Preparation method of composite admixture capable of changing rheological property of concrete

Abstract

The invention discloses a preparation method of a composite admixture capable of changing rheological property of concrete, which comprises the following steps: S1, premixing mineral powder, fly ash, quartz powder and desulfurized gypsum according to a set proportion, and adjusting the mixing proportion through synergistic self-adaptive fractal analysis to obtain a first mixture with a uniform microstructure; s2, the first mixture is ground, a dynamic grinding regulation and control algorithm based on fractal dimension regression is adopted, the grinding pressure and time are adjusted, the fineness of the mixture reaches 600 meshes, and a uniform second mixture is obtained; s3, mixing the second mixture with hollow microsphere powder, and optimizing microscopic flow characteristics by applying a multi-dimensional vector field intelligent regulation and control algorithm to obtain a third mixture; s4, the third mixture is screened, unqualified particles are removed, and the composite admixture with the uniform particle size is obtained; and S5, packaging and storing the composite admixture so as to maintain the stability of the composite admixture.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete engineering, and particularly to a preparation method of a composite admixture for changing the rheology of concrete. Background Art

[0002] First of all, as a material widely used in construction engineering, the performance of concrete directly affects the quality and durability of buildings. With the improvement of the requirements for the performance of concrete in modern architecture, especially in high-rise buildings, long-span bridges and projects in special environments, the rheological properties, strength, durability and environmental friendliness of concrete have become the focus of attention. In the prior art, the fluidity and other properties of concrete are usually improved by adjusting the mix proportion of concrete, using water reducers or admixtures. However, these traditional methods have certain limitations. For example, excessive reliance on water reducers may cause bleeding, segregation and other phenomena in concrete, thus affecting the quality and performance of concrete.

[0003] Secondly, although the existing concrete admixture technologies can improve the rheological properties of concrete to a certain extent, most of them adopt a single material or a simple way of mixing multiple materials, and it is difficult to achieve effective optimization at the microscopic structure. For example, although common admixtures such as fly ash and ground granulated blast-furnace slag can improve the fluidity and strength of concrete, their improvement effects are limited by the physical properties of the materials, and it is difficult to achieve the best effect through simple proportion adjustment. At the same time, the existing grinding and mixing technologies usually cannot achieve fine control of the particle size and microscopic structure of the materials when dealing with these admixtures, which results in poor performance consistency of the final products.

[0004] Thirdly, in practical applications, due to the complexity of the concrete formula and the variability of the construction environment, it is difficult for the prior art to effectively control the rheological properties and other key performance indicators of concrete. Especially in the preparation process of high-performance concrete, how to further improve its fluidity while ensuring the high strength and high durability of concrete remains an unsolved technical problem. Most of the existing research and technologies are limited to improving the rheology of concrete by increasing the dosage of admixtures or relying on high-performance water reducers, but this often brings other problems, such as an increase in the viscosity of concrete, an increase in the incidence of bleeding and segregation phenomena, etc.

[0005] In summary, the following main problems exist in the prior art in improving the rheological properties of concrete: First, although the application of traditional admixtures and water reducers can improve the performance of concrete to a certain extent, it is prone to cause a series of negative effects, such as bleeding, segregation, uneven microstructure, etc.; Second, the existing mixing and grinding processes are difficult to precisely control the particle size distribution and microstructure of materials at the microscopic level, resulting in the consistency and performance of the final product being difficult to meet expectations; Third, the existing technical means are difficult to significantly improve the rheology of concrete without increasing costs while maintaining high strength and high durability. Therefore, how to provide a preparation method for a composite admixture that changes the rheology of concrete is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] An object of the present invention is to provide a preparation method for a composite admixture that changes the rheology of concrete. The present invention details the whole process from raw material premixing to the preparation of the final composite admixture through advanced technologies such as cooperative adaptive fractal analysis, dynamic grinding control algorithm based on fractal dimension regression, and multi-dimensional vector field intelligent control algorithm. It significantly improves the uniformity of concrete and the consistency of the microstructure, and has the advantages of high efficiency, stability, and environmental protection.

[0007] A preparation method for a composite admixture that changes the rheology of concrete according to an embodiment of the present invention includes the following steps:

[0008] S1. Premix the mineral powder, fly ash, quartz powder, and desulfurized gypsum prepared in claim 1 in a set ratio, and use cooperative adaptive fractal analysis to adjust the mixing ratio to make the microstructure of the mixture uniform, obtaining a first mixture;

[0009] S2. Grind the obtained first mixture, adopt a dynamic grinding control algorithm based on fractal dimension regression, calculate and adjust the grinding pressure and time in real time to make the fineness of the mixture reach 600 mesh, and the surface structure and particle size distribution of each component are uniform, obtaining a second mixture;

[0010] S3. Mix the ground second mixture with the hollow microsphere powder weighed in proportion, apply a multi-dimensional vector field intelligent control algorithm, and make each component evenly distributed by real-time monitoring and optimizing the microscopic flow characteristics during the mixing process, obtaining a third mixture;

[0011] S4. Screen the obtained third mixture, use a sieve with a mesh size not greater than 600 mesh to remove unqualified particles, and the product particle size is uniform, obtaining a composite admixture;

[0012] S5. Package the obtained composite admixture and store it in a sealed container to prevent moisture absorption and caking, and the admixture remains stable for a long time;

[0013] S6. During the concrete mixing process, add the composite admixture at 10% - 30% of the weight of cement in one cubic meter of concrete, and uniformly stir it with other mixtures;

[0014] S7. Conduct a rheological test on the prepared concrete mixture to verify the improvement effect of the composite admixture on the rheological properties of concrete.

[0015] Optionally, S1 includes the following steps:

[0016] S11. Weigh the mineral powder M, fly ash P, quartz powder Q, and desulfurized gypsum G in claim 1, and weigh them respectively according to the set mass percentages. The initial mixing ratio is set as P 0 , and the initial mass ratio of each component can be expressed as P 0 = {P M , P P , P Q , P G};

[0017] S12. Add the weighed mineral powder M, fly ash P, quartz powder Q, and desulfurized gypsum G to the high - efficiency mixing equipment in sequence. The initial mixing time is determined according to the set equipment parameters, and the rotation speed is controlled between 800 - 1200 rpm;

[0018] S13. After the initial mixing, measure the particle size distribution curve of the mixture through an on - line particle size analysis instrument. If the particle size distribution range exceeds the set standard, adjust the rotation speed and mixing time of the mixing equipment, and mix again until the particle size distribution curve meets the set non - linear distribution conditions;

[0019] S14. After the particle size distribution meets the requirements, perform the following operations using the collaborative adaptive fractal analysis technology:

[0020] Calculate the initial fractal dimension D f0 of the mixture. The calculation of the fractal dimension D f0 takes into account the structural characteristics at multiple different scales:

[0021]

[0022] where, N i (∈ i ) represents the number of the i - th fractal element at the scale factor ∈ i , and N is the total number of elements;

[0023] Set the target fractal dimension D ft , and according to the expected performance requirements of the mixture, set the corresponding D ft by analyzing the uniformity and denseness of the target microstructure;

[0024] Apply the collaborative adaptive algorithm to adjust by monitoring the change ΔD of the current fractal dimension D in real time: f of the change ΔD f = D ft - D f and dynamically adjust the proportions of different components through the weight function F(P):

[0025] P new = P old + β × ΔD f × F(P);

[0026] where P new is the new adjusted mixing ratio; P old is the original mixing ratio; β is the adjustment coefficient; ΔD f is the change in the fractal dimension; F(P) is a non-linear weight function that takes into account the influence of each component on the overall fractal dimension during the mixing process:

[0027]

[0028] where w i is the weight coefficient of each component; P i is the mixing ratio of the current component; ζ i is the ratio influence factor; λ is the convergence coefficient;

[0029] During the adjustment process, update the value of P in real time and gradually adjust the mixing ratio of each component until the current fractal dimension D f approaches the target fractal dimension D ft ;

[0030] S15. When the fractal dimension reaches or approaches the target value D ft stop the adjustment, and the obtained material is the optimized first mixture;

[0031] S16. Conduct a final quality inspection on the obtained first mixture, confirm that it meets the predetermined standards, and record the data for analysis and quality control.

[0032] Optionally, the S2 includes the following steps:

[0033] S21. Import the first mixture obtained in claim 3 into a high-efficiency grinding device, and the initial grinding pressure P init and the grinding time t init are determined based on the initial fractal dimension D f,init and the target fractal dimension D f,target The initial grinding pressure P init is determined by the following formula:

[0034]

[0035] Among them, α 1 is the equipment characteristic coefficient, β 1 is the regression adjustment coefficient, δ 1 is the material characteristic correction coefficient, V mix is the volume of the mixture, μ mix is the viscosity of the mixture, R grind is the radius of the grinding equipment, η device is the equipment efficiency parameter;

[0036] S22. Start the grinding process and monitor the change of the fractal dimension D f (t) of the mixture over time t. Calculate the change of the fractal dimension through an extended regression model and adjust the grinding parameters accordingly:

[0037]

[0038] Among them, D f (t) is the fractal dimension at time t; is the sensitivity coefficient of the fractal dimension to the grinding pressure; P grind (t) is the grinding pressure at time t; is the change rate of the fractal dimension over time; ψ 1 is the energy consumption correction coefficient; E grind (t) is the energy consumption at time t; is the energy consumption rate, indicating the energy consumption per unit time during the grinding process;

[0039] During the grinding process, calculate the change rate of the current fractal dimension D f (t) in real time, and adjust the grinding pressure P grind (t) and the grinding time t grind to gradually approach the target fractal dimension D f,target ;

[0040] S23. Based on the real-time monitored fractal dimension D f (t) and the energy consumption E grind (t), dynamically adjust the grinding parameters; the adjusted grinding pressure P grind (t):

[0041]

[0042] Among them, γ 1 is the control gain parameter; ΔD f (t) is the fractal dimension deviation at time t; κ 1 is the energy consumption influence coefficient; is the sensitivity coefficient of the fractal dimension to the energy consumption;

[0043] S24. During the grinding process, continuously record the change of the fractal dimension D f (t), and judge whether the grinding process reaches the target according to the following conditions:

[0044] When D f ( t ) approaches or reaches the target fractal dimension D f,target , automatically terminate the grinding process;

[0045] When the set grinding time t grind or the energy consumption E grind ( t ) reaches the predetermined threshold, judge whether the grinding process needs to be extended or adjusted;

[0046] S25. After grinding is completed, the fineness of the obtained mixture should reach 600 mesh, and the particle size distribution is detected at the end of grinding;

[0047] S26. The finally obtained second mixture.

[0048] Optionally, the S3 includes the following steps:

[0049] S31. Preliminary mixing and parameter setting: preliminarily mix the ground second mixture with the hollow microsphere powder M weighed in proportion hollow in a set ratio; the initial mixing ratio P init,mix is calculated based on the final fractal dimension D f,final of the second mixture and the mixture viscosity parameter η mix , and the specific formula is:

[0050]

[0051] where ζ 1 is the mixing efficiency correction coefficient, and λ 1 is the regulation factor;

[0052] S32. At the beginning of the preliminary mixing, construct a multi-dimensional vector field V(t) describing the physical state of the mixture; V(t) includes the fluidity field temperature field T(t) and stress field σ(t);

[0053] S34. Based on the real-time feedback of the multi-dimensional vector field V(t), dynamically adjust the mixing pressure P mix (t), mixing time t mix and temperature T mix through the control system to optimize the distribution of each component; the adjustment formula of the mixing pressure P mix (t) is as follows:

[0054]

[0055] where: γ 2 is the control gain parameter that adjusts the influence of the mixing pressure on the fluidity; κ 2 is the temperature adjustment coefficient, which is adjusted according to the real-time change of the temperature field T(t); θ 1 is the microstructure adjustment coefficient, which is used to optimize the microstructure of the mixture; is the change amount of the fluidity parameter, which reflects the difference between the fluidity and the target value; represents the sensitivity of the fluidity to temperature, and is adjusted in real time based on the change of the temperature field T(t); is the change rate of the microstructure, which describes the evolution of the microstructure during the mixing process and is adjusted according to the dynamic change of the stress field σ(t);

[0056] S35. Optimization of the micro-flow characteristics: Through vector field gradient analysis, according to the state of the fluidity field the distribution of hollow microsphere powder in the mixture is adjusted. The optimization of the fluidity field is achieved through the following formula:

[0057]

[0058] where: ξ 1 is the fluidity correction coefficient; V mix,2 and ρ mix,2 are the volume and density of the second mixture respectively; η flow is the viscosity parameter related to fluidity;

[0059] S36. Adjustment of the stress field and temperature field: Based on the real-time monitoring data of the stress field σ(t) and temperature field T(t), the operating parameters of the mixing equipment are adjusted to ensure that the microstructure ψ(t) is within the set range;

[0060] S37. When the fluidity and the microstructure ψ(t) reach the predetermined target values and ψ target the mixing process is terminated, and the final mixture is detected. The standards are as follows:

[0061]

[0062] where, λ 2 and λ 3 are the control parameters of the fluidity and microstructure respectively.

[0063] Optionally, the said S4 includes the following steps:

[0064] S41. Before screening, perform particle size pre - treatment on the third mixture, measure and record the initial particle size distribution d(x) parameters of the mixture, where x represents the particle size, and analyze the initial particle size distribution to determine the parameters required for screening;

[0065] S42. According to the particle size distribution of the third mixture, select a screen mesh with a particle size upper limit not greater than 600 mesh. The specific setting of the screen mesh number N is combined with the screening requirements and the actual particle size range of the mixture to achieve the consistency of the screening effect and the target particle size;

[0066] S43. During the screening process, monitor the rate v s (t) at which particles pass through the screen mesh and the particle size distribution of the undersize material in real - time, record the screening process, and adjust the relevant parameters during the screening process to keep the screening efficiency within the set standard range;

[0067] S44. Based on the real - time monitoring data, dynamically adjust the vibration frequency f s (t) and the screening time t s to optimize the screening effect; the adjustment of the vibration frequency is based on the difference between the current particle size distribution and the target particle size distribution to regulate the particle size distribution of the oversize material and the undersize material during the screening process;

[0068] S45. After screening is completed, collect the oversize material and the undersize material, detect the particle size distribution of the undersize material, verify that the particle size uniformity meets the set standards, and focus on monitoring the cumulative particle size distribution during the detection process to maintain product consistency;

[0069] S46. After screening is completed, clean the screening equipment to remove residual particles and perform regular equipment maintenance to maintain the long - term stability and accuracy of the screening equipment;

[0070] S47. After the qualified composite admixture after screening is tested, it is collected and packaged; during the packaging process, it is stored in a sealed container to prevent the admixture from getting damp or caking during storage and transportation.

[0071] The beneficial effects of the present invention are:

[0072] (1) The present invention proposes a preparation method of a composite admixture for changing the rheology of concrete. Through the collaborative adaptive fractal analysis technology, the mixing ratio of mineral powder, fly ash, quartz powder, and desulfurized gypsum is optimized and adjusted in the premixing stage to ensure the uniformity of the microstructure of the mixture. By this method, not only the initial uniformity of the mixture is improved, but also a foundation is laid for the subsequent grinding and mixing processes, effectively improving the overall performance of the concrete.

[0073] (2) The present invention adopts a dynamic grinding control algorithm based on fractal dimension regression, which monitors and adjusts the grinding pressure and time in real time during the grinding process, enabling the fineness of the mixture to reach 600 mesh, and ensuring uniform surface structure and particle size distribution of each component. Through this algorithm, the problem of difficult precise control in the existing grinding process is solved, ensuring the consistency of the microstructure of the mixture and significantly improving the strength and durability of concrete.

[0074] (3) The present invention introduces a multi-dimensional vector field intelligent control algorithm. During the mixing process of the second mixture and hollow microsphere powder, by monitoring and optimizing the microscopic flow characteristics in real time, uniform distribution of each component is achieved. This method optimizes the mixture in terms of microscopic structure and fluidity by dynamically adjusting the mixing parameters, and is particularly suitable for the preparation of high-performance concrete, improving the rheological properties and construction performance of concrete. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, but do not constitute a limitation to the present invention. In the drawings:

[0076] Figure 1 is a flowchart of a preparation method of a composite admixture for changing the rheology of concrete proposed by the present invention;

[0077] Figure 2 is a schematic diagram of the dynamic grinding control algorithm based on fractal dimension regression proposed by the present invention for adjusting the grinding pressure and time in real time during the grinding process;

[0078] Figure 3 is a schematic diagram of the application of the multi-dimensional vector field intelligent control algorithm proposed by the present invention during the mixing process of the second mixture and hollow microsphere powder. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0079] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only showing the basic structure of the present invention in a schematic way, so they only show the components related to the present invention.

[0080] Refer to Figures 1-3 , a preparation method of a composite admixture for changing the rheology of concrete, comprising the following steps:

[0081] S1. Premix the mineral powder, fly ash, quartz powder, and desulfurized gypsum prepared in claim 1 according to a set ratio, and adjust the mixing ratio using cooperative adaptive fractal analysis to make the microstructure of the mixture uniform, obtaining a first mixture;

[0082] S2. Grind the obtained first mixture, and adopt a dynamic grinding control algorithm based on fractal dimension regression to calculate and adjust the grinding pressure and time in real time, so that the fineness of the mixture reaches 600 mesh, and the surface structures and particle size distributions of all components are uniform, obtaining a second mixture;

[0083] S3. Mix the ground second mixture with the hollow microsphere powder weighed in proportion, and apply a multi-dimensional vector field intelligent control algorithm. By real-time monitoring and optimizing the microscopic flow characteristics during the mixing process, make all components evenly distributed, obtaining a third mixture;

[0084] S4. Screen the obtained third mixture, use a sieve with a mesh size not greater than 600 mesh, remove unqualified particles, and the product particle size is uniform, obtaining a composite admixture;

[0085] S5. Package the obtained composite admixture and store it in a sealed container to prevent moisture absorption and caking, and the admixture remains stable for a long time;

[0086] S6. During the concrete mixing process, incorporate the composite admixture into the concrete at 10% - 30% of the weight of cement per cubic meter of concrete, and stir it evenly with other mixtures;

[0087] S7. Conduct a rheology test on the prepared concrete mixture to verify the improvement effect of the composite admixture on the rheological properties of concrete.

[0088] In this embodiment, S1 includes the following steps:

[0089] S11. Weigh the mineral powder M, fly ash P, quartz powder Q, and desulfurized gypsum G in Claim 1, and weigh them respectively according to the set mass percentages. The initial mixing ratio is set as P 0 , and the initial mass ratios of all components can be expressed as P 0 = {P M , P P , P Q , P G};

[0090] S12. Add the weighed mineral powder M, fly ash P, quartz powder Q, and desulfurized gypsum G to a high-efficiency mixing device in sequence. The initial mixing time is determined according to the set device parameters, and the rotation speed is controlled between 800 - 1200 rpm;

[0091] S13. After the initial mixing, measure the particle size distribution curve of the mixture through an on-line particle size analyzer. If the particle size distribution range exceeds the set standard, adjust the rotation speed and mixing time of the mixing device, and mix again until the particle size distribution curve meets the set non-linear distribution conditions;

[0092] S14. After the particle size distribution meets the requirements, the following operations are carried out using the collaborative adaptive fractal analysis technique:

[0093] Calculate the initial fractal dimension D of the mixture f0 , the calculation of the fractal dimension D f0 considers the structural characteristics at multiple different scales:

[0094]

[0095] where N i (∈ i ) represents the number of the i-th fractal element at the scale factor ∈ i , and N is the total number of elements;

[0096] Set the target fractal dimension D ft , according to the expected performance requirements of the mixture, by analyzing the uniformity and compactness of the target microstructure, set the corresponding D ft ;

[0097] Apply the collaborative adaptive algorithm, and adjust by monitoring the change ΔD f of the current fractal dimension D f = D ft - D f , and dynamically adjust the proportion of different components through the weight function F(P):

[0098] P new = P old + β × ΔD f × F(P);

[0099] where P new is the new mixing ratio after adjustment; P old is the original mixing ratio; β is the adjustment coefficient; ΔD f is the change in the fractal dimension; F(P) is a non-linear weight function that considers the influence of each component on the overall fractal dimension during the mixing process:

[0100]

[0101] where w i is the weight coefficient of each component; P i is the mixing ratio of the current component; ζ i is the ratio influence factor; λ is the convergence coefficient;

[0102] During the adjustment process, update the value of P in real time, and gradually adjust the mixing ratio of each component until the current fractal dimension D f is close to the target fractal dimension D ft ;

[0103] S15. When the fractal dimension reaches or approaches the target value D ft stop the adjustment, and the obtained material is the optimized first mixture;

[0104] S16. Conduct a final quality inspection on the obtained first mixture, confirm that it meets the predetermined standards, and record the data for analysis and quality control.

[0105] In this embodiment, S2 includes the following steps:

[0106] S21. Introduce the first mixture obtained in claim 3 into a high-efficiency grinding device, with the initial grinding pressure P init and the grinding time t init determined based on the initial fractal dimension D f,init and the target fractal dimension D f,target The initial grinding pressure P init is determined by the following formula:

[0107]

[0108] where α 1 is the device characteristic coefficient, β 1 is the regression adjustment coefficient, δ 1 is the material characteristic correction coefficient, V mix is the volume of the mixture, μ mix is the viscosity of the mixture, R grind is the radius of the grinding device, η device is the device efficiency parameter;

[0109] S22. Start the grinding process, and monitor in real time the change of the fractal dimension D f (t) of the mixture with time t, calculate the change of the fractal dimension through an extended regression model, and adjust the grinding parameters accordingly:

[0110]

[0111] where D f (t) is the fractal dimension at time t; is the sensitivity coefficient of the fractal dimension to the grinding pressure; P grind (t) is the grinding pressure at time t; is the rate of change of the fractal dimension with time; ψ 1 is the energy consumption correction coefficient; E grind (t) is the energy consumption at time t; is the energy consumption rate, representing the energy consumption per unit time during the grinding process;

[0112] During the grinding process, calculate the current fractal dimension D f(t) and adjust the grinding pressure P according to the change rate grind (t) and the grinding time t grind to gradually approach the target fractal dimension D f,target ;

[0113] S23. Based on the real-time monitored fractal dimension D f (t) and the energy consumption E grind (t), dynamically adjust the grinding parameters; the adjusted grinding pressure P grind (t):

[0114]

[0115] where γ 1 is the control gain parameter; ΔD f is the deviation of the fractal dimension at time t; κ 1 is the energy consumption influence coefficient; is the sensitivity coefficient of the fractal dimension to the energy consumption;

[0116] S24. During the grinding process, continuously record the change of the fractal dimension D f (t) and judge whether the grinding process reaches the target according to the following conditions:

[0117] When D f (t) approaches or reaches the target fractal dimension D f,target , automatically terminate the grinding process;

[0118] When the set grinding time t grind or the energy consumption E grind (t) reaches the predetermined threshold, judge whether the grinding process needs to be extended or adjusted;

[0119] S25. After the grinding is completed, the fineness of the obtained mixture should reach 600 mesh, and the particle size distribution is detected at the end of the grinding;

[0120] S26. The finally obtained second mixture.

[0121] In this embodiment, S3 includes the following steps:

[0122] S31. Preliminary mixing and parameter setting: Preliminarily mix the ground second mixture with the hollow microsphere powder M weighed in proportion hollow in a set ratio; the initial mixing ratio P init,mix is calculated based on the final fractal dimension D f,final of the second mixture and the mixture viscosity parameter η mix , and the specific formula is:

[0123]

[0124] Among them, ζ 1 is the mixing efficiency correction coefficient, and λ 1 is the regulation factor;

[0125] S32. At the beginning of the preliminary mixing, construct a multi-dimensional vector field V(t) that describes the physical state of the mixture; V(t) includes the fluidity field temperature field T(t) and stress field σ(t);

[0126] S34. Based on the real-time feedback of the multi-dimensional vector field V(t), dynamically adjust the mixing pressure P mix (t), mixing time t mix and temperature T mix through the control system to optimize the distribution of each component; the adjustment formula of the mixing pressure P mix (t) is as follows:

[0127]

[0128] Among them: γ 2 is the control gain parameter, which adjusts the influence of the mixing pressure on the fluidity; κ 2 is the temperature adjustment coefficient, which is adjusted according to the real-time change of the temperature field T(t); θ 1 is the microstructure adjustment coefficient, which is used to optimize the microstructure of the mixture; is the change amount of the fluidity parameter, which reflects the difference between the fluidity and the target value; represents the sensitivity of the fluidity to the temperature, and is adjusted in real time based on the change of the temperature field T(t); is the change rate of the microstructure, which describes the evolution of the microstructure during the mixing process and is adjusted according to the dynamic change of the stress field σ(t);

[0129] S35. Optimization of the micro-flow characteristics: Through vector field gradient analysis, adjust the distribution of hollow microsphere powder in the mixture according to the state of the fluidity field , and the optimization of the fluidity field is achieved through the following formula:

[0130]

[0131] Among them: ξ 1 is the fluidity correction coefficient; V mix,2 and ρ mix,2 are the volume and density of the second mixture respectively; η flow is the viscosity parameter related to the fluidity;

[0132] S36. Adjustment of stress field and temperature field: Based on the real-time monitoring data of the stress field σ(t) and the temperature field T(t), adjust the operating parameters of the mixing equipment to ensure that the microstructure ψ(t) is within the set range;

[0133] S37. When the fluidity and the microstructure ψ(t) reach the predetermined target values and ψ target terminate the mixing process, and conduct inspections on the final mixture according to the following standards:

[0134]

[0135] where λ 2 and λ 3 are the control parameters for fluidity and microstructure respectively.

[0136] In this embodiment, S4 includes the following steps:

[0137] S41. Before screening, perform particle size pretreatment on the third mixture, measure and record the initial particle size distribution d(x) parameters of the mixture, where x represents the particle size, and analyze the initial particle size distribution to determine the parameters required for screening;

[0138] S42. According to the particle size distribution of the third mixture, select a screen with a particle size upper limit not greater than 600 mesh. The specific setting of the screen mesh number N is combined with the screening requirements and the actual particle size range of the mixture to achieve the consistency between the screening effect and the target particle size;

[0139] S43. During the screening process, monitor in real time the rate v s (t) of particles passing through the screen and the particle size distribution of the undersize material, record the screening process, and adjust the relevant parameters during the screening process to maintain the screening efficiency within the set standard range;

[0140] S44. Based on the real-time monitoring data, dynamically adjust the vibration frequency f s (t) and the screening time t s during the screening process to optimize the screening effect; The vibration frequency adjustment is based on the difference between the current particle size distribution and the target particle size distribution, and adjusts the particle size distribution of the oversize material and the undersize material during the screening process;

[0141] S45. After screening is completed, collect the oversize material and the undersize material, conduct particle size distribution detection on the undersize material, verify that the particle size uniformity meets the set standards, and focus on monitoring the cumulative particle size distribution during the detection process to maintain product consistency;

[0142] S46. After screening is completed, clean the screening equipment to remove residual particles, and perform regular equipment maintenance to maintain the long-term stability and accuracy of the screening equipment;

[0143] After the qualified composite admixture after screening is tested, it is collected and packaged; during the packaging process, it is stored in a closed container to prevent the admixture from getting damp or caking during storage and transportation.

[0144] Example 1:

[0145] In a large bridge construction project in a coastal city in the south, the construction side faced a relatively severe concrete construction environment. Since the bridge foundation is located near the coastline and is affected by adverse factors such as high humidity and salt spray erosion all year round, traditional concrete is prone to segregation and bleeding in such an environment, and may have problems such as reduced strength and insufficient durability after long-term use. The project team hopes to improve the rheology of concrete, enhance the construction performance, and strengthen the overall durability of the bridge by using the composite admixture of the present invention.

[0146] In this bridge construction project, the construction side decided to introduce the preparation method of the composite admixture of the present invention. At the laboratory stage, the project team first conducted small-scale tests according to the preparation method of the present invention to determine the optimal raw material ratio and preparation process flow.

[0147] First, the project team pre-mixed mineral powder, fly ash, quartz powder and desulfurized gypsum in a set ratio. During the pre-mixing process, the cooperative adaptive fractal analysis technology was applied to adjust the mixing ratio in real time to ensure the uniformity of the microstructure of the mixture. Through preliminary tests, the fractal dimension of the first mixture reached the target value, indicating that the microstructure was uniform and met the design requirements.

[0148] Next, the first mixture was ground, and a dynamic grinding control algorithm based on fractal dimension regression was used to monitor and adjust the grinding pressure and time in real time, so that the fineness of the mixture reached 600 mesh, and the surface structure and particle size distribution of each component were uniform. The second mixture obtained after grinding was carefully tested, and the particle size distribution was uniform, meeting the design standards.

[0149] Subsequently, the project team mixed the second mixture with the hollow microsphere powder weighed in proportion. By applying the multi-dimensional vector field intelligent control algorithm, the microscopic flow characteristics were monitored and optimized in real time during the mixing process to make each component evenly distributed, forming a third mixture. After testing, the rheology and microstructure of the third mixture both reached the expected effects.

[0150] During the preparation process of the third mixture, the project team carried out screening treatment on it, using a sieve with a particle size not greater than 600 mesh to remove unqualified particles to ensure uniform product particle size. The screened composite admixture was packaged and stored in a closed container to prevent moisture absorption and caking.

[0151] During actual construction, the project team incorporated a composite admixture at 20% of the cement weight during the concrete mixing process, evenly stirred it with other mixtures, and prepared a concrete mixture with optimized rheological properties. The fluidity of the concrete mixture was significantly improved, the bleeding phenomenon was significantly reduced, and the viscosity of the concrete was moderate, making it easy to pump and construct.

[0152] Data proves

[0153] To verify the beneficial effects of the present invention, the project team made detailed data records and analyses before and after actual construction. Before using the composite admixture of the present invention, the construction party conducted a test using a traditional concrete formula, and the results are as follows:

[0154] Table 1 Comparison of the properties of traditional concrete and concrete using the composite admixture

[0155]

[0156]

[0157] Table 2 Performance data of concrete during construction after using the composite admixture

[0158]

[0159] Table 3 Test records of the properties of concrete using the composite admixture at the bridge construction site

[0160]

[0161] The data of this embodiment shows that using the composite admixture of the present invention for construction in a coastal high-humidity and salt-fog environment not only improves the various performance indicators of the concrete, but also provides sufficient working time for the project, which helps to ensure the smooth completion and long-term stability of the project.

[0162] As described above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for preparing a composite admixture for changing the rheological properties of concrete, characterized in that: The composite admixture is prepared from the following raw materials in parts by weight: 400-600 parts of mineral powder; 100-300 parts of fly ash; 200-300 parts of hollow microsphere powder; 5-10 parts of quartz powder; 3-5 parts of desulfurized gypsum; 1-3 parts of grinding aid.

2. A method for preparing a composite admixture for changing the rheological properties of concrete, characterized in that: The steps include: S1. Premixing the mineral powder, fly ash, quartz powder and desulfurized gypsum prepared in claim 1 according to a set ratio, adjusting the mixing ratio by using collaborative adaptive fractal analysis to make the microstructure of the mixture uniform, and obtaining a first mixture; S2, grinding the obtained first mixture, using a dynamic grinding control algorithm based on fractal dimension regression, calculating and adjusting the grinding pressure and time in real time, so that the fineness of the mixture reaches 600 meshes, and the surface structure and particle size distribution of each component are uniform, to obtain a second mixture; S3, mixing the ground second mixture with hollow microbead powder weighed in proportion, applying a multi-dimensional vector field intelligent control algorithm, and making each component evenly distributed by real-time monitoring and optimizing the microscopic flow characteristics during the mixing process to obtain a third mixture; S4, sieving the obtained third mixture, using a sieve with a mesh size of no more than 600 meshes to remove unqualified particles, so that the product particle size is uniform, and a composite admixture is obtained; S5. Packing the obtained composite admixture and storing it in a sealed container to prevent moisture absorption and agglomeration, so that the admixture remains stable for a long time; S6. During the concrete mixing process, 10% to 30% of the weight of cement in one cubic meter of concrete is added to the composite admixture and mixed evenly with other mixed materials; S7. Perform rheological tests on the prepared concrete mixture to verify the improvement effect of the composite admixture on the rheological properties of concrete.

3. The method for preparing a composite admixture for changing the rheological properties of concrete according to claim 2, characterized in that: The S1 comprises the following steps: S11. Weigh the mineral powder M, fly ash P, quartz powder Q and desulfurized gypsum G in claim 1, respectively, according to the set mass percentage, and set the initial mixing ratio to P0. The initial mass ratio of each component can be expressed as P0 = {P M ,P P ,P Q ,P G }; S12, adding the weighed mineral powder M, fly ash P, quartz powder Q and desulfurized gypsum G into the high-efficiency mixing equipment in sequence, the initial mixing time is determined according to the set equipment parameters, and the rotation speed is controlled between 800 and 1200 rpm; S13. After preliminary mixing, the particle size distribution curve of the mixture is measured by an online particle size analyzer. If the particle size distribution range exceeds the set standard, the rotation speed and mixing time of the mixing device are adjusted, and the mixture is mixed again until the particle size distribution curve meets the set nonlinear distribution condition; S14. After the particle size distribution meets the requirements, the following operations are performed using the collaborative adaptive fractal analysis technology: Calculate the initial fractal dimension D of the mixture f0 , fractal dimension D f0 The calculation of takes into account structural features at multiple different scales: Among them, N i (∈ i ) indicates that the scale factor ∈ i The number of the i-th fractal element under , N is the total number of elements; Set the target fractal dimension D ft According to the expected performance requirements of the mixture, the corresponding D is set by analyzing the uniformity and density of the target microstructure. ft ; The collaborative adaptive algorithm is applied to monitor the current fractal dimension D in real time. f Change of ΔD f =D ft -D f Adjustments are made to dynamically adjust the proportions of different components through the weight function F(P): P new =P old +β×ΔD f ×F(P); Among them, P new is the new mixing ratio after adjustment; P old is the original mixing ratio; β is the adjustment coefficient; ΔD f is the change in fractal dimension; F(P) is a nonlinear weight function that takes into account the influence of each component on the overall fractal dimension during the mixing process: Among them, w i is the weight coefficient of each component; P i is the mixing ratio of the current components; i is the proportional influence factor; λ is the convergence coefficient; During the adjustment process, the value of P is updated in real time, and the mixing ratio of each component is gradually adjusted until the current fractal dimension D f Approaching the target fractal dimension D ft ; S15. When the fractal dimension reaches or approaches the target value D ft After that, stop adjusting, and the obtained material is the optimized first mixture; S16, performing a final quality test on the obtained first mixture to confirm that it meets the predetermined standard, and recording the data for analysis and quality control.

4. The method for preparing a composite admixture for changing the rheological properties of concrete according to claim 2, characterized in that: The S2 comprises the following steps: S21, introducing the first mixture obtained in claim 3 into a high-efficiency grinding device, with an initial grinding pressure P init and grinding time t init Based on the initial fractal dimension D f,init and the target fractal dimension D f,target Determine the initial grinding pressure P init Determined by the following formula: Among them, α1 is the equipment characteristic coefficient, β1 is the regression adjustment coefficient, δ1 is the material characteristic correction coefficient, V mix is the volume of the mixture, μ mix is the viscosity of the mixture, R grind is the radius of the grinding equipment, η device is the equipment efficiency parameter; S22, start the grinding process, and monitor the fractal dimension D of the mixture in real time f (t) With the change of time t, the change of fractal dimension is calculated by the extended regression model, and the grinding parameters are adjusted accordingly: Among them, D f (t) is the fractal dimension at time t; is the sensitivity coefficient of fractal dimension to grinding pressure; P grind (t) is the grinding pressure at time t; is the rate of change of fractal dimension over time; ψ1 is the energy consumption correction coefficient; E grind (t) is the energy consumption at time t; is the energy consumption rate, which indicates the energy consumption per unit time during the grinding process; During the grinding process, the current fractal dimension D is calculated in real time. f (t) and adjust the grinding pressure P according to the change rate grind (t) and grinding time t grind , to gradually approach the target fractal dimension D f,target ; S23. Fractal dimension D based on real-time monitoring f (t) and energy consumption E grind (t), dynamically adjust the grinding parameters; the adjusted grinding pressure P grind (t): Among them, γ1 controls the gain parameter; ΔD f (t) fractal dimension deviation at time t; κ1 energy consumption influence coefficient; The sensitivity coefficient of fractal dimension to energy consumption; S24. During the grinding process, the fractal dimension D is continuously recorded. f (t) and judge whether the grinding process has reached the target according to the following conditions: When D f (t) Approach or reach the target fractal dimension D f,target When the grinding process is automatically terminated; When the set grinding time t is reached grind or energy consumption E grind (t) when a predetermined threshold is reached, determining whether the grinding process needs to be extended or adjusted; S25. After grinding, the fineness of the obtained mixture should reach 600 mesh, and the particle size distribution should be tested at the end of grinding; S26, the second mixture finally obtained.

5. The method for preparing a composite admixture for changing the rheological properties of concrete according to claim 2, characterized in that: The S3 comprises the following steps: S31, preliminary mixing and parameter setting: the ground second mixture and hollow microbead powder M weighed in proportion are mixed. hollow Perform preliminary mixing according to the set ratio; the initial mixing ratio P init,mix The final fractal dimension D based on the second mixture f,final and the mixture viscosity parameter η mix Calculation, the specific formula is: Among them, ζ1 is the mixed efficiency correction coefficient, λ1 is the control factor; S32. At the beginning of the initial mixing, a multidimensional vector field V(t) is constructed to describe the physical state of the mixture; V(t includes the flowability field Temperature field T(t) and stress field σ(t); S34, based on the real-time feedback of the multi-dimensional vector field V(t), the mixed pressure P is dynamically adjusted through the control system mix (t), mixing time t mix and temperature T mix , to optimize the distribution of each component; mixing pressure P mix The adjustment formula for (t) is as follows: Where: γ2 is the control gain parameter, which adjusts the effect of the mixing pressure on fluidity; κ2 is the temperature adjustment coefficient, which is adjusted according to the real-time change of the temperature field T(t); θ1 is the microstructure adjustment coefficient, which is used to optimize the microstructure of the mixture; is the change in liquidity parameter, reflecting the difference between liquidity and target value; Indicates the sensitivity of fluidity to temperature and makes real-time adjustments based on changes in the temperature field T(t); is the rate of change of the microstructure, describing the evolution of the microstructure during the mixing process, and is adjusted according to the dynamic changes of the stress field σ(t); S35, Optimization of microscopic flow characteristics: Through vector field gradient analysis, according to the flow field state, adjust the distribution of hollow microbead powder in the mixture, and the flow field The optimization is achieved through the following formula: Where: ξ1 is the liquidity correction coefficient; V mix,2 and ρ mix,2 are the volume and density of the second mixture respectively; η flow is the viscosity parameter related to fluidity; S36, adjustment of stress field and temperature field: based on the real-time monitoring data of stress field σ(t) and temperature field T(t), adjusting the operating parameters of the mixing equipment to ensure that the microstructure ψ(t) is within the set range; S37, when liquidity and microstructure ψ(t) reaches the predetermined target value and ψ target When the mixing process is terminated, the final mixture is tested according to the following standards: Among them, λ2 and λ3 are the control parameters of fluidity and microstructure, respectively.

6. The method for preparing a composite admixture for changing the rheological properties of concrete according to claim 2, characterized in that: The S4 comprises the following steps: S41, before screening, performing particle size pretreatment on the third mixture, measuring and recording the initial particle size distribution d(x) parameter of the mixture, where x represents the particle size, and analyzing the initial particle size distribution to determine the parameters required for screening; S42, according to the particle size distribution of the third mixture, selecting a sieve with an upper limit of particle size not greater than 600 meshes, and setting the specific number of meshes N of the sieve in combination with the screening requirements and the actual particle size range of the mixture to achieve consistency between the screening effect and the target particle size; S43, during the screening process, real-time monitoring of the speed v of particles passing through the screen s (t) and the particle size distribution of the sieve material, record the screening process, and adjust the relevant parameters in the screening process to maintain the screening efficiency within the set standard range; S44, based on real-time monitoring data, dynamically adjust the vibration frequency f during the screening process s (t) and screening time t s , in order to optimize the screening effect; the vibration frequency is adjusted according to the difference between the current particle size distribution and the target particle size distribution, and the particle size distribution of the oversize and undersize during the screening process is adjusted; S45. After the screening is completed, the sieve material and the sieve material are collected, and the particle size distribution of the sieve material is tested to verify that the particle size uniformity meets the set standard. During the test, the cumulative particle size distribution is monitored to maintain product consistency; S46. After the screening is completed, the screening equipment is cleaned to remove residual particles, and the equipment is regularly maintained to maintain the long-term stability and accuracy of the screening equipment; S47. After screening, qualified composite admixtures are collected and packaged after testing. During the packaging process, they are stored in sealed containers to prevent the admixtures from getting damp or agglomerating during storage and transportation.

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