Method for predicting the quality of cement mixtures, system for predicting the quality of cement mixtures, program

Abstract

This technology provides a non-contact method for predicting the quality of cement mixtures, regardless of the skill of the person performing the measurement. [Solution] The method for predicting the quality of a cement mixture includes the steps of predicting air bubble information of the cement mixture based on load information of a mixer that mixes the cement mixture, and predicting the quality of the cement mixture based on the predicted air bubble information.

Description

Technical Field

[0001] The present disclosure relates to a technique for predicting the quality of cementitious materials such as concrete.

Background Art

[0002] As quality evaluation values of cementitious materials such as concrete, four are known: strength, slump or slump flow, air content, and chloride content. The materials constituting concrete include water, cement, sand, and gravel, but also include air. The air content of concrete affects the workability during concrete placement, freeze-thaw resistance, strength, etc., so it is important that the air content of concrete is an appropriate amount.

[0003] As methods for measuring the air content in concrete, the air chamber pressure method (JIS A 1128: Test method for air content of fresh concrete by pressure), the mass method (JIS A 1116: Test method for unit volume mass of fresh concrete and test method for air content by mass), the volume method (JIS A 1118: Test method for air content of fresh concrete by volume), etc. are known. However, these measurement methods require sampling and measuring a sample of concrete after mixing, and a certain level of skill is required for measurement. Therefore, it is preferable to be able to predict the quality such as the air content of concrete without contact with the concrete, independent of the skill of the measurer. This problem is not limited to the air content, and the same can be said for the quality other than the above air content.

[0004] Patent Document 1 discloses a technique for measuring the air content of fresh concrete based on the pressure in the concrete pumping pipe, the measured unit volume mass of fresh concrete, and the unit volume mass of fresh concrete without air.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

[0006] This disclosure provides a technology that enables non-contact prediction of the quality of cement mixtures, independent of the skill of the measurer. [Means for solving the problem]

[0007] The method for predicting the quality of a cement mixture according to this disclosure includes the steps of predicting air bubble information of the cement mixture based on load information of a mixer that mixes the cement mixture, and predicting the quality of the cement mixture based on the predicted air bubble information.

[0008] The cement mixture quality prediction system of this disclosure comprises: an acquisition unit that acquires load information of a mixer for mixing the cement mixture; a bubble prediction unit that predicts bubble information of the cement mixture based on the acquired load information; and a quality prediction unit that predicts the quality of the cement mixture based on the bubble information. [Effects of the Invention]

[0009] According to this disclosure, since load information from the mixer used to mix the cement mixture is used, the quality of the unhardened cement mixture can be predicted non-contact. Furthermore, predictions that do not depend on the skill of the person taking the measurement can be made. [Brief explanation of the drawing]

[0010] [Figure 1] This flowchart shows the method for predicting the quality of an uncured cement mixture according to the present disclosure. [Figure 2] This block diagram shows the configuration of a cement mixture quality prediction system that implements the method described in Figure 1. [Figure 3] This diagram shows the change in the load (power consumption) of the mixing machine. [Figure 4]This figure shows a linear approximation equation (correlation equation) generated based on the power consumption of the mixer measured during the mixing of fresh concrete and the total number of air bubbles measured after the fresh concrete hardened. [Figure 5] This figure shows an example of bubble quality correlation data, which is a correlation equation linking bubble parameters (total number of bubbles) and quality (measured air volume). [Figure 6] This figure shows the correlation between air volume, a type of bubble information (bubble parameter), and compressive strength. [Modes for carrying out the invention]

[0011] Hereinafter, embodiments of this disclosure will be described with reference to the drawings.

[0012] The prediction method of this embodiment includes the steps of predicting the air bubble information of a cement mixture based on load information (e.g., power consumption) of a mixer used to mix an unhardened cement mixture, and predicting the quality of the cement mixture based on the predicted air bubble information. It has been found that the load information of the mixer correlates with the quality of the cement mixture, such as the amount of air in concrete. Therefore, the quality of the cement mixture is estimated based on the load information of the mixer used to mix the cement mixture, such as concrete.

[0013] This method utilizes load information related to the measured load of the mixer, making it possible to predict the quality of the unhardened cement mixture, such as the air content, non-contact. Furthermore, it enables predictions that are independent of the skill of the person taking the measurement.

[0014] Uncured cement mixtures include fresh concrete or fresh mortar. Figure 1 is a flowchart of the method for predicting the quality of an uncured cement mixture according to this disclosure. Figure 2 is a block diagram showing the configuration of a cement mixture quality prediction system that implements the method described in Figure 1. The prediction method and prediction system will be described below with reference to Figures 1 and 2.

[0015] (Measurement step) In step ST1 shown in FIG. 1, as shown in FIG. 2, the load (power consumption) of the kneader 10 while kneading the cement kneaded material is measured by a sensor 12 (wattmeter), and measurement data is acquired. FIG. 3 is a diagram showing the change in the load (power consumption) of the kneader 10. The vertical axis of FIG. 3 indicates the load (instantaneous value of power consumption) [kW], and the horizontal axis of FIG. 3 indicates the elapsed time. The measurement data shown in FIG. 3 is an example, and the measurement of power consumption by the sensor 12 (wattmeter) is started simultaneously with the start of kneading the cement kneaded material (time point T0). The measurement of power consumption is performed until the time point T1 when a predetermined time (for example, 60 seconds) has elapsed. After the time point T1, the cement kneaded material is discharged from the kneader 10. The measurement data includes the measured values (instantaneous values of power consumption) at each time point from the measurement start time point T0 to the measurement end time point T1 as shown in FIG. 3. The measured values at a plurality of time points included in the measurement data are, for example, measured values for each unit time such as every 1 second.

[0016] The kneader 10 is driven based on predetermined operating conditions. The predetermined operating conditions of the kneader 10 in the present embodiment is to make the number of rotations per unit time constant, and for this purpose, in the initial stage of kneading, the torque load for kneading becomes large, so the power consumption relatively increases, and as the kneading progresses, the torque load becomes small, so the power consumption relatively decreases. The kneader 10 includes a housing portion that houses an uncured cementitious kneaded material, and a kneading member that kneads the cementitious kneaded material in the housing portion. The kneading member has kneading blades and a rotating shaft. Note that the housing portion and the kneading member may be configured such that they are not physically connected to each other, and the kneading member may employ a structure in which the kneading blades and the rotating shaft are integrated. A mechanism in which the kneading member rotates with respect to the fixed housing portion may be used, or a mechanism in which the housing portion rotates with respect to the fixed kneading member may be used. Also, the kneading member may be one or a plurality. When there are a plurality of kneading members, the value of the load information of any one of them, or the value obtained by averaging the respective load information, can be used as the load information for use in the control described later.

[0017] (Prediction step) In step ST2 following step ST1 shown in FIG. 1, the bubble prediction unit 23 (see FIG. 2) generates load information (for example, load parameters) based on the acquired measurement data.

[0018] The load information is information regarding the load of the kneader 10 included in the acquired measurement data. The load information in the present embodiment is the power consumption of the kneader 10. The load information can be represented by load parameters. The load parameters are, for example, as listed below. · Moving average value of power consumption: It is the moving average value of the measured values at a plurality of time points included in the measurement data. · Most frequent value of power consumption: It is the most frequent value of the measured values at a plurality of time points included in the measurement data. · Slope of power consumption: It is the slope of at least two of the measured values at a plurality of time points included in the measurement data. · Differential value of power consumption: It is the differential value of at least two of the measured values at a plurality of time points included in the measurement data. · Integral value of power consumption: It is the integral value of at least two of the measured values at a plurality of time points included in the measurement data. · Integrated value of power consumption: It is the integrated value of the measured values within a predetermined period among the measured values at a plurality of time points included in the measurement data. (Power consumption amount)

[0019] In step ST3 following step ST2 shown in FIG. 1, the bubble prediction unit 23 (see FIG. 2) predicts bubble information based on the load information. Specifically, using the load-bubble correlation data 20, the bubble information corresponding to the load information is specified as a predicted value. The load-bubble correlation data 20 is data in which the load information and the bubble information are associated. The load-bubble correlation data 20 may be any data as long as the load information and the bubble information are associated. For example, it may be an approximate formula or a correlation formula generated based on a plurality of measured data including load parameters and bubble parameters to be described later, or it may be a prediction model generated by machine learning that outputs bubble parameters using one or more load parameters as input data. In the following example, the load-bubble correlation data 20 associating the load parameters and the bubble parameters is given as an example.

[0020] Bubble information refers to information about the bubbles contained in the cement mixture. Bubble information can be expressed using bubble parameters. Specifically, bubble information refers to information about the number, diameter, and proportion of bubbles inside the hardened cement mixture, and can be obtained, for example, from a cross-section of the center of a specimen using a linear traverse method in accordance with ASTM C457 for the hardened cement mixture.

[0021] The linear traverse method is one method for measuring the number of bubbles generated, as described later. In this method, the number of bubbles is measured by counting bubbles while moving linearly within the measurement area. The specific procedure consists of the following steps 1 to 4. 1. Setting the measurement area: Set the measurement area for observing the generation of bubbles. 2. Preparation of the measuring device: Prepare the measuring device, which includes observation equipment such as microscopes and high-speed cameras, and a stage and traverse equipment for movement. 3. Measurement process: Using a stage or traverse device, observe the bubbles using an observation device while moving linearly within the measurement area, and count the number of bubbles. 4. Measurement result analysis process: The number of bubbles generated per unit area is calculated by dividing the number of measured bubbles by the area or distance traveled in the measurement region. The linear traverse method is a relatively simple method that can accurately measure the number of bubbles generated within a measurement area.

[0022] The bubble parameters are listed below, for example: • Total number of bubbles: This is the sum of the number of bubbles across all bubble diameters. • Total bubble ratio: This is the value obtained by dividing the number of bubbles within a specific bubble diameter range by the total number of bubbles. The specific bubble diameter range is defined as 0.000 to 0.299 mm for entrained air and 0.400 to 5.000 mm for entrapped air. Therefore, the total bubble ratio for entrained air and the total bubble ratio for entrapped air can be used. • Number of bubbles generated: This is the number of bubbles generated per unit area, obtained by measuring bubbles while moving linearly within a specific area and dividing the number of measured bubbles by the area of ​​the region or the distance traveled. • Bubble diameter: This is the diameter corresponding to the size of a bubble measured while it moves linearly within a specific area. The obtained bubble diameters are classified into several types. • Pore diameter: This is the pore diameter obtained by the mercury intrusion method. The mercury intrusion method involves injecting mercury into the pores of a material and calculating the pore diameter from the amount and rate of injection. The specific procedure is as follows (1-4). 1. Prepare a sample of the substance and smooth its surface as much as possible. 2. Place the sample in a mercury container and gradually increase the mercury pressure. 3. Mercury penetrates the pores of materials, and the amount injected increases in proportion to the pressure. 4. The pore size of the material is calculated by measuring the relationship between the injection volume and the pressure. • Bubble generation rate: This is the ratio of bubbles of a specific diameter to the total number of bubbles generated. • Cumulative number of bubbles: This is the cumulative number of bubbles generated when distributed by bubble diameter, from smallest to largest bubble diameter. • Total pore volume (cc / g): This is the total pore volume (cc / g) in the mercury intrusion method, and is the value obtained by dividing the total volume of pores in the sample by the mass of the sample.

[0023] Here, as one example, we have prepared 100 data points from when mortar or concrete is being mixed in a twin-shaft mixer. The data items are as follows: Water-cement ratio (W / C): 35-65% • Types of cement: 5 types • Types of admixtures: 10 types ·Admixture amount: 2~10kg / m 3 • Unit water volume: 160-180 kg / m³ 3

[0024] The quality items obtained from 100 samples are as follows: • Slump: 10-25.5cm • Slump flow: 22.5~74.5cm • Air volume: 1.5-7.0% • Power consumption (moving average): 4-5kW • Outside temperature: 5~31℃ ·Training length: 4m 3

[0025] Using some of the above 100 data points, load bubble correlation data 20 (see Figure 2) was created. Figure 4 shows an example of load bubble correlation data 20 that correlates power consumption (moving average value) with the total number of bubbles. In the load bubble correlation data 20 shown in Figure 4, the load parameter is the "moving average value of power consumption" and the bubble parameter is the "total number of bubbles". Figure 4 shows a linear approximation formula (correlation formula) generated based on the power consumption of the mixer 10 measured during the mixing of fresh concrete and the total number of bubbles measured after the fresh concrete hardened. It can be confirmed that there is a correlation between the load information and the bubble information.

[0026] Figure 5 shows an example of bubble quality correlation data 21, which is a correlation formula linking bubble parameters (total number of bubbles) and quality (measured air content). As shown in Figure 5, it can be confirmed that there is a correlation between bubble information and quality (air content). The air content is measured using the air chamber pressure method (JIS A 1128: Pressure test method for air content of fresh concrete) of the unhardened cement mixture. The total number of bubbles is measured by measuring the bubbles in the hardened cement mixture as described above. The bubble quality correlation data 21 is an approximate formula calculated by fitting each measurement point using methods such as the least squares method.

[0027] The bubble quality correlation data 21 can be any data that associates bubble information with quality information. For example, it may be an approximation formula or correlation formula generated based on multiple measured data including bubble parameters and quality (such as air content), or it may be a predictive model generated by machine learning that takes one or more bubble parameters as input data and outputs a quality value.

[0028] As another example, the following conversion formula can be given as bubble quality correlation data 21. Air volume = Σ Total number of bubbles × Cumulative volume / Sampling volume

[0029] As another example, the quality correlated with air bubble information is not limited to the amount of air mentioned above. For example, strength (e.g., initial strength, compressive strength, flexural strength, splitting strength) can be cited. Figure 6 shows the correlation between the amount of air, which is a type of air bubble information (air bubble parameter), and compressive strength. In the example in Figure 6, several samples of fresh concrete were prepared. The amount of air in the fresh concrete of each sample was measured. The compressive strength of each hardened sample was also measured. The compressive strength was measured at the age shown in Figure 6, in accordance with JIS A 1108 "Test Method for Compressive Strength of Concrete". As shown in Figure 6, it can be confirmed that there is a correlation between the amount of air as air bubble information and compressive strength. Based on the above, parameters that can capture the mass scale of volume (volume factor quality), such as air content, strength, surface moisture content of concrete raw materials, and particle size, have a high correlation with air bubble information (air bubble parameters), and thus can be used to obtain higher prediction accuracy.

[0030] As another example, the following conversion formula can be given as bubble quality correlation data 21. Strength = Air volume × A + B The amount of air can be calculated from the bubble information, and the strength can be calculated from the calculated amount of air.

[0031] Next, we will show an example of the accuracy rate when quality is defined as air content. Correlation data was created by linking air bubble information of hardened concrete with load information based on the power consumption of the mixer 10 measured during the mixing of fresh concrete. Correlation data was also created by linking air bubble information of hardened concrete with the air content of fresh concrete. Subsequently, the air content was predicted based on the power consumption of the mixer 10 measured during the mixing of the cement mixture, and then the air content was measured. The accuracy rate for accuracy within ±0.5% was 67%, for accuracy within ±1.0% was 80%, and for accuracy within ±1.5% was 96%. As described above, the quality can be predicted based on the load information (power consumption) of the mixer 10 measured during the mixing of the cement mixture.

[0032] (Prediction system) The above prediction method may be performed by a person, but as shown in Figure 2, it may also be performed by a system. As shown in Figure 2, the cement mixture quality system includes a sensor 12 (power meter) that measures the load of a mixer 10 that mixes the cement mixture, an acquisition unit 22 that acquires the measurement data obtained by the sensor 12 (power meter), a bubble prediction unit 23 that predicts bubble information of the cement mixture based on the measurement data, and a quality prediction unit 24 that predicts the quality of the cement mixture based on the bubble information. The prediction system is configured to store load bubble correlation data 20 and bubble quality correlation data 21 or to acquire them from an external source via a network. The acquisition unit 22, bubble prediction unit 23, and quality prediction unit 24 are realized by one or more processors of a computer reading a predetermined program. This enables the system to automatically predict the quality of the cement mixture.

[0033] [Alternative Embodiment] (A) In Figure 2 of the above embodiment, the load (power consumption) of the mixer 10 while mixing concrete (cement mixture) is measured by the sensor 12 (power meter), but the invention is not limited to this. For example, a torque sensor may be directly or indirectly attached to the rotation axis of the mixing blade of the mixer 10, and the torque acting on the rotation axis may be measured as the load of the mixer 10 by the torque sensor. When using torque, the load parameter can be calculated using the maximum value, moving average value, mode, etc., listed above.

[0034] (B) In the above embodiment, the load bubble correlation data 20 and the bubble quality correlation data 21 are conversion formulas, but are not limited thereto.

[0035] (C) In the above embodiment, the amount of air in the hardened cement mixture is predicted based on the load information of the mixer 10, and the amount of air in the unhardened cement mixture is predicted based on the amount of air in the hardened cement mixture, but the embodiment is not limited to this. For example, correction data can be created to correct the amount of air in the hardened cement mixture from the amount of air in the hardened cement mixture to the amount of air in the unhardened cement mixture based on the amount of air in the hardened cement mixture (measured) and the amount of air in the unhardened cement mixture (measured). Using the correction data, the amount of air in the hardened cement mixture predicted based on the load information of the mixer 10 can be converted to the amount of air in the unhardened cement mixture, and the amount of air in the unhardened cement mixture can be predicted based on the converted amount of air in the unhardened cement mixture. This makes it possible to make more accurate predictions.

[0036] (D) In ​​the above embodiment, the predetermined operating condition of the kneader 10 is constant rotation speed control, but is not limited to this. For example, the predetermined operating condition may be the application of a rated voltage, and the current may change according to the torque load, causing the power consumption to change. Any other control is acceptable as long as the power consumption changes.

[0037] [1] As described above in the embodiment, the method for predicting the quality of a cement mixture may include the steps of predicting air bubble information of the cement mixture based on load information of the mixer 10 that mixes the cement mixture, and predicting the quality of the cement mixture based on the predicted air bubble information. In this way, since the load information of the mixer 10 that mixes the cement mixture is used, the quality of the unhardened cement mixture can be predicted non-contact. Furthermore, predictions that do not depend on the skill of the measurer become possible.

[0038] [2] The method for predicting the quality of the cement mixture described in [1] above may also be such that the load information is a load parameter based on the power consumption of the mixer 10. Since the power consumption of the mixing machine 10 is correlated with the bubble information, it may be possible to make more accurate predictions.

[0039] [3] The method for predicting the quality of cement mixture described in [2] above, wherein the load parameter may be one of the moving average, mode, slope, derivative, integral, or cumulative values ​​of the power consumption of the mixer. An example of a preferred load parameter is shown.

[0040] [4] The method for predicting the quality of a cement mixture described in [1] above may be such that the load information is a load parameter based on the torque acting on the rotation axis of the mixing blade in the mixer 10. Since the torque acting on the rotation axis of the mixing blades in the mixing machine 10 correlates with bubble information, it may be possible to make more accurate predictions.

[0041] [5] A method for predicting the quality of a cement mixture described in any of [1] to [4] above, wherein load parameters representing load information and bubble parameters representing bubble information are associated with load-bubble correlation data 20 to predict bubble parameters corresponding to load parameters based on load information, and bubble quality correlation data 21 representing bubble parameters representing bubble information of the cement mixture and the quality of the cement mixture are associated to predict the quality corresponding to the predicted bubble parameters. Since load bubble correlation data 20 and bubble quality correlation data 21 obtained in advance through actual measurements are used, it may be possible to make more accurate predictions.

[0042] [6] A method for predicting the quality of a cement mixture as described in any of [1] to [5] above, wherein the quality is the amount of air in the cement mixture. This makes it possible to predict the amount of air in the cement mixture as a quality indicator.

[0043] [7] A method for predicting the quality of a cement mixture as described in any of [1] to [6] above, wherein the quality is defined as the strength of the cement mixture, the particle size, or the surface moisture content of the raw materials of the cement mixture. In terms of quality, it becomes possible to predict one of the following: the strength of the cement mixture (e.g., initial strength, compressive strength, flexural strength, splitting strength), particle size, or the surface moisture content of the raw materials (concrete raw materials) of the cement mixture.

[0044] [8] As in the embodiment described above, the cement mixture quality prediction system may include a bubble prediction unit 23 that predicts bubble information of the cement mixture based on load information of the mixer 10 that mixes the cement mixture, and a quality prediction unit 24 that predicts the quality of the cement mixture based on the bubble information. This allows the system to automatically predict the quality of the cement mixture.

[0045] [9] The present invention can also be specified as a computer program invention. That is, the program may cause one or more processors to perform the following: predict bubble information of the cement mixture based on load information of a mixer 10 that mixes the cement mixture, and predict the quality of the cement mixture based on the bubble information. By running a program with such functionality, it becomes possible to automatically predict the quality of the cement mixture.

[0046] Although embodiments of this disclosure have been described above with reference to the drawings, it should be understood that the specific configurations are not limited to these embodiments. The scope of this disclosure is indicated not only by the description of the embodiments above but also by the claims, and further includes all modifications within the meaning and scope equivalent to the claims.

[0047] The structures adopted in each of the above embodiments can be adopted in any other embodiment. The specific configuration of each part is not limited to the embodiments described above, and various modifications are possible without departing from the spirit of this disclosure. [Explanation of symbols]

[0048] 10: Mixing machine 20: Load bubble correlation data 21: Bubble quality correlation data 23: Bubble prediction unit 24: Quality Forecasting Department

Claims

1. A step of predicting air bubble information of the cement mixture based on load information of a mixer that mixes the cement mixture, A method for predicting the quality of a cement mixture, comprising the step of predicting the quality of the cement mixture based on the predicted bubble information.

2. The method for predicting the quality of a cement mixture according to claim 1, wherein the load information is a load parameter based on the power consumption of the mixer.

3. The method for predicting the quality of a cement mixture according to claim 2, wherein the load parameter is one of the moving average, mode, slope, derivative, integral, or cumulative value of the power consumption of the mixer.

4. The method for predicting the quality of a cement mixture according to claim 1, wherein the load information is a load parameter based on the torque acting on the rotation axis of the mixing blade in the mixing machine.

5. Using load-bubble correlation data, in which load parameters representing the load information and bubble parameters representing the bubble information are associated, the bubble parameters corresponding to the load parameters based on the load information are predicted. A method for predicting the quality of a cement mixture according to any one of claims 1 to 4, wherein the quality corresponding to the predicted bubble parameter is predicted using bubble parameter data which associates bubble information of the cement mixture with the quality of the cement mixture.

6. The method for predicting the quality of a cement mixture according to any one of claims 1 to 4, wherein the quality is the amount of air in the cement mixture.

7. The method for predicting the quality of a cement mixture according to any one of claims 1 to 4, wherein the quality is any of the strength, particle size, or surface moisture content of the raw materials of the cement mixture.

8. A bubble prediction unit that predicts bubble information of the cement mixture based on load information of a mixer that mixes the cement mixture, A cement mixture quality prediction system comprising a quality prediction unit that predicts the quality of the cement mixture based on the aforementioned bubble information.

9. Predicting air bubble information in the cement mixture based on load information of the mixer used to mix the cement mixture, To predict the quality of the cement mixture based on the aforementioned bubble information, A program that causes one or more processors to execute.

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