Control method for improving quality of pumped concrete

Abstract

The invention discloses a control method for improving the quality of pump concrete, which comprises the following steps: aggregate grading optimization: calculating an aggregate grading deviation value D (Dgt) through a grading deviation degree model; when 5%, triggering an automatic material supplementing system to correct the gradation; dynamic mix proportion adjustment: the water-cement ratio and the sand ratio of the aggregate subjected to grading optimization are synchronously adjusted according to the slump # imgabs0 # detected in real time; monitoring the uniformity of the mixture: detecting the color variance of the adjusted concrete mixture through a multispectral sensor; carrying out compensation control in a transportation process: calculating a loss value # imgabs1 # of the concrete with qualified uniformity according to a slump loss prediction model, and automatically injecting a plastic retaining agent; dynamically adjusting pumping parameters; and quality tracing and optimization.

Description

Technical Field

[0001] The present invention relates to the field of pumped concrete, and particularly to a method for improving the quality control of pumped concrete. Background Art

[0002] In the construction of pumped concrete, the key defect of the existing technology lies in that the quality control parameters of each production link are isolated from each other and lack dynamic correlation. For example, the real-time change of the aggregate gradation cannot automatically trigger the adjustment of the mix ratio, and the vibration data during transportation cannot be used to optimize the pumping parameters, resulting in the continuous attenuation of the workability of concrete between different processes. This split control leads to a high pipe blockage rate and a high coefficient of variation of the later strength, and heavily relies on manual experience for post-event remedies. Summary of the Invention

[0003] To achieve the above object and other related objects, the present invention discloses a method for improving the quality control of pumped concrete, including the following steps executed in sequence: S1: Aggregate gradation optimization: Calculate the aggregate gradation deviation value D through the gradation deviation model. When D>5%, trigger the automatic feeding system to correct the gradation; wherein, the gradation deviation model is: In the formula, : The actual sieve residue percentage of the i-th grade aggregate (i is the serial number of the aggregate particle size classification); : The target sieve residue percentage of the i-th grade aggregate in the preset ideal gradation curve; : The particle size weight coefficient, with a value range of 0.5 to 1.2, and the larger the particle size, The higher the value; S2: Dynamic mix ratio adjustment: Based on the aggregate with optimized gradation obtained in step S1, synchronously adjust the water-cement ratio and sand ratio according to the slump detected in real time ; wherein, The water-cement ratio adjustment formula is: In the formula, : The preset design slump target value, with the unit of mm; : The adjustment coefficient, with a value range of 0.02 to 0.05; : The initial water-cement ratio reference value; The sand ratio adjustment formula is: In the formula, : The initial sand ratio; : The real-time detected value of the pumping pressure; : Rated pressure of the pumping system; : Base value for sand ratio adjustment range, with a value range of 1% - 3%; : Exponential coefficient, with a value range of 0.5 - 0.8; S3: Monitoring of the uniformity of the mixture: For the concrete mixture adjusted in step S2, detect the color variance through a multispectral sensor , when three consecutive sampling periods trigger secondary stirring; S4: Compensation control during transportation: For the concrete determined to have qualified uniformity in step S3, calculate the loss value according to the slump loss prediction model , when automatically inject a plasticizer preservative; where the slump loss prediction model is: In the formula, : Time decay coefficient, with a value range of 0.3 - 0.5 mm / h; : Concrete transportation time, in hours; : Vibration sensitivity coefficient, with a value range of 0.02 - 0.05 mm / (m 2 ·s 3 ); : Real - time detection value of the acceleration sensor of the transport vehicle, in m / s 2 ; S5: Dynamic adjustment of pumping parameters: Based on the concrete compensated in step S4, adjust the pumping rate according to the pressure - flow coordinated control equation; where the pressure - flow equation is: In the formula, : Real - time pumping flow rate; : Maximum designed flow rate of the pumping system; : Real - time pumping pressure; : Minimum allowable pressure of the pumping system; : Maximum allowable pressure of the pumping system; : Exponential coefficient, with a value range of 1.5 - 2.0; S6: Quality traceability and optimization: Substitute the whole - process data of steps S1 - S5 into the hardened strength prediction model to generate a report and store it on the blockchain; where the hardened strength prediction model is: In the formula, : Predicted compressive strength of concrete; : Material coefficient, with a value range of 8 - 12; : Cement - water ratio; : Strength growth index, with a value range of 0.6 - 0.8; : Temperature influence coefficient, with a value range of 0.03 - 0.05 / °C; : The maximum temperature difference during the period from concrete pouring to hardening, with the unit of °C.

[0004] Further, the association relationship between step S1 and step S2 is: The initial water-cement ratio reference value is set to meet: When the gradation deviation D increases by 5% each time, it is adjusted down by 0.05, and the minimum is not lower than 0.35.

[0005] Further, in step S3: The triggering condition of the secondary mixing is linked with the sand ratio adjustment result of step S2. When the adjusted sand ratio is, the color variance threshold is changed from to .

[0006] Further, in step S5: The exponential coefficient of the pressure-flow equation is dynamically adjusted according to the superplasticizer injection amount Q in step S4: In the formula, is the maximum allowable single superplasticizer injection amount.

[0007] On the other hand, the present invention discloses a concrete quality control system for implementing the above method, including the following linkage modules: Gradation optimization module: including an online screening instrument and a feeding controller, and outputting the gradation deviation D to the dynamic proportioning module; Dynamic proportioning module: receiving the D value and calculating the initial water-cement ratio , and simultaneously connecting to a slump sensor and a sand ratio regulator; Uniformity detection module: receiving the mixture data and sending a reset instruction to the dynamic proportioning module when the secondary mixing is triggered; Transportation compensation module: communicating with the uniformity detection module, receiving the acceleration data and controlling the superplasticizer injection; Intelligent pumping module: receiving the concrete state data of the compensation module and dynamically adjusting the pumping parameters; Data traceability module: collecting the data of all modules and generating a blockchain certificate. Description of the Drawings

[0008] In conjunction with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The accompanying drawings are used to better understand the solution and do not limit the present disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, where: Figure 1 This is a flowchart of the present invention. Detailed implementation manners

[0009] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0010] Referring to Figure 1 , an embodiment of the present invention provides a method for improving the quality control of pumped concrete, including the following steps executed in sequence: S1: Aggregate gradation optimization: Calculate the aggregate gradation deviation value D through the gradation deviation model. When D > 5%, trigger the automatic feeding system to correct the gradation; where the gradation deviation model is: In the formula, : The actual sieve residue percentage of the i-th grade aggregate (i is the aggregate particle size classification serial number); : The target sieve residue percentage of the i-th grade aggregate in the preset ideal gradation curve; : The particle size weight coefficient, with a value range of 0.5 to 1.2, and the larger the particle size, The higher the value.

[0011] By dynamically calculating the gradation deviation and triggering automatic feeding, the instability of the mix ratio caused by the fluctuation of the aggregate particle size is eliminated, ensuring the reliability of the benchmark for subsequent mix ratio adjustment.

[0012] S2: Dynamic mix ratio adjustment: Based on the aggregate with optimized gradation obtained in step S1, synchronously adjust the water-cement ratio and sand ratio according to the slump detected in real time; where The water-cement ratio adjustment formula is: In the formula, : The preset design slump target value, in mm; : The adjustment coefficient, with a value range of 0.02 to 0.05; : The initial water-cement ratio reference value; The sand ratio adjustment formula is as follows: In the formula, : the initial sand ratio; : the real-time detected value of the pumping pressure; : the rated pressure of the pumping system; : the base number of the sand ratio adjustment range, with a value range of 1% - 3%; : the exponential coefficient, with a value range of 0.5 - 0.8; As mentioned above, the double closed-loop feedback mechanism synchronously optimizes the water-cement ratio and the sand ratio, and in real time cancels the coupling effect of the slump fluctuation and the pumping pressure change on the fluidity, avoiding the chain quality defects caused by the adjustment of a single parameter.

[0013] S3: Monitoring the uniformity of the mixture: For the concrete mixture adjusted in step S2, detect the color variance through a multi-spectral sensor , and trigger secondary mixing when for three consecutive sampling periods; As mentioned above, triggering secondary mixing based on the multi-spectral color variance threshold directly intervenes in the problem of uneven material distribution, and eliminates the pumping segregation or local strength abnormality caused by insufficient mixing.

[0014] S4: Compensation control during transportation: For the concrete determined to be qualified in uniformity in step S3, calculate the loss value according to the slump loss prediction model , and automatically inject the plasticizer when ; among them, the slump loss prediction model is: In the formula, : the time decay coefficient, with a value range of 0.3 - 0.5 mm / h; : the concrete transportation time, in hours; : the vibration sensitivity coefficient, with a value range of 0.02 - 0.05 mm / (m 2 ·s 3 ); : the real-time detected value of the acceleration sensor of the transport vehicle, in m / s 2 ; As mentioned above, quantifying the acceleration integral into the slump loss factor realizes the accurate conversion of the vibration energy, and avoids the over-dose or under-dose of the plasticizer caused by ignoring the transportation bumps in the traditional empirical compensation method.

[0015] S5: Dynamic adjustment of pumping parameters: Based on the concrete compensated in step S4, adjust the pumping rate according to the pressure-flow collaborative control equation; among them, the pressure-flow equation is: In the formula, : Real-time pumping flow rate; : Maximum design flow rate of the pumping system; : Real-time pumping pressure; : Minimum allowable pressure of the pumping system; : Maximum allowable pressure of the pumping system; : Exponential coefficient, with a value range of 1.5 to 2.0; As described above, the exponential decay characteristic of the pressure-flow equation automatically matches the change in the rheological properties of concrete, and realizes flexible pressure relief through the velocity gradient descent before the risk of pipe blockage occurs, reducing mechanical impact damage.

[0016] S6: Quality traceability and optimization: Substitute the whole-process data of steps S1 to S5 into the hardened strength prediction model to generate a report, and store it on the blockchain; among them, the hardened strength prediction model is: In the formula, : Predicted compressive strength of concrete; : Material coefficient, with a value range of 8 to 12; : Ash-water ratio; : Strength growth index, with a value range of 0.6 to 0.8; : Temperature influence coefficient, with a value range of 0.03 to 0.05 / °C; : Maximum temperature difference during the period from concrete pouring to hardening, with the unit of °C.

[0017] As described above, the hardened strength model integrates the key parameters of the whole process, establishes a reproducible quality causal relationship chain, provides a clear correction direction with physical meaning for the mixture ratio iteration, and avoids blind trial and error.

[0018] Among them, the correlation relationship between step S1 and step S2 is: The initial water-cement ratio reference value is set to meet: When the grading deviation D increases by 5% each time, it is lowered by 0.05, and the minimum is not lower than 0.35.

[0019] Furthermore, in step S3: The triggering condition of the secondary mixing is linked to the sand ratio adjustment result of step S2. When the adjusted sand ratio is the color variance threshold is adjusted from .

[0020] Further, in step S5: The exponential coefficient of the pressure-flow equation is dynamically adjusted according to the plasticizer injection amount Q in step S4: wherein, is the maximum allowable plasticizer injection amount per time.

[0021] On the other hand, the present invention discloses a concrete quality control system for implementing the above method, including the following linkage modules: Gradation optimization module: including an on-line sieve shaker and a feeding controller, and outputting the gradation deviation D to the dynamic proportioning module; Dynamic proportioning module: receiving the D value and calculating the initial water-cement ratio , and connecting to a slump sensor and a sand ratio regulator at the same time; Uniformity detection module: receiving the mixture data and sending a reset instruction to the dynamic proportioning module when secondary mixing is triggered; Transport compensation module: communicating with the uniformity detection module, receiving acceleration data and controlling the plasticizer filling; Intelligent pumping module: receiving the concrete state data of the compensation module and dynamically adjusting the pumping parameters; Data traceability module: collecting the data of all modules and generating a blockchain certificate.

[0022] Those skilled in the art of the present technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used here have the same meaning as the general understanding of those of ordinary skill in the art in the field to which the present invention belongs. It should also be understood that those terms defined in a general dictionary, such as those, should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined.

[0023] For the method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of the present invention are not limited by the described action sequences, because according to the embodiments of the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential for the embodiments of the present invention.

[0024] As can be seen from the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0025] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the quality control of pumped concrete, characterized in that, It includes the following steps executed in sequence: S1: Aggregate gradation optimization: Calculate the aggregate gradation deviation value D through the gradation deviation model. When D > 5%, trigger the automatic feeding system to correct the gradation. Among them, the gradation deviation model is: ； In the formula, : The actual sieve residue percentage of the i-th grade aggregate (i is the serial number of the aggregate particle size classification); : The target sieve residue percentage of the i-th grade aggregate in the preset ideal grading curve; : The particle size weight coefficient, with a value range of 0.5 to 1.2, and the larger the particle size, the higher the value; S2: Dynamic mix ratio adjustment: Based on the aggregate with optimized gradation obtained in step S1, synchronously adjust the water-cement ratio and sand ratio according to the slump detected in real time ; where The water-cement ratio adjustment formula is: ； In the formula, : The preset target value of the design slump, unit: mm; : The adjustment coefficient, with a value range of 0.02 - 0.05; : The initial water-cement ratio reference value; The sand ratio adjustment formula is: ； In the formula, : Initial sand ratio; : Real-time detected value of pumping pressure; : Rated pressure of pumping system; : Base number of sand ratio adjustment range, with a value range of 1% - 3%; : Exponential coefficient, with a value range of 0.5 - 0.8; S3: Monitoring the uniformity of the mixture: For the concrete mixture adjusted in step S2, detect the color variance through a multispectral sensor , and trigger secondary mixing when three consecutive sampling periods are reached; S4: Compensation control during transportation: For the concrete determined to have qualified uniformity in step S3, calculate the loss value according to the slump loss prediction model , and when , automatically inject a plasticizer preservative; wherein, the slump loss prediction model is: ； In the formula, : Time decay coefficient, with a value range of 0.3 - 0.5 mm / h; : Concrete transportation time, in hours; : Vibration sensitivity coefficient, with a value range of 0.02 - 0.05 mm / (m 2 ·s 3 ); : Real-time detection value of the acceleration sensor of the transport vehicle, in m / s 2 ; S5: Dynamic adjustment of pumping parameters: Based on the concrete compensated in step S4, adjust the pumping rate according to the pressure-flow collaborative control equation. Among them, the pressure-flow equation is: ； In the formula, : Real-time pumping flow rate; : Maximum designed flow rate of the pumping system; : Real-time pumping pressure; : Minimum allowable pressure of the pumping system; : Maximum allowable pressure of the pumping system; : Exponential coefficient, with a value range of 1.5 to 2.0; S6: Quality traceability and optimization: Substitute the whole-process data of steps S1 to S5 into the hardened strength prediction model to generate a report and store it on the blockchain. Among them, the hardened strength prediction model is: ； In the formula, : Predicted compressive strength of concrete; : Material coefficient, with a value range of 8 - 12; : Cement - water ratio; : Strength growth index, with a value range of 0.6 - 0.8; : Temperature influence coefficient, with a value range of 0.03 - 0.05 / ℃; : Maximum temperature difference during the period from concrete pouring to hardening, with the unit of ℃.

2. The method according to claim 1, wherein The correlation between step S1 and step S2 is: The initial water-cement ratio reference value is set to satisfy: ； When the grading deviation D increases by 5% each time, it is lowered by 0.05, and the minimum is not lower than 0.

35.

3. The method according to claim 1, characterized in that, In step S3: The triggering condition of the secondary stirring is linked to the sand ratio adjustment result in step S2. When the adjusted sand ratio is reached, the color variance threshold is changed from to .

4. The method according to claim 1, characterized in that, In step S5: The exponential coefficient of the pressure-flow equation Dynamically adjusted according to the plasticizer injection amount Q in step S4: ； In the formula, is the maximum allowable single injection amount of the plasticizer.

5. A concrete quality control system for implementing any one of the methods of claims 1 to 4, characterized in that It includes the following linkage modules: Gradation optimization module: It includes an online sieve analyzer and a feeding controller, and outputs the gradation deviation D to the dynamic proportioning module; Dynamic ratio module: Receive the D value and calculate the initial water-cement ratio , and connect to the slump sensor and sand ratio regulator at the same time; Uniformity detection module: Receive the mixture data and send a reset command to the dynamic proportioning module when secondary mixing is triggered; Transportation compensation module: Communicate with the uniformity detection module, receive the acceleration data and control the addition of plasticizer; Intelligent pumping module: Receive the concrete state data of the compensation module and dynamically adjust the pumping parameters; Data traceability module: Collect the data of all modules and generate a blockchain deposit certificate.

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