Equipment and method for detecting content and orientation of steel fibers in steel fiber reinforced concrete

Abstract

The invention discloses equipment and a method for detecting the content and orientation of steel fibers in steel fiber reinforced concrete. The detection equipment comprises a data acquisition system, a magnetization device, an induction device and a data processing and display system, the data acquisition system is respectively in communication connection with the magnetization device, the induction device and the data processing and display system; the magnetizing device is used for generating a uniform magnetic field and magnetizing steel fibers in the steel fiber reinforced concrete; the sensing device is used for measuring residual magnetism in the steel fiber reinforced concrete; and the data acquisition system is used for acquiring and processing residual magnetism measured by the sensing device in real time. According to the method, the steel fibers in the steel fiber concrete are magnetized through the magnetizing device, the residual magnetism data of each sampling point are measured through the sensing device, the content and orientation of the steel fibers are analyzed and determined in combination with the spatial distribution model and the orientation probability model, efficient, accurate and nondestructive detection of steel fiber distribution is achieved, and the method is suitable for real-time evaluation of a construction site.

Description

Technical Field

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

[0002] Steel fiber concrete is a composite material that is made by adding steel fibers to concrete to improve its crack resistance, tensile strength, impact resistance, fatigue resistance and durability. Whether the steel fibers are evenly distributed in the concrete and the orientation of the steel fibers are crucial to the mechanical properties and durability of the concrete. However, in actual applications, due to various factors, the distribution of steel fibers in concrete may result in excessive (agglomerated) or insufficient steel fibers in some areas, thus affecting the overall performance of the concrete.

[0003] The distribution of steel fibers in concrete is usually evaluated through destructive testing (such as core drilling and cutting sampling). Destructive testing methods are not only time-consuming and labor-intensive, but also cannot be used for large-scale and comprehensive testing. In addition, the destructive effects on components or structures are not conducive to safety. In addition, destructive testing methods cannot be carried out in real time at the construction site, so it is difficult to adjust and control the uniform distribution of steel fibers in a timely manner according to the test results during the construction process.

[0004] In recent years, non-destructive testing (NDT) technology has received extensive attention and application in concrete engineering. At present, the NDT methods used for steel fiber concrete detection mainly include ultrasonic testing, X-ray computed tomography (CT), magnetic resonance imaging (MRI), resistivity measurement, radar detection technology (GPR) and electromagnetic induction method. Although ultrasonic testing is easy to operate, the sound wave attenuation in steel fiber concrete is large, the reflection signal is complex, and it is difficult to accurately identify the distribution characteristics of steel fibers. X-ray computed tomography can provide a clearer three-dimensional image of the internal structure, but the equipment is expensive and the detection speed is slow, which is not suitable for rapid detection on the construction site. Nuclear magnetic resonance imaging can obtain the internal structure information of the material non-destructively, but there are obvious limitations in the detection of metal materials such as steel fibers. The resistivity measurement method is based on the resistivity difference between steel fibers and concrete matrix, but it is greatly affected by environmental humidity and it is difficult to distinguish the orientation information of steel fibers. Radar detection technology has strong penetration ability, but limited resolution, and it is difficult to accurately distinguish densely distributed steel fibers. Although the electromagnetic induction method is theoretically suitable for metal material detection, it has limited detection depth and poor recognition ability for 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 comprising a data acquisition system, a magnetization device, a sensing 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; the data acquisition system is used to collect and process the residual magnetism measured by the induction device in real time.

[0008] The technical solution of the present invention to solve the technical problem of the method is to provide a method for detecting the content and orientation of steel fibers in steel fiber concrete based on the detection device, and the detection method comprises the following steps: Step 1, building the detection equipment; Step 2: Place the sensing device in the on-site construction environment and measure the background reference value; Step 3, use a magnetizing device 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, and during the scanning process, the magnetic field generated by the magnetizing device magnetizes the steel fibers inside the steel fiber concrete to magnetize the steel fibers inside the steel fiber concrete; the moving speed is constant to ensure a consistent degree of magnetization; Step 4: define a detection area on the surface of the steel fiber concrete, select several sampling points in the detection area, and then place the sensing device at the sampling points. Use the sensing device to measure the initial residual magnetism data at each sampling point, and the initial residual magnetism data includes the initial residual magnetism size and the initial residual magnetism direction; Step 5: The data acquisition system acquires the initial residual magnetic data of each sampling point measured by the sensing device in real time, and transmits the initial residual magnetic data of each sampling point to the data processing and display system; Step 6: In the data processing and display system, first correct the initial remanence data of each sampling point obtained in the previous step with the background reference value to obtain the remanence calibration data; then, perform machine learning filtering on the remanence calibration data to eliminate the influence of steel bars and metal embedded parts in the steel fiber concrete and obtain the remanence data of the steel fibers at each sampling point; the remanence data includes the magnitude and direction of the remanence; then, according to the remanence direction of the steel fibers at each sampling point, combine the electromagnetic laws to obtain the orientation of the steel fibers at each sampling point; at the same time, establish a spatial distribution model and an orientation probability model in the data processing and display system respectively; according to the remanence magnitude of the steel fibers at each sampling point, use the spatial distribution model to obtain the content of the steel fibers at each sampling point, the content of the steel fibers in the detection area, and the content of the steel fibers at each non-sampling point in the detection area; according to the remanence direction of the steel fibers at each sampling point, use the orientation probability model to obtain the orientation of the steel fibers in the detection area.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention magnetizes the steel fibers inside the steel fiber concrete through a magnetization device, measures the remanence data of each sampling point by an induction device, and combines the spatial distribution model and the orientation probability model to analyze and determine the content and orientation of the steel fibers, realizing efficient, accurate, and non-destructive detection of the steel fiber distribution, being applicable to real-time evaluation at the construction site, and having the advantages of high efficiency, accuracy, convenience, and non-destructiveness. Without damaging the component or structure, the detection accuracy of the fiber distribution and content in the steel fiber concrete is improved, thus providing guarantee for the construction quality of the steel fiber concrete.

[0010] (2) High efficiency: The present invention adopts the magnetic vector method. Through the cooperation of the magnetization device and the induction device, it can quickly scan and measure the magnitude and direction of the remanence of the steel fiber concrete, and combine with the data processing and display system to realize real-time data acquisition, intelligent analysis, and graphical presentation. Compared with the disadvantages of slow detection speed and inapplicability to on-site use of traditional X-ray CT and the long imaging process of nuclear magnetic resonance imaging (MRI), the present invention can quickly generate results through one-way scanning without cumbersome post-analysis, being particularly suitable for the needs of real-time detection at the construction site and greatly improving the detection efficiency.

[0011] (3) Accuracy: Traditional methods such as ultrasonic detection are difficult to accurately identify the fiber distribution due to the limitations of acoustic wave attenuation and signal complexity. The present invention adopts a highly sensitive induction device and machine learning algorithms, which can intelligently analyze and eliminate the influence of the geomagnetic field, steel bars in the concrete, metal embedded parts, and surrounding environmental noise, accurately analyze and determine the content and orientation of the steel fibers, and significantly improve the detection accuracy.

[0012] (4)Convenience: Compared with the complex radar detection technology (GPR) equipment with limited resolution and high operation threshold, the equipment of the present invention has a relatively simple composition, a compact design, and is easy and intuitive to operate. It does not require a professional environment or lengthy calibration, is suitable for on-site detection, and does not require complex preparation work, greatly improving the convenience of on-site application.

[0013] (5)Non-destructiveness: Compared with traditional destructive tests (such as cutting and polishing) that need to damage the sample and cannot comprehensively detect, the detection process of the present invention will 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 detection results.

[0014] (6)In summary, the present detection method has the characteristics of high efficiency, accuracy, convenience, and comprehensiveness. 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, judge defects such as steel fiber agglomeration, effectively guide the construction process, and ensure the mechanical properties and durability of concrete.

[0015] The present detection equipment is applicable to existing and newly built steel fiber concrete projects, ensures the quality of steel fiber concrete for major infrastructure projects such as tunnels and bridges, and has the characteristics of high detection accuracy, strong applicability, and low cost. Brief Description of the Drawings

[0016] Figure 1 is a schematic diagram of the overall structure of the detection equipment of the present invention; Figure 2 is a structural diagram of the magnetization device of the present invention; Figure 3 is a schematic diagram of a specimen of a steel fiber concrete shield segment in Embodiment 1 of the present invention; Figure 4 is a distribution diagram of sampling points of a steel fiber concrete shield segment in Embodiment 1 of the present invention; Figure 5 is a visualization diagram of the steel fiber distribution in Embodiment 1 of the present invention; Figure 6 is a visualization diagram of the steel fiber distribution in Embodiment 2 of the present invention; Figure 7 is a visualization diagram of the steel fiber distribution in Embodiment 3 of the present invention.

[0017] In the figure, data acquisition system 1, magnetization device 2, induction device 3, data processing and display system 4, electromagnet 21, magnetization device housing 22, DC power supply 23, steel fiber concrete shield segment 5, detection area 6, sampling point 7. Detailed Embodiment

[0018] Specific embodiments of the present invention are given below. The specific embodiments are only used to further illustrate the present invention in detail and do not limit the protection scope of the present invention.

[0019] 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, as Figure 1 shown), which includes 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 communicatively connected to the magnetization device 2, the induction device 3, and the data processing and display system 4 through wired transmission 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 (hereinafter referred to as remanence) inside the steel fiber concrete; the data acquisition system 1 is used to collect and process in real time the remanence data measured by the induction device 3.

[0020] Preferably, the magnetization device 2 (as Figure 2 shown) includes an electromagnet 21, a magnetization device housing 22, and a DC power supply 23; several groups of electromagnets 21 arranged in a straight line are uniformly arranged inside the magnetization device housing 22, and the directions of the inductance coils on all the electromagnets 21 are exactly the same; the DC power supply 23 supplies power to the electromagnet 21.

[0021] Preferably, the induction device 3 includes a fluxgate sensor and an induction device housing; m rows and n columns of fluxgate sensors are arranged in an array inside 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; the accuracy of the induction device 3 is related to the number and arrangement of the set fluxgate sensors.

[0022] Preferably, the distance between adjacent two fluxgate sensors is 0.5 - 5 cm; the sensitivity of the fluxgate sensors is in the order of nT, which can not only measure the magnitude of the magnetic induction intensity, but also determine the direction of the magnetic field.

[0023] The present invention also provides a detection method for the content and orientation of steel fibers in steel fiber concrete (hereinafter referred to as the detection method), which includes the following steps: Step 1, set up the detection device; Step 2, place the induction device 3 in the on-site construction environment and measure the background reference value; Step 3, use the magnetization device 2 to perform a one-way scan on the surface of the steel fiber concrete from one end to the other end in one direction, and do not scan back and forth repeatedly. During the scanning process, the magnetic field generated by the magnetization device 2 magnetizes the steel fibers inside the steel fiber concrete to magnetize the steel fibers inside the steel fiber concrete; the moving speed is constant to ensure the same degree of magnetization; Preferably, in step 3, the moving speed is 0.01~0.1 m / s, and the movement is kept at a constant speed during the movement for uniform magnetization.

[0024] Preferably, in step 3, the magnetization process will also magnetize the steel bars and metal embedded parts in the steel fiber concrete.

[0025] Step 4: Mark the detection area 6 on the surface of the steel fiber concrete, then select a number of sampling points 7 within the detection area 6, and then place the induction device 3 at the sampling point 7. Use the induction device 3 to measure the initial remanence data at each sampling point 7. The initial remanence data includes the magnitude and direction of the initial remanence. Preferably, in step 4, according to the accuracy requirements, each fluxgate sensor of the induction device 3 corresponds to a sampling point 7 to realize batch detection of the sampling point 7.

[0026] Step 5: The data acquisition system 1 real-time collects the initial remanence data of each sampling point 7 measured by the induction device 3 and transmits the initial remanence data of each sampling point 7 to the data processing and display system 4. Step 6: In the data processing and display system 4, first correct the initial remanence data of each sampling point 7 obtained in step 4 with the background reference value to obtain the remanence calibration data; then, perform machine learning filtering on the remanence calibration data to eliminate the influence of the steel bars and metal embedded parts in the steel fiber concrete, and obtain the remanence data of the steel fibers at each sampling point 7. The remanence data includes the magnitude and direction of the remanence; then, since the background reference value correction and machine learning filtering will not affect the remanence direction, the remanence direction of the steel fibers at each sampling point 7 is the same as the initial remanence direction. The initial remanence direction measured by the induction device 3 at each sampling point 7 is the remanence direction of the steel fibers at each sampling point 7. Combine the electromagnetic laws to obtain the orientation of the steel fibers at each sampling point 7; at the same time, establish a spatial distribution model and an orientation probability model in the data processing and display system 4 respectively; according to the magnitude of the remanence of the steel fibers at each sampling point 7, use the spatial distribution model to obtain the content of the steel fibers at each sampling point 7, the content of the steel fibers in the detection area 6, and the content of the steel fibers at each non-sampling point within the detection area 6; according to the remanence direction of the steel fibers at each sampling point 7, use the orientation probability model to obtain the orientation of the steel fibers in the detection area 6.

[0027] Preferably, in step 6, the larger the remanence, the higher the corresponding content of steel fibers; according to electromagnetics, the initial remanence direction at each sampling point 7 measured by the induction device 3 in step 4 is the orientation of the steel fibers at each sampling point 7. The principle is that the magnetization device 2 scans the steel fiber concrete to magnetize the steel fibers inside the concrete without magnetizing the concrete. Therefore, the remanence direction of the steel fibers detected by the induction device 3 at each sampling point 7 is the orientation of the steel fibers at each sampling point 7 in the concrete.

[0028] Preferably, in step 6, the background reference value correction is to eliminate the influence of the geomagnetic field and ambient noise.

[0029] Preferably, in step 6, the machine learning filtering is specifically as follows: test the concrete samples containing known steel bars and metal embedded parts, record the remanence data to form a data set; then use the data set as the input to train a neural network model to obtain a trained neural network model; then input the remanence calibration data into the trained neural network model for recognition processing to filter and shield the remanence of the steel bars and metal embedded parts, eliminate the influence of the steel bars and metal embedded parts in the steel fiber concrete, and obtain the remanence data of the steel fibers at each sampling point 7. This is an existing mature technology.

[0030] Preferably, in step 6, the calculation process of the spatial distribution model is specifically as follows: Solution 1: According to the remanence of the steel fibers at each sampling point 7, use the inverse distance weighted interpolation algorithm to calculate the remanence of the steel fibers at several non-sampling points in the detection area 6 (the number of non-sampling points selected is related to the requirements or the calculation accuracy to be achieved); then sum the remanence of the steel fibers at each sampling point 7 and the remanence of the steel fibers at several non-sampling points, and divide the sum by the sum of the number of sampling points 7 and non-sampling points. The obtained average value is used as the remanence of the steel fibers in the detection area 6; then, in combination with the preset remanence-steel fiber content calibration relationship, the content of the steel fibers at each sampling point 7, the content of the steel fibers in the detection area 6, and the content of the steel fibers at several non-sampling points are obtained respectively; Solution 2: According to the remanence of the steel fibers at each sampling point 7, use the inverse distance weighted interpolation algorithm to calculate the remanence of the steel fibers at several non-sampling points in the detection area 6 (the number of non-sampling points selected is related to the requirements or the calculation accuracy to be achieved); then, in combination with the preset remanence-steel fiber content calibration relationship, the content of the steel fibers at each sampling point 7 and the content of the steel fibers at several non-sampling points are obtained respectively; then sum the content of the steel fibers at each sampling point 7 and the content of the steel fibers at several non-sampling points, and divide the sum by the sum of the number of sampling points 7 and non-sampling points. The obtained average value is used as the content of the steel fibers in the detection area 6.

[0031] Preferably, in step 6, a three-dimensional rectangular coordinate system is established with the surface of the steel fiber concrete 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 ) of each sampling point 7 and the corresponding residual magnetic flux density B of the steel fibers i , using the inverse distance weighted interpolation algorithm, the residual magnetic flux density B(x, y, z) of the steel fibers at any position (x, y, z) to be measured (i.e., non-sampling point) in the detection area 6 is calculated as follows: (1) In formula (1), is the spatial distance from the position (x, y, z) to be measured to the sampling point 7(x i , y i , z i ); j is the number of sampling points 7 in the detection area 6; p is the distance weight parameter, and the value range is 1 to 3, corresponding to the spatial distances along the x-axis, y-axis, and z-axis respectively.

[0032] Preferably, in step 6, the preset calibration relationship between the residual magnetic flux density and the steel fiber content: use the detection equipment of the present invention to test the steel fiber concrete samples with known steel fiber content, for example, test samples with steel fiber contents of 15 kg / m 3 , 30 kg / m 3 , 50 kg / m 3 , 80 kg / m 3 , measure the residual magnetic flux density of the samples through the induction device 3, and establish the calibration relationship between the residual magnetic flux density and the steel fiber content. This is an existing mature technology.

[0033] Preferably, in step 6, the specific process of the orientation probability model is: establish a two-dimensional local coordinate system with the boundary of the detection area 6 as the coordinate axis, and count the angle A i between the residual magnetic direction of the steel fibers at all sampling points 7 and the coordinate axis of the two-dimensional local coordinate system, A i ∈[0~90°]; then set the angle interval of the angle A i , construct a two-dimensional angle histogram, the abscissa of the two-dimensional angle histogram is the angle interval, and the ordinate is the number of sampling points 7 with the angle A i in the corresponding angle interval; then, according to the number of sampling points 7 in each angle interval and the total number of sampling points 7, determine the proportion 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 proportion in the two-dimensional angle histogram is determined as the orientation of the steel fibers in the detection area 6; when there are multiple angle intervals corresponding to the proportion values that are all the maximum proportion, then take the angle average value or the intermediate value of these multiple angle intervals as the orientation of the steel fibers in the detection area 6.

[0034] Preferably, in step 6, the data processing and display system 4 visually presents the final result after analysis and processing in a graphical manner, intuitively showing the content and orientation of steel fibers at different spatial positions; the graphing includes a vector distribution map or a contour map.

[0035] Example 1: The detection device includes a data acquisition system 1, a magnetization device 2, an induction device 3, and a data processing and display system 4; In the magnetization 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.

[0036] In the induction device 3, there are 60×20 fluxgate sensors, forming an array; the distance between adjacent two fluxgate sensors is 0.5 cm, that is, the distance between adjacent two sampling points 7 is 0.5 cm. The material of the induction device housing is made of polycarbonate (PC).

[0037] The detection object is: steel fiber reinforced concrete shield segment 5 (see Figure 3 ), with the size of the inner arc surface being 3.5 m×1.5 m and the outer arc surface being 3.9 m×1.5 m, and the designed value of the steel fiber content is 30 kg / m 3 . Select a 60 cm×30 cm 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 in is only for illustration and not the actual number) as the detection area 6 to test the content and orientation of steel fibers therein.

[0038] The detection result is: Draw a visualization map of the steel fiber distribution, as shown in Figure 5 . Figure 5 The line length at the sampling point 7 in represents the relative content of steel fibers, the longer the length, the higher the content, and the line direction represents the orientation of steel fibers. According to Figure 5 , it can be obtained that the steel fiber content of the specimen is 30 kg / m 3 , and the steel fiber content meets the design requirements; the highest steel fiber content at all sampling points 7 is 33 kg / m 3 ( Figure 5 Sampling point one of), the lowest steel fiber content at all sampling points 7 is 28 kg / m 3 ( Figure 5 Sampling point two of), there is no area where steel fibers agglomerate (local steel fiber content is much higher than the overall content) or the steel fiber content is too low (local steel fiber content is much lower than the overall content), and the overall dispersion of steel fibers is good. The orientation angle of steel fibers in the detection area 6 is 45°.

[0039] From Figure 5It can be seen that the directions of steel fibers at each sampling point 7 are irregular and random. From Figure 5 the local steel fiber distribution state can be clearly identified, and the minute changes in fiber orientation can be accurately captured. However, the data volume is large and the single detection time is long, which is applicable to the detection of precast components, construction sites, existing structures with high precision requirements or the testing of laboratory specimens.

[0040] Example 2: 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; In the magnetization 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.

[0041] In the induction device 3, there are 12×6 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 material of the induction device housing is polycarbonate (PC).

[0042] The detection object is: steel fiber reinforced concrete shield segment 5 (see Figure 3 ), with the size of the inner arc surface being 3.5 m×1.5 m and the outer arc surface being 3.9 m×1.5 m. The designed value of the steel fiber content is 25 kg / m 3 . A 60 cm×30 cm area on the inner arc surface of the steel fiber reinforced concrete shield segment 5 (such as Figure 4 ) is selected as the detection area 6 to test the content and orientation of steel fibers therein.

[0043] The detection results are as follows: A visualization graph of the steel fiber distribution is drawn, as shown in Figure 6 . Figure 6 In it, the line length at the sampling point 7 represents the relative content of steel fibers, the longer the length, the higher the content, and the line direction represents the orientation of steel fibers. According to Figure 6 , the steel fiber content of the specimen is 24 kg / m 3 , and the steel fiber content meets the design requirements; the highest steel fiber content at all sampling points 7 is 31 kg / m 3 (sampling point three in Figure 6 ), and the lowest steel fiber content at all sampling points 7 is 22 kg / m 3 (sampling point four in Figure 6 ). The highest steel fiber content is slightly on the high side, but there are no areas where steel fibers agglomerate (local steel fiber content is much higher than the overall content) or the steel fiber content is too low (local steel fiber content is much lower than the overall content), and the overall dispersion of steel fibers is good. The orientation angle of steel fibers in the detection area 6 is 55°.

[0044] From Figure 6It can be seen that the directions of the steel fibers at each sampling point 7 are irregular and random. Figure 6 The results accurately reflect the distribution trend of the local steel fibers. The distance between two adjacent fluxgate sensors is 2.5 cm. The sampling balances efficiency and accuracy, shortens the detection time, and is suitable for rapid assessment of precast components, construction sites, or existing structures.

[0045] Example 3: The detection device includes a data acquisition system 1, a magnetization device 2, an induction device 3, and a data processing and display system 4; In the magnetization 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.

[0046] In the induction device 3, there are 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 material of the induction device housing is polycarbonate (PC).

[0047] The detection object is: a steel fiber reinforced concrete shield segment 5 (see Figure 3 ), with dimensions of 3.5 m×1.5 m for the inner arc surface and 3.9 m×1.5 m for the outer arc surface. The designed value of the steel fiber content is 40 kg / m 3 . A 60 cm×30 cm area on the inner arc surface of the steel fiber reinforced concrete shield segment 5 (such as Figure 4 ) is selected as the detection area 6 to test the content and orientation of the steel fibers therein.

[0048] The detection results are as follows: A visualization diagram of the steel fiber distribution is drawn, as shown in Figure 7 . Figure 7 In it, the line length at the sampling point 7 represents the relative content of the steel fibers, the longer the length, the higher the content, and the line direction represents the orientation of the steel fibers. According to Figure 7 , the steel fiber content of the specimen is 41 kg / m 3 , and the steel fiber content meets the design requirements; the highest steel fiber content at all sampling points 7 is 43 kg / m 3 (sampling point five in Figure 7 ), and the lowest steel fiber content at all sampling points 7 is 38 kg / m 3 (sampling point six in Figure 7 ). There are no areas where steel fibers agglomerate (local steel fiber content is much higher than the overall content) or the steel fiber content is too low (local steel fiber content is much lower than the overall content), and the overall dispersion of the steel fibers is good. The orientation angle of the steel fibers in the detection area 6 is 35°.

[0049] From Figure 7It can be seen that the directions of the steel fibers at each sampling point 7 are irregular and random. Figure 7 The results reflect the general distribution of steel fibers in the segment concrete of the detection area. The distance between two adjacent fluxgate sensors is 5 cm. The number of sampling points is relatively small, the sampling and data processing efficiency is high, the detection duration is short, and it is suitable for the rapid evaluation of large structures.

[0050] Where the present invention is not described shall apply to the prior art.

Claims

1. A detection device for the content and orientation of steel fibers in steel fiber concrete, characterized in that, The detection device includes 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 communicatively connected to the magnetization device (2), the induction device (3), and the data processing and display system (4) respectively; 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 magnetism inside the steel fiber concrete; the data acquisition system (1) is used to collect and process in real time the residual magnetism data measured by the induction device (3).

2. The detection device for the steel fiber content and orientation in steel fiber concrete according to claim 1, characterized in that, The magnetization device (2) includes an electromagnet (21), a magnetization device housing (22), and a DC power supply (23); several groups of electromagnets (21) arranged in a straight line are uniformly arranged inside the magnetization device housing (22); the DC power supply (23) supplies power to the electromagnet (21).

3. The detection device for the steel fiber content and orientation in steel fiber concrete according to claim 1, characterized in that, The induction device (3) includes a fluxgate sensor and an induction device housing; m rows and n columns of fluxgate sensors are arranged in an array inside 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; the distance between adjacent two fluxgate sensors is 0.5 - 5 cm; the sensitivity of the fluxgate sensors is in the order of nT.

4. A method for detecting the steel fiber content and orientation in steel fiber concrete based on the detection device according to any one of claims 1-3, characterized in that, The detection method includes the following steps: Step 1, build the detection device according to any one of claims 1 - 3; Step 2, place the induction device (3) in the on-site construction environment to measure the background reference value; Step 3, use the magnetization device (2) to perform a one-way scan on the surface of the steel fiber concrete from one end to the other end in one direction, and during the scan, the magnetic field generated by the magnetization device (2) magnetizes the steel fibers inside the steel fiber concrete to magnetize the steel fibers inside the steel fiber concrete; the moving speed is constant to ensure the same degree of magnetization; Step 4, demarcate a detection area (6) on the surface of the steel fiber concrete, then select several sampling points (7) within the detection area (6), and then place the induction device (3) at the sampling points (7), and use the induction device (3) to measure the initial residual magnetism data at each sampling point (7), and the initial residual magnetism data includes the initial residual magnetism magnitude and the initial residual magnetism direction; Step 5, the data acquisition system (1) collects in real time the initial residual magnetism data of each sampling point (7) measured by the induction device (3), 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), first, correct the initial remanence data of each sampling point (7) obtained in Step 4 with the background reference value to obtain the remanence calibration data; then, perform machine learning filtering on the remanence calibration data to eliminate the influence of steel bars and metal embedded parts in the steel fiber concrete, and obtain the remanence data of the steel fibers at each sampling point (7); the remanence data includes the magnitude and direction of the remanence; then, according to the remanence direction of the steel fibers at each sampling point (7), combine the electromagnetic laws to obtain the orientation of the steel fibers at each sampling point (7); meanwhile, establish a spatial distribution model and an orientation probability model in the data processing and display system (4) respectively; according to the remanence magnitude of the steel fibers at each sampling point (7), use the spatial distribution model to obtain the content of the steel fibers at each sampling point (7), the content of the steel fibers in the detection area (6), and the content of the steel fibers at each non-sampling point in the detection area (6); according to the remanence direction of the steel fibers at each sampling point (7), use the orientation probability model to obtain the orientation of the steel fibers in the detection area (6).

5. The detection method of the steel fiber content and orientation in steel fiber concrete according to claim 4, characterized in that, In Step 3, the moving speed is 0.01~0.1 m / s.

6. The detection method of the steel fiber content and orientation in steel fiber concrete according to claim 4, characterized in that, In Step 4, each fluxgate sensor of the induction device (3) corresponds to a sampling point (7) to achieve batch detection of the sampling points (7).

7. The detection method of the steel fiber content and orientation in steel fiber concrete according to claim 4, characterized in that In Step 6, the background reference value correction is to eliminate the influence of the geomagnetic field and ambient noise.

8. The detection method for the steel fiber content and orientation in steel fiber concrete according to claim 4, characterized in that, In Step 6, the specific calculation process of the spatial distribution model is as follows Scheme 1: According to the remanence magnitude of the steel fibers at each sampling point (7), use the inverse distance weighted interpolation algorithm to calculate the remanence magnitude of the steel fibers at several non-sampling points in the detection area (6), and the number of non-sampling points selected is related to the requirements or the calculation accuracy to be achieved; then sum the remanence magnitude of the steel fibers at each sampling point (7) and the remanence magnitude of the steel fibers at several non-sampling points, and divide the sum by the sum of the number of sampling points (7) and non-sampling points, and use the obtained average value as the remanence magnitude of the steel fibers in the detection area (6); then, in combination with the preset calibration relationship between the remanence magnitude and the steel fiber content, obtain the content of the steel fibers at each sampling point (7), the content of the steel fibers in the detection area (6), and the content of the steel fibers at several non-sampling points respectively. Scheme 2: According to the remanence magnitude of the steel fibers at each sampling point (7), use the inverse distance weighted interpolation algorithm to calculate the remanence magnitude of the steel fibers at several non-sampling points in the detection area (6); then, in combination with the preset calibration relationship between the remanence magnitude and the steel fiber content, obtain the content of the steel fibers at each sampling point (7) and the content of the steel fibers at several non-sampling points respectively; then sum the content of the steel fibers at each sampling point (7) and the content of the steel fibers at several non-sampling points, and divide the sum by the sum of the number of sampling points (7) and non-sampling points, and use the obtained average value as the content of the steel fibers in the detection area (6).

9. The detection method of the steel fiber content and orientation in steel fiber concrete according to claim 8, characterized in that, In step 6, a three-dimensional rectangular coordinate system is established with the surface of the steel fiber concrete 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 ) of each sampling point (7) and the corresponding residual magnetic flux density B of the steel fibers i , using the inverse distance weighted interpolation algorithm, the residual magnetic flux density B(x, y, z) of the steel fibers at any position (x, y, z) to be measured within the detection area (6) is calculated as follows: （1） In formula (1), is the spatial distance from the position to be measured (x, y, z) to the sampling point (7) (x i , y i , z i ); j is the number of sampling points (7) in the detection area (6); p is the distance weight parameter, and its value range is 1 to 3, corresponding to the spatial distances along the x-axis, y-axis, and z-axis respectively.

10. The detection method of the steel fiber content and orientation in steel fiber concrete according to claim 4, characterized in that, In step 6, the specific process of the orientation probability model is as follows: A two-dimensional local coordinate system is established with the boundary of the detection area (6) as the coordinate axes, and the included angle A between the residual magnetic direction of the steel fibers at all sampling points (7) and the coordinate axes of the two-dimensional local coordinate system is statistically analyzed i , A i ∈[0~90°]; then the angle range of the included angle A i is set, and a two-dimensional angle histogram is constructed. The abscissa of the two-dimensional angle histogram is the angle range, and the ordinate is the number of sampling points (7) in the corresponding angle range of the included angle A i ; then, according to the number of sampling points (7) in each angle range and the total number of sampling points (7), the proportion of the number of sampling points (7) in each angle range to the total number of sampling points (7) is determined; the angle range corresponding to the maximum proportion value in the two-dimensional angle histogram is determined as the orientation of the steel fibers in the detection area (6); when there are multiple angle ranges corresponding to the proportion values that are all the maximum proportion, the average value or the intermediate value of the angles of these multiple angle ranges is taken as the orientation of the steel fibers in the detection area (6).

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