A calculation method of a compressive strength prediction model of recycled fine aggregate 3D printing concrete

Abstract

The application belongs to the field of civil engineering recycled fine aggregate 3D printing concrete compressive strength prediction, and relates to a calculation method of a recycled fine aggregate 3D printing concrete compressive strength prediction model. A complex prediction formula is established by means of effective mixing water amount, effective cementitious material, recycled fine aggregate replacement rate, attached old mortar content, three-dimensional porous graphene content, cementitious material total amount, carbon black amount, 3D printing layer thickness, reference layer thickness, printing speed, reference printing speed, age, and reference age, which is of great significance for accurately predicting the compressive strength of recycled fine aggregate 3D printing concrete.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of strength prediction methods for 3D printed concrete, and particularly relates to a calculation method of a compressive strength prediction model for recycled fine aggregate 3D printed concrete. BACKGROUND

[0002] Due to the differences in physical properties such as high water absorption, high porosity, and high content of attached old mortar between recycled fine aggregate and natural fine aggregate, recycled fine aggregate concrete often has lower compressive strength than ordinary concrete. However, by ingeniously introducing modified three-dimensional porous graphene and carbon black nanomaterials, the internal microstructure of recycled fine aggregate 3D printed concrete is significantly optimized. These advanced materials can efficiently fill voids, effectively reduce porosity and refine pore size, thereby greatly improving the density of the cement matrix and enhancing the strength of 3D printed concrete. This innovative strategy not only successfully breaks through the bottleneck of insufficient strength of recycled fine aggregate 3D printed concrete, but also opens up a new path to solve this problem. The breakthrough of this technology is expected to accelerate the popularization of recycled fine aggregate 3D printed concrete in the commercial field and inject strong impetus for the vigorous development of the industry. From an economic perspective, the application of recycled fine aggregate 3D printed concrete will greatly promote the efficient use of resources and effective cost control; from an environmental perspective, it helps to reduce the burden of construction waste on the ecological environment; from the perspective of social development, the popularization and application of this technology will lead the construction industry towards a more green and sustainable future. Therefore, the widespread application of this technology will undoubtedly have far-reaching and immeasurable positive impacts.

[0003] Recycled fine aggregate 3D printed concrete (RFA-3DPC) is a new type of building material that utilizes construction waste and combines 3D printing technology. Due to the differences in physical properties between recycled fine aggregate and natural fine aggregate, such as high water absorption, high porosity, and high content of attached old mortar, the compressive strength of recycled fine aggregate concrete is usually lower than that of ordinary concrete. To improve the mechanical properties of recycled fine aggregate concrete, three-dimensional porous graphene (3D-PG) and carbon black (CB) nanomaterials can be added to enhance the microstructure of the concrete and improve its compressive strength. Therefore, it is important to establish a complex prediction formula that considers the content of three-dimensional porous graphene, carbon black, recycled fine aggregate, attached old mortar, age, and 3D printing process parameters to accurately predict the compressive strength of recycled fine aggregate 3D printed concrete. SUMMARY

[0004] In view of the limitations of the prior art, the core problem that the present application aims to solve is to develop a new method for predicting the compressive strength of recycled fine aggregate 3D printed concrete, which simplifies the operation process, shortens the time consumption, and ensures that the prediction result has a small error and high accuracy compared to the actual measured value.

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

[0006] Step 1, establish a prediction model for the compressive strength of recycled fine aggregate 3D printed concrete, as shown in formula (2)

[0007]

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

[0009] k is an empirical coefficient related to data fitting, with a range of data fitting: 13-26; alpha is an empirical index related to the effective glue ratio, with a range of: 0.8-0.9; beta is an empirical index related to the replacement rate of recycled fine aggregate, with a range of: 0.3-0.6; gamma is an empirical index related to the content of attached old mortar, with a range of: 0.2-0.4; n is an empirical index related to the mass percentage of graphene in cementitious materials, with a range of: 0.5-0.7; m is an empirical index related to the mass percentage of carbon black in cementitious materials, with a range of: 0.4-0.6; lambda is an empirical index related to the age ratio, with a range of 0.23-0.26; k R is an empirical coefficient related to data fitting, with a range of: 0.2-0.6; k M is an empirical coefficient related to data fitting, with a range of: 0.23-0.26; k G is an empirical coefficient related to data fitting, with a range of: 13-16; k CB is an empirical coefficient related to data fitting, with a range of: 8-11; k L is an empirical index related to data fitting, with a range of: 0.03-0.06; k S is an empirical index related to data fitting, with a range of: 0.01-0.04;

[0010] C eff : effective cementitious material dosage, unit: kg / m 3 ;

[0011] W eff : effective mixing water, unit: kg / m 3 ;

[0012] R: recycled fine aggregate, unit: kg / m 3 ;

[0013] A: total amount of fine aggregate, unit: kg / m 3 , i.e. the sum of natural fine aggregate and recycled fine aggregate;

[0014] M: content of attached old mortar;

[0015] G: amount of three-dimensional porous graphene, unit: kg / m 3 ;

[0016] CB: amount of carbon black, unit: kg / m 3 ;

[0017] B: total amount of cementitious materials, unit: kg / m 3 ;

[0018] t: age, unit: days;

[0019] t0: reference age;

[0020] L t : 3D printing layer thickness, unit: mm;

[0021] L0: reference layer thickness, unit: mm, taking a standard value;

[0022] S: printing speed, unit: mm / s;

[0023] S0: reference printing speed, unit: mm / s, taking a standard value;

[0024] Step 2, set the effective cementitious material amount C eff and the effective mixing water amount W eff , the recycled fine aggregate amount R, the total fine aggregate amount A and the old mortar content M.

[0025] Step 3, set the amount of three-dimensional porous graphene G, the amount of carbon black CB, the total amount of cementitious materials B, and the age t.

[0026] Step 4, set the reference age t0, the 3D printing layer thickness L t , the reference layer thickness L0, the printing speed S, and the reference printing speed S0.

[0027] Step 5, substitute the relevant parameters in Step 2, Step 3 and Step 4 into formula (1) to realize the prediction of the compressive strength of recycled fine aggregate 3D printing concrete.

[0028] As a further scheme of the application, the calculation method of the compressive strength prediction model of the recycled fine aggregate 3D printing concrete, the effective cementitious material amount C eff :

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

[0030] C: cement dosage, unit: kg / m 3 ;

[0031] F, SF, SL: respectively, fly ash, silica fume, slag powder dosage, unit: kg / m 3 ;

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

[0033] Effective mixing water dosage W eff :

[0034] W eff = W + R x W abs

[0035] W: mixing water, unit: kg / m 3 ;

[0036] W abs : water absorption rate of recycled fine aggregate.

[0037] As a further scheme of the application, the effective water-binder ratio value range is 0.3-0.45; the mixing water dosage value range is 135-270 kg / m 3 , the total amount of cementitious material value range is 450-600 kg / m 3 , the amount of each cementitious material is calculated: C = a c x B, F = a F x B, SF = a SF x B, SL = a SL x B, a c value range 30%-50%; a F value range 10%-20%; a SF value range 5%-10%; a SL value range 20%-40%; determine the amount of three-dimensional porous graphene G, determined in percentage of the total amount of cementitious material; determine the amount of carbon black CB, determined in percentage of the total amount of cementitious material,; Set the fine aggregate replacement rate 0%, 25%, 50%, 75%, 100%; the mass percentage M of the old mortar attached in the recycled fine aggregate: 0, 10%, 15%, 20%, 25%.

[0038] As a further scheme of the present application, the calculation method of the compressive strength prediction model of the recycled fine aggregate 3D printing concrete, the layer thickness L t 5-15mm, the printing speed S is 30-100mm / s, the reference value L0 and S0: L0=10mm, S0=50mm / s.

[0039] As a further scheme of the present application, the calculation method of the compressive strength prediction model of the recycled fine aggregate 3D printing concrete, the material mixing: (1) dry mixing: mixing the cementing material containing graphene, fine aggregate and carbon black uniformly; (2) wet mixing: adding effective mixing water and admixture, stirring uniformly, and ensuring that the nanomaterial is fully dispersed.

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

[0041] (1) The present application can quickly predict the compressive strength of the recycled fine aggregate 3D printing concrete by using the three-dimensional porous graphene content, carbon black content, recycled fine aggregate content, attached old mortar content, age and 3D printing process parameters.

[0042] (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

[0043] The technical solutions will be described in detail below with reference to the specific embodiments of the present application. It should be noted that the embodiments described herein are only a part of the examples of the present application, but not 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 scope of protection of the present application.

[0044] In the present embodiment, the production raw materials of the recycled fine aggregate 3D printing concrete include cement, fly ash, slag, silica fume, three-dimensional porous graphene, fine aggregate and natural fine aggregate, and in addition, the recycled fine aggregate 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, the fine aggregate uses medium sand, the recycled fine aggregate is mixed with the natural fine aggregate, and the gradation is qualified.

[0045] Five groups of recycled fine aggregate 3D printing concrete tests and verifications are carried out, and the concrete raw material mixing ratio is as shown in Table 1:

[0046] Table 1

[0047]

[0048] The unit of each raw material in Table 1 is kg, wherein the cement is ordinary Portland cement with a mark of P.O 42.5.

[0049] By publicizing:

[0050]

[0051] The calculated predicted value (unit: MPa) is compared with the measured value (unit: MPa) at 28d, and the comparison is as follows:

[0052] As shown in Table 2

[0053] No. Observed Predicted Absolute error Relative error Y 预测 / Y 实测 ]]> 1 40 39.5 0.5 1.25 0.988 2 44.1 43.6 0.5 1.13 0.989 3 47.8 47.6 0.2 0.42 0.996 4 49.9 49.4 0.5 1.00 0.990 5 56.6 56.1 0.5 0.88 0.991

[0054] Through in-depth analysis of the data in Table 2, it is found that the error between the predicted value and the measured value in the prediction formula in the application is controlled within 0.5 MPa when predicting the strength of concrete, wherein the minimum error is only 0.2 MPa, the maximum error is 0.5 MPa, and the average error reaches 0.44 MPa. More noteworthy is that the average relative error is only 0.94%, and the ratio of the predicted value to the measured value (Ypredicted / Ymeasured) is stable in the range of 0.983-0.994, showing an accuracy of up to 95% or more. These data fully prove that the prediction formula in the application not only has a high degree of correlation when calculating the 28d compressive strength of concrete, but also shows excellent prediction precision and accuracy.

[0055] Finally, it needs to be emphasized that the above implementation cases are only intended to illustrate the technical solutions of the application, but not constitute a limitation on its application. Although we have described the application in detail by referring to the preferred implementation cases, 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 application defined in the appended claims.

Claims

1. A calculation method for a compressive strength prediction model of 3D-printed concrete using recycled fine aggregate, characterized in that... Includes the following steps: Step 1: Establish a predictive model for the compressive strength of 3D-printed concrete containing recycled fine aggregate, as shown in formula (1). f c (t): Compressive strength at age t days, in MPa; k is an empirical coefficient related to data fitting, ranging from 13 to 26; α is an empirical index related to the effective glue ratio, ranging from 0.8 to 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 content of attached old mortar, ranging from 0.2 to 0.4; n is an empirical index related to the mass percentage of graphene in the cementitious material, ranging from 0.5 to 0.7; m is an empirical index related to the mass percentage of carbon black in the cementitious material, ranging from 0.4 to 0.6; λ is an empirical index related to the age ratio, ranging from 0.23 to 0.26; k R These are empirical coefficients related to data fitting, ranging from 0.2 to 0.

6. k M These are empirical coefficients related to data fitting, ranging from 0.23 to 0.26; k G These are empirical coefficients related to data fitting, ranging from 13 to 16; k CB These are empirical coefficients related to data fitting, ranging from 8 to 11; k L It is an empirical index related to data fitting, with a range of 0.03 to 0.06; k S It is an empirical index related to data fitting, and its range is: 0.01～0.04； C eff Effective cementitious material dosage, in kg / m³ 3 ; W eff Effective mixing water quantity, in kg / m³ 3 ; R: Recycled fine aggregate dosage, in kg / m³ 3 ; A: Total fine aggregate, in kg / m³ 3 That is, the sum of natural fine aggregate and recycled fine aggregate; M: Content of attached old mortar; G: Amount of three-dimensional porous graphene used, in kg / m³ 3 ; CB: Carbon black dosage, 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 effective amount of cementitious material C eff and effective mixing water volume W eff , the amount of recycled fine aggregate R, the total amount of fine aggregate A, and the content of old mortar M; Step 3: Set the amount of three-dimensional porous graphene (G), carbon black (CB), total amount of cementitious materials (B), and age (t); Step 4: Set the reference age t0 and 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 predict the compressive strength of 3D printed concrete made from recycled fine aggregate.

2. The calculation method for the compressive strength prediction model of 3D printed concrete using recycled fine aggregate 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 Activity coefficients of each mineral admixture; Effective mixing water dosage W eff : IN eff =W+R×W abs W: Mixing water quantity, unit is kg / m³ 3 ; W abs Water absorption rate of recycled fine aggregate.

3. The calculation method for the compressive strength prediction model of 3D printed concrete using recycled fine aggregate 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 ranges from 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%; determine the amount of three-dimensional porous graphene G: determined as a percentage of the total amount of cementitious materials; determine the amount of carbon black CB: determined as a percentage of the total amount of cementitious materials; set the fine aggregate replacement rate. 0%, 25%, 50%, 75%, 100%; the mass percentage of old mortar adhering to recycled coarse aggregate, M: 0%, 10%, 15%, 20%, 25%.

4. The calculation method for the compressive strength prediction model of 3D printed concrete using recycled fine aggregate 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 the compressive strength prediction model of 3D printed concrete using recycled fine aggregate according to claim 1, characterized in that, Material mixing: (1) Dry mixing: Mix the graphene-containing cementitious material, fine aggregate, and carbon black evenly; (2) Wet mixing: Add effective mixing water and additives, stir evenly, and ensure that the nanomaterials are fully dispersed.

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