Prediction model for chloride ion diffusion of three-dimensional porous graphene 3D printing concrete

Abstract

The invention belongs to the field of chloride ion diffusion prediction of civil engineering 3D printing concrete, and relates to a chloride ion diffusion prediction model of three-dimensional porous graphene 3D printing concrete. 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 and the 3D printing process parameters, and the method has important significance on accurate prediction of chloride ion diffusion of the three-dimensional porous graphene 3D printing concrete.

Description

Technical Field

[0001] The present invention relates to a chloride ion diffusion prediction method for 3D printed concrete, and in particular to a chloride ion diffusion prediction model for three-dimensional porous graphene 3D printed concrete. Background Art

[0002] Chloride ion corrosion 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 will cause corrosion of the steel bars, which in turn causes the deterioration of structural performance. The chloride ion diffusion coefficient is an important parameter to measure the ability of concrete to resist chloride ion corrosion.

[0003] As a new type of nanomaterial, 3D-Porous Graphene (3D-PG) has been used in the study of reinforced concrete due to its excellent 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, it can realize the construction of complex structures and improve construction efficiency. Therefore, it is of great significance to establish a complex prediction formula that comprehensively considers factors such as 3D porous graphene content, concrete mix ratio, age, and 3D printing process parameters for evaluating the durability of 3D printed concrete. Summary of the invention

[0004] 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 predicting chloride ion diffusion in 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.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

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

[0007]

[0008] D C1 (t): Chloride ion diffusion coefficient of concrete at age t days (m2 / s);

[0009] D 0 : Base chloride ion diffusion coefficient, a constant related to material properties (m 2 / s);

[0010] D 0 The range of relevant empirical coefficients is: 39~46×10 -12α is an empirical index related to the effective water-binder ratio, ranging from 0.7 to 0.9; β is an empirical index related to the pulp-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 The experience index ranges from 18 to 21; k L The range of relevant experience index is: 0.04~0.06; k S The range of relevant experience index is: 0.02~0.04; k t The range of the relevant experience index is: 0.1~0.3;

[0011] C eff :Effective cementitious material dosage (kg / m 3 );

[0012] W: mixing water consumption (kg / m3);

[0013] The volume ratio of paste to aggregate (paste-to-aggregate ratio);

[0014] G: Amount of three-dimensional porous graphene (kg / m 3 );

[0015] B: total amount of cementitious materials (kg / m3);

[0016] t: age (days);

[0017] t 0 : Reference age, generally 28 days;

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

[0019] L 0 : Reference layer thickness (mm), take the standard value, such as 10mm;

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

[0021] S 0 : Reference printing speed (mm / s), take the standard value, such as 50mm / s;

[0022] Step 2: Set the quasi-reference chloride ion diffusion coefficient D 0 , effective cementitious material dosage C eff , mixing water consumption W, paste-bone ratio

[0023] Step 3: Set the amount of three-dimensional porous graphene G, the total amount of gelling material B, the age t, and the reference age t 0 .

[0024] Step 4: Set the 3D printing layer thickness L t , reference layer thickness L 0 , Print speed S, Reference print speed S 0 .

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

[0026] As a further solution of the present invention, the prediction model of chloride ion diffusion 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 consumption (kg / m3);

[0029] F, SF, SL: the amount of fly ash, silica fume, and slag powder respectively (kg / m3);

[0030] k F , k SF , k SL :Activity coefficient of each mineral admixture.

[0031] As a further solution of the present invention, the prediction model of chloride ion diffusion of a three-dimensional porous graphene 3D printed concrete has an effective water-cement ratio ranging from 0.3 to 0.45; the mixing water dosage is 135 to 270 kg / m3, the total amount of cementitious materials is in the range of 450 to 600 kg / m3, and the dosage of each cementitious material is calculated as follows: 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 material, such as 0.1%, 0.3%, 0.5%, 1.0%; set the pulp-bone ratio range: 0.35 to 0.45.

[0032] As a further solution of the present invention, in step 1, the three-dimensional porous graphene (G): a high-quality, well-dispersed graphene material; a 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.

[0033] As a further solution of the present invention, the prediction model of chloride ion diffusion of a three-dimensional porous graphene 3D printed concrete comprises the following material mixing: (1) dry mixing: mixing the cementitious material (including graphene) and the aggregate evenly; and (2) wet mixing: adding water and admixtures and stirring evenly to ensure that the graphene is fully dispersed.

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

[0035] (1) The present invention utilizes the three-dimensional porous graphene content, concrete mix ratio, mortar-to-brick ratio, age and 3D printing process parameters to rapidly predict the chloride ion diffusion of three-dimensional porous graphene 3D printed concrete.

[0036] (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

[0037] 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.

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

[0039] 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:

[0040] Table 1

[0041]

[0042] The unit of each raw material in Table 1 is kg, among which the cement grade is ordinary Portland cement with PO 42.5.

[0043] By formula:

[0044]

[0045] The calculated prediction value (unit × 10 -12 ), according to the measured value of 28d (unit × 10 -12 ) for comparison, for example

[0046] As shown in Table 2:

[0047] serial number Measured value Predicted value Absolute error Relative error <![CDATA[Y 预测 / AND 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] After in-depth analysis of the data in Table 2, we found that the error between the prediction formula in the present invention and the measured value when predicting the concrete resistivity is controlled within 0.5×10 -12 The minimum error is only 0.2×10 -12 , the maximum error is 0.5×10 -12 , the average error reaches 0.26×10 -12 . It is worth mentioning that the average relative error is only 4.02%, and the ratio of the predicted value to the measured value (Y predicted / Y measured) is stable in the range of 1.022 to 1.065, showing an accuracy of more than 95%. These data fully prove that the prediction formula in the present invention is not only highly relevant when calculating the chloride ion diffusion of concrete at 28 days, but also has excellent prediction precision and accuracy.

[0049] 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 chloride ion diffusion in three-dimensional porous graphene 3D printed concrete, characterized in that The following steps are involved: Step 1: Establish a prediction 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 (m 2 / s); D0: Base chloride ion diffusion coefficient, a constant related to material properties (m 2 / s); The range of D0 empirical coefficient is: 39~46×10 -12 α is an empirical index related to the effective water-binder ratio, ranging from 0.7 to 0.9; β is an empirical index related to the pulp-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 The experience index ranges from 18 to 21; k L The range of relevant experience index is: 0.04~0.06; k S The range of relevant experience index is: 0.02~0.04; k t The range of relevant experience index is: 0.1~0.3; C eff :Effective cementitious material dosage (kg / m 3 ); W: mixing water consumption (kg / m3); The volume ratio of paste to aggregate (paste-to-aggregate ratio); G: Amount of three-dimensional porous graphene (kg / m 3 ); B: total amount of cementitious materials (kg / m3); t: age (days); t0: reference age, generally 28 days; 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 quasi-reference chloride ion diffusion coefficient D0 and the effective gelling material dosage C eff , mixing water consumption W, paste-bone ratio Step 3, setting the amount of three-dimensional porous graphene G, the total amount of gelling 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 in steps 2, 3 and 4 into formula (1) to obtain a prediction model for chloride ion diffusion in three-dimensional porous graphene 3D printed concrete.

2. A prediction model for chloride ion diffusion in 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 consumption (kg / m3); F, SF, SL: the amount of fly ash, silica fume, and slag powder (kg / m 3 ); k F , k SF , k SL :Activity coefficient of each mineral admixture.

3. A prediction model for chloride ion diffusion in 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 dosage 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 material, such as 0.1%, 0.3%, 0.5%, 1.0%; set the pulp-bone ratio The value range is 0.35~0.

45.

4. A prediction model for chloride ion diffusion in 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 chloride ion diffusion in 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.

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