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

Abstract

The invention belongs to the field of compressive strength prediction of 3D printing concrete in civil engineering, and relates to a model for predicting compressive strength of recycled fine aggregate three-dimensional porous graphene 3D printing concrete. Through a complex prediction formula of factors such as the content of the three-dimensional porous graphene, the replacement rate of the recycled fine aggregate, the age, the effective glue ratio, 3D printing process parameters and the like, the method has important significance for accurately predicting the compressive strength of the recycled fine aggregate three-dimensional porous graphene 3D printing concrete.

Description

Technical Field

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

[0002] The use of recycled fine aggregate (RFA) can effectively reduce construction waste and is of great significance to environmental protection and resource recycling. However, due to the differences in the physical and chemical properties of recycled fine aggregate and natural fine aggregate, such as high water absorption, low strength, and many impurities, the mechanical properties of recycled fine aggregate concrete are reduced. In order to improve the performance of recycled fine aggregate concrete, three-dimensional porous graphene (3D-Porous Graphene, 3D-PG), as a new type of nanomaterial, has been used in the study of enhancing concrete performance due to its excellent mechanical properties and nanostructure.

[0003] Combined with 3D printing technology, efficient construction of complex structures can be achieved, and the utilization efficiency and performance of materials can be improved. Therefore, establishing a complex prediction formula that comprehensively considers the content of three-dimensional porous graphene, the content of recycled fine aggregate, age, and 3D printing process parameters is of great significance for accurately predicting the compressive strength of 3D printed recycled fine aggregate 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 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.

[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 the compressive strength of 3D printed concrete containing recycled fine aggregate and three-dimensional porous graphene, as shown in formula (2):

[0007]

[0008] f c (t): compressive strength at age t days (MPa);

[0009] k is related to the empirical coefficient, and its range is: 11 ~ 23; α is related to the empirical index of the effective glue ratio, and its range is:

[0010] 0.8~0.9; β is an empirical index related to the replacement rate of recycled fine aggregate, ranging from 0.3 to 0.6; γ is an empirical index related to the age ratio, 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 R The range of relevant empirical coefficients is: 0.3~0.5; k G The relevant empirical coefficient ranges from 13 to 16; k L The range of relevant experience index is: 0.03~0.06; k S The experience index

[0011] Range: 0.02~0.04;

[0012] C eff : Effective amount of cementitious material (kg / m3);

[0013] W eff : Effective mixing water (kg / m3), taking into account the water absorption rate of recycled fine aggregate;

[0014] R: amount of recycled fine aggregate (kg / m3);

[0015] A: Total amount of fine aggregate (kg / m3), i.e. the sum of natural fine aggregate and recycled fine aggregate;

[0016] G: three-dimensional porous graphene dosage (kg / m3);

[0017] B: total amount of cementitious materials (kg / m3);

[0018] t: age (days);

[0019] t 0 : Reference age, generally 28 days;

[0020] L t :3D printing layer thickness (mm);

[0021] L 0 : Reference layer thickness (mm), take the standard value, such as 10mm;

[0022] S: printing speed (mm / s);

[0023] S 0 : Reference printing speed (mm / s), take the standard value, such as 50mm / s;

[0024] Step 2: Set the effective cementitious material dosage C eff And mixing water W eff , Recycled fine aggregate replacement rate

[0025] 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 .

[0026] Step 4: Set the 3D printing layer thickness L t , reference layer thickness L 0 , Print speed S, Reference print speed S 0 .

[0027] Step 5: Substitute the relevant parameters in steps 2, 3 and 4 into formula (2) to predict the compressive strength of recycled fine aggregate three-dimensional porous graphene 3D printed concrete.

[0028] As a further solution of the present invention, the prediction model of the compressive strength of the recycled fine aggregate three-dimensional porous graphene 3D printed concrete, the effective cementitious material dosage C eff :

[0029] C eff =C+k F F+k SF SF+k SL SL

[0030] C: cement consumption (kg / m 3 );

[0031] F, SF, SL: the amount of fly ash, silica fume, and slag powder (kg / m 3 );

[0032] k F , k SF , k SL : Activity coefficient of each mineral admixture;

[0033] Effective mixing water consumption W eff :

[0034] W eff =W+R×W abs

[0035] W: Mixing water consumption (kg / m 3 );

[0036] W abs :Water absorption rate of recycled fine aggregate (%).

[0037] As a further solution of the present invention, the prediction model of the compressive strength of the recycled fine aggregate 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 of 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 materials, such as 0.1%, 0.3%, 0.5%, 1.0%; the replacement rate of recycled fine aggregate The value range is 0, 25%, 50%, 75%, 100%.

[0038] As a further solution of the present invention, the prediction model of the compressive strength of the recycled fine aggregate three-dimensional porous graphene 3D printed concrete, 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.

[0039] As a further solution of the present invention, a prediction model for the compressive strength of a recycled fine aggregate three-dimensional porous graphene 3D printed concrete is provided, wherein the materials are mixed: (1) dry mixing: the cementitious material (including graphene), natural fine aggregate, and recycled fine aggregate are mixed evenly; and (2) wet mixing: effective mixing water and admixtures are added and stirred evenly to ensure that the graphene and recycled fine aggregate are fully dispersed and wetted.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] (1) The present invention utilizes factors such as three-dimensional porous graphene content, recycled fine aggregate replacement rate, age, effective water-binder ratio, and 3D printing process parameters to quickly predict the compressive strength of three-dimensional porous graphene 3D printed concrete.

[0042] (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

[0043] 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.

[0044] In this specific embodiment, the raw materials for producing the regenerated three-dimensional porous graphene 3D printing concrete include cement, fly ash, slag, silica fume, three-dimensional porous graphene, recycled fine 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. The cement in the above raw materials is ordinary silicate cement, and the fine aggregate is medium sand, with a grade matching standard.

[0045] Five groups of recycled fine aggregate three-dimensional porous graphene 3D printing concrete tests and verifications were carried out. The concrete raw material mix ratio is shown in Table 1:

[0046] Table 1

[0047]

[0048] The unit of each raw material in Table 1 is kg, among which the cement grade is ordinary Portland cement with PO 42.5.

[0049] By formula:

[0050]

[0051] The predicted value (in MPa) is calculated and then compared with the actual measured value (in MPa) at 28 days.

[0052] As shown in Table 2:

[0053] serial number Measured value Predicted value Absolute error Relative error <![CDATA[Y 预测 / AND 实测 ]]> 1 33.7 33.5 0.2 0.59 0.994 2 32.7 32.2 0.5 1.53 0.985 3 32.8 32.6 0.2 0.61 0.994 4 32.7 32.2 0.5 1.53 0.985 5 33.8 33.6 0.2 0.59 0.994

[0054] After in-depth analysis of the data in Table 2, we found that the prediction formula in the present invention is within 0.5MPa when predicting concrete strength, with the error between the actual value and the actual value being controlled within 0.5MPa, of which the minimum error is only 0.2MPa, the maximum error is 0.5MPa, and the average error reaches 0.32MPa. It is worth mentioning that the average relative error is only 0.97%, and the ratio of the predicted value to the actual value (Y predicted / Y measured) is stable in the range of 0.985 to 0.994, 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.

[0055] 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 recycled fine aggregate 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 recycled fine aggregate and 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: 11 ~ 23; α is related to the empirical index of the effective glue ratio, and its range is: 0.8~0.9; β is an empirical index related to the replacement rate of recycled fine aggregate, ranging from 0.3 to 0.6; γ is an empirical index related to the age ratio, 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 R The range of relevant empirical coefficients is: 0.3~0.5; k G The relevant empirical coefficient ranges from 13 to 16; k L The range of relevant experience index is: 0.03~0.06; k S The range of the relevant experience index is: 0.02~0.04; C eff : Effective amount of cementitious material (kg / m3); W eff : Effective mixing water (kg / m3), taking into account the water absorption rate of recycled fine aggregate; R: amount of recycled fine aggregate (kg / m3); A: Total amount of fine aggregate (kg / m3), i.e. the sum of natural fine aggregate and recycled fine aggregate; G: three-dimensional porous graphene dosage (kg / m3); 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 effective cementitious material dosage C eff and effective mixing water volume W eff , Recycled fine aggregate replacement rate 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 recycled fine aggregate three-dimensional porous graphene 3D printed concrete.

2. The prediction model for compressive strength of recycled fine aggregate 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 fume, and slag powder (kg / m 3 ); k F , k SF , k SL : Activity coefficient of each mineral admixture; Effective mixing water consumption W eff : IN eff =W+R×W abs W: Mixing water consumption (kg / m 3 ); W abs :Water absorption rate of recycled fine aggregate (%).

3. The prediction model for compressive strength of recycled fine aggregate 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 materials, such as 0.1%, 0.3%, 0.5%, 1.0%; the replacement rate of recycled fine aggregate Value range: 0, 25%, 50%, 75%, 100%.

4. The prediction model for compressive strength of recycled fine aggregate 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. The prediction model for compressive strength of recycled fine aggregate 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), natural fine aggregate and recycled fine aggregate evenly; (2) Wet mixing: Add effective mixing water and admixtures, stir evenly to ensure that the graphene and recycled fine aggregate are fully dispersed and wetted.

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