Concrete based on mix ratio optimization and performance prediction and preparation method thereof

Abstract

The application discloses a kind of concrete based on mix proportion optimization and performance prediction and preparation method thereof, belong to building material technical field.The concrete preparation method of the application, with water-cement ratio, mineral admixture content, admixture composition as orthogonal test factor, the air void ratio of each test group cement stone, slurry volume fraction and in the case of considering air content are calculated, and the 28d compressive strength of each test group is calculated and predicted;Through contour map, the influence of mix proportion parameters on the 28d compressive strength and microstructure of concrete is obtained, and the water-cement ratio and cementitious material composition are selected from the contour map according to the performance design requirements of concrete.The application can design multiple strength grade concrete simultaneously, and is suitable for wharf, approach bridge, pile cap, wave protection wall and offshore wind power foundation and other marine structures in marine chlorine salt environment, to provide scientific basis and reliable technical support for preparing low-carbon, high-performance, ultra-high performance, mix proportion optimization and performance prediction based concrete, especially marine concrete.

Description

Technical Field

[0001] This invention relates to the field of building materials technology, and more specifically to a concrete based on mix proportion optimization and performance prediction, and its preparation method, which is particularly suitable for the preparation of marine concrete. Background Technology

[0002] As the most widely used building material, the mechanical properties and durability of concrete largely depend on its mix design. Traditional concrete mix design methods rely heavily on empirical formulas or extensive trial mix experiments, which are time-consuming, costly, and fail to fully reveal the intrinsic relationship between the structural evolution and performance of cement paste. For example, Chinese Patent Publication No. CN120496654 A, published on August 15, 2025, discloses a mix design method for ultra-high performance concrete based on intelligent algorithms and functional requirements, combining data mining, machine learning, and intelligent optimization into UHPC design. However, the model is not linked to cement chemistry theory, thus limiting the algorithm selection. Another example is Chinese Patent Publication No. CN118598603 A, which combines thermodynamic principles, packing theory, and low-carbon concepts to propose a mix design method for low-carbon high-performance concrete, but this method does not form a systematic performance design framework. For example, Chinese patent publication number CN117034431 A proposed a concrete low-carbon mix design method based on a close packing model. Under the guidance of low carbon, it aims to reduce carbon by reducing the amount of paste and utilizing mineral admixtures. However, the low-carbon effect lacks quantitative verification and does not introduce a multi-objective optimization algorithm.

[0003] With the widespread application of admixtures and low-carbon mineral admixtures, the influence of water-cement ratio, admixture type, and dosage on concrete structure and strength has become increasingly complex, making traditional methods insufficient to meet the demands of high-performance concrete preparation. While existing research has attempted to incorporate cement chemistry theory into calculations, an effective method for systematically simulating concrete performance prediction under varying mix proportion parameters is still lacking. Therefore, a computational simulation method based on cement chemistry theory is urgently needed to achieve rapid prediction of concrete performance and optimized mix proportion design. Summary of the Invention

[0004] 1. The technical problem that the invention aims to solve

[0005] In response to the increasing complexity of the influence of existing concrete structures and strength, and the difficulty of traditional methods in preparing high-performance concrete, this invention provides a concrete and its preparation method based on mix proportion optimization and performance prediction. By combining cement chemistry theory and orthogonal experimental design, the cement-aggregate-void ratio is calculated, and the 28-day compressive strength is predicted under the condition of considering air content. This establishes a quantitative relationship between mix proportion parameters and mechanical properties, thereby achieving the goal of optimizing concrete mix proportion and predicting performance.

[0006] 2. Technical Solution To achieve the above objectives, the technical solution provided by the present invention is as follows: A method for preparing concrete based on mix proportion optimization and performance prediction, comprising the following steps: Step 1: Design multiple simulation calculation test groups using water-cement ratio, mineral admixture dosage and admixture composition as orthogonal experimental factors; Step 2: Based on cement chemistry theory, simulate and calculate the cement-stone-gel-void ratio of each test group, and predict the 28-day compressive strength of each test group considering the air content; Step 3: Obtain the influence of mix proportion parameters on the 28-day compressive strength and microstructure of concrete through contour maps, and optimize the water-cement ratio and cementitious material composition from the contour maps according to the concrete performance design requirements. Step 4: Calculate the slurry volume ratio and water consumption according to the concrete workability design requirements, and determine the optimal sand ratio based on the calculated value of the densest bulk density of the aggregate. Step 5: Determine the preliminary concrete mix proportion based on the calculation results of Steps 1 to 4; Step 6: Conduct concrete mix design to determine the admixture dosage and obtain a concrete mix proportion that meets the design strength, workability, and durability requirements.

[0007] Further preparation methods include orthogonal experimental levels for the water-cement ratio: 0.882 × statistical water-cement ratio, statistical water-cement ratio, and 1.118 × statistical water-cement ratio; the statistical water-cement ratio is obtained through machine learning; the orthogonal experimental levels for the mineral admixture dosage are 25%, 40%, and 55%; the mineral admixture composition is a binary mineral admixture, consisting of any two of fly ash, mineral powder, and silica fume, and its orthogonal experimental levels are: the mass ratio of the component with the larger dosage to the component with the smaller dosage is 80:20, 65:35, and 50:50.

[0008] Further preparation methods for concrete 28-day compressive strength f cu It is the cement-void ratio of cement stone. X The exponential function, and considering the design gas content V a and slurry volume fraction V p Calculate and predict according to formula (1), where the glue-to-air ratio is calculated according to formula (2);

[0009] In the formula, n c and n si Cement and the first i Specific volume (m³) of component mineral admixtures 3 / kg); α c and α si Cement and the first i The degree of hydration of the component mineral admixtures; c and s i For cement and the first i Component mineral admixture dosage (kg / m³) 3 ); w / c This refers to the water-cement ratio.

[0010] A concrete based on mix proportion optimization and performance prediction, wherein the optimized mix proportion obtained by any of the above preparation methods is produced by a pre-mixing process, wherein admixtures matching the performance requirements are added to meet the workability and durability requirements.

[0011] 3. Beneficial effects Compared with the prior art, the technical solution provided by this invention has the following advantages: (1) The concrete preparation method based on mix proportion optimization and performance prediction of the present invention combines cement chemical theory with orthogonal experimental design, which can systematically analyze the influence of water-cement ratio, mineral admixture dosage and composition on cement stone structure and concrete performance; by calculating cement stone gel-void ratio, paste volume fraction and chemical shrinkage rate, and predicting concrete 28d compressive strength based on air content design value. (2) This invention establishes a quantitative relationship between concrete mix proportion parameters and mechanical properties. Compared with traditional methods that rely on a large number of trial mixes and actual measurements, it can significantly reduce the amount of experimental work and improve the efficiency and accuracy of concrete performance prediction, thereby providing a reliable theoretical basis and optimization design means for the research and development and engineering application of high-performance concrete. (3) The concrete preparation method based on mix proportion optimization and performance prediction of the present invention can design concrete of multiple strength grades at the same time. It is applicable to marine engineering components such as wharves, approach bridges, piers, wave walls and offshore wind power foundations in marine chloride environments. It provides a scientific basis and reliable technical support for the preparation of low-carbon, high-performance, ultra-high-performance concrete based on mix proportion optimization and performance prediction, especially marine concrete. Attached Figure Description

[0012] Figure 1 Contour plot showing the effect of water-cement ratio and mineral admixture dosage on the 28-day compressive strength of concrete in a specific embodiment; Figure 2 This is a contour map showing the effect of the dosage and composition of mineral admixtures on the 28-day compressive strength of concrete in a specific embodiment. Detailed Implementation

[0013] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings.

[0014] Example The concrete preparation method based on mix proportion optimization and performance prediction in this embodiment includes the following steps: Step 1: Obtain the empirical water-cement ratio that meets the concrete mix design strength requirements through machine learning. Design multiple simulation test groups using water-cement ratio, mineral admixture dosage, and admixture composition as orthogonal experimental factors. The orthogonal experimental levels for water-cement ratio are: 0.882 × statistical water-cement ratio, statistical water-cement ratio, and 1.118 × statistical water-cement ratio. The orthogonal experimental levels for mineral admixture dosage are: 25%, 40%, and 55%. A binary mineral admixture system is adopted, and the orthogonal experimental levels for admixture composition are: the mass ratio of the component with the larger dosage to the component with the smaller dosage is 80:20, 65:35, and 50:50.

[0015] Step 2: Based on cement chemistry theory, simulate and calculate the cement-void ratio X of cement paste in each test group, and calculate the 28-day compressive strength of concrete according to formula (1). f cu The relationship between the cement-to-void ratio X and the cement stone, taking into account the design air content. V a and slurry volume fraction V p Under the premise of calculating the 28-day compressive strength of concrete, the cement-void ratio X is calculated according to formula (2);

[0016] In the formula, n c and n si Cement and the first i Specific volume (m³) of component mineral admixtures 3 / kg); α c and α si Cement and the first i The degree of hydration of the component mineral admixtures; c and s i For cement and the first i Component mineral admixture dosage (kg / m³) 3 ); w / c The water-cement ratio is given by the coefficients in formula (1), which are obtained by fitting experimental data. The physical meaning of the coefficient 2.06 in formula (2) is that 1 volume of cement is completely hydrated to obtain 2.06 volumes of hydrated products. The coefficient 2.52 means that 1 volume of mineral admixture is completely hydrated to obtain 2.52 net volumes of hydrated products.

[0017] Step 3: Through Figure 1 and 2 The contour map shown illustrates the influence of mix proportion parameters on the 28-day compressive strength and microstructure of concrete. Based on the concrete performance design requirements, the water-cement ratio and cementitious material composition are optimized from the contour map. Step 4: Based on the concrete workability design requirements, calculate the slurry volume ratio and water consumption, and determine the optimal sand ratio based on the calculated value of the densest bulk density of the aggregate. Example Design Requirements 1: C30~C50 pumped concrete, with a design slump flow of not less than 400mm and an air content of less than 3.0%. Raw materials: apparent density of 3150kg / m³. 3 P.Ⅱ52.5 grade cement, apparent density 2652kg / m³ 3 4.75mm~19.5mm continuously graded limestone crushed stone, apparent density 2648kg / m³ 3 The mixture consists of manufactured sand with a stone powder content of 5.3%, polycarboxylate superplasticizer with a solid content of 10%, composite defoamer, and water; the mineral admixtures are binary mineral admixtures with an apparent density of 2250 kg / m³. 3 Class F, Grade II fly ash with an apparent density of 2950 kg / m³ 3 S95 mineral powder.

[0018] Based on machine learning, the statistical median water-cement ratio was taken as 0.40, and simulation calculations were performed according to Table 1 to determine the preliminary mix proportion of concrete.

[0019] Table 1. Orthogonal test table for simulation calculation of C30~C50 concrete strength.

[0020] Considering an air content of 2% to 3.0%, the simulation calculation results of concrete according to the slurry mix proportions listed in Table 1 are shown in Table 2.

[0021] Table 2 Simulation Calculation Results of C30~C50 Concrete Strength

[0022] Based on the simulation results, contour maps were plotted to obtain the influence of paste composition on the 28-day compressive strength of concrete, thereby determining: The composition of C30 concrete paste is as follows: water-cement ratio 0.42, admixture to cementitious material mass ratio 0.50, and fly ash to admixture mass ratio 0.30. The composition of C40 concrete paste is as follows: water-cement ratio 0.40, admixture to cementitious material mass ratio 0.40, and fly ash to admixture mass ratio 0.25. The composition of C50 concrete paste is as follows: water-cement ratio 0.36, admixture to cementitious material mass ratio 0.30, and fly ash to admixture mass ratio 0.25.

[0023] Calculations show that the paste ratio of all three strength grades of concrete is less than 0.35.

[0024] Fresh concrete was prepared and its performance tested according to GB / T50080-2016 "Standard for Test Methods of Performance of Ordinary Concrete Mixtures". Concrete based on mix proportion optimization and performance prediction was prepared, and 150mm×150mm×150mm concrete cube specimens were formed and cured under standard curing conditions for 28 days. Compressive strength was tested according to GB / T50081-2019 "Standard for Test Methods of Physical and Mechanical Properties of Concrete". The test results showed that the aggregate bulk density was the highest when the sand ratio was 38%, reaching 0.74; the maximum bulk densities of granular materials in C30, C40, and C50 concrete were 0.81, 0.82, and 0.82, respectively.

[0025] The optimized formula obtained from the above steps is produced using a premixing process. In order to meet the requirements of workability and durability, additives that match the performance requirements are added, such as water-reducing agents, defoamers, etc.

[0026] Based on workability requirements, with a excess paste ratio of 0.05~0.10, the mix proportions shown in Table 3 were calculated. After experimental verification, the basic concrete properties shown in Table 4 were obtained. Table 4 shows that the air content of C30, C40, and C50 concrete is 2.5%, 2.1%, and 2.0%, respectively, meeting the design requirement of less than 3.0% air content. The slump flow of the three strength grades all reached greater than 400mm, and the 28-day compressive strength distribution reached 41.3MPa, 52.1MPa, and 61.9MPa, exceeding the trial mix strength requirements of 38.2MPa, 48.2MPa, and 59.9MPa.

[0027] Table 3 Mix proportions for C30~C50 concrete (kg / m³) 3 )

[0028] Table 4 Basic Properties of C30~C50 Concrete

[0029] The trial mix results demonstrate the feasibility of the simulation calculation method proposed in this embodiment in producing concrete based on mix proportion optimization and performance prediction in practical applications.

[0030] Example 2 Design Requirements: C60~C80 self-compacting concrete, with a design slump flow of not less than 660mm and an air content of less than 3.0%. Raw materials: apparent density of 3150kg / m³. 3 The P.Ⅱ52.5 grade cement has an apparent density of 2250 kg / m³. 3 The silica fume has an apparent density of 2950 kg / m³. 3 S95 mineral powder, apparent density 2652 kg / m³ 3 Continuously graded limestone crushed stone with a thickness of 4.75mm~9.5mm and 9.5mm~19.5mm, with an apparent density of 2648kg / m³. 3 Manufactured sand with a stone powder content of 5.3%, polycarboxylate superplasticizer with a solid content of 10%, composite defoamer, and water.

[0031] Based on machine learning, the statistical median water-to-glue ratio was taken as 0.28, and simulation calculations were performed according to Table 5.

[0032] Table 5. Orthogonal test table for simulation calculation of C60~C80 self-compacting concrete strength.

[0033] Considering an air content of 2% to 3.0%, the simulation calculation results of concrete according to the slurry mix proportions listed in Table 5 are shown in Table 6.

[0034] Based on the simulation results, contour maps were plotted to obtain the influence of paste composition on the 28-day compressive strength of self-compacting concrete, thereby determining: The composition of C60 self-compacting slurry is as follows: water-cement ratio 0.315, mass ratio of admixtures to cementitious materials 0.55, and mass ratio of silica fume to admixtures 0.20. The composition of C70 self-compacting concrete paste is as follows: water-cement ratio 0.285, admixture to cementitious material mass ratio 0.50, and silica fume to admixture mass ratio 0.30. The composition of C80 self-compacting concrete paste is as follows: water-cement ratio 0.255, admixture to cementitious material mass ratio 0.50, and silica fume to admixture mass ratio 0.40.

[0035] Table 6. Simulation Calculation Results of Strength of C60~C80 Self-Compacting Concrete

[0036] The performance of fresh concrete was tested according to GB / T50080-2016 "Standard for Test Methods of Performance of Ordinary Concrete Mixtures". Simultaneously, 150mm×150mm×150mm cubic test blocks were formed and cured under standard curing conditions for 28 days. Compressive strength was then tested according to GB / T50081-2019 "Standard for Test Methods of Physical and Mechanical Properties of Concrete". Based on the test results, the sand ratio at the densest aggregate packing was 47%, with a bulk density of 0.76. The maximum bulk densities of the granular materials in C60, C70, and C80 self-compacting concrete were 0.82, 0.83, and 0.83, respectively. Based on workability requirements, an excess paste ratio of 0.12~0.14 was used, and the mix proportions shown in Table 7 were calculated. The paste ratio of each test group was less than 0.35. The basic properties of the self-compacting concrete are shown in Table 8.

[0037] Table 7 Mix proportions for C60~C80 self-compacting concrete (kg / m³) 3 )

[0038] Table 8 Basic Properties of C60~C80 Self-Compacting Concrete

[0039] As shown in Table 8, the air content of C60, C70, and C80 concrete is 2.1%, 2.0%, and 1.8%, respectively, which meets the design requirement of less than 3.0% air content. The slump spread of the self-compacting concrete of the three strength grades all meet the requirement of greater than 660 mm, and the 28-day compressive strength distribution reaches 71.2 MPa, 80.0 MPa, and 90.4 MPa, respectively, which meets the requirements of the trial mix strength.

[0040] The trial mix results demonstrate the feasibility of the simulation calculation method proposed in this embodiment in producing concrete based on mix proportion optimization and performance prediction in practical applications, providing a scientific basis and reliable technical support for the preparation of low-carbon, high-performance, and ultra-high-performance concrete based on mix proportion optimization and performance prediction.

[0041] Example 3: C55 marine pumped concrete, with a design slump of 220±20mm, spread of not less than 580mm, and air content of less than 3.0%, meeting the requirements for resistance to chloride ion intrusion and impermeability in a marine environment. Raw materials are: P·Ⅱ52.5 grade cement with an apparent density of 3150kg / m³, S95 mineral powder with an apparent density of 2950kg / m³, silica fume with an apparent density of 2250kg / m³, manufactured sand with an apparent density of 2650kg / m³, 5mm~20mm continuously graded crushed stone with an apparent density of 2700kg / m³, polycarboxylate superplasticizer with a solid content of 10%, composite defoamer, and water. The mineral admixture composition is a binary mineral admixture, consisting of mineral powder and silica fume.

[0042] Based on machine learning, the median statistical water-cement ratio is taken as 0.31. Following the same three-factor, three-level orthogonal design approach as the original patent, the three levels of water-cement ratio are 0.882 × statistical water-cement ratio, statistical water-cement ratio, and 1.118 × statistical water-cement ratio; the three levels of mineral admixture dosage are 25%, 40%, and 55%; and the three levels of admixture composition are the mass ratios of the component with the larger dosage to the component with the smaller dosage of 80:20, 65:35, and 50:50.

[0043] Table 9. Orthogonal Test Table for Simulation Calculation of C55 Marine Concrete Strength

[0044] Considering an air content of 2% to 3.0%, the simulation calculation results of concrete according to the slurry mix proportions listed in Table 9 are shown in Table 10.

[0045] Table 10 Simulation Calculation Results of C55 Marine Concrete Strength

[0046] Based on the simulation results, contour maps were plotted to determine the influence of slurry composition on the 28-day compressive strength of marine concrete. Therefore, the slurry composition for C55 marine concrete was determined to be: water-cement ratio 0.31, admixture-cement material mass ratio 0.45, and silica fume-admixture mass ratio 0.20. Calculations showed that the slurry ratio for this strength grade of concrete is less than 0.35.

[0047] Fresh concrete was prepared and its performance was tested in accordance with GB / T50080-2016 "Standard for Test Methods of Performance of Ordinary Concrete Mixtures". At the same time, 150mm×150mm×150mm concrete cube test blocks were formed and cured under standard curing conditions for 28 days. The compressive strength was tested in accordance with GB / T50081-2019 "Standard for Test Methods of Physical and Mechanical Properties of Concrete".

[0048] Experiments showed that the aggregate bulk density was highest at a sand ratio of 39%, reaching 0.75; the maximum bulk density of granular materials in C55 marine concrete was 0.82. Based on workability requirements, an excess paste ratio of 0.08 was taken, and the mix proportions shown in Table 11 were calculated. After experimental verification, the basic concrete properties shown in Table 12 were obtained; further durability tests were conducted, and the durability properties shown in Table 13 were obtained.

[0049] Table 11 C55 Marine Concrete Mix Proportion (kg / m³) 3 )

[0050] Table 12 Basic Properties of C55 Marine Concrete

[0051] Table 13 Durability of C55 Marine Concrete

[0052] As shown in Tables 12 and 13, the air content of C55 marine concrete is 2.2%, meeting the design requirement of less than 3.0%; the slump and spread reach 230 mm and 605 mm respectively, meeting the requirements for marine pumping construction; the 28-day compressive strength reaches 63.8 MPa, meeting and exceeding the C55 design target. Meanwhile, the concrete's 56-day rapid electrical flux is 780C, and the RCM chloride ion migration coefficient is 2.3 × 10⁻⁶. - The ¹² m² / s indicates that it has good resistance to chloride ion intrusion; the impermeability grade reaches P14, and the compressive strength ratio after 90d sulfate corrosion is 94%, indicating that it is suitable for marine engineering components such as wharves, approach bridges, piers, and breakwaters in marine chloride environments, as well as offshore wind power foundations.

[0053] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the figures shown are only one embodiment of the present invention. The actual structure and manufacturing steps are not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for preparing concrete based on mix proportion optimization and performance prediction, characterized in that, The steps are as follows: Step 1: Design and simulate experimental groups using water-cement ratio, mineral admixture dosage, and admixture composition as orthogonal experimental factors; Step 2: Based on cement chemistry theory, simulate and calculate the cement-stone-gel-void ratio of each test group, and predict the 28-day compressive strength of each test group considering the air content; Step 3: Obtain the influence of mix proportion parameters on the 28-day compressive strength and microstructure of concrete through contour maps, and select the water-cement ratio and cementitious material composition from the contour maps according to the concrete performance design requirements. Step 4: Calculate the slurry volume ratio and water consumption according to the concrete workability design requirements, and determine the optimal sand ratio based on the calculated value of the densest bulk density of the aggregate. Step 5: Determine the preliminary concrete mix proportion based on the calculation results of Steps 1 to 4; Step 6: Conduct concrete mix design trials to determine the admixture dosage and obtain a concrete mix proportion that meets the requirements.

2. The concrete preparation method based on mix proportion optimization and performance prediction according to claim 1, characterized in that: The orthogonal experimental levels for the water-to-binder ratio are: 0.882 × statistical water-to-binder ratio, statistical water-to-binder ratio, and 1.118 × statistical water-to-binder ratio; The statistical water-cement ratio was obtained through machine learning.

3. The concrete preparation method based on mix proportion optimization and performance prediction according to claim 2, characterized in that: The orthogonal experimental levels for the mineral admixture dosage were 25%, 40%, and 55%.

4. The concrete preparation method based on mix proportion optimization and performance prediction according to claim 3, characterized in that: The mineral admixture is a binary mineral admixture, composed of any two of fly ash, mineral powder and silica fume. Its orthogonal experimental level is: the mass ratio of the component with larger admixture to the component with smaller admixture is 80:20, 65:35 and 50:

50.

5. The concrete preparation method based on mix proportion optimization and performance prediction according to claim 4, characterized in that: The 28-day compressive strength of the concrete f cu It is an exponential function of the cement-void ratio X of cement stone, taking into account the design air content. V a and slurry volume fraction V p Calculate and predict according to formula (1), where the glue-to-air ratio is calculated according to formula (2); In the formula, ν c and ν si Cement and the first i Specific volume (m³) of component mineral admixtures 3 / kg); α c and α si Cement and the first i The degree of hydration of the component mineral admixtures; c and s i For cement and the first i Component mineral admixture dosage (kg / m³) 3 ); w / c This refers to the water-cement ratio.

6. A type of concrete based on mix proportion optimization and performance prediction, characterized in that, The optimized formulation obtained by any of the preparation methods of claims 1-5 is produced using a premixing process and incorporates additives that match the performance requirements.

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