Thickening agent and water reducing agent co-doping optimization method based on polyvinyl alcohol fiber concrete

Abstract

The invention discloses a thickening agent and water reducing agent co-doping optimization method based on polyvinyl alcohol fiber concrete. The method comprises the following steps: S1) constructing a multi-layer pre-analysis framework model; s2) considering input data and output data of all decision-making units according to the pre-analysis framework model, obtaining an optimal effect site and an effect leading edge surface through boundary constraint and planning solution, and evaluating relative efficiency of homogeneous decision-making units under various input index and effect index conditions; and S3) according to the efficiency score of the decision-making unit, judging whether the decision-making unit is valid or invalid, accurately adjusting the mixing amount configuration of the thickening agent and the water reducing agent for the invalid decision-making unit, and gradually optimizing to the efficiency leading edge calibrated in the final target. According to the method, the defects that in the prior art, a method for designing the compound doping of the thickening agent and the water reducing agent in the polyvinyl alcohol fiber concrete is not standard, data are not accurate, and application is not wide can be overcome, and systematicness and accuracy of a scheme for designing the compound doping of the thickening agent and the water reducing agent in the polyvinyl alcohol fiber concrete are improved.

Description

Technical Field

[0001] The present application relates to the technical field of building materials, and in particular to a method for optimizing the compounding of a thickener and a water reducer based on polyvinyl alcohol fiber concrete. Background Art

[0002] Polyvinyl alcohol fiber concrete is a composite material that adds polyvinyl alcohol fiber materials to ordinary concrete to improve its mechanical properties, durability and stability. In the preparation process of polyvinyl alcohol fiber concrete, the water reducer plays the role of adsorption, dispersion, wetting and lubrication, while the thickener reduces the mutual attraction and cohesion between cement particles by changing the properties of the particle surface and reducing the surface tension of the mixture. The two enhance the overall performance of polyvinyl alcohol fiber concrete by improving its fluidity and cohesion. Due to the complex compatibility and adsorption behavior between thickeners and water reducers and cementitious materials, fine aggregates, polyvinyl alcohol fibers, and between the two, the reinforcement effect of thickeners and water reducers on polyvinyl alcohol fiber concrete varies depending on the mixing scheme of thickeners and water reducers.

[0003] In the current existing technical solutions, the design of the compounding scheme of thickener and water reducer in polyvinyl alcohol fiber concrete is mainly manifested in three aspects:

[0004] One is to conduct flowability and slump tests in the laboratory, and use the parameters when the material has the best flowability and consistency as the mixing ratio of thickener and water reducer; this scheme has the size effect, test standardization and reproducibility differences, and has the limitations of solving the problem and the inaccuracy of the data results;

[0005] The second is to refer to the existing industry standards and empirical formulas to determine the composite ratio of thickener and water reducer; this scheme relies on empirical preference design and lacks standardization, rationality and universality;

[0006] The third is to use computer simulation software to simulate the fluidity and stability of polyvinyl alcohol fiber concrete, so as to predict the effect of additives in different mixing ratios; this scheme is extremely dependent on the accuracy of the simulation, and the validity and applicability of the data are still questionable. So far, the current common design methods have the problems of "non-standard methods", "inaccurate data" and "not widely used", and cannot achieve the optimization of the compounding of thickeners and water reducers in polyvinyl alcohol fiber concrete.

[0007] Therefore, the optimization of the co-addition scheme of thickener and water reducer suitable for PVA fiber reinforced concrete has become one of the decision-making challenges faced by the field of building materials. Summary of the invention

[0008] In order to solve the above-mentioned technical problems, the embodiment of the present application provides a method for optimizing the co-blending of thickeners and water reducers based on polyvinyl alcohol fiber concrete, which overcomes the defects of "non-standard methods", "inaccurate data" and "not widely used" in the prior art of the co-blending design of thickeners and water reducers in polyvinyl alcohol fiber concrete, and improves the systematicness and accuracy of the co-blending scheme of thickeners and water reducers in polyvinyl alcohol fiber concrete.

[0009] The present application provides a method for optimizing the compounding of a thickener and a water reducer based on polyvinyl alcohol fiber concrete, comprising the following steps:

[0010] Step S1) constructing a multi-level pre-analysis framework model, which includes a total target unit, a decision unit, an input index unit and an effect index unit, wherein the total target unit is the ultimate goal of the optimization decision of the composite mixing ratio of thickener and water reducer in polyvinyl alcohol fiber concrete;

[0011] The decision unit includes several groups, including different composite mixing ratio schemes of thickener and water reducer in polyvinyl alcohol fiber concrete; each decision unit corresponds to a composite mixing ratio scheme of thickener and water reducer;

[0012] The input index unit is the mass mixing ratio set of thickener and water reducer in each group of decision units;

[0013] The effect index unit is the set of flow expansion degrees of the corresponding polyvinyl alcohol fiber concrete in each group of decision units;

[0014] Step S2) According to the pre-analysis framework model, the input data and output data of all decision-making units are considered, and the maximum efficiency index parameters of each group of decision-making units are obtained through the planning model and boundary constraints, and the effectiveness of homogeneous decision-making units under various input index and effect index conditions is evaluated; homogeneity means that the decision-making units for comparison have similar properties or characteristics to ensure the fairness and rationality of the comparison process; homogeneous decision-making units represent different composite ratios of thickener and water reducer in polyvinyl alcohol fiber concrete under the same design principle and comparison environment;

[0015] Step S3) According to the efficiency scores of all decision-making units, the effective / ineffective decision-making units are identified, and the dosage configuration of the thickener and water reducer of the ineffective decision-making units is accurately adjusted, and gradually optimized to the efficiency frontier (efficiency index parameter is equal to 1), so as to achieve the optimization of the thickener and water reducer compounding scheme in polyvinyl alcohol fiber reinforced concrete.

[0016] Furthermore, step 2) includes the following steps:

[0017] Step S21) establishing the mixing ratios of various thickeners and water reducers for polyvinyl alcohol fiber reinforced concrete;

[0018] Step S22) constructing n groups of decision-making units by matching different composite mixing ratios of thickener and water reducer;

[0019] The dosage of thickener and water reducer corresponding to each group of decision-making units is used as input elements to construct the input vector X in the production process represented by the decision-making unit;

[0020] The flow expansion degree corresponding to each group of decision-making units is used as the effect element to construct the effect vector Y in the production process of the decision-making unit;

[0021] Define (X, Y) as the possible set of all possible decisions;

[0022] Step S23) Obtain the input comprehensive evaluation index P and output comprehensive evaluation index P of each group of decision units ' The effect evaluation index parameter K is obtained; the efficiency is evaluated through the effect evaluation index parameter K of each group of decision units. The larger the K is, the better the effect of the composite dosage of thickener and water reducer in this group of schemes on the fluidity of polyvinyl alcohol fiber concrete is.

[0023] Further, step S23) specifically includes:

[0024] Each group of decision units contains two input variables, thickener and water reducer, and one effect variable, flow expansion. Let: , , , , ;

[0025] Among them, i represents the i-th scheme of thickener and water reducer co-addition, with a total of n groups of decision-making units; represents the dosage of thickener in the jth decision plan, represents the dosage of water reducing agent in the jth decision plan; represents the flow expansion of PVA fiber reinforced concrete in the jth decision scheme; represents the corresponding weight coefficient of thickener input variable, Represents the corresponding weight coefficient of the water reducer input variable; Represents the weight coefficient corresponding to the flow expansion effect variable; the output comprehensive evaluation index of the jth decision unit , the comprehensive evaluation index of the input of the jth decision unit ; The jth decision-making unit effect evaluation index parameter .

[0026] Furthermore, the efficiency scoring of the decision-making unit in step S3) includes determining input and output, building a linear programming model, setting constraints and solving the linear programming problem, and result analysis and evaluation, specifically including:

[0027] Step 31) Determine input and output: Select input for decision-making unit j and effect evaluation index parameters, where decision-making unit j selects As input, effect evaluation index parameter selection As input;

[0028] Step 32) Construct a linear programming model: Construct the objective function for decision-making unit j, the purpose of which is to achieve the effect evaluation index parameter of decision-making unit j under specific weight conditions Maximize, that is ;

[0029] Step 33) Set constraints and solve the linear programming problem: the efficiency index parameter K of decision unit j j If it does not exceed 1, it will not be more than 100% efficient.

[0030] Setting constraints , , ; The weight coefficient , is an unknown number, and finally solves the effect evaluation index parameter of decision unit j The specific weighted combination of inputs and outputs to achieve the maximization goal;

[0031] Step 34) Result analysis and evaluation: Based on the specific weight combination in the linear programming solution, the maximization effect evaluation index parameter of decision unit j is used Evaluate the effectiveness of decision-making unit j;

[0032] Step 35) Based on the effectiveness evaluation results of each group of decision-making units, the non-strong effectiveness (maximizing the efficiency index parameter K jmax ≠1) The unit adjusts the dosage configuration of its input variables (thickener and water reducer), performs effectiveness analysis, production effect analysis, input redundancy analysis and effect deficiency analysis through boundary constraints and dual values ​​of planning models, and adjusts the mixing ratio of thickener and water reducer in each group of decision units to guide the inefficient decision units to gradually optimize to the effect frontier (i.e., maximize the efficiency index parameter K jmax =1).

[0033] Further, step 35) includes:

[0034] Convert the constraint programming model in step 33) into a dual model, introduce the non-Archimedean infinitesimal ε, and input the variable S - , effect slack variable S +， , , , , , , where λ j is the weight combination coefficient of the jth group of decision units, and the optimal value θ obtained in the dual model j0 is the efficiency value, when θ j0 <1 indicates that this group of decision-making units is inefficient. j0 =1 indicates that the efficiency of the phrase decision unit is relatively optimal.

[0035] Furthermore, step 35) also includes: optimizing to the calibrated efficiency frontier (i.e., maximizing the efficiency index parameter K) through input effect L, effect benefit M, comprehensive benefit N, and quantitative guidance of the slack variable S. jmax =1), and finally the optimization of the co-addition scheme of thickener and water reducer in PVA fiber reinforced concrete was achieved.

[0036] Beneficial effects of the present invention:

[0037] 1. A reliable, scientific and feasible method for optimizing the compounding of thickeners and water reducers in polyvinyl alcohol fiber concrete is provided, which overcomes the defects of "non-standard methods", "inaccurate data" and "not widely used" in the prior art of compounding design of thickeners and water reducers in polyvinyl alcohol fiber concrete, and improves the systematicness and accuracy of the compounding scheme of thickeners and water reducers in polyvinyl alcohol fiber concrete;

[0038] 2. By optimizing the compounding ratio of thickener and water reducer, the fluidity and stability of polyvinyl alcohol fiber concrete can be effectively regulated to avoid poor concrete working performance due to improper proportion, which in turn leads to insufficient strength, cracking and other quality problems, thereby improving the construction, mechanics and durability of polyvinyl alcohol fiber concrete;

[0039] 3. By optimizing the use of thickeners and water reducers in polyvinyl alcohol fiber concrete, the cost problems caused by unreasonable excessive use can be reduced, and cost control and resource conservation can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 is a flow chart of the method provided by this application;

[0042] Figure 2 It is the logic block diagram of this application. DETAILED DESCRIPTION

[0043] In order to make the purpose, features, and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described below are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments.

[0045] In the description of the present application, it should be understood that the terms "upper", "lower", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0046] The present application is described below in conjunction with specific embodiments:

[0047] In this embodiment, polyvinyl alcohol fiber concrete is prepared by cementitious materials, water, fine aggregate, polyvinyl alcohol fiber, and external admixtures, wherein the cementitious materials are cement and fly ash, the fine aggregate is quartz sand, the polyvinyl alcohol fiber is Japanese Kuraray PVA fiber, and the external admixtures are thickeners and water reducers.

[0048] The raw material details and technical indicators of each component in polyvinyl alcohol fiber concrete are shown in Table 1.

[0049]

[0050] Table 1 Details of raw materials of each component in polyvinyl alcohol fiber concrete

[0051] The compounding scheme of thickener and water reducer is the ultimate research goal of this example. Except for the mixing ratio of admixtures, the proportions of other components are shown in Table 2 below.

[0052]

[0053] Table 2 Proportion of each component of polyvinyl alcohol fiber concrete

[0054] Note: The PVA fiber ratio is the volume ratio, and the other component ratios are the mass ratios; water and quartz sand are the ratios compared to the cementitious material.

[0055] The composite mixing ratio of thickener and water reducer is shown in Table 3.

[0056]

[0057] Table 3 Thickener and water reducer compound ratio scheme

[0058] Note: The mixing ratio of thickener and water reducer is the proportion of cementitious materials.

[0059] According to Table 2 and Table 3, cement, fly ash, quartz sand, water, PVA fiber, thickener and water reducer are mixed and stirred, and the flow expansion of the polyvinyl alcohol fiber concrete composite material is measured in the laboratory to provide basic data for the case demonstrated in the present invention.

[0060] As stated in this application:

[0061] Step 1) The optimization problem of the composite design of thickener and water reducer in polyvinyl alcohol fiber concrete is classified according to the overall goal, decision-making unit, input index (input) and effect index (output), and a multi-level pre-analysis framework model for evaluation and decision-making is constructed based on the interaction form between the categories. The model includes the overall goal unit, decision-making unit, input index unit and effect index unit.

[0062] The overall target unit is the ultimate goal of optimizing the composite ratio of thickener and water reducer in polyvinyl alcohol fiber concrete.

[0063] The decision unit includes several groups (the decision unit in this example is a set of different composite mixing ratios of thickeners and water reducers in polyvinyl alcohol fiber concrete, with a total of 20 decision units), including different composite mixing ratios of thickeners and water reducers in polyvinyl alcohol fiber concrete, and each decision unit corresponds to a composite mixing ratio of thickeners and water reducers.

[0064] The input indicator unit is the mass mixing ratio set of thickener and water reducer in each group of decision units.

[0065] The effect index unit is the set of flow expansion degrees of the corresponding polyvinyl alcohol fiber concrete in each group of decision units.

[0066] In this example, the input index is the mass ratio set of the specific thickener and water reducer in each decision unit, and the effect index is defined as the flow expansion set of the corresponding polyvinyl alcohol fiber concrete in each decision unit, with a total of 20 effect index variables.

[0067] The pre-analysis framework model in this example is a multi-level model guided by the overall goal and constructed based on the interaction mechanism relationship between decision-making units, input indicators, and effect indicators, such as Figure 2 shown.

[0068] Step 2) Based on the multi-level pre-analysis framework model, considering the input and output data of all decision-making units, the optimal effect site and effect frontier are obtained through boundary constraints and planning solutions, and the relative efficiency of homogeneous decision-making units under various input indicators and effect indicators is evaluated.

[0069] As in step 22), the data of all decision-making unit-specific input indicators and effect indicators in this example are derived from laboratory fluidity tests, and the test results are shown in Table 4 below;

[0070]

[0071] Table 4 Laboratory fluidity test results

[0072] In this example, the dosage of thickener and water reducer specific to each group of decision-making units is used as input elements to construct the input vector X=(X1, X2) in the production process of the decision-making unit;

[0073] X1=(0.1, 0.2, 0.3, 0.4), X2= (0.1, 0.2, 0.4, 0.6, 0.8)

[0074] In this example, the specific flow expansion degree of each group of decision-making units is used as the effect element to construct the effect vector Y= (Y1) in the production process of the decision-making unit;

[0075] Y1=(0.1, 0.2, 0.4, 0.6, 0.8)

[0076] In this example, (X, Y) is defined as the possible set of all possible decisions;

[0077] (X1, Y1) = (0.1, 0.1, 140)

[0078] (X2, Y2) = (0.2, 0.1, 123)

[0079] (X3, Y3) = (0.3, 0.1, 116)

[0080] (X4, Y4) = (0.4, 0.1, 106)

[0081] …

[0082] (X 20 , Y 20 ) = (0.4, 0.8, 186)

[0083] In this example, the optimal effect site and effect frontier of each group of decision-making units are obtained through boundary constraints and planning solution.

[0084] As shown in step 23), in this example, the effect evaluation index parameter K of each group of decision-making units is obtained by inputting the comprehensive evaluation index P and outputting the comprehensive evaluation index P ' get;

[0085] The input comprehensive evaluation index is obtained by performing algebraic operations on the input vector X = (X1, X2) and the corresponding weight vector:

[0086] In this example, there are 20 mixed schemes for thickeners and water reducers. Each decision scheme has two input variables, thickener and water reducer, and one effect variable, flow expansion. The comprehensive evaluation index P of the 20 decision units is ' And input comprehensive evaluation index P:

[0087] ,

[0088] ,

[0089] …

[0090] ,

[0091] The j-th decision-making unit effect evaluation index parameter .

[0092] In the example, the efficiency is evaluated by the effect evaluation index parameter K of each group of decision-making units. The larger the K, the better the effect of the composite dosage of thickener and water reducer on the fluidity of polyvinyl alcohol fiber concrete in this group of schemes. The decision-making unit with an effect evaluation index parameter K score of 1 is located on the effect frontier, indicating that this decision-making unit has the best efficiency; the decision-making unit with a score less than 1 is lower than the effect frontier, indicating that there is room for efficiency improvement.

[0093] Step 3) According to the efficiency scores of all decision-making units, the effective / ineffective decision-making units are identified. For the ineffective decision-making units, the dosage configuration of thickener and water reducer is precisely adjusted to gradually optimize them to the calibrated efficiency frontier (maximizing the efficiency index parameter K jmax =1), to achieve the optimization of the co-addition scheme of thickener and water reducer in polyvinyl alcohol fiber reinforced concrete. Specifically, it can be divided into the following steps:

[0094] Step 31) Determine input and output: Select input for decision-making unit j and effect evaluation index parameters, where decision-making unit j selects As input, effect evaluation index parameter selection As input;

[0095] Step 32) Construct a linear programming model: Construct the objective function for decision-making unit j, the purpose of which is to achieve the effect evaluation index parameter of decision-making unit j under specific weight conditions Maximize, that is ;

[0096] Step 33) Set constraints and solve the linear programming problem: the efficiency index parameter K of decision unit j j If it does not exceed 1, it will not be more than 100% efficient.

[0097] Setting constraints , , ; The weight coefficient , is an unknown number, and finally solves the effect evaluation index parameter of decision unit j The specific weighted combination of inputs and outputs to achieve the maximization goal;

[0098] Step 34) Result analysis and evaluation: Based on the specific weight combination in the linear programming solution, the maximization effect evaluation index parameter of decision unit j is used Evaluate the effectiveness of decision-making unit j;

[0099] Step 35) Based on the effectiveness evaluation results of each group of decision-making units, the non-strong effectiveness (maximizing the efficiency index parameter K jmax ≠1) The unit adjusts the dosage configuration of its input variables (thickener and water reducer), performs effectiveness analysis, production effect analysis, input redundancy analysis and effect deficiency analysis through boundary constraints and dual values ​​of planning models, and adjusts the mixing ratio of thickener and water reducer in each group of decision units to guide the inefficient decision units to gradually optimize to the effect frontier (i.e., maximize the efficiency index parameter K jmax =1).

[0100] Here, the dual value of the constraint programming model is to transform the original constraint programming model into a dual model, introduce the non-Archimedean infinitesimal ε, and input the variable S - , effect slack variable S + . , , , , , , where λ j is the weight combination coefficient of the jth group of decision units, and the optimal value θ obtained in the dual model j0 is the efficiency value, when θ j0 <1 indicates that this group of decision-making units is inefficient. j0 =1 indicates that the efficiency of the phrase decision unit is relatively optimal.

[0101] In this example, the input effect L, effect benefit M, comprehensive benefit N, and relaxation variable S are used for quantitative guidance until the calibrated efficiency frontier is optimized, and finally the optimization of the co-addition scheme of thickener and water reducer in polyvinyl alcohol fiber reinforced concrete is achieved.

[0102] In this example, the input effect L and effect benefit M of the 20 decision-making units are shown in Table 5. Comprehensive benefit N, input / effect slack variable S - / S + As shown in Table 6 below.

[0103]

[0104] Table 5 Input, effect, and comprehensive benefit indicators

[0105]

[0106] Table 6 Input / Effect Slack Variables

[0107] In this example, the comprehensive benefit indicators N, S and - and S + There are 3 indicators in total to judge the effectiveness of each set of decision-making solutions:

[0108] If the comprehensive benefit is 1, and the input / effect slack variables are both 0, the decision plan is strongly effective;

[0109] If the comprehensive benefit is 1, but the input or effect slack variable is greater than 0, the decision plan is weakly effective;

[0110] If the comprehensive benefit is less than 1, the decision plan is not effective.

[0111] The results show that decision units 1, 13, and 17 are strongly effective, indicating that the compounding ratio of these three groups of thickeners and water reducers makes the fluidity of polyvinyl alcohol fiber concrete reach a relatively optimal state. The remaining decision units are not effective units, and their thickener and water reducer dosage configuration needs to be adjusted.

[0112] In this example, the ratio between the input of thickener and water reducer and the flow expansion effect of polyvinyl alcohol fiber concrete is analyzed through scale returns, and the input configuration of thickener and water reducer in each decision unit is optimized.

[0113]

[0114] Table 7 Input / Effect Slack Variables

[0115] In this example, the scale return coefficients of 20 decision-making units are used as indicator parameters for scale return analysis:

[0116] When the scale return coefficient is less than 1, the production scale output is small, and the ratio of the input of thickener and water reducer to the output of fluid expansion will increase rapidly with the increase of scale, which is increasing scale return (if the scale is too small, the scale can be expanded to increase benefits);

[0117] When the scale return coefficient = 1, the production reaches its peak, the input of thickener and water reducer is proportional to the ratio of the output of flow expansion, and the optimal production scale is reached, which is called fixed scale return;

[0118] When the coefficient of returns to scale is greater than 1, the production scale is too large, resulting in a slowdown in output, which is called diminishing returns to scale. That is, when input increases, the proportion of output increase will be less than the proportion of input increase (excessive scale can reduce the benefits of scale increase).

[0119] The results show that the strong effective decision units 1, 13, and 17 have fixed scale returns, and the current input resource allocation of thickeners and water reducers should be maintained; decision units 2, 3, 4, 5, 6, 7, 8, and 9 have increasing scale returns, and the input of thickeners and water reducers should be increased to use the scale effect to improve the efficiency of the flow expansion of polyvinyl alcohol fiber concrete; decision units 10, 11, 12, 14, 15, 16, 18, 19, and 20 have decreasing scale returns, and the input of thickeners and water reducers should be reduced to optimize resource allocation and avoid efficiency decline.

[0120] In this example, the specific dosage configuration of thickener and water reducer that needs to be adjusted is quantitatively analyzed through input redundancy analysis (difference variable analysis) in order to achieve the target efficiency of optimal fluidity of polyvinyl alcohol fiber concrete.

[0121] In this example, the input redundancy analysis of 20 groups of decision-making units is shown in Table 8 below.

[0122]

[0123] Table 8 Input / Effect Slack Variables

[0124] In this example, the relaxation variables S of 20 decision-making units are - (Difference variable) is used as an indicator parameter for input redundancy analysis:

[0125] (1) Relaxation variable S - Refers to the amount of input that needs to be reduced to achieve the target efficiency;

[0126] (2) Input redundancy rate refers to the ratio of “excessive input” to “already invested”. The larger the value, the more “excessive input” there is.

[0127] The results show that the thickener dosage in decision units 2, 3, 4, 7, and 8 is redundant by 0.087, 0.165, 0.227, 0.042, and 0.076 units, respectively, accounting for 0.439, 0.552, 0.567, 0.142, and 0.191 of the input, respectively. In groups 2, 3, 4, 7, and 8, the thickener dosage needs to be reduced according to the corresponding redundancy to optimize the fluidity of polyvinyl alcohol fiber concrete, while the water reducer dosage is not adjusted and can be kept as it is.

[0128] Combined with the above-mentioned scale returns analysis, except for 2, 3, 4, 7, and 8, non-effective decision-making units 5, 6, and 9 have increasing scale returns, and non-effective decision-making units 10, 11, 12, 14, 15, 16, 18, 19, and 20 have decreasing scale returns. Their slack variables are all 0. After proportional adjustment (radial adjustment), they can reach the optimal effect frontier without the need for additional non-radial adjustment (slack adjustment).

[0129] Combined with the laboratory fluidity test phenomenon, a comprehensive judgment is made, and the design scheme of the thickener and water reducer for the polyvinyl alcohol fiber reinforced concrete in this example is preferred. Although decision units 1, 13, and 17 are strongly effective, group 1 has poor workability and is not easy to form, and groups 13 and 17 both have slurry bleeding. Although decision unit 14 is judged to be invalid, the laboratory test results of this group not only have excellent workability, but also no slurry bleeding. By proportionally adjusting (radially adjusting) the input configuration of this group of thickeners and water reducers, the polyvinyl alcohol fiber concrete can achieve the optimal fluidity. So far, the design scheme of the thickener and water reducer for the polyvinyl alcohol fiber reinforced concrete in this example is as follows.

[0130] Table 9 Optimization scheme of thickener and water reducer for polyvinyl alcohol fiber reinforced concrete in this example

[0131]

[0132] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0133] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations (such as quantity, shape, position, etc.) can be made to the technical scheme of the present invention, and these equivalent transformations are all protected by the present invention.

Claims

1. A method for optimizing the compounding of a thickener and a water reducer based on polyvinyl alcohol fiber concrete, characterized in that: The steps include: Step S1) construct a multi-level pre-analysis framework model, which includes a total goal unit, a decision unit, an input indicator unit, and an effect indicator unit. The overall target unit is the optimized composite ratio of thickener and water reducer in polyvinyl alcohol fiber concrete to ensure the excellent working performance of fiber concrete; The decision unit includes several groups, including different composite mixing ratio schemes of thickener and water reducer in polyvinyl alcohol fiber concrete; each decision unit corresponds to a composite mixing design ratio of thickener and water reducer; The input index unit is a mass mixing ratio set of thickener and water reducer in each group of decision units; The effect index unit is a set of flow expansion degrees of the corresponding polyvinyl alcohol fiber concrete in each group of decision units; Step S2) According to the pre-analysis framework model, the input data and output data of all decision-making units are considered, and the maximum efficiency index parameters of each group of decision-making units are obtained through the planning model and boundary constraints, and the effectiveness of homogeneous decision-making units under various input index and effect index conditions is evaluated; Step S3) According to the efficiency scores of all decision making units, the effective / ineffective decision making units are identified, and the dosage configuration of the thickener and water reducer of the ineffective decision making units is accurately adjusted, and gradually optimized to the efficiency frontier, so as to realize the optimization of the thickener and water reducer compounding scheme in the polyvinyl alcohol fiber reinforced concrete.

2. The method for optimizing the compounding of thickener and water reducer based on polyvinyl alcohol fiber concrete according to claim 1, characterized in that: The step 2) comprises the following steps: Step S21) establishing the mixing ratios of various thickeners and water reducers for polyvinyl alcohol fiber reinforced concrete; Step S22) constructing n groups of decision-making units by matching different composite mixing ratios of thickener and water reducer; The dosage of thickener and water reducer corresponding to each group of decision-making units is used as input elements to construct the input vector X in the production process represented by the decision-making unit; The flow expansion degree corresponding to each group of decision-making units is used as the effect element to construct the effect vector Y in the production process of the decision-making unit; Define (X, Y) as the possible set of all possible decisions; Step S23) Obtain the input comprehensive evaluation index P and the output comprehensive evaluation index P' of each group of decision-making units to obtain the effect evaluation index parameter K; perform efficiency evaluation based on the effect evaluation index parameter K of each group of decision-making units.

3. The method for optimizing the compounding of thickener and water reducer based on polyvinyl alcohol fiber concrete according to claim 2, characterized in that: The step S23) specifically includes: Each group of decision units contains two input variables, thickener and water reducer, and one effect variable, flow expansion. Let: , , , , ; Among them, i represents the i-th scheme of thickener and water reducer co-addition, with a total of n groups of decision-making units; represents the dosage of thickener in the jth decision plan, represents the dosage of water reducing agent in the jth decision plan; represents the flow expansion of PVA fiber reinforced concrete in the jth decision scheme; represents the corresponding weight coefficient of thickener input variable, Represents the corresponding weight coefficient of the water reducer input variable; Represents the weight coefficient corresponding to the flow expansion effect variable; the output comprehensive evaluation index of the jth decision unit ; Comprehensive evaluation index of input of the jth decision unit ; Effect evaluation index parameter of the jth decision unit .

4. The method for optimizing the compounding of thickener and water reducer based on polyvinyl alcohol fiber concrete according to claim 3, characterized in that: The step S3) specifically includes: Step 31) Determine input and output: Select input for decision-making unit j and effect evaluation index parameters, where decision-making unit j selects As input, effect evaluation index parameter selection As input; Step 32) Construct a linear programming model: Construct the objective function for decision-making unit j, the purpose of which is to achieve the effect evaluation index parameter of decision-making unit j under specific weight conditions Maximize, that is ; Step 33) Set constraints and solve the linear programming problem: Set constraints , , ; The weight coefficient , is an unknown number, and finally solves the effect evaluation index parameter of decision unit j The specific weighted combination of inputs and outputs to achieve the maximization goal; Step 34) Result analysis and evaluation: Based on the specific weight combination in the linear programming solution, the evaluation index parameters are evaluated by maximizing the effect of decision unit j Evaluate the effectiveness of decision-making unit j; Step 35) According to the effectiveness evaluation results of each group of decision-making units, the dosage configuration of the input variables of the non-strongly effective units is adjusted, and the dual value of the boundary constraint and the planning model is analyzed to adjust the mixing ratio of the thickener and the water reducer in each group of decision-making units to guide the non-efficient decision-making units to be gradually optimized to the effect frontier.

5. The method for optimizing the compounding of thickener and water reducer based on polyvinyl alcohol fiber concrete according to claim 4, characterized in that: The step 35) comprises: Convert the constraint programming model in step 33) into a dual model, introduce the non-Archimedean infinitesimal ε, and input the variable , effect slack variable , , ; Solution , Constraints: , , ; Among them, E m is the vector matrix of m-dimensional space, E s is the vector matrix of s-dimensional space, e is the unit vector, λ j is the weight combination coefficient of the jth group of decision units, and the optimal value θ obtained in the dual model j0 As the efficiency value of the decision-making unit, when θ j0 <1 indicates that this group of decision-making units is inefficient. j0 =1 indicates that the efficiency of the phrase decision unit is relatively optimal; By introducing input variables , effect slack variable Each group of decision-making units is analyzed and the input of each group of decision-making units is adjusted to optimize the co-addition scheme of thickener and water reducer in polyvinyl alcohol fiber reinforced concrete.

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