A calculation method of a chloride ion diffusion prediction model of three-dimensional porous graphene 3D printing concrete

Abstract

The application belongs to the field of 3D printing concrete chloride ion diffusion prediction in civil engineering, and relates to a calculation method of a chloride ion diffusion 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 and 3D printing process parameters, which is of great significance for accurately predicting the chloride ion diffusion of three-dimensional porous graphene 3D printing concrete.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of chloride ion diffusion prediction methods for 3D printed concrete, and particularly relates to a calculation method of a chloride ion diffusion prediction model for three-dimensional porous graphene 3D printed concrete. BACKGROUND

[0002] Chloride ion erosion is one of the main causes of durability problems in reinforced concrete structures. When chloride ions penetrate into the surface of steel bars through the pores and cracks of concrete, they can cause corrosion of the steel bars, which in turn leads to deterioration of the structural performance. The chloride ion diffusion coefficient is an important parameter for measuring the ability of concrete to resist chloride ion erosion.

[0003] Three-dimensional porous graphene (3D-Porous Graphene, 3D-PG) is a new type of nanomaterial that has been applied to the study of reinforced concrete due to its excellent electrical conductivity and mechanical properties. The addition of 3D-PG can improve the microstructure of concrete, reduce porosity and connectivity, and thus reduce the penetration of chloride ions. Combined with 3D printing technology, complex structures can be constructed, and construction efficiency can be improved. Therefore, it is of great significance to establish a complex prediction formula that takes into account factors such as the content of three-dimensional porous graphene, the mix proportion of concrete, the age, and the 3D printing process parameters, for evaluating the durability of 3D printed concrete. SUMMARY

[0004] 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 predicting the chloride ion diffusion of three-dimensional porous graphene 3D printed concrete that simplifies the operation process, shortens the time consumption, and ensures that the prediction results have a small error and high accuracy compared to actual measurements.

[0005] To solve the above technical problems, the present application adopts the following technical solutions:

[0006] Step 1, establish a prediction model for the chloride ion diffusion of three-dimensional porous graphene 3D printed concrete, as shown in formula (2)

[0007]

[0008] D C1 (t): chloride ion diffusion coefficient of concrete at age t days, unit m 2 / s;

[0009] D0: reference chloride ion diffusion coefficient, constant related to material properties, unit m 2 / s;

[0010] D0 is a data fitting related empirical coefficient, its range is: (39-46) x 10 -12; a is an empirical index related to the effective water-binder ratio, which ranges from 0.7 to 0.9; b is an empirical index related to the paste-aggregate ratio, which ranges from 0.4 to 0.6; n is an empirical index related to the mass percentage of graphene in the cementitious material, which ranges from 0.5 to 0.7; k G is an empirical index related to data fitting, which ranges from 18 to 21; k L is an empirical index related to data fitting, which ranges from 0.04 to 0.06; k S is an empirical index related to data fitting, which ranges from 0.02 to 0.04; k t is an empirical index related to data fitting, which ranges from 0.1 to 0.3;

[0011] C eff : effective cementitious material dosage, in kg / m 3 ;

[0012] W: mixing water dosage, in kg / m 3 ;

[0013] paste-aggregate volume ratio;

[0014] G: three-dimensional porous graphene dosage, in kg / m 3 ;

[0015] B: total cementitious material dosage, in kg / m 3 ;

[0016] t: age, in days;

[0017] t0: reference age;

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

[0019] L0: reference layer thickness, in mm, taking a standard value;

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

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

[0022] Step 2, setting the quasi-reference chloride ion diffusion coefficient D0, effective cementitious material dosage C eff , mixing water dosage W, paste-aggregate ratio

[0023] Step 3, setting the three-dimensional porous graphene dosage G, total cementitious material dosage B, age t, and reference age t0.

[0024] Step 4, setting the 3D printing layer thickness Lt , 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 chloride ion diffusion of the three-dimensional porous graphene 3D printed concrete.

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

[0027] C eff =C+k F F+k SF SF+k SL SL

[0028] C: cement dosage, unit kg / m 3 ;

[0029] F, SF, SL: the dosages of fly ash, silica fume and slag powder, respectively, unit kg / m 3 ;

[0030] k F , k SF , k SL : the activity coefficients of various mineral admixtures.

[0031] As a further scheme of the application, the calculation method of the chloride ion diffusion prediction model of the three-dimensional porous graphene 3D printed concrete, the effective water-binder ratio value range is 0.3-0.45; the mixing water dosage is 135-270 kg / m 3 , the total amount of cementitious materials is 450-600 kg / m 3 , the dosages of various cementitious materials are calculated: C=α c ×B, F=α F ×B, SF=α SF ×B, SL=α SL ×B, α c is 30%-50%; α F is 10%-20%; α SF is 5%-10%; α SL is 20%-40%; the dosage of three-dimensional porous graphene G is determined as a percentage of the total amount of cementitious materials; the slurry-bone ratio range is set to 0.35-0.45.

[0032] As a further scheme of the application, the calculation method of the chloride ion diffusion prediction model of the three-dimensional porous graphene 3D printed concrete, the layer thickness L tThe printing speed S is 30-100 mm / s, and the reference values L0 and S0 are: L0=10 mm, S0=50 mm / s

[0033] As a further scheme of the present application, the calculation method of the chloride ion diffusion prediction model of the three-dimensional porous graphene 3D printing concrete, the material mixing includes: (1) dry mixing: mix the cementing material containing graphene and the aggregate uniformly; (2) wet mixing: add water and additives, stir uniformly, and ensure that the graphene is fully dispersed.

[0034] Compared with the prior art, the present application has the following beneficial effects:

[0035] (1) The present application can quickly predict the chloride ion diffusion of the three-dimensional porous graphene 3D printing concrete by using the three-dimensional porous graphene content, the concrete mixing ratio, the paste-aggregate ratio, the age, and the 3D printing process parameters.

[0036] (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

[0037] 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 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.

[0038] In the present specific embodiment, the production raw materials of the three-dimensional porous graphene 3D printing concrete include cement, fly ash, slag, silica fume, three-dimensional porous graphene, 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.

[0039] Five groups of three-dimensional porous graphene 3D printing concrete tests and verifications are carried out, and the concrete raw material mixing ratio is as shown in Table 1:

[0040] Table 1

[0041]

[0042] The units of each raw material in Table 1 are kg, and the cement is P.O 42.5 ordinary Portland cement.

[0043] Through the formula:

[0044]

[0045] The calculated predicted value (unit: 10 -12 ) is compared with the measured value (unit: 10 -12 ) at 28d, and the comparison is as follows:

[0046] Table 2 shows:

[0047] No. Observed Predicted Absolute error Relative error Y 预测 / Y 实测 ]]> 1 12 12.5 0.5 4.17 1.042 2 8.9 9.1 0.2 2.25 1.022 3 6.2 6.4 0.2 3.23 1.032 4 5.0 5.2 0.2 4.00 1.040 5 3.1 3.3 0.2 6.45 1.065

[0048] 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 resistivity in the present application is controlled within 0.5x10 -12 , wherein the minimum error is only 0.2x10 -12 , the maximum error is 0.5x10 -12 , and the average error reaches 0.26x10 -12 . More notably, the average relative error is only 4.02%, and the ratio of the predicted value to the measured value (Ypredicted / Ymeasured) is stable in the range of 1.022-1.065, showing an accuracy of up to 95%. These data fully prove that the prediction formula in the present application in calculating the 28d chloride ion diffusion of concrete not only has a high degree of correlation, but also shows excellent prediction accuracy and accuracy.

[0049] Finally, it should be emphasized that the above implementation cases are only intended to illustrate the technical solutions of the present application, but not to constitute a limitation on its application. Although we have described the present application in detail by referring to the preferred implementation cases, those skilled in the art should recognize 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 by the appended claims.

Claims

1. A calculation method for a chloride ion diffusion prediction model of three-dimensional porous graphene 3D printed concrete, characterized in that... Includes the following steps: Step 1: Establish a predictive model for chloride ion diffusion in 3D-printed concrete containing three-dimensional porous graphene, as shown in formula (1). D C1 (t): Chloride ion diffusion coefficient of concrete at age t days, in m³. 2 / s; D0: Reference chloride ion diffusion coefficient, a constant related to material properties, in units of m. 2 / s; D0 is an empirical coefficient related to data fitting, and its range is (39~46)×10. -12 α is an empirical index related to the effective water-cement ratio, ranging from 0.7 to 0.9; β is an empirical index related to the paste-binder ratio, ranging from 0.4 to 0.6; n is an empirical index related to the mass percentage of graphene in the cementitious material, ranging from 0.5 to 0.7; k G It is an empirical index related to data fitting, with a range of 18–21; k L This is an empirical index related to data fitting, with a range of 0.04 to 0.06; k S It is an empirical index related to data fitting, with a range of 0.02 to 0.04; k t It is an empirical index related to data fitting, and its range is 0.1 to 0.3; C eff Effective cementitious material dosage, in kg / m³ 3 ; W: Mixing water quantity, unit is kg / m³ 3 ; The volume ratio of slurry to aggregate; G: Amount of three-dimensional porous graphene used, in kg / m³ 3 ; B: Total amount of cementitious materials, in kg / m³ 3 ; t: Age, in days; t0: Reference age; L t 3D printing layer thickness, in mm; L0: Reference layer thickness, in mm, using the standard value; S: Printing speed, in mm / s; S0: Reference printing speed, in mm / s, using standard values; Step 2: Set the quasi-reference chloride ion diffusion coefficient D0 and the effective amount of cementitious material C. eff Water volume for mixing (W), paste-to-aggregate ratio Step 3: Set the amount of three-dimensional porous graphene (G), the total amount of cementitious material (B), the age (t), and the reference age (t0); 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 from Step 2, Step 3 and Step 4 into Formula (1) to obtain the prediction model for chloride ion diffusion in three-dimensional porous graphene 3D printed concrete.

2. The calculation method for a chloride ion diffusion prediction model of three-dimensional porous graphene 3D printed concrete according to claim 1, characterized in that, Effective cementitious material dosage C eff : C eff =C+k F F+k SF SF+k SL SL C: Cement usage, in kg / m³ 3 ; F, SF, and SL: These represent the dosage of fly ash, silica fume, and slag powder, respectively, in kg / m³. 3 ; k F k SF k SL : The activity coefficient of each mineral admixture.

3. The calculation method for a chloride ion diffusion prediction model of three-dimensional porous graphene 3D printed concrete according to claim 2, characterized in that, In step 1, the effective water-cement ratio ranges from 0.3 to 0.45; the mixing water dosage is 135 to 270 kg / m³. 3 The total amount of cementitious material is taken in the range of 450-600 kg / m³. 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 ranges from 30% to 50%; α F The value ranges from 10% to 20%; α SF The value ranges from 5% to 10%; α SL The value range is 20% to 40%; the amount of three-dimensional porous graphene G is determined as a percentage of the total amount of cementitious material; the paste-aggregate ratio is set. The value range is 0.35 to 0.

45.

4. The calculation method for a chloride ion diffusion prediction model of three-dimensional porous graphene 3D printed concrete according to claim 1, characterized in that, In step 1, the layer thickness L t The thickness is 5-15mm, the printing speed S is 30-100mm / s, and the reference values ​​L0 and S0 are: L0 = 10mm, S0 = 50mm / s.

5. The calculation method for a chloride ion diffusion prediction model of three-dimensional porous graphene 3D printed concrete according to claim 1, characterized in that, Material mixing: (1) Dry mixing: Mix the graphene-containing cementitious materials and aggregates evenly; (2) Wet mixing: Add water and additives, stir evenly, and ensure that the graphene is fully dispersed.

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