Geopolymer mortar matching parameter optimization method, geopolymer mortar matching parameter optimization model construction method and geopolymer mortar matching parameter optimization model construction system

Abstract

The invention discloses a geopolymer mortar proportioning parameter optimization method, a model construction method and a system thereof. The method comprises the following steps: constructing a geopolymer mortar ternary interaction model of interaction between geopolymer mortar compressive strength and blast furnace slag powder mass fraction, coal series kaolin powder mass fraction and exciting agent mass fraction; predetermining the compressive strength of the geopolymer mortar; respectively inputting the predetermined compressive strength and the selected and fixed mass fraction of any component element into the geopolymer mortar ternary interaction model, and calculating mass fractions of the other two component elements in a mass fraction value range of the model, and selecting and fixing the mass fraction of one component element again, and inputting the mass fraction into the geopolymer mortar ternary interaction model to calculate and determine the mass fraction of the last component element. According to the method, the matching parameters of the blast furnace slag powder, the coal series kaolin powder and the exciting agent in the geopolymer mortar corresponding to the compressive strength can be obtained.

Description

Technical Field

[0001] The invention belongs to the technical field of concrete testing, and in particular relates to a geopolymer mortar mix parameter optimization method, a model building method and a system 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 design of mortar mix parameters is currently more of an art than a science. The design of mortar mix parameters is not simply calculating the amount of each component. Excellent mortar mix parameters 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 design of mortar mix parameters 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 mortar mix 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 mortar, and only understands the action mechanism of a single raw material in geopolymer mortar. The influence of the interaction of various raw materials on the performance evolution of geopolymer mortar after addition is still unclear. Usually, the formula is adjusted through small-scale orthogonal tests, and the performance of geopolymer mortar cannot be accurately predicted when the mix 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 geopolymer mortar, a model building method and a system 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 geopolymer mortar mix parameters, comprising the following steps:

[0008] A ternary interaction model of geopolymer mortar was constructed to show the interaction between the compressive strength of geopolymer mortar and the mass fractions of blast furnace slag powder, coal-series kaolin powder and activator.

[0009] Predetermine the compressive strength of geopolymer mortar according to actual performance requirements;

[0010] The predetermined compressive strength and the mass fraction of any one component element are respectively input into the ternary interaction model of geopolymer mortar. 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 geopolymer mortar to calculate and determine the mass fraction of the last component element, thereby obtaining the proportion parameters of blast furnace slag powder, coal-based kaolin powder and activator in the geopolymer mortar corresponding to the predetermined compressive strength of the geopolymer mortar.

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

[0012] Obtain a certain number of experimental real data samples of geopolymer mortars 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 geopolymer mortar compressive strength;

[0013] The experimental real data samples were input into the ternary interaction model and the data normalization calculation was performed repeatedly to obtain the final geopolymer mortar ternary interaction model.

[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 mortar prepared by different mix 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 mortar ternary interaction model is expressed by a mathematical formula:

[0018] C 3d =-6.23x 1 -11.82x 2 -12.84x 3 +0.36x 1 x 2 +0.33x 1 x 3 +0.16x 2 x 3

[0019] In the formula, C 3d Indicates compressive strength; 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.

[0020] 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%.

[0021] In a second aspect, the present invention provides a method for constructing a geopolymer mortar mix parameter optimization model, comprising the following method steps:

[0022] Obtain a certain number of experimental real data samples of geopolymer mortars with different ratios of blast furnace slag powder mass fraction, coal-based kaolin powder mass fraction, and activator mass fraction, and the corresponding compressive strength;

[0023] The experimental real data samples were input into the ternary interaction model and the data normalization calculation was performed repeatedly to obtain the final geopolymer mortar ternary interaction model.

[0024] Furthermore, the ternary interaction model is expressed by a mathematical formula:

[0025] 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

[0026] Where Y is the response value, which represents the performance index of geopolymer mortar prepared by different mix 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 ).

[0027] Furthermore, the geopolymer mortar ternary interaction model is expressed by a mathematical formula:

[0028] C 3d =-6.23x 1 -11.82x 2 -12.84x 3 +0.36x 1 x 2 +0.33x 1 x 3 +0.16x 2 x 3

[0029] In the formula, C 3d Indicates compressive strength; 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 +x2 +x 3 =1.

[0030] 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%.

[0031] In a third aspect, the present invention provides a geopolymer mortar ratio parameter optimization system, comprising the following modules:

[0032] Interaction model building module, used to build a ternary interaction model of geopolymer mortar, which shows the interaction between the compressive strength of geopolymer mortar and the mass fractions of three component elements: blast furnace slag powder mass fraction, coal-based kaolin powder mass fraction, and activator mass fraction;

[0033] A performance parameter input processing module is used to predetermine the compressive strength of the geopolymer mortar according to actual performance requirements;

[0034] It is also used to input the predetermined compressive strength and the mass fraction of any selected component element into the ternary interaction model of the geopolymer mortar respectively; within the mass fraction value range of the other two component element mass fractions calculated by the model, the mass fraction of one component element is again selected and input into the ternary interaction model of the geopolymer mortar to calculate and determine the mass fraction of the last component element, thereby obtaining the proportion parameters of blast furnace slag powder, coal-based kaolin powder and activator in the geopolymer mortar corresponding to the predetermined compressive strength of the geopolymer mortar.

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

[0036] The geopolymer mortar ratio parameter optimization method, model construction method and system provided by the present invention adopt a ternary interaction model of geopolymer mortar to obtain the optimal formula ratio parameters between blast furnace slag powder, coal-based kaolin powder and activator, and apply it to obtain the preparation of high-performance geopolymer mortar, thereby achieving effective prediction of geopolymer mortar performance and clarifying the influence of the interaction of various raw material components on the evolution of geopolymer mortar performance.

[0037] It can effectively absorb solid waste (coal-based kaolin powder and blast furnace slag powder) and timely adjust the geopolymer mortar ratio parameters and achieve effective performance prediction according to actual project needs, significantly reducing the difficulty of actual construction. In addition, the design technology used has no pollution emissions during the process, 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 2It is a ternary interaction model diagram of the influence of the interaction of raw material components on the 3-day compressive strength of geopolymer mortar in an embodiment of the present invention;

[0040] Figure 3 A ternary interaction model verification diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0041] 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.

[0042] Example

[0043] The present invention provides a geopolymer mortar mix ratio parameter optimization method, a model construction method and a system thereof. Through the geopolymer mortar mix ratio and a ternary interaction model, the required composition ratio of the polymer mortar is deduced from the mortar compressive strength parameters, which greatly reduces the time for finding suitable geopolymer mortar mix ratio parameters corresponding to mechanical property parameters through experiments one by one.

[0044] The optimization design technology and application of geopolymer mortar ratio parameters are to establish the compressive strength C of geopolymer mortar and the mass fraction x of blast furnace slag powder. 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:

[0045] C 3d =-6.23x 1 -11.82x 2 -12.84x 3 +0.36x 1 x 2 +0.33x 1 x 3 +0.16x 2 x 3

[0046] 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 It can also be seen from the contour map that each geopolymer mortar compressive strength C actually corresponds to a contour line, in which each component element x 1 、x 2 、x 3 Or they are all range values, and the points can be selected according to actual needs.

[0047] Compressive strength C of geopolymer mortar and mass fraction x of blast furnace slag powder 1 , mass fraction of coal-bearing kaolin powder x2 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:

[0048] 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

[0049] Where Y is the response value, which can represent the performance index of geopolymer mortar prepared by different mix parameters; x i The three compositions of the mixture, x i ≥0(i=1,2,3),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 ).

[0050] Substituting the above formula into the pre-test data sample, the determined compressive strength C of geopolymer mortar and the mass fraction x of blast furnace slag powder are obtained. 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 compressive strength C of geopolymer mortar and the mass fraction x of blast furnace slag powder. 1 , mass fraction of coal-bearing kaolin powder x 2 and the activator mass fraction x 3 Ternary interaction model between geopolymer mortar.

[0051] 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 compressive strength C of geopolymer mortar. Each point on 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.

[0052] The pre-experimental data sample is based on 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 For the factors, single centroid experimental schemes were designed respectively.

[0053] The design of the three-way interactive influence model is attached. Figure 1 The experimental scheme is shown in Table 1. The geopolymer mortar is stirred at room temperature and then formed and placed in a 20±2°C environment for 24 hours, then taken out and removed from the mold for curing, and then sealed and cured in a 20±2°C environment to a specified age, and then a compressive strength test is performed to obtain performance data. In this embodiment, zero-water sodium metasilicate is used as the activator.

[0054] Table 1 Experimental plan of the three-way interaction model

[0055] 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

[0056] Table 2 shows the comparison of 3d compressive strength development of 10 groups of test mix specimens at room temperature.

[0057] Changes in the mass fraction of blast furnace slag powder, coal-based kaolin powder and activator can significantly affect the development of the internal "interlocking" structure and overall density of geopolymer mortar, thereby causing fluctuations in the compressive strength of the mortar.

[0058] Table 2 Performance test results

[0059] Group 3d compressive strength (MPa) Group 3d compressive strength (MPa) 1 45.9 6 46.9 2 47.8 7 43.7 3 48.6 8 57.6 4 37.2 9 41.0 5 44.4 10 64.0

[0060] Experiments are carried out according to the formula generated by the system to obtain experimental data for further simulation.

[0061] The present invention also provides a geopolymer mortar mix parameter optimization system, comprising the following modules:

[0062] Interaction model building module, used to build a ternary interaction model of geopolymer mortar, which shows the interaction between the compressive strength of geopolymer mortar and the mass fractions of three component elements: blast furnace slag powder mass fraction, coal-based kaolin powder mass fraction, and activator mass fraction;

[0063] A performance parameter input processing module is used to predetermine the compressive strength of the geopolymer mortar according to actual performance requirements;

[0064] It is also used to input the predetermined compressive strength and the mass fraction of any selected component element into the ternary interaction model of the geopolymer mortar respectively; within the mass fraction value range of the other two component element mass fractions calculated by the model, the mass fraction of one component element is again selected and input into the ternary interaction model of the geopolymer mortar to calculate and determine the mass fraction of the last component element, thereby obtaining the proportion parameters of blast furnace slag powder, coal-based kaolin powder and activator in the geopolymer mortar corresponding to the predetermined compressive strength of the geopolymer mortar.

[0065] The geopolymer mortar ratio parameter optimization system can be applied to corresponding software and APP. 1 , mass fraction of coal-bearing kaolin powder x 2 and the activator mass fraction x 3 The ternary interaction model and preset mechanical property parameter values ​​are used to establish the software and APP, input the set performance parameters, and display the corresponding composition ratio of the geopolymer mortar.

[0066] How to operate the system:

[0067] Fixed one factor in the software to generate a three-way interaction model diagram, because Figure 3 It is a two-dimensional graph, so the activator mass fraction x is fixed 3 ;

[0068] Then the dosage of the other two raw materials is selected according to the required performance. 3 You can also Figure 2 The model diagram can be used to directly select points to change. After the change, the corresponding raw material dosage of the two-dimensional image will also change. You can follow the same steps to select the required raw material dosage. After selecting a point on the plane, double-click to open the data corresponding to the point and record x 1 With x 2 Numeric value;

[0069] pass Figure 3 It can be clearly seen that they are respectively 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 .

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

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

[0072] Step 2: Conduct parallel tests based on the single variable principle to determine the compressive strength of hardened geopolymer mortars of different groups;

[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 mortar mix parameters based on the geopolymer mortar ternary interaction model.

[0075] The compressive strength of the optimal group after 3-day curing was tested to be 64.0MPa.

[0076] Figure 2 This is a ternary interaction model diagram of the influence of raw material interaction on the 3-day compressive strength of geopolymer mortar in an embodiment of the present invention.

[0077] Table 3 Comparison of the set mechanical properties parameters of the predicted geopolymer mortar mix and the measured performance parameters

[0078]

[0079] Take verification example 3 as an example for verification:

[0080] C 3d =-6.23x 1 -11.82x 2 -12.84x 3 +0.36x 1 x 2 +0.33x 1 x 3 +0.16x 2 x 3

[0081] Based on the above geopolymer mortar ternary interaction model formula, the assumed performance requirement of compressive strength is 45.5MPa, and the fixed parameter activator mass fraction x is 3 , that is, the mass fraction of the activator is 10.4. The above geopolymer mortar ternary interaction model formula is simplified to:

[0082] 168.6 = -2.80x 1 -10.16x 2 +0.36x 1 x 2

[0083] from Figure 2 The contour map shows that each performance value corresponds to a contour line. 1 or x 2 They are all range values, so we can determine one unknown factor and then find the other.

[0084] Now assume that the parameter x 1 , that is, the mass fraction of blast furnace slag powder is 53.6, and then x is obtained by substituting it into the above simplified model. 2 , that is, the mass fraction of coal-bearing kaolin powder is 36.0.

[0085] 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 geopolymer mortar mix parameters, characterized in that: The method comprises the following steps: A ternary interaction model of geopolymer mortar was constructed to show the interaction between the compressive strength of geopolymer mortar and the mass fractions of blast furnace slag powder, coal-series kaolin powder and activator. Predetermine the compressive strength of geopolymer mortar according to actual performance requirements; The predetermined compressive strength and the mass fraction of any one component element are respectively input into the ternary interaction model of geopolymer mortar. 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 geopolymer mortar to calculate and determine the mass fraction of the last component element, thereby obtaining the proportion parameters of blast furnace slag powder, coal-based kaolin powder and activator in the geopolymer mortar corresponding to the predetermined compressive strength of the geopolymer mortar.

2. The method for optimizing the mix ratio parameters of geopolymer mortar according to claim 1, characterized in that: The steps of constructing the ternary interaction model of geopolymer mortar are as follows: Obtain a certain number of experimental real data samples of geopolymer mortars 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 geopolymer mortar compressive strength; The experimental real data samples were input into the ternary interaction model and the data normalization calculation was performed repeatedly to obtain the final geopolymer mortar ternary interaction model.

3. The method for optimizing the mix ratio parameters of 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 mortar prepared by different mix 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 mix ratio parameters of geopolymer mortar according to any one of claims 1 to 3, characterized in that: The geopolymer mortar ternary interaction model is expressed by the mathematical formula: C 3d =-6.23x1-11.82x2-12.84x3+0.36x1x2+0.33x1x3+0.16x2x3 In the formula, C 3d represents compressive strength; 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 activator, and x1+x2+x3=1.

5. The method for optimizing the mix ratio parameters of 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. A method for constructing a geopolymer mortar mix parameter optimization model, characterized in that: The method comprises the following steps: Obtain a certain number of experimental real data samples of geopolymer mortars with different ratios of blast furnace slag powder mass fraction, coal-based kaolin powder mass fraction, and activator mass fraction, and the corresponding compressive strength; The experimental real data samples were input into the ternary interaction model and the data normalization calculation was performed repeatedly to obtain the final geopolymer mortar ternary interaction model.

7. The method for constructing a geopolymer mortar mix parameter optimization model 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 mortar prepared by different mix 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 geopolymer mortar mix parameter optimization model according to claim 6 or 7, characterized in that: The geopolymer mortar ternary interaction model is expressed by the mathematical formula: C 3d =-6.23x1-11.82x2-12.84x3+0.36x1x2+0.33x1x3+0.16x2x3 In the formula, C 3d represents compressive strength; 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 activator, and x1+x2+x3=1.

9. The method for constructing a geopolymer mortar mix 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. A geopolymer mortar mix parameter optimization system, characterized in that: Includes the following modules: Interaction model building module, used to build a ternary interaction model of geopolymer mortar, which shows the interaction between the compressive strength of geopolymer mortar and the mass fractions of three component elements: blast furnace slag powder mass fraction, coal-based kaolin powder mass fraction, and activator mass fraction; A performance parameter input processing module is used to predetermine the compressive strength of the geopolymer mortar according to actual performance requirements; It is also used to input the predetermined compressive strength and the mass fraction of any selected component element into the ternary interaction model of the geopolymer mortar respectively; within the mass fraction value range of the other two component element mass fractions calculated by the model, the mass fraction of one component element is again selected and input into the ternary interaction model of the geopolymer mortar to calculate and determine the mass fraction of the last component element, thereby obtaining the proportion parameters of blast furnace slag powder, coal-based kaolin powder and activator in the geopolymer mortar corresponding to the predetermined compressive strength of the geopolymer mortar.

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