A calculation method of an elastic modulus prediction model of three-dimensional porous graphene 3D printing concrete

Abstract

The application belongs to the field of elastic modulus prediction of civil engineering 3D printing concrete, and relates to a calculation method of an elastic modulus prediction model of three-dimensional porous graphene 3D printing concrete. A complex prediction formula is established by three-dimensional porous graphene content, concrete mix proportion, paste-aggregate ratio, age, compressive strength, density and 3D printing process parameters, which is of great significance for accurately predicting the elastic modulus of three-dimensional porous graphene 3D printing concrete.

Description

TECHNICAL FIELD

[0001] The application belongs to the elastic modulus prediction method of 3D printing concrete, and particularly relates to a calculation method of an elastic modulus prediction model of three-dimensional porous graphene 3D printing concrete. BACKGROUND

[0002] Elastic modulus is an important mechanical parameter for measuring 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, it is of great value to study the elastic modulus of 3D printing concrete under different 3D-PG contents and ages, so as to optimize material performance and guide engineering application. The introduction of 3D-PG can improve the microstructure of concrete and increase the elastic modulus; and 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 considering 3D-PG content, concrete mix proportion, age and 3D printing process parameters for accurately predicting the elastic modulus of 3D printing concrete. SUMMARY

[0003] In view of the limitations of the current technology, the core problem to be solved by the present application is to develop a new method for measuring the elastic modulus of recycled coarse aggregate three-dimensional porous graphene 3D printing concrete, which simplifies the operation process, shortens the time consumption, and ensures that the prediction result has small error and high accuracy compared with the actual measured value.

[0004] To solve the above technical problems, the technical scheme adopted by the present application is as follows:

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

[0006]

[0007] Among them, the elastic modulus related term is:

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

[0009] E c0 : reference elastic modulus, taking the elastic modulus of ordinary concrete at 28 days of age and under standard conditions, unit: GPa;

[0010] ρ c : concrete density, unit: kg / m 3 ;

[0011] ρ0: reference density, taking the density of ordinary concrete;

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

[0013] f c0 : reference compressive strength, taking the compressive strength of ordinary concrete at 28 days, unit: MPa;

[0014] k1 is an empirical index related to the ratio of actual density to reference density, ranging from 1.3 to 1.6; k2 is an empirical index related to the ratio of compressive strength at t days to reference compressive strength at 28 days, ranging from 0.1 to 0.4; k G is an empirical coefficient related to data fitting, ranging from 8 to 11; n is an empirical coefficient related to the mass percentage of graphene in cementitious materials, ranging from 0.3 to 0.6; k L is an empirical index related to data fitting, ranging from 0.04 to 0.06; k S is an empirical coefficient related to data fitting, ranging from 0.02 to 0.04; reference elastic modulus E c0 is an empirical coefficient related to data fitting, ranging from 25 to 35 GPa;

[0015] G: amount of three-dimensional porous graphene, unit: kg / m 3 ;

[0016] B: total amount of cementitious materials, unit: kg / m 3 ;

[0017] Mass percentage of graphene in cementitious materials;

[0018] L t : 3D printing layer thickness, unit: mm;

[0019] L0: reference layer thickness, unit: mm, taking the standard value;

[0020] S: printing speed, unit: mm / s;

[0021] S0: reference printing speed, unit: mm / s, taking the standard value;

[0022] Step 2, set reference elastic modulus E c0 , actual density of concrete ρ c and reference density ρ0, compressive strength f c at t days, reference compressive strength f c0 .

[0023] Step 3, set the amount of three-dimensional porous graphene G, the total amount of cementitious materials B.

[0024] Step 4, setting 3D printing layer thickness L t , reference layer thickness L0, printing speed S, reference printing speed S0.

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

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

[0027] C = a c × B

[0028] F = a F × B

[0029] SF = a SF × B

[0030] SL = a SL × B

[0031] C, F, SF, SL: the dosages of cement, fly ash, silica fume and slag powder, respectively, in kg / m 3 ; a c , a F , a SF , a SL : the coefficients of each mineral admixture to the cementitious material.

[0032] As a further scheme of the present application, the calculation method of the elastic modulus prediction model of the three-dimensional porous graphene 3D printed concrete, the effective water-binder ratio value range is 0.3-0.45; the total amount of cementitious material value range is 450-600 kg / m 3 , the dosages of each cementitious material are calculated: C = a c × B, F = a F × B, SF = a SF × B, SL = a SL × B, a c value range is 30%-50%; a F value range is 10%-20%; a SF value range is 5%-10%; a SL value range is 20%-40%; the amount of three-dimensional porous graphene G is determined as a percentage of the total amount of cementitious material; the density of concrete p c : obtained by experiment.

[0033] As a further scheme of the present application, the calculation method of the elastic modulus prediction model of the three-dimensional porous graphene 3D printing concrete, the layer thickness L t is 5-15mm, the printing speed S is 30-100mm / s, and the reference values L0 and S0 are L0=10mm and S0=50mm / s.

[0034] As a further scheme of the present application, the calculation method of the elastic modulus prediction model of the three-dimensional porous graphene 3D printing concrete, the material mixing: (1) dry mixing: mixing the cementitious material containing graphene and the aggregate uniformly; (2) wet mixing: adding water and admixtures and stirring uniformly to ensure that the graphene is fully dispersed.

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

[0036] (1) The present application can accurately predict the elastic modulus of the three-dimensional porous graphene 3D printing concrete by using the complex prediction formula of the three-dimensional porous graphene content, the concrete mix proportion, the paste-aggregate ratio, the age, the compressive strength, the density and the 3D printing process parameters.

[0037] (2) The present application reduces the repeated test time, accelerates the test progress, and significantly improves the accuracy and reliability of the prediction results. DETAILED DESCRIPTION

[0038] The specific embodiments of the present application will be introduced in detail below, and the technical solutions will be described in detail. It should be clear that the embodiments listed here are only a part of the numerous examples of the present application, and do not cover all possible implementation manners. Based on these specific embodiments, all other implementation forms that can be reasonably deduced by those skilled in the art without additional creative efforts should be considered to fall within the protection scope of the claims of the present application.

[0039] In this specific embodiment, the production raw materials of the three-dimensional porous graphene 3D printing concrete include cement, fly ash, slag, silica fume, coarse aggregate and fine aggregate, and in addition, the three-dimensional porous graphene 3D printing concrete in the specific implementation of the present application is not limited to the above raw materials. The cement in the above raw materials is ordinary portland cement, and the coarse aggregate and the fine aggregate are mixed and used, and the gradation is qualified.

[0040] Five groups of three-dimensional porous graphene 3D printing concrete tests and verifications are carried out, and the concrete raw material mix proportions are as shown in Table 1:

[0041] Table 1

[0042]

[0043] The unit of each raw material in Table 1 is kg, wherein the cement is ordinary Portland cement with a mark of P.O 42.5.

[0044] By the formula:

[0045]

[0046] The calculated predicted value (unit: GPa) is compared with the measured value (unit: GPa) at 28d, and the comparison is as follows:

[0047] As shown in Table 2:

[0048] No. Observed Predicted Absolute error Relative error Y 预测 / Y 实测 <!-- 3 -->]]> 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] Through in-depth analysis of the data in Table 2, it is found that the error between the predicted value and the measured value of the concrete in the present application is controlled within 0.5 GPa, wherein the minimum error is only 0.2 GPa, the maximum error is 0.5 GPa, and the average error reaches 0.44 GPa. More noteworthy is that the average relative error is only 1.16%, and the ratio of the predicted value to the measured value (Ypredicted / Ymeasured) is stable in the range of 0.982-0.9996, showing an accuracy of up to 95%. These data fully prove that the prediction formula in the present application in calculating the elastic modulus of concrete at 28d not only has a high degree of correlation, but also shows excellent prediction precision and accuracy.

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

Claims

1. A computational method for a model for predicting the elastic modulus of a three- dimensional porous graphene 3D-printed concrete, characterized by Comprising the following steps: Step 1, establishing a prediction model of the elastic modulus of the three-dimensional porous graphene 3D printing concrete, as shown in formula (1): Wherein the elastic modulus related term: E c (t): Elastic modulus of 3D-printed concrete at age t days, in GPa; E c0 : reference elastic modulus, the elastic modulus of ordinary concrete under standard conditions at 28 days of age, in GPa; ρ c : concrete density in kg / m 3 ; P0: reference density, taking the density of ordinary concrete; f c (t): compressive strength at age t days, in MPa; f c0 : reference compressive strength, the compressive strength of ordinary concrete at 28 days of age, in MPa; k1 is an empirical index related to the ratio of actual density to reference density, which ranges from 1.3 to 1.6; k2 is an empirical index related to the ratio of compressive strength at t days to the reference compressive strength at 28 days, which ranges from 0.1 to 0.4; k G is an empirical coefficient related to data fitting, which ranges from 8 to 11; n is an empirical coefficient related to the mass percentage of graphene in the cementitious material, which ranges from 0.3 to 0.6; k L is an empirical index related to data fitting, which ranges from 0.04 to 0.06; k S is an empirical coefficient related to data fitting, which ranges from 0.02 to 0.04; reference elastic modulus E c0 is an empirical coefficient related to data fitting, which ranges from 25 to 35 GPa; G: amount of three-dimensional porous graphene, in kg / m 3 ; B: total amount of cementitious material, in kg / m 3 ; The percentage of graphene in the cementitious material by mass; L t :3D printing layer thickness in mm; L0: reference layer thickness, unit: mm, taking the standard value; S: printing speed, unit: mm / s; S0: reference printing speed, unit: mm / s, taking the standard value; Step 2, setting the reference elastic modulus E c0 , the density of the concrete p c and the reference density p0, the compressive strength f c (t) at the age t, the reference compressive strength f c0 ; Step 3, setting the amount of three-dimensional porous graphene G and the total amount of cementitious materials B; Step 4, setting 3D printing layer thickness L t , reference layer thickness L0, printing speed S, reference printing speed S0; Step 5, substituting the related parameters in steps 2, 3 and 4 into formula (1), the prediction model of the elastic modulus of the three-dimensional porous graphene 3D printing concrete.

2. The method of claim 1, wherein the method is characterized by: The amount of each cementitious material: C = a c x B F = a F xB SF = a SF x B SL = a SL x B C, F, SF, SL: the amount of cement, fly ash, silica fume, and slag powder, respectively, in kg / m 3 ; α c , α F , α SF , α SL : Coefficient of each mineral admixture to cementitious materials.

3. The method of claim 2, wherein the method is characterized by: In step 1, the effective water-binder ratio is in the range of 0.3-0.45; the mixing water amount is in the range of 135-270 kg / m 3 , the total amount of cementitious materials is in the range of 450-600 kg / m 3 , the amount of each cementitious material is calculated as follows: C = a c × B, F = a F × B, SF = a SF × B, SL = a SL × B, a c is in the range of 30%-50%; a F is in the range of 10%-20%; a SF is in the range of 5%-10%; a SL is in the range of 20%-40%; the amount of three-dimensional porous graphene G is determined as a percentage of the total amount of cementitious materials; the density of concrete p c is obtained through experiments.

4. The method of claim 1, wherein the method is characterized by: In step 1, the layer thickness L t was 5-15 mm, the printing speed S was 30-100 mm / s, and the reference values L0and S0: L0= 10 mm, S0= 50 mm / s.

5. The method of claim 1, wherein the method is characterized by: Material mixing: (1) dry mixing: mix the cementitious material containing graphene and the aggregate uniformly; (2) wet mixing: add water and admixtures, stir uniformly to ensure that the graphene is fully dispersed.

Images