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

Abstract

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

Description

TECHNICAL FIELD

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

[0002] With the increasing standards of environmental protection and sustainable resource utilization in the construction industry, recycled coarse aggregate (RCA) as an economical and environmentally friendly alternative material is increasingly widely used in the field of concrete. However, the old mortar attached to the surface of the recycled aggregate may have an adverse effect on the mechanical properties of the concrete, especially the compressive strength. In the field of 3D printing concrete, the printing quality not only depends on the material ratio, but also is closely related to the 3D printing process parameters (such as printing speed, interlayer waiting time, etc.) and the introduction of additives (such as three-dimensional porous graphene). Therefore, how to accurately predict the compressive strength of recycled coarse aggregate 3D printing concrete has become a key problem to be solved at present.

[0003] Most current researches focus on the influence of a single factor on the performance of concrete, and there is still a lack of a perfect model that comprehensively considers the effects of multiple factors on the prediction of the compressive strength of recycled coarse aggregate 3D printing concrete. In view of this, it is of great significance to construct a compressive strength prediction model of recycled coarse aggregate 3D printing concrete that covers multiple factors such as effective water-binder ratio, recycled coarse aggregate replacement rate, old mortar attachment amount, three-dimensional porous graphene addition amount, age, and 3D printing process parameters, for scientifically predicting the compressive strength of concrete and guiding its application in engineering practice. SUMMARY

[0004] 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 recycled coarse aggregate 3D printing concrete that simplifies the operation process, shortens the time consumption, and ensures that the prediction results have small errors and high accuracy compared with the actual measured values.

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

[0006] Step 1, a prediction model of the compressive strength of recycled coarse aggregate 3D printing concrete is established, 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, ranging from 14 to 19; a is an empirical index related to the effective glue ratio, ranging from 0.8 to 1.0; b is an empirical index related to the replacement rate of recycled coarse aggregate, ranging from 0.3 to 0.6;

[0010] g is an empirical index related to the reference age ratio, ranging from 0.20 to 0.30; d is an empirical index related to the attached old mortar content, ranging from 0.2 to 0.4; n is an empirical index related to the mass percentage of graphene in cementitious materials, ranging from 0.5 to 0.7; k R is an empirical coefficient related to data fitting, ranging from 0.30 to 0.40;

[0011] k M is an empirical coefficient related to data fitting, ranging from 0.24 to 0.26; k G is an empirical coefficient related to data fitting, ranging from 17 to 19; k L is an empirical index related to data fitting, ranging from 0.03 to 0.06; k S is an empirical

[0012] index related to data fitting, ranging from 0.01 to 0.04;

[0013] C eff : effective cementitious material dosage, unit kg / m 3 ;

[0014] W eff : effective mixing water dosage, unit kg / m 3 , considering the water absorption of recycled coarse aggregate and the water absorption of attached old mortar;

[0015] R: recycled fine aggregate dosage, unit kg / m 3 ;

[0016] G t : total coarse aggregate, unit kg / m 3 , i.e. the sum of natural coarse aggregate and recycled coarse aggregate;

[0017] M: mass percentage of attached old mortar in recycled coarse aggregate;

[0018] G: three-dimensional porous graphene dosage, unit kg / m 3 ;

[0019] B: total cementitious material, unit kg / m 3 ;

[0020] t: age, unit day;

[0021] t0: reference age;

[0022] L t :3D printing layer thickness, unit: mm;

[0023] L0: reference layer thickness, unit: mm, take standard value;

[0024] S: printing speed, unit: mm / s;

[0025] S0: reference printing speed, unit: mm / s, take standard value;

[0026] Step 2, set the effective amount of cementitious material C eff , the effective amount of mixing water W eff , the replacement rate of recycled coarse aggregate The mass percentage of the attached old mortar in the recycled coarse aggregate M.

[0027] Step 3, set the amount of three-dimensional porous graphene G, the total amount of cementitious material B, the age t, and the reference age t0.

[0028] Step 4, set the 3D printing layer thickness L t , the reference layer thickness L0, the printing speed S, and the reference printing speed S0.

[0029] 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 coarse aggregate 3D printing concrete.

[0030] As a further scheme of the application, the calculation method of the recycled coarse aggregate 3D printing concrete compressive strength prediction model, the effective amount of cementitious material C eff :

[0031] C eff =C+k F F+k SF SF+k SL SL

[0032] C: cement dosage, unit: kg / m 3 ;

[0033] F, SF, SL: the dosage of fly ash, silica fume, and slag powder, respectively, unit: kg / m 3 ;

[0034] k F , k SF , k SL : the activity coefficient of each mineral admixture;

[0035] The effective amount of mixing water W eff :

[0036] W eff= W + R x W abs + R x M x W old

[0037] W: the amount of mixing water, in kg / m 3

[0038] W abs : the water absorption rate of the recycled coarse aggregate, expressed as a percentage by mass;

[0039] W old : the water absorption rate of the attached old mortar, expressed as a percentage by mass.

[0040] As a further aspect of the present application, the effective water-binder ratio is in the range of 0.3 to 0.45, the amount of mixing water is in the range of 135 to 270 kg / m 3 , the total amount of cementitious materials is in the range of 450 to 600 kg / m 3 , the amount of three-dimensional porous graphene is determined as a percentage of the total amount of cementitious materials, the mass percentage of attached old mortar in the recycled coarse aggregate is 0, 10%, 15%, 20%, or 25%, and the recycled coarse aggregate replacement rate

[0041] As a further aspect of the present application, the layer thickness L t is in the range of 5 to 15 mm, the printing speed S is in the range of 30 to 100 mm / s, and the reference values L0 and S0 are L0 = 10 mm and S0 = 50 mm / s.

[0042] As a further aspect of the present application, the material mixing includes: (1) dry mixing, in which the cementitious materials containing graphene, natural coarse aggregate, recycled coarse aggregate, and natural fine aggregate are mixed uniformly; and (2) wet mixing, in which the effective mixing water and additives are added and stirred uniformly to ensure that the graphene and recycled coarse aggregate are fully dispersed and wetted.

[0043] Compared with the prior art, the present application has the following advantages:

[0044] (1) The present application can quickly predict the compressive strength of recycled coarse aggregate 3D-printed concrete by using the content of three-dimensional porous graphene, the content of recycled coarse aggregate, the content of attached old mortar, the age, and the 3D printing process parameters.

[0045] (2) The present application reduces the time for repeated tests, accelerates the test progress, and significantly improves the accuracy and reliability of the prediction results. DETAILED DESCRIPTION

[0046] The technical solutions in the present application will be described in detail below with reference to specific embodiments. 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 work should be considered to fall within the protection scope of the present application.

[0047] In the specific embodiment, the raw materials for producing the recycled coarse aggregate 3D printing concrete include cement, fly ash, slag, silica fume, three-dimensional porous graphene, recycled coarse aggregate and natural coarse aggregate. In addition, the recycled coarse aggregate 3D printing concrete in the specific implementation of the present application is not limited to the above raw materials. In the above raw materials, the cement is ordinary Portland cement, and the recycled coarse aggregate is mixed with the natural coarse aggregate for use, and the gradation is qualified.

[0048] Five groups of recycled coarse aggregate 3D printing concrete tests and verifications were carried out, and the concrete raw material mix proportions are shown in Table 1:

[0049] Table 1

[0050]

[0051] The units of each raw material in Table 1 are kg, and the cement is ordinary Portland cement with a mark of P.O 42.5.

[0052] Through the formula:

[0053]

[0054] The predicted value (unit: MPa) is calculated, and then compared with the measured value (unit: MPa) at 28d, and the comparison is as follows:

[0055] Table 2:

[0056] No. Observed Predicted Absolute error Relative error Y 预测 / Y 实测 ]]> 1 39.3 38.8 0.5 1.27 0.987 2 34.7 34.2 0.5 1.44 0.986 3 34.6 34.4 0.2 0.58 0.994 4 33.9 33.4 0.5 1.47 0.985 5 35.5 34.5 1 2.82 0.972

[0057] After in-depth analysis of the data in Table 2, it is found that the prediction formula in the present application has an error of less than 1 MPa between the predicted concrete strength and the measured value, wherein the minimum error is only 0.2 MPa, the maximum error is 1 MPa, and the average error is 0.54 MPa. More noteworthy is that the average relative error is only 1.52%, and the ratio of the predicted value to the measured value (Y predicted / Y measured) is stable in the range of 0.972-0.994, showing an accuracy of more than 95%. These data fully prove that the prediction formula in the present application has high correlation when calculating the 28d compressive strength of concrete, and the prediction accuracy and accuracy are excellent.

[0058] Finally, it should be noted that the above-mentioned embodiments are only intended to illustrate the technical solutions of the present application, and are not intended to limit the application thereof. Although the present application has been described in detail through reference to the preferred embodiments thereof, it will be appreciated by those skilled in the art that the application is subject to many different alterations and modifications, and that many equivalents thereof can be substituted without departing from the spirit and scope of the present application as defined in the appended claims.

Claims

1. A computing method of a compressive strength prediction model of recycled coarse aggregate 3D printing concrete, characterized by Comprising the following steps: Step 1, establishing a prediction model of the compressive strength of recycled coarse aggregate 3D printing concrete, 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 14 to 19; a is an empirical index related to the effective glue ratio, ranging from 0.8 to 1.0; b is an empirical index related to the replacement rate of recycled coarse aggregate, ranging from 0.3 to 0.6; g is an empirical index related to the reference age ratio, ranging from 0.20 to 0.30; d is an empirical index related to the attached old mortar content, ranging from 0.2 to 0.4; n is an empirical index related to the mass percentage of graphene in cementitious materials, ranging from 0.5 to 0.7; k R is an empirical coefficient related to data fitting, ranging from 0.30 to 0.40; k M is an empirical coefficient related to data fitting, ranging from 0.24 to 0.26; k G is an empirical coefficient related to data fitting, ranging from 17 to 19; k L is an empirical index related to data fitting, ranging from 0.03 to 0.06; k S is an empirical index related to data fitting, ranging from 0.01 to 0.04; C eff : Effective gelling material amount, in kg / m 3 ; W eff : Effective mixing water quantity, in kg / m 3 , taking into account the water absorption of the recycled coarse aggregate and the water absorption of the attached old mortar R: recycled fine aggregate amount, in kg / m 3 ; G t : total amount of coarse aggregate, in kg / m 3 , i.e. the sum of natural coarse aggregate and recycled coarse aggregate; M: the mass percentage of the attached old mortar in the recycled coarse 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, take the standard value; S: printing speed, unit: mm / s; S0: reference printing speed, unit: mm / s, take the standard value; Step 2, Set the effective cementitious material amount C eff and effective mixing water amount W eff , recycled coarse aggregate replacement rate The mass percentage of the old mortar attached in the recycled coarse aggregate M; 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 the layer thickness L of the 3D printing t , the reference layer thickness L0, the printing speed S, and the reference printing speed S0. Step 5, substituting the related parameters in step 2, step 3 and step 4 into formula (1) to predict the compressive strength of recycled coarse aggregate 3D printing concrete.

2. The method of claim 1, wherein the method is characterized by: 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 Effective amount of water for mixing W eff : W eff = W + R x W abs + R x M x W old W: amount of water for mixing, unit: kg / m 3 W abs : water absorption of the recycled coarse aggregate, expressed as a percentage by mass; W old : Water absorption of the old mortar-attached sand, expressed as a percentage by mass.

3. The method of 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 135-270 kg / m 3 ; the total amount of binder is in the range of 450-600 kg / m 3 , the amount of each binder 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 binder; the percentage of the mass of the attached old mortar in the recycled coarse aggregate M is 0, 10%, 15%, 20%, 25%; the replacement rate of the recycled coarse aggregate 4. The method of claim 1, wherein the method is characterized by: 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 of claim 1, wherein the method is characterized by: Material mixing: (1) dry mixing: mix the cementitious materials containing graphene, natural coarse aggregate, recycled coarse aggregate, and natural fine aggregate uniformly; (2) wet mixing: add effective mixing water and admixtures, stir uniformly to ensure that the graphene and recycled coarse aggregate are fully dispersed and wetted.

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