CN114656204B - Method for designing mixing proportion of ecological ultrahigh-performance concrete containing multi-element material - Google Patents
Method for designing mixing proportion of ecological ultrahigh-performance concrete containing multi-element material Download PDFInfo
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- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B28/00—Compositions of mortars, concrete or artificial stone, containing inorganic binders or the reaction product of an inorganic and an organic binder, e.g. polycarboxylate cements
- C04B28/02—Compositions of mortars, concrete or artificial stone, containing inorganic binders or the reaction product of an inorganic and an organic binder, e.g. polycarboxylate cements containing hydraulic cements other than calcium sulfates
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- C04B2111/00—Mortars, concrete or artificial stone or mixtures to prepare them, characterised by specific function, property or use
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- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
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Abstract
The invention discloses a design method of the mixing proportion of ecological ultrahigh-performance concrete containing a multi-element material, which comprises the following steps: s1, establishing a numerical model by adopting a secondary saturation D-optimization design, performing optimization design, and determining a basic mix proportion; s2, preparing and maintaining a UHPC test piece, performing a test and acquiring UHPC performance index test data; s3, performing multiple linear regression analysis, determining a numerical model and performing accuracy evaluation; and S4, establishing a multivariate optimization combination model, and carrying out ecological UHPC mix proportion design. The method can design the ecological UHPC, obviously reduces the using amount of cement and improves the utilization rate of solid waste while meeting the requirements of excellent working performance and compressive strength, thereby reducing the production cost of the UHPC, reducing environmental pollution, realizing the ecology of the UHPC and having important value for popularizing the engineering application of the UHPC.
Description
Technical Field
The invention relates to the technical field of concrete, in particular to a design method of ecological mix proportion of ultra-high performance concrete containing a multi-element material.
Background
Compared with common concrete, the ultra-high performance concrete (UHPC) has higher strength, durability and toughness, and shows huge potential and value in civil engineering construction as a novel cement-based composite material. The UHPC has excellent mechanical property and durability, due to the closest packing of particles in the matrix and the large amount of cementing material, the water-cement ratio of the UHPC is generally 0.14-0.27, and the cement usage amount is as high as 900-1100 kg/m 3 The production cost is several times of that of common concrete. And 1t CO is discharged every 1t of portland cement is produced 2 And the increase of carbon emission can cause environmental and climate problems, which is contrary to the development goals of building a green low-carbon cycle development economic system and realizing carbon peak reaching and carbon neutralization in China. Therefore, it has become a considerable problem to reduce the amount of cement used to realize the ecology of UHPC while ensuring the excellent performance of UHPC.
At present, with the continuous expansion of the infrastructure scale of China, the demand of concrete is continuously increased, and the price of cementing materials such as cement is continuously increased. The prices of admixtures such as mineral powder and fly ash are rising and are in short supply. Therefore, the environment-friendly composite admixture with high cost performance is urgently needed to be used as a supplement of a cementing material to replace fly ash and slag powder with rising prices and obtain better concrete performance. The production process of the environment-friendly composite material is characterized in that various industrial solid wastes such as steel slag, furnace bottom slag, ore tailings and the like are combined into a multi-component material, and the composite admixture is compounded into the UHPC by reasonably matching the proportion of different materials to form the high-performance environment-friendly composite material which is doped into the UHPC and is beneficial to improving the mechanical property and durability of concrete.
The secondary saturation D-optimization design method is an effective statistical design method. The method can convert the influence of a plurality of variables on the response value and the interaction among the variables into a mathematical model, and has the advantages of simple and convenient calculation, high precision, consideration of variable variation and the like. Currently, researches of scholars prove that the secondary saturation D-optimization design has excellent prediction capability, applicability and reliability, and can be effectively applied to the optimization design of concrete.
Disclosure of Invention
The invention takes UHPC multi-component material components as independent variables, concrete performance indexes such as fluidity, compressive strength and the like as response values, utilizes a secondary saturation D-optimization design method to establish a prediction model between the multiple independent variables and the multiple responses, carries out multiple response analysis, aims at maximally reducing the cement consumption and improving the environment-friendly composite material doping amount, determines the optimal doping amount among the materials, and designs the ecological mix proportion of the ultra-high performance concrete containing the multi-component material. Experiments prove that the ecological UHPC containing the multi-component material designed according to the technical scheme disclosed by the invention can obviously reduce the using amount of cement while ensuring the excellent performance of the UHPC, and has important values for reducing the production cost of the UHPC and promoting the engineering application thereof.
In order to achieve the aim, the invention discloses a method for designing the ecological mix proportion of ultrahigh-performance concrete containing a multi-element material, which is characterized by comprising the following steps:
s1, establishing a numerical model by adopting a secondary saturation D-optimization design, performing optimization design, and determining a basic mix proportion;
s2, preparing and maintaining a UHPC test piece, performing a test and acquiring UHPC performance index test data;
s3, performing multiple linear regression analysis, determining a numerical model and performing accuracy evaluation;
and S4, establishing a multivariate optimization combination model, and carrying out ecological UHPC mix proportion design.
Preferably, the specific steps of step 1) are as follows:
s11, establishing a mathematical relation between each component in the UHPC and a response value by adopting a secondary saturation D-optimization design method: adopting a quadratic polynomial widely applied in a mixed material test to fit the mathematical relationship between each component of the UHPC and the response value and establishing a numerical model, wherein the formula of the numerical model is as follows:
in the formula: e (y) is the response value of the system; x is the number of i Is the proportion of various raw materials; beta is a i Coefficients representing the corresponding terms; q is the number of arguments.
S12, determining independent variables and dependent variables in the UHPC mix proportion and the value range of the independent variables and the dependent variables: before the design of the mix proportion of the UHPC containing the multi-component material is carried out, the material composition of a concrete system needs to be determined, the composition is used as an independent variable, the fluidity and the compressive strength are used as dependent variables, and finally the value ranges of the respective variables are set. In the concrete mix proportion design, the concrete is assumed to be composed of q materials, the sum of the proportions of all the components is 1, and the mathematical relationship among the components is as follows:
in the formula: q is the material composition quantity of the concrete; x is a radical of a fluorine atom i X is more than or equal to 0 and less than or equal to 1.
The components of UHPC containing multi-component materials are mutually restricted, so that the proportion (x) of the materials of each component i ) StoreAt the upper and lower limits, the proportional relationship is:
0≤L i ≤x i ≤U I ≤1 i＝1,2,3...q (3)
in the formula: x is the number of i Is the proportion of each component; l is i And U i Respectively the lower limit and the upper limit of the proportion of each component.
S13, carrying out optimization design by means of a matrix algorithm of secondary saturation D-optimization design to obtain a basic mix proportion of the test: a quadratic polynomial regression model is built in Design-Expert, SAS and other test Design software, the functional relation between the independent variable and the response value can be fitted, and according to the independent variable value range and the group number n set in the step S12, the software can automatically carry out optimization Design and obtain n groups of test basic mix proportion.
Preferably, the specific steps of step 2) are as follows:
s21, preparing and curing a UHPC concrete test piece, preferably, the preparation process of the UHPC is shown in figure 2, and mainly comprises the following steps:
(1) Weighing the raw materials according to the n groups of test basic mixing ratios;
(2) Pouring raw materials such as cement, lime powder, silica fume, fine sand, environment-friendly composite material and the like into a stirrer, slowly stirring for 90s, and uniformly mixing;
(3) Mixing about 75% of water with a water reducing agent, gradually adding into the uniformly mixed powder, and stirring at a low speed for 90s;
(4) Pouring the residual water into a stirrer, and quickly stirring for 120s;
(5) Slowly stirring for 90s, stopping stirring, pouring into a mold for molding, and maintaining at normal temperature for 28 days.
S22, carrying out a fluidity test to obtain UHPC fluidity test data: the fluidity of UHPC is tested according to the requirements of GB/T2419-2005, namely a cement mortar fluidity test method, the stirred slurry is poured into a mould, a trowel is used for scraping redundant parts to ensure that the volume of the slurry is equal during each measurement, then the mould is lifted vertically upwards at a constant speed slowly to allow the slurry to slide downwards freely, the slurry on the inner wall of the mould is scraped until the average value of the vertical longest side and the vertical shortest side is measured when the slurry stops flowing.
S23, performing a compressive strength test to obtain 28-day compressive strength test data of UHPC: the compressive strength of UHPC is tested according to the requirements of the Cement mortar Strength test method (ISO method) GB/T17671-2020, each group of 3 test blocks of 40mm × 40mm × 160mm is cut into 6 parts, the loading rate is set to be 2.4kN/s, the maximum stress applied is measured, and the average value is obtained as the final compressive strength.
Preferably, the specific steps of step 3) are:
s31, performing multiple linear regression analysis on the test data, estimating each coefficient of the numerical model, and determining the numerical model between the independent variable and the response value: performing multiple linear regression analysis by using the n groups of combination ratios designed in the step S1 and the test data obtained in the step S2 and statistical software such as Design-Expert, SAS and Minitab, and the like, and estimating coefficients (beta) of respective variables in the numerical model i ) Obtaining a final mathematical fitting equation, namely a numerical model; the numerical model can predict the corresponding working performance and compressive strength according to the test mixture ratio.
S32, evaluating the fitting accuracy of the numerical model by using the regression evaluation index, and judging the reliability of the prediction result: the accuracy of the model fit was evaluated by performing analysis of variance (ANOVA) using a variety of regression evaluation metrics, including model F values, model P values, and coefficient of determination (R-Square, R) 2 ) Correction decision coefficient (Adjusted R-Square, adj-R) 2 ) And model signal-to-noise ratio (Adeq Precision); f value and P value of the model are used for independent variable significance test, P value less than 5% is used as significance evaluation standard, and F value is used for evaluating the homogeneity of variance; r of the model 2 And Adj-R 2 The test result is used for goodness-of-fit test and is used for evaluating the fitting degree of the regression straight line to a test value, the value range is 0-1, and the closer the value is to 1, the better the fitting degree is; the model signal-to-noise ratio represents the ratio of information that can be interpreted by the model to information that cannot be interpreted by the model to assist in determining the accuracy of the model.
Preferably, the specific steps of step 4) are as follows:
s41, determining a response optimization function, and establishing a multivariate optimization combination model: based on a response surface method, a total expectation function D developed by Derringer and Suich is utilized, a relative importance factor r is introduced, and the expectation value of each component when acting alone is optimized and combined, wherein the formula of the total expectation function is as follows:
in the formula: n is the optimized response number; r is i As a function d i The value range of the relative importance factor of (2) is 1 to 5, which respectively corresponds to the least important factor to the most important factor; d i For the individual expectation functions, the values range from 0 to 1, 0 indicating a completely undesired reaction and 1 indicating a completely desired reaction; d is the overall expectation function, with values closer to 1 indicating response values closer to the target value.
When any one reaction or function is out of the expected range, the whole function becomes zero, and the expected goal cannot be realized; in the condition setting, the function d is expected individually i Three target ranges exist, namely maximum, minimum or within range, and the optimization of the mixed variables is realized by setting the target range of the independent variables; the individual expectation function calculation methods for the case where the target range of the independent variable is maximum, minimum and in-range are shown in formula (5) and formula (6), respectively:
in the formula: d i As individual expectation functions; u shape i 、L i Upper and lower limits for the independent variable, respectively; c i Is the most ideal value of the independent variable; wr i 、 ws i 、wt i All weights are given response values, the value range is between 0.1 and 10, more emphasis is given to the target when the value is higher than 1, and the opposite is given when the value is lower than 1.
S42 sets target ranges for all variables and response values (to achieve optimization of mixed variables): selecting the value range of each component according to the expected performance requirement of the UHPC, and setting the target ranges of the working performance and the compressive strength; the method has the advantages that the cement consumption is minimum, the steel slag powder and other environment-friendly composite materials are maximum, the compressive strength is improved as much as possible while the working performance of the UHPC is guaranteed, and the ecology of the UHPC is realized.
S43, carrying out numerical optimization, designing the ecotype UHPC mixing ratio: carrying out numerical optimization according to a set target range to obtain an optimal solution, and determining a low cement consumption UHPC mixing ratio which meets excellent working performance and compressive strength, namely an ecological UHPC mixing ratio; the numerical optimization work is carried out by using Design-Expert, SAS, minitab and other test Design software.
Drawings
FIG. 1 is a schematic flow chart of a method for designing an ecological mix ratio of ultra-high performance concrete containing a plurality of materials;
FIG. 2 is a flow chart of a UHPC stirring preparation process;
FIG. 3 is a microscopic morphology of steel slag powder in the UHPC component;
FIG. 4 is a comparison of predicted and true values of UHPC performance;
FIG. 5 is a graph comparing the predicted and true values of compressive strength for UHPC;
FIG. 6 is a drawing of a desirable area of cement, silica fume and steel slag powder;
FIG. 7 is a drawing of a desirable area of cement, silica fume and lime powder.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention; it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
The invention provides a method for designing ecological mix proportion of ultra-high performance concrete containing a plurality of materials, which has a flow schematic diagram shown in figure 1 and mainly comprises the following steps:
s1, carrying out optimization design and determining a basic mix proportion;
s11, establishing a mathematical relation between each component and a response value in the UHPC by adopting a secondary saturation D-optimization design method: adopting a quadratic polynomial widely applied in a mixed material test to fit the mathematical relationship between each component of the UHPC and the response value and establishing a numerical model, wherein the formula of the numerical model is as follows:
in the formula: e (y) is the response value of the system; x is a radical of a fluorine atom i Is the proportion of various raw materials; beta is a i Coefficients representing the corresponding terms; q is the number of arguments.
S12, determining an independent variable, a dependent variable and a value range of the independent variable in the UHPC mixture ratio: the components of the ultrahigh-performance concrete containing the multi-component material in the embodiment are cement, silica fume, lime powder, steel slag powder, fine sand, a water reducing agent and water, the 7 materials are used as independent variables, the fluidity and the compressive strength of UHPC are used as response values, and the value range of the independent variables is set as shown in Table 1.
TABLE 1 Secondary saturation D-optimization design variables and their value ranges
Note: OPC is ordinary portland cement; SF is silica fume; LP is lime powder; SSP is steel slag powder; RS is fine sand (0-0.6 mm); SP is a water reducing agent; w is water; the value range in the table is the mass ratio of each component material in the total material, and the unit is%.
S13, carrying out optimization design by means of a matrix algorithm of secondary saturation D-optimization design to obtain a basic mix proportion of the test: and (4) performing optimization Design by adopting test Design software Design-Expert, setting independent variables and value ranges of the independent variables according to the step S12, and designing 38 groups of test basic mix proportion, wherein relevant data are shown in a table 2.
TABLE 2 test base match ratio and response values
Note: WA is the fluidity of UHPC; CS is the 28-day compressive strength of UHPC.
S2, testing and acquiring UHPC performance test data;
s21, preparing and curing a UHPC concrete sample:
(1) Test raw materials:
cement: PII52.5 cement produced by Huaxin Cement GmbH, with apparent density of 3144kg/m 3 (ii) a Steel slag powder: the steel-making plant waste steel slag has an apparent density of 2900kg/m 3 The microscopic morphology of the steel slag powder is shown in FIG. 3, and most of the particle size is less than 10 μm; lime powder: white powder with apparent density of 2700kg/m, produced by Guangdong New Marte 3 (ii) a Silica fume: silica fume from Aiken corporation, blue powder, apparent density 2200kg/m 3 The chemical compositions of the steel slag powder, the lime powder and the silica fume as the auxiliary cementing materials and the cement are shown in table 1; fine sand: the apparent density of the cleaned common river sand is 2630kg/m 3 (ii) a Water reducing agent: the high-performance polycarboxylic acid water reducing agent produced by Jiangsu Su Bote GmbH has good compatibility and can be used for adjusting the workability of UHPC.
TABLE 3 chemical composition of cement and steel slag powder (mass fraction/%)
Note: OPC is ordinary portland cement; SSP is steel slag powder; LP is lime powder; SF is silica fume.
(2) The UHPC preparation process comprises the following steps:
preferably, the process for preparing UHPC is shown in FIG. 2 and essentially comprises the following steps:
1) Weighing the raw materials according to the 38 groups of test basic mix proportions determined in the step S13;
2) Pouring raw materials such as cement, lime powder, silica fume, fine sand, steel slag powder and the like into a stirrer, slowly stirring for 90s, and uniformly mixing;
3) Mixing about 75% of water with a water reducing agent, gradually adding the mixture into the uniformly mixed powder, and stirring the mixture at a low speed for 90s;
4) Pouring the residual water into a stirrer, and quickly stirring for 120s;
5) Slowly stirring for 90s, stopping stirring, performing fluidity test on part of the stirred slurry, pouring the rest slurry into a mold of 40mm × 40mm × 160mm for molding, and naturally curing at normal temperature for 28 days for subsequent compressive strength test.
S22, carrying out a fluidity test to obtain UHPC fluidity test data: testing the fluidity of UHPC according to the related requirements of a cement mortar fluidity testing method GB/T2419-2005, pouring the stirred slurry into a mold, scraping redundant parts by using a trowel and leveling the slurry on the top of the mold to ensure that the volume of the slurry is equal during each testing, then vertically and slowly lifting the mold at a constant speed upwards to allow the slurry to freely slide downwards, and scraping the slurry on the inner wall of the mold until the average value of the diameters of the vertical longest edge and the shortest edge is measured when the slurry stops flowing; the results of the UHPC fluidity test at the base test mix ratio of 38 sets are shown in Table 2.
S23, performing a compressive strength test to obtain 28-day compressive strength test data of UHPC: testing the compressive strength of UHPC according to the requirements of a cement mortar strength test method (ISO method) GB/T17671-2020, cutting 3 test blocks of 40mm × 40mm × 160mm in each group into 6 parts, setting the loading rate to be 2.4kN/s, measuring the maximum applied stress, and obtaining the average value as the final compressive strength; the results of the UHPC compression strength test at the 38 base test mix ratios are shown in Table 2.
S3, establishing a numerical model and carrying out accuracy evaluation;
s31, carrying out multiple linear regression analysis on the test data, estimating each coefficient of the numerical model, and determiningNumerical model between variables and response values: performing multiple linear regression analysis by using 38 groups of basic mix proportions designed in the step S1 and the test data obtained in the step S2 and adopting test Design software Design-Expert to estimate coefficients (beta) of respective variables in the numerical model i ) Obtaining a final numerical model as shown in formulas (2) and (3); the formula (2) is a multiple regression model of the fluidity of UHPC, and the formula (3) is a multiple regression model of the compressive strength of UHPC, and the specific formula is as follows:
E(y 2 )＝-4560.33995x 1 +1468.46991x 2 -2675.41213x 3 -20432.99167x 4 (3)
-1284.03424x 5 +16471.76054x 6 -27485.24172x 7 -3827.80153x 1 x 2
+2951.75879x 1 x 3 +20318.93862x 1 x 4 +2926.95680x 1 x 5 +102.38417x 1 x 6
+51352.28055x 1 x 7 +2728.59036x 2 x 3 +12508.35892x 2 x 4 -4719.93700x 2 x 5
-25299.73107x 2 x 6 +47611.95755x 2 x 7 +20286.61890x 3 x 4 +988.53042x 3 x 5
-16913.15263x 3 x 6 +44293.60820x 3 x 7 +22224.86834x 4 x 5 +10884.26201x 4 x 6
+62278.58636x 4 x 7 -15587.85056x 5 x 6 +40594.65297x 5 x 7 -1479.97922x 6 x 7
s32, evaluating the accuracy of the numerical model fitting by using the regression evaluation index, and judging the reliability of the prediction result: by performing analysis of variance (ANOVA), using multipleThe regression evaluation index evaluates the accuracy of model fitting, including model F value, model P value, and coefficient of determination (R-Square, R) 2 ) Correction decision coefficient (Adjusted R-Square, adj-R) 2 ) And model signal-to-noise ratio (Adeq Precision); FIG. 4 is a graph comparing predicted values and actual values of the operating performance of UHPC, and FIG. 5 is a graph comparing predicted values and actual values of the compressive strength of UHPC; table 4 shows the evaluation results of the accuracy of the numerical model, in which the minimum F value of the model is 17.80, and the p values are all less than 0.01%, indicating that the model is very significant, and the respective variables can effectively predict the changes in response values; determining the coefficient R 2 And a correction decision coefficient Adj-R 2 The highest values of 0.9818 and 0.9325 are respectively close to 1, so that the model fitting degree is good, and the model fitting degree has high reliability and prediction accuracy; the signal-to-noise ratio of the fluidity model and the compressive strength model is 14.930, and both are greater than 4, so that the applicability of the prediction model is further shown; in conclusion, the accuracy of the numerical model is high, the prediction result is reliable, and the next design step can be carried out to design the ecological UHPC mix proportion.
TABLE 4 evaluation results of model accuracy
Note: WA is the UHPC working performance, i.e. fluidity; CS is the 28-day compressive strength of UHPC.
S4, designing the ecological UHPC mix proportion;
s41, determining a response optimization function, and establishing a multivariate optimization combination model: based on a response surface method, a total expectation function D developed by Derringer and Suich is utilized, a relative importance factor r is introduced, and the expectation value of each component when acting alone is optimized and combined, wherein the formula of the total expectation function is as follows:
in the formula: n is the optimized response number; r is i As a function d i Is relatively importantSex factors, the value range of which is 1 to 5, respectively corresponding to the least important to the most important; d i For the individual expectation functions, the values range from 0 to 1, with 0 indicating a completely undesired reaction and 1 indicating a completely desired reaction; d is the overall expectation function, with values closer to 1 indicating response values closer to the target value.
The individual expectation function calculation methods in the case where the target range of the independent variable is maximum, minimum and within the range in the present invention are shown in formula (5) and formula (6), respectively:
in the formula: d i As individual expectation functions; u shape i 、L i Upper and lower limits for the independent variable, respectively; c i Is the most ideal value of the independent variable; wr i 、 ws i 、wt i All are weights of given response, the value range is between 0.1 and 10, more emphasis is given to the target when the value is higher than 1, and the opposite is given when the value is lower than 1.
S42 sets target ranges for all variables and response values (to achieve optimization of mixed variables): selecting the value range of each component according to the expected performance requirement of the UHPC, and setting the target ranges of the working performance and the compressive strength; in the embodiment, in order to realize the ecology of UHPC and ensure the excellent working performance and certain compressive strength of UHPC, the dosage of cement is determined to be minimum, the dosage of steel slag powder is determined to be maximum, meanwhile, the working performance (fluidity) is in the range of 260mm to 300mm, and the compressive strength is above 120MPa in 28 days, as shown in Table 5.
TABLE 5 high Performance ecotype UHPC variable and response value target Range
Note: the unit of the upper limit and the lower limit in the table indicates that OPC, SF, LP, SSP, RS, SP, and W are mass ratios of the total raw materials, and the unit is%; WA is the UHPC working performance, and the unit is mm; CS is the compressive strength of UHPC in MPa.
S43, carrying out numerical optimization, designing the ecotype UHPC mixing ratio: setting target ranges of respective variables and response values in Design-Expert software, and carrying out numerical optimization; FIGS. 6 and 7 show the values of cement, silica fume, steel slag powder and cement, silica fume, and lime powder in a set range, respectively, and Table 6 shows the optimized UHPC mix proportion to finally obtain 5 different optimal solutions (G1, G2, G3, G4, and G5), with a total expectation of 0.94-1; the optimized result shows that the expected value of the overall function is as high as 1 under a certain value, and the target requirement can be fully realized. The slag powder in the optimized mixing proportion G1 replaces 30.1 percent of cement, and the using amount of the cement is reduced to 471.6kg/m 3 Is obviously lower than the average dosage (900-1100 kg/m) of cement blended in the common UHPC 3 ) The utilization rate of solid waste is improved while the consumption of cement is reduced, and the ecology of UHPC is realized.
TABLE 6 optimized UHPC blend ratio
The foregoing is only a preferred embodiment of the present invention; the scope of the invention is not limited thereto. Any person skilled in the art should also be able to cover the technical scope of the present invention by the equivalent or modified embodiments and the modified concepts of the present invention.
Claims (1)
1. A design method of ecological mix proportion of ultra-high performance concrete containing multi-element materials is characterized by comprising the following steps: the method comprises the following steps:
s1, establishing a numerical model by adopting a secondary saturation D-optimization design, performing optimization design, and determining a basic mix proportion;
s2, preparing and maintaining a UHPC test piece, performing a test and acquiring UHPC performance index test data;
s3, performing multiple linear regression analysis, determining a numerical model and performing accuracy evaluation;
s4, establishing a multi-element optimization combination model, and carrying out ecological UHPC mix proportion design;
the specific steps of the step S1 are as follows:
s11, establishing a numerical model between each component and a response value in the UHPC by adopting a secondary saturation D-optimization design method;
s12, determining independent variables, dependent variables and value ranges of the independent variables and the dependent variables in the mixing ratio according to the material composition of the ecological UHPC;
s13, carrying out optimization design by means of a matrix algorithm of secondary saturation D-optimization design to obtain a basic mix proportion of the test;
the specific formula of the numerical model between each component and the response value in the UHPC is as follows:
in the formula: e (y) is the response value of the system; x is the number of i The proportion of various raw materials; beta is a i Coefficients representing the corresponding terms; q is the number of independent variables;
the UHPC is composed of q materials, the sum of the proportions of all the components is 1, and the mathematical relationship among the components is as follows:
in the formula: q is the material composition quantity of the concrete; x is a radical of a fluorine atom i X is more than or equal to 0 and less than or equal to 1;
the components of UHPC containing multi-component materials are mutually restricted, and the proportion (x) of the materials of the components i ) There are upper and lower limits, the proportional relationship of which is:
0≤L i ≤x i ≤U I ≤1 i＝1,2,3...q (3)
in the formula: x is the number of i Is the proportion of each component; l is i And U i Respectively the lower limit and the upper limit of the proportion of each component;
the ecotypic UHPC consists of cement, lime powder, silica fume, fine sand and an environment-friendly composite material, wherein the environment-friendly composite material comprises steel slag, nickel-iron slag, copper slag, bottom slag, phosphorous slag, manganese slag, refining slag, lithium slag, fly ash, granite saw mud, alum mud, desulfurized gypsum, limestone, granite tailings, zeolite tailings, pumice and a mixture thereof;
the specific steps of the step S2 are as follows:
s21, preparing and maintaining a UHPC test piece;
s22, carrying out a fluidity test to obtain UHPC fluidity test data;
s23, performing a compressive strength test to obtain UHPC 28-day compressive strength test data;
the preparation process of the UHPC comprises the following steps:
(1) Weighing the raw materials according to n groups of test basic mix proportions;
(2) Pouring the raw materials of the cement, the lime powder, the silica fume, the fine sand and the environment-friendly composite material into a stirrer, slowly stirring for 90s, and uniformly mixing;
(3) Mixing 75% of water with a water reducing agent, gradually adding into the uniformly mixed powder, and stirring at a low speed for 90s;
(4) Pouring the residual water into a stirrer, and quickly stirring for 120s;
(5) Slowly stirring for 90s, stopping stirring, pouring into a mold for molding, and maintaining at normal temperature for 28 days;
the specific steps of the step S3 are as follows:
s31, performing multiple linear regression analysis on the test data, estimating each coefficient of the numerical model, and determining the numerical model between the independent variable and the response value;
s32, evaluating the fitting accuracy of the numerical model by using the regression evaluation index, and judging the reliability of a prediction result;
the numerical model accuracy evaluation index comprises a model F value, a model P value and a determination coefficient (R-Square, R) 2 ) And correctingDetermining coefficients (Adjusted R-Square, adj-R) 2 ) And model signal-to-noise ratio (Adeq Precision);
the specific steps of the step S4 are as follows:
s41, establishing a multivariate optimization combination model by using an overall expectation function developed by Derringer and Suich;
s42, selecting the value range of each component according to the expected performance requirement of the UHPC, and setting the target ranges of the working performance and the compressive strength;
s43, carrying out numerical optimization according to a set target range to obtain an optimal solution, and determining a low cement consumption UHPC (ultra high Performance concrete) mixing ratio which meets excellent working performance and compressive strength, namely a ecological UHPC mixing ratio;
the calculation method of the overall expectation function comprises the following steps:
in the formula: n is the optimized response number; r is i As a function d i The value range of the relative importance factor of (2) is 1 to 5, which respectively corresponds to the least important factor to the most important factor; d i For the individual expectation functions, the values range from 0 to 1, 0 indicating a completely undesired reaction and 1 indicating a completely desired reaction; d is an overall expectation function, the closer the value is to 1, the closer the response value is to the target value;
the single expectation function has three target ranges, namely maximum, minimum or within the range, and the optimization of the mixed variables is realized by setting the target range of the response value; the individual expectation function calculation methods for the case where the target range of the independent variable is maximum, minimum and in-range are shown in formula (5) and formula (6), respectively:
in the formula: d i As individual expectation functions; u shape i 、L i Upper and lower limits for the independent variable, respectively; c i Is the most ideal value of the independent variable; wr (w) i 、ws i 、wt i All weights are given response values, the value range is between 0.1 and 10, more emphasis is given to the target when the value is higher than 1, and the opposite is given when the value is lower than 1.
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