Technology for intelligently and rapidly detecting quality of fly ash glass beads

Abstract

The invention discloses a fly ash glass bead quality intelligent rapid detection technology, which comprises: sampling: taking an undried fly ash sample, placing in a mortar, grinding with a pestle, and placing in a drying vessel to prevent dampness; sample preparation: placing the ground sample in a round hole of a glass plate, then sealing the round hole, vibrating the glass plate to uniformly spread the sample, and removing the redundant sample; image acquisition: the glass plate adhered with the sample is placed under a microscope of an electronic eyepiece acquisition system, at least three objective lenses with different focal lengths are adopted for clear focusing, and the electronic eyepiece acquisition system acquires at least three images under different objective lens focal lengths; image analysis: naming and packaging the collected images, importing the images into an analysis system for intelligent identification, and automatically calculating the number and proportion of the glass beads and other particles; and result judgment: repeating the steps to obtain three detection results, and taking the average value of the contents of the glass beads in the three samples as a test result.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of building material detection, and in particular to a fly ash glass bead quality intelligent rapid detection process. BACKGROUND

[0002] Fly ash is a common admixture in concrete, and its quality directly affects the performance of high-performance concrete. The glass bead content is a key indicator of fly ash quality. Traditional detection methods require drying and sieving of fly ash first, then observing under a microscope and manually calculating the glass bead content. However, there are the following defects: • Low precision: only the surface can be observed, and the glass bead content cannot be accurately calculated; • Slow speed: requires pretreatment steps such as drying and sieving, and the operation is cumbersome; • Low efficiency: long detection time, difficult to meet the timeliness requirements of fly ash incoming vehicle detection in large-scale construction; • Poor result traceability: manual recording is prone to errors, and data storage is not standardized.

[0003] Therefore, the present application provides a fly ash glass bead quality intelligent rapid detection process to solve the above problems. SUMMARY

[0004] The present application overcomes the shortcomings of the prior art and provides a fly ash glass bead quality intelligent rapid detection process.

[0005] To achieve the above purpose, the technical solution adopted by the present application is as follows: a fly ash glass bead quality intelligent rapid detection process, comprising the following steps: Step one, sampling: take the un-dried fly ash sample, put it in a mortar with a pestle, and place it in a drying dish to prevent moisture; Step two, sample preparation: place the ground sample in a glass plate round hole, then seal the round hole, shake the glass plate to evenly spread the sample, and remove the excess sample; Step three, image acquisition: place the glass plate with the sample under the electronic ocular collection system microscope, and place the sample under the electronic ocular lens of the microscope, focus the objective lens clearly, the objective lens is at least three different focal lengths, and the electronic ocular collection system collects at least three images under different objective lens focal lengths; Step four, image analysis: name and package the collected images, import them into the analysis system for intelligent identification, and automatically calculate the number and proportion of glass beads and other particles; Step five, result determination: repeat the above steps to obtain three detection results, and take the average value of the glass bead content in the three samples as the test result.

[0006] In a preferred embodiment of the present application, the step five, the result determination further comprises a correction step, and the correction step is as follows: If one of the results exceeds the average value by ±20%, the value is removed, and a new average value is calculated; If there are still results exceeding ±20%, the test is invalid and the experiment is re-performed.

[0007] In a preferred embodiment of the present application, the focal lengths of the three objectives are 10X / 0.25, 50X / 0.55 and 100X / 0.80 respectively.

[0008] In a preferred embodiment of the present application, the electronic eyepiece acquisition system comprises a metallographic optical microscope, supports the conversion of optical images and electronic images, and the image acquisition scale is adjustable.

[0009] In a preferred embodiment of the present application, the electronic eyepiece acquisition system further comprises an analysis host and an image analysis software installed on the analysis host, and the image analysis software can automatically store the detection results.

[0010] In a preferred embodiment of the present application, the image analysis software is used for feature extraction, particle identification and content calculation of the collected images. The specific steps are as follows: the collected image particles are extracted according to the features of smoothing, circular separation, thinning and boundarying, and the glass beads are detected according to the extracted features, and then the content of the glass beads is calculated.

[0011] In a preferred embodiment of the present application, the feature extraction and the particle identification analysis are based on particle projection contour identification. The content calculation specifically calculates the number, area, perimeter, maximum diameter and shape parameter of the glass beads.

[0012] In a preferred embodiment of the present application, the step of sealing the circular hole in the step two is as follows: transparent tape is pasted on one side of the glass plate with holes to seal the circular hole.

[0013] In a preferred embodiment of the present application, the side length of the glass plate is 10 cm, and the aperture of the circular hole of the glass plate is 10 mm.

[0014] In a preferred embodiment of the present application, the step of removing the excess sample in the step two is as follows: the glass plate is turned over to pour out the excess sample, and an ear bulb is used to blow away the sample that is not firmly adhered.

[0015] The present application solves the defects in the background art, and has the following beneficial effects: The application directly takes the sample to be detected from the non-dried fly ash, without drying and sieving pretreatment, directly detects, greatly shortens the detection time, and then utilizes the image analysis technology, can accurately identify the glass beads and calculate the content, overcomes the limitation that the traditional method can only observe the surface, the sample processing to image acquisition steps are simple and automatic, reduces the skill requirement for the operator, meanwhile avoids the construction delay caused by the detection lag, and reduces the engineering cost. BRIEF DESCRIPTION OF DRAWINGS

[0016] The application will be further described below in combination with the drawings and embodiments. Figure 1 is a detection process flowchart of the preferred embodiment of the application; Figure 2 is an image acquisition interface on the analysis host of the preferred embodiment of the application; Figure 3 is an analysis result interface on the analysis host of the preferred embodiment of the application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the application will be clearly and completely described below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the application.

[0018] It should be noted that when a component is referred to as being "fixed" to another component, it can be directly on the other component or can be present with another intermediate component fixed thereto. When a component is referred to as being "connected" to another component, it can be directly connected to the other component or can be present with another intermediate component. When a component is referred to as being "disposed" on another component, it can be directly disposed on the other component or can be present with another intermediate component. The terms "vertical", "horizontal", "left", "right", and similar expressions used herein are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0020] As Figure 1 , Figure 2 and Figure 3As shown, the fly ash glass bead mass intelligent rapid detection process comprises the following steps: step one, sampling: take the un-dried fly ash sample, place it in a mortar with a pestle, and place it in a drying dish to prevent moisture; Step two, sample preparation: place the ground sample in a glass plate round hole, then seal the round hole, shake the glass plate to evenly lay the sample, and remove the excess sample; Specifically, the step of sealing the round hole in step two is as follows: use transparent tape to paste on one side of the hole glass plate to seal the round hole, preferably, the side length of the glass plate is 10 cm, and the diameter of the glass plate round hole is 10 mm.

[0021] The step of removing excess sample in step two is as follows: turn over the glass plate to pour off the excess sample, and use an ear cleaning ball to blow away the sample that is not firmly adhered.

[0022] Step three, image acquisition: place the glass plate with the sample on the electronic ocular acquisition system microscope, and place the sample under the electronic ocular of the microscope, focus the objective lens clearly, and use at least three objective lenses with different focal lengths, and the electronic ocular acquisition system collects at least three images under different objective lens focal lengths; Preferably, the focal lengths of the three objective lenses are 10X / 0.25, 50X / 0.55, and 100X / 0.80, respectively, and the electronic ocular acquisition system includes a metallographic optical microscope, supports optical image and electronic image conversion, and the image acquisition scale is adjustable.

[0023] Step four, image analysis: name and package the collected images, import them into the analysis system for intelligent recognition, and automatically calculate the number and proportion of glass beads and other particles; Preferably, the electronic ocular acquisition system further comprises an analysis host and an image analysis software installed on the analysis host, the image analysis software can automatically store the detection results, and the image analysis software is used for feature extraction, particle recognition and content calculation of the collected images; The specific steps are as follows: the collected image particles are extracted according to the characteristics of smoothing, circular separation, thinning and boundary, the glass beads are detected according to the extracted characteristics, and then the content of the glass beads is calculated.

[0024] Specifically, the feature extraction and particle recognition analysis are based on particle projection contour recognition; the content calculation specifically calculates the number, area, perimeter, maximum diameter and shape parameter of the glass beads.

[0025] Step five, result determination: repeat the above steps to obtain three detection results, and take the average value of the glass bead content in the three samples as the test result; The result determination also includes a correction step, which is as follows: If one of the results is more than ± 20% of the average value, the value is discarded and a new test is performed and the average value is recalculated. If there are still results that exceed ± 20%, the test is invalid and the experiment is performed again.

[0026] The sample to be detected is directly taken from the un-dried fly ash, without the need for drying and sieving pretreatment, and is directly detected, greatly shortening the detection time. Then, by using image analysis technology, glass beads can be accurately identified and the content can be calculated, overcoming the limitation of traditional methods that can only observe the surface. From sample processing to image acquisition, the steps are simple and automated, reducing the skill requirements for operators. At the same time, it avoids the delay of construction caused by detection lag, and reduces the engineering cost.

[0027] Example one: Chengyu middle line railway Sichuan section 9 bid approach car detection application This example is applied to the fly ash approach car detection of the center laboratory of Chengyu middle line railway Sichuan section 9 bid, which fully embodies the technical advantages and economic value of the process by comparing with the traditional car detection screening fineness and visual glass bead content method, as follows: Detection process Sampling: Randomly take un-dried samples from the approach fly ash transport vehicle, take about 100g and put it into a mortar, gently grind it with a pestle with a rubber head, remove the agglomerates and put it into a dry dish with color-changing silica gel for standby to avoid sample moisture.

[0028] Sample preparation: Take a glass plate with a hole of 10cm in length and 10mm in diameter, and stick it flat on the glass plate with a hole with transparent tape, completely sealing the round hole. Take about 0.3g of ground sample from the dry dish and place it in the glass plate hole, gently shake the glass plate to evenly spread the sample on the tape surface, turn the glass plate to remove the excess sample that is not adhered, and then use a dry ear bulb to blow the surface of the round hole to remove the powder that is not firmly adhered.

[0029] Image acquisition: Place the glass plate with the sample and tape on the top of the upright metallographic optical microscope stage, and focus on the three objectives of 10X / 0.25, 50X / 0.55 and 100X / 0.80 in turn. Through the electronic ocular lens acquisition system, 1 clear image is collected at each objective focal length, a total of 3 images are obtained, named "CYZX-20241001-1", "CYZX-20241001-2", "CYZX-20241001-3".

[0030] Image analysis: 3 images were stored in a compressed package named "CYZX-20241001", and imported into an analysis host computer installed with image analysis software. The software automatically processed the images: first, remove noise by smoothing, then separate circles, thin line processing to highlight the grain outline, and finally extract the glass bead features by boundary detection; according to the grain projection profile, identify the glass beads, and count the number, area, perimeter, maximum diameter and shape parameter, and calculate the glass bead ratio as 82.3%, 83.1%, and 82.7%.

[0031] Results: The average of three test results is (82.3%+83.1%+82.7%) ÷ 3 = 82.7%, and the three results are within ±20% of the average (66.2%~99.2%), so the final test result is 82.7%, and the fly ash glass bead content of this batch is qualified.

[0032] Technical and economic comparison analysis Technical advantages: This system significantly improves the accuracy of fly ash glass bead content identification. Based on the origin and morphology of fly ash glass beads, image analysis technology can not only provide multiple particle size distribution and morphology distribution parameters, but also count particles, which is a new generation of fly ash identification technology that replaces traditional detection methods and chemical detection methods, effectively avoiding misjudgment caused by personnel experience differences in traditional visual inspection.

[0033] Direct economic benefits: To ensure the accuracy of screening, the sample needs to be dried. There are about 450,000 square meters of remaining concrete in this bid section, which requires about 54,000 tons of fly ash, and about 1,637 times of detection. According to the calculation of 5KW oven drying for 1 hour per vehicle detection, the total energy consumption is 8185kw・h, and the electricity cost is 8185×0.7=5729.5 yuan. At the same time, the traditional vehicle detection needs 1.5 hours, and each batch of fly ash is about 200 tons (about 33 tons per vehicle), so 6 vehicles need to be detected, and the traditional method takes 9 hours (equivalent to 1 working day) for each batch of detection. To ensure the timeliness of detection, 3 dedicated detection personnel are needed for 3 mixing stations in this bid section, and the labor cost is 1 working day × 220 yuan / working day × 365 working days / 2 × 3 = 120450 yuan. The direct total cost of the traditional method is 5729.5+120450=126179.5 yuan ≈ 12.62 million yuan. After using this technology, no dedicated detection personnel are needed, and the direct cost is only 2.7 million yuan for the glass bead identification system, with a direct economic benefit of 12.62-2.7=9.92 million yuan.

[0034] Indirect economic benefits: To meet the construction needs, one additional fly ash tank for detection is required for each mixing machine. In this bid section, five mixing machines are installed in three mixing stations, which requires five additional fly ash tanks and supporting facilities. The construction cost of each fly ash tank is 150,000 yuan, and the total cost is 750,000 yuan. After using this process, the detection efficiency is greatly improved, and no additional detection tank is needed, which indirectly saves 750,000 yuan in cost.

[0035] Example Two Quality Control Application of CYZXZQ-9 Section of New Chengyu Middle Line Railway Station Front Project This example is applied to the CYZXZQ-9 section of the new Chengyu Middle Line Railway Station Front Project, which has a mileage range from the DK131+059 Youfanggou Reservoir Super Major Bridge Chengdu End Abutment to DK102+706 Provincial Boundary, with a main line length of 28.353 km. The main engineering content includes: 6.024 km of roadbed, 38 bridges / 20.665 km (including 548 box girder precast erection holes), and 3 tunnels / 1.661 km. Among them, Rongjiawan Tunnel (high gas, 1.302 km long), crossing Tianliuwan Super Major Bridge (1.65 km long, special bridge span 1 hole, maximum hole span 80 m), Maxiwan Super Major Bridge (1.278 km long, special bridge span 1 hole, maximum hole span 72 m), and crossing Zitong Expressway Super Major Bridge (0.788 km long, special bridge span 1 hole, maximum hole span 64 m) are the key and difficult projects of the section.

[0036] The design concrete volume of this section is about 650,000 cubic meters, and the concrete environmental conditions of the whole section are mostly H1 and Y1 environments. According to the Railway Concrete Durability Design Specification, at least 20% of the total amount of cementitious materials should be fly ash, and the total amount of fly ash in the whole section is about 65,000 tons. Since the quality of fly ash is directly related to the quality and durability of concrete products, and the project progress is tight, if the detection is not timely, it will seriously affect the construction on site, therefore, the accuracy and efficiency of fly ash detection are extremely high.

[0037] After the development and application of this fly ash glass microsphere quality intelligent rapid detection process in the central laboratory of this section, significant results have been achieved: the detection efficiency is greatly improved compared to traditional methods, no pre-treatment steps such as drying and sieving are needed, and the single vehicle detection time is shortened from 1.5 hours to 40 minutes, ensuring the timely acceptance of incoming fly ash and avoiding construction delays due to delayed detection; through image analysis technology, the accurate identification of glass microsphere content is realized, effectively improving the fly ash quality control level. So far, the fly ash detected by this process has not had any quality problems, fully meeting the acceptance standard requirements, and providing a strong guarantee for the realization of the high-quality goal of the section concrete.

[0038] Example Three Outlier Processing Scene Detection This example simulates the processing of outliers in the detection process, with the following steps: Sampling and sample preparation: same as example 1, a batch of fly ash sample was treated to ensure uniform sample laying.

[0039] Image acquisition: 3 images were collected by microscope, and the glass bead content was 75.0%, 95.0%, and 82.0% respectively after being imported into the analysis system.

[0040] Result determination: the average value of the first calculation was (75.0%+95.0%+82.0%) ÷3=84.0%. The ±20% range of 84.0% was 67.2%~100.8%, and 95.0% was within this interval, so the average value was valid. If the second detection result was 60.0%, 85.0%, and 80.0%, the average value was 75.0%, 60.0% was equal to 75.0%-20%×75.0%=60.0% (critical value), and 60.0% needed to be removed and retested once. Assuming the retest result was 82.0%, the new average value was (85.0%+80.0%+82.0%) ÷3=82.3%, which was determined to be qualified. If the retest result was still outside the ±20% range (for example, the retest result was 50.0%, the average value of 85.0%, 80.0%, and 50.0% was 71.7%, and 50.0%<71.7%×80%=57.4%), the test was invalid, and the sample needed to be retested.

[0041] The above examples fully embody the characteristics of the process, such as "no need for drying and sieving, intelligent identification, and fast and efficient", in the scene of railway engineering and other scenes with strict requirements on material quality and construction progress, which can not only ensure the detection accuracy, but also significantly reduce the cost and improve the efficiency, and has strong practical value.

[0042] The above examples only express several embodiments of the present application, and the description is more specific and detailed, but it cannot be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are equivalent modifications and evolution of the above examples according to the essential technology of the present application, and these all belong to the protection scope of the present application.

Claims

1. A process for intelligent and rapid detection of the mass of fly ash glass microspheres, characterized in that, It comprises the following steps: Step one, sampling: take the un-dried fly ash sample, put it in a mortar and pestle, and place it in a drying dish to prevent moisture; Step two, sample preparation: place the ground sample in a glass plate round hole, then seal the round hole, shake the glass plate to evenly spread the sample, and remove the excess sample; Step three, image acquisition: place the glass plate with the sample under the electronic ocular collection system microscope, and position the sample under the microscope electronic ocular, focus the objective lens clearly, the objective lens is at least three different focal lengths, and the electronic ocular collection system collects at least three images under different objective lens focal lengths; Step four, image analysis: name the collected images, package them, and import them into the analysis system for intelligent recognition and automatic calculation of the number and proportion of glass beads and other particles; Step five, result determination: repeat the above steps to obtain three test results, and take the average of the glass bead content in the three samples as the test result. 2.The process for intelligent and rapid detection of fly ash glass microsphere mass according to claim 1, characterized in that: The step five, result determination also includes a correction step, which is as follows: If one of the results is more than ±20% of the average, remove the value and do it again to calculate the average; If the result is still more than ±20%, the test is invalid and the experiment is repeated. 3.The process for intelligent and rapid detection of fly ash glass microsphere mass according to claim 1, characterized in that: The focal lengths of the three objective lenses are 10X / 0.25, 50X / 0.55, and 100X / 0.80, respectively. 4.The process for intelligent and rapid detection of fly ash glass microsphere mass according to claim 1, characterized in that: The electronic ocular collection system includes a metallographic optical microscope that supports optical image and electronic image conversion, and the image collection scale is adjustable. 5.The process for intelligent and rapid detection of fly ash glass microsphere mass according to claim 1, characterized in that: The electronic ocular collection system also includes an analysis host and an image analysis software installed on the analysis host, which can automatically store the test results. 6.The process for intelligent and rapid detection of fly ash glass microsphere mass according to claim 5, characterized in that: The image analysis software is used for feature extraction, particle recognition, and content calculation of the collected images; The specific steps are as follows: the collected image particles are extracted according to the characteristics of smoothing, circular separation, thinning, and boundary, the glass beads are detected according to the extracted features, and the content of the glass beads is calculated. 7.The process for intelligent and rapid detection of fly ash glass microsphere mass according to claim 6, characterized in that: The feature extraction and particle recognition analysis are based on particle projection contour recognition; The content calculation specifically calculates the number, area, perimeter, maximum diameter, and shape parameters of the glass beads. 8.The process for intelligent and rapid detection of fly ash glass microsphere mass according to claim 1, characterized in that: The step of sealing the round hole in step two is as follows: use transparent tape to paste on one side of the hole glass plate to seal the round hole. 9.The process for intelligent and rapid detection of fly ash glass microsphere mass according to claim 8, characterized in that: The side length of the glass plate is 10 cm, and the glass plate round hole diameter is 10 mm. 10.The fly ash glass microsphere quality intelligent rapid detection process of claim 1, characterized in that: The step of removing excess sample in step two is as follows: turn over the glass plate to pour out the excess sample, and use an ear bulb to blow away the sample that is not firmly adhered.

Images