, the mixture is added to the segment mold for segment processing, and then solidified and cured for 20 days to obtain the segment. S1102 on-site processing The segments are assembled together to obtain the components to be monitored.
[0033] S12. Infrared simulation monitoring: S1201 segment failure Using a hammer to strike the tunnel segments causes incomplete structural damage to the segments, resulting in visible cracks. S1202 Data Monitoring Thermal images were obtained by monitoring the walls of subway tunnels using an infrared imager; S1203 Data Processing The thermal image is binarized to obtain the base image; The binarization process is performed as follows: First, convert the thermal image to a grayscale image; Obtain the grayscale value s of each pixel in a grayscale image i The maximum value is s max The minimum value is s min ; Calculate the average gray value S of all pixels in a grayscale image; Get the basic white marker pixels: For grayscale value s i In [s max -(s max -S)*0.1,s max The grayscale value of the pixels within the brackets is set to 255. For grayscale value s i In [s min s min +(Ss min The grayscale value of the pixels within [0.1] is set to 0; Then, secondary assignments are performed on the grayscale values of other pixels that are adjacent to pixels that have already been assigned values.
[0034] The secondary assignment is set as follows: The selection of other pixels is based on the size of the number N of adjacent pixels that have already been assigned a value. First, the pixel with the largest N is assigned a value in the following manner. First, calculate the average value of the surrounding pixels. If the gray value of the point is greater than the average value of the surrounding pixels, then set the gray value of the point to 255; otherwise, set it to 0. Then, based on the number N of adjacent pixels that have already been assigned values, other pixels are selected, and the pixel with the largest N is assigned a value; this process is repeated until the binarization of all pixels is completed.
[0035] S1204 analyzes and measures the number, length, and width of cracks in the binarized image.
[0036] S1205 conducted an on-site investigation, measuring the actual number, length, and width of the cracks, and comparing this data with information obtained from image analysis. The number of cracks obtained through binarization was 34, and the number obtained from on-site measurement was 38. On-site verification was carried out on the length and width of the cracks obtained through binarization. Based on the on-site measurement values, the number of cracks with fluctuations in length and width within 10% was considered basically accurate, with 30 cracks. The number of cracks with fluctuations in length and width within 5% was considered completely accurate, with 25 cracks.
[0037] Example 2: S21. Structural fabrication: S2101 segment processing Mix the following components in parts by weight: cement: 350 parts; fly ash: 60 parts; sand: 650 parts; crushed stone: 500 parts; water-reducing agent: 15 parts, with a water-cement ratio of 0.3. After mixing evenly, the mixture is added to the segment mold for segment processing, and then solidified and cured for 20 days to obtain the segment. S2102 on-site processing The segments are assembled together to obtain the components to be monitored.
[0038] S22. Infrared simulation monitoring: S2201 segment failure Using a hammer to strike the tunnel segments causes incomplete structural damage to the segments, resulting in visible cracks. S2202 Data Monitoring Thermal images were obtained by monitoring the walls of subway tunnels using an infrared imager; S2203 Data Processing The thermal image is binarized to obtain the base image; The binarization process is performed as follows: First, convert the thermal image to a grayscale image; Obtain the grayscale value s of each pixel in a grayscale image i The maximum value is s max The minimum value is s min ; Calculate the average gray value S of all pixels in a grayscale image; Get the basic white marker pixels: For grayscale value s i In [s max -(s max -S)*0.1,s max The grayscale value of the pixels within the brackets is set to 255. For grayscale value s i In [s min s min +(Ss min The grayscale value of the pixels within [0.1] is set to 0; Then, secondary assignments are performed on the grayscale values of other pixels that are adjacent to pixels that have already been assigned values.
[0039] The secondary assignment is set as follows: The selection of other pixels is based on the size of the number N of adjacent pixels that have already been assigned a value. First, the pixel with the largest N is assigned a value in the following manner. First, calculate the average value of the surrounding pixels. If the gray value of the point is greater than the average value of the surrounding pixels, then set the gray value of the point to 255; otherwise, set it to 0. Then, based on the number N of adjacent pixels that have already been assigned values, other pixels are selected, and the pixel with the largest N is assigned a value; this process is repeated until the binarization of all pixels is completed.
[0040] S2204 analyzes and measures the number, length, and width of cracks in the binarized image.
[0041] S2205 conducted an on-site investigation, measuring the actual number, length, and width of the cracks, and comparing this data with information obtained from image analysis. The number of cracks obtained through binarization was 28, and the number obtained through on-site measurement was 31. The length and width of the cracks obtained through binarization were verified on-site. Based on the on-site measurement values, the number of cracks with fluctuations in length and width within 10% was considered basically accurate, with 23 cracks. The number of cracks with fluctuations in length and width within 5% was considered completely accurate, with 20 cracks.
[0042] Example 3: S31. Structural fabrication: S3101 segment processing Carbon black and fly ash are premixed as follows: First, dissolve 8 parts by weight of carbon black in 6 times its weight of water, then heat to 80°C and stir evenly. Then add it to 50 parts by weight of fly ash and continue stirring for at least 1 hour. The slurry can be used directly.
[0043] Mix the following components in parts by weight: cement: 300 parts; fly ash: 50 parts; sand: 600 parts; crushed stone: 400 parts; water-reducing agent: 10 parts, with a water-cement ratio of 0.25. After mixing evenly, the mixture is added to the segment mold for segment processing, and then solidified and cured for 20 days to obtain the segment. S3102 on-site processing The segments are assembled together to obtain the components to be monitored.
[0044] S32. Infrared Simulation Monitoring: S3201 segmental damage Using a hammer to strike the tunnel segments causes incomplete structural damage to the segments, resulting in visible cracks. S3202 Data Monitoring Thermal images were obtained by monitoring the walls of subway tunnels using an infrared imager; S3203 Data Processing The thermal image is binarized to obtain the base image; The binarization process is performed as follows: First, convert the thermal image to a grayscale image; Obtain the grayscale value s of each pixel in a grayscale image i The maximum value is s max The minimum value is s min ; Calculate the average gray value S of all pixels in a grayscale image; Get the basic white marker pixels: For grayscale value s i In [s max -(s max -S)*0.1,s max The grayscale value of the pixels within the brackets is set to 255. For grayscale value s i In [s min s min +(Ss min The grayscale value of the pixels within [0.1] is set to 0; Then, secondary assignments are performed on the grayscale values of other pixels that are adjacent to pixels that have already been assigned values.
[0045] The secondary assignment is set as follows: The selection of other pixels is based on the size of the number N of adjacent pixels that have already been assigned a value. First, the pixel with the largest N is assigned a value in the following manner. First, calculate the average value of the surrounding pixels. If the gray value of the point is greater than the average value of the surrounding pixels, then set the gray value of the point to 255; otherwise, set it to 0. Then, based on the number N of adjacent pixels that have already been assigned values, other pixels are selected, and the pixel with the largest N is assigned a value; this process is repeated until the binarization of all pixels is completed.
[0046] The S3204 analyzes and measures the number, length, and width of cracks in the binarized image.
[0047] S3205 conducted an on-site investigation, measuring the actual number, length, and width of the cracks, and comparing this data with information obtained from image analysis. The number of cracks obtained through binarization was 45, and the number obtained through on-site measurement was 46. The length and width of the cracks obtained through binarization were verified on-site. Based on the on-site measurement values, the number of cracks with fluctuations in length and width within 10% was considered basically accurate, with 44 cracks. The number of cracks with fluctuations in length and width within 5% was considered completely accurate, with 42 cracks.
[0048] Example 4: S41. Structural fabrication: S4101 segment processing First, dissolve 10 parts by weight of carbon black in 8 times its weight of water, then heat to 90°C and stir evenly. Then add it to 60 parts by weight of fly ash. Continue stirring for at least 1 hour. Spray dry the slurry to obtain a solid mixture of carbon black and fly ash for later use.
[0049] Mix the following components in parts by weight: cement: 350 parts; sand: 650 parts; crushed stone: 500 parts; water-reducing agent: 15 parts, with a water-cement ratio of 0.3. After mixing evenly, the mixture is added to the segment mold for segment processing, and then solidified and cured for 20 days to obtain the segment. S4102 on-site processing The segments are assembled together to obtain the components to be monitored.
[0050] S42. Infrared Simulation Monitoring: S4201 segmental damage Using a hammer to strike the tunnel segments causes incomplete structural damage to the segments, resulting in visible cracks. S4202 Data Monitoring Thermal images were obtained by monitoring the walls of subway tunnels using an infrared imager; S4203 Data Processing The thermal image is binarized to obtain the base image; The binarization process is performed as follows: First, convert the thermal image to a grayscale image; Obtain the grayscale value s of each pixel in a grayscale image i The maximum value is s max The minimum value is s min ; Calculate the average gray value S of all pixels in a grayscale image; Get the basic white marker pixels: For grayscale value s i In [s max -(s max -S)*0.1,s max The grayscale value of the pixels within the brackets is set to 255. For grayscale value s i In [s min s min +(Ss min The grayscale value of the pixels within [0.1] is set to 0; Then, secondary assignments are performed on the grayscale values of other pixels that are adjacent to pixels that have already been assigned values.
[0051] The secondary assignment is set as follows: The selection of other pixels is based on the size of the number N of adjacent pixels that have already been assigned a value. First, the pixel with the largest N is assigned a value in the following manner. First, calculate the average value of the surrounding pixels. If the gray value of the point is greater than the average value of the surrounding pixels, then set the gray value of the point to 255; otherwise, set it to 0. Then, based on the number N of adjacent pixels that have already been assigned values, other pixels are selected, and the pixel with the largest N is assigned a value; this process is repeated until the binarization of all pixels is completed.
[0052] The S4204 analyzes and measures the number, length, and width of cracks in the binarized image.
[0053] S4205 conducted an on-site investigation, measuring the actual number, length, and width of the cracks, and comparing this data with information obtained from image analysis. The number of cracks obtained through binarization processing was 38, and the number obtained from on-site measurement was also 38. On-site verification was carried out on the length and width of the cracks obtained through binarization processing. Based on the on-site measurement values, the number of cracks with fluctuations in length and width within 10% was considered basically accurate, with 37 cracks. The number of cracks with fluctuations in length and width within 5% was considered completely accurate, with 36 cracks.
[0054] Comparative Example 1: Based on Example 1, the number of cracks obtained by binarization using the bimodal method was 30, while the number obtained by field measurement was 38. The length and width of the cracks obtained by binarization were verified on-site. Based on the on-site measurement values, the number of cracks with fluctuations in length and width within 10% was considered basically accurate, with 21 cracks. The number of cracks with fluctuations in length and width within 5% was considered completely accurate, with 15 cracks.
[0055] Comparative Example 2: Based on Example 3, the method of segment assembly is changed to the following steps: Mix the following components in parts by weight: cement: 300 parts; fly ash: 50 parts; sand: 600 parts; crushed stone: 400 parts; water-reducing agent: 10 parts; carbon black: 8 parts, with a water-cement ratio of 0.25. The number of cracks obtained through binarization was 38, and the number obtained through on-site measurement was 43. The length and width of the cracks obtained through binarization were verified on-site. Based on the on-site measurement values, the number of cracks with fluctuations in length and width within 10% was considered basically accurate, with 32 cracks. The number of cracks with fluctuations in length and width within 5% was considered completely accurate, with 26 cracks.
[0056] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method of monitoring a subway tunnel gap, the method comprising: The method comprises the following steps: monitoring the subway tunnel wall to obtain a thermal image by using an infrared imager; preprocessing the thermal image to obtain a basic image; taking the abutted joint between the segments as a position base point, and obtaining the change trend of the segment crack according to the position base point; determining whether intervention is needed according to the change trend of the segment crack.
2. The method according to claim 1, wherein the preprocessing is binaryzation.
3. The method according to claim 2, wherein the length and width of the crack are obtained by analyzing and measuring the image after the binaryzation.
4. The method according to claim 3, wherein the binaryzation is performed in the following manner: firstly, the thermal image is converted into a gray image; then, the average value S of the gray values of all the pixel points in the gray image is calculated; the basic white marker pixel point is obtained; and then, the gray values of other pixel points adjacent to the pixel points that have been assigned are assigned. Obtaining the gray value s of each pixel point of the gray image i , the maximum value is s max , and the minimum value is s min ; 5. The method according to claim 4, wherein the secondary assignment is set in the following manner: the selection of other pixel points is performed according to the number N of the pixel points that have been assigned, the assignment of the pixel points with the largest N is performed in the following manner: For the gray value s i In [s max -(s max -S)*0.1, the gray value of the pixel point in s max ] is set to 255; For the gray value s i In [s min , s min +(s-s min )*0.1] the gray value of the pixel point is set to 0; firstly, the average value of the surrounding pixel points is calculated, if the gray value of the pixel point is greater than the average value of the surrounding pixel points, the gray value of the pixel point is set to 255, otherwise, the gray value of the pixel point is set to 0; then, the selection of other pixel points is performed according to the number N of the pixel points that have been assigned, the assignment of the pixel points with the largest N is performed; and the above steps are repeated until the binaryzation of all the pixel points is completed.
6. The method according to claim 3, wherein the subway tunnel wall is assembled by segments.
7. The method according to claim 6, wherein the position base point is arranged at the corner of the segment, and the segment is positioned according to the abutted joint between the segments. The carbon material is carbon black. The segment raw material comprises the following components in mass fraction: cement: 300-350 parts; fly ash: 50-60 parts; sand: 600-650 parts; gravel: 400-500 parts; water reducing agent: 10-15 parts; carbon material: 8-10 parts; water-cement ratio: 0.25-0.
3.
8. The method of claim 7, wherein: During the synthesis of the segment raw material, the carbon black is first mixed with the fly ash, then mixed with the cement, sand, gravel, water reducing agent and water to obtain a slurry, then introduced into a mold, compacted, cured, demolded to obtain the segment.
9. The subway tunnel gap monitoring method according to claim 8, characterized in that: The carbon black and fly ash are mixed in the following manner: firstly, the carbon black is dissolved in 6-8 times of water, then heated to 80-90℃, stirred uniformly, and then added to the fly ash; the slurry is directly used or spray-dried to obtain a solid mixture of carbon black and fly ash for standby. 10. The method of claim 9, wherein: