A method for calculating a compressive strength prediction model of three-dimensional porous graphene 3D printing concrete

Abstract

The application belongs to the field of civil engineering 3D printing concrete compressive strength prediction, and relates to a calculation method of a compressive strength 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, age, paste-aggregate ratio and 3D printing process parameters (such as layer thickness, printing speed, nozzle diameter and the like), and the formula is of great significance for accurately predicting the compressive strength of three-dimensional porous graphene 3D printing concrete.

Description

TECHNICAL FIELD

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

[0002] Three-dimensional porous graphene (3D-Porous Graphene, 3D-PG) as a new type of nanomaterial is widely used in the research of reinforced concrete due to its excellent mechanical properties and nano-porous structure. Combined with 3D printing technology, 3D printing concrete can realize the construction of complex structures and has the advantages of saving materials and improving efficiency. However, the compressive strength of 3D printing concrete is affected by many factors, such as three-dimensional porous graphene content, concrete mix proportion, age, 3D printing process parameters, etc. In order to accurately predict the compressive strength of 3D printing concrete with different three-dimensional porous graphene contents and different ages, it is of great significance to establish a complex prediction formula that comprehensively considers these factors. 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 predicting the compressive strength of three-dimensional porous graphene 3D printing concrete that simplifies the operation process, shortens the time consumption, and ensures that the prediction result has a small error and high accuracy compared with the actual measured value.

[0004] To solve the above technical problems, the application adopts the following technical solutions:

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

[0006]

[0007] f c (t): the compressive strength at t days, unit: MPa;

[0008] k is an empirical coefficient related to data fitting, ranging from 26 to 33; α is an empirical index related to the effective glue ratio, ranging from 0.8 to 1.0; β is an empirical index related to the slurry bone ratio, ranging from 0.3 to 0.6; γ is an empirical index related to the ratio of the compressive strength at t days to the 28-day reference compressive strength, ranging from 0.15 to 0.35;

[0009] n is an empirical index related to the mass percentage of graphene in cementitious materials, ranging from 0.5 to 0.7; k G is an empirical coefficient related to data fitting, ranging from 19 to 22; k L is an empirical index related to data fitting, ranging from 0.5 to 0.7

[0010] is the data fitting related empirical index, which ranges from 0.04 to 0.06; S

[0011] C eff : effective cementitious material amount, unit: kg / m 3 ;

[0012] W: mixing water amount, unit: kg / m 3 ;

[0013] VP / VA: volume ratio of paste to aggregate;

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

[0015] B: total cementitious material amount, unit: kg / m 3 ;

[0016] t: age, unit: day;

[0017] t0: reference age;

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

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

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

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

[0022] Step 2, setting the effective cementitious material amount C eff , the mixing water amount W, and the volume ratio of paste to aggregate

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

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

[0025] Step 5, substituting the related parameters in Step 2, Step 3, and Step 4 into Formula (2) to realize the prediction of the compressive strength of the three-dimensional porous graphene 3D printing concrete.

[0026] ​As a further scheme of the present application, the calculation method of the compressive strength prediction model of the three-dimensional porous graphene 3D printing 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: respectively, the dosage of fly ash, silica fume, slag powder, unit kg / m 3 ;

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

[0031] As a further scheme of the present application, the calculation method of the compressive strength prediction model of the three-dimensional porous graphene 3D printing concrete, the effective water-binder ratio value range is 0.3-0.45; the mixing water dosage value range is 150-270 kg / m 3 , the total amount of cementitious material value range is 450-600 kg / m 3 , the dosage of each cementitious material is calculated: C=alpha c * B, F=alpha F * B, SF=alpha SF * B, SL=alpha SL * B, alpha c The value range is 30%-50%; alpha F The value range is 10%-20%; alpha SF The value range is 5%-10%; alpha SL The value range is 20%-40%; the amount of three-dimensional porous graphene G is determined: determined in percentage of the total amount of cementitious material; the slurry bone ratio The value range is 0.35-0.45.

[0032] As a further scheme of the present application, the calculation method of the compressive strength prediction model of the three-dimensional porous graphene 3D printing concrete, the layer thickness L t 5-15 mm, the printing speed S is 30-100 mm / s, the reference value L0 and S0: L0=10 mm, S0=50 mm / s.

[0033] As a further scheme of the application, the material mixing of the calculation method of the compressive strength prediction model of the three-dimensional porous graphene 3D printing concrete is as follows: (1) dry mixing: uniformly mix the cementing material and aggregate including three-dimensional porous graphene; (2) wet mixing: add mixing water and admixture, and stir until uniform to ensure that the graphene is fully dispersed.

[0034] (1) The application can quickly predict the compressive strength of the three-dimensional porous graphene 3D printing concrete by using the three-dimensional porous graphene content, the concrete mix ratio, the age, the paste-aggregate ratio, and the 3D printing process parameters.

[0035] (2) The application reduces the repeated test time, accelerates the test progress, and significantly improves the accuracy and reliability of the prediction results. DETAILED DESCRIPTION

[0036] The technical solutions will be described in detail below with reference to specific embodiments of the application. It should be noted that the embodiments described herein are only a part of the examples of the application, rather than all possible embodiments. Based on these embodiments, all other embodiments that can be deduced by those skilled in the art without creative labor should be considered to fall within the protection scope of the application.

[0037] In the 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 natural fine aggregate. In addition, the three-dimensional porous graphene 3D printing concrete in the specific implementation of the application is not limited to the above raw materials. The cement in the above raw materials is ordinary Portland cement, the fine aggregate uses medium sand, the coarse aggregate uses gravel, and the gradation is qualified.

[0038] Five groups of three-dimensional porous graphene 3D printing concrete tests and verifications are carried out, and the concrete raw material mix ratio is as shown in Table 1:

[0039] Table 1

[0040]

[0041] The units of each raw material in Table 1 are kg, and the cement is P.O 42.5 ordinary Portland cement.

[0042] The predicted value (unit: MPa) is calculated by the formula:

[0043]

[0044] The predicted value (unit: MPa) is calculated by the formula:

[0045] Table 2 shows the comparison of the measured value (unit: MPa) at 28d.

[0046] No. Observed Predicted Absolute error Relative error Y 预测 / Y 实测 ]]> 1 25.1 24.9 0.2 0.80 0.992 2 33.3 32.8 0.5 1.50 0.985 3 59.2 58.7 0.5 0.84 0.992 4 67.4 66.6 0.8 1.19 0.988 5 84.4 83.4 1 1.18 0.988

[0047] Through the deep analysis of the data in Table 2, we found that the prediction formula in the present application in predicting the strength of concrete, the error between the measured value is controlled within 1.0MPa, wherein the minimum error is only 0.2MPa, the maximum error is 1.0MPa, the average error reaches 0.6MPa. More worth mentioning is that the average relative error is only 1.1%, and the ratio of the predicted value and the measured value (Ypredicted / Ymeasured) is stable in the range of 0.985-0.992, showing a high accuracy of up to 95%. These data fully prove that the prediction formula in the present application in calculating the 28d compressive strength of concrete, not only has a high degree of correlation, but also the prediction accuracy and accuracy are excellent.

[0048] 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, the 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 computing method of a compressive strength prediction model of three-dimensional porous graphene 3D-printed concrete, characterized in that The method comprises the following steps: Step 1, establishing a prediction model of the compressive strength of the three-dimensional porous graphene 3D printing concrete, as shown in formula (1) f c (t): compressive strength at age t days, in MPa; k is a data fitting related empirical coefficient, ranging from 26 to 33; alpha is an empirical index related to the effective glue ratio, ranging from 0.8 to 1.0; beta is an empirical index related to the slurry aggregate ratio, ranging from 0.3 to 0.6; gamma is an empirical index related to the ratio of the compressive strength at t days to the 28-day reference compressive strength, ranging from 0.15 to 0.35; 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 coefficient related to the data fitting, which ranges from 19 to 22; k L is an empirical index related to the data fitting, which ranges from 0.05 to 0.15; k S is an empirical index related to the data fitting, which ranges from 0.04 to 0.06; C eff : Effective gelling material amount, in kg / m 3 ; W: amount of water for mixing, unit: kg / m 3 ; VP / VA: volume ratio of slurry to aggregate; G: three-dimensional porous graphene amount, in kg / m 3 ; B: total amount of cementitious material, in kg / m 3 ; t: age, unit: day; t0: reference age; L t :3D printing layer thickness in mm; L0: reference layer thickness, unit: mm, taking a standard value; S: printing speed, unit: mm / s; S0: reference printing speed, unit: mm / s, taking a standard value; Step 2, Set the effective cementitious material amount C eff and the amount of mixing water W, the volume ratio of paste to aggregate: Step 3, setting the amount of three-dimensional porous graphene G, the total amount of cementitious materials B, the age t, and the reference age t0; 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) to predict the compressive strength of the three-dimensional porous graphene 3D printing concrete.

2. The calculation method for the compressive strength prediction model of three-dimensional porous graphene 3D printed concrete according to claim 1, characterized in that, Effective gelling material amount C eff : C eff = C + k F F + k SF SF + k SL SL C: cement consumption in kg / m 3 ; F, SF, SL: the amount of fly ash, silica ash, and slag powder, respectively, in kg / m 3 ; k F , k SF , k SL : Activity coefficient of each mineral admixture.

3. The method according to 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 amount of mixing water is in the range of 150-270 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, S = a SF × B, SF = 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 slurry-bone ratio is in the range of 0.35-0.

45.

4. The method according to claim 1, wherein, 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 according to claim 1, wherein, Material mixing: (1) dry mixing: mix the cementitious materials including three-dimensional porous graphene and the aggregate uniformly; (2) wet mixing: add mixing water and admixtures, stir until uniform, and ensure that the graphene is fully dispersed.

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