Rapid detection method for mica content in tunnel hole slag mother rock

Abstract

The invention discloses a method for rapidly detecting the content of mica in tunnel hole slag mother rock, and relates to the technical field of mica content detection.The method comprises the following steps that S1, in the indoor environment, a camera is used for photographing a sample to be detected; s2, performing background denoising on a picture obtained by photographing of the camera; and S3, marking and counting reflective points in the photo, calculating to obtain the reflective rate of mica in the sample to be detected, and calculating the content of mica by utilizing a reflective rate regression equation. The relative error of the measured mica content is about 5.227%, the detection accuracy is as high as 90% or above, it is proved that a light reflection method has high practicability and stability, and the requirement for mica content detection in actual engineering can be met.

Description

Technical Field

[0001] The invention relates to the technical field of molds, and in particular to a method for quickly detecting the mica content in tunnel slag matrix. Background Art

[0002] Mica is one of the important rock-forming minerals and has a certain content in most rocks. As the volume of tunnel construction continues to increase, tunnel excavation will be accompanied by the generation of a large amount of tunnel slag. As the natural fine aggregate that can be used in concrete is becoming less and less, the proportion of machine-made sand used in concrete engineering is increasing. In order to save the consumption of natural fine aggregate, this kind of waste slag is often used as the raw material of fine aggregate for concrete in engineering. Since it is the waste tailings generated by tunnel excavation, it is low in cost and is an ideal raw material for fine aggregate.

[0003] However, the fine aggregate used in construction has certain requirements on the material composition. Tunnel slag is generally composed of gneiss and granite, mainly containing quartz, feldspar and mica. Among them, mica particles are flaky, with smooth and jointed surfaces, and have low bonding strength with cement binder materials. It is one of the causes of original cracks and defects in concrete and is a harmful substance in concrete fine aggregate.

[0004] The national standard GB / T 14684-2011 "Construction Sand" and the industry standard SL 352-2006 "Testing Procedures for Hydraulic Concrete" have put forward specific limit requirements for the mica content in fine aggregate for concrete. The test methods in the current mica content testing specifications and standards are based on a certain amount of samples, and are manually selected under a 3-5 times magnifying glass for quantitative testing. This method is time-consuming and labor-intensive, and there are human errors. Frontline workers cannot accurately and quickly judge whether the slag can meet the engineering requirements of concrete.

[0005] At the current stage of the project, most of the concrete projects use tunnel slag as the main fine aggregate. Due to the large-scale and wide-range existence of mica in nature, if the mica content is not controlled, project quality problems may arise.

[0006] Therefore, developing new rapid and efficient detection methods for mica content in field engineering is an inevitable need to adapt to engineering construction and engineering quality control in the new era. Summary of the invention

[0007] The purpose of the present invention is to provide a method for quickly detecting the mica content in tunnel slag matrix, which solves the technical problem in the prior art that front-line workers cannot quickly and accurately detect the mica content.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for quickly detecting the mica content in tunnel slag parent rock, comprising the following steps:

[0009] S1. In an indoor environment, use a camera to take photos of the samples to be tested. The photos should be taken with consistency in clarity and brightness to avoid omissions or duplications.

[0010] S2. Perform background denoising on the photos taken by the camera, adjust the photo parameters, and extract the reflective point information in the sample to be tested.

[0011] S3. Mark and count the reflective points in the photo, calculate the reflectivity of mica in the sample to be tested, and use the reflectivity regression equation to calculate the mica content.

[0012] In a specific embodiment, in S3, the reflective points in the photo are extracted by image processing software, and the reflectivity of the mica in the sample to be tested is obtained by using the ratio of the area of ​​the reflective points to the total area of ​​the sample to be tested in the photo.

[0013] In a specific embodiment, in S3, the reflectance regression equation is:

[0014] Y = 0.2123x + 8.6004, where R 2 =0.948;

[0015] Where Y is the reflectivity and x is the mica content.

[0016] In a specific embodiment, obtaining the reflectance regression equation in S3 comprises the following steps:

[0017] S31. Mixing different weights of pure mica flakes with a purity greater than 99.8% with mica ore, feldspar and quartz particles to obtain mixtures with different mica contents.

[0018] S32. The mixture in S31 is naturally placed at the center of the upper sample box by using a funnel. A camera is fixed just above the sample box, and the mixture is photographed by the camera. The position and angle of the camera remain unchanged during the photographing process.

[0019] S33. Using image processing software to perform background denoising on the photograph, calculating the ratio of the area of ​​the reflective point in the photograph to the area of ​​the mixture in the photograph, and using the known weight of mica to obtain the reflectivity of mica in the mixture with different mica contents.

[0020] S34. Using the reflectivity obtained in S33, a reflectivity-mica content function curve is established, and a reflectivity regression equation is obtained by fitting.

[0021] In a specific embodiment, in S31, the weight of the mica is 5g, 10g, 15g, 20g, 25g, 30g, and 50g respectively, and the others are mixtures of mica ore, feldspar and quartz particles. The total weight of the mixtures with different mica contents is 100g, and the particle size of the mica ore, feldspar and quartz particles is 3mm-5mm.

[0022] In a specific embodiment, in S32, the sample box is made of non-reflective material and has a black inner wall to avoid the influence of reflection during the photographing process on the acquisition of reflective points in the experiment.

[0023] In a specific embodiment, in S2, background denoising of the photo includes adjusting the brightness, color, contrast, etc. of the photo, the range value in the color range is 240, the color tolerance is 15%, and the highlight mode is selected.

[0024] In a specific embodiment, a full-band hyperspectral imager is used to scan the sample to be tested, and the obtained image is processed by ENVI software band to obtain intuitive distribution data of mica in the sample to be tested, thereby verifying whether the content of mica in S3 is correct.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The relative error of the mica content measured by the present invention is about 5.227%, and the detection accuracy is as high as more than 90%, which proves that the reflection method has high practicality and stability and can meet the requirements for mica content detection in actual engineering.

[0027] The invention is easy to operate, does not require complicated detection equipment, and can quickly detect the mica content. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Schematic diagrams of the present invention after the flash points of different mica contents are extracted; wherein (a) is a schematic diagram of the flash points of 5% mica content after extraction, and (b) is a schematic diagram of the flash points of 50% mica content after extraction;

[0029] Figure 2 A schematic diagram of the relationship curve between the reflectivity of the mica flash point extracted by the present invention and the mica content;

[0030] Figure 3 This is a schematic diagram of the flash point extraction effect of the original rock sample of the slag of the present invention;

[0031] Figure 4 is the spectrum diagram of mica of the present invention;

[0032] Figure 5This is a hyperspectral image of a mica-containing rock sample after the band combination processing of the present invention (R: 969nm, G: 1321nm; B: 1999nm) (mica appears gray-green). DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] See also Figure 1-Figure 5 A method for rapidly detecting the mica content in tunnel slag matrix comprises the following steps:

[0035] Step 1, mix different masses of pure mica flakes with a purity greater than 99.8% after physical sorting with mica ore, feldspar and quartz that have been physically crushed to a particle size between 3mm and 5mm. The mass of the pure mica flakes can be selected from 5g, 10g, 15g, 20g, 25g, 30g, 50g respectively, and the mica ore, feldspar and quartz are selected from the corresponding masses to form a 100g mixture. For example, select 15g of pure mica flakes, 85g of mica ore, feldspar and quartz, and finally form a 100g mixture. The content of mica ore, feldspar and quartz in the mixture of 85g of mica ore, feldspar and quartz is randomly selected.

[0036] Step 2: Preparation of testing equipment.

[0037] In order to eliminate the influence of background reflection on the experimental acquisition of reflective points, a non-reflective plate is selected to make a rectangular sample box, such as a wooden board, a steel plate, etc. The inner wall of the sample box is coated with a black coating.

[0038] Precision balance, used to accurately weigh mica and quartz, feldspar mixture, convenient for mixing and verification.

[0039] Use cardboard to make a funnel; camera or video camera; LED light source to provide a stable light source for the camera to take pictures. Fix the camera just above the center of the sample box and adjust the position and angle to keep it unchanged.

[0040] Step 3: Place the mixture with different mica contents naturally in the center of the sample box using a funnel to prevent sample unevenness caused by human adjustment, start the LED light source, and take a picture of the mixture using a camera in an indoor environment.

[0041] Step 4: Take clear photos with consistent brightness, use image processing software such as Photoshop to process the photos, remove background noise, and adjust image brightness, color, contrast and other parameters, such as Figure 1 As shown, the reflective information in the photo is extracted through the color range in Photoshop software and the calculation module in the image option. The range value in the color range is set to 240, the color tolerance is set to 15%, and the highlight area is selected.

[0042] Step 5: Count the ratio between the reflective area of ​​mica in the photo and the total area of ​​the mixture in the photo, and use the different known contents of mica in the mixture to establish a reflectivity-mica content function curve, such as Figure 2 As shown, and fitted into the reflectivity linear regression equation:

[0043] Y = 0.2123x + 8.6004, where R 2 =0.948;

[0044] Where Y is the reflectivity and x is the mica content.

[0045] Step 6: In an indoor environment, place the sample to be tested in a sample box and use a camera to take a photo of the sample to be tested; repeat steps 3 to 4. Figure 3 shown.

[0046] Step 8: Mark and count the reflective points in the photo, calculate the reflectivity of mica in the sample to be tested, and use the reflectivity regression equation to calculate the mica content.

[0047] In order to verify the mica content of the mica ore, 100g of mica ore was selected. The mica content of the 100g mica ore was 28.754% obtained in the laboratory using an optical microscope, and it was physically crushed. Repeat the measurement steps. The reflectivity test verified that the reflectivity of 100g of the mica ore was 15.024%. From the reflectivity change trend chart of 100g mica mixture, it can be seen that when the reflectivity is 15.024%, the corresponding mica content measured should be 30.2571g. It shows that the absolute error of the mica raw material measured by the reflection method is 1.5031%, and the relative error is 5.227%.

[0048] The mica ore was scanned with a full-band hyperspectral imager, and the obtained image was processed with ENVI software to obtain the distribution data of mica in the ore, which helped verify the mica content obtained in the above steps. Figure 4The following is a spectrum image of mica. Mica has obvious absorption in the 1408-1415nm band, 1909-1916nm band, and 2127-2130nm band. The rock hyperspectral image is processed by changing the band. The image scanned by the hyperspectral imager is compared with the band in the library by using the ENVI band processing to highlight the color of the mica. Figure 5 As shown, the left side of the figure is the original rock, and the right side is the mica distribution data diagram in the mica ore produced after processing by ENVI software. In the right figure, through the comparative analysis of lithology, the gray-green part is mica. Figure 5 The mica distribution can be directly reflected on the original rock, and the mica content can be further verified.

Claims

1. A method for rapid detection of mica content in tunnel slag matrix, characterized in that: The following steps are involved: S1. In an indoor environment, use a camera to take pictures of the sample to be tested; S2, performing background denoising on the photos taken by the camera; S3. Mark and count the reflective points in the photo, calculate the reflectivity of mica in the sample to be tested, and use the reflectivity regression equation to calculate the mica content.

2. A rapid detection method for mica content in tunnel slag matrix according to claim 1, characterized in that: In S3, the reflective points in the photo are extracted by image processing software, and the reflectivity of the mica in the sample to be tested is obtained by using the ratio of the area of ​​the reflective points to the total area of ​​the sample to be tested in the photo.

3. The method for rapid detection of mica content in tunnel slag matrix according to claim 1, characterized in that: In S3, the reflectivity regression equation is: Y = 0.2123x + 8.6004, where R 2 =0.948; Where Y is the reflectivity and x is the mica content.

4. The method for rapid detection of mica content in tunnel slag matrix according to claim 1, characterized in that: Obtaining the reflectance regression equation in S3 comprises the following steps: S31, mixing different weights of pure mica flakes with a purity greater than 99.8% with mica ore, feldspar, and quartz particles to obtain mixtures with different mica contents; S32, placing the mixture in S31 naturally at the center of the upper sample box using a funnel, fixing a camera just above the sample box, and photographing the mixture using the camera; S33, using image processing software to perform background denoising on the photographed photos, calculating the ratio of the area of ​​the reflective points in the photos to the area of ​​the mixture in the photos, and obtaining the reflectivity of mica in the mixtures with different mica contents; S34. Using the reflectivity obtained in S33, a reflectivity-mica content function curve is established, and a reflectivity regression equation is obtained by fitting.

5. A method for rapid detection of mica content in tunnel slag matrix according to claim 4, characterized in that: In S31, the weight of the mica is 5g, 10g, 15g, 20g, 25g, 30g, and 50g respectively, and the others are mixtures of mica ore, feldspar and quartz particles. The total weight of the mixtures with different mica contents is 100g. The particle sizes of the mica ore, feldspar and quartz particles are 3mm-5mm.

6. A method for rapid detection of mica content in tunnel slag matrix according to claim 4, characterized in that: In S32, the sample box is made of non-reflective material and the inner wall is black.

7. A method for rapid detection of mica content in tunnel slag matrix according to claim 1 or 4, characterized in that: In S2, background denoising is performed on the photo, including adjusting the brightness, color, and contrast of the photo. The range value in the color range is 240, the color tolerance is 15%, and the highlight mode is selected.

8. A method for rapid detection of mica content in tunnel slag matrix according to claim 1, characterized in that: The samples to be tested were scanned using a full-band hyperspectral imager, and the obtained images were processed using the ENVI software band to obtain intuitive distribution data of mica in the samples to be tested, thereby verifying whether the mica content in S3 was correct.

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