High-strength corrosion-resistant geopolymer mortar proportioning parameter optimization method and high-strength corrosion-resistant geopolymer mortar model construction method

Abstract

The invention discloses a high-strength corrosion-resistant geopolymer mortar proportioning parameter optimization method and a model construction method thereof, and the method comprises the following steps: constructing a geopolymer slurry ternary interaction model of interaction influence between geopolymer slurry rheological properties and blast furnace slag powder mass fraction, coal series kaolinite powder mass fraction and exciting agent mass fraction; determining the optimal proportioning parameter of the geopolymer slurry according to the optimal proportioning parameter of the geopolymer slurry; the aggregate gradation is optimized by adopting a directional gradation composite technology so as to determine the optimal aggregate gradation; the geopolymer mortar is soaked in a magnesium sulfate and sodium chloride composite solution, and the formula of the high-strength corrosion-resistant mortar is determined on the basis. According to the high-strength corrosion-resistant geopolymer mortar proportioning parameter optimization method and the model construction method thereof provided by the invention, a high-strength corrosion-resistant mortar product which can meet the requirements of a harsh service environment can be obtained.

Description

Technical Field

[0001] The invention belongs to the technical field of concrete testing, and particularly relates to a method for optimizing the proportion parameters of a high-strength and corrosion-resistant geopolymer mortar and a model building method thereof. Background Art

[0002] Geopolymer is a new type of green cementitious material obtained by alkali-activated aluminosilicate raw materials at room temperature or high temperature. Its reaction products are mostly calcium aluminosilicate hydrate gel (CASH) or zeolite-like aluminosilicate core (MAS-(H), M = Na, K, etc.), which are quite different from the reaction products of silicate cement. The unique three-dimensional network product structure gives geopolymer good mechanical strength and resistance to chloride and sulfate corrosion. As one of the new cementitious materials with great potential to replace traditional cement, geopolymer has attracted continuous attention from researchers and industry at home and abroad for its remarkable characteristics of solid waste disposal, energy conservation and emission reduction.

[0003] The process of optimizing the mix parameters of cementitious materials is currently more of an art than a science. The design of mix parameters of cementitious materials is not simply calculating the amount of each component. Excellent mix parameters of cementitious materials should not only meet the workability requirements of the casting process, but also meet the mechanical properties, durability, economy and even ecology requirements of engineering applications. However, there are always various problems when optimizing specific parameters to meet the above performances, for example: high-flow mortar can be obtained by increasing the water-binder ratio and replacing rough crushed stones with round and smooth natural pebbles. For high-strength concrete, the water-binder ratio needs to be reduced and coarse crushed stone aggregates need to be used. Therefore, how to optimize the mix parameters of cementitious materials and strike a balance between workability, mechanical properties, durability, ecology and even economy is a difficult and time-consuming task.

[0004] For the traditional optimization design of geopolymer slurry ratio parameters, people often use trial and error method or single factor variable method to meet multiple performance requirements. However, such optimization design method cannot effectively predict the performance evolution law of geopolymer slurry, and only understands the action mechanism of a single raw material in geopolymer slurry. The influence of the interaction of each raw material on the performance evolution of geopolymer slurry after addition is still unclear. Usually, the formula is adjusted through small-scale orthogonal tests, and the performance of geopolymer slurry cannot be accurately predicted when the ratio parameters are not adjusted according to the actual engineering needs, and the formula adjustment cycle is long. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method for optimizing the mix parameters of a high-strength and corrosion-resistant geopolymer mortar and a method for constructing a model thereof.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a method for optimizing the ratio parameters of a high-strength and corrosion-resistant geopolymer mortar, comprising the following method steps:

[0008] A ternary interaction model of geopolymer slurry was constructed to analyze the interaction between the rheological properties of geopolymer slurry and the mass fractions of blast furnace slag powder, coal-bearing kaolin powder and activator.

[0009] To predetermine the rheological properties of the geopolymer slurry according to actual performance requirements;

[0010] The predetermined rheological properties and the mass fraction of any one component element are respectively input into the ternary interaction model of the geopolymer slurry. Within the mass fraction value range of the other two component elements calculated by the model, the mass fraction of one component element is again selected and fixed and input into the ternary interaction model of the geopolymer slurry to calculate and determine the mass fraction of the last component element, thereby obtaining the ratio parameters of blast furnace slag powder, coal-based kaolin powder and activator in the geopolymer slurry corresponding to the predetermined rheological properties of the geopolymer slurry.

[0011] Furthermore, the steps of constructing the ternary interaction model of geopolymer slurry are as follows:

[0012] Obtain a certain number of experimental real data samples of geopolymer slurries with different ratios of blast furnace slag powder mass fraction, coal-based kaolin powder mass fraction, and activator mass fraction, as well as the corresponding rheological properties of the geopolymer slurries;

[0013] The experimental real data samples are input into the ternary interaction model and the data normalization calculation is repeated to obtain the final ternary interaction model of geopolymer paste.

[0014] Furthermore, the ternary interaction model is expressed by a mathematical formula:

[0015] Y=β 1 x 1 +β 2 x 2 +β 3 x 3 +β 12 x 1 x 2 +β 13 x 1 x 3 +β 23 x 2 x 3

[0016] Where Y is the response value, which represents the performance index of geopolymer slurry prepared by different mixing parameters; x iRespectively represent the mass fractions of the three components of the mixture: blast furnace slag powder, coal-based kaolin powder, and activator, x i ≥0(i=1,2,3) and x 1 +x 2 +x 3 =1;β i is the component x i The corresponding action coefficient of i =Y i , β ij =4Y ij -2(Y i +Y j ).

[0017] Furthermore, the geopolymer slurry ternary interaction model is expressed by a mathematical formula:

[0018] τ 0 =-39.45x 1 +293.74x 2 -2459.70x 3 -6.36x 1 x 2 +41.63x 1 x 3 +19.60x 2 x 3

[0019] μ=-0.39x 1 +2.94x 2 -24.59x 3 -0.06x 1 x 2 +0.42x 1 x 3 +0.20x 2 x 3

[0020] In the formula, τ 0 represents the slurry yield stress (Pa), μ represents the slurry plastic viscosity (Pa·s); x 1 represents the mass fraction of blast furnace slag powder, x 2 represents the mass fraction of coal-bearing kaolin powder, x 3 represents the mass fraction of the exciter, and x 1 +x 2 +x 3 =1.

[0021] Furthermore, the mass fraction of the blast furnace slag powder is 48-58%, the mass fraction of the coal-based kaolin powder is 32-42%, and the mass fraction of the activator is 10-20%.

[0022] In a second aspect, the present invention provides a method for constructing a high-strength and corrosion-resistant geopolymer slurry ratio parameter optimization model, comprising the following method steps:

[0023] Obtain a certain number of experimental real data samples of geopolymer slurries with different ratios of blast furnace slag powder mass fraction, coal-based kaolin powder mass fraction, and activator mass fraction, and the corresponding rheological properties;

[0024] The experimental real data samples are input into the ternary interaction model and the data normalization calculation is repeated to obtain the final ternary interaction model of geopolymer paste.

[0025] Furthermore, the ternary interaction model is expressed by a mathematical formula:

[0026] Y=β 1 x 1 +β 2 x 2 +β 3 x 3 +β 12 x 1 x 2 +β 13 x 1 x 3 +β 23 x 2 x 3

[0027] Where Y is the response value, which represents the performance index of geopolymer slurry prepared by different mixing parameters; x i Respectively represent the mass fractions of the three components of the mixture: blast furnace slag powder, coal-based kaolin powder, and activator, x i ≥0(i=1,2,3) and x 1 +x 2 +x 3 =1;β i is the component x i The corresponding action coefficient of i =Y i , β ij =4Y ij -2(Y i +Y j ).

[0028] Furthermore, the geopolymer slurry ternary interaction model is expressed by a mathematical formula:

[0029] τ 0 =-39.45x 1 +293.74x 2 -2459.70x 3 -6.36x 1 x2 +41.63x 1 x 3 +19.60x 2 x 3

[0030] μ=-0.39x 1 +2.94x 2 -24.59x 3 -0.06x 1 x 2 +0.42x 1 x 3 +0.20x 2 x 3

[0031] In the formula, τ 0 represents the slurry yield stress (Pa), μ represents the slurry plastic viscosity (Pa·s); x 1 represents the mass fraction of blast furnace slag powder, x 2 represents the mass fraction of coal-bearing kaolin powder, x 3 represents the mass fraction of the exciter, and x 1 +x 2 +x 3 =1.

[0032] Furthermore, the mass fraction of the blast furnace slag powder is 48-58%, the mass fraction of the coal-based kaolin powder is 32-42%, and the mass fraction of the activator is 10-20%.

[0033] Furthermore, the method further comprises the following steps: using a directional grading composite technology to prepare river sands of different gradations, and selecting the optimal river sand gradation according to the working and mechanical property test results, and screening the river sand according to the particle size to prepare aggregates with particle sizes ranging from 2.36-4.75 mm, 1.18-2.36 mm, 0.60-1.18 mm, 0.30-0.60 mm, 0.15-0.30 mm and 0.075-0.15 mm;

[0034] Geopolymer mortar was prepared according to the optimal mix parameters of the obtained geopolymer slurry and the optimal grading of river sand. The geopolymer mortar samples were immersed in a composite solution of magnesium sulfate and sodium chloride, and the mix parameters of the high-strength and corrosion-resistant mortar were determined according to the performance degradation.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The high-strength and corrosion-resistant geopolymer mortar mix parameter optimization method and model construction method provided by the present invention both adopt a geopolymer slurry ternary interaction model to obtain the optimal formula ratio parameters among blast furnace slag powder, coal-based kaolin powder and activator, and apply it to obtain the preparation of high-performance geopolymer slurry, thereby achieving effective prediction of geopolymer slurry performance and clarifying the influence of the interaction of each raw material component on the evolution of geopolymer slurry performance; adopting directional grading composite technology to balance the relationship between aggregate grading and mortar performance, and thereby obtain the optimal graded river sand; geopolymer mortar is prepared according to the optimal ratio parameters of geopolymer slurry and the optimal grading of river sand obtained by the performance prediction model and the directional grading composite technology, and the high-strength and corrosion-resistant mortar mix parameters are determined according to the performance degradation of the mortar when immersed in magnesium sulfate and sodium chloride solutions.

[0037] It can effectively absorb solid waste (coal-based kaolin powder and blast furnace slag powder) and produce high-strength corrosion-resistant mortar that can meet the application requirements of harsh service environments. In addition, the design technology used in the process has no pollution emissions, and resources are maximized. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A three-element interactive influence model composition design diagram provided by an embodiment of the present invention;

[0039] Figure 2 It is a ternary interaction model diagram of the influence of the interaction of raw material components on the yield stress of geopolymer slurry in an embodiment of the present invention;

[0040] Figure 3 It is a ternary interaction model diagram of the influence of the interaction of raw material components on the plastic viscosity of geopolymer slurry in an embodiment of the present invention;

[0041] Figure 4 It is a river sand gradation curve diagram in an embodiment of the present invention;

[0042] Figure 5-a , Figure 5-b , Figure 5-c A performance comparison diagram of geopolymer mortar provided by an embodiment of the present invention;

[0043] Figure 6 A comparison diagram of the mechanical properties of the geopolymer mortar before and after erosion provided by an embodiment of the present invention;

[0044] Figure 7 A comparison diagram of the pore structure of the geopolymer mortar before and after erosion provided by an embodiment of the present invention;

[0045] Figure 8 The present invention provides a flowchart of a method for optimizing the proportion parameters of high-strength and corrosion-resistant geopolymer mortar provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0047] Example 1

[0048] The present invention provides a method for optimizing the proportion parameters of a high-strength and corrosion-resistant geopolymer mortar and a model building method. Through the geopolymer slurry proportion and a ternary interaction model, the required composition proportion of the geopolymer slurry is deduced from the slurry rheological property parameters, which greatly reduces the time for finding suitable geopolymer slurry proportion parameters corresponding to the mechanical property parameters through experiments one by one.

[0049] The optimization design technology and application of geopolymer slurry ratio parameters are to establish the yield stress τ of geopolymer slurry respectively. 0 and plastic viscosity μ and blast furnace slag powder mass fraction x 1 , mass fraction of coal-bearing kaolin powder x 2 and the activator mass fraction x 3 The three-way interaction model is expressed as:

[0050] τ 0 =-39.45x 1 +293.74x 2 -2459.70x 3 -6.36x 1 x 2 +41.63x 1 x 3 +19.60x 2 x 3

[0051] μ=-0.39x 1 +2.94x 2 -24.59x 3 -0.06x 1 x 2 +0.42x 1 x 3 +0.20x 2 x 3

[0052] A quadratic equation can be derived by fixing one of the components, and then by assuming that an unknown component is a specific value, the other value can be obtained. Figure 2 And attached Figure 3 It can also be seen from the contour map that the yield stress τ of each geopolymer paste 0 The plastic viscosity μ corresponds to a contour line, in which the components x 1 、x 2 、x3 Or they are all range values, and the points can be selected according to actual needs.

[0053] Rheological properties of geopolymer slurry and mass fraction of blast furnace slag powder x 1 , mass fraction of coal-bearing kaolin powder x 2 and the activator mass fraction x 3 The three-variable interaction model is obtained by the three-variable interaction formula with the mass fraction of blast furnace slag powder, the mass fraction of coal-based kaolin powder and the mass fraction of activator as variables, as follows:

[0054] Y=β 1 x 1 +β 2 x 2 +β 3 x 3 +β 12 x 1 x 2 +β 13 x 1 x 3 +β 23 x 2 x 3

[0055] Where Y is the response value, which represents the performance index of geopolymer slurry prepared by different mixing parameters; x i Respectively represent the mass fractions of the three components of the mixture: blast furnace slag powder, coal-based kaolin powder, and activator, x i ≥0(i=1,2,3) and x 1 +x 2 +x 3 =1;β i is the component x i The corresponding action coefficient of i =Y i , β ij =4Y ij -2(Y i +Y j ).

[0056] Substituting the above formula into the pre-test data sample, the rheological properties of the geopolymer slurry and the mass fraction of blast furnace slag powder x are determined. 1 , mass fraction of coal-bearing kaolin powder x 2 and the activator mass fraction x 3 The three-variable interaction model is used to obtain the rheological properties of geopolymer slurry and the mass fraction of blast furnace slag powder x 1 , mass fraction of coal-bearing kaolin powder x 2 and the activator mass fraction x 3 Ternary interaction model between geopolymer paste and slurry.

[0057] We obtain the true value of the above array experiment and substitute the data results into the Simplex centroid design model, and then perform repeated simulation calculations, and finally obtain a formula model exclusive to the rheological properties of geopolymer slurry. Each point in the model is actually a data point. This array is actually equivalent to a special point extracted from a huge data system, but when simulating, the system will complete other points according to these rules to finally form a complete data graph.

[0058] The design of the three-way interactive influence model is attached. Figure 1 The experimental scheme is shown in Table 1. The geopolymer slurry is stirred at room temperature and then subjected to rheological property testing to obtain performance data. In this embodiment, zero-water sodium metasilicate is used as the activator.

[0059] Table 1 Experimental plan of the three-way interaction model

[0060] Group Blast furnace slag powder Coal-bearing kaolin powder Stimulant water 1 58 32 10 35 2 48 42 10 35 3 48 32 20 35 4 53 37 10 35 5 53 32 15 35 6 48 37 15 35 7 51.33 35.33 13.33 35 8 54.67 33.67 11.67 35 9 49.67 38.67 11.67 35 10 49.67 33.67 16.67 35

[0061] Table 2 is a comparison of the rheological properties of 10 groups of test specimens at room temperature.

[0062] The changes in the mass fraction of blast furnace slag powder, coal-bearing kaolin powder and activator can significantly affect the polymerization process of geopolymer slurry, thereby causing the rheological properties of the slurry to fluctuate.

[0063] Table 2 Performance test results

[0064] Group Yield stress(Pa) Plastic viscosity (Pa·s) Group Yield stress(Pa) Plastic viscosity (Pa·s) 1 1002.5 10.03 6 1688.7 16.89 2 1179.6 11.80 7 1183.3 11.83 3 1043.4 10.44 8 2014.8 20.16 4 962.03 9.62 9 1687.9 16.88 5 2117.8 21.18 10 1851.9 18.52

[0065] Experiments are carried out according to the formula generated by the system to obtain experimental data for further simulation.

[0066] The present invention also provides a geopolymer slurry ratio parameter optimization system, comprising the following modules:

[0067] Interaction model building module, used to build a ternary interaction model of geopolymer slurry in which the rheological properties of geopolymer slurry are interactively affected by the mass fractions of three components: blast furnace slag powder, coal-based kaolin powder, and activator.

[0068] A performance parameter input processing module is used to determine the rheological properties of the geopolymer slurry according to actual performance requirements;

[0069] The optimal mix parameters of geopolymer slurry are determined based on the performance prediction model.

[0070] In this embodiment, the geopolymer slurry ratio parameters are optimized by constructing a ternary interaction model of the geopolymer slurry, and the specific steps are as follows:

[0071] Step 1: Design the geopolymer slurry ratio parameters with the mass fraction of blast furnace slag powder (48-58%), the mass fraction of coal-based kaolin powder (32-42%) and the mass fraction of activator (10-20%) as variables;

[0072] Step 2: Conduct parallel tests based on the single variable principle to determine the rheological properties of different groups of hardened geopolymer pastes;

[0073] Step 3: According to the model formula: Y = β 1 x 1 +β 2 x 2 +β 3 x 3 +β 12 x 1 x 2 +β 13 x 1 x 3 +β 23 x 2 x 3 , model the above experimental results and establish a three-way interaction model between experimental factors, levels and response values;

[0074] Step 4: Optimize the geopolymer slurry ratio parameters according to the geopolymer slurry ternary interaction model.

[0075] The optimal group rheological properties tested corresponded to 1183.3Pa yield stress and 11.83Pa·s plastic viscosity respectively.

[0076] refer to Figure 2 This is a ternary interaction model diagram of the influence of the interaction of raw material components on the yield stress of geopolymer slurry in an embodiment of the present invention.

[0077] refer to Figure 3 This is a ternary interaction model diagram of the effect of the interaction of raw material components on the plastic viscosity of geopolymer slurry in an embodiment of the present invention.

[0078] Example 2

[0079] The river sand was sieved according to the particle size to obtain aggregates with particle sizes ranging from 2.36-4.75 mm, 1.18-2.36 mm, 0.60-1.18 mm, 0.30-0.60 mm, 0.15-0.30 mm and 0.075-0.15 mm.

[0080] The river sand with different gradations is made by using the directional grading composite technology. The river sand grading curve is attached. Figure 4 ;

[0081] Based on the optimal mix parameters of geopolymer slurry, different graded river sands were used to prepare geopolymer mortar and its working and mechanical properties were tested. The performance test results are shown in Figure 5;

[0082] The optimum river sand gradation was selected based on the test results of geopolymer mortar working and mechanical properties.

[0083] In this embodiment, the aggregate gradation is optimized by using the directional grading composite technology, and the specific steps are as follows:

[0084] Step 1: Screening the river sand according to the particle size to obtain aggregates with particle sizes ranging from 2.36-4.75 mm, 1.18-2.36 mm, 0.60-1.18 mm, 0.30-0.60 mm, 0.15-0.30 mm and 0.075-0.15 mm;

[0085] Step 2: Prepare river sands of different grades by using directional grading composite technology;

[0086] Step 3: Based on the optimal mix parameters of geopolymer slurry, river sands of different grades are used to prepare geopolymer mortar;

[0087] Step 4: Select the optimal river sand gradation based on the geopolymer mortar working and mechanical properties test results.

[0088] Test its comprehensive optimal group II 0 The corresponding performance test results are: 42.3% river sand porosity, 2281kg / m 3 The mortar has a bulk density of 220 mm, a mortar expansion of 220 mm, a 3d flexural strength of 12.0 MPa and a 3d compressive strength of 76.1 MPa.

[0089] refer to Figure 4 It is the river sand grading curve in the embodiment of the present invention.

[0090] refer to Figure 5-a , Figure 5-b , Figure 5-c This is a comparison chart of the performance of geopolymer mortar provided in an embodiment of the present invention.

[0091] Example 3

[0092] Geopolymer mortar was prepared according to the results of Example 1 and Example 2. The experimental scheme is shown in Table 3, and the traditional cement mortar was compared in parallel.

[0093] Each group of mortar samples was immersed in a composite solution of magnesium sulfate and sodium chloride to measure the properties of mortars at different ages. The mechanical properties test results are shown in the attached Figure 6 , the hole structure test results are attached Figure 7 .

[0094] Determine the mix parameters of high-strength corrosion-resistant mortar based on the performance degradation.

[0095] In this embodiment, a method for preparing high-strength corrosion-resistant mortar is provided, and the specific steps are as follows:

[0096] Step 1: Prepare geopolymer mortar according to the optimal mix parameters of geopolymer slurry and the optimal gradation of river sand obtained by the performance prediction model and directional gradation composite technology;

[0097] Step 2: Soak the geopolymer mortar sample in a composite solution of magnesium sulfate and sodium chloride;

[0098] Step 3: Determine the proportion parameters of high-strength corrosion-resistant mortar based on performance degradation.

[0099] Table 3 Proportion parameters of high-strength corrosion-resistant mortar

[0100] Group Portland cement slag Coal-bearing kaolin Alkali activator water sand PC 100 40 300 <![CDATA[GC 1 ]]> 51.33 35.33 13.33 35 300 <![CDATA[GC 2 ]]> 51.33 35.33 13.33 35 300 <![CDATA[GC 3 ]]> 51.33 35.33 13.33 35 300 <![CDATA[GC 4 ]]> 51.33 35.33 13.33 35 300 <![CDATA[GC 5 ]]> 51.33 35.33 13.33 35 300

[0101] From Table 3 above, test the optimal group GC 3 The corresponding performance test results are: uncorroded compressive strength of 106.6MPa, uncorroded porosity of 3.09%, 90d corroded compressive strength of 98.0MPa and 90d corroded porosity of 3.30%.

[0102] The corresponding performance test results of the comparative cement mortar PC group were: uncorroded compressive strength of 59.9 MPa, uncorroded porosity of 4.11%, 90d post-corrosion compressive strength of 40.2 MPa and 90d post-corrosion porosity of 5.22%.

[0103] refer to Figure 6 This is a comparison chart of the mechanical properties of the geopolymer mortar before and after erosion provided in this experimental example.

[0104] refer to Figure 7 A comparison of the pore structures of the geopolymer mortar before and after erosion provided in this experimental example.

[0105] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing the proportion parameters of high-strength and corrosion-resistant geopolymer mortar, characterized in that: The method comprises the following steps: A ternary interaction model of geopolymer slurry was constructed to analyze the interaction between the rheological properties of geopolymer slurry and the mass fractions of blast furnace slag powder, coal-bearing kaolin powder and activator. To predetermine the rheological properties of the geopolymer slurry according to actual performance requirements; The predetermined rheological properties and the mass fraction of any one component element are respectively input into the ternary interaction model of the geopolymer slurry. Within the mass fraction value range of the other two component elements calculated by the model, the mass fraction of one component element is again selected and fixed and input into the ternary interaction model of the geopolymer slurry to calculate and determine the mass fraction of the last component element, thereby obtaining the ratio parameters of blast furnace slag powder, coal-based kaolin powder and activator in the geopolymer slurry corresponding to the predetermined rheological properties of the geopolymer slurry.

2. The method for optimizing the ratio parameters of high-strength and corrosion-resistant geopolymer mortar according to claim 1, characterized in that: The steps of constructing the ternary interaction model of geopolymer slurry are as follows: Obtain a certain number of experimental real data samples of geopolymer slurries with different ratios of blast furnace slag powder mass fraction, coal-based kaolin powder mass fraction, and activator mass fraction, as well as the corresponding rheological properties of the geopolymer slurries; The experimental real data samples are input into the ternary interaction model and the data normalization calculation is repeated to obtain the final ternary interaction model of geopolymer paste.

3. The method for optimizing the ratio parameters of high-strength and corrosion-resistant geopolymer mortar according to claim 2, characterized in that: The three-element interaction model is expressed in mathematical formula as follows: Y=β1x1+β2x2+β3x3+β 12 x1x2+b 13 x1x3+b 23 x2x3 Where Y is the response value, which represents the performance index of geopolymer slurry prepared by different mixing parameters; x i Respectively represent the mass fractions of the three components of the mixture: blast furnace slag powder, coal-based kaolin powder, and activator, x i ≥0 (i=1, 2, 3) and x1+x2+x3=1; β i is the component x i The corresponding action coefficient of i =Y i , β ij =4Y ij -2(Y i +Y j ).

4. The method for optimizing the ratio parameters of high-strength and corrosion-resistant geopolymer mortar according to any one of claims 1 to 3, characterized in that: The geopolymer slurry ternary interaction model is expressed by a mathematical formula: τ0=-39.45x1+293.74x2-2459.70x3-6.36x1x2+41.63x1x3+19.60x2x3 μ=-0.39x1+2.94x2-24.59x3-0.06x1x2+0.42x1x3+0.20x2x3 Wherein, τ0 represents the slurry yield stress (Pa), μ represents the slurry plastic viscosity (Pa·s); x1 represents the mass fraction of blast furnace slag powder, x2 represents the mass fraction of coal-based kaolin powder, x3 represents the mass fraction of the activator, and x1+x2+x3=1.

5. The method for optimizing the ratio parameters of high-strength and corrosion-resistant geopolymer mortar according to claim 4, characterized in that: The mass fraction of the blast furnace slag powder is 48-58%, the mass fraction of the coal-based kaolin powder is 32-42% and the mass fraction of the activator is 10-20%.

6. The method for optimizing the ratio parameters of high-strength and corrosion-resistant geopolymer mortar according to claim 5, characterized in that: The following steps are also included: The directional grading composite technology is used to produce river sands of different gradations, and the optimal river sand gradation is selected according to the working and mechanical properties test results. The river sand is sieved according to the particle size to produce aggregates with particle sizes ranging from 2.36-4.75mm, 1.18-2.36mm, 0.60-1.18mm, 0.30-0.60mm, 0.15-0.30mm and 0.075-0.15mm; Geopolymer mortar was prepared according to the optimal mix parameters of the obtained geopolymer slurry and the optimal grading of river sand. The geopolymer mortar samples were immersed in a composite solution of magnesium sulfate and sodium chloride, and the mix parameters of the high-strength and corrosion-resistant mortar were determined according to the performance degradation.

7. A method for constructing a high-strength and corrosion-resistant geopolymer slurry ratio parameter optimization model, characterized in that: The method comprises the following steps: Obtain a certain number of experimental real data samples of geopolymer slurries with different ratios of blast furnace slag powder mass fraction, coal-based kaolin powder mass fraction, and activator mass fraction, and the corresponding rheological properties; The experimental real data samples are input into the ternary interaction model and the data normalization calculation is repeated to obtain the final geopolymer slurry ternary interaction model; The three-element interaction model is expressed in mathematical formula as follows: Y=β1x1+β2x2+β3x3+β 12 x1x2+b 13 x1x3+b 23 x2x3 Where Y is the response value, which represents the performance index of geopolymer slurry prepared by different mixing parameters; x i Respectively represent the mass fractions of the three components of the mixture: blast furnace slag powder, coal-based kaolin powder, and activator, x i ≥0 (i=1, 2, 3) and x1+x2+x3=1; β i is the component x i The corresponding action coefficient of i =Y i , β ij =4Y ij -2(Y i +Y j ).

8. The method for constructing a high-strength and corrosion-resistant geopolymer slurry ratio parameter optimization model according to claim 7, characterized in that: The geopolymer slurry ternary interaction model is expressed by a mathematical formula: τ0=-39.45x1+293.74x2-2459.70x3-6.36x1x2+41.63x1x3+19.60x2x3 μ=-0.39x1+2.94x2-24.59x3-0.06x1x2+0.42x1x3+0.20x2x3 Wherein, τ0 represents the slurry yield stress (Pa), μ represents the slurry plastic viscosity (Pa·s); x1 represents the mass fraction of blast furnace slag powder, x2 represents the mass fraction of coal-based kaolin powder, x3 represents the mass fraction of the activator, and x1+x2+x3=1.

9. The method for constructing a high-strength and corrosion-resistant geopolymer slurry ratio parameter optimization model according to claim 4, characterized in that: The mass fraction of the blast furnace slag powder is 48-58%, the mass fraction of the coal-based kaolin powder is 32-42%, and the mass fraction of the activator is 10-20%.

10. The method for constructing a high-strength and corrosion-resistant geopolymer slurry ratio parameter optimization model according to claim 9, characterized in that: The following steps are also included: The directional grading composite technology is used to produce river sands of different gradations, and the optimal river sand gradation is selected according to the working and mechanical properties test results. The river sand is sieved according to the particle size to produce aggregates with particle sizes ranging from 2.36-4.75mm, 1.18-2.36mm, 0.60-1.18mm, 0.30-0.60mm, 0.15-0.30mm and 0.075-0.15mm; Geopolymer mortar was prepared according to the optimal mix parameters of the obtained geopolymer slurry and the optimal grading of river sand. The geopolymer mortar samples were immersed in a composite solution of magnesium sulfate and sodium chloride, and the mix parameters of the high-strength and corrosion-resistant mortar were determined according to the performance degradation.

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