A rheology-based concrete mix design and optimization method and system

Abstract

This application relates to the field of concrete materials engineering technology, specifically disclosing a method for concrete mix design and optimization based on rheology, including the following steps: Preliminary mix design: Based on the concrete workability requirements, a preliminary concrete mix design is performed using a quantitative model established between concrete mix proportion parameters and conventional workability indicators; Real-time monitoring of rheological parameters: After the concrete mix proportion is initially determined, the concrete rheological parameters are monitored in real time; Mix proportion adjustment: By using the monitored concrete rheological parameter data and combining it with the quantitative model between the concrete mix proportion parameters and rheological parameters, the concrete mix proportion parameters are adjusted to ensure that the concrete rheological properties and conventional workability meet construction requirements. This invention integrates rheological principles into concrete mix design, and the scientific design and dynamic optimization of the mix proportion ensures the quality of concrete construction, reduces trial mixing costs and design cycles, and is applicable to modern concrete engineering.

Description

Technical Field

[0001] This application relates to the field of concrete materials engineering technology, and more specifically, to a method and system for concrete mix design and optimization based on rheology. Background Technology

[0002] In modern concrete engineering construction, the precise design of concrete mix proportions is crucial to ensuring project quality and construction efficiency. Traditional concrete mix design mainly relies on empirical formulas based on slump-strength and trial mix tests, focusing on conventional performance indicators such as slump and spread.

[0003] However, in complex engineering scenarios such as ultra-high-rise pumping construction, large-volume concrete pouring, and the application of self-compacting concrete, the rheological properties of concrete, such as fluidity, filling capacity, and segregation resistance, have a decisive impact on construction quality and structural performance.

[0004] In existing technologies, the correlation mechanism between concrete's yield stress (characterizing resistance to shear failure) and plastic viscosity (characterizing flow resistance) and workability is not yet clear. There is a lack of effective methods to deeply integrate rheological parameters with concrete mix design parameters and achieve real-time monitoring and intelligent control. This leads to problems such as low efficiency, poor accuracy, delayed control, and difficulty in adapting to diverse construction needs in concrete mix design, failing to meet the stringent requirements of modern engineering for high-performance and refined concrete construction. Summary of the Invention

[0005] This application provides a rheology-based method and system for concrete mix design and optimization. By constructing a quantitative relationship system between concrete mix parameters, conventional workability indicators and rheological parameters, it realizes the scientific design and dynamic intelligent optimization of concrete mix proportions, thereby significantly improving concrete workability and construction quality, while reducing trial mixing costs and design cycle, providing an efficient and reliable technical solution for concrete engineering construction.

[0006] Firstly, this application provides a rheology-based method for concrete mix design and optimization, employing the following technical solution: A rheology-based method for concrete mix design and optimization includes the following steps: Preliminary mix design: Based on the concrete workability requirements, and using a quantitative model relating the concrete mix design parameters to conventional workability indicators, the concrete mix design is preliminarily designed. The concrete mix design parameters include the paste-aggregate ratio (p / ag), sand ratio (s / ag), and admixture dosage (admix). The quantitative model relating the concrete mix design parameters to conventional workability indicators includes: For slump (T, unit: mm), the established quantitative model is: T = a×(p / ag) + b×(s / ag) + c×admix + d, where a, b, c, and d are coefficients obtained through regression analysis of experimental data; For the expansion (S, unit: mm), the established quantitative model is: S = k×(p / ag) + l×(s / ag) + m×admix + n, where k, l, m, and n are coefficients obtained through regression analysis of experimental data; Real-time monitoring of rheological parameters: After the concrete mix proportions are initially determined, the rheological parameters of the concrete are monitored in real time. Mix proportion control: By monitoring concrete rheological parameter data and combining it with a quantitative model between concrete mix proportion parameters and rheological parameters, concrete mix proportions that do not meet workability requirements are controlled. The concrete mix proportion parameters are adjusted to ensure that the rheological properties and conventional workability of the concrete meet construction requirements. The quantitative model between concrete mix proportion parameters and rheological parameters includes: For static yield stress (τ) 0s (Unit: Pa), the established quantization model is: τ 0s = e×(p / ag) + f×(s / ag) + g×admix + h, where e, f, g, and h are coefficients obtained through regression analysis of experimental data; For dynamic yield stress (τ) 0d (Unit: Pa), the established quantization model is: τ 0d = o×(p / ag) + p×(s / ag) + q×admix + r, where o, p, q, and r are coefficients obtained through regression analysis of experimental data; For plastic viscosity (μ, unit: Pa·s), the established quantitative model is: μ = s×(p / ag) + t×(s / ag) + u×admix + v, where s, t, u, and v are coefficients obtained through regression analysis of experimental data.

[0007] It should be noted that working performance is a general term for both conventional working performance and rheological performance, with rheological performance corresponding to rheological parameters.

[0008] Furthermore, the quantitative model between concrete mix proportion parameters and conventional work performance indicators includes: The quantification model established for slump (T, unit: mm) is: T = 500×(p / ag) - 300×(s / ag)+ 200×admix +50; The quantization model established for the scalability (S, unit: mm) is: S = 1200×(p / ag) -500×(s / ag)+ 300×admix + 100.

[0009] Furthermore, the quantitative model between concrete mix proportion parameters and rheological parameters includes: For static yield stress (τ) 0s (Unit: Pa), the established quantization model is: τ 0s = -80×(p / ag) + 50×(s / ag) -30×admix +40; For dynamic yield stress (τ) 0d (Unit: Pa), the established quantization model is: τ 0d = -60×(p / ag) + 40×(s / ag) -25×admix +30; For plastic viscosity (μ, unit: Pa·s), the established quantitative model is: μ = -15×(p / ag) + 10×(s / ag) -5×admix +8.

[0010] The establishment of the quantization model includes the following steps: (1) Database establishment: Select different concretes and conduct concrete mix design tests according to different mix proportion parameters, obtain test data, establish a database containing concrete mix proportion parameters, conventional work performance indicators and rheological parameters, and store the established database in the storage module; (2) Quantitative model establishment: Using multiple linear regression analysis, the experimental data were processed in depth, and quantitative models were established between concrete mix proportion parameters and conventional work performance indicators, as well as between concrete mix proportion parameters and rheological parameters. The established quantitative models were stored in the storage module.

[0011] Furthermore, in step (2), the quantitative model construction for concrete mix proportion parameters and conventional workability indicators and rheological parameters all adopt a structurally unified multiple linear regression model. The core independent variables of the model are all concrete mix proportion parameters; the dependent variables include specific workability indicators and regression coefficients, and the dependent variables vary depending on the type of indicator. Furthermore, the specific values ​​of the regression coefficients are determined by fitting the concrete rheological performance test results from the database.

[0012] Furthermore, in the real-time monitoring step of rheological parameters, the rheometer is connected to the data processor via a serial port, and the real-time monitoring of concrete rheological parameters is achieved using the data processor and programming tools.

[0013] Furthermore, in the mix proportion control step, by monitoring the concrete rheological parameter data and combining it with the quantitative model between the concrete mix proportion parameters and rheological parameters in the storage module, the concrete mix proportion that does not meet the workability requirements is intelligently controlled by programming tools, and the concrete mix proportion parameters are adjusted so that the concrete rheological properties and conventional workability meet the construction requirements.

[0014] Furthermore, in the mix proportion control step, by monitoring the concrete rheological parameter data and combining it with the quantitative model between the concrete mix proportion parameters and rheological parameters in the storage module, the concrete mix proportion that does not meet the workability requirements is intelligently controlled by programming tools, and the concrete mix proportion parameters are adjusted so that the concrete rheological properties and conventional workability meet the construction requirements.

[0015] Furthermore, the programming tools include the MATLAB program.

[0016] Secondly, this application provides a system for the above-mentioned rheology-based concrete mix design and optimization method, employing the following technical solution: A system for the above-mentioned rheology-based concrete mix design and optimization method, the system comprising an experimental data acquisition module, a quantitative model construction module, a storage module, a preliminary mix design module, a real-time rheological parameter detection module, a performance judgment module, and a mix intelligent control module; The test data acquisition module is used to acquire test data including concrete mix proportion parameters, conventional work performance indicators, and rheological parameters, and to establish a database; The quantitative model building module is used to establish quantitative models between concrete mix proportion parameters and conventional work performance indicators, as well as quantitative models between concrete mix proportion parameters and rheological parameters. The storage module is used to store the experimental data acquired by the experimental data acquisition module; and to store the quantitative models constructed by the quantitative model construction module. The preliminary mix design module is used for the preliminary design of concrete mix proportions; The real-time rheological parameter detection module is used to detect the rheological parameters of concrete in the preliminary design mix proportion in real time. The performance assessment module is used to determine whether the rheological properties and conventional working properties of concrete meet the construction requirements. The intelligent mix proportion control module is used to intelligently control the concrete mix proportions that do not meet the performance requirements.

[0017] In summary, this application has the following beneficial effects: (1) This application deeply integrates the principles of rheology into concrete mix design. By establishing a multi-parameter database and a quantitative model, it comprehensively considers the intrinsic relationship between concrete mix parameters, conventional work performance indicators and rheological parameters, which changes the traditional design mode that relies on experience, making the mix design more scientific and reasonable, and able to accurately meet the diverse requirements of concrete work performance in different construction scenarios.

[0018] (2) This application realizes real-time monitoring of concrete rheological parameters and intelligent control of mix proportions. Compared with traditional methods, it eliminates the need for extensive and repeated trial mixing, significantly improving the efficiency of concrete mix design. Intelligent control reduces material waste, lowers construction and time costs, and has good economic and social benefits. Attached Figure Description

[0019] Figure 1 This is a diagram showing the interaction relationships between modules in the system provided in this application. Detailed Implementation

[0020] The present application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0021] Example This application provides a rheology-based method for concrete mix design and optimization, comprising the following steps: (1) Database establishment: Select different concretes and conduct concrete mix design tests according to different mix proportion parameters, obtain test data, establish a database containing concrete mix proportion parameters, conventional work performance indicators and rheological parameters, and store the established database in the storage module; The concrete mix proportion parameters include the paste-aggregate ratio p / ag, sand ratio s / ag, and admixture dosage admix; the conventional work performance indicators include slump and spread; and the rheological parameters include static yield stress, dynamic yield stress, and plastic viscosity.

[0022] In the embodiments of this application, in a laboratory environment, different types of cement, aggregates, and admixtures were selected, and a large number of concrete mix design tests were conducted according to different gradients of admixture dosage (0.5%-1.2%, with intervals of 0.1%), paste-aggregate ratio (0.35-0.50, with intervals of 0.05), and sand ratio (0.30-0.50, with intervals of 0.05). Multiple sets of test data were obtained, and a database containing concrete mix design parameters, conventional workability indicators, and rheological parameters was established.

[0023] (2) Quantitative model establishment: Using multiple linear regression analysis, the experimental data were processed in depth, and quantitative models were established between concrete mix proportion parameters and conventional work performance indicators, as well as between concrete mix proportion parameters and rheological parameters. The established quantitative models were stored in the storage module. Both models employ a unified multiple linear regression model, with the core independent variables being concrete mix proportion parameters (including: paste-aggregate ratio p / ag, sand ratio s / ag, and admixture dosage admix). Only the dependent variable (corresponding to specific performance indicators) and regression coefficients (obtained from fitting experimental data) vary depending on the type of indicator.

[0024] For slump (T, unit: mm), the established quantitative model is: T = a×(p / ag) + b×(s / ag) + c×admix + d, where a, b, c, and d are coefficients obtained through regression analysis of experimental data; For the expansion (S, unit: mm), the established quantitative model is: S = k×(p / ag) + l×(s / ag) + m×admix + n, where k, l, m, and n are coefficients obtained through regression analysis of experimental data; For static yield stress (τ) 0s (Unit: Pa), the established quantization model is: τ 0s = e×(p / ag) + f×(s / ag) + g×admix + h, where e, f, g, and h are coefficients obtained through regression analysis of experimental data; For dynamic yield stress (τ) 0d (Unit: Pa), the established quantization model is: τ 0d = o×(p / ag) + p×(s / ag) + q×admix + r, where o, p, q, and r are coefficients obtained through regression analysis of experimental data; For plastic viscosity (μ, unit: Pa·s), the established quantitative model is: μ = s×(p / ag) + t×(s / ag) + u×admix + v, where s, t, u, and v are coefficients obtained through regression analysis of experimental data; (3) Preliminary design of mix proportion: Based on the concrete workability requirements, the concrete mix proportion is preliminarily designed using the quantitative model between the concrete mix proportion parameters and conventional workability indicators established in the storage module; (4) Real-time monitoring of rheological parameters: After the concrete mix proportion is initially determined, the rheometer is connected to the data processor, and the data processor and MATLAB program are used to realize the real-time monitoring of concrete rheological parameters. A rheometer is connected externally to the mixer outlet and connected to a data processor via a serial port. A MATLAB program reads the data measured by the rheometer through the serial port, realizing real-time acquisition and visualization of rheological parameters. The implementation logic of this serial port data reading function falls within the conventional technical scope of those skilled in the art. The code example provided below is merely an exemplary implementation of the "real-time monitoring of rheological parameters" stage of this invention. The serial port parameter configuration can be adjusted according to the actual hardware connection, and the data reading and display logic can also be optimized based on conventional programming techniques. These adjustments do not deviate from the conventional understanding and practical abilities of those skilled in the art. The specific code for this embodiment is as follows:

[0025] (5) Intelligent control of mix proportion: By monitoring the concrete rheological parameter data, and combining the quantitative model between concrete mix proportion parameters and rheological parameters in the storage module, the concrete mix proportion that does not meet the work performance requirements is intelligently controlled by the MATLAB program, and the concrete mix proportion parameters are adjusted so that the concrete rheological properties and conventional work performance meet the construction requirements.

[0026] This embodiment also provides a system for the above-mentioned rheology-based concrete mix design and optimization method, including an experimental data acquisition module, a quantitative model construction module, a storage module, a preliminary mix design module, a real-time rheological parameter detection module, a performance judgment module, and a mix design intelligent control module; wherein the experimental data acquisition module, combined with a database, forms a data layer, the quantitative model construction module and the storage module form a model layer, and the preliminary mix design module, the real-time rheological parameter detection module, the performance judgment module, and the mix design intelligent control module form an application layer; the workflow of the above system is as follows: Figure 1 As shown, the details are as follows: Test data acquisition module: Acquires test data including concrete mix proportion parameters, conventional work performance indicators, and rheological parameters, and establishes a database; Quantitative model construction module: Establishes quantitative models between concrete mix proportion parameters and conventional work performance indicators, as well as quantitative models between concrete mix proportion parameters and rheological parameters; Storage module: Stores experimental data acquired by the experimental data acquisition module; and stores the quantitative models constructed by the quantitative model construction module; Mix proportion preliminary design module: After inputting the performance requirements, the storage module uses the established quantitative model to preliminarily design the concrete mix proportion; Real-time rheological parameter detection module: Real-time detection of concrete rheological parameters based on preliminary design mix proportions; Performance assessment module: Determines whether the rheological properties and conventional working properties of concrete meet the construction requirements, and outputs the construction mix proportion parameters that meet the standards; Intelligent mix proportion control module: Intelligently controls concrete mix proportions that do not meet workability requirements, repeating the above steps until the rheological properties and conventional workability of the concrete meet construction requirements.

[0027] The following explanation is provided through specific examples.

[0028] Examples 1-3 The quantization models and design and optimization process of Examples 1-3 are shown in Table 1. The relevant parameters are detailed in Table 1.

[0029] Table 1. Parameters for the design optimization process of Examples 1-3

[0030] Accuracy verification (1) Verification scheme: Using the final mix proportions of Examples 1-3 as the subjects, three parallel trial mix experiments were conducted for each group, and the results were tested separately: Standard working performance: Slump and spread are measured according to GB / T 50080-2016 "Standard for Test Methods of Performance of Ordinary Concrete Mixtures"; Rheological parameters: Static yield stress, dynamic yield stress, and plastic viscosity were measured using a rotational rheometer (such as the NELD concrete rheometer). Comparative group: The traditional "slump-strength" empirical method was used to design the mix proportions for the same scenario. Three parallel tests were conducted, and the number of trial mixes, design cycle, and material waste rate were statistically analyzed. The design process is as follows: 1. Clearly define and specify the corresponding intensity level (C30); 2. Estimate the benchmark mix proportion based on the experience tables in the "Specification for Mix Proportion Design of Ordinary Concrete" (JGJ55-2011); 3. Mix according to the benchmark mix ratio, and only test the slump and strength: if the slump does not meet the standard, adjust the sand ratio / admixture dosage based on experience; if the strength is insufficient, adjust the water-cement ratio. Repeat the process until both indicators meet the standard. 4. The final mix proportions are determined as follows: paste-aggregate ratio 0.40, sand ratio 0.38, and admixture dosage 1.0% (without rheological parameter control).

[0031] (2) Verification results, see Table 2 for details. Table 2 Verification Results

[0032] (3) Conclusion: The conventional working performance indicators and measured values ​​of rheological parameters of the method in this application are all within the target range, with a relative error of ≤5%, and the accuracy is significantly better than that of traditional methods. The number of trial mixes is reduced by 75%, the design cycle is shortened by 75%, and the material waste rate is reduced by 66%, which verifies the beneficial effects of "improving efficiency and reducing costs" and meets the requirements of complex engineering for refined concrete design.

[0033] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

Claims

1. A method for concrete mix design and optimization based on rheology, characterized in that, Includes the following steps: Preliminary mix design: Based on the concrete workability requirements, and using a quantitative model relating the concrete mix design parameters to conventional workability indicators, the concrete mix design is preliminarily designed. The concrete mix design parameters include the paste-aggregate ratio (p / ag), sand ratio (s / ag), and admixture dosage (admix). The quantitative model relating the concrete mix design parameters to conventional workability indicators includes: For slump (T, unit: mm), the established quantitative model is: T = a×(p / ag) + b×(s / ag) + c×admix + d, where a, b, c, and d are coefficients obtained through regression analysis of experimental data; For the expansion (S, unit: mm), the established quantitative model is: S = k×(p / ag) + l×(s / ag) + m×admix + n, where k, l, m, and n are coefficients obtained through regression analysis of experimental data; Real-time monitoring of rheological parameters: After the concrete mix proportions are initially determined, the rheological parameters of the concrete are monitored in real time. Mix proportion control: By monitoring concrete rheological parameter data and combining it with a quantitative model between concrete mix proportion parameters and rheological parameters, concrete mix proportions that do not meet workability requirements are controlled. The concrete mix proportion parameters are adjusted to ensure that the rheological properties and conventional workability of the concrete meet construction requirements. The quantitative model between concrete mix proportion parameters and rheological parameters includes: For static yield stress (τ) 0s (Unit: Pa), the established quantization model is: τ 0s = e×(p / ag) + f×(s / ag) + g×admix + h, where e, f, g, and h are coefficients obtained through regression analysis of experimental data; For dynamic yield stress (τ) 0d (Unit: Pa), the established quantization model is: τ 0d = o×(p / ag) + p×(s / ag) +q×admix + r, where o, p, q, and r are coefficients obtained through regression analysis of experimental data; For plastic viscosity (μ, unit: Pa·s), the established quantitative model is: μ = s×(p / ag) + t×(s / ag) + u×admix + v, where s, t, u, and v are coefficients obtained through regression analysis of experimental data.

2. The method for concrete mix design and optimization based on rheology according to claim 1, characterized in that, The quantitative models between concrete mix proportion parameters and conventional workability indicators include: The quantification model established for slump (T, unit: mm) is: T = 500×(p / ag) - 300×(s / ag) +200×admix +50; The quantization model established for the scalability (S, unit: mm) is: S = 1200×(p / ag) -500×(s / ag) +300×admix + 100.

3. The method for concrete mix design and optimization based on rheology according to claim 1, characterized in that, The quantitative models between concrete mix proportion parameters and rheological parameters include: For static yield stress (τ) 0s (Unit: Pa), the established quantization model is: τ 0s = -80×(p / ag) + 50×(s / ag) -30×admix +40; For dynamic yield stress (τ) 0d (Unit: Pa), the established quantization model is: τ 0d = -60×(p / ag) + 40×(s / ag) -25×admix +30; For plastic viscosity (μ, unit: Pa·s), the established quantitative model is: μ = -15×(p / ag) + 10×(s / ag) -5×admix +8.

4. The method for concrete mix design and optimization based on rheology according to claim 1, characterized in that, The establishment of the quantization model includes the following steps: (1) Database establishment: Select different concretes and conduct concrete mix design tests according to different mix proportion parameters, obtain test data, establish a database containing concrete mix proportion parameters, conventional work performance indicators and rheological parameters, and store the established database in the storage module; (2) Quantitative model establishment: Using multiple linear regression analysis, the experimental data were processed in depth, and quantitative models were established between concrete mix proportion parameters and conventional work performance indicators, as well as between concrete mix proportion parameters and rheological parameters. The established quantitative models were stored in the storage module.

5. The method for concrete mix design and optimization based on rheology according to claim 1, characterized in that, In the real-time monitoring step of rheological parameters, the rheometer is connected to the data processor via a serial port, and the real-time monitoring of concrete rheological parameters is achieved using the data processor and programming tools.

6. The method for concrete mix design and optimization based on rheology according to claim 1, characterized in that, In the mix proportion control step, by monitoring the concrete rheological parameter data and combining it with the quantitative model between the concrete mix proportion parameters and rheological parameters in the storage module, the concrete mix proportion that does not meet the workability requirements is intelligently controlled by programming tools, and the concrete mix proportion parameters are adjusted so that the concrete rheological properties and conventional workability meet the construction requirements.

7. The method for concrete mix design and optimization based on rheology according to claim 6, characterized in that, In the mix proportion adjustment step, the concrete mix proportion parameter adjustment value is calculated based on the set target rheological parameters and the quantitative model.

8. A method for designing and optimizing concrete mix proportions based on rheology according to claim 5 or 6, characterized in that, The stream programming tools include the MATLAB program.

9. A system for a rheology-based concrete mix design and optimization method as described in any one of claims 1-8, characterized in that, The system includes an experimental data acquisition module, a quantitative model construction module, a storage module, a preliminary mix design module, a real-time rheological parameter detection module, a performance judgment module, and a mix intelligent control module. The test data acquisition module is used to acquire test data including concrete mix proportion parameters, conventional work performance indicators, and rheological parameters, and to establish a database; The quantitative model building module is used to establish quantitative models between concrete mix proportion parameters and conventional work performance indicators, as well as quantitative models between concrete mix proportion parameters and rheological parameters. The storage module is used to store the test data acquired by the test data acquisition module; And the quantization model used to build the quantization model building module; The preliminary mix design module is used for the preliminary design of concrete mix proportions; The real-time rheological parameter detection module is used to detect the rheological parameters of concrete in the preliminary design mix proportion in real time. The performance assessment module is used to determine whether the rheological properties and conventional working properties of concrete meet the construction requirements. The intelligent mix proportion control module is used to intelligently control the concrete mix proportions that do not meet the performance requirements.

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