A method for optimizing design of concrete mix proportion against sulfate

Abstract

The application discloses a kind of sulphate resisting concrete mix proportion optimization design methods, comprising the following steps: establishing the raw material performance database of sulphate resisting concrete, establishing the damage evolution equation of multi-factor coupling;Prepare sulphate resisting concrete of different proportions to carry out immersion test, the coefficient of damage evolution equation is calibrated, the damage evolution equation of output calibration is completed, and the expression of the service life of sulphate resisting erosion is solved;Design several test groups, fit the relationship between response data and independent variable;Based on the relationship between response data and independent variable, the objective function of raw material proportioning optimization is constructed, and the constraint condition of independent variable, response data is constructed, the proportion of sulphate resisting concrete is optimized using NSGA-Ⅱ algorithm, and the optimal proportioning solution set of sulphate resisting concrete is output. Sulphate resisting concrete mix proportion that can quickly generate and optimize to meet different engineering requirements has important engineering application value.

Description

Technical Field

[0001] This invention relates to the field of concrete mix design optimization, and more specifically to a method for optimizing the mix design of sulfate-resistant concrete. Background Technology

[0002] Sulfate corrosion is one of the main factors leading to the deterioration of the durability of concrete structures. In the northwest and southwest salt lake areas, coastal areas and industrial pollution areas, a large number of concrete structures have failed prematurely due to sulfate corrosion, causing huge economic losses and safety hazards.

[0003] The existing methods for designing mix proportions for sulfate-resistant concrete have the following main technical defects: 1. Highly empirical and lacking scientific quantitative basis: Most of them are based on single-factor experiments or engineering experience, and cannot systematically consider the coupled effects of multiple factors such as water-cement ratio, type and dosage of mineral admixtures, aggregate gradation, and admixture dosage.

[0004] 2. Disconnect between durability and mix design: Mix design mainly focuses on workability and strength, while sulfate resistance is only used as a post-construction verification indicator. It is impossible to accurately predict the long-term sulfate resistance life of concrete during the design stage.

[0005] 3. Single optimization objective: Usually only the highest sulfate resistance is pursued, ignoring the balance between concrete workability, mechanical properties and economy, resulting in excessively high project costs or poor construction performance. Summary of the Invention

[0006] To address the aforementioned shortcomings of existing technologies, this invention provides a method for optimizing the mix design of sulfate-resistant concrete, which comprehensively considers the coupling effects of multiple factors to achieve multi-performance and multi-objective optimization of the sulfate-resistant concrete mix design.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for optimizing the mix design of sulfate-resistant concrete is provided, which includes the following steps: S1: Establish a database of raw material properties for sulfate-resistant concrete, and based on the physical mechanism of sulfate erosion damage, establish a multi-factor coupled damage evolution equation. S2: Retrieve mix proportion data from the raw material performance database, prepare sulfate-resistant concrete with different mix proportions for immersion tests, detect the sulfate ion concentration and calcium hydroxide concentration at different locations of the sulfate-resistant concrete during the immersion test, output the effective diffusion coefficient and reaction rate constant based on the transport-reaction coupling model, calibrate the coefficients of the damage evolution equation, output the calibrated damage evolution equation, and solve the expression for the sulfate erosion resistance life. S3: Using the raw material mix ratio of sulfate-resistant concrete as the independent variable, and the slump, 28-day compressive strength and sulfate erosion resistance life of the specimens as the response data, design several test groups, prepare sulfate-resistant concrete specimens, calculate the sulfate erosion resistance life of the specimens, and fit the relationship between the response data and the independent variable. S4: Based on the relationship between response data and independent variables, construct the objective function for raw material ratio optimization, and construct the constraints of independent variables and response data. Use the NSGA-II algorithm to perform multi-objective optimization of the mix ratio of sulfate-resistant concrete, and output the optimal mix ratio solution set of sulfate-resistant concrete.

[0008] Further, step S1 includes: S11: Establish a database of raw material properties for sulfate-resistant concrete; S12: Establish a transport-reaction coupling model of sulfate ions in sulfate-resistant concrete; S13: Considering the influence of water-cement ratio and mineral admixtures on the effective diffusion coefficient, construct a calculation model for the effective diffusion coefficient; S14: Construct a computational model for the reaction rate constant between sulfate ions and calcium hydroxide; S15: Based on the physical mechanism of sulfate erosion damage, a multi-factor coupled damage evolution equation is established according to the calculation model of effective diffusion coefficient and reaction rate constant.

[0009] Further, step S2 includes: S21: Retrieve mix proportion data from the raw material performance database, prepare sulfate-resistant concrete with different mix proportions for immersion tests, set different immersion times, detect the sulfate ion concentration and calcium hydroxide concentration at different locations in the sulfate-resistant concrete during the immersion test, output the effective diffusion coefficient and reaction rate constant based on the transport-reaction coupling model, calibrate the coefficients of the damage evolution equation, and output the calibrated damage evolution equation. S22: Set the ideal damage variables for sulfate-resistant concrete, solve the calibrated damage evolution equation, and obtain the expression for the sulfate erosion resistance life of sulfate-resistant concrete.

[0010] Further, step S3 includes: S31: Using the raw material ratio of sulfate-resistant concrete as the independent variable, several test groups are designed based on different independent variables. Sulfate-resistant concrete specimens are prepared according to the raw material ratio of each test group. The slump and 28-day compressive strength of each specimen are tested. The sulfate erosion resistance of the specimen is calculated using the expression for sulfate erosion resistance life. S32: Using the slump, 28-day compressive strength, and sulfate resistance life of the specimen as response data, construct a response model between each response data and the independent variable; S33: Fit the response model using the response data and independent variables of several specimens, output the coefficients of the first-order and second-order terms and the constant term of the fitted, and obtain the relationship between each type of response data and the independent variable.

[0011] Further, step S4 includes: S41: Construct an objective function for optimizing the raw material ratio based on the relationship between each response data and the independent variable, and construct constraints on the independent variables and response data; S42: Based on the constraints of independent variables and response data and the objective function of raw material ratio optimization, the NSGA-II algorithm is used to perform multi-objective optimization of the mix proportion of sulfate-resistant concrete, and output the optimal mix proportion solution set of sulfate-resistant concrete.

[0012] Further, step S42 includes: S421: Based on the constraints of the independent variable, perform random uniform sampling to generate samples of size... N The independent variable is the initial population, where each individual in the population represents a set of matching data obtained by random uniform sampling. S422: Input each individual from the initial population into the objective function. In the process, the objective function corresponding to each individual is calculated. value, For the first n Individual, n Individuals are assigned numbers, and a constraint penalty function is introduced to penalize individuals that exceed the constraints of the response data. This is incorporated into the objective function. In the value, the objective function after penalty is obtained. value; ; in, For the penalty weighting coefficient, This exceeds the constraint amount; S423: Remove individuals from the initial population that do not meet the response data constraints, and perform fast non-dominated sorting on the remaining individuals; For any two individuals retained If satisfied And there exists at least one objective function. Then it is called an individual Dominate Based on the dominance relationships among the retained individuals, individuals who do not dominate each other are selected as the elite population. S424: Set the crossover and mutation probabilities between individuals, perform crossover and mutation genetic operations on individuals in the elite population, and output the offspring population; return to step S422 to perform iterative genetic evolution of the population; S425: Output the elite population until the number of iterations of genetics reaches the set maximum number of generations, and obtain the optimal mix design solution for sulfate-resistant concrete.

[0013] The beneficial effects of this invention are as follows: This invention establishes a multi-factor coupled model for sulfate-resistant concrete damage evolution, considering factors such as water-cement ratio, type and dosage of mineral admixtures, sulfate concentration, temperature, and time. This model can accurately predict the long-term sulfate resistance life of sulfate-resistant concrete. Simultaneously considering four optimization objectives—workability, strength, durability, and economy—the NSGA-II algorithm is used to solve for the optimal mix design of sulfate-resistant concrete, achieving the best balance among various properties and avoiding the limitations of single-objective optimization. It can quickly generate and optimize sulfate-resistant concrete mix designs that meet different engineering requirements, and has significant engineering application value. Attached Figure Description

[0014] Figure 1 A flowchart for the optimal mix design method of sulfate-resistant concrete. Detailed Implementation

[0015] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0016] like Figure 1 As shown, a method for optimizing the mix design of sulfate-resistant concrete includes the following steps: S1: Establish a database of raw material properties for sulfate-resistant concrete, and based on the physical mechanism of sulfate erosion damage, establish a multi-factor coupled damage evolution equation.

[0017] Step S1 specifically includes: S11: Conduct performance tests on the raw materials for preparing sulfate-resistant concrete, obtain physicochemical parameters, and establish a raw material performance database.

[0018] This embodiment conducts performance tests on raw materials such as cement, mineral admixtures (fly ash, slag powder, silica fume), aggregates, and additives in sulfate concrete. The raw material performance database contains information such as the chemical composition, physical properties, and price of each raw material.

[0019] The performance data of the mineral admixtures obtained in this embodiment are shown in Table 1: Table 1 Performance Data of Mineral Admixtures

[0020] The performance data of coarse and fine aggregates obtained in this embodiment are shown in Table 2: Table 2. Performance data of coarse and fine aggregates

[0021] The performance data of the admixture and mixing water obtained in this embodiment are shown in Table 3: Table 3 Data on Admixtures and Mixing Water Performance

[0022] S12: Establish a transport-reaction coupling model of sulfate ions in sulfate-resistant concrete; ; in, For a moment t Distance from sulfate-resistant concrete surface x The sulfate ion concentration (mol / m³) at the location. D The effective diffusion coefficient of sulfate ions in concrete (m² / s) is given. k The reaction rate constant (m³ / (mol)) between sulfate ions and calcium hydroxide in sulfate-resistant concrete. s)), For a moment t Sulfate-resistant concrete interior distance from surface x The concentration of calcium hydroxide at that location (mol / m³).

[0023] The left side of the transport-reaction coupling model represents the rate of change of sulfate ion concentration over time, while the first term on the right side represents diffusion caused by the concentration gradient, and the second term represents sulfate ion consumption caused by chemical reactions. This transport-reaction coupling model accurately describes the transport and chemical reaction processes of sulfate ions within concrete, laying the foundation for the development of subsequent damage evolution models.

[0024] S13: Considering the influence of water-cement ratio and mineral admixtures on the effective diffusion coefficient, construct the effective diffusion coefficient. D The computational model; ; in, As the reference diffusion coefficient, this embodiment takes (1.0 × 10⁻⁶). -11 )m² / s, The water-cement ratio influence coefficient is set to 6.5 in this embodiment (calibrated through experiments). The values ​​are the influence coefficients for fly ash, slag powder, and silica fume, respectively. In this embodiment, based on experimental calibration, these coefficients are taken as 3.2, 4.1, and 8.7, respectively. This refers to the total mass of mixing water per unit volume of sulfate-resistant concrete. B This refers to the total mass of cementitious materials per unit volume of sulfate-resistant concrete. The cementitious materials include cement, fly ash, slag powder, and silica fume. These represent the mass of cement, fly ash, slag powder, and silica fume per unit volume of sulfate-resistant concrete.

[0025] The lower the water-cement ratio, the denser the concrete and the higher the effective diffusion coefficient. D The smaller the effective diffusion coefficient, the better. The addition of mineral admixtures can refine the pore structure of concrete and reduce the diffusion coefficient, and different mineral admixtures have different degrees of influence. D The computational model can quantitatively calculate the effective diffusion coefficient of sulfate ions in concrete with different mix proportions, providing key parameters for the transport-reaction coupling model.

[0026] S14: Constructing the reaction rate constant between sulfate ions and calcium hydroxide k The computational model; ; in, Pre-exponential factor, The activation energy of the reaction. R The gas constant is... T This refers to absolute temperature.

[0027] S15: Based on the physical mechanism of sulfate erosion damage, according to the effective diffusion coefficient D Calculation model, reaction rate constant k The computational model establishes a damage evolution equation involving multiple coupled factors; ; in, i It consists of fly ash, slag powder, and silica fume. For the quality of fly ash, slag powder and silica fume, The damage inhibition coefficients for fly ash, slag powder, and silica fume are given. p The water-cement ratio affects the index. n The index is influenced by sulfate concentration. The concentration of sulfate in the environment. The damage evolution coefficient is... t 1 represents the duration of sulfate erosion. The time variable is the time process of sulfate erosion. For time t Damage variables.

[0028] The degree of damage to concrete is closely related to sulfate attack time, environmental sulfate concentration, temperature, water-cement ratio, and the type and dosage of mineral admixtures. Damage accumulates over time, and the accumulation rate is positively correlated with sulfate concentration, temperature, and water-cement ratio, and negatively correlated with the dosage of mineral admixtures. The damage evolution coefficient in this embodiment... The effect of sulfate concentration on the index n Water-to-cement ratio influence index p Take 2.5 × 10 -7 (mol / m 3 ) -n ·s -1 The damage inhibition coefficients for fly ash, slag powder, and silica fume were 1.2, 1.5, and 2.8, respectively.

[0029] The multi-factor coupled damage evolution equation constructed in this invention can comprehensively consider the influence of multiple factors on sulfate attack damage to concrete, and accurately predict the damage evolution process of concrete under different mix proportions and environmental conditions.

[0030] S2: Retrieve mix proportion data from the raw material performance database, prepare sulfate-resistant concrete with different mix proportions for immersion tests, detect the sulfate ion concentration and calcium hydroxide concentration at different locations in the sulfate-resistant concrete during the immersion test, output the effective diffusion coefficient and reaction rate constant based on the transport-reaction coupling model, calibrate the coefficients of the damage evolution equation, output the calibrated damage evolution equation, and solve the expression for the sulfate erosion resistance lifetime.

[0031] Step S2 specifically includes: S21: Retrieve mix proportion data from the raw material performance database, prepare sulfate-resistant concrete with different mix proportions, and conduct immersion tests. Set different immersion times, and detect the sulfate ion concentration and calcium hydroxide concentration at different locations within the sulfate-resistant concrete during the immersion test. Output the effective diffusion coefficient based on the transport-reaction coupling model. D and reaction rate constant k The coefficients of the damage evolution equation are calibrated, and the calibrated damage evolution equation is output.

[0032] This embodiment calibrates the parameters of the above model through erosion tests. The test uses a 5% Na2SO4 solution immersion method. Mix proportion data is retrieved from the raw material performance database to prepare sulfate-resistant concrete with different mix proportions and different immersion times. The concentrations of sulfate ions and calcium hydroxide at different locations within the sulfate-resistant concrete are measured, and the effective diffusion coefficient is output based on the transport-reaction coupling model. D and reaction rate constant kThis allows for the calibration of the parameters of the aforementioned model, outputting the calibrated damage evolution equation. The coefficients that need to be calibrated in the damage evolution equation include the damage evolution coefficient, the sulfate concentration influence index, the water-cement ratio influence index, and the damage inhibition coefficients of fly ash, slag powder, and silica fume.

[0033] S22: Set the ideal damage variable for sulfate-resistant concrete The damage evolution equation that has been calibrated is solved to obtain the expression for the sulfate erosion resistance life of sulfate-resistant concrete; ; in, The lifespan of sulfate-resistant concrete is characterized by the time required to reach the failure state.

[0034] S3: Using the mix proportions of sulfate-resistant concrete as the independent variable, and the slump, 28-day compressive strength, and sulfate attack resistance life of the specimens as response data, design several test groups, prepare sulfate-resistant concrete specimens, calculate the sulfate attack resistance life of the specimens, and fit the relationship between the response data and the independent variables. Step S3 specifically includes: S31: Use the raw material mix proportions of sulfate-resistant concrete as the independent variable. X i , i Number the independent variables, design several experimental groups based on different independent variables, and prepare sulfate-resistant concrete specimens according to the raw material ratio of each experimental group. Test the slump and 28-day compressive strength of each specimen, and calculate the sulfate erosion resistance of the specimens using the expression for sulfate erosion resistance life. In this embodiment, five raw material ratios were selected as independent variables from the raw material performance database. X i Including: water-to-binder ratio ( X 1) Fly ash content ( X 2) Slag powder content ( X 3) Silica fume content ( X 4) Water-reducing agent dosage ( X 5) Each independent variable has 3 levels, and the values ​​of the independent variables are shown in Table 4 below: Table 4 Independent Variable Data Table

[0035] Note: The dosage of mineral admixtures is a percentage of the total mass of cementitious materials; the dosage of water-reducing agent is a percentage of the total mass of cementitious materials.

[0036] S32: Use the slump, 28-day compressive strength, and sulfate resistance lifetime of the specimen as response data. Y j, j In response to the numbering of data, this embodiment j =4 (including subsequent economic objectives), construct a response model between each response data and independent variables; ; in, These are the coefficients of the linear and quadratic terms, respectively. c This is a constant term; subsequent economic objectives do not require building a response model between independent variables, but are only used as optimization targets in the subsequent raw material ratio optimization process, so the response data here does not include economic objectives.

[0037] S33: Fit the response model using response data and independent variables from several specimens, output the coefficients of the first-order and second-order terms and the constant term, and obtain the relationship between each type of response data and the independent variable. ; Based on the independent variable data in Table 4, this embodiment constructed 46 experimental groups, resulting in 46 specimens, including 6 groups of center-point repeated experiments, to estimate the fitting error of the relation. The relation between each response data and the independent variable was fitted. Through verification, the F-value of the fitted relation met the requirements, and the P-value < 0.0001, indicating that the model is highly significant; the coefficient of determination R² met the requirements, indicating that the fitted relation can accurately predict the sulfate erosion resistance life, slump, and 28-day compressive strength of sulfate-resistant concrete.

[0038] S4: Based on the relationship between response data and independent variables, construct the objective function for raw material ratio optimization, and construct the constraints of independent variables and response data. Use the NSGA-II algorithm to perform multi-objective optimization of the mix ratio of sulfate-resistant concrete, and output the optimal mix ratio solution set of sulfate-resistant concrete.

[0039] Step S4 specifically includes: S41: Based on the relationship between each response data and the independent variable Constructing the objective function for optimizing the raw material ratio And construct the constraints on the independent variables: and constraints on the response data; The objective function for optimizing the raw material ratio is related to slump. , Design target value for slump, This is the relationship between slump and the independent variable. The relative error is used as the objective function for optimizing slump, aiming to make the slump as close as possible to the design target value, avoiding excessively large or small slumps, and meeting the requirements for pumping and pouring.

[0040] Objective function for optimizing raw material ratio for 28-day compressive strength , For the designed 28-day compressive strength, The relationship between 28-day compressive strength and independent variables; objective function. The smaller the value, the higher the concrete strength and the greater the strength margin.

[0041] Objective function for optimizing raw material ratio to improve sulfate resistance lifetime , For the design service life of concrete structures, The relationship between sulfate resistance life and independent variables; objective function. The smaller the value, the longer the concrete's resistance to sulfate attack and the better its durability, ensuring that the concrete structure will not be damaged by sulfate attack within its design service life.

[0042] For the economic objective, the objective function for optimizing the raw material ratio is... , The unit price of the raw materials. The quality of raw materials used; the cost per unit volume of concrete is the sum of the costs of each raw material; the objective function is... The value is the one that minimizes the total cost.

[0043] This embodiment uses the proportions of five raw materials as independent variables. X i The specific independent variables and response data constraints are as follows: , .

[0044] S42: Objective function for optimizing raw material ratio based on independent variables, response data constraints, and raw material ratio. The NSGA-II algorithm is used to perform multi-objective optimization of the mix proportion of sulfate-resistant concrete, and the optimal mix proportion solution set of sulfate-resistant concrete is output.

[0045] This embodiment utilizes the NSGA-II algorithm to perform multi-objective optimization of the mix design for sulfate-resistant concrete, outputting the optimal mix design solution set for sulfate-resistant concrete, specifically including: S421: Based on the constraints of the independent variable, perform random uniform sampling to generate samples of size... N The independent variable is the initial population, where each individual in the population represents a set of matching data obtained by random uniform sampling. S422: Input each individual from the initial population into the objective function. In the process, the objective function corresponding to each individual is calculated. value, For the first n Individual, nIndividuals are assigned numbers, and a constraint penalty function is introduced to penalize individuals that exceed the constraints of the response data. This is incorporated into the objective function. In the value, the objective function after penalty is obtained. value; ; in, For the penalty weighting coefficient, This exceeds the constraint amount; S423: Remove individuals from the initial population that do not meet the response data constraints, and perform fast non-dominated sorting on the remaining individuals; For any two individuals retained If satisfied And there exists at least one objective function. Then it is called an individual Dominate Based on the dominance relationships among the retained individuals, individuals who do not dominate each other are selected as the elite population. S424: Set the crossover and mutation probabilities between individuals, perform crossover and mutation genetic operations on individuals in the elite population, and output the offspring population; return to step S422 to perform iterative genetic evolution of the population; S425: Output the elite population until the number of iterations of genetics reaches the set maximum number of generations, and obtain the optimal mix design solution for sulfate-resistant concrete.

[0046] This embodiment, based on the raw material ratio table in Table 4, performed 200 generations of evolution, with the crossover probability set to 0.8, the mutation probability set to 0.1, and the penalty weighting coefficient... Setting it to 1000 yields a partial set of optimal ratio solutions as shown in Table 5 below. The response data for the partial optimal ratio solutions in Table 5 are shown in Table 6 below.

[0047] Table 5 Partial optimal ratio solution set data table

[0048] Table 6 Performance data of some optimal sizing solutions

[0049] The NSGA-II algorithm employed in this invention can quickly converge to the optimal frontier while maintaining solution diversity. It can simultaneously optimize multiple conflicting objectives, providing various optimal mix proportion schemes that satisfy constraints, allowing engineers to choose according to actual needs. In practical applications, relevant raw material data and set raw material proportions can be directly retrieved from the raw material performance database. The NSGA-II algorithm directly outputs the optimal mix proportion solution set that meets the requirements. Engineers can then use the optimal mix proportion with outstanding performance to prepare sulfate-resistant concrete according to actual engineering requirements.

Claims

1. A method for optimizing the mix design of sulfate-resistant concrete, characterized in that, Includes the following steps: S1: Establish a database of raw material properties for sulfate-resistant concrete, and based on the physical mechanism of sulfate erosion damage, establish a multi-factor coupled damage evolution equation. S2: Retrieve mix proportion data from the raw material performance database, prepare sulfate-resistant concrete with different mix proportions for immersion tests, detect the sulfate ion concentration and calcium hydroxide concentration at different locations of the sulfate-resistant concrete during the immersion test, output the effective diffusion coefficient and reaction rate constant based on the transport-reaction coupling model, calibrate the coefficients of the damage evolution equation, output the calibrated damage evolution equation, and solve the expression for the sulfate erosion resistance life. S3: Using the raw material mix ratio of sulfate-resistant concrete as the independent variable, and the slump, 28-day compressive strength and sulfate erosion resistance life of the specimens as the response data, design several test groups, prepare sulfate-resistant concrete specimens, calculate the sulfate erosion resistance life of the specimens, and fit the relationship between the response data and the independent variable. S4: Based on the relationship between response data and independent variables, construct the objective function for raw material ratio optimization, and construct the constraints of independent variables and response data. Use the NSGA-II algorithm to perform multi-objective optimization of the mix ratio of sulfate-resistant concrete, and output the optimal mix ratio solution set of sulfate-resistant concrete.

2. The method for optimizing the mix design of sulfate-resistant concrete according to claim 1, characterized in that, Step S1 includes: S11: Establish a database of raw material properties for sulfate-resistant concrete; S12: Establish a transport-reaction coupling model of sulfate ions in sulfate-resistant concrete; S13: Considering the influence of water-cement ratio and mineral admixtures on the effective diffusion coefficient, construct a calculation model for the effective diffusion coefficient; S14: Construct a computational model for the reaction rate constant between sulfate ions and calcium hydroxide; S15: Based on the physical mechanism of sulfate erosion damage, a multi-factor coupled damage evolution equation is established according to the calculation model of effective diffusion coefficient and reaction rate constant.

3. The method for optimizing the mix design of sulfate-resistant concrete according to claim 2, characterized in that, Step S2 includes: S21: Retrieve mix proportion data from the raw material performance database, prepare sulfate-resistant concrete with different mix proportions for immersion tests, set different immersion times, detect the sulfate ion concentration and calcium hydroxide concentration at different locations in the sulfate-resistant concrete during the immersion test, output the effective diffusion coefficient and reaction rate constant based on the transport-reaction coupling model, calibrate the coefficients of the damage evolution equation, and output the calibrated damage evolution equation. S22: Set the ideal damage variables for sulfate-resistant concrete, solve the calibrated damage evolution equation, and obtain the expression for the sulfate erosion resistance life of sulfate-resistant concrete.

4. The method for optimizing the mix design of sulfate-resistant concrete according to claim 3, characterized in that, Step S3 includes: S31: Using the raw material ratio of sulfate-resistant concrete as the independent variable, several test groups are designed according to different independent variables. Sulfate-resistant concrete specimens are prepared according to the raw material ratio of each test group. The slump and 28-day compressive strength of each specimen are tested. The sulfate erosion resistance of the specimen is calculated using the expression for sulfate erosion resistance life. S32: Using the slump, 28-day compressive strength, and sulfate resistance life of the specimen as response data, construct a response model between each response data and the independent variable; S33: Fit the response model using the response data and independent variables of several specimens, output the coefficients of the first-order and second-order terms and the constant term of the fitted, and obtain the relationship between each type of response data and the independent variable.

5. The method for optimizing the mix design of sulfate-resistant concrete according to claim 4, characterized in that, Step S4 includes: S41: Construct an objective function for optimizing the raw material ratio based on the relationship between each response data and the independent variable, and construct constraints on the independent variables and response data; S42: Based on the constraints of independent variables and response data and the objective function of raw material ratio optimization, the NSGA-II algorithm is used to perform multi-objective optimization of the mix proportion of sulfate-resistant concrete, and output the optimal mix proportion solution set of sulfate-resistant concrete.

6. The method for optimizing the mix design of sulfate-resistant concrete according to claim 5, characterized in that, Step S42 includes: S421: Based on the constraints of the independent variable, perform random uniform sampling to generate samples of size... N The independent variable is the initial population, where each individual in the population represents a set of matching data obtained by random uniform sampling. S422: Input each individual from the initial population into the objective function. In the process, the objective function corresponding to each individual is calculated. value, For the first n Individual, n Individuals are assigned numbers, and a constraint penalty function is introduced to penalize individuals that exceed the constraints of the response data. This is incorporated into the objective function. In the value, the objective function after penalty is obtained. value; ； in, For the penalty weighting coefficient, This exceeds the constraint amount; S423: Remove individuals from the initial population that do not meet the response data constraints, and perform fast non-dominated sorting on the remaining individuals; For any two individuals retained If satisfied And there exists at least one objective function. Then it is called an individual Dominate Based on the dominance relationships among the retained individuals, individuals who do not dominate each other are selected as the elite population. S424: Set the crossover and mutation probabilities between individuals, perform crossover and mutation genetic operations on individuals in the elite population, and output the offspring population; return to step S422 to perform iterative genetic evolution of the population; S425: Output the elite population until the number of iterations of genetics reaches the set maximum number of generations, and obtain the optimal mix design solution for sulfate-resistant concrete.

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