Prediction model for compressive strength of three-dimensional porous graphene 3D printing concrete

Abstract

The invention belongs to the field of civil engineering 3D printing concrete compressive strength prediction, and relates to a model for predicting compressive strength 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 age, the slurry-to-bone ratio and 3D printing process parameters (such as the layer thickness, the printing speed and the nozzle diameter), and the method has important significance on accurate prediction of the compressive strength of the three-dimensional porous graphene 3D printing concrete.

Description

Technical Field

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

[0002] As a new type of nanomaterial, 3D-Porous Graphene (3D-PG) is widely used in the research of reinforced concrete due to its excellent mechanical properties and nanoporous structure. Combined with 3D printing technology, 3D printed concrete can realize the construction of complex structures and has the advantages of saving materials and improving efficiency. However, the compressive strength of 3D printed concrete is affected by many factors, such as 3D porous graphene content, concrete mix ratio, age, 3D printing process parameters, etc. In order to accurately predict the compressive strength of 3D printed concrete with different 3D porous graphene contents and different ages, it is of great significance to establish a complex prediction formula that comprehensively considers these factors. 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 predicting the compressive strength of 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 compressive strength of 3D printed concrete containing three-dimensional porous graphene, as shown in formula (2):

[0006]

[0007] f c (t): compressive strength at age t days (MPa);

[0008] k is related to the empirical coefficient, and its range is: 26 ~ 33; α is related to the empirical index of the effective glue ratio, and its range is:

[0009] 0.8~1.0; β is an empirical index related to the pulp-bone ratio, ranging from 0.3 to 0.6; γ is an empirical index related to the reference age (28 days), ranging from 0.15 to 0.35; 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 relevant empirical coefficient ranges from 19 to 22; k L related

[0010] The range of the empirical index is: 0.05~0.15; kS The range of the relevant experience index is: 0.04~0.06;

[0011] C eff : Effective amount of cementitious material (kg / m3);

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

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

[0014] G: three-dimensional porous graphene dosage (kg / m3);

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

[0016] t: age (days), such as 7 days, 28 days, 56 days, etc.;

[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 effective cementitious material dosage C eff , mixing water volume W, volume ratio of paste to aggregate

[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 compressive strength of three-dimensional porous graphene 3D printed concrete.

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

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

[0028] C: cement consumption (kg / m 3 );

[0029] F, SF, SL: the amount of fly ash, silica fume, and slag powder (kg / m 3 );

[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 the compressive strength of the three-dimensional porous graphene 3D printed concrete has an effective water-binder ratio range of 0.3 to 0.45 and a mixing water dosage range of 150 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%; pulp-bone ratio The value range is 0.35~0.45.

[0032] As a further solution of the present invention, the prediction model of the compressive strength 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 / s.

[0033] As a further solution of the present invention, a prediction model for the compressive strength of a three-dimensional porous graphene 3D printed concrete is provided, wherein the materials are mixed: (1) dry mixing: the cementitious material (including the three-dimensional porous graphene) and the aggregate are mixed evenly; and (2) wet mixing: mixing water and admixtures are added, and the mixture is stirred until uniform 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, age, mortar-brick ratio, and 3D printing process parameters to quickly predict the compressive strength 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 following will be combined with the specific embodiments of the present invention to elaborate the technical solutions therein in detail and comprehensively. It should be noted that the embodiments described here are only some examples of the present invention, rather than exhaustive of all possible implementation methods. Based on these embodiments, all other embodiments that can be derived by those skilled in the art without the need for creative work should be deemed to fall within the scope of protection 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 natural 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, wherein the fine aggregate is medium sand, and the coarse aggregate is crushed stone, and the grade is matched.

[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 predicted value (in MPa) is calculated and then compared with the actual measured value (in MPa) at 28 days.

[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 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

[0048] After in-depth analysis of the data in Table 2, we found that the prediction formula in the present invention is within 1.0MPa of the measured value when predicting concrete strength, with the minimum error being only 0.2MPa, the maximum error being 1.0MPa, and the average error reaching 0.6MPa. It is worth mentioning that the average relative error is only 1.1%, and the ratio of the predicted value to the measured value (Y predicted / Y measured) is stable in the range of 0.985 to 0.992, showing an accuracy rate of more than 95%. These data fully prove that the prediction formula in the present invention is not only highly correlated when calculating the 28d compressive strength of concrete, but also has excellent prediction accuracy and precision.

[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 the compressive strength of three-dimensional porous graphene 3D printed concrete, characterized in that The following steps are involved: Step 1: Establish a prediction model for the compressive strength of 3D printed concrete containing three-dimensional porous graphene, as shown in formula (1): f c (t): compressive strength at age t days (MPa); k is related to the empirical coefficient, and its range is: 26 ~ 33; α is related to the empirical index of the effective glue ratio, and its range is: 0.8~1.0; β is an empirical index related to the pulp-bone ratio, ranging from 0.3 to 0.6; γ is an empirical index related to the reference age (28 days), ranging from 0.15 to 0.35; 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 relevant empirical coefficient ranges from 19 to 22; k L The range of relevant experience index is: 0.05~0.15; k S The range of the relevant experience index is: 0.04~0.06; C eff : Effective cementitious material dosage (kg / m3); W: mixing water consumption (kg / m3); VP / VA: volume ratio of paste to aggregate (paste-to-aggregate ratio); G: three-dimensional porous graphene dosage (kg / m3); B: total amount of cementitious materials (kg / m3); t: age (days), such as 7 days, 28 days, 56 days, etc.; 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 effective cementitious material dosage C eff and 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 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 predict the compressive strength of three-dimensional porous graphene 3D printed concrete.

2. The prediction of compressive strength of a 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 / m 3 ); F, SF, SL: the amount of fly ash, silica ash, and slag powder (kg / m 3 ); k F , k SF , k SL :Activity coefficient of each mineral admixture.

3. A prediction model for compressive strength of 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 150 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%; pulp-bone ratio The value range is 0.35~0.

45.

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

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