A device and method for detecting steel fiber content and orientation in steel fiber concrete

Abstract

The present invention discloses a device and method for detecting the content and orientation of steel fibers in steel fiber concrete. The detection device includes a data acquisition system, a magnetization device, an induction device, and a data processing and display system; the data acquisition system is respectively connected to the magnetization device, the induction device, and the data processing and display system; the magnetization device is used to generate a uniform magnetic field and magnetize the steel fibers inside the steel fiber concrete; the induction device is used to measure the residual magnetism inside the steel fiber concrete; and the data acquisition system is used to collect and process the residual magnetism measured by the induction device in real time. The present invention magnetizes the steel fibers inside the steel fiber concrete using a magnetization device, measures the residual magnetism data of each sampling point using an induction device, and analyzes and determines the content and orientation of the steel fibers in combination with a spatial distribution model and an orientation probability model, thereby achieving efficient, accurate, and non-destructive detection of the steel fiber distribution and being suitable for real-time evaluation at the construction site.

Description

Technical Field

[0001] The present invention relates to the field of nondestructive testing of civil engineering, in particular to a device and method for detecting the content and orientation of steel fibers in steel fiber concrete. Background Art

[0002] Steel fiber reinforced concrete (SFRC) is a composite material that incorporates steel fibers into concrete to improve its crack resistance, tensile strength, impact resistance, fatigue resistance, and durability. The uniform distribution and orientation of the steel fibers within the concrete are crucial to the concrete's mechanical properties and durability. However, in practice, various factors can lead to localized excess (clumping) or deficiency of steel fibers within the concrete, compromising the overall performance of the concrete.

[0003] The distribution of steel fibers in concrete is typically assessed through destructive testing (such as core drilling and cutting sampling). Destructive testing methods are not only time-consuming and labor-intensive, but also impractical for large-scale, comprehensive inspections. Furthermore, the damage to components or structures is detrimental to safety. Furthermore, destructive testing cannot be conducted in real time at the construction site, making it difficult to adjust and control the uniform distribution of steel fibers based on the test results during construction.

[0004] In recent years, nondestructive testing (NDT) technology has gained widespread attention and application in concrete engineering. Currently, the main NDT methods used for steel fiber reinforced concrete testing include ultrasonic testing, X-ray computed tomography (CT), magnetic resonance imaging (MRI), resistivity measurement, ground-based radar (GPR), and electromagnetic induction. While ultrasonic testing is simple to perform, the sound wave attenuation in steel fiber reinforced concrete is significant, and the reflected signal is complex, making it difficult to accurately identify the distribution characteristics of steel fibers. X-ray computed tomography can provide relatively clear three-dimensional images of the internal structure, but the equipment is expensive and the testing speed is slow, making it unsuitable for rapid on-site testing. Magnetic resonance imaging can non-destructively obtain information about the internal structure of a material, but it has significant limitations when testing metallic materials such as steel fibers. Resistivity measurement relies on the resistivity difference between steel fibers and the concrete matrix, but it is significantly affected by ambient humidity and has difficulty distinguishing the orientation of steel fibers. Radar detection technology has strong penetration but limited resolution, making it difficult to accurately distinguish densely distributed steel fibers. While electromagnetic induction is theoretically suitable for testing metallic materials, it has limited testing depth and poor ability to identify fiber orientation.

[0005] All of the above traditional NDT methods have their own limitations. Therefore, developing a detection device and method for the content and orientation of steel fibers in steel fiber concrete is of great significance for ensuring the construction quality and performance of steel fiber concrete. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a device and method for detecting the content and orientation of steel fibers in steel fiber concrete.

[0007] The technical solution of the present invention to solve the technical problem of the equipment is to provide a detection device for the content and orientation of steel fibers in steel fiber concrete, the detection device including a data acquisition system, a magnetization device, a sensing device and a data processing and display system;

[0008] The data acquisition system is respectively connected to the magnetization device, the induction device and the data processing and display system; the magnetization device is used to generate a uniform magnetic field and magnetize the steel fibers inside the steel fiber concrete; the induction device is used to measure the residual magnetism inside the steel fiber concrete; the data acquisition system is used to collect and process the residual magnetism measured by the induction device in real time.

[0009] The present invention solves the technical problem of the method by providing a method for detecting the content and orientation of steel fibers in steel fiber concrete based on the detection device, the detection method comprising the following steps:

[0010] Step 1: Building the detection equipment;

[0011] Step 2: Place the sensing device in the on-site construction environment and measure the background reference value;

[0012] Step 3: Use a magnetizing device to scan the surface of the steel fiber concrete in one direction from one end of the steel fiber concrete to the other end. During the scanning process, the magnetic field generated by the magnetizing device magnetizes the steel fibers inside the steel fiber concrete, thereby magnetizing the steel fibers inside the steel fiber concrete. The moving speed is constant to ensure a consistent degree of magnetization.

[0013] Step 4: Delineate a detection area on the steel fiber concrete surface, select several sampling points within the detection area, and then place a sensing device at the sampling points. Use the sensing device to measure the initial residual magnetization data at each sampling point. The initial residual magnetization data includes the initial residual magnetization magnitude and the initial residual magnetization direction.

[0014] Step 5: The data acquisition system collects the initial residual magnetism data of each sampling point measured by the sensing device in real time, and transmits the initial residual magnetism data of each sampling point to the data processing and display system;

[0015] Step 6: In the data processing and display system, the initial residual magnetization data of each sampling point obtained in the step is first corrected for the background reference value to obtain residual magnetization calibration data; then, the residual magnetization calibration data is subjected to machine learning filtering to eliminate the influence of the steel bars and metal embedded parts in the steel fiber concrete, and the residual magnetization data of the steel fiber at each sampling point is obtained; the residual magnetization data includes the magnitude and direction of the residual magnetization; then, according to the residual magnetization direction of the steel fiber at each sampling point, the orientation of the steel fiber at each sampling point is obtained in combination with electromagnetic laws; at the same time, a spatial distribution model and an orientation probability model are respectively established in the data processing and display system; according to the magnitude of the residual magnetization of the steel fiber at each sampling point, the spatial distribution model is used to obtain the content of the steel fiber at each sampling point, the content of the steel fiber in the detection area, and the content of the steel fiber at each non-sampling point in the detection area; according to the residual magnetization direction of the steel fiber at each sampling point, the orientation probability model is used to obtain the orientation of the steel fiber in the detection area.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] (1) The present invention magnetizes the steel fibers within steel fiber concrete using a magnetizing device, measures the residual magnetization data of each sampling point using an induction device, and analyzes and determines the content and orientation of the steel fibers by combining a spatial distribution model and an orientation probability model. This method achieves efficient, accurate, and non-destructive testing of the steel fiber distribution, making it suitable for real-time assessment at the construction site. It has the advantages of high efficiency, accuracy, convenience, and non-destructiveness. Without destroying the components or structure, the accuracy of detecting the fiber distribution and content in steel fiber concrete is improved, thereby providing a guarantee for the construction quality of steel fiber concrete.

[0018] (2) High efficiency: The present invention uses a magnetic vector method. Through the combination of a magnetizing device and an induction device, it can quickly scan and measure the magnitude and direction of the residual magnetism of steel fiber concrete. Combined with a data processing and display system, it can achieve real-time data acquisition, intelligent analysis, and graphical presentation. Compared with the shortcomings of traditional X-ray CT detection, which is slow and unsuitable for on-site use, and the lengthy imaging process of magnetic resonance imaging (MRI), the present invention can quickly generate results through unidirectional scanning without the need for tedious post-analysis. It is particularly suitable for the needs of real-time detection on construction sites and greatly improves detection efficiency.

[0019] (3) Accuracy: Traditional methods such as ultrasonic testing are limited by sound wave attenuation and signal complexity and are difficult to accurately identify fiber distribution. The present invention uses a highly sensitive sensing device and machine learning algorithm to intelligently analyze and eliminate the influence of the geomagnetic field, steel bars in concrete, metal embedded parts and surrounding environmental noise, accurately analyze and determine the content and orientation of steel fibers, and significantly improve detection accuracy.

[0020] (4) Convenience: Compared with the complex and limited resolution radar detection technology (GPR) equipment and the high operating threshold, the equipment of the present invention is relatively simple in composition, compact in design, easy and intuitive to operate, does not require a professional environment or lengthy calibration, is suitable for on-site detection, does not require complicated preparation work, and greatly improves the convenience of on-site application.

[0021] (5) Non-destructive: Compared to traditional destructive testing (such as cutting and polishing), which requires sample destruction and cannot fully detect the sample, the detection process of the present invention does not cause any damage to the concrete, can ensure the integrity of the material, and is suitable for real-time evaluation of the distribution of steel fibers in concrete at the construction site. While maintaining non-destructiveness, it provides efficient and accurate test results.

[0022] (6) In summary, this detection method is efficient, accurate, convenient, and comprehensive. It can detect the content and orientation of steel fibers in concrete in real time, in situ, with high precision, and non-destructively at the construction site, identify defects such as steel fiber agglomeration, effectively guide the construction process, and ensure the mechanical properties and durability of concrete.

[0023] This detection equipment is suitable for existing and new steel fiber concrete projects, ensuring the quality of steel fiber concrete in major infrastructure projects such as tunnels and bridges. It has the characteristics of high detection accuracy, strong applicability and low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Schematic diagram of the overall structure of the detection device of the present invention;

[0025] Figure 2 is a structural diagram of the magnetization device of the present invention;

[0026] Figure 3 Schematic diagram of a sample of a steel fiber reinforced concrete shield segment according to Example 1 of the present invention;

[0027] Figure 4 This is a sampling point distribution diagram of the steel fiber concrete shield segment of Example 1 of the present invention;

[0028] Figure 5 This is a visualization diagram of steel fiber distribution in Example 1 of the present invention;

[0029] Figure 6 This is a visualization diagram of steel fiber distribution in Example 2 of the present invention;

[0030] Figure 7 This is a visualization diagram of the steel fiber distribution of Example 3 of the present invention.

[0031] In the figure, there are a data acquisition system 1, a magnetization device 2, a sensing device 3, a data processing and display system 4, an electromagnet 21, a magnetization device housing 22, a DC power supply 23, a steel fiber concrete shield segment 5, a detection area 6, and a sampling point 7. DETAILED DESCRIPTION

[0032] The specific embodiments of the present invention are given below. The specific embodiments are only used to further illustrate the present invention and do not limit the scope of protection of the present invention.

[0033] The present invention also provides a detection device for the content and orientation of steel fibers in steel fiber concrete (hereinafter referred to as the detection device, such as Figure 1 As shown), the detection equipment includes a data acquisition system 1, a magnetization device 2, an induction device 3 and a data processing and display system 4;

[0034] The data acquisition system 1 is connected to the magnetization device 2, the induction device 3 and the data processing and display system 4 through wired or wireless transmission. The magnetization device 2 is used to generate a uniform magnetic field and magnetize the steel fibers inside the steel fiber concrete. The induction device 3 is used to measure the residual magnetic induction intensity (residual magnetism) inside the steel fiber concrete. The data acquisition system 1 is used to collect and process the residual magnetism data measured by the induction device 3 in real time.

[0035] Preferably, the magnetizing device 2 (such as Figure 2 ) includes an electromagnet 21, a magnetizing device housing 22, and a DC power supply 23; a plurality of groups of electromagnets 21 arranged in a straight line are evenly arranged in the magnetizing device housing 22, and the directions of the inductance coils on all electromagnets 21 are completely consistent; the DC power supply 23 supplies power to the electromagnets 21.

[0036] Preferably, the sensing device 3 includes a fluxgate sensor and a sensing device housing; m rows and n columns of fluxgate sensors are arranged in an array in the sensing device housing; the fluxgate sensor is communicatively connected to the data acquisition system 1; m and n are arbitrary integers greater than or equal to 1; the accuracy of the sensing device 3 is related to the number and arrangement of the fluxgate sensors arranged.

[0037] Preferably, the distance between two adjacent fluxgate sensors is 0.5-5 cm; the sensitivity of the fluxgate sensor is nT level, which can not only measure the magnitude of the magnetic induction intensity, but also determine the direction of the magnetic field.

[0038] The present invention also provides a method for detecting the content and orientation of steel fibers in steel fiber concrete (hereinafter referred to as the detection method), which comprises the following steps:

[0039] Step 1: Building the detection equipment;

[0040] Step 2: Place the sensing device 3 in the on-site construction environment to measure the background reference value;

[0041] Step 3: Use the magnetizing device 2 to move the surface of the steel fiber concrete from one end of the steel fiber concrete to the other end in one direction to perform a one-way scan. Do not scan back and forth repeatedly. During the scanning process, the magnetic field generated by the magnetizing device 2 magnetizes the steel fibers inside the steel fiber concrete, thereby magnetizing the steel fibers inside the steel fiber concrete. The moving speed is constant to ensure a consistent degree of magnetization.

[0042] Preferably, in step 3, the moving speed is 0.01-0.1 m / s, and a uniform speed is maintained during the moving process to ensure uniform magnetization.

[0043] Preferably, in step 3, the magnetization process also magnetizes the steel bars and metal embedded parts in the steel fiber concrete.

[0044] Step 4: Delineate a detection area 6 on the surface of the steel fiber concrete, select a number of sampling points 7 within the detection area 6, and then place the sensing device 3 at the sampling points 7. Use the sensing device 3 to measure the initial residual magnetization data at each sampling point 7, where the initial residual magnetization data includes the initial residual magnetization magnitude and the initial residual magnetization direction.

[0045] Preferably, in step 4, according to the accuracy requirement, each fluxgate sensor of the sensing device 3 corresponds to one sampling point 7, so as to realize batch detection of the sampling points 7.

[0046] Step 5: The data acquisition system 1 collects the initial residual magnetism data of each sampling point 7 measured by the sensing device 3 in real time, and transmits the initial residual magnetism data of each sampling point 7 to the data processing and display system 4;

[0047] Step 6: In the data processing and display system 4, the initial residual magnetization data of each sampling point 7 obtained in step 4 is first corrected for the background reference value to obtain residual magnetization calibration data; then, the residual magnetization calibration data is subjected to machine learning filtering to eliminate the influence of the steel bars and metal embedded parts in the steel fiber concrete, and the residual magnetization data of the steel fiber at each sampling point 7 is obtained; the residual magnetization data includes the magnitude and direction of the residual magnetization; then, since the background reference value correction and machine learning filtering will not affect the residual magnetization direction, the residual magnetization direction of the steel fiber at each sampling point 7 is consistent with the initial residual magnetization direction, and the residual magnetization direction of each sampling point 7 is obtained according to the measurement of the sensing device 3. The initial remanent magnetization direction at 7 is the remanent magnetization direction of the steel fiber at each sampling point 7, and the orientation of the steel fiber at each sampling point 7 is obtained by combining the electromagnetic law; at the same time, a spatial distribution model and an orientation probability model are respectively established in the data processing and display system 4; according to the remanent magnetization size of the steel fiber at each sampling point 7, the spatial distribution model is used to obtain the steel fiber content at each sampling point 7, the steel fiber content in the detection area 6, and the steel fiber content at each non-sampling point in the detection area 6; according to the remanent magnetization direction of the steel fiber at each sampling point 7, the orientation probability model is used to obtain the orientation of the steel fiber in the detection area 6.

[0048] Preferably, in step 6, the larger the residual magnetism, the higher the corresponding steel fiber content; according to electromagnetism, the initial residual magnetism direction at each sampling point 7 measured by the induction device 3 in step 4 is the orientation of the steel fiber at each sampling point 7. The principle is to use the magnetization device 2 to scan the steel fiber concrete, magnetize the steel fibers inside the concrete, and not magnetize the concrete. Therefore, the residual magnetism direction of the steel fiber at each sampling point 7 detected by the induction device 3 is the orientation of the steel fiber at each sampling point 7 in the concrete.

[0049] Preferably, in step 6, the background reference value correction is to eliminate the influence of the geomagnetic field and the surrounding environmental noise.

[0050] Preferably, in step 6, the machine learning filtering specifically involves testing a concrete sample containing known steel bars and metal embedded parts, recording the residual magnetization data to form a data set; then using the data set as input to train a neural network model to obtain a trained neural network model; then inputting the residual magnetization calibration data into the trained neural network model for recognition processing, filtering out the residual magnetization of the steel bars and metal embedded parts, eliminating the influence of the steel bars and metal embedded parts in the steel fiber reinforced concrete, and obtaining the residual magnetization data of the steel fibers at each sampling point 7. This is an existing mature technology.

[0051] Preferably, in step 6, the calculation process of the spatial distribution model is specifically:

[0052] Solution 1: Based on the residual magnetism of the steel fiber at each sampling point 7, an inverse distance weighted interpolation algorithm is used to calculate the residual magnetism of the steel fiber at several non-sampling points in the detection area 6 (the number of non-sampling points selected is related to the demand or the calculation accuracy to be achieved); then the residual magnetism of the steel fiber at each sampling point 7 and the residual magnetism of the steel fiber at several non-sampling points are summed, and the sum is divided by the sum of the number of sampling points 7 and the number of non-sampling points. The obtained average value is used as the residual magnetism of the steel fiber in the detection area 6; then, combined with the preset residual magnetism-steel fiber content calibration relationship, the steel fiber content at each sampling point 7, the steel fiber content in the detection area 6, and the steel fiber content at several non-sampling points are respectively obtained;

[0053] Option 2: Based on the residual magnetism of the steel fiber at each sampling point 7, the inverse distance weighted interpolation algorithm is used to calculate the residual magnetism of the steel fiber at several non-sampling points in the detection area 6 (the number of non-sampling points selected is related to the demand or the calculation accuracy to be achieved); then, combined with the preset residual magnetism size-steel fiber content calibration relationship, the steel fiber content at each sampling point 7 and the steel fiber content at several non-sampling points are obtained respectively; then, the steel fiber content at each sampling point 7 and the steel fiber content at several non-sampling points are summed, and the sum is divided by the sum of the number of sampling points 7 and the number of non-sampling points, and the obtained average value is used as the steel fiber content of the detection area 6.

[0054] Preferably, in step 6, a three-dimensional rectangular coordinate system is established with the steel fiber concrete surface as the xy plane and the direction perpendicular to the surface as the z axis; according to the coordinates (x i ,y i ,z i ) and its corresponding remanence size B of steel fiber i , using the inverse distance weighted interpolation algorithm, the residual magnetism size B(x,y,z) of the steel fiber at any position to be measured (x,y,z) (i.e. non-sampling point) in the detection area 6 is calculated as:

[0055] (1)

[0056] In formula (1), The distance from the position to be measured (x, y, z) to the sampling point 7 (x i ,y i ,z i ) spatial distance; j is the number of sampling points 7 in the detection area 6; p is a distance weight parameter, which ranges from 1 to 3, corresponding to the spatial distances along the x-axis, y-axis, and z-axis, respectively.

[0057] Preferably, in step 6, the preset residual magnetism size-steel fiber content calibration relationship is: use the detection equipment of the present invention to test the steel fiber concrete sample with known steel fiber content, for example, test 15kg / m 3 、30kg / m 3 , 50kg / m 3 、80kg / m 3 The residual magnetism of the steel fiber content sample is measured by the induction device 3, and a calibration relationship between residual magnetism and steel fiber content is established. This is a mature technology.

[0058] Preferably, in step 6, the specific process of the orientation probability model is: establishing a two-dimensional local coordinate system with the boundary of the detection area 6 as the coordinate axis, and counting the angle A between the remanent magnetization direction of the steel fiber at all sampling points 7 and the coordinate axis of the two-dimensional local coordinate system. i , A i ∈[0~90°]; then set the angle A i Angle interval, construct a two-dimensional angle histogram, the horizontal axis of the two-dimensional angle histogram is the angle interval, and the vertical axis is the angle A i The number of sampling points 7 in the corresponding angle interval; then, based on the number of sampling points 7 in each angle interval and the total number of sampling points 7, the ratio of the number of sampling points 7 in each angle interval to the total number of sampling points 7 is determined; the angle interval corresponding to the maximum ratio in the two-dimensional angle histogram is determined as the orientation of the steel fiber in the detection area 6; when there are multiple angle intervals whose corresponding ratio values ​​are all the maximum ratio, the angle average value or median value of these multiple angle intervals is taken as the orientation of the steel fiber in the detection area 6.

[0059] Preferably, in step 6, the data processing and display system 4 visualizes the final results after analysis and processing in a graphical manner, intuitively showing the content and orientation of steel fibers at different spatial positions; the graphics include vector distribution diagrams or cloud diagrams.

[0060] Example 1:

[0061] The detection equipment includes a data acquisition system 1, a magnetization device 2, a sensing device 3, and a data processing and display system 4;

[0062] In the magnetizing device 2 , the maximum inductance of the electromagnet 21 is 30 mH; the voltage of the DC power supply 23 is 24 V, and the maximum current is 10 A.

[0063] The sensing device 3 has 60×20 fluxgate sensors in an array. The spacing between two adjacent fluxgate sensors is 0.5 cm, which means the spacing between two adjacent sampling points 7 is 0.5 cm. The sensing device housing is made of polycarbonate (PC).

[0064] The test object is: Steel fiber reinforced concrete shield segment 5 (see Figure 3 ), the dimensions are 3.5m×1.5m for the inner arc surface and 3.9m×1.5m for the outer arc surface, and the design value of the steel fiber content is 30kg / m 3 Select the 60cm×30cm area on the inner arc surface of the steel fiber reinforced concrete shield segment 5 (such as Figure 4 , Figure 4 The number of sampling points 7 is only for reference and not the actual number) as the detection area 6 to test the content and orientation of the steel fibers therein.

[0065] The test results are:

[0066] Draw a visualization of the steel fiber distribution, such as Figure 5 As shown, Figure 5 The length of the line at sampling point 7 represents the relative content of steel fibers. The longer the length, the higher the content. The direction of the line represents the orientation of the steel fibers. Figure 5 It can be seen that the steel fiber content of the sample is 30kg / m 3 The steel fiber content meets the design requirements; the highest steel fiber content at all sampling points 7 is 33kg / m 3 ( Figure 5 The steel fiber content at all sampling points 7 is at least 28kg / m 3 ( Figure 5 At sampling point 2, there were no areas of steel fiber clumping (where the local steel fiber content was much higher than the overall content) or areas of low steel fiber content (where the local steel fiber content was much lower than the overall content), indicating good overall steel fiber dispersion. The steel fiber orientation angle in test area 6 was 45°.

[0067] Depend on Figure 5 It can be seen that the direction of the steel fibers at each sampling point 7 has no regularity and is random. Figure 5 The system can clearly identify the local distribution of steel fibers and accurately capture subtle changes in fiber orientation. However, the data volume is large and the single test time is long. It is suitable for prefabricated components, construction sites, existing structures, or laboratory specimen testing where high precision is required.

[0068] Example 2:

[0069] The detection equipment includes a data acquisition system 1, a magnetization device 2, a sensing device 3, and a data processing and display system 4;

[0070] In the magnetizing device 2 , the maximum inductance of the electromagnet 21 is 30 mH; the voltage of the DC power supply 23 is 24 V, and the maximum current is 10 A.

[0071] The sensing device 3 has 12 x 6 fluxgate sensors forming an array. The distance between two adjacent fluxgate sensors is 2.5 cm, that is, the distance between two adjacent sampling points 7 is 2.5 cm. The sensing device housing is made of polycarbonate (PC).

[0072] The test object is: Steel fiber reinforced concrete shield segment 5 (see Figure 3 ), the dimensions are 3.5m×1.5m for the inner arc surface and 3.9m×1.5m for the outer arc surface, and the design value of the steel fiber content is 25kg / m 3 Select the 60cm×30cm area on the inner arc surface of the steel fiber reinforced concrete shield segment 5 (such as Figure 4 ) as the detection area 6, to test the content and orientation of steel fibers therein.

[0073] The test results are:

[0074] Draw a visualization of the steel fiber distribution, such as Figure 6 As shown, Figure 6 The length of the line at sampling point 7 represents the relative content of steel fibers. The longer the length, the higher the content. The direction of the line represents the orientation of the steel fibers. Figure 6 It can be seen that the steel fiber content of the sample is 24kg / m 3 The steel fiber content meets the design requirements; the highest steel fiber content at all sampling points 7 is 31kg / m 3 ( Figure 6 The steel fiber content at all sampling points 7 was at least 22 kg / m 3 ( Figure 6 At sampling point 4, the maximum steel fiber content was slightly higher, but there were no areas of steel fiber agglomeration (localized steel fiber content much higher than the overall content) or areas of low steel fiber content (localized steel fiber content much lower than the overall content). Overall, the steel fibers were well dispersed. The steel fiber orientation angle at test area 6 was 55°.

[0075] Depend on Figure 6 It can be seen that the directions of the steel fibers at each sampling point 7 have no regularity and are random. Figure 6 The results accurately reflect the distribution trend of local steel fibers. The spacing between adjacent fluxgate sensors is 2.5 cm, balancing sampling efficiency and accuracy while shortening detection time. This makes it suitable for rapid assessment of prefabricated components, construction sites, or existing structures.

[0076] Example 3:

[0077] The detection equipment includes a data acquisition system 1, a magnetization device 2, a sensing device 3, and a data processing and display system 4;

[0078] In the magnetizing device 2 , the maximum inductance of the electromagnet 21 is 30 mH; the voltage of the DC power supply 23 is 24 V, and the maximum current is 10 A.

[0079] The sensing device 3 has 6×6 fluxgate sensors forming an array. The distance between two adjacent fluxgate sensors is 5 cm, that is, the distance between two adjacent sampling points 7 is 5 cm. The sensing device housing is made of polycarbonate (PC).

[0080] The test object is: Steel fiber reinforced concrete shield segment 5 (see Figure 3 ), the dimensions are 3.5m×1.5m for the inner arc surface and 3.9m×1.5m for the outer arc surface, and the design value of the steel fiber content is 40kg / m 3 Select the 60cm×30cm area on the inner arc surface of the steel fiber reinforced concrete shield segment 5 (such as Figure 4 ) as the detection area 6, to test the content and orientation of steel fibers therein.

[0081] The test results are:

[0082] Draw a visualization of the steel fiber distribution, such as Figure 7 As shown, Figure 7 The length of the line at sampling point 7 represents the relative content of steel fibers. The longer the length, the higher the content. The direction of the line represents the orientation of the steel fibers. Figure 7 It can be seen that the steel fiber content of the sample is 41kg / m 3 The steel fiber content meets the design requirements; the highest steel fiber content at all sampling points 7 is 43kg / m 3 ( Figure 7 The steel fiber content at all sampling points 7 was at least 38 kg / m 3 ( Figure 7 At sampling point 6, there were no areas of steel fiber clumping (where the local steel fiber content was much higher than the overall content) or areas of low steel fiber content (where the local steel fiber content was much lower than the overall content), indicating good overall steel fiber dispersion. The steel fiber orientation angle in test area 6 was 35°.

[0083] Depend on Figure 7 It can be seen that the directions of the steel fibers at each sampling point 7 have no regularity and are random. Figure 7 The results provide an overview of the distribution of steel fibers within the segment concrete in the test area. The 5-cm spacing between adjacent fluxgate sensors results in a relatively small number of sampling points, resulting in efficient sampling and data processing and a short test time, making it suitable for rapid assessment of large structures.

[0084] Any matters not described in the present invention are applicable to the prior art.

Claims

1. A device for detecting the content and orientation of steel fibers in steel fiber concrete, characterized in that: The detection device comprises a data acquisition system (1), a magnetization device (2), an induction device (3) and a data processing and display system (4); The data acquisition system (1) is respectively connected to the magnetizing device (2), the sensing device (3) and the data processing and display system (4); the magnetizing device (2) is used to generate a uniform magnetic field and magnetize the steel fibers inside the steel fiber concrete; the sensing device (3) is used to measure the residual magnetism inside the steel fiber concrete; the data acquisition system (1) is used to collect and process the residual magnetism data measured by the sensing device (3) in real time; The induction device (3) includes a fluxgate sensor and an induction device housing; the fluxgate sensors are arranged in an array in m rows and n columns in the induction device housing; the fluxgate sensors are communicatively connected to the data acquisition system (1); m and n are any integers greater than or equal to 1; A spatial distribution model and an orientation probability model are respectively established in the data processing and display system (4); based on the residual magnetism of the steel fiber at each sampling point (7), the content of the steel fiber at each sampling point (7), the content of the steel fiber in the detection area (6), and the content of the steel fiber at each non-sampling point in the detection area (6) are obtained by using the spatial distribution model; based on the residual magnetism direction of the steel fiber at each sampling point (7), the orientation of the steel fiber in the detection area (6) is obtained by using the orientation probability model.

2. The device for detecting the content and orientation of steel fibers in steel fiber concrete according to claim 1, characterized in that: The magnetizing device (2) comprises an electromagnet (21), a magnetizing device housing (22) and a DC power supply (23); a plurality of groups of electromagnets (21) arranged in a straight line are evenly arranged in the magnetizing device housing (22); and the DC power supply (23) supplies power to the electromagnets (21).

3. The device for detecting the content and orientation of steel fibers in steel fiber concrete according to claim 1, characterized in that: The distance between two adjacent fluxgate sensors is 0.5 to 5 cm; the sensitivity of the fluxgate sensor is nT level.

4. A method for detecting the content and orientation of steel fibers in steel fiber concrete based on the detection device according to any one of claims 1 to 3, characterized in that: The detection method comprises the following steps: Step 1: Build the detection device according to any one of claims 1 to 3; Step 2: placing the sensing device (3) in the on-site construction environment and measuring the background reference value; Step 3, using the magnetizing device (2) to move the surface of the steel fiber concrete from one end of the steel fiber concrete to the other end in one direction to perform a unidirectional scan, during which the magnetic field generated by the magnetizing device (2) magnetizes the steel fibers inside the steel fiber concrete, thereby magnetizing the steel fibers inside the steel fiber concrete; the moving speed is constant to ensure a consistent degree of magnetization; Step 4: defining a detection area (6) on the surface of the steel fiber concrete, selecting a plurality of sampling points (7) within the detection area (6), placing the sensing device (3) at the sampling points (7), and using the sensing device (3) to measure initial remanent magnetization data at each sampling point (7), the initial remanent magnetization data including initial remanent magnetization magnitude and initial remanent magnetization direction; Step 5, the data acquisition system (1) collects the initial residual magnetism data of each sampling point (7) measured by the sensing device (3) in real time, and transmits the initial residual magnetism data of each sampling point (7) to the data processing and display system (4); Step 6: In the data processing and display system (4), the initial residual magnetization data of each sampling point (7) obtained in step 4 is first corrected for the background reference value to obtain residual magnetization calibration data; then, the residual magnetization calibration data is subjected to machine learning filtering to eliminate the influence of the steel bars and metal embedded parts in the steel fiber concrete, and the residual magnetization data of the steel fiber at each sampling point (7) is obtained; the residual magnetization data includes the magnitude and direction of the residual magnetization; then, according to the residual magnetization direction of the steel fiber at each sampling point (7), the orientation of the steel fiber at each sampling point (7) is obtained in combination with the electromagnetic law; at the same time, a spatial distribution model and an orientation probability model are respectively established in the data processing and display system (4); according to the magnitude of the residual magnetization of the steel fiber at each sampling point (7), the content of the steel fiber at each sampling point (7), the content of the steel fiber in the detection area (6), and the content of the steel fiber at each non-sampling point in the detection area (6) are obtained by using the spatial distribution model; according to the residual magnetization direction of the steel fiber at each sampling point (7), the orientation of the steel fiber in the detection area (6) is obtained by using the orientation probability model.

5. The method for detecting the content and orientation of steel fibers in steel fiber concrete according to claim 4, characterized in that: In step 3, the moving speed is 0.01 to 0.1 m / s.

6. The method for detecting the content and orientation of steel fibers in steel fiber concrete according to claim 4, wherein: In step 4, each fluxgate sensor of the sensing device (3) corresponds to a sampling point (7), thereby realizing batch detection of the sampling points (7).

7. The method for detecting the content and orientation of steel fibers in steel fiber concrete according to claim 4, wherein: In step 6, background reference value correction is to eliminate the influence of the geomagnetic field and surrounding environmental noise.

8. The method for detecting the content and orientation of steel fibers in steel fiber concrete according to claim 4, wherein: In step 6, the calculation process of the spatial distribution model is as follows: Solution 1: Based on the residual magnetism of the steel fiber at each sampling point (7), an inverse distance weighted interpolation algorithm is used to calculate the residual magnetism of the steel fiber at several non-sampling points in the detection area (6), and the number of non-sampling points selected is related to the demand or the calculation accuracy to be achieved; then the residual magnetism of the steel fiber at each sampling point (7) and the residual magnetism of the steel fiber at several non-sampling points are summed, and the sum is divided by the sum of the number of sampling points (7) and the number of non-sampling points, and the obtained average value is used as the residual magnetism of the steel fiber in the detection area (6); then, combined with a preset residual magnetism size-steel fiber content calibration relationship, the steel fiber content at each sampling point (7), the steel fiber content in the detection area (6), and the steel fiber content at several non-sampling points are respectively obtained; Solution 2: Based on the residual magnetism of the steel fiber at each sampling point (7), an inverse distance weighted interpolation algorithm is used to calculate the residual magnetism of the steel fiber at several non-sampling points in the detection area (6); then, combined with a preset residual magnetism size-steel fiber content calibration relationship, the steel fiber content at each sampling point (7) and the steel fiber content at several non-sampling points are obtained respectively; then, the steel fiber content at each sampling point (7) and the steel fiber content at several non-sampling points are summed, and the sum is divided by the sum of the number of sampling points (7) and the number of non-sampling points, and the obtained average value is used as the steel fiber content of the detection area (6).

9. The method for detecting the content and orientation of steel fibers in steel fiber concrete according to claim 8, characterized in that: In step 6, a three-dimensional rectangular coordinate system is established with the steel fiber concrete surface as the xy plane and the direction perpendicular to the surface as the z axis; according to the coordinates (x i ,y i ,z i ) and its corresponding remanence size B of steel fiber i , using the inverse distance weighted interpolation algorithm, the residual magnetism size B(x,y,z) of the steel fiber at any position (x,y,z) to be tested in the detection area (6) is calculated as: In formula (1), The distance from the position to be measured (x, y, z) to the sampling point (7)(x i ,y i ,z i ) spatial distance; j is the number of sampling points (7) in the detection area (6); p is the distance weight parameter, which ranges from 1 to 3, corresponding to the spatial distance along the x-axis, y-axis and z-axis respectively.

10. The method for detecting the content and orientation of steel fibers in steel fiber concrete according to claim 4, wherein: In step 6, the specific process of the orientation probability model is: a two-dimensional local coordinate system is established with the boundary of the detection area (6) as the coordinate axis, and the angle A between the residual magnetization direction of the steel fiber at all sampling points (7) and the coordinate axis of the two-dimensional local coordinate system is calculated. i , A i ∈[0~90°]; then set the angle A i Angle interval, construct a two-dimensional angle histogram, the horizontal axis of the two-dimensional angle histogram is the angle interval, and the vertical axis is the angle A i The number of sampling points (7) in the corresponding angle interval; then, based on the number of sampling points (7) in each angle interval and the total number of sampling points (7), determining the ratio of the number of sampling points (7) in each angle interval to the total number of sampling points (7); the angle interval corresponding to the maximum ratio value in the two-dimensional angle histogram is determined as the orientation of the steel fiber in the detection area (6); when there are multiple angle intervals corresponding to the maximum ratio values, the angle average value or the median value of the multiple angle intervals is taken as the orientation of the steel fiber in the detection area (6).

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