Model for predicting elastic modulus of three-dimensional porous graphene 3D printing concrete

Abstract

The invention belongs to the field of elasticity modulus prediction of 3D printing concrete in civil engineering, and relates to a prediction model of elasticity modulus of 3D printing concrete with three-dimensional porous graphene. A complex prediction formula is established through the content of the three-dimensional porous graphene, the concrete mix proportion, the slurry-to-bone ratio, the age, the compressive strength, the density and the 3D printing process parameters, and the method has important significance for accurately predicting the elastic modulus of the three-dimensional porous graphene 3D printing concrete.

Description

Technical Field

[0001] The present invention relates to a method for predicting the elastic modulus of 3D printed concrete, and in particular to a prediction model for the elastic modulus of three-dimensional porous graphene 3D printed concrete. Background Art

[0002] Elastic modulus is an important mechanical parameter to measure the deformation capacity of concrete materials, and is of great significance for structural design and performance analysis. With the application of three-dimensional porous graphene (3D-Porous Graphene, 3D-PG) in concrete and the development of 3D printing technology, studying the elastic modulus of 3D printed concrete at different 3D-PG contents and ages is of great value for optimizing material properties and guiding engineering applications. The introduction of 3D-PG can improve the microstructure of concrete and increase the elastic modulus; while 3D printing process parameters (such as layer thickness and printing speed) will also affect the internal structure and mechanical properties of concrete. Therefore, it is of great significance to establish a complex prediction formula that comprehensively considers 3D-PG content, concrete mix ratio, age and 3D printing process parameters for accurately predicting the elastic modulus of 3D printed concrete. Summary of the invention

[0003] In view of the limitations of current technology, the core problem that the present invention aims to solve is to develop a new method for measuring the elastic modulus of recycled coarse aggregate three-dimensional porous graphene 3D printed concrete, which can not only simplify the operation process and shorten the time, but also ensure that the error between the predicted results and the actual measured values ​​is small and the accuracy is high.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] Step 1: Establish a prediction model for the elastic modulus of 3D printed concrete containing three-dimensional porous graphene, as shown in formula (2):

[0006]

[0007] Among them, the elastic modulus related terms are:

[0008] E c (t): elastic modulus of 3D printed concrete at age t days (GPa);

[0009] E c0 : Reference elastic modulus, the elastic modulus of ordinary concrete at 28 days of age and under standard conditions (GPa);

[0010] ρ c :Concrete density (kg / m 3 );

[0011] ρ0: Base density, ordinary concrete density, about 2400kg / m3 ;

[0012] f c (t): compressive strength at age t days (MPa);

[0013] f c0 : Baseline compressive strength, the compressive strength of ordinary concrete at 28 days of age (MPa);

[0014] k1 is an empirical index related to the ratio of actual density to reference density, and its range is 1.3 to 1.6; k2 is an empirical index related to the ratio of t day to 28 reference compressive strength, and its range is 0.1 to 0.4; k G The relevant empirical coefficient ranges from 8 to 11; n is the empirical coefficient related to the mass percentage of graphene in the gelling material, and its range is from 0.3 to 0.6; k L The range of relevant experience index is: 0.04~0.06; k S The relevant empirical coefficient ranges from 0.02 to 0.04; the reference elastic modulus E c0 The range of relevant empirical coefficients is: 25~35GPa;

[0015] G: Amount of three-dimensional porous graphene (kg / m 3 );

[0016] B: Total amount of cementitious materials (kg / m 3 );

[0017] The mass percentage of graphene in the cementitious material;

[0018] L t :3D printing layer thickness (mm);

[0019] L0: reference layer thickness (mm), take the standard value, such as 10mm;

[0020] S: printing speed (mm / s);

[0021] S0: Reference printing speed (mm / s), take the standard value, such as 50mm / s;

[0022] Step 2: Set the reference elastic modulus E c0 , actual density of concrete ρ c and the base density ρ0, the compressive strength f at age t c (t), baseline compressive strength f c0 .

[0023] Step 3: Set the amount of three-dimensional porous graphene G and the total amount of gelling material B.

[0024] Step 4: Set the 3D printing layer thickness Lt , reference layer thickness L0, printing speed S, reference printing speed S0.

[0025] Step 5: Substitute the relevant parameters in steps 2, 3 and 4 into formula (2) to predict the elastic modulus of three-dimensional porous graphene 3D printed concrete.

[0026] As a further solution of the present invention, the prediction model of elastic modulus of the three-dimensional porous graphene 3D printed concrete, the effective cementitious material dosage C eff :

[0027] C=α c ×B

[0028] F=α F ×B

[0029] SF=α SF ×B

[0030] SL=α SL ×B

[0031] C, F, SF, SL: the amount of cement, fly ash, silica fume, and slag powder (kg / m 3 );

[0032] α c , α F , α SF , α SL :The coefficient of each mineral admixture in cementitious materials.

[0033] As a further solution of the present invention, the prediction model of the elastic modulus of the three-dimensional porous graphene 3D printed concrete has an effective water-binder ratio ranging from 0.3 to 0.45; the total amount of cementitious material ranging from 450 to 600 kg / m 3 , calculate the amount of each cementitious material: C = α c ×B,F=α F ×B,SF=α SF ×B,SL=α SL ×B,α c The value range is 30% to 50%; α F The value range is 10% to 20%; α SF The value range is 5% to 10%; α SL The value range is 20% to 40%; determine the amount of three-dimensional porous graphene G: determined by the percentage of the total amount of cementitious materials, such as 0.1%, 0.3%, 0.5%, 1.0%; concrete density ρ c : Obtained through experimental measurement.

[0034] As a further solution of the present invention, the prediction model of the elastic modulus of the three-dimensional porous graphene 3D printed concrete, the three-dimensional porous graphene (G): high-quality, well-dispersed graphene material; layer thickness L t : Generally 5-15mm, printing speed S: Generally 30-100mm / s, reference value L o and S o : Take L o =10mm,S o =50mm.

[0035] As a further solution of the present invention, a prediction model for the elastic modulus of a three-dimensional porous graphene 3D printed concrete is provided, wherein the materials are mixed: (1) dry mixing: the cementitious material (including graphene) and the aggregate are mixed evenly; and (2) wet mixing: water and an admixture are added and stirred evenly to ensure that the graphene is fully dispersed.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] (1) The present invention utilizes a complex prediction formula of three-dimensional porous graphene content, concrete mix ratio, mortar-to-aggregate ratio, age, compressive strength, density and 3D printing process parameters to accurately predict the elastic modulus of three-dimensional porous graphene 3D printed concrete.

[0038] (2) The present invention reduces the time for repeated tests, accelerates the test progress, and significantly improves the accuracy and reliability of the prediction results. DETAILED DESCRIPTION

[0039] The specific embodiments of the present invention will be introduced in depth and comprehensively below, and the technical solutions therein will be elaborated in detail. It should be clear that the embodiments listed here are only a part of the many examples of the present invention and do not cover all possible implementation methods. Based on these specific embodiments, all other implementation forms that can be reasonably derived by those skilled in the art without additional creative efforts should be deemed to fall within the scope of protection of the claims of the present invention.

[0040] In this specific embodiment, the raw materials for producing the three-dimensional porous graphene 3D printing concrete include cement, fly ash, slag, silica fume, coarse aggregate and fine aggregate. In addition, the three-dimensional porous graphene 3D printing concrete is not limited to the above raw materials when the present invention is implemented. Among the above raw materials, cement is ordinary silicate cement, and coarse aggregate and fine aggregate are mixed and used in a graded manner.

[0041] Five groups of three-dimensional porous graphene 3D printing concrete tests and verifications were carried out, and the concrete raw material ratios are shown in Table 1:

[0042] Table 1

[0043]

[0044] The unit of each raw material in Table 1 is kg, among which the cement grade is ordinary Portland cement with PO 42.5.

[0045] By formula:

[0046]

[0047] The calculated predicted values ​​(in GPa) are compared with the measured values ​​(in GPa) at 28 days, as shown in Table 2:

[0048] serial number Measured value Predicted value Absolute error Relative error <![CDATA[Y 预测 / AND 实测 ]]> 1 28.0 27.5 0.5 1.79 0.982 2 36.7 36.2 0.5 1.36 0.986 3 42.4 41.9 0.5 1.18 0.988 4 46.6 46.1 0.5 1.07 0.989 5 52.4 52.2 0.2 0.38 0.996

[0049] After in-depth analysis of the data in Table 2, we found that the prediction formula in the present invention controls the error between the actual measured value and the actual measured value within 0.5GPa when predicting concrete, with the minimum error being only 0.2GPa, the maximum error being 0.5GPa, and the average error reaching 0.44GPa. It is worth mentioning that the average relative error is only 1.16%, and the ratio of the predicted value to the actual measured value (Y predicted / Y measured) is stable in the range of 0..982 to 0.9996, showing an accuracy rate of more than 95%. These data fully prove that the prediction formula in the present invention not only has a high correlation when calculating the 28d elastic modulus of concrete, but also has excellent prediction accuracy and precision.

[0050] Finally, it should be emphasized that the above implementation cases are only intended to illustrate the technical solutions of the present invention, and do not constitute a limitation on its application. Although we have described the present invention in detail by referring to the preferred implementation cases, professionals in this field should recognize that it is entirely possible to make various adjustments and changes in form and details without departing from the core spirit and protection scope of the present invention defined by the attached claims.

Claims

1. A prediction model for the elastic modulus of three-dimensional porous graphene 3D printed concrete, characterized in that The following steps are involved: Step 1: Establish a prediction model for the elastic modulus of 3D printed concrete containing three-dimensional porous graphene, as shown in formula (1): Among them, the elastic modulus related terms are: E c (t): elastic modulus of 3D printed concrete at age t days (GPa); E c0 : Reference elastic modulus, the elastic modulus of ordinary concrete at 28 days of age and under standard conditions (GPa); ρ c :Concrete density (kg / m 3 ); ρ0: Base density, ordinary concrete density, about 2400kg / m 3 ; f c (t): compressive strength at age t days (MPa), which can be obtained from existing formulas or experimental data; f c0 : Baseline compressive strength, the compressive strength of ordinary concrete at 28 days of age (MPa); k1 is an empirical index related to the ratio of actual density to reference density, and its range is 1.3 to 1.6; k2 is an empirical index related to the ratio of t day to 28 reference compressive strength, and its range is 0.1 to 0.4; k G The relevant empirical coefficient ranges from 8 to 11; n is the empirical coefficient related to the mass percentage of graphene in the gelling material, and its range is from 0.3 to 0.6; k L The range of relevant experience index is: 0.04~0.06; k S The relevant empirical coefficient ranges from 0.02 to 0.04; the reference elastic modulus E c0 The range of relevant empirical coefficients is: 25~35GPa; G: Amount of three-dimensional porous graphene (kg / m 3 ); B: Total amount of cementitious materials (kg / m 3 ); The mass percentage of graphene in the cementitious material; L t :3D printing layer thickness (mm); L0: reference layer thickness (mm), take the standard value, such as 10mm; S: printing speed (mm / s); S0: Reference printing speed (mm / s), take the standard value, such as 50mm / s; Step 2: Set the reference elastic modulus E c0 , concrete density ρ c and the base density ρ0, the compressive strength f at age t c (t), baseline compressive strength f c0 ; Step 3, setting the amount of three-dimensional porous graphene G and the total amount of gelling material B; Step 4: Set the 3D printing layer thickness L t , reference layer thickness L0, printing speed S, reference printing speed S0; Step 5: Substitute the relevant parameters in steps 2, 3 and 4 into formula (1) to obtain a prediction model for the elastic modulus of three-dimensional porous graphene 3D printed concrete.

2. A prediction model for the elastic modulus of a three-dimensional porous graphene 3D printed concrete according to claim 1, characterized in that: Amount of each cementitious material: C=α×B F=a F ×B SF=α SF ×B SL=α SL ×B C, F, SF, SL: the amount of cement, fly ash, silica fume, and slag powder (kg / m 3 ); α c , α F , α SF , α SL :The coefficient of each mineral admixture in cementitious materials.

3. A prediction model for the elastic modulus of a three-dimensional porous graphene 3D printed concrete according to claim 1, characterized in that: In step 1, the effective water-binder ratio ranges from 0.3 to 0.45; the mixing water volume ranges from 135 to 270 kg / m 3 The total amount of cementitious materials ranges from 450 to 600 kg / m 3 , calculate the amount of each cementitious material: C = α c ×B,F=α F ×B,SF=α SF ×B,SL=α SL ×B,α c The value range is 30% to 50%; α F The value range is 10% to 20%; α SF The value range is 5% to 10%; α SL The value range is 20% to 40%; determine the amount of three-dimensional porous graphene G: determined by the percentage of the total amount of cementitious materials, such as 0.1%, 0.3%, 0.5%, 1.0%; concrete density ρ c : Obtained through experimental measurement.

4. A prediction model for the elastic modulus of a three-dimensional porous graphene 3D printed concrete according to claim 1, characterized in that: In step 1, three-dimensional porous graphene (G): high-quality, well-dispersed graphene material; layer thickness L t : Generally 5-15mm, printing speed S: Generally 30-100mm / s, reference value L o and S o : Take L o =10mm,S o =50mm / s.

5. A prediction model for the elastic modulus of a three-dimensional porous graphene 3D printed concrete according to claim 1, characterized in that: Material mixing: (1) Dry mixing: Mix the cementitious material (including graphene) and aggregate evenly; (2) Wet mixing: Add water and admixtures, stir evenly to ensure that the graphene is fully dispersed.

Images